diff --git a/README.md b/README.md
index a74d80c..643aa76 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,8 @@
-**NOTE:** If you're trying to run the RollDieDynamic.py script in Chapter 1 and the window does not appear, this is due to a known issue in the software for which a fix is already in process. You can instead run the program with 
-
-> `ipython -i RollDieDynamic.py 6000 1`
-    
-Then terminate IPython interactive mode (Ctrl + D twice) after you close the script's window.
-
-
 # IntroToPython
 This repository contains the source code and supporting files associated with our book: <a href=https://amzn.to/2KfCptN>_Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud_.</a>
     
 ![Cover image for Intro to Python for Computer Science and Data Science: 
-    Learning to Program with AI, Big Data and the Cloud](http://deitel.com/bookresources/IntroToPython/IntroToPythonCover.png)
+    Learning to Program with AI, Big Data and the Cloud](https://deitel.com/wp-content/uploads/2020/01/intro-to-python-for-computer-science-and-data-science.jpg)
 
 These files are Copyright 2020 by Pearson Education, Inc. All Rights Reserved. 
 
diff --git a/examples/ch01/RollDieDynamic.py b/examples/ch01/RollDieDynamic.py
index ebb6c48..45845fc 100755
--- a/examples/ch01/RollDieDynamic.py
+++ b/examples/ch01/RollDieDynamic.py
@@ -14,7 +14,7 @@ def update(frame_number, rolls, faces, frequencies):
 
     # reconfigure plot for updated die frequencies
     plt.cla()  # clear old contents contents of current Figure
-    axes = sns.barplot(faces, frequencies, palette='bright')  # new bars
+    axes = sns.barplot(x=faces, y=frequencies, palette='bright')  # new bars
     axes.set_title(f'Die Frequencies for {sum(frequencies):,} Rolls')
     axes.set(xlabel='Die Value', ylabel='Frequency')  
     axes.set_ylim(top=max(frequencies) * 1.10)  # scale y-axis by 10%
@@ -37,7 +37,7 @@ def update(frame_number, rolls, faces, frequencies):
 
 # configure and start animation that calls function update
 die_animation = animation.FuncAnimation(
-    figure, update, repeat=False, frames=number_of_frames, interval=33,
+    figure, update, repeat=False, frames=number_of_frames - 1, interval=33,
     fargs=(rolls_per_frame, values, frequencies))
 
 plt.show()  # display window
diff --git a/examples/ch01/snippets_ipynb/.ipynb_checkpoints/IPython_selfcheck-checkpoint.ipynb b/examples/ch01/snippets_ipynb/.ipynb_checkpoints/IPython_selfcheck-checkpoint.ipynb
deleted file mode 100644
index 2d8cb19..0000000
--- a/examples/ch01/snippets_ipynb/.ipynb_checkpoints/IPython_selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** Evaluate the expression `5 * (3 + 4)` both with and without the parentheses. Do you get the same result? Why or why not?\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "5 * (3 + 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "5 * 3 + 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_04-checkpoint.py b/examples/ch02/snippets_py/.ipynb_checkpoints/02_04-checkpoint.py
deleted file mode 100755
index e191182..0000000
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_04-checkpoint.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Section 2.4 snippets
-print('Welcome to Python!')
-
-print("Welcome to Python!")
-
-# Printing a Comma-Separated List of Items
-print('Welcome', 'to', 'Python!')
-
-# Printing Many Lines of Text with One Statement
-print('Welcome\nto\n\nPython!')
-
-# Other Escape Sequences
-
-# Ignoring a Line Break in a Long String
-print('this is a longer string, so we \
-split it over two lines')
-
-# Printing the Value of an Expression
-print('Sum is', 7 + 3)
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_04selfcheck-checkpoint.py b/examples/ch02/snippets_py/.ipynb_checkpoints/02_04selfcheck-checkpoint.py
deleted file mode 100755
index 13bca1d..0000000
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_04selfcheck-checkpoint.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Section 2.4 Self Check snippets
-type('word')
-print('int(5.2)', 'truncates 5.2 to', int(5.2))
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_05-checkpoint.py b/examples/ch02/snippets_py/.ipynb_checkpoints/02_05-checkpoint.py
deleted file mode 100755
index 4cadaf0..0000000
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_05-checkpoint.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Section 2.5 snippets
-
-# Including Quotes in Strings
-print('Display "hi" in quotes')
-
-print('Display 'hi' in quotes')
-
-print('Display \'hi\' in quotes')
-
-print("Display the name O'Brien")
-
-print("Display \"hi\" in quotes")
-
-print("""Display "hi" and 'bye' in quotes""")
-
-# Multiline Strings
-triple_quoted_string = """This is a triple-quoted
-string that spans two lines"""
-
-print(triple_quoted_string)
-
-triple_quoted_string
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_06selfcheck-checkpoint.py b/examples/ch02/snippets_py/.ipynb_checkpoints/02_06selfcheck-checkpoint.py
deleted file mode 100755
index 48f4544..0000000
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_06selfcheck-checkpoint.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Section 2.6 Self Check snippets
-float('6.2') * 3.3
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_08-checkpoint.py b/examples/ch02/snippets_py/.ipynb_checkpoints/02_08-checkpoint.py
deleted file mode 100755
index e19bce5..0000000
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_08-checkpoint.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Section 2.8 snippets
-
-type(7)
-
-type(4.1)
-
-type('dog')
-
-# Variables Refer to Objects
-x = 7
-
-x + 10
-
-x
-
-x = x + 10
-
-x
-
-# Dynamic Typing
-type(x)
-
-x = 4.1
-
-type(x)
-
-x = 'dog'
-
-type(x)
-
-# Garbage Collection
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_09-checkpoint.py b/examples/ch02/snippets_py/.ipynb_checkpoints/02_09-checkpoint.py
deleted file mode 100755
index 5f7815d..0000000
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_09-checkpoint.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Section 2.9 snippets
-min(36, 27, 12)
-max(36, 27, 12)
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_09selfcheck-checkpoint.py b/examples/ch02/snippets_py/.ipynb_checkpoints/02_09selfcheck-checkpoint.py
deleted file mode 100755
index bf77b38..0000000
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_09selfcheck-checkpoint.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Section 2.9 Self Checksnippets
-min(47, 95, 88, 73, 88, 84)
-max(47, 95, 88, 73, 88, 84)
-print('Range:', min(47, 95, 88, 73, 88, 84), '-',
-    max(47, 95, 88, 73, 88, 84))
-    
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/03_05-checkpoint.py b/examples/ch03/snippets_py/.ipynb_checkpoints/03_05-checkpoint.py
deleted file mode 100755
index 9a2086c..0000000
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/03_05-checkpoint.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# Section 3.5 snippets
-
-grade = 85
-
-if grade >= 60:
-    print('Passed')
-
-# Suite Indentation
-if grade >= 60:
-print('Passed')  # statement is not indented properly
-
-if grade >= 60:
-    print('Passed')
-  print('Good job!)
-        
-# if Statement Flowchart
-
-# Every Expression Can Be Treated as True or False
-if 1:
-    print('Nonzero values are true, so this will print')
-if 0:
-    print('Zero is false, so this will not print')
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/03_05selfcheck-checkpoint.py b/examples/ch03/snippets_py/.ipynb_checkpoints/03_05selfcheck-checkpoint.py
deleted file mode 100755
index 144d283..0000000
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/03_05selfcheck-checkpoint.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Section 3.5 Self Check snippets
-
-# Exercise 2
-grade = 85
-
-if grade >= 60:
-    print('Passed')
-    
-grade = 55
-
-if grade >= 60:
-    print('Passed')
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/03_12selfcheck-checkpoint.py b/examples/ch03/snippets_py/.ipynb_checkpoints/03_12selfcheck-checkpoint.py
deleted file mode 100644
index 8cb7617..0000000
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/03_12selfcheck-checkpoint.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Section 3.12 Self Check snippets
-
-# Exercise 1
-
-for count in range(2):
-    value = int(input('Enter an integer: '))
-    if value % 2 == 0:
-        print(f'{value} is even')
-    else:
-        print(f'{value} is odd')
-        
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_03-checkpoint.py b/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_03-checkpoint.py
deleted file mode 100644
index b56001c..0000000
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_03-checkpoint.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Exercise 3.3
-for row in range(10):
-    for column in range(10):
-        print('<' if row % 2 == 1 else '>', end='')
-    print()
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_04-checkpoint.py b/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_04-checkpoint.py
deleted file mode 100644
index 1e697c8..0000000
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_04-checkpoint.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Exercise 3.4
-for ***:
-    for ***:
-        print('@')
-    print()
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_22-checkpoint.py b/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_22-checkpoint.py
deleted file mode 100644
index 626d238..0000000
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_22-checkpoint.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Exercise 3.22
-for i in range(2):
-    value = int(input('Enter an integer (-1 to break): '))
-    print('You entered:', value)
-    
-    if value == -1:
-        break
-else:
-    print('The loop terminated without executing the break')
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_02-checkpoint.py b/examples/ch03/validate_indents.py
similarity index 89%
rename from examples/ch03/snippets_py/.ipynb_checkpoints/ex03_02-checkpoint.py
rename to examples/ch03/validate_indents.py
index fff7838..2a318a6 100644
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/ex03_02-checkpoint.py
+++ b/examples/ch03/validate_indents.py
@@ -1,7 +1,10 @@
-# Exercise 3.2
-a = b = 7
-print('a =', a, '\\nb =', b)
+# validate_indents.py
+grade = 93
 
+if grade >= 90:
+    print('A')
+  print('Great Job!')
+   print('Take a break from studying')
 
 ##########################################################################
 # (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_02-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_02-checkpoint.py
deleted file mode 100755
index 0b52206..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_02-checkpoint.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Section 4.2 snippets
-
-def square(number):
-    """Calculate the square of number."""
-    return number ** 2
-
-square(7)
-
-square(2.5)
-
-# Returning a Result to a Function’s Caller
-print('The square of 7 is', square(7))
-
-# Accessing a Function’s Docstring Via IPython’s Help Mechanism 
-square?
-
-square??
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_03-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_03-checkpoint.py
deleted file mode 100755
index c855981..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_03-checkpoint.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Section 4.3 snippets
-
-def maximum(value1, value2, value3):
-    """Return the maximum of three values."""
-    max_value = value1
-    if value2 > max_value:
-        max_value = value2
-    if value3 > max_value:
-        max_value = value3
-    return max_value
-
-maximum(12, 27, 36)
-
-maximum(12.3, 45.6, 9.7)
-
-maximum('yellow', 'red', 'orange')
-
-maximum(13.5, -3, 7)
-
-# Python’s Built-In max and min Functions
-max('yellow', 'red', 'orange', 'blue', 'green')
-
-min(15, 9, 27, 14)
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_03selfcheck-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_03selfcheck-checkpoint.py
deleted file mode 100755
index 8c21e57..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_03selfcheck-checkpoint.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Section 4.3 Self Check snippets
-
-# Exercise 3
-max([14, 27, 5, 3])
-
-min('orange')
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_04selfcheck-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_04selfcheck-checkpoint.py
deleted file mode 100755
index 77fdffa..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_04selfcheck-checkpoint.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Section 4.4 Self Check snippets
-
-# Exercise 3
-import random
-
-for i in range(20):
-    print('H' if random.randrange(2) == 0 else 'T', end=' ')
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_13-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_13-checkpoint.py
deleted file mode 100755
index 2b93236..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_13-checkpoint.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Section 4.13 snippets
-
-# Accessing a Global Variable from a Function 
-x = 7
-
-def access_global():
-    print('x printed from access_global:', x)
-    
-access_global()
-
-def try_to_modify_global():
-    x = 3.5
-    print('x printed from try_to_modify_global:', x)
-    
-try_to_modify_global()
-
-x
-
-def modify_global():
-    global x;
-    x = 'hello'
-    print('x printed from modify_global:', x)
-    
-modify_global()
-
-x
-
-# Shadowing Functions
-sum = 10 + 5
-
-sum
-
-sum([10, 5])
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_14selfcheck-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_14selfcheck-checkpoint.py
deleted file mode 100755
index 9687480..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_14selfcheck-checkpoint.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Section 4.14 Self Check snippets
-
-# Exercise 2
-import decimal as dec
-
-dec.Decimal('2.5') ** 2
-Decimal('6.25')
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_15selfcheck-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_15selfcheck-checkpoint.py
deleted file mode 100755
index 5fc74d1..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_15selfcheck-checkpoint.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Section 4.15 Self Check snippets
-
-# Exercise 3
-width = 15.5
-
-print('id:', id(width), ' value:', width)
-
-width = width * 3
-
-print('id:', id(width), ' value:', width)
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_18selfcheck-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/04_18selfcheck-checkpoint.py
deleted file mode 100755
index f3e0add..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_18selfcheck-checkpoint.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Section 4.18 Self Check snippets
-
-# Exercise 3
-import statistics
-
-statistics.variance([1, 3, 4, 2, 6, 5, 3, 4, 5, 2])
-
-statistics.stdev([1, 3, 4, 2, 6, 5, 3, 4, 5, 2])
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/ex04_05-checkpoint.py b/examples/ch04/snippets_py/.ipynb_checkpoints/ex04_05-checkpoint.py
deleted file mode 100755
index 9e37d8c..0000000
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/ex04_05-checkpoint.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Exercise 4.5
-def seconds_since_midnight(***, ***, ***):
-    hour_in_seconds = ***
-    minute_in_seconds = ***
-    return ***
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch05/RollDie.py b/examples/ch05/RollDie.py
index e49ab9b..d6f07fe 100755
--- a/examples/ch05/RollDie.py
+++ b/examples/ch05/RollDie.py
@@ -14,7 +14,7 @@
 
 title = f'Rolling a Six-Sided Die {len(rolls):,} Times'
 sns.set_style('whitegrid')  # white backround with gray grid lines
-axes = sns.barplot(values, frequencies, palette='bright')  # create bars
+axes = sns.barplot(x=values, y=frequencies, palette='bright')  # create bars
 axes.set_title(title)  # set graph title
 axes.set(xlabel='Die Value', ylabel='Frequency')  # label the axes
 
diff --git a/examples/ch05/snippets_py/.ipynb_checkpoints/05_17-checkpoint.py b/examples/ch05/snippets_py/.ipynb_checkpoints/05_17-checkpoint.py
deleted file mode 100755
index 3ac5560..0000000
--- a/examples/ch05/snippets_py/.ipynb_checkpoints/05_17-checkpoint.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Section 5.17 snippets
-
-# 5.17.1 Sample Graphs for 600, 60,000 and 6,000,000 Die Rolls
-
-# 5.17.2 Visualizing Die-Roll Frequencies and Percentages
-
-# Launching IPython for Interactive Matplotlib Development
-
-
-# Importing the Libraries
-import matplotlib.pyplot as plt
-
-import numpy as np
-
-import random
-
-import seaborn as sns
-
-# Rolling the Die and Calculating Die Frequencies
-rolls = [random.randrange(1, 7) for i in range(600)]
-
-values, frequencies = np.unique(rolls, return_counts=True)
-
-# Creating the Initial Bar Plot
-title = f'Rolling a Six-Sided Die {len(rolls):,} Times'
-
-sns.set_style('whitegrid')
-
-axes = sns.barplot(values, frequencies, palette='bright')
-
-# Setting the Window Title and Labeling the x- and y-Axes
-axes.set_title(title)
-
-axes.set(xlabel='Die Value', ylabel='Frequency')  
-
-# Finalizing the Bar Plot
-axes.set_ylim(top=max(frequencies) * 1.10)
-
-for bar, frequency in zip(axes.patches, frequencies):
-    text_x = bar.get_x() + bar.get_width() / 2.0  
-    text_y = bar.get_height() 
-    text = f'{frequency:,}\n{frequency / len(rolls):.3%}'
-    axes.text(text_x, text_y, text, 
-              fontsize=11, ha='center', va='bottom')
-              
-# Rolling Again and Updating the Bar Plot—Introducing IPython Magics
-plt.cla()
-
-%recall 5
-
-rolls = [random.randrange(1, 7) for i in range(600)]
-
-rolls = [random.randrange(1, 7) for i in range(60000)]
-
-%recall 6-13
-
-values, frequencies = np.unique(rolls, return_counts=True)
-title = f'Rolling a Six-Sided Die {len(rolls):,} Times'
-sns.set_style('whitegrid')
-axes = sns.barplot(values, frequencies, palette='bright')
-axes.set_title(title)
-axes.set(xlabel='Die Value', ylabel='Frequency')  
-axes.set_ylim(top=max(frequencies) * 1.10)
-for bar, frequency in zip(axes.patches, frequencies):
-    text_x = bar.get_x() + bar.get_width() / 2.0  
-    text_y = bar.get_height() 
-    text = f'{frequency:,}\n{frequency / len(rolls):.3%}'
-    axes.text(text_x, text_y, text, 
-              fontsize=11, ha='center', va='bottom')
-              
-# Saving Snippets to a File with the %save Magic 
-%save RollDie.py 1-13
-
-
-
-
-
-   
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch06/RollDieDynamic.py b/examples/ch06/RollDieDynamic.py
index c7bf9ae..b95023e 100755
--- a/examples/ch06/RollDieDynamic.py
+++ b/examples/ch06/RollDieDynamic.py
@@ -14,7 +14,7 @@ def update(frame_number, rolls, faces, frequencies):
 
     # reconfigure plot for updated die frequencies
     plt.cla()  # clear old contents contents of current Figure
-    axes = sns.barplot(faces, frequencies, palette='bright')  # new bars
+    axes = sns.barplot(x=faces, y=frequencies, palette='bright')  # new bars
     axes.set_title(f'Die Frequencies for {sum(frequencies):,} Rolls')
     axes.set(xlabel='Die Value', ylabel='Frequency')  
     axes.set_ylim(top=max(frequencies) * 1.10)  # scale y-axis by 10%
@@ -37,7 +37,7 @@ def update(frame_number, rolls, faces, frequencies):
 
 # configure and start animation that calls function update
 die_animation = animation.FuncAnimation(
-    figure, update, repeat=False, frames=number_of_frames, interval=33,
+    figure, update, repeat=False, frames=number_of_frames - 1, interval=33,
     fargs=(rolls_per_frame, values, frequencies))
 
 plt.show()  # display window
diff --git a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.01selfcheck-checkpoint.ipynb b/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.01selfcheck-checkpoint.ipynb
deleted file mode 100644
index 4eec882..0000000
--- a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.01selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,101 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 6.2.1 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** `________` can be thought of as unordered collections in which each value is accessed through its corresponding key. \n",
-    "\n",
-    "**Answer:** Dictionaries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Dictionaries may contain duplicate keys.\n",
-    "\n",
-    "**Answer:** False. Dictionary keys must be unique. However, multiple keys may have the same value."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** Create a dictionary named `states` that maps three state abbreviations to their state names, then display the dictionary.\n",
-    "\n",
-    "**Answer:**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "states = {'VT': 'Vermont', 'NH': 'New Hampshire', \n",
-    "          'MA': 'Massachusetts'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "states"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.02-checkpoint.ipynb b/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.02-checkpoint.ipynb
deleted file mode 100644
index e109ff0..0000000
--- a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.02-checkpoint.ipynb
+++ /dev/null
@@ -1,83 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6.2.2 Iterating through a Dictionary "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "days_per_month = {'January': 31, 'February': 28, 'March': 31}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "days_per_month"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for month, days in days_per_month.items():\n",
-    "    print(f'{month} has {days} days')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.03.04-checkpoint.ipynb b/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.03.04-checkpoint.ipynb
deleted file mode 100644
index fff4353..0000000
--- a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.03.04-checkpoint.ipynb
+++ /dev/null
@@ -1,81 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6.3.4 Set Comprehensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numbers = [1, 2, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10, 10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "evens = {item for item in numbers if item % 2 == 0}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "evens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.04.02selfcheck-checkpoint.ipynb b/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.04.02selfcheck-checkpoint.ipynb
deleted file mode 100644
index 689a13b..0000000
--- a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.04.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 6.4.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The Matplotlib `________` module’s `________` function dynamically updates a visualization.\n",
-    "\n",
-    "**Answer:** `animation`, `FuncAnimation`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(Fill-In)_** `FuncAnimation`’s `________` keyword argument enables you to pass custom arguments to the function that’s called once per animation frame.\n",
-    "\n",
-    "**Answer:** `fargs`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/ex06.04-checkpoint.ipynb b/examples/ch06/snippets_ipynb/.ipynb_checkpoints/ex06.04-checkpoint.ipynb
deleted file mode 100644
index 0de765a..0000000
--- a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/ex06.04-checkpoint.ipynb
+++ /dev/null
@@ -1,109 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**6.4. _(Fill in the Missing Code)_** In each of the following expressions, replace the `***`s with a set operator that produces the result shown in the comment. The last operation should check whether the left operand is an improper subset of the right operand. For each of the first four expressions, specify the name of the set operation that produces the specified result.\n",
-    "\n",
-    "**a.** `{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {1,2,4,8,16,64,256} `\n",
-    "\n",
-    "**b.** `{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {1,4,16} `\n",
-    "\n",
-    "**c.** `{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {2,8} `\n",
-    "\n",
-    "**d.** `{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {2,8,64,256}  `\n",
-    "\n",
-    "**e.** `{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # False`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {1,2,4,8,16,64,256} "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {1,4,16} "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {2,8} "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # {2,8,64,256}  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{1, 2, 4, 8, 16} *** {1, 4, 16, 64, 256}  # False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch07/snippets_py/.ipynb_checkpoints/07_08selfcheck-checkpoint.py b/examples/ch07/snippets_py/.ipynb_checkpoints/07_08selfcheck-checkpoint.py
deleted file mode 100755
index 1e23c1d..0000000
--- a/examples/ch07/snippets_py/.ipynb_checkpoints/07_08selfcheck-checkpoint.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Section 7.8 Self Check snippets
-
-# Exercise 2
-import numpy as np
-
-grades = np.random.randint(60, 101, 12).reshape(3, 4)
-
-grades
-
-grades.mean()
-
-grades.mean(axis=0)
-
-grades.mean(axis=1)
-
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch09/snippets_py/.ipynb_checkpoints/09_03.01selfcheck-checkpoint.py b/examples/ch09/snippets_py/.ipynb_checkpoints/09_03.01selfcheck-checkpoint.py
deleted file mode 100755
index 5569f5b..0000000
--- a/examples/ch09/snippets_py/.ipynb_checkpoints/09_03.01selfcheck-checkpoint.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Section 9.3.1 Self Check snippets
-
-# Exercise 3
-with open('grades.txt', mode='w') as grades:
-    grades.write('1 Red A\n')
-    grades.write('2 Green B\n')
-    grades.write('3 White A\n')
-    
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch09/snippets_py/.ipynb_checkpoints/09_12.03-05-checkpoint.py b/examples/ch09/snippets_py/.ipynb_checkpoints/09_12.03-05-checkpoint.py
deleted file mode 100755
index a7640a3..0000000
--- a/examples/ch09/snippets_py/.ipynb_checkpoints/09_12.03-05-checkpoint.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# NOTE: This file contains all the snippets for Sections 9.12.3-9.12.5,
-# including the 9.12.5 Self Check
-
-# Section 9.12.3 snippets
-
-# Loading the Titanic Dataset via a URL
-import pandas as pd
-
-titanic = pd.read_csv('https://vincentarelbundock.github.io/' +
-    'Rdatasets/csv/carData/TitanicSurvival.csv')
-                      
-# Viewing Some of the Rows in the Titanic Dataset
-pd.set_option('precision', 2)  # format for floating-point values
-
-titanic.head()
-
-titanic.tail()
-
-# Customizing the Column Names
-titanic.columns = ['name', 'survived', 'sex', 'age', 'class']
-
-titanic.head()
-
-# Section 9.12.4 snippets
-titanic.describe()
-
-(titanic.survived == 'yes').describe()
-
-# Section 9.12.5 snippets
-%matplotlib
-
-histogram = titanic.hist()
-
-# Section 9.12.5 Self Check snippets
-
-# Exercise 2
-pd.read_csv('grades.csv', names=['ID', 'Name', 'Grade'])
-
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.01-checkpoint.ipynb
deleted file mode 100755
index 9df9044..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.01-checkpoint.ipynb
+++ /dev/null
@@ -1,80 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.3.1 Writing to a Text File; Introducing the `with` Statement "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('accounts.txt', mode='w') as accounts:\n",
-    "    accounts.write('100 Jones 24.98\\n')\n",
-    "    accounts.write('200 Doe 345.67\\n')\n",
-    "    accounts.write('300 White 0.00\\n')\n",
-    "    accounts.write('400 Stone -42.16\\n')\n",
-    "    accounts.write('500 Rich 224.62\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "\n",
-    "### The `with` Statement\n",
-    "### Built-In Function `open` \n",
-    "### Writing to the File \n",
-    "### Contents of `accounts.txt` File "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.01selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.01selfcheck-checkpoint.ipynb
deleted file mode 100755
index cb4ed42..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.01selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.3.1 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** When the `________` statement terminates, it implicitly releases the resource that it acquired before its suite began executing.\n",
-    "\n",
-    "**Answer:** `with`.\n",
-    "\n",
-    "**2. _(True/False)_** It’s good practice to keep resources open until your program terminates. \n",
-    "\n",
-    "**Answer:** False. It’s good practice to close resources as soon as the program no longer needs them.\n",
-    "\n",
-    "**3. _(IPython Session)_** Create a `grades.txt` file and write to it the following three records consisting of student IDs, last names and letter grades:\n",
-    "```\n",
-    "1 Red A\n",
-    "2 Green B\n",
-    "3 White A\n",
-    "```\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "with open('grades.txt', mode='w') as grades:\n",
-    "    grades.write('1 Red A\\n')\n",
-    "    grades.write('2 Green B\\n')\n",
-    "    grades.write('3 White A\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.02-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.02-checkpoint.ipynb
deleted file mode 100755
index da26a72..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.02-checkpoint.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.3.2 Reading Data from a Text File"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('accounts.txt', mode='r') as accounts:\n",
-    "    print(f'{\"Account\":<10}{\"Name\":<10}{\"Balance\":>10}')\n",
-    "    for record in accounts:\n",
-    "        account, name, balance = record.split()\n",
-    "        print(f'{account:<10}{name:<10}{balance:>10}')\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### File Method `readlines`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Seeking to a Specific File Position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.02selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index f3d8f46..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_03.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,86 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.3.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** A file object’s ‘________‘ method can be used to reposition the file position pointer.\n",
-    "\n",
-    "**Answer:** `seek`.\n",
-    "\n",
-    "**2. _(True/False)_** By default, iterating through a file object with a `for` statement reads one line at a time from the file and returns it as a string. \n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**3. _(IPython Session)_** Read the file `grades.txt` that you created in the previous section’s Self Check and display it in columns with the column heads `'ID'`, `'Name'` and `'Grade'`.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('grades.txt', 'r') as grades:\n",
-    "    print(f'{\"ID\":<4}{\"Name\":<7}{\"Grade\"}')\n",
-    "    for record in grades:  \n",
-    "        student_id, name, grade = record.split()\n",
-    "        print(f'{student_id:<4}{name:<7}{grade}')\n",
-    "       "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_04-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_04-checkpoint.ipynb
deleted file mode 100755
index d4a4cf9..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_04-checkpoint.ipynb
+++ /dev/null
@@ -1,130 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 9.4 Updating Text Files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Updating `accounts.txt` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accounts = open('accounts.txt', 'r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_file = open('temp_file.txt', 'w')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with accounts, temp_file:\n",
-    "    for record in accounts:\n",
-    "        account, name, balance = record.split()\n",
-    "        if account != '300':\n",
-    "            temp_file.write(record)\n",
-    "        else:\n",
-    "            new_record = ' '.join([account, 'Williams', balance])\n",
-    "            temp_file.write(new_record + '\\n')\n",
-    "                    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `os` Module File Processing Functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.remove('accounts.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.rename('temp_file.txt', 'accounts.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_04selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_04selfcheck-checkpoint.ipynb
deleted file mode 100755
index 66884ec..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_04selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.4 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The `os` module’s ‘________‘ and ‘________‘ functions delete a file and specify a new name for a file, respectively. \n",
-    "\n",
-    "**Answer:** `remove`, `rename`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Formatted data in a text file can be updated in place because records and their fields are fixed in size. \n",
-    "\n",
-    "**Answer:** False. Such data cannot be modified without the risk of destroying other data in the file because records and their fields can vary in size."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** In the `accounts.txt` file, update the last name `'Doe'` to `'Smith'`.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accounts = open('accounts.txt', 'r')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_file = open('temp_file.txt', 'w')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with accounts, temp_file:\n",
-    "    for record in accounts:\n",
-    "        account, name, balance = record.split()\n",
-    "        if account != '200':\n",
-    "            temp_file.write(record)\n",
-    "        else:\n",
-    "            new_record = ' '.join([account, 'Smith', balance])\n",
-    "            temp_file.write(new_record + '\\n')\n",
-    "                    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.remove('accounts.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.rename('temp_file.txt', 'accounts.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_05-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_05-checkpoint.ipynb
deleted file mode 100755
index d57cf32..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_05-checkpoint.ipynb
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 9.5 Serialization with JSON "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### JSON Data Format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Python Standard Library Module `json` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accounts_dict = {'accounts': [\n",
-    "    {'account': 100, 'name': 'Jones', 'balance': 24.98},\n",
-    "    {'account': 200, 'name': 'Doe', 'balance': 345.67}]}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Serializing an Object to JSON"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('accounts.json', 'w') as accounts:\n",
-    "    json.dump(accounts_dict, accounts)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deserializing the JSON Text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('accounts.json', 'r') as accounts:\n",
-    "    accounts_json = json.load(accounts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accounts_json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accounts_json['accounts']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accounts_json['accounts'][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accounts_json['accounts'][1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying the JSON Text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('accounts.json', 'r') as accounts:\n",
-    "    print(json.dumps(json.load(accounts), indent=4))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_05selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_05selfcheck-checkpoint.ipynb
deleted file mode 100755
index 6d36244..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_05selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,123 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.5 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Converting objects to JSON text format is known as ‘________‘, and reconstructing the original Python object from the JSON text is known as ‘________‘.\n",
-    "\n",
-    "**Answer:** serialization, deserialization.\n",
-    "\n",
-    "**2. _(True/False)_** JSON is both a human-readable and computer-readable format that makes it convenient to send and receive objects across the Internet. \n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**3. _(IPython Session)_** Create a JSON file named `grades.json` and write into it the following dictionary:\n",
-    "```python\n",
-    "grades_dict = {'gradebook': \n",
-    "    [{'student_id': 1, 'name': 'Red', 'grade': 'A'},\n",
-    "     {'student_id': 2, 'name': 'Green', 'grade': 'B'},\n",
-    "     {'student_id': 3, 'name': 'White', 'grade': 'A'}]}\n",
-    "```\n",
-    "Then, read the file and display its pretty-printed JSON.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grades_dict = {'gradebook': \n",
-    "    [{'student_id': 1, 'name': 'Red', 'grade': 'A'},\n",
-    "     {'student_id': 2, 'name': 'Green', 'grade': 'B'},\n",
-    "     {'student_id': 3, 'name': 'White', 'grade': 'A'}]}\n",
-    "     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('grades.json', 'w') as grades:\n",
-    "    json.dump(grades_dict, grades)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('grades.json', 'r') as grades:\n",
-    "    print(json.dumps(json.load(grades), indent=4))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_07selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_07selfcheck-checkpoint.ipynb
deleted file mode 100755
index 0c34751..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_07selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,68 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.7 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The classes that Python uses to create file objects are defined in the Python Standard Library’s ‘________‘ module. \n",
-    "\n",
-    "**Answer:** `io`.\n",
-    "\n",
-    "**2. _(True/False)_** The `read` method always returns the entire contents of a file. \n",
-    "\n",
-    "**Answer:** False. You may specify an argument indicating the number of characters to read from the file.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.01-checkpoint.ipynb
deleted file mode 100755
index 791841e..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.01-checkpoint.ipynb
+++ /dev/null
@@ -1,86 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.8.1 Division by Zero and Invalid Input"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Division By Zero  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "10 / 0 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Invalid Numeric Input "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "value = int(input('Enter an integer: '))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.02-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.02-checkpoint.ipynb
deleted file mode 100755
index b50fd0d..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.02-checkpoint.ipynb
+++ /dev/null
@@ -1,90 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.8.2 `try` Statements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dividebyzero.py\n",
-    "\"\"\"Simple exception handling example.\"\"\"\n",
-    "\n",
-    "while True:\n",
-    "    # attempt to convert and divide values\n",
-    "    try:\n",
-    "        number1 = int(input('Enter numerator: '))\n",
-    "        number2 = int(input('Enter denominator: '))\n",
-    "        result = number1 / number2\n",
-    "    except ValueError:  # tried to convert non-numeric value to int\n",
-    "        print('You must enter two integers\\n')\n",
-    "    except ZeroDivisionError:  # denominator was 0\n",
-    "        print('Attempted to divide by zero\\n')\n",
-    "    else:  # executes only if no exceptions occur\n",
-    "        bprint(f'{number1:.3f} / {number2:.3f} = {result:.3f}')\n",
-    "        break  # terminate the loop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `try` Clause\n",
-    "### `except` Clause\n",
-    "### `else` Clause\n",
-    "### Flow of Control for a `ZeroDivisionError` \n",
-    "### Flow of Control for a `ValueError` \n",
-    "### Flow of Control for a Successful Division "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.02selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index b15ff82..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_08.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,123 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.8.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The statement that raises an exception is sometimes called the ‘________‘ of the exception. \n",
-    "\n",
-    "**Answer:** raise point. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** In Python, it’s possible to return to the raise point of an exception via keyword `return`.\n",
-    "\n",
-    "**Answer:** False. Program control continues from the first statement after the `try` statement in which the exception was handled. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** Before executing the IPython session, determine what the following function displays if you call it with the value `10.7` then the value `'Python'`?\n",
-    "```python\n",
-    "def try_it(value)\n",
-    "    try:\n",
-    "        x = int(value)\n",
-    "    except ValueError:\n",
-    "        print(f'{value} could not be converted to an integer')\n",
-    "    else:\n",
-    "        print(f'int({value}) is {int(value)}')\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def try_it(value):\n",
-    "    try:\n",
-    "        x = int(value)\n",
-    "    except ValueError:\n",
-    "        print(f'{value} could not convert to an integer')\n",
-    "    else:\n",
-    "        print(f'int({value}) is {int(value)}')\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try_it(10.7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try_it('Python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_09-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_09-checkpoint.ipynb
deleted file mode 100755
index 68fea03..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_09-checkpoint.ipynb
+++ /dev/null
@@ -1,125 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 9.9 finally Clause\n",
-    "### The `finally` Clause of the `try` Statement\n",
-    "### Example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    print('try suite with no exceptions raised')\n",
-    "except:\n",
-    "    print('this will not execute')\n",
-    "else:\n",
-    "    print('else executes because no exceptions in the try suite')\n",
-    "finally:  \n",
-    "    print('finally always executes')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    print('try suite that raises an exception')\n",
-    "    int('hello')\n",
-    "    print('this will not execute')\n",
-    "except ValueError:\n",
-    "    print('a ValueError occurred')\n",
-    "else:\n",
-    "    print('else will not execute because an exception occurred')\n",
-    "finally:  \n",
-    "    print('finally always executes')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Combining `with` Statements and `try`…`except` Statements "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "open('gradez.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    with open('gradez.txt', 'r') as accounts:\n",
-    "        print(f'{\"ID\":<3}{\"Name\":<7}{\"Grade\"}')\n",
-    "        for record in accounts:  \n",
-    "            student_id, name, grade = record.split()\n",
-    "            print(f'{student_id:<3}{name:<7}{grade}')\n",
-    "except FileNotFoundError:\n",
-    "    print('The file name you specified does not exist')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_09selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_09selfcheck-checkpoint.ipynb
deleted file mode 100755
index bfd6834..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_09selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,127 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.9 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(True/False)_** If a `finally` clause appears in a function, that `finally` clause is guaranteed to execute when the function executes, regardless of whether the function raises an exception.\n",
-    "\n",
-    "**Answer:** False. The `finally` clause will execute only if program control enters the corresponding `try` suite. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(Fill-In)_** Closing a file helps prevent a(n) ‘________‘ in which the file resource is not available to other programs because a program using the file never closes it.\n",
-    "\n",
-    "**Answer:** resource leak."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** Before executing the IPython session, determine what the following function displays if you call it with the value `10.7` then the value `'Python'`?\n",
-    "```python\n",
-    "def try_it(value)\n",
-    "    try:\n",
-    "        x = int(value)\n",
-    "    except ValueError:\n",
-    "        print(f'{value} could not be converted to an integer')\n",
-    "    else:\n",
-    "        print(f'int({value}) is {int(value)}')\n",
-    "    finally:\n",
-    "        print('finally executed')\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def try_it(value):\n",
-    "    try:\n",
-    "        x = int(value)\n",
-    "    except ValueError:\n",
-    "        print(f'{value} could not convert to an integer')\n",
-    "    else:\n",
-    "        print(f'int({value}) is {int(value)}')\n",
-    "    finally:\n",
-    "        print('finally executed')\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try_it(10.7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try_it('Python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_10selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_10selfcheck-checkpoint.ipynb
deleted file mode 100755
index b0b6a0d..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_10selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,64 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 8.10 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Use the ‘________‘ statement to indicate that a problem occurred at execution time.\n",
-    "\n",
-    "**Answer:** raise.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_11-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_11-checkpoint.ipynb
deleted file mode 100755
index 5354592..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_11-checkpoint.ipynb
+++ /dev/null
@@ -1,94 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 9.11 (Optional) Stack Unwinding and Tracebacks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def function1():\n",
-    "    function2()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def function2():\n",
-    "    raise Exception('An exception occurred')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "function1()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Traceback Details\n",
-    "### Stack Unwinding\n",
-    "### Tip for Reading Tracebacks\n",
-    "### Exceptions in finally Suites"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_11selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_11selfcheck-checkpoint.ipynb
deleted file mode 100755
index 3bf1790..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_11selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.11 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** An uncaught exception in a function causes ‘________‘. The function’s stack frame is removed from the function call stack.\n",
-    "\n",
-    "**Answer:** stack unwinding. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Exceptions always are handled in the function that raises the exception.\n",
-    "\n",
-    "**Answer:** False. Although it is possible to handle an exception in the function that raises it. Normally an exception is handled by a calling function on the function call stack. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(True/False)_** Exceptions can be raised only by code in `try` statements.\n",
-    "\n",
-    "**Answer:** False. Exceptions can be raised by any code, regardless of whether the code is wrapped in a `try` statement. The interpreter also can raise exceptions, like `ZeroDivisionError`s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.01-checkpoint.ipynb
deleted file mode 100755
index 3351fe1..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.01-checkpoint.ipynb
+++ /dev/null
@@ -1,103 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.12.1 Python Standard Library Module `csv` \n",
-    "### Writing to a CSV File"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import csv\n",
-    "\n",
-    "with open('accounts.csv', mode='w') as accounts:\n",
-    "    writer = csv.writer(accounts)\n",
-    "    writer.writerow([100, 'Jones', 24.98])\n",
-    "    writer.writerow([200, 'Doe', 345.67])\n",
-    "    writer.writerow([300, 'White', 0.00])\n",
-    "    writer.writerow([400, 'Stone', -42.16])\n",
-    "    writer.writerow([500, 'Rich', 224.62])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reading from a CSV File"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('accounts.csv', 'r') as accounts:\n",
-    "    print(f'{\"Account\":<10}{\"Name\":<10}{\"Balance\":>10}')\n",
-    "    reader = csv.reader(accounts)\n",
-    "    for record in reader:  \n",
-    "        account, name, balance = record\n",
-    "        print(f'{account:<10}{name:<10}{balance:>10}')\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Caution: Commas in CSV Data Fields\n",
-    "### Caution: Missing Commas and Extra Commas in CSV Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.01selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.01selfcheck-checkpoint.ipynb
deleted file mode 100755
index 919fcaf..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.01selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,116 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.12.1 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The `csv` module provides capabilities for writing and reading files in ‘________‘ (CSV) format.\n",
-    "\n",
-    "**Answer:** comma-separated values.\n",
-    "\n",
-    "**2. _(True/False)_** The `csv` module’s `reader` function returns an object that reads from the specified file object CSV-format data. \n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**3. _(IPython Session)_** Create a text file named `grades.csv` and write to it the following three records consisting of student IDs, last names and letter grades:\n",
-    "```\n",
-    "1,Red,A\n",
-    "2,Green,B\n",
-    "3,White,A\n",
-    "```\n",
-    "Then, read the file `grades.csv` and display it in columns with the column heads `'ID'`, `'Name'` and `'Grade'`.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import csv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('grades.csv', mode='w') as grades:\n",
-    "    writer = csv.writer(grades)\n",
-    "    writer.writerow([1, 'Red', 'A'])\n",
-    "    writer.writerow([2, 'Green', 'B'])\n",
-    "    writer.writerow([3, 'White', 'A'])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('grades.csv', 'r') as grades:\n",
-    "    print(f'{\"ID\":<4}{\"Name\":<7}{\"Grade\"}')\n",
-    "    reader = csv.reader(grades)\n",
-    "    for record in reader:  \n",
-    "        student_id, name, grade = record\n",
-    "        print(f'{student_id:<4}{name:<7}{grade}')\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.02-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.02-checkpoint.ipynb
deleted file mode 100755
index b8367c9..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.02-checkpoint.ipynb
+++ /dev/null
@@ -1,84 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.12.2 Reading CSV Files into Pandas `DataFrame`s \n",
-    "### Datasets\n",
-    "### Working with Locally Stored CSV Files "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv('accounts.csv', \n",
-    "                 names=['account', 'name', 'balance'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv('accounts_from_dataframe.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.03-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.03-checkpoint.ipynb
deleted file mode 100755
index 2c7f174..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.03-checkpoint.ipynb
+++ /dev/null
@@ -1,134 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.12.3 Reading the Titanic Disaster Dataset \n",
-    "### Loading the Titanic Dataset via a URL"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "titanic = pd.read_csv('https://vincentarelbundock.github.io/' +\n",
-    "    'Rdatasets/csv/carData/TitanicSurvival.csv')\n",
-    "                      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Viewing Some of the Rows in the Titanic Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('precision', 2)  # format for floating-point values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "titanic.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "titanic.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Customizing the Column Names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "titanic.columns = ['name', 'survived', 'sex', 'age', 'class']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "titanic.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.04-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.04-checkpoint.ipynb
deleted file mode 100755
index 098c47a..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.04-checkpoint.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.12.4 Simple Data Analysis with the Titanic Disaster Dataset "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "titanic.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(titanic.survived == 'yes').describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.05-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.05-checkpoint.ipynb
deleted file mode 100755
index 0539ab0..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.05-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9.12.5 Passenger Age Histogram\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "histogram = titanic.hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.01-checkpoint.ipynb
deleted file mode 100755
index 74d1e06..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.01-checkpoint.ipynb
+++ /dev/null
@@ -1,161 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.2.1 Test-Driving Class `Account` "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Importing Classes Account and Decimal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from account import Account"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from decimal import Decimal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create an `Account` Object with a Constructor Expression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1 = Account('John Green', Decimal('50.00'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting an `Account`’s Name and Balance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.balance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Depositing Money into an `Account` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.deposit(Decimal('25.53'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.balance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `Account` Methods Perform Validation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.deposit(Decimal('-123.45'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.02-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.02-checkpoint.ipynb
deleted file mode 100644
index c88565a..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.02-checkpoint.ipynb
+++ /dev/null
@@ -1,92 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.2.2 `Account` Class Definition\n",
-    "### Defining a Class \n",
-    "```python\n",
-    "# account.py\n",
-    "\"\"\"Account class definition.\"\"\"\n",
-    "from decimal import Decimal\n",
-    "\n",
-    "class Account:\n",
-    "    \"\"\"Account class for maintaining a bank account balance.\"\"\"\n",
-    "    \n",
-    "\n",
-    "```\n",
-    "\n",
-    "### Initializing Account Objects: Method `__init__` \n",
-    "```python\n",
-    "    def __init__(self, name, balance):\n",
-    "        \"\"\"Initialize an Account object.\"\"\"\n",
-    "\n",
-    "        # if balance is less than 0.00, raise an exception\n",
-    "        if balance < Decimal('0.00'):\n",
-    "            raise ValueError('Initial balance must be >= to 0.00.')\n",
-    "\n",
-    "        self.name = name\n",
-    "        self.balance = balance\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Method deposit \n",
-    "```python\n",
-    "    def deposit(self, amount):\n",
-    "        \"\"\"Deposit money to the account.\"\"\"\n",
-    "\n",
-    "        # if amount is less than 0.00, raise an exception\n",
-    "        if amount < Decimal('0.00'):\n",
-    "            raise ValueError('amount must be positive.')\n",
-    "\n",
-    "        self.balance += amount\n",
-    "\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.03selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.03selfcheck-checkpoint.ipynb
deleted file mode 100644
index cbf2fb5..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.03selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,148 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.2.3 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** A class’s `________` method is called by a constructor expression to initialize a new object of the class.\n",
-    "\n",
-    "**Answer:** `__init__`. \n",
-    "\n",
-    "**2. _(True/False)_** A class’s `__init__` method returns an object of the class.\n",
-    "\n",
-    "**Answer:** False. A class’s `__init__` method initializes an object of the class and implicitly returns None. \n",
-    "\n",
-    "**3. _(IPython Session)_** Add a `withdraw` method to class Account. If the withdrawal `amount` is greater than the `balance`, raise a `ValueError`, indicating that the withdrawal `amount` must be less than or equal to the `balance`. If the withdrawal `amount` is less than `0.00`, raise a `ValueError` indicating that the withdrawal `amount` must be positive. If the withdrawal `amount` is valid, subtract it from the `balance` attribute. Create an `Account` object then test method `withdraw` first with a valid withdrawal `amount`, then with a withdrawal `amount` greater than the `balance` and finally with a negative withdrawal `amount`. \n",
-    "\n",
-    "**Answer:** The new method in class Account is:\n",
-    "```python\n",
-    "def withdraw(self, amount):\n",
-    "    \"\"\"Withdraw money from the account.\"\"\"\n",
-    "\n",
-    "    # if amount is greater than balance, raise an exception\n",
-    "    if amount > self.balance:\n",
-    "        raise ValueError('amount must be <= to balance.')\n",
-    "    elif amount < Decimal('0.00'):\n",
-    "        raise ValueError('amount must be positive.')\n",
-    "\n",
-    "    self.balance -= amount\n",
-    "```\n",
-    "Testing method withdraw:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from account import Account"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from decimal import Decimal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1 = Account('John Green', Decimal('50.00'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.withdraw(Decimal('20.00'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.balance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.withdraw(Decimal('100.00'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.withdraw(Decimal('-10.00'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_03-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_03-checkpoint.ipynb
deleted file mode 100755
index 8bf8195..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_03-checkpoint.ipynb
+++ /dev/null
@@ -1,116 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10.3 Controlling Access to Attributes "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from account import Account"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from decimal import Decimal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1 = Account('John Green', Decimal('50.00'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.balance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.balance = Decimal('-1000.00')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "account1.balance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Encapsulation \n",
-    "### Leading Underscore (`_`) Naming Convention"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.01-checkpoint.ipynb
deleted file mode 100755
index 41fd14e..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.01-checkpoint.ipynb
+++ /dev/null
@@ -1,186 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.4.1 Test-Driving Class `Time` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from timewithproperties import Time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a `Time` Object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up = Time(hour=6, minute=30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying a `Time` Object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(wake_up)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting an Attribute Via a Property "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up.hour"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setting the `Time` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up.set_time(hour=7, minute=45)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setting an Attribute via a Property "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up.hour = 6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Attempting to Set an Invalid Value "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up.hour = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.01selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.01selfcheck-checkpoint.ipynb
deleted file mode 100755
index 0031956..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.01selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,80 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.4.1 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Self Check\n",
-    "**1. _(Fill-In)_** The `print` function implicitly invokes special method `________`.\n",
-    "\n",
-    "**Answer:** `__str__`. \n",
-    "\n",
-    "**2. _(Fill-In)_** IPython calls an object’s special method `________` to produce a string representation of the object\n",
-    "\n",
-    "**Answer:** `__repr__`.\n",
-    "\n",
-    "**3. _(True/False)_** Properties are used like methods.\n",
-    "\n",
-    "**Answer:** False. Properties are used like data attributes, but (as we’ll see in the next section) are implemented as methods.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.02-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.02-checkpoint.ipynb
deleted file mode 100644
index c68417b..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.02-checkpoint.ipynb
+++ /dev/null
@@ -1,145 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.4.2 Class `Time` Definition\n",
-    "### Class Time: `__init__` Method with Default Parameter Values\n",
-    "```python\n",
-    "# time1.py\n",
-    "\"\"\"Class Time with read-write properties.\"\"\"\n",
-    "\n",
-    "class Time:\n",
-    "    \"\"\"Class Time with read-write properties.\"\"\"\n",
-    "\n",
-    "    def __init__(self, hour=0, minute=0, second=0):\n",
-    "        \"\"\"Initialize each attribute.\"\"\"\n",
-    "        self.hour = hour  # 0-23\n",
-    "        self.minute = minute  # 0-59\n",
-    "        self.second = second  # 0-59\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Class `Time`: `hour` Read-Write Property\n",
-    "```python\n",
-    "    @property\n",
-    "    def hour(self):\n",
-    "        \"\"\"Return the hour.\"\"\"\n",
-    "        return self._hour\n",
-    "\n",
-    "    @hour.setter\n",
-    "    def hour(self, hour):\n",
-    "        \"\"\"Set the hour.\"\"\"\n",
-    "        if not (0 <= hour < 24):\n",
-    "            raise ValueError(f'Hour ({hour}) must be 0-23')\n",
-    "\n",
-    "        self._hour = hour\n",
-    "\n",
-    "\n",
-    "``` \n",
-    "### Class `Time`: `minute` and `second` Read-Write Properties\n",
-    "```python\n",
-    "    @property\n",
-    "    def minute(self):\n",
-    "        \"\"\"Return the minute.\"\"\"\n",
-    "        return self._minute\n",
-    "\n",
-    "    @minute.setter\n",
-    "    def minute(self, minute):\n",
-    "        \"\"\"Set the minute.\"\"\"\n",
-    "        if not (0 <= minute < 60):\n",
-    "            raise ValueError(f'Minute ({minute}) must be 0-59')\n",
-    "\n",
-    "        self._minute = minute\n",
-    "\n",
-    "    @property\n",
-    "    def second(self):\n",
-    "        \"\"\"Return the second.\"\"\"\n",
-    "        return self._second\n",
-    "\n",
-    "    @second.setter\n",
-    "    def second(self, second):\n",
-    "        \"\"\"Set the second.\"\"\"\n",
-    "        if not (0 <= second < 60):\n",
-    "            raise ValueError(f'Second ({second}) must be 0-59')\n",
-    "\n",
-    "        self._second = second\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Class `Time`: Method `set_time` \n",
-    "```python\n",
-    "    def set_time(self, hour=0, minute=0, second=0):\n",
-    "        \"\"\"Set values of hour, minute, and second.\"\"\"\n",
-    "        self.hour = hour\n",
-    "        self.minute = minute\n",
-    "        self.second = second\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Class `Time`: Special Method `__repr__`\n",
-    "```python\n",
-    "    def __repr__(self):\n",
-    "        \"\"\"Return Time string for repr().\"\"\"\n",
-    "        return (f'Time(hour={self.hour}, minute={self.minute}, ' + \n",
-    "                f'second={self.second})')\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Class `Time`: Special Method `__str__` \n",
-    "```python\n",
-    "    def __str__(self):\n",
-    "        \"\"\"Print Time in 12-hour clock format.\"\"\"\n",
-    "        return (('12' if self.hour in (0, 12) else str(self.hour % 12)) + \n",
-    "                f':{self.minute:0>2}:{self.second:0>2}' + \n",
-    "                (' AM' if self.hour < 12 else ' PM'))\n",
-    "\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.02selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.02selfcheck-checkpoint.ipynb
deleted file mode 100644
index d8f8a66..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,151 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.4.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The `print` function implicitly invokes special method `________`.\n",
-    "\n",
-    "**Answer:** `__str__`. \n",
-    "\n",
-    "**2. _(Fill-In)_** A(n) `________` property has both a getter and setter. If only a getter is provided, the property is a(n) `________` property, meaning that you only can get the property’s value.\n",
-    "\n",
-    "**Answer:** read-write, read-only.\n",
-    "\n",
-    "**3. _(IPython Session)_** Add to class `Time` a read-write property `time` in which the getter returns a tuple containing the values of the `hour`, `minute` and `second` properties, and the _setter_ receives a tuple containing hour, minute and second values and uses them to set the time. Create a `Time` object and test the new property.\n",
-    "\n",
-    "**Answer:** The new read-write property definition is shown below:\n",
-    "```python\n",
-    "@property \n",
-    "def time(self):\n",
-    "    \"\"\"Return hour, minute and second as a tuple.\"\"\"\n",
-    "    return (self.hour, self.minute, self.second)\n",
-    "\n",
-    "@time.setter\n",
-    "def time(self, time_tuple):\n",
-    "    \"\"\"Set time from a tuple containing hour, minute and second.\"\"\"\n",
-    "    self.set_time(time_tuple[0], time_tuple[1], time_tuple[2])\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from timewithproperties import Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = Time()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t.time = (12, 30, 45)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t.time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the `self.set_time` call in the time property’s `setter` method may be expressed more concisely as\n",
-    "```python\n",
-    "self.set_time(*time_tuple)\n",
-    "```\n",
-    "The expression `*time_tuple` uses the unary `*` operator to _unpack_ the `time_tuple`’s values then passes them as individual arguments. In the preceding IPython session, the `setter` would receive the tuple `(12, 30, 45)`, then unpack the tuple and call `self.set_time` as follows:\n",
-    "```python\n",
-    "self.set_time(12, 30, 45)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.03-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.03-checkpoint.ipynb
deleted file mode 100644
index 2beddf7..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.03-checkpoint.ipynb
+++ /dev/null
@@ -1,112 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.4.3 Class `Time` Definition Design Notes \n",
-    "### Interface of a Class\n",
-    "### Attributes Are Always Accessible"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from timewithproperties import Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up = Time(hour=7, minute=45, second=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up._hour"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up._hour = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wake_up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Internal Data Representation\n",
-    "### Evolving a Class’s Implementation Details\n",
-    "### Properties\n",
-    "### Utility Methods\n",
-    "### Module `datetime` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_05-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_05-checkpoint.ipynb
deleted file mode 100755
index 152e38a..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_05-checkpoint.ipynb
+++ /dev/null
@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10.5 Simulating “Private” Attributes \n",
-    "\n",
-    "**Note: This notebook contains the Self Check exercises because the Self Check IPython session continues from the section.**\n",
-    "\n",
-    "### IPython Auto-Completion Shows Only “Public” Attributes\n",
-    "### Demonstrating “Private” Attributes\n",
-    "```python\n",
-    "# private.py\n",
-    "\"\"\"Class with public and private attributes.\"\"\"\n",
-    "\n",
-    "class PrivateClass:\n",
-    "    \"\"\"Class with public and private attributes.\"\"\"\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        \"\"\"Initialize the public and private attributes.\"\"\"\n",
-    "        self.public_data = \"public\"  # public attribute\n",
-    "        self.__private_data = \"private\"  # private attribute\n",
-    "\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from private import PrivateClass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_object = PrivateClass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_object.public_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_object.__private_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.5 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** Python mangles attribute names that begin with `________` underscore(s).\n",
-    "\n",
-    "**Answer:** two. \n",
-    "\n",
-    "**2. _(True/False)_** An attribute that begins with a single underscore is a private attribute.\n",
-    "\n",
-    "**Answer:** False. An attribute that begins with a single underscore simply conveys the convention that a client of the class should not access the attribute directly, but it does allow access. Again, “nothing in Python makes it possible to enforce data hiding—it is all based upon convention.”\n",
-    "\n",
-    "**4. _(IPython Session)_** Even with double-underscore (`__`) naming, we can still access and modify `__private_data`, because we know that Python renames attributes simply by prefixing their names with `'_`_ClassName_`'`. Demonstrate this for class `PrivateData`’s data attribute __`private_data`. \n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_object._PrivateClass__private_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_object._PrivateClass__private_data = 'modified'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_object._PrivateClass__private_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.01-checkpoint.ipynb
deleted file mode 100755
index 6ae6e85..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.01-checkpoint.ipynb
+++ /dev/null
@@ -1,151 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.6.1 Test-Driving Classes `Card` and `DeckOfCards` \n",
-    "### Creating, Shuffling and Dealing the Cards "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from deck import DeckOfCards"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deck_of_cards = DeckOfCards()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(deck_of_cards)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deck_of_cards.shuffle()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(deck_of_cards)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dealing Cards"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "  \n",
-    "deck_of_cards.deal_card()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Class Card’s Other Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card = deck_of_cards.deal_card()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "str(card)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card.image_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.02-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.02-checkpoint.ipynb
deleted file mode 100644
index 99dee9e..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.02-checkpoint.ipynb
+++ /dev/null
@@ -1,114 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.6.2 Class `Card`—Introducing Class Attributes \n",
-    "### Class Attributes `FACES` and `SUITS` \n",
-    "```python\n",
-    "# card.py\n",
-    "\"\"\"Card class that represents a playing card and its image file name.\"\"\"\n",
-    "\n",
-    "class Card:\n",
-    "    FACES = ['Ace', '2', '3', '4', '5', '6',\n",
-    "             '7', '8', '9', '10', 'Jack', 'Queen', 'King']\n",
-    "    SUITS = ['Hearts', 'Diamonds', 'Clubs', 'Spades']\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Card Method `__init__` \n",
-    "```python\n",
-    "\n",
-    "    def __init__(self, face, suit):\n",
-    "        \"\"\"Initialize a Card with a face and suit.\"\"\"\n",
-    "        self._face = face\n",
-    "        self._suit = suit\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Read-Only Properties `face`, `suit` and `image_name` \n",
-    "```python\n",
-    "    @property\n",
-    "    def face(self):\n",
-    "        \"\"\"Return the Card's self._face value.\"\"\"\n",
-    "        return self._face\n",
-    "\n",
-    "    @property\n",
-    "    def suit(self):\n",
-    "        \"\"\"Return the Card's self._suit value.\"\"\"\n",
-    "        return self._suit\n",
-    "\n",
-    "    @property\n",
-    "    def image_name(self):\n",
-    "        \"\"\"Return the Card's image file name.\"\"\"\n",
-    "        return str(self).replace(' ', '_') + '.png'\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Methods that Return String Representations of a Card \n",
-    "```python\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "        \"\"\"Return string representation for repr().\"\"\"\n",
-    "        return f\"Card(face='{self.face}', suit='{self.suit}')\"     \n",
-    "\n",
-    "\n",
-    "\n",
-    "    def __str__(self):\n",
-    "        \"\"\"Return string representation for str().\"\"\"\n",
-    "        return f'{self.face} of {self.suit}'\n",
-    "\n",
-    "\n",
-    "\n",
-    "    def __format__(self, format):\n",
-    "        \"\"\"Return formatted string representation for str().\"\"\"\n",
-    "        return f'{str(self):{format}}'\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.03-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.03-checkpoint.ipynb
deleted file mode 100644
index c31639e..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.03-checkpoint.ipynb
+++ /dev/null
@@ -1,113 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.6.3 Class `DeckOfCards` \n",
-    "### Method `__init__`\n",
-    "```python\n",
-    "# deck.py\n",
-    "\"\"\"Deck class represents a deck of Cards.\"\"\"\n",
-    "import random \n",
-    "from card import Card\n",
-    "\n",
-    "class DeckOfCards:\n",
-    "    NUMBER_OF_CARDS = 52  # constant number of Cards\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        \"\"\"Initialize the deck.\"\"\"\n",
-    "        self._current_card = 0\n",
-    "        self._deck = []\n",
-    "\n",
-    "        for count in range(DeckOfCards.NUMBER_OF_CARDS):  \n",
-    "            self._deck.append(Card(Card.FACES[count % 13], \n",
-    "                Card.SUITS[count // 13]))\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Method `shuffle`\n",
-    "```python\n",
-    "    def shuffle(self):\n",
-    "        \"\"\"Shuffle deck.\"\"\"\n",
-    "        self._current_card = 0\n",
-    "        random.shuffle(self._deck)    \n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Method `deal_card`\n",
-    "```python\n",
-    "\n",
-    "    def deal_card(self):\n",
-    "        \"\"\"Return one Card.\"\"\"\n",
-    "        try:\n",
-    "            card = self._deck[self._current_card]\n",
-    "            self._current_card += 1\n",
-    "            return card\n",
-    "        except:\n",
-    "            return None  \n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Method `__str__`\n",
-    "```python\n",
-    "\n",
-    "    def __str__(self):\n",
-    "        \"\"\"Return a string representation of the current _deck.\"\"\"\n",
-    "        s = ''\n",
-    "\n",
-    "        for index, card in enumerate(self._deck):\n",
-    "            s += f'{self._deck[index]:<19}'\n",
-    "            if (index + 1) % 4 == 0:\n",
-    "                s += '\\n'\n",
-    "        \n",
-    "        return s\n",
-    "\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.04-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.04-checkpoint.ipynb
deleted file mode 100644
index e19e33d..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_06.04-checkpoint.ipynb
+++ /dev/null
@@ -1,289 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.6.4 Displaying Card Images with Matplotlib \n",
-    "\n",
-    "**Note: This notebook contains the Self Check exercises because the Self Check IPython session continues from the section.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from deck import DeckOfCards"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deck_of_cards = DeckOfCards()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Enable Matplotlib in IPython"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create the Base `Path` for Each Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path = Path('.').joinpath('card_images')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Import the Matplotlib Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.image as mpimg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create the `Figure` and `Axes` Objects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "figure, axes_list = plt.subplots(nrows=4, ncols=13)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Configure the `Axes` Objects and Display the Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for axes in axes_list.ravel():\n",
-    "    axes.get_xaxis().set_visible(False)\n",
-    "    axes.get_yaxis().set_visible(False)\n",
-    "    image_name = deck_of_cards.deal_card().image_name\n",
-    "    img = mpimg.imread(str(path.joinpath(image_name).resolve()))\n",
-    "    axes.imshow(img)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Maximize the Image Sizes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "figure.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Shuffle and Re-Deal the Deck"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deck_of_cards.shuffle()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for axes in axes_list.ravel():\n",
-    "    axes.get_xaxis().set_visible(False)\n",
-    "    axes.get_yaxis().set_visible(False)\n",
-    "    image_name = deck_of_cards.deal_card().image_name\n",
-    "    img = mpimg.imread(str(path.joinpath(image_name).resolve()))\n",
-    "    axes.imshow(img)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.6.4 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Matplotlib function `________` returns a tuple containing a `Figure` and a list of `Axes` objects.\n",
-    "\n",
-    "**Answer:** `subplots`.\n",
-    "\n",
-    "**2. _(True/False)_** `Path` method `appendpath` appends to a `Path` object. \n",
-    "\n",
-    "**Answer:** False, `Path` method `joinpath` appends to a `Path` object\n",
-    "\n",
-    "**4. _(IPython Session)_** Continue this section’s session by reshuffling the cards, then creating a new `Figure` containing two rows of five cards each—these might represent two five-card-stud poker hands.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deck_of_cards.shuffle()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "figure, axes_list = plt.subplots(nrows=2, ncols=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for axes in axes_list.ravel():\n",
-    "     axes.get_xaxis().set_visible(False)\n",
-    "     axes.get_yaxis().set_visible(False)\n",
-    "     image_name = deck_of_cards.deal_card().image_name\n",
-    "     img = mpimg.imread(str(path.joinpath(image_name).resolve()))\n",
-    "     axes.imshow(img)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "figure.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_07selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_07selfcheck-checkpoint.ipynb
deleted file mode 100755
index 962f9d8..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_07selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,68 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.7 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** A base class exists in a `________` relationship with its subclasses.\n",
-    "\n",
-    "**Answer:** hierarchical. \n",
-    "\n",
-    "**2. _(Fill-In)_** In this section’s `Shape` class hierarchy, `TwoDimensionalShape` is a `________`  of  `Shape` and a `________` of `Circle`, `Square` and `Triangle`. \n",
-    "\n",
-    "**Answer:** subclass, base class.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_08-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_08-checkpoint.ipynb
deleted file mode 100755
index 40007e4..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_08-checkpoint.ipynb
+++ /dev/null
@@ -1,461 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10.8 Building an Inheritance Hierarchy; Introducing Polymorphism\n",
-    "\n",
-    "**Note: This notebook contains all of Section 10.8, including its Self Check exercises because the IPython session continues through the entire section.\n",
-    "\n",
-    "## 10.8.1 Base Class `CommissionEmployee` \n",
-    "```python\n",
-    "# commmissionemployee.py\n",
-    "\"\"\"CommissionEmployee base class.\"\"\"\n",
-    "from decimal import Decimal\n",
-    "\n",
-    "class CommissionEmployee:\n",
-    "    \"\"\"An employee who gets paid commission based on gross sales.\"\"\"\n",
-    "\n",
-    "    def __init__(self, first_name, last_name, ssn, \n",
-    "                 gross_sales, commission_rate):\n",
-    "        \"\"\"Initialize CommissionEmployee's attributes.\"\"\"\n",
-    "        self._first_name = first_name\n",
-    "        self._last_name = last_name\n",
-    "        self._ssn = ssn\n",
-    "        self.gross_sales = gross_sales  # validate via property\n",
-    "        self.commission_rate = commission_rate  # validate via property\n",
-    "\n",
-    "    @property\n",
-    "    def first_name(self):\n",
-    "        return self._first_name\n",
-    "\n",
-    "    @property\n",
-    "    def last_name(self):\n",
-    "        return self._last_name\n",
-    "\n",
-    "    @property\n",
-    "    def ssn(self):\n",
-    "        return self._ssn\n",
-    "\n",
-    "    @property\n",
-    "    def gross_sales(self):\n",
-    "        return self._gross_sales\n",
-    "\n",
-    "    @gross_sales.setter\n",
-    "    def gross_sales(self, sales):\n",
-    "        \"\"\"Set gross sales or raise ValueError if invalid.\"\"\"\n",
-    "        if sales < Decimal('0.00'):\n",
-    "            raise ValueError('Gross sales must be >= to 0')\n",
-    "        \n",
-    "        self._gross_sales = sales\n",
-    "        \n",
-    "    @property\n",
-    "    def commission_rate(self):\n",
-    "        return self._commission_rate\n",
-    "\n",
-    "    @commission_rate.setter\n",
-    "    def commission_rate(self, rate):\n",
-    "        \"\"\"Set commission rate or raise ValueError if invalid.\"\"\"\n",
-    "        if not (Decimal('0.0') < rate < Decimal('1.0')):\n",
-    "            raise ValueError(\n",
-    "               'Interest rate must be greater than 0 and less than 1')\n",
-    "        \n",
-    "        self._commission_rate = rate\n",
-    "\n",
-    "    def earnings(self):\n",
-    "        \"\"\"Calculate earnings.\"\"\"   \n",
-    "        return self.gross_sales * self.commission_rate\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "        \"\"\"Return string representation for repr().\"\"\"\n",
-    "        return ('CommissionEmployee: ' + \n",
-    "            f'{self.first_name} {self.last_name}\\n' +\n",
-    "            f'social security number: {self.ssn}\\n' +\n",
-    "            f'gross sales: {self.gross_sales:.2f}\\n' +\n",
-    "            f'commission rate: {self.commission_rate:.2f}')\n",
-    "\n",
-    "```\n",
-    "### All Classes Inherit Directly or Indirectly from Class `object`\n",
-    "### Testing Class `CommissionEmployee`  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from commissionemployee import CommissionEmployee"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from decimal import Decimal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = CommissionEmployee('Sue', 'Jones', '333-33-3333', \n",
-    "    Decimal('10000.00'), Decimal('0.06'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'{c.earnings():,.2f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c.gross_sales = Decimal('20000.00')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c.commission_rate = Decimal('0.1')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'{c.earnings():,.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.8.1 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** When a base-class method implementation is inappropriate for a derived class, that method can be `________` (i.e., redefined) in the derived class with an appropriate implementation. \n",
-    "\n",
-    "**Answer:** overridden.\n",
-    "\n",
-    "**2. _(What does this code do?)_** In this section’s IPython session, explain in detail what snippet `[6]` does:\n",
-    "```python\n",
-    "c.gross_sales = Decimal('20000.00')\n",
-    "```\n",
-    "\n",
-    "**Answer:** This statement creates a `Decimal` object and assigns it to a `CommissionEmployee`’s `gross_sales` property, invoking the property’s `setter`. The `setter` checks whether the new value is less than `Decimal('0.00')`. If so, the `setter` raises a `ValueError`, indicating that the value must be greater than or equal to 0; otherwise, the `setter` assigns the new value to the `CommissionEmployee`’s `_gross_sales` attribute.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.8.2 Subclass `SalariedCommissionEmployee` \n",
-    "### Declaring Class `SalariedCommissionEmployee` \n",
-    "```python\n",
-    "# basepluscommissionemployee.py\n",
-    "\"\"\"SalariedCommissionEmployee derived from CommissionEmployee.\"\"\"\n",
-    "from commissionemployee import CommissionEmployee\n",
-    "from decimal import Decimal\n",
-    "\n",
-    "class SalariedCommissionEmployee(CommissionEmployee):\n",
-    "    \"\"\"An employee who gets paid a salary plus \n",
-    "    commission based on gross sales.\"\"\"\n",
-    "\n",
-    "    def __init__(self, first_name, last_name, ssn, \n",
-    "                 gross_sales, commission_rate, base_salary):\n",
-    "        \"\"\"Initialize SalariedCommissionEmployee's attributes.\"\"\"\n",
-    "        super().__init__(first_name, last_name, ssn, \n",
-    "                         gross_sales, commission_rate)\n",
-    "        self.base_salary = base_salary  # validate via property\n",
-    "\n",
-    "    @property\n",
-    "    def base_salary(self):\n",
-    "        return self._base_salary\n",
-    "\n",
-    "    @base_salary.setter\n",
-    "    def base_salary(self, salary):\n",
-    "        \"\"\"Set base salary or raise ValueError if invalid.\"\"\"\n",
-    "        if salary < Decimal('0.00'):\n",
-    "            raise ValueError('Base salary must be >= to 0')\n",
-    "        \n",
-    "        self._base_salary = salary\n",
-    "\n",
-    "    def earnings(self):\n",
-    "        \"\"\"Calculate earnings.\"\"\"   \n",
-    "        return super().earnings() + self.base_salary\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "        \"\"\"Return string representation for repr().\"\"\"\n",
-    "        return ('Salaried' + super().__repr__() +      \n",
-    "            f'\\nbase salary: {self.base_salary:.2f}')\n",
-    "\n",
-    "```\n",
-    "### Inheriting from Class `CommissionEmployee`\n",
-    "### Method `__init__` and Built-In Function `super` \n",
-    "### Overriding Method `earnings`\n",
-    "### Overriding Method `__repr__`\n",
-    "# Testing Class `SalariedCommissionEmployee` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from salariedcommissionemployee import SalariedCommissionEmployee"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = SalariedCommissionEmployee('Bob', 'Lewis', '444-44-4444',\n",
-    "         Decimal('5000.00'), Decimal('0.04'), Decimal('300.00'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(s.first_name, s.last_name, s.ssn, s.gross_sales, \n",
-    "       s.commission_rate, s.base_salary)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'{s.earnings():,.2f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s.gross_sales = Decimal('10000.00')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s.commission_rate = Decimal('0.05')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s.base_salary = Decimal('1000.00')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'{s.earnings():,.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the “is a” Relationship "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issubclass(SalariedCommissionEmployee, CommissionEmployee)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isinstance(s, CommissionEmployee)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isinstance(s, SalariedCommissionEmployee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.8.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Function `________` determines whether an object has an “is a” relationship with a specific type. \n",
-    "\n",
-    "**Answer:** `isinstance`. \n",
-    "\n",
-    "**2. _(Fill-In)_** Function `________` determines whether one class is derived from another. \n",
-    "\n",
-    "**Answer:** `issubclass`.\n",
-    "\n",
-    "**3. _(What does this code do?)_** Explain in detail what the following statement from class `SalariedCommissionEmployee`’s `earnings` method does:\n",
-    "```python\n",
-    "return super().earnings() + self.base_salary\n",
-    "```\n",
-    "\n",
-    "**Answer:** This statement calculates a `SalariedCommissionEmployee`’s earnings by using the built-in function `super` to invoke the base class `CommissionEmployee`’s version of method earnings then adding to the result the `base_salary`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.8.3 Processing `CommissionEmployee`s and `SalariedCommissionEmployee`s Polymorphically"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "employees = [c, s]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for employee in employees:\n",
-    "     print(employee)\n",
-    "     print(f'{employee.earnings():,.2f}\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.8.3 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** `________` enables us to take advantage of the “subclass-object-is-a-base-class-object” relationship to process objects in a general way.\n",
-    "\n",
-    "**Answer:** Polymorphism."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_09-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_09-checkpoint.ipynb
deleted file mode 100755
index 0938170..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_09-checkpoint.ipynb
+++ /dev/null
@@ -1,143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10.9 Duck Typing and Polymorphism"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class WellPaidDuck:\n",
-    "    def __repr__(self):\n",
-    "        return 'I am a well-paid duck'\n",
-    "    def earnings(self):\n",
-    "        return Decimal('1_000_000.00')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from decimal import Decimal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from commissionemployee import CommissionEmployee"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from salariedcommissionemployee import SalariedCommissionEmployee"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = CommissionEmployee('Sue', 'Jones', '333-33-3333',\n",
-    "                       Decimal('10000.00'), Decimal('0.06'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = SalariedCommissionEmployee('Bob', 'Lewis', '444-44-4444',\n",
-    "    Decimal('5000.00'), Decimal('0.04'), Decimal('300.00'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = WellPaidDuck()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "employees = [c, s, d]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for employee in employees:\n",
-    "    print(employee)\n",
-    "    print(f'{employee.earnings():,.2f}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.01-checkpoint.ipynb
deleted file mode 100755
index af19b8b..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.01-checkpoint.ipynb
+++ /dev/null
@@ -1,153 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.10.1 Test-Driving Class Complex "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from complexnumber import Complex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = Complex(real=2, imaginary=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = Complex(real=5, imaginary=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x + y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x += y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.02-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.02-checkpoint.ipynb
deleted file mode 100644
index f3e665b..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.02-checkpoint.ipynb
+++ /dev/null
@@ -1,103 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.10.2 Class `Complex` Definition\n",
-    "### Method `__init__` \n",
-    "```python\n",
-    "# complexnumber.py\n",
-    "\"\"\"Complex class with overloaded operators.\"\"\"\n",
-    "\n",
-    "class Complex:\n",
-    "    \"\"\"Complex class that represents a complex number \n",
-    "    with real and imaginary parts.\"\"\"\n",
-    "\n",
-    "    def __init__(self, real, imaginary):\n",
-    "        \"\"\"Initialize Complex class's attributes.\"\"\"\n",
-    "        self.real = real\n",
-    "        self.imaginary = imaginary\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Overloaded `+` Operator\n",
-    "```python\n",
-    "\n",
-    "    def __add__(self, right):\n",
-    "        \"\"\"Overrides the + operator.\"\"\"\n",
-    "        return Complex(self.real + right.real, \n",
-    "                       self.imaginary + right.imaginary)\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Overloaded `+=` Augmented Assignment\n",
-    "```python\n",
-    "\n",
-    "    def __iadd__(self, right):\n",
-    "        \"\"\"Overrides the += operator.\"\"\"\n",
-    "        self.real += right.real\n",
-    "        self.imaginary += right.imaginary\n",
-    "        return self\n",
-    "\n",
-    "\n",
-    "```\n",
-    "### Method `__repr__`\n",
-    "```python\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "       \"\"\"Return string representation for repr().\"\"\"\n",
-    "        return (f'({self.real} ' + \n",
-    "                ('+' if self.imaginary >= 0 else '-') +\n",
-    "                f' {abs(self.imaginary)}i)')\n",
-    "\n",
-    "```\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.02selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index a33f1bc..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_10.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,147 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.10.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Suppose `a` and `b` are integer variables and a program calculates `a + b`. Now suppose `c` and `d` are string variables and a program performs the concatenation `c + d`. The two `+` operators here are clearly being used for different purposes. This is an example of `________`.\n",
-    "\n",
-    "**Answer:** operator overloading. \n",
-    "\n",
-    "**2. _(True/False)_** Python allows you to create new operators to overload and to change how existing operators work for built-in types.\n",
-    "\n",
-    "**Answer:** False. Python prohibits you from creating new operators, and operator overloading cannot change how an operator works with built-in types.\n",
-    "\n",
-    "**3. _(IPython Session)_** Modify class `Complex` to support operators `-` and `-=`, then test these operators.\n",
-    "\n",
-    "**Answer:** The new method definitions are:\n",
-    "```python\n",
-    "def __sub__(self, right):\n",
-    "    \"\"\"Overrides the - operator.\"\"\"\n",
-    "    return Complex(self.real - right.real, \n",
-    "                   self.imaginary - right.imaginary)\n",
-    "\n",
-    "def __isub__(self, right):\n",
-    "    \"\"\"Overrides the -= operator.\"\"\"\n",
-    "    self.real -= right.real\n",
-    "    self.imaginary -= right.imaginary\n",
-    "    return self\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from complexnumber2 import Complex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = Complex(real=2, imaginary=4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = Complex(real=5, imaginary=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x - y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x -= y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_11selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_11selfcheck-checkpoint.ipynb
deleted file mode 100755
index cfeb609..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_11selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,69 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.11 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Most exceptions you’ll encounter inherit from base class `________` and are defined in module `________`.\n",
-    "\n",
-    "**Answer:** Exception, exceptions.\n",
-    "\n",
-    "**2. _(True/False)_** When you raise an exception from your code, you should generally use a new exception class. \n",
-    "When you raise an exception from your code, you should generally use one of the existing exception classes from the \n",
-    "\n",
-    "**Answer:** Python Standard Library.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_12-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_12-checkpoint.ipynb
deleted file mode 100755
index a9416cb..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_12-checkpoint.ipynb
+++ /dev/null
@@ -1,156 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10.12 Named Tuples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import namedtuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Card = namedtuple('Card', ['face', 'suit'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card = Card(face='Ace', suit='Spades')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card.face"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card.suit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Other Named Tuple Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values = ['Queen', 'Hearts']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card = Card._make(values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "card._asdict()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_12selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_12selfcheck-checkpoint.ipynb
deleted file mode 100755
index 9a46bbf..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_12selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,113 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.12 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The Python Standard Library’s `collections` module’s `________` function creates a custom tuple type that enables you to reference the tuple’s members by name rather than by index number.\n",
-    "\n",
-    "**Answer:** `namedtuple`.\n",
-    "\n",
-    "**2. _(IPython Session)_** Create a `namedtuple` called `Time` with members named `hour`, `minute` and `second`. Then, create a `Time` object, access its members and display its string representation.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import namedtuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Time = namedtuple('Time', ['hour', 'minute', 'second'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = Time(13, 30, 45)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(t.hour, t.minute, t.second)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.01-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.01-checkpoint.ipynb
deleted file mode 100644
index 8b54a73..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.01-checkpoint.ipynb
+++ /dev/null
@@ -1,113 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 10.13.1 Creating a `Card` Data Class \n",
-    "### Importing the `dataclasses` and `typing` Modules\n",
-    "```python\n",
-    "# carddataclass.py\n",
-    "\"\"\"Card data class with class attributes, data attributes, \n",
-    "autogenerated methods and explicitly defined methods.\"\"\"\n",
-    "from dataclasses import dataclass, field\n",
-    "from typing import ClassVar, List\n",
-    "```\n",
-    "### Using the `@dataclass` Decorator\n",
-    "```python\n",
-    "@dataclass\n",
-    "class Card:\n",
-    "```\n",
-    "### Variable Annotations: Class Attributes\n",
-    "```python\n",
-    "    FACES: ClassVar[List[str]] = ['Ace', '2', '3', '4', '5', '6', '7', \n",
-    "                                  '8', '9', '10', 'Jack', 'Queen', 'King']\n",
-    "    SUITS: ClassVar[List[str]] = ['Hearts', 'Diamonds', 'Clubs', 'Spades']\n",
-    "```\n",
-    "### Variable Annotations: Data Attributes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from dataclasses import dataclass\n",
-    "\n",
-    "@dataclass\n",
-    "class Demo:\n",
-    "    x  # attempting to create a data attribute x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "    face: str\n",
-    "    suit: str\n",
-    "```\n",
-    "\n",
-    "### Defining a Property and Other Methods\n",
-    "```python\n",
-    "    @property\n",
-    "    def image_name(self):\n",
-    "        \"\"\"Return the Card's image file name.\"\"\"\n",
-    "        return str(self).replace(' ', '_') + '.png'\n",
-    "\n",
-    "    def __str__(self):\n",
-    "        \"\"\"Return string representation for str().\"\"\"\n",
-    "        return f'{self.face} of {self.suit}'\n",
-    "    \n",
-    "    def __format__(self, format):\n",
-    "        \"\"\"Return formatted string representation.\"\"\"\n",
-    "        return f'{str(self):{format}}'\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.01selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.01selfcheck-checkpoint.ipynb
deleted file mode 100644
index 87b5894..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.01selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,76 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.13.1 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Data classes require `________` that specify each class attribute’s or data attribute’s data type.\n",
-    "\n",
-    "**Answer:** variable annotations.\n",
-    "\n",
-    "**2. _(Fill-In)_** The `________` decorator specifies that a new class is a data class.\n",
-    "\n",
-    "**Answer:** `@dataclass`.\n",
-    "\n",
-    "**3. _(True/False)_** The Python Standard Library’s `annotations` module defines the variable annotations that are required in data class definitions.\n",
-    "\n",
-    "**Answer:** False. The `typing` module defines the variable annotations that are required in data class definitions.\n",
-    "\n",
-    "**4. _(True/False)_** Data classes have auto-generated `<`, `<=`, `>` and `>=` operators, by default\n",
-    "\n",
-    "**Answer:** False. The `==` and `!=` operators are auto-generated by default. The `<`, `<=`, `>` and `>=` operators are autogenerated only if the `@dataclass` decorator specifies the keyword argument `order=True`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.02selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.02selfcheck-checkpoint.ipynb
deleted file mode 100644
index 020fb2c..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,127 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.13.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(IPython Session)_** Variable annotations are not enforced at execution time. To prove this, create a `Card` object, then assign the integer `100` to its `face` attribute and display the `Card`.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from carddataclass import Card"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c = Card('Ace', 'Spades')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(c.face)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c.face = 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(c.face)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13selfcheck-checkpoint.ipynb
deleted file mode 100644
index 6de7289..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,70 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.13 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "**1. _(True/False)_** The autogenerated `__eq__` method for a data class creates tuples containing each of its operands’ data attributes then compares the tuples with `==`.\n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**2. _(True/False)_** Data classes have auto-generated `<`, `<=`, `>` and `>=` operators, by default.\n",
-    "\n",
-    "**Answer:** False. The `==` and `!=` operators are auto-generated by default. The `<`, `<=`, `>` and `>=` operators are autogenerated only if the `@dataclass` decorator specifies the keyword argument `order=True`. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_14-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_14-checkpoint.ipynb
deleted file mode 100644
index 65978d7..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_14-checkpoint.ipynb
+++ /dev/null
@@ -1,117 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10.14 Unit Testing with Docstrings and `doctest` \n",
-    "\n",
-    "**For your convenience, this notebook includes the entire contents accountdoctest.py so you can modify it and rerun the tests. When you execute the cell containing accountdoctest.py, the doctests will execute.**\n",
-    "\n",
-    "### Module `doctest` and the `testmod` Function\n",
-    "### Modified `Account` Class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accountdoctest.py\n",
-    "\"\"\"Account class definition.\"\"\"\n",
-    "from decimal import Decimal\n",
-    "\n",
-    "class Account:\n",
-    "    \"\"\"Account class for demonstrating doctest.\"\"\"\n",
-    "    \n",
-    "    def __init__(self, name, balance):\n",
-    "        \"\"\"Initialize an Account object.\n",
-    "        \n",
-    "        >>> account1 = Account('John Green', Decimal('50.00'))\n",
-    "        >>> account1.name\n",
-    "        'John Green'\n",
-    "        >>> account1.balance\n",
-    "        Decimal('50.00')\n",
-    "\n",
-    "        The balance argument must be greater than or equal to 0.\n",
-    "        >>> account2 = Account('John Green', Decimal('-50.00'))\n",
-    "        Traceback (most recent call last):\n",
-    "            ...\n",
-    "        ValueError: Initial balance must be >= to 0.00.\n",
-    "        \"\"\"\n",
-    "\n",
-    "        # if balance is less than 0.00, raise an exception\n",
-    "        if balance < Decimal('0.00'):\n",
-    "            raise ValueError('Initial balance must be >= to 0.00.')\n",
-    "\n",
-    "        self.name = name\n",
-    "        self.balance = balance\n",
-    "\n",
-    "    def deposit(self, amount):\n",
-    "        \"\"\"Deposit money to the account.\"\"\"\n",
-    "\n",
-    "        # if amount is less than 0.00, raise an exception\n",
-    "        if amount < Decimal('0.00'):\n",
-    "            raise ValueError('amount must be positive.')\n",
-    "\n",
-    "        self.balance += amount\n",
-    "\n",
-    "if __name__ == '__main__':\n",
-    "    import doctest\n",
-    "    doctest.testmod(verbose=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Module `__main__` \n",
-    "### Running Tests "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness.The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_14selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_14selfcheck-checkpoint.ipynb
deleted file mode 100644
index 41a1409..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_14selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,149 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.14 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** When you execute a Python source file as a script, Python creates a global attribute `__name__` and assigns it the string `________`. \n",
-    "\n",
-    "**Answer:** '__main__'.\n",
-    "\n",
-    "**2. _(True/False)_** When you execute the `doctest` module’s `testmod` function, it inspects your code and automatically creates tests for you.\n",
-    "\n",
-    "**Answer:** False. When you execute the `doctest` module’s `testmod` function, it inspects your code’s function, method and class docstrings looking sample Python statements preceded by `>>>`, each followed on the next line by the given statement’s expected output (if any). \n",
-    "\n",
-    "**3. _(IPython Session)_** Add tests to the `deposit` method’s docstring, then execute the tests using the standard `python` interpreter’s verbose mode. Your test should create an `Account` object, deposit a valid amount into it, then attempt to deposit an invalid negative amount, which raises a `ValueError`.\n",
-    "\n",
-    "**Answer:** The updated docstring for method deposit is shown below followed by the verbose `doctest` results:\n",
-    "```python\n",
-    "\"\"\"Deposit money to the account.\n",
-    "\n",
-    ">>> account1 = Account('John Green', Decimal('50.00'))\n",
-    ">>> account1.deposit(Decimal('10.55'))\n",
-    ">>> account1.balance\n",
-    "Decimal('60.55')\n",
-    "\n",
-    ">>> account1.deposit(Decimal('-100.00'))\n",
-    "Traceback (most recent call last):\n",
-    "    ...\n",
-    "ValueError: amount must be positive.\n",
-    "\"\"\"\n",
-    "```\n",
-    "\n",
-    "**For your convenience, this notebook includes the entire contents accountdoctest2.py so you can modify it and rerun the tests. When you execute the cell containing accountdoctest2.py, the doctests will execute.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# accountdoctest2.py\n",
-    "\"\"\"Account class definition.\"\"\"\n",
-    "from decimal import Decimal\n",
-    "\n",
-    "class Account:\n",
-    "    \"\"\"Account class for demonstrating doctest.\"\"\"\n",
-    "    \n",
-    "    def __init__(self, name, balance):\n",
-    "        \"\"\"Initialize an Account object.\n",
-    "        \n",
-    "        >>> account1 = Account('John Green', Decimal('50.00'))\n",
-    "        >>> account1.name\n",
-    "        'John Green'\n",
-    "        >>> account1.balance\n",
-    "        Decimal('50.00')\n",
-    "\n",
-    "        The balance argument must be greater than or equal to 0.\n",
-    "        >>> account2 = Account('John Green', Decimal('-50.00'))\n",
-    "        Traceback (most recent call last):\n",
-    "            ...\n",
-    "        ValueError: Initial balance must be >= to 0.00.\n",
-    "        \"\"\"\n",
-    "\n",
-    "        # if balance is less than 0.00, raise an exception\n",
-    "        if balance < Decimal('0.00'):\n",
-    "            raise ValueError('Initial balance must be >= to 0.00.')\n",
-    "\n",
-    "        self.name = name\n",
-    "        self.balance = balance\n",
-    "\n",
-    "    def deposit(self, amount):\n",
-    "        \"\"\"Deposit money to the account.\n",
-    "        \n",
-    "        >>> account1 = Account('John Green', Decimal('50.00'))\n",
-    "        >>> account1.deposit(Decimal('10.55'))\n",
-    "        >>> account1.balance\n",
-    "        Decimal('60.55')\n",
-    "        \n",
-    "        >>> account1.deposit(Decimal('-100.00'))\n",
-    "        Traceback (most recent call last):\n",
-    "            ...\n",
-    "        ValueError: amount must be positive.\n",
-    "        \"\"\"\n",
-    "\n",
-    "        # if amount is less than 0.00, raise an exception\n",
-    "        if amount < Decimal('0.00'):\n",
-    "            raise ValueError('amount must be positive.')\n",
-    "\n",
-    "        self.balance += amount\n",
-    "\n",
-    "if __name__ == '__main__':\n",
-    "    import doctest\n",
-    "    doctest.testmod(verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_15selfcheck-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_15selfcheck-checkpoint.ipynb
deleted file mode 100755
index c21cfa5..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_15selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,69 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.15 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** A function’s `________` namespace stores information about identifiers created in the function, such as its parameters and local variables.\n",
-    "\n",
-    "**Answer:** local.\n",
-    "\n",
-    "**2. _(True/False)_** When a function attempts to get an attribute’s value, Python searches the local namespace, then the global namespace, then the built-in namespace until it finds the attribute; otherwise, a `NameError` occurs. \n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_16-checkpoint.ipynb b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_16-checkpoint.ipynb
deleted file mode 100644
index fd2c410..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_16-checkpoint.ipynb
+++ /dev/null
@@ -1,441 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 10.16 Intro to Data Science: Time Series and Simple Linear Regression \n",
-    "\n",
-    "**This file includes the Self Check snippets which continue from the section body.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Time Series\n",
-    "### Simple Linear Regression\n",
-    "### Linear Relationships"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "c = lambda f: 5 / 9 * (f - 32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temps = [(f, c(f)) for f in range(0, 101, 10)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temps_df = pd.DataFrame(temps, columns=['Fahrenheit', 'Celsius'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = temps_df.plot(x='Fahrenheit', y='Celsius', style='.-')\n",
-    "\n",
-    "y_label = axes.set_ylabel('Celsius')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extra cell added to keep subsequent snippet numbers the same as the chapter.\n",
-    "# Had to merge the two prior cells for use in the notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Components of the Simple Linear Regression Equation \n",
-    "### SciPy’s `stats` Module\n",
-    "### Pandas\n",
-    "### Seaborn Visualization\n",
-    "### Getting Weather Data from NOAA\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading the Average High Temperatures into a `DataFrame` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc = pd.read_csv('ave_hi_nyc_jan_1895-2018.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cleaning the Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.columns = ['Date', 'Temperature', 'Anomaly']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.Date.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.Date = nyc.Date.floordiv(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.head(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Calculating Basic Descriptive Statistics for the Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('precision', 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.Temperature.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Forecasting Future January Average High Temperatures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression = stats.linregress(x=nyc.Date,\n",
-    "                                     y=nyc.Temperature)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression.slope"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression.intercept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression.slope * 2019 + linear_regression.intercept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression.slope * 1850 + linear_regression.intercept"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting the Average High Temperatures and a Regression Line "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set_style('whitegrid')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = sns.regplot(x=nyc.Date, y=nyc.Temperature)\n",
-    "\n",
-    "axes.set_ylim(10, 70)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extra cell added to keep subsequent snippet numbers the same as the chapter.\n",
-    "# Had to merge the two prior cells for use in the notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting Time Series Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.16 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_**  Time series `________` looks at existing time series data for patterns, helping data analysts understand the data. Time series `________` uses data from the past to predict the future. \n",
-    "\n",
-    "**Answer:** analysis, forecasting.\n",
-    "\n",
-    "**2. _(True/False)_** In the formula, `c` `=` `5` `/` `9` `*` `(f` `-` `32)`, `f` (the Fahrenheit temperature) is the independent variable and `c` (the Celsius temperature) is the dependent variable.\n",
-    "\n",
-    "**Answer:** True. \n",
-    "\n",
-    "**3. _(IPython Session)_** Based on the slope and intercept values calculated in this section’s interactive session, in what year might the average January temperature in New York City reach 40 degrees Fahrenheit.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "year = 2019"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slope = linear_regression.slope"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "intercept = linear_regression.intercept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temperature = slope * year + intercept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "while temperature < 40.0:\n",
-    "    year += 1\n",
-    "    temperature = slope * year + intercept"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/account-checkpoint.py b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/account-checkpoint.py
deleted file mode 100755
index 914f8a4..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/account-checkpoint.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# account.py
-"""Account class definition."""
-from decimal import Decimal
-
-class Account:
-    """Account class for maintaining a bank account balance."""
-    
-    def __init__(self, name, balance):
-        """Initialize an Account object."""
-
-        # if balance is less than 0.00, raise an exception
-        if balance < Decimal('0.00'):
-            raise ValueError('Initial balance must be >= to 0.00.')
-
-        self.name = name
-        self.balance = balance
-
-    def deposit(self, amount):
-        """Deposit money to the account."""
-
-        # if amount is less than 0.00, raise an exception
-        if amount < Decimal('0.00'):
-            raise ValueError('amount must be positive.')
-
-        self.balance += amount
-
-    def withdraw(self, amount):
-        """Withdraw money from the account."""
-
-        # if amount is greater than balance, raise an exception
-        if amount > self.balance:
-            raise ValueError('amount must be <= to balance.')
-        elif amount < Decimal('0.00'):
-            raise ValueError('amount must be positive.')
-
-        self.balance -= amount
-        
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/carddataclass-checkpoint.py b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/carddataclass-checkpoint.py
deleted file mode 100755
index 653cf76..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/carddataclass-checkpoint.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# carddataclass.py
-"""Card data class with class attributes, data attributes,
-autogenerated methods and explicitly defined methods."""
-from dataclasses import dataclass
-from typing import ClassVar, List
-
-@dataclass
-class Card:
-    FACES: ClassVar[List[str]] = ['Ace', '2', '3', '4', '5', '6', '7', 
-                                  '8', '9', '10', 'Jack', 'Queen', 'King']
-    SUITS: ClassVar[List[str]] = ['Hearts', 'Diamonds', 'Clubs', 'Spades']
-
-    face: str
-    suit: str
-
-    @property
-    def image_name(self):
-        """Return the Card's image file name."""
-        return str(self).replace(' ', '_') + '.png'
-
-    def __str__(self):
-        """Return string representation for str()."""
-        return f'{self.face} of {self.suit}'
-    
-    def __format__(self, format):
-        """Return formatted string representation."""
-        return f'{str(self):{format}}'
-        
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/private-checkpoint.py b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/private-checkpoint.py
deleted file mode 100755
index 59d4f9e..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/private-checkpoint.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# private.py
-"""Class with public and private attributes."""
-
-class PrivateClass:
-    """Class with public and private attributes."""
-
-    def __init__(self):
-        """Initialize the public and private attributes."""
-        self.public_data = "public"  # public attribute
-        self.__private_data = "private"  # private attribute
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/salariedcommissionemployee-checkpoint.py b/examples/ch10/snippets_ipynb/.ipynb_checkpoints/salariedcommissionemployee-checkpoint.py
deleted file mode 100755
index f069b71..0000000
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/salariedcommissionemployee-checkpoint.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# salariedcommissionemployee.py
-"""SalariedCommissionEmployee derived from CommissionEmployee."""
-from commissionemployee import CommissionEmployee
-from decimal import Decimal
-
-class SalariedCommissionEmployee(CommissionEmployee):
-    """An employee who gets paid a salary plus 
-    commission based on gross sales."""
-
-    def __init__(self, first_name, last_name, ssn, 
-                 gross_sales, commission_rate, base_salary):
-        """Initialize SalariedCommissionEmployee's attributes."""
-        super().__init__(first_name, last_name, ssn, 
-                         gross_sales, commission_rate)
-        self.base_salary = base_salary  # validate via property
-
-    @property
-    def base_salary(self):
-        return self._base_salary
-
-    @base_salary.setter
-    def base_salary(self, salary):
-        """Set base salary or raise ValueError if invalid."""
-        if salary < Decimal('0.00'):
-            raise ValueError('Base salary must be >= to 0')
-        
-        self._base_salary = salary
-
-    def earnings(self):
-        """Calculate earnings."""   
-        return super().earnings() + self.base_salary
-
-    def __repr__(self):
-        """Return string representation for repr()."""
-        return ('Salaried' + super().__repr__() +      
-            f'\nbase salary: {self.base_salary:.2f}')
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
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diff --git a/examples/ch10/snippets_ipynb/__pycache__/timewithproperties.cpython-37.pyc b/examples/ch10/snippets_ipynb/__pycache__/timewithproperties.cpython-37.pyc
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diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_02.01-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_02.01-checkpoint.py
deleted file mode 100755
index abe7a78..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_02.01-checkpoint.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Section 10.2.1 and 10.2.2 Snippets
-
-# Importing Classes Account and Decimal
-from account import Account
-
-from decimal import Decimal
-
-# Create an Account Object with a Constructor Expression
-account1 = Account('John Green', Decimal('50.00'))
-
-# Getting an Account’s Name and Balance
-account1.name
-
-account1.balance
-
-# Depositing Money into an Account
-account1.deposit(Decimal('25.53'))
-
-account1.balance
-
-# Account Methods Perform Validation
-account1.deposit(Decimal('-123.45'))
-    
-# Section 10.2.2
-# Defining a Class 
-Account?
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_04.03-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_04.03-checkpoint.py
deleted file mode 100755
index 9b780e1..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_04.03-checkpoint.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Section 10.4.3 snippets
-
-# Attributes Are Always Accessible
-from timewithproperties import Time
-
-wake_up = Time(hour=7, minute=45, second=30)
-
-wake_up._hour
-
-wake_up._hour = 100
-
-wake_up
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_09-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_09-checkpoint.py
deleted file mode 100755
index c286788..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_09-checkpoint.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Section 10.9 snippets
-
-class WellPaidDuck:
-    def __repr__(self):
-        return 'I am a well-paid duck'
-    def earnings(self):
-        return Decimal('1_000_000.00')
-    
-from decimal import Decimal
-
-from commissionemployee import CommissionEmployee
-
-from salariedcommissionemployee import SalariedCommissionEmployee
-
-c = CommissionEmployee('Sue', 'Jones', '333-33-3333',
-                       Decimal('10000.00'), Decimal('0.06'))
-                       
-s = SalariedCommissionEmployee('Bob', 'Lewis', '444-44-4444',
-    Decimal('5000.00'), Decimal('0.04'), Decimal('300.00'))
-    
-d = WellPaidDuck()
-
-employees = [c, s, d]
-
-for employee in employees:
-    print(employee)
-    print(f'{employee.earnings():,.2f}\n')
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_12-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_12-checkpoint.py
deleted file mode 100755
index 1df875c..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_12-checkpoint.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Section 10.12 snippets
-
-from collections import namedtuple
-
-Card = namedtuple('Card', ['face', 'suit'])
-
-card = Card(face='Ace', suit='Spades')
-
-card.face
-
-card.suit
-
-card
-
-# Other Named Tuple Features
-values = ['Queen', 'Hearts']
-
-card = Card._make(values)
-
-card
-
-card._asdict()
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.01-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.01-checkpoint.py
deleted file mode 100644
index d1538cc..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.01-checkpoint.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Section 10.13.1 snippets
-
-from dataclasses import dataclass
-
-@dataclass
-class Demo:
-    x  # attempting to create a data attribute x
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.02-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.02-checkpoint.py
deleted file mode 100644
index d1fb42d..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.02-checkpoint.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Section 10.13.2 snippets
-
-from carddataclass import Card
-from carddataclass import Card
-
-c1 = Card(Card.FACES[0], Card.SUITS[3])
-
-c1
-
-print(c1)
-
-c1.face
-
-c1.suit
-
-c1.image_name
-
-c2 = Card(Card.FACES[0], Card.SUITS[3])
-
-c2
-
-c3 = Card(Card.FACES[0], Card.SUITS[0])
-
-c3
-
-c1 == c2
-
-c1 == c3
-
-c1 != c3
-
-from deck2 import DeckOfCards  # uses Card data class
-
-deck_of_cards = DeckOfCards()
-
-print(deck_of_cards)
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_15-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_15-checkpoint.py
deleted file mode 100755
index 5193f88..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_15-checkpoint.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Section 10.15 snippets
-z = 'global z'
-
-def print_variables():
-    y = 'local y in print_variables'
-    print(y)
-    print(z)   
-
-print_variables()
-
-y
-
-z
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_16-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/10_16-checkpoint.py
deleted file mode 100644
index dd1bca8..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_16-checkpoint.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# Section 10.16 Snippets
-# This file includes the Self Check snippets which continue from the section body.
-
-# Time Series
-# Simple Linear Regression
-# Linear Relationships
-
-c = lambda f: 5 / 9 * (f - 32)
-
-temps = [(f, c(f)) for f in range(0, 101, 10)]
-
-import pandas as pd
-
-temps_df = pd.DataFrame(temps, columns=['Fahrenheit', 'Celsius'])
-
-axes = temps_df.plot(x='Fahrenheit', y='Celsius', style='.-')
-
-y_label = axes.set_ylabel('Celsius')
-
-# Components of the Simple Linear Regression Equation
-# SciPy’s stats Module
-# Pandas
-# Seaborn Visualization
-# Getting Weather Data from NOAA
-
-# Loading the Average High Temperatures into a DataFrame
-nyc = pd.read_csv('ave_hi_nyc_jan_1895-2018.csv')
-
-nyc.head()
-
-nyc.tail()
-
-# Cleaning the Data
-nyc.columns = ['Date', 'Temperature', 'Anomaly']
-
-nyc.head(3)
-
-nyc.Date.dtype
-
-nyc.Date = nyc.Date.floordiv(100)
-
-nyc.head(3)
-
-# Calculating Basic Descriptive Statistics for the Dataset
-pd.set_option('precision', 2)
-
-nyc.Temperature.describe()
-
-# Forecasting Future January Average High Temperatures
-from scipy import stats
-
-linear_regression = stats.linregress(x=nyc.Date,
-                                     y=nyc.Temperature)
-
-linear_regression.slope
-
-linear_regression.intercept
-
-linear_regression.slope * 2019 + linear_regression.intercept
-
-linear_regression.slope * 1850 + linear_regression.intercept
-
-# Plotting the Average High Temperatures and a Regression Line
-import seaborn as sns
-
-sns.set_style('whitegrid')
-
-axes = sns.regplot(x=nyc.Date, y=nyc.Temperature)
-
-axes.set_ylim(10, 70)
-
-# Getting Time Series Datasets
-
-# Self Check Exercises 
-# Exercise 3
-year = 2019
-
-slope = linear_regression.slope
-
-intercept = linear_regression.intercept
-
-temperature = slope * year + intercept
-
-while temperature < 40.0:
-    year += 1
-    temperature = slope * year + intercept
-
-year
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/account-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/account-checkpoint.py
deleted file mode 100755
index 914f8a4..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/account-checkpoint.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# account.py
-"""Account class definition."""
-from decimal import Decimal
-
-class Account:
-    """Account class for maintaining a bank account balance."""
-    
-    def __init__(self, name, balance):
-        """Initialize an Account object."""
-
-        # if balance is less than 0.00, raise an exception
-        if balance < Decimal('0.00'):
-            raise ValueError('Initial balance must be >= to 0.00.')
-
-        self.name = name
-        self.balance = balance
-
-    def deposit(self, amount):
-        """Deposit money to the account."""
-
-        # if amount is less than 0.00, raise an exception
-        if amount < Decimal('0.00'):
-            raise ValueError('amount must be positive.')
-
-        self.balance += amount
-
-    def withdraw(self, amount):
-        """Withdraw money from the account."""
-
-        # if amount is greater than balance, raise an exception
-        if amount > self.balance:
-            raise ValueError('amount must be <= to balance.')
-        elif amount < Decimal('0.00'):
-            raise ValueError('amount must be positive.')
-
-        self.balance -= amount
-        
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/carddataclass-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/carddataclass-checkpoint.py
deleted file mode 100755
index 5722606..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/carddataclass-checkpoint.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# carddataclass.py
-"""Card data class with class attributes, data attributes,
-autogenerated methods and explicitly defined methods."""
-from dataclasses import dataclass, field
-from typing import ClassVar, List
-
-@dataclass
-class Card:
-    FACES: ClassVar[List[str]] = ['Ace', '2', '3', '4', '5', '6', '7', 
-                                  '8', '9', '10', 'Jack', 'Queen', 'King']
-    SUITS: ClassVar[List[str]] = ['Hearts', 'Diamonds', 'Clubs', 'Spades']
-
-    face: str
-    suit: str
-
-    @property
-    def image_name(self):
-        """Return the Card's image file name."""
-        return str(self).replace(' ', '_') + '.png'
-
-    def __str__(self):
-        """Return string representation for str()."""
-        return f'{self.face} of {self.suit}'
-    
-    def __format__(self, format):
-        """Return formatted string representation."""
-        return f'{str(self):{format}}'
-        
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/private-checkpoint.py b/examples/ch10/snippets_py/.ipynb_checkpoints/private-checkpoint.py
deleted file mode 100755
index 59d4f9e..0000000
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/private-checkpoint.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# private.py
-"""Class with public and private attributes."""
-
-class PrivateClass:
-    """Class with public and private attributes."""
-
-    def __init__(self):
-        """Initialize the public and private attributes."""
-        self.public_data = "public"  # public attribute
-        self.__private_data = "private"  # private attribute
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch10/snippets_py/__pycache__/account.cpython-37.pyc b/examples/ch10/snippets_py/__pycache__/account.cpython-37.pyc
deleted file mode 100644
index 340ab57..0000000
Binary files a/examples/ch10/snippets_py/__pycache__/account.cpython-37.pyc and /dev/null differ
diff --git a/examples/ch10/snippets_py/__pycache__/timewithproperties.cpython-37.pyc b/examples/ch10/snippets_py/__pycache__/timewithproperties.cpython-37.pyc
deleted file mode 100644
index 7378491..0000000
Binary files a/examples/ch10/snippets_py/__pycache__/timewithproperties.cpython-37.pyc and /dev/null differ
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_03-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_03-checkpoint.ipynb
deleted file mode 100755
index 5c6959a..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_03-checkpoint.ipynb
+++ /dev/null
@@ -1,183 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 11.3 Recursive Factorial Example\n",
-    "\n",
-    "**Note: This notebook contains the section's Self Check also because the IPython session continues into the exercises.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursive Factorial Approach "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing Recursion "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Implementing a Recursive Factorial Function "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def factorial(number):\n",
-    "    \"\"\"Return factorial of number.\"\"\"\n",
-    "    if number <= 1:\n",
-    "        return 1\n",
-    "    return number * factorial(number - 1)  # recursive call"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(11):\n",
-    "    print(f'{i}! = {factorial(i)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Indirect Recursion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stack Overflow and Infinite Recursion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursion and the Function-Call Stack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.3 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** A `________` case is needed to successfully terminate recursion.\n",
-    "\n",
-    "**Answer:**  base."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** A function calling itself indirectly is not an example of recursion.\n",
-    "\n",
-    "**Answer:** False. A function calling itself in this manner is an example of indirect recursion."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(True/False)_** When a recursive function is called to solve a problem, it’s capable of solving only the simplest case(s), or base case(s).\n",
-    "\n",
-    "**Answer:** True. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**4. _(True/False)_** To make recursion feasible, the recursion step in a recursive solution must resemble the original problem, but be a slightly larger version of it.\n",
-    "\n",
-    "**Answer:** False. To make recursion feasible, the recursion step in a recursive solution must resemble the original problem, but be a slightly smaller or simpler version of it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**5. _(IPython Session)_** Most other programming languages store integers in a fixed amount of space. So their built-in integer types can represent only a limited range of integer values. For example, Java’s `int` type can represent only values in the range –2,147,483,648 to +2,147,483,647. Python allows integers to become arbitrarily large. Continue this section’s IPython session and execute the function call `factorial(50)` to demonstrate that Python supports much larger integers.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factorial(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_04-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_04-checkpoint.ipynb
deleted file mode 100755
index 1b4720c..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_04-checkpoint.ipynb
+++ /dev/null
@@ -1,212 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 11.4 Recursive Fibonacci Series Example\n",
-    "\n",
-    "**Note: This notebook contains the section's Self Check also because the IPython session continues into the exercises.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fibonacci(n):\n",
-    "    if n in (0, 1):  # base cases\n",
-    "        return n\n",
-    "    else:\n",
-    "        return fibonacci(n - 1) + fibonacci(n - 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "\n",
-    "### Testing Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for n in range(41):\n",
-    "    print(f'Fibonacci({n}) = {fibonacci(n)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analyzing the Calls to Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Complexity Issues"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.4 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The ratio of successive Fibonacci numbers converges on a constant value of 1.618…, a number that has been called the `________` or the `________`.\n",
-    "\n",
-    "**Answer:** golden ratio, golden mean. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** In the field of complexity theory, computer scientists study how hard algorithms work to complete their tasks. \n",
-    "\n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** Continuing this section’s IPython session, create a function named `iterative_fibonacci` that uses looping rather than recursion to calculate Fibonacci numbers. Use both the iterative and recursive versions to calculate the 32nd, 33rd and 34th Fibonacci numbers. Time the calls with `%timeit` to see the difference in computation time.\n",
-    " \n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def iterative_fibonacci(n):\n",
-    "    result = 0\n",
-    "    temp = 1\n",
-    "    for j in range(0, n):\n",
-    "        temp, result = result, result + temp\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(33)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(33)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(34)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(34)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_05selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_05selfcheck-checkpoint.ipynb
deleted file mode 100755
index 5cc8ff4..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_05selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 11.5 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Recursion terminates when `________`.\n",
-    " \n",
-    "**Answer:** a base case is reached."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Iteration and recursion can occur infinitely.\n",
-    " \n",
-    "**Answer:** True. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_07-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_07-checkpoint.ipynb
deleted file mode 100755
index 7c6aa71..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_07-checkpoint.ipynb
+++ /dev/null
@@ -1,135 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 11.7 Linear Search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear Search Algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear Search Implementation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def linear_search(data, search_key):\n",
-    "    for index, value in enumerate(data):\n",
-    "        if value == search_key:\n",
-    "            return index\n",
-    "    return -1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values = np.random.randint(10, 91, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 78)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 61)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 66)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_07selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_07selfcheck-checkpoint.ipynb
deleted file mode 100755
index 60f6d54..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_07selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 11.7 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The `________` algorithm searches each element in an array sequentially. \n",
-    " \n",
-    "**Answer:** linear search."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** If an array contains duplicate values, the linear search finds the last matching value. \n",
-    " \n",
-    "**Answer:** False. If there are duplicate values, linear search finds the first matching value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_08selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_08selfcheck-checkpoint.ipynb
deleted file mode 100755
index 9448036..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_08selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 11.8 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** `________` notation indicates how hard an algorithm may have to work to solve a problem. \n",
-    " \n",
-    "**Answer:** Big O."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** An O(n) algorithm is referred to as having a quadratic run time. \n",
-    " \n",
-    "**Answer:** False. An O(n) algorithm is referred to as having a linear run time. O(n2) algorithms have quadratic run time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(Discussion)_** When might you choose to write an O(n2) algorithm? \n",
-    " \n",
-    "**Answer:** When n is small and you do not have the time (or need) to invest in developing a better performing algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_09.00selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_09.00selfcheck-checkpoint.ipynb
deleted file mode 100755
index 20b3e88..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_09.00selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 11.9 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(True/False)_** The linear search and binary search algorithms require arrays to be sorted. \n",
-    " \n",
-    "**Answer:** False. Only the binary search requires a sorted array."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(Fill-In)_** With binary search, the smallest number of comparisons that would be needed to find a matching element in a 1,000,001-element array is `________`.\n",
-    " \n",
-    "**Answer:** One. This happens if on the first comparison the key matches the middle array element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_09.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_09.01-checkpoint.ipynb
deleted file mode 100755
index 5e5d86b..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_09.01-checkpoint.ipynb
+++ /dev/null
@@ -1,165 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 11.9.1 Binary Search Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `binary_search` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# binarysearch.py\n",
-    "\"\"\"Use binary search to locate an item in an array.\"\"\"\n",
-    "import numpy as np\n",
-    "\n",
-    "def binary_search(data, key):\n",
-    "    \"\"\"Perform binary search of data looking for key.\"\"\"\n",
-    "    low = 0   # low end of search area\n",
-    "    high = len(data) - 1  # high end of search area\n",
-    "    middle = (low + high + 1) // 2  # middle element index\n",
-    "    location = -1  # return value -1 if not found\n",
-    "    \n",
-    "    # loop to search for element\n",
-    "    while low <= high and location == -1:\n",
-    "        # print remaining elements of array\n",
-    "        print(remaining_elements(data, low, high))\n",
-    "\n",
-    "        print('   ' * middle, end='')  # output spaces for alignment \n",
-    "        print(' * ')  # indicate current middle\n",
-    "\n",
-    "        # if the element is found at the middle                    \n",
-    "        if key == data[middle]:                                   \n",
-    "            location = middle  # location is the current middle     \n",
-    "        elif key < data[middle]:  # middle element is too high\n",
-    "            high = middle - 1  # eliminate the higher half          \n",
-    "        else:  # middle element is too low                         \n",
-    "            low = middle + 1  # eliminate the lower half            \n",
-    "                                                                        \n",
-    "        middle = (low + high + 1) // 2  # recalculate the middle\n",
-    "\n",
-    "    return location  # return location of search key\n",
-    "                            "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `remaining_elements` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def remaining_elements(data, low, high):\n",
-    "    \"\"\"Display remaining elements of the binary search.\"\"\"\n",
-    "    return '   ' * low + ' '.join(str(s) for s in data[low:high + 1])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main` \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    # create and display array of random values\n",
-    "    data = np.random.randint(10, 91, 15)\n",
-    "    data.sort()\n",
-    "    print(data, '\\n')\n",
-    "\n",
-    "    search_key = int(input('Enter an integer value (-1 to quit): ')) \n",
-    "\n",
-    "    # repeatedly input an integer; -1 terminates the program\n",
-    "    while search_key != -1:\n",
-    "        location = binary_search(data, search_key)  # perform search\n",
-    "\n",
-    "        if location == -1:  # not found\n",
-    "            print(f'{search_key} was not found\\n') \n",
-    "        else:\n",
-    "            print(f'{search_key} found in position {location}\\n')\n",
-    "\n",
-    "        search_key = int(input('Enter an integer value (-1 to quit): '))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execte the search \n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_11.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_11.01-checkpoint.ipynb
deleted file mode 100755
index 6a62a6a..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_11.01-checkpoint.ipynb
+++ /dev/null
@@ -1,121 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 11.11.1 Selection Sort Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `selection_sort` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# selectionsort.py\n",
-    "\"\"\"Sorting an array with selection sort.\"\"\"\n",
-    "import numpy as np\n",
-    "from ch11utilities import print_pass\n",
-    "\n",
-    "def selection_sort(data):\n",
-    "    \"\"\"Sort array using selection sort.\"\"\"\n",
-    "    # loop over len(data) - 1 elements      \n",
-    "    for index1 in range(len(data) - 1):\n",
-    "        smallest = index1  # first index of remaining array\n",
-    "\n",
-    "        # loop to find index of smallest element                      \n",
-    "        for index2 in range(index1 + 1, len(data)): \n",
-    "            if data[index2] < data[smallest]:\n",
-    "                smallest = index2\n",
-    "                                              \n",
-    "        # swap smallest element into position\n",
-    "        data[smallest], data[index1] = data[index1], data[smallest]  \n",
-    "        print_pass(data, index1 + 1, smallest)\n",
-    "        \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main(): \n",
-    "    data = np.array([34, 56, 14, 20, 77, 51, 93, 30, 15, 52])\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    selection_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to run the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_11.03selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_11.03selfcheck-checkpoint.ipynb
deleted file mode 100755
index c2bded9..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_11.03selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 11.11.3 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_ A selection sort application would take approximately `________` times as long to run on a 128-million-element array as on a 32-million-element array.\n",
-    "16, because an O(n2) algorithm takes 16 times as long to sort four times as much information. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_12.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_12.01-checkpoint.ipynb
deleted file mode 100755
index 6cefa97..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_12.01-checkpoint.ipynb
+++ /dev/null
@@ -1,114 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 11.12.1 Insertion Sort Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `insertion_sort`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# insertionsort.py\n",
-    "\"\"\"Sorting an array with insertion sort.\"\"\"\n",
-    "import numpy as np\n",
-    "from ch11utilities import print_pass\n",
-    "\n",
-    "def insertion_sort(data):\n",
-    "    \"\"\"Sort an array using insertion sort.\"\"\"\n",
-    "    # loop over len(data) - 1 elements      \n",
-    "    for next in range(1, len(data)):\n",
-    "        insert = data[next]  # value to insert \n",
-    "        move_item = next  # location to place element\n",
-    "\n",
-    "        # search for place to put current element         \n",
-    "        while move_item > 0 and data[move_item - 1] > insert:  \n",
-    "            # shift element right one slot\n",
-    "            data[move_item] = data[move_item - 1]         \n",
-    "            move_item -= 1                                      \n",
-    "                                              \n",
-    "        data[move_item] = insert  # place inserted element \n",
-    "        print_pass(data, next, move_item)  # output pass of algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main(): \n",
-    "    data = np.array([34, 56, 14, 20, 77, 51, 93, 30, 15, 52])\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    insertion_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call mainto execute the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_12.02selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_12.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index 4711f51..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_12.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 11.12.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(True/False)_** Like the selection sort algorithm, the insertion sort algorithm has linear run time. \n",
-    " \n",
-    "**Answer:** False. Both algorithms have quadratic run time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Each iteration of the selection sort algorithm inserts one value into sorted order among the values that have been sorted so far.\n",
-    " \n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_13.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_13.01-checkpoint.ipynb
deleted file mode 100755
index 622573d..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_13.01-checkpoint.ipynb
+++ /dev/null
@@ -1,211 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 11.13.1 Merge Sort Implementation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `merge_sort` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# mergesort.py\n",
-    "\"\"\"Sorting an array with merge sort.\"\"\"\n",
-    "import numpy as np \n",
-    "\n",
-    "# calls recursive sort_array method to begin merge sorting\n",
-    "def merge_sort(data):\n",
-    "    sort_array(data, 0, len(data) - 1)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursive Function `sort_array` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sort_array(data, low, high):\n",
-    "    \"\"\"Split data, sort subarrays and merge them into sorted array.\"\"\"\n",
-    "    # test base case size of array equals 1     \n",
-    "    if (high - low) >= 1:  # if not base case\n",
-    "        middle1 = (low + high) // 2  # calculate middle of array\n",
-    "        middle2 = middle1 + 1  # calculate next element over     \n",
-    "\n",
-    "        # output split step\n",
-    "        print(f'split:   {subarray_string(data, low, high)}') \n",
-    "        print(f'         {subarray_string(data, low, middle1)}') \n",
-    "        print(f'         {subarray_string(data, middle2, high)}\\n') \n",
-    "\n",
-    "        # split array in half then sort each half (recursive calls)\n",
-    "        sort_array(data, low, middle1)  # first half of array       \n",
-    "        sort_array(data, middle2, high)  # second half of array     \n",
-    "\n",
-    "        # merge two sorted arrays after split calls return\n",
-    "        merge(data, low, middle1, middle2, high)              \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `merge` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# merge two sorted subarrays into one sorted subarray             \n",
-    "def merge(data, left, middle1, middle2, right):\n",
-    "    left_index = left  # index into left subarray              \n",
-    "    right_index = middle2  # index into right subarray         \n",
-    "    combined_index = left  # index into temporary working array\n",
-    "    merged = [0] * len(data)  # working array        \n",
-    "  \n",
-    "    # output two subarrays before merging\n",
-    "    print(f'merge:   {subarray_string(data, left, middle1)}') \n",
-    "    print(f'         {subarray_string(data, middle2, right)}') \n",
-    "\n",
-    "    # merge arrays until reaching end of either         \n",
-    "    while left_index <= middle1 and right_index <= right:\n",
-    "        # place smaller of two current elements into result  \n",
-    "        # and move to next space in arrays                   \n",
-    "        if data[left_index] <= data[right_index]:       \n",
-    "            merged[combined_index] = data[left_index]\n",
-    "            combined_index += 1\n",
-    "            left_index += 1\n",
-    "        else:                                                 \n",
-    "            merged[combined_index] = data[right_index] \n",
-    "            combined_index += 1\n",
-    "            right_index += 1\n",
-    "\n",
-    "    # if left array is empty                                \n",
-    "    if left_index == middle2:  # if True, copy in rest of right array\n",
-    "        merged[combined_index:right + 1] = data[right_index:right + 1]\n",
-    "    else:  # right array is empty, copy in rest of left array\n",
-    "        merged[combined_index:right + 1] = data[left_index:middle1 + 1]\n",
-    "\n",
-    "    data[left:right + 1] = merged[left:right + 1]  # copy back to data\n",
-    "\n",
-    "    # output merged array\n",
-    "    print(f'         {subarray_string(data, left, right)}\\n') \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `subarray_string` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# method to output certain values in array\n",
-    "def subarray_string(data, low, high):\n",
-    "    temp = '   ' * low  # spaces for alignment\n",
-    "    temp += ' '.join(str(item) for item in data[low:high + 1])\n",
-    "    return temp\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    data = np.array([34, 56, 14, 20, 77, 51, 93, 30, 15, 52])\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    merge_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execute the sort (we removed the if statement from the script in the book)\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_13.02selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_13.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index 428915f..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_13.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,64 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 11.13.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The efficiency of merge sort is         .\n",
-    " \n",
-    "**Answer:** O(n log n)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_15-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_15-checkpoint.ipynb
deleted file mode 100644
index ed92d92..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_15-checkpoint.ipynb
+++ /dev/null
@@ -1,255 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 11.15 Visualizing Algorithms\n",
-    "\n",
-    "Before running this notebook, be sure that you have previously executed the following commands, which we specified in the Before You Begin section of the book:\n",
-    "\n",
-    "```\n",
-    "pip install ipympl\n",
-    "conda install nodejs\n",
-    "jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-    "jupyter labextension install jupyter-matplotlib\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# selectionsortanimation.py\n",
-    "\"\"\"Animated selection sort visualization.\"\"\"\n",
-    "from matplotlib import animation\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import sys\n",
-    "from ch11soundutilities import play_sound"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `update` Function That Displays Each Animation Frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update(frame_data):\n",
-    "    \"\"\"Display bars representing the current state.\"\"\"    \n",
-    "    # unpack info for graph update\n",
-    "    data, colors, swaps, comparisons = frame_data\n",
-    "    plt.cla()  # clear old contents contents of current Figure\n",
-    "\n",
-    "    # create barplot and set its xlabel\n",
-    "    bar_positions = np.arange(len(data))\n",
-    "    axes = sns.barplot(bar_positions, data, palette=colors)  # new bars\n",
-    "    axes.set(xlabel=f'Comparisons: {comparisons}; Swaps: {swaps}',\n",
-    "             xticklabels=data)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `flash_bars` Function That Flashes the Bars About to be Swapped"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def flash_bars(index1, index2, data, colors, swaps, comparisons):\n",
-    "    \"\"\"Flash the bars about to be swapped and play their notes.\"\"\"\n",
-    "    for x in range(0, 2):\n",
-    "        colors[index1], colors[index2] = 'white', 'white'\n",
-    "        yield (data, colors, swaps, comparisons) \n",
-    "        colors[index1], colors[index2] = 'purple', 'purple'\n",
-    "        yield (data, colors, swaps, comparisons) \n",
-    "        play_sound(data[index1], seconds=0.05)\n",
-    "        play_sound(data[index2], seconds=0.05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `selection_sort` as a Generator Function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def selection_sort(data):\n",
-    "    \"\"\"Sort data using the selection sort algorithm and\n",
-    "    yields values that update uses to visualize the algorithm.\"\"\"\n",
-    "    swaps = 0  \n",
-    "    comparisons = 0\n",
-    "    colors = ['lightgray'] * len(data)  # list of bar colors\n",
-    "    \n",
-    "    # display initial bars representing shuffled values\n",
-    "    yield (data, colors, swaps, comparisons)    \n",
-    "    \n",
-    "    # loop over len(data) - 1 elements    \n",
-    "    for index1 in range(0, len(data) - 1):\n",
-    "        print('outerloop')\n",
-    "        smallest = index1\n",
-    "        \n",
-    "        # loop to find index of smallest element's index   \n",
-    "        for index2 in range(index1 + 1, len(data)):\n",
-    "            print('innerloop')\n",
-    "            comparisons += 1\n",
-    "            colors[smallest] = 'purple'\n",
-    "            colors[index2] = 'red'            \n",
-    "            yield (data, colors, swaps, comparisons) \n",
-    "            play_sound(data[index2], seconds=0.05)\n",
-    "\n",
-    "            # compare elements at positions index and smallest\n",
-    "            if data[index2] < data[smallest]:\n",
-    "                colors[smallest] = 'lightgray'\n",
-    "                smallest = index2\n",
-    "                colors[smallest] = 'purple'\n",
-    "                yield (data, colors, swaps, comparisons) \n",
-    "            else: \n",
-    "                colors[index2] = 'lightgray'\n",
-    "                yield (data, colors, swaps, comparisons) \n",
-    "            \n",
-    "        # ensure that last bar is not purple\n",
-    "        colors[-1] = 'lightgray'\n",
-    "        \n",
-    "        # flash the bars about to be swapped\n",
-    "        yield from flash_bars(index1, smallest, data, colors, \n",
-    "                              swaps, comparisons)\n",
-    "\n",
-    "        # swap the elements at positions index1 and smallest\n",
-    "        swaps += 1\n",
-    "        data[smallest], data[index1] = data[index1], data[smallest]  \n",
-    "        \n",
-    "        # flash the bars that were just swapped\n",
-    "        yield from flash_bars(index1, smallest, data, colors, \n",
-    "                              swaps, comparisons)\n",
-    "        \n",
-    "        # indicate that bar index1 is now in its final spot\n",
-    "        colors[index1] = 'lightgreen'\n",
-    "        yield (data, colors, swaps, comparisons)    \n",
-    "\n",
-    "    # indicate that last bar is now in its final spot\n",
-    "    colors[-1] = 'lightgreen'\n",
-    "    yield (data, colors, swaps, comparisons)    \n",
-    "    play_sound(data[-1], seconds=0.05)\n",
-    "\n",
-    "    # play each bar's note once and color it darker green\n",
-    "    for index in range(len(data)):\n",
-    "        colors[index] = 'green'\n",
-    "        yield (data, colors, swaps, comparisons)\n",
-    "        play_sound(data[index], seconds=0.05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `main` Function That Launches the Animation\n",
-    "\n",
-    "Note that we made some changes to execute this code in the notebook. The animation works best when run from the command line. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#def main():\n",
-    "number_of_values = int(sys.argv[1]) if len(sys.argv) == 2 else 10 \n",
-    "\n",
-    "figure = plt.figure('Selection Sort')  # Figure to display barplot\n",
-    "numbers = np.arange(1, number_of_values + 1)  # create array \n",
-    "np.random.shuffle(numbers)  # shuffle the array\n",
-    "\n",
-    "# start the animation\n",
-    "anim = animation.FuncAnimation(figure, update, repeat=False,\n",
-    "    frames=selection_sort(numbers), interval=50)\n",
-    "\n",
-    "plt.show()  # display the Figure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execute the animation (we removed the if statement from the script in the book)\n",
-    "#main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_Exercises-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_Exercises-checkpoint.ipynb
deleted file mode 100644
index 83706c7..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_Exercises-checkpoint.ipynb
+++ /dev/null
@@ -1,147 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 11 Exercises with Code Snippets\n",
-    "\n",
-    "**11.1** What does the following code do?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mystery(a, b):\n",
-    "    if b == 1:\n",
-    "        return a\n",
-    "    else:\n",
-    "        return a + mystery(a, b - 1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mystery(2, 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**11.2** Find the logic error(s) in the following recursive function, and explain how to correct it (them). This function should find the sum of the values from 0 to `n`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sum(n):\n",
-    "    if n == 0:\n",
-    "        return 0\n",
-    "    else: \n",
-    "        return n + sum(n)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**11.3** What does the following code do?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mystery(a_array, size):\n",
-    "    if size == 1:\n",
-    "        return a_array[0]\n",
-    "    else: \n",
-    "        return a_array[size - 1] + mystery(a_array, size - 1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numbers = np.arange(1, 11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mystery(numbers, len(numbers))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_02-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_02-checkpoint.ipynb
deleted file mode 100755
index 6b63fd8..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_02-checkpoint.ipynb
+++ /dev/null
@@ -1,89 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 12.2 Factorials"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Iterative Factorial Approach"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factorial = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in range(5, 0, -1):\n",
-    "    factorial *= number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factorial"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_03-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_03-checkpoint.ipynb
deleted file mode 100755
index 507211b..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_03-checkpoint.ipynb
+++ /dev/null
@@ -1,183 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 12.3 Recursive Factorial Example\n",
-    "\n",
-    "**Note: This notebook contains the section's Self Check also because the IPython session continues into the exercises.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursive Factorial Approach "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing Recursion "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Implementing a Recursive Factorial Function "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def factorial(number):\n",
-    "    \"\"\"Return factorial of number.\"\"\"\n",
-    "    if number <= 1:\n",
-    "        return 1\n",
-    "    return number * factorial(number - 1)  # recursive call"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(11):\n",
-    "    print(f'{i}! = {factorial(i)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Indirect Recursion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stack Overflow and Infinite Recursion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursion and the Function-Call Stack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.3 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** A `________` case is needed to successfully terminate recursion.\n",
-    "\n",
-    "**Answer:**  base."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** A function calling itself indirectly is not an example of recursion.\n",
-    "\n",
-    "**Answer:** False. A function calling itself in this manner is an example of indirect recursion."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(True/False)_** When a recursive function is called to solve a problem, it’s capable of solving only the simplest case(s), or base case(s).\n",
-    "\n",
-    "**Answer:** True. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**4. _(True/False)_** To make recursion feasible, the recursion step in a recursive solution must resemble the original problem, but be a slightly larger version of it.\n",
-    "\n",
-    "**Answer:** False. To make recursion feasible, the recursion step in a recursive solution must resemble the original problem, but be a slightly smaller or simpler version of it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**5. _(IPython Session)_** Most other programming languages store integers in a fixed amount of space. So their built-in integer types can represent only a limited range of integer values. For example, Java’s `int` type can represent only values in the range –2,147,483,648 to +2,147,483,647. Python allows integers to become arbitrarily large. Continue this section’s IPython session and execute the function call `factorial(50)` to demonstrate that Python supports much larger integers.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factorial(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_04-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_04-checkpoint.ipynb
deleted file mode 100755
index a94e06b..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_04-checkpoint.ipynb
+++ /dev/null
@@ -1,212 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 12.4 Recursive Fibonacci Series Example\n",
-    "\n",
-    "**Note: This notebook contains the section's Self Check also because the IPython session continues into the exercises.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fibonacci(n):\n",
-    "    if n in (0, 1):  # base cases\n",
-    "        return n\n",
-    "    else:\n",
-    "        return fibonacci(n - 1) + fibonacci(n - 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "\n",
-    "### Testing Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for n in range(41):\n",
-    "    print(f'Fibonacci({n}) = {fibonacci(n)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analyzing the Calls to Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Complexity Issues"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.4 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The ratio of successive Fibonacci numbers converges on a constant value of 1.618…, a number that has been called the `________` or the `________`.\n",
-    "\n",
-    "**Answer:** golden ratio, golden mean. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** In the field of complexity theory, computer scientists study how hard algorithms work to complete their tasks. \n",
-    "\n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** Continuing this section’s IPython session, create a function named `iterative_fibonacci` that uses looping rather than recursion to calculate Fibonacci numbers. Use both the iterative and recursive versions to calculate the 32nd, 33rd and 34th Fibonacci numbers. Time the calls with `%timeit` to see the difference in computation time.\n",
-    " \n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def iterative_fibonacci(n):\n",
-    "    result = 0\n",
-    "    temp = 1\n",
-    "    for j in range(0, n):\n",
-    "        temp, result = result, result + temp\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(33)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(33)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(34)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(34)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_05selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_05selfcheck-checkpoint.ipynb
deleted file mode 100755
index f0777f3..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_05selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.5 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Recursion terminates when `________`.\n",
-    " \n",
-    "**Answer:** a base case is reached."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Iteration and recursion can occur infinitely.\n",
-    " \n",
-    "**Answer:** True. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_07-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_07-checkpoint.ipynb
deleted file mode 100755
index 567836b..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_07-checkpoint.ipynb
+++ /dev/null
@@ -1,135 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 12.7 Linear Search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear Search Algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear Search Implementation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def linear_search(data, search_key):\n",
-    "    for index in range(len(data)):\n",
-    "        if data[index] == search_key:\n",
-    "            return index\n",
-    "    return -1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values = np.random.randint(10, 91, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 78)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 61)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 66)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_07selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_07selfcheck-checkpoint.ipynb
deleted file mode 100755
index eb4949b..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_07selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.7 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The `________` algorithm searches each element in an array sequentially. \n",
-    " \n",
-    "**Answer:** linear search."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** If an array contains duplicate values, the linear search finds the last matching value. \n",
-    " \n",
-    "**Answer:** False. If there are duplicate values, linear search finds the first matching value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_08selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_08selfcheck-checkpoint.ipynb
deleted file mode 100755
index 6760c16..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_08selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.8 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** `________` notation indicates how hard an algorithm may have to work to solve a problem. \n",
-    " \n",
-    "**Answer:** Big O."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** An O(n) algorithm is referred to as having a quadratic run time. \n",
-    " \n",
-    "**Answer:** False. An O(n) algorithm is referred to as having a linear run time. O(n2) algorithms have quadratic run time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(Discussion)_** When might you choose to write an O(n2) algorithm? \n",
-    " \n",
-    "**Answer:** When n is small and you do not have the time (or need) to invest in developing a better performing algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_09.00selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_09.00selfcheck-checkpoint.ipynb
deleted file mode 100755
index 88013a8..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_09.00selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.9 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(True/False)_** The linear search and binary search algorithms require arrays to be sorted. \n",
-    " \n",
-    "**Answer:** False. Only the binary search requires a sorted array."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(Fill-In)_** With binary search, the smallest number of comparisons that would be needed to find a matching element in a 1,000,001-element array is `________`.\n",
-    " \n",
-    "**Answer:** One. This happens if on the first comparison the key matches the middle array element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_09.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_09.01-checkpoint.ipynb
deleted file mode 100755
index 2962a5c..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_09.01-checkpoint.ipynb
+++ /dev/null
@@ -1,165 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 12.9.1 Binary Search Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `binary_search` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# binarysearch.py\n",
-    "\"\"\"Use binary search to locate an item in an array.\"\"\"\n",
-    "import numpy as np\n",
-    "\n",
-    "def binary_search(data, key):\n",
-    "    \"\"\"Perform binary search of data looking for key.\"\"\"\n",
-    "    low = 0   # low end of search area\n",
-    "    high = len(data) - 1  # high end of search area\n",
-    "    middle = (low + high + 1) // 2  # middle element index\n",
-    "    location = -1  # return value -1 if not found\n",
-    "    \n",
-    "    # loop to search for element\n",
-    "    while low <= high and location == -1:\n",
-    "        # print remaining elements of array\n",
-    "        print(remaining_elements(data, low, high))\n",
-    "\n",
-    "        print('   ' * middle, end='')  # output spaces for alignment \n",
-    "        print(' * ')  # indicate current middle\n",
-    "\n",
-    "        # if the element is found at the middle                    \n",
-    "        if key == data[middle]:                                   \n",
-    "            location = middle  # location is the current middle     \n",
-    "        elif key < data[middle]:  # middle element is too high\n",
-    "            high = middle - 1  # eliminate the higher half          \n",
-    "        else:  # middle element is too low                         \n",
-    "            low = middle + 1  # eliminate the lower half            \n",
-    "                                                                        \n",
-    "        middle = (low + high + 1) // 2  # recalculate the middle\n",
-    "\n",
-    "    return location  # return location of search key\n",
-    "                            "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `remaining_elements` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def remaining_elements(data, low, high):\n",
-    "    \"\"\"Display remaining elements of the binary search.\"\"\"\n",
-    "    return '   ' * low + ' '.join(str(s) for s in data[low:high + 1])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main` \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    # create and display array of random values\n",
-    "    data = np.random.randint(10, 91, 15)\n",
-    "    data.sort()\n",
-    "    print(data, '\\n')\n",
-    "\n",
-    "    search_key = int(input('Enter an integer value (-1 to quit): ')) \n",
-    "\n",
-    "    # repeatedly input an integer; -1 terminates the program\n",
-    "    while search_key != -1:\n",
-    "        location = binary_search(data, search_key)  # perform search\n",
-    "\n",
-    "        if location == -1:  # not found\n",
-    "            print(f'{search_key} was not found\\n') \n",
-    "        else:\n",
-    "            print(f'{search_key} found in position {location}\\n')\n",
-    "\n",
-    "        search_key = int(input('Enter an integer value (-1 to quit): '))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execte the search \n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_11.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_11.01-checkpoint.ipynb
deleted file mode 100755
index c512996..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_11.01-checkpoint.ipynb
+++ /dev/null
@@ -1,121 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 12.11.1 Selection Sort Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `selection_sort` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# selectionsort.py\n",
-    "\"\"\"Sorting an array with selection sort.\"\"\"\n",
-    "import numpy as np\n",
-    "from ch12utilities import print_pass\n",
-    "\n",
-    "def selection_sort(data):\n",
-    "    \"\"\"Sort array using selection sort.\"\"\"\n",
-    "    # loop over len(data) - 1 elements      \n",
-    "    for index1 in range(len(data) - 1):\n",
-    "        smallest = index1  # first index of remaining array\n",
-    "\n",
-    "        # loop to find index of smallest element                      \n",
-    "        for index2 in range(index1 + 1, len(data)): \n",
-    "            if data[index2] < data[smallest]:\n",
-    "                smallest = index2\n",
-    "                                              \n",
-    "        # swap smallest element into position\n",
-    "        data[smallest], data[index1] = data[index1], data[smallest]  \n",
-    "        print_pass(data, index1 + 1, smallest)\n",
-    "        \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main(): \n",
-    "    data = np.random.randint(10, 91, 10)\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    selection_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to run the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_11.03selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_11.03selfcheck-checkpoint.ipynb
deleted file mode 100755
index 3f29d27..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_11.03selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.11.3 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_ A selection sort application would take approximately `________` times as long to run on a 128-million-element array as on a 32-million-element array.\n",
-    "16, because an O(n2) algorithm takes 16 times as long to sort four times as much information. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_12.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_12.01-checkpoint.ipynb
deleted file mode 100755
index c870f5d..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_12.01-checkpoint.ipynb
+++ /dev/null
@@ -1,115 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 12.12.1 Insertion Sort Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `insertion_sort`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# insertionsort.py\n",
-    "\"\"\"Sorting an array with insertion sort.\"\"\"\n",
-    "import numpy as np\n",
-    "from ch12utilities import print_pass\n",
-    "\n",
-    "def insertion_sort(data):\n",
-    "    \"\"\"Sort an array using insertion sort.\"\"\"\n",
-    "    # loop over len(data) - 1 elements      \n",
-    "    for next in range(1, len(data)):\n",
-    "        insert = data[next]  # value to insert \n",
-    "        move_item = next  # location to place element\n",
-    "\n",
-    "        # search for place to put current element         \n",
-    "        while move_item > 0 and data[move_item - 1] > insert:  \n",
-    "            # shift element right one slot\n",
-    "            data[move_item] = data[move_item - 1]         \n",
-    "            move_item -= 1                                      \n",
-    "                                              \n",
-    "        data[move_item] = insert  # place inserted element \n",
-    "        print_pass(data, next, move_item)  # output pass of algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main(): \n",
-    "    \"\"\"Main application.\"\"\"\n",
-    "    data = np.random.randint(10, 91, 10)\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    insertion_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call mainto execute the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_12.02selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_12.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index a507534..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_12.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.12.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(True/False)_** Like the selection sort algorithm, the insertion sort algorithm has linear run time. \n",
-    " \n",
-    "**Answer:** False. Both algorithms have quadratic run time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Each iteration of the selection sort algorithm inserts one value into sorted order among the values that have been sorted so far.\n",
-    " \n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_13.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_13.01-checkpoint.ipynb
deleted file mode 100755
index 412933c..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_13.01-checkpoint.ipynb
+++ /dev/null
@@ -1,211 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 12.13.1 Merge Sort Implementation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `merge_sort` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# mergesort.py\n",
-    "\"\"\"Sorting an array with merge sort.\"\"\"\n",
-    "import numpy as np \n",
-    "\n",
-    "# calls recursive sort_array method to begin merge sorting\n",
-    "def merge_sort(data):\n",
-    "    sort_array(data, 0, len(data) - 1)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursive Function `sort_array` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sort_array(data, low, high):\n",
-    "    \"\"\"Split data, sort subarrays and merge them into sorted array.\"\"\"\n",
-    "    # test base case size of array equals 1     \n",
-    "    if (high - low) >= 1:  # if not base case\n",
-    "        middle1 = (low + high) // 2  # calculate middle of array\n",
-    "        middle2 = middle1 + 1  # calculate next element over     \n",
-    "\n",
-    "        # output split step\n",
-    "        print(f'split:   {subarray_string(data, low, high)}') \n",
-    "        print(f'         {subarray_string(data, low, middle1)}') \n",
-    "        print(f'         {subarray_string(data, middle2, high)}\\n') \n",
-    "\n",
-    "        # split array in half  sort each half (recursive calls)\n",
-    "        sort_array(data, low, middle1)  # first half of array       \n",
-    "        sort_array(data, middle2, high)  # second half of array     \n",
-    "\n",
-    "        # merge two sorted arrays after split calls return\n",
-    "        merge(data, low, middle1, middle2, high)              \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `merge` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# merge two sorted subarrays into one sorted subarray             \n",
-    "def merge(data, left, middle1, middle2, right):\n",
-    "    left_index = left  # index into left subarray              \n",
-    "    right_index = middle2  # index into right subarray         \n",
-    "    combined_index = left  # index into temporary working array\n",
-    "    merged = [0] * len(data)  # working array        \n",
-    "  \n",
-    "    # output two subarrays before merging\n",
-    "    print(f'merge:   {subarray_string(data, left, middle1)}') \n",
-    "    print(f'         {subarray_string(data, middle2, right)}') \n",
-    "\n",
-    "    # merge arrays until reaching end of either         \n",
-    "    while left_index <= middle1 and right_index <= right:\n",
-    "        # place smaller of two current elements into result  \n",
-    "        # and move to next space in arrays                   \n",
-    "        if data[left_index] <= data[right_index]:       \n",
-    "            merged[combined_index] = data[left_index]\n",
-    "            combined_index += 1\n",
-    "            left_index += 1\n",
-    "        else:                                                 \n",
-    "            merged[combined_index] = data[right_index] \n",
-    "            combined_index += 1\n",
-    "            right_index += 1\n",
-    "\n",
-    "    # if left array is empty                                \n",
-    "    if left_index == middle2:  # if True, copy in rest of right array\n",
-    "        merged[combined_index:right + 1] = data[right_index:right + 1]\n",
-    "    else:  # right array is empty, copy in rest of left array\n",
-    "        merged[combined_index:middle1 + 1] = data[left_index:middle1 + 1]\n",
-    "\n",
-    "    data[left:right + 1] = merged[left:right + 1]  # copy back to data\n",
-    "\n",
-    "    # output merged array\n",
-    "    print(f'         {subarray_string(data, left, right)}\\n') \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `subarray_string` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# method to output certain values in array\n",
-    "def subarray_string(data, low, high):\n",
-    "    temp = '   ' * low  # spaces for alignment\n",
-    "    temp += ' '.join(str(item) for item in data[low:high + 1])\n",
-    "    return temp\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    data = np.random.randint(10, 91, 10)\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    merge_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execute the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_13.02selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_13.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index 963f841..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_13.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,64 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 12.13.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The efficiency of merge sort is         .\n",
-    " \n",
-    "**Answer:** O(n log n)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_15-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_15-checkpoint.ipynb
deleted file mode 100644
index 0b3c497..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_15-checkpoint.ipynb
+++ /dev/null
@@ -1,246 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 12.15 Visualizing Algorithms\n",
-    "\n",
-    "**If you are using JupyterLab, it does not currently support JavaScript output in a notebook, which is required for Matplotlib animations. This example will run in a classic Jupyter Notebook, but not JupyterLab.** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# selectionsortanimation.py\n",
-    "\"\"\"Animated selection sort visualization.\"\"\"\n",
-    "from matplotlib import animation\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import sys\n",
-    "from ch12soundutilities import play_sound"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `update` Function That Displays Each Animation Frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update(frame_data):\n",
-    "    \"\"\"Display bars representing the current state.\"\"\"    \n",
-    "    # unpack info for graph update\n",
-    "    data, colors, swaps, comparisons = frame_data\n",
-    "    plt.cla()  # clear old contents contents of current Figure\n",
-    "\n",
-    "    # create barplot and set its xlabel\n",
-    "    bar_positions = np.arange(len(data))\n",
-    "    axes = sns.barplot(bar_positions, data, palette=colors)  # new bars\n",
-    "    axes.set(xlabel=f'Comparisons: {comparisons}; Swaps: {swaps}',\n",
-    "             xticklabels=data)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `flash_bars` Function That Flashes the Bars About to be Swapped"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def flash_bars(index1, index2, data, colors, swaps, comparisons):\n",
-    "    \"\"\"Flash the bars about to be swapped and play their notes.\"\"\"\n",
-    "    for x in range(0, 2):\n",
-    "        colors[index1], colors[index2] = 'white', 'white'\n",
-    "        yield (data, colors, swaps, comparisons) \n",
-    "        colors[index1], colors[index2] = 'purple', 'purple'\n",
-    "        yield (data, colors, swaps, comparisons) \n",
-    "        play_sound(data[index1], seconds=0.05)\n",
-    "        play_sound(data[index2], seconds=0.05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `selection_sort` as a Generator Function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def selection_sort(data):\n",
-    "    \"\"\"Sort data using the selection sort algorithm and\n",
-    "    yields values that update uses to visualize the algorithm.\"\"\"\n",
-    "    swaps = 0  \n",
-    "    comparisons = 0\n",
-    "    colors = ['lightgray'] * len(data)  # list of bar colors\n",
-    "    \n",
-    "    # display initial bars representing shuffled values\n",
-    "    yield (data, colors, swaps, comparisons)    \n",
-    "    \n",
-    "    # loop over len(data) - 1 elements    \n",
-    "    for index1 in range(0, len(data) - 1):\n",
-    "        print('outerloop')\n",
-    "        smallest = index1\n",
-    "        \n",
-    "        # loop to find index of smallest element's index   \n",
-    "        for index2 in range(index1 + 1, len(data)):\n",
-    "            print('innerloop')\n",
-    "            comparisons += 1\n",
-    "            colors[smallest] = 'purple'\n",
-    "            colors[index2] = 'red'            \n",
-    "            yield (data, colors, swaps, comparisons) \n",
-    "            play_sound(data[index2], seconds=0.05)\n",
-    "\n",
-    "            # compare elements at positions index and smallest\n",
-    "            if data[index2] < data[smallest]:\n",
-    "                colors[smallest] = 'lightgray'\n",
-    "                smallest = index2\n",
-    "                colors[smallest] = 'purple'\n",
-    "                yield (data, colors, swaps, comparisons) \n",
-    "            else: \n",
-    "                colors[index2] = 'lightgray'\n",
-    "                yield (data, colors, swaps, comparisons) \n",
-    "            \n",
-    "        # ensure that last bar is not purple\n",
-    "        colors[-1] = 'lightgray'\n",
-    "        \n",
-    "        # flash the bars about to be swapped\n",
-    "        yield from flash_bars(index1, smallest, data, colors, \n",
-    "                              swaps, comparisons)\n",
-    "\n",
-    "        # swap the elements at positions index1 and smallest\n",
-    "        swaps += 1\n",
-    "        data[smallest], data[index1] = data[index1], data[smallest]  \n",
-    "        \n",
-    "        # flash the bars that were just swapped\n",
-    "        yield from flash_bars(index1, smallest, data, colors, \n",
-    "                              swaps, comparisons)\n",
-    "        \n",
-    "        # indicate that bar index1 is now in its final spot\n",
-    "        colors[index1] = 'lightgreen'\n",
-    "        yield (data, colors, swaps, comparisons)    \n",
-    "\n",
-    "    # indicate that last bar is now in its final spot\n",
-    "    colors[-1] = 'lightgreen'\n",
-    "    yield (data, colors, swaps, comparisons)    \n",
-    "    play_sound(data[-1], seconds=0.05)\n",
-    "\n",
-    "    # play each bar's note once and color it darker green\n",
-    "    for index in range(len(data)):\n",
-    "        colors[index] = 'green'\n",
-    "        yield (data, colors, swaps, comparisons)\n",
-    "        play_sound(data[index], seconds=0.05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `main` Function That Launches the Animation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    number_of_values = int(sys.argv[1]) if len(sys.argv) == 2 else 10 \n",
-    "\n",
-    "    figure = plt.figure('Selection Sort')  # Figure to display barplot\n",
-    "    numbers = np.arange(1, number_of_values + 1)  # create array \n",
-    "    np.random.shuffle(numbers)  # shuffle the array\n",
-    "\n",
-    "    # start the animation\n",
-    "    anim = animation.FuncAnimation(figure, update, repeat=False,\n",
-    "        frames=selection_sort(numbers), interval=50)\n",
-    "    \n",
-    "    plt.show()  # display the Figure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execute the animation\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_Exercises-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_Exercises-checkpoint.ipynb
deleted file mode 100644
index 684f00a..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/12_Exercises-checkpoint.ipynb
+++ /dev/null
@@ -1,147 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 12 Exercises with Code Snippets\n",
-    "\n",
-    "**12.1** What does the following code do?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mystery(a, b):\n",
-    "    if b == 1:\n",
-    "        return a\n",
-    "    else:\n",
-    "        return a + mystery(a, b - 1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mystery(2, 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**12.2** Find the logic error(s) in the following recursive function, and explain how to correct it (them). This function should find the sum of the values from 0 to `n`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sum(n):\n",
-    "    if n == 0:\n",
-    "        return 0\n",
-    "    else: \n",
-    "        return n + sum(n)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**12.3** What does the following code do?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mystery(a_array, size):\n",
-    "    if size == 1:\n",
-    "        return a_array[0]\n",
-    "    else: \n",
-    "        return a_array[size - 1] + mystery(a_array, size - 1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numbers = np.arange(1, 11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mystery(numbers, len(numbers))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_02-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_02-checkpoint.ipynb
deleted file mode 100755
index 4767596..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_02-checkpoint.ipynb
+++ /dev/null
@@ -1,89 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 15.2 Factorials"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Iterative Factorial Approach"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factorial = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for counter in range(5, 0, -1):\n",
-    "    factorial *= counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factorial"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_03-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_03-checkpoint.ipynb
deleted file mode 100755
index 34de11d..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_03-checkpoint.ipynb
+++ /dev/null
@@ -1,183 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 15.3 Recursive Factorial Example\n",
-    "\n",
-    "**Note: This notebook contains the section's Self Check also because the IPython session continues into the exercises.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursive Factorial Approach "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing Recursion "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Implementing a Recursive Factorial Function "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def factorial(number):\n",
-    "    \"\"\"Return factorial of number.\"\"\"\n",
-    "    if number <= 1:\n",
-    "        return 1\n",
-    "    return number * factorial(number - 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(11):\n",
-    "    print(f'{i}! = {factorial(i)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Indirect Recursion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stack Overflow and Infinite Recursion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursion and the Function-Call Stack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 8.3 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** A `________` case is needed to successfully terminate recursion.\n",
-    "\n",
-    "**Answer:**  base."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** A function calling itself indirectly is not an example of recursion.\n",
-    "\n",
-    "**Answer:** False. A function calling itself in this manner is an example of indirect recursion."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(True/False)_** When a recursive function is called to solve a problem, it’s capable of solving only the simplest case(s), or base case(s).\n",
-    "\n",
-    "**Answer:** True. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**4. _(True/False)_** To make recursion feasible, the recursion step in a recursive solution must resemble the original problem, but be a slightly larger version of it.\n",
-    "\n",
-    "**Answer:** False. To make recursion feasible, the recursion step in a recursive solution must resemble the original problem, but be a slightly smaller or simpler version of it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**5. _(IPython Session)_** Most other programming languages store integers in a fixed amount of space. So their built-in integer types can represent only a limited range of integer values. For example, Java’s `int` type can represent only values in the range –2,147,483,648 to +2,147,483,647. Python allows integers to become arbitrarily large. Continue this section’s IPython session and execute the function call `factorial(50)` to demonstrate that Python supports much larger integers.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "factorial(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_04-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_04-checkpoint.ipynb
deleted file mode 100755
index 81c4430..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_04-checkpoint.ipynb
+++ /dev/null
@@ -1,214 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 15.4 Recursive Fibonacci Series Example\n",
-    "\n",
-    "**Note: This notebook contains the section's Self Check also because the IPython session continues into the exercises.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fibonacci(n):\n",
-    "    if n in (0, 1):\n",
-    "        return n\n",
-    "    else:\n",
-    "        return fibonacci(n - 1) + fibonacci(n - 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "\n",
-    "### Testing Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    " \n",
-    "for n in range(41):\n",
-    "    print(f'Fibonacci({n}) = {fibonacci(n)}')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analyzing the Calls to Function `fibonacci` "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Complexity Issues"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.4 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The ratio of successive Fibonacci numbers converges on a constant value of 1.618…, a number that has been called the `________` or the `________`.\n",
-    "\n",
-    "**Answer:** golden ratio, golden mean. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** In the field of complexity theory, computer scientists study how hard algorithms work to complete their tasks. \n",
-    "\n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(IPython Session)_** Continuing this section’s IPython session, create a function named `iterative_fibonacci` that uses looping rather than recursion to calculate Fibonacci numbers. Use both the iterative and recursive versions to calculate the 32nd, 33rd and 34th Fibonacci numbers. Time the calls with `%timeit` to see the difference in computation time.\n",
-    " \n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def iterative_fibonacci(n):\n",
-    "    result = 0\n",
-    "    temp = 1\n",
-    "    for j in range(0, n):\n",
-    "        temp, result = result, result + temp\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(33)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(33)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit fibonacci(34)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%timeit iterative_fibonacci(34)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_05selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_05selfcheck-checkpoint.ipynb
deleted file mode 100755
index 918cd1f..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_05selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.5 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** Recursion terminates when `________`.\n",
-    " \n",
-    "**Answer:** a base case is reached."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Iteration and recursion can occur infinitely.\n",
-    " \n",
-    "**Answer:** True. **"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_07-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_07-checkpoint.ipynb
deleted file mode 100755
index 7c73e98..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_07-checkpoint.ipynb
+++ /dev/null
@@ -1,135 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 15.7 Linear Search"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear Search Algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear Search Implementation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def linear_search(data, search_key):\n",
-    "    for index in range(len(data)):\n",
-    "        if data[index] == search_key:\n",
-    "            return index\n",
-    "    return -1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values = np.random.randint(10, 91, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 78)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 61)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_search(values, 66)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_07selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_07selfcheck-checkpoint.ipynb
deleted file mode 100755
index 07d6ee1..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_07selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.7 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The `________` algorithm searches each element in an array sequentially. \n",
-    " \n",
-    "**Answer:** linear search."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** If an array contains duplicate values, the linear search finds the last matching value. \n",
-    " \n",
-    "**Answer:** False. If there are duplicate values, linear search finds the first matching value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_08selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_08selfcheck-checkpoint.ipynb
deleted file mode 100755
index 8c32447..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_08selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.8 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** `________` notation indicates how hard an algorithm may have to work to solve a problem. \n",
-    " \n",
-    "**Answer:** Big O."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** An O(n) algorithm is referred to as having a quadratic run time. \n",
-    " \n",
-    "**Answer:** False. An O(n) algorithm is referred to as having a linear run time. O(n2) algorithms have quadratic run time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**3. _(Discussion)_** When might you choose to write an O(n2) algorithm? \n",
-    " \n",
-    "**Answer:** When n is small and you do not have the time (or need) to invest in developing a better performing algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_09.00selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_09.00selfcheck-checkpoint.ipynb
deleted file mode 100755
index dd8783d..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_09.00selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.9 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(True/False)_** The linear search and binary search algorithms require arrays to be sorted. \n",
-    " \n",
-    "**Answer:** False. Only the binary search requires a sorted array."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(Fill-In)_** With binary search, the smallest number of comparisons that would be needed to find a matching element in a 1,000,001-element array is `________`.\n",
-    " \n",
-    "**Answer:** One. This happens if on the first comparison the key matches the middle array element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_09.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_09.01-checkpoint.ipynb
deleted file mode 100755
index 9bf0312..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_09.01-checkpoint.ipynb
+++ /dev/null
@@ -1,165 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 15.9.1 Binary Search Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `binary_search` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# binarysearch.py\n",
-    "\"\"\"Use binary search to locate an item in an array.\"\"\"\n",
-    "import numpy as np\n",
-    "\n",
-    "def binary_search(data, key):\n",
-    "    \"\"\"Perform binary search of data looking for key.\"\"\"\n",
-    "    low = 0   # low end of search area\n",
-    "    high = len(data) - 1  # high end of search area\n",
-    "    middle = (low + high + 1) // 2  # middle element index\n",
-    "    location = -1  # return value -1 if not found\n",
-    "    \n",
-    "    # loop to search for element\n",
-    "    while low <= high and location == -1:\n",
-    "        # print remaining elements of array\n",
-    "        print(remaining_elements(data, low, high))\n",
-    "\n",
-    "        print('   ' * middle, end='')  # output spaces for alignment \n",
-    "        print(' * ')  # indicate current middle\n",
-    "\n",
-    "        # if the element is found at the middle                    \n",
-    "        if key == data[middle]:                                   \n",
-    "            location = middle  # location is the current middle     \n",
-    "        elif key < data[middle]:  # middle element is too high\n",
-    "            high = middle - 1  # eliminate the higher half          \n",
-    "        else:  # middle element is too low                         \n",
-    "            low = middle + 1  # eliminate the lower half            \n",
-    "                                                                        \n",
-    "        middle = (low + high + 1) // 2  # recalculate the middle\n",
-    "\n",
-    "    return location  # return location of search key\n",
-    "                            "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `remaining_elements` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def remaining_elements(data, low, high):\n",
-    "    \"\"\"Display remaining elements of the binary search.\"\"\"\n",
-    "    return '   ' * low + ' '.join(str(s) for s in data[low:high + 1])\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main` \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    # create and display array of random values\n",
-    "    data = np.random.randint(10, 91, 15)\n",
-    "    data.sort()\n",
-    "    print(data, '\\n')\n",
-    "\n",
-    "    search_key = int(input('Enter an integer value (-1 to quit): ')) \n",
-    "\n",
-    "    # repeatedly input an integer; -1 terminates the program\n",
-    "    while search_key != -1:\n",
-    "        location = binary_search(data, search_key)  # perform search\n",
-    "\n",
-    "        if location == -1:  # not found\n",
-    "            print(f'{search_key} was not found\\n') \n",
-    "        else:\n",
-    "            print(f'{search_key} found in position {location}\\n')\n",
-    "\n",
-    "        search_key = int(input('Enter an integer value (-1 to quit): '))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execte the search \n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_11.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_11.01-checkpoint.ipynb
deleted file mode 100755
index 01a6bd2..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_11.01-checkpoint.ipynb
+++ /dev/null
@@ -1,121 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 15.11.1 Selection Sort Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `selection_sort` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# selectionsort.py\n",
-    "\"\"\"Sorting an array with selection sort.\"\"\"\n",
-    "import numpy as np\n",
-    "from ch15utilities import print_pass\n",
-    "\n",
-    "def selection_sort(data):\n",
-    "    \"\"\"Sort array using selection sort.\"\"\"\n",
-    "    # loop over data.length - 1 elements      \n",
-    "    for index1 in range(len(data) - 1):\n",
-    "        smallest = index1  # first index of remaining array\n",
-    "\n",
-    "        # loop to find index of smallest element                      \n",
-    "        for index2 in range(index1 + 1, len(data)): \n",
-    "            if data[index2] < data[smallest]:\n",
-    "                smallest = index2\n",
-    "                                              \n",
-    "        # swap smallest element into position\n",
-    "        data[smallest], data[index1] = data[index1], data[smallest]  \n",
-    "        print_pass(data, index1 + 1, smallest)\n",
-    "        \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main(): \n",
-    "    data = np.random.randint(10, 91, 10)\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    selection_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to run the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_11.03selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_11.03selfcheck-checkpoint.ipynb
deleted file mode 100755
index 91755ba..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_11.03selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.11.3 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_ A selection sort application would take approximately `________` times as long to run on a 128-million-element array as on a 32-million-element array.\n",
-    "16, because an O(n2) algorithm takes 16 times as long to sort four times as much information. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_12.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_12.01-checkpoint.ipynb
deleted file mode 100755
index 12496e3..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_12.01-checkpoint.ipynb
+++ /dev/null
@@ -1,115 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 15.12.1 Insertion Sort Implementation\n",
-    "\n",
-    "**Note: The last two lines of source code in this example have been modified from the print book so you can execute the example inside the notebook.**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `insertion_sort`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# insertionsort.py\n",
-    "\"\"\"Sorting an array with insertion sort.\"\"\"\n",
-    "import numpy as np\n",
-    "from ch15utilities import print_pass\n",
-    "\n",
-    "def insertion_sort(data):\n",
-    "    \"\"\"Sort an array using insertion sort.\"\"\"\n",
-    "    # loop over data.length - 1 elements      \n",
-    "    for next in range(1, len(data)):\n",
-    "        insert = data[next]  # value to insert \n",
-    "        move_item = next  # location to place element\n",
-    "\n",
-    "        # search for place to put current element         \n",
-    "        while move_item > 0 and data[move_item - 1] > insert:  \n",
-    "            # shift element right one slot\n",
-    "            data[move_item] = data[move_item - 1]         \n",
-    "            move_item -= 1                                      \n",
-    "                                              \n",
-    "        data[move_item] = insert  # place inserted element \n",
-    "        print_pass(data, next, move_item)  # output pass of algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main(): \n",
-    "    \"\"\"Main application.\"\"\"\n",
-    "    data = np.random.randint(10, 91, 10)\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    insertion_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call mainto execute the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_12.02selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_12.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index fae4dd0..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_12.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.12.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(True/False)_** Like the selection sort algorithm, the insertion sort algorithm has linear run time. \n",
-    " \n",
-    "**Answer:** False. Both algorithms have quadratic run time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(True/False)_** Each iteration of the selection sort algorithm inserts one value into sorted order among the values that have been sorted so far.\n",
-    " \n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_13.01-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_13.01-checkpoint.ipynb
deleted file mode 100755
index e20acae..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_13.01-checkpoint.ipynb
+++ /dev/null
@@ -1,211 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 15.13.1 Merge Sort Implementation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `merge_sort` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# mergesort.py\n",
-    "\"\"\"Sorting an array with merge sort.\"\"\"\n",
-    "import numpy as np \n",
-    "\n",
-    "# calls recursive sort_array method to begin merge sorting\n",
-    "def merge_sort(data):\n",
-    "    sort_array(data, 0, len(data) - 1)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Recursive Function `sort_array` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sort_array(data, low, high):\n",
-    "    \"\"\"Split data, sort subarrays and merge them into sorted array.\"\"\"\n",
-    "    # test base case size of array equals 1     \n",
-    "    if (high - low) >= 1:  # if not base case\n",
-    "        middle1 = (low + high) // 2  # calculate middle of array\n",
-    "        middle2 = middle1 + 1  # calculate next element over     \n",
-    "\n",
-    "        # output split step\n",
-    "        print(f'split:   {subarray_string(data, low, high)}') \n",
-    "        print(f'         {subarray_string(data, low, middle1)}') \n",
-    "        print(f'         {subarray_string(data, middle2, high)}\\n') \n",
-    "\n",
-    "        # split array in half  sort each half (recursive calls)\n",
-    "        sort_array(data, low, middle1)  # first half of array       \n",
-    "        sort_array(data, middle2, high)  # second half of array     \n",
-    "\n",
-    "        # merge two sorted arrays after split calls return\n",
-    "        merge(data, low, middle1, middle2, high)              \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `merge` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# merge two sorted subarrays into one sorted subarray             \n",
-    "def merge(data, left, middle1, middle2, right):\n",
-    "    left_index = left  # index into left subarray              \n",
-    "    right_index = middle2  # index into right subarray         \n",
-    "    combined_index = left  # index into temporary working array\n",
-    "    merged = [0] * len(data)  # working array        \n",
-    "  \n",
-    "    # output two subarrays before merging\n",
-    "    print(f'merge:   {subarray_string(data, left, middle1)}') \n",
-    "    print(f'         {subarray_string(data, middle2, right)}') \n",
-    "\n",
-    "    # merge arrays until reaching end of either         \n",
-    "    while left_index <= middle1 and right_index <= right:\n",
-    "        # place smaller of two current elements into result  \n",
-    "        # and move to next space in arrays                   \n",
-    "        if data[left_index] <= data[right_index]:       \n",
-    "            merged[combined_index] = data[left_index]\n",
-    "            combined_index += 1\n",
-    "            left_index += 1\n",
-    "        else:                                                 \n",
-    "            merged[combined_index] = data[right_index] \n",
-    "            combined_index += 1\n",
-    "            right_index += 1\n",
-    "\n",
-    "    # if left array is empty                                \n",
-    "    if left_index == middle2:  # if True, copy in rest of right array\n",
-    "        merged[combined_index:right + 1] = data[right_index:right + 1]\n",
-    "    else:  # right array is empty, copy in rest of left array\n",
-    "        merged[combined_index:middle1 + 1] = data[left_index:middle1 + 1]\n",
-    "\n",
-    "    data[left:right + 1] = merged[left:right + 1]  # copy back to data\n",
-    "\n",
-    "    # output merged array\n",
-    "    print(f'         {subarray_string(data, left, right)}\\n') \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `subarray_string` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# method to output certain values in array\n",
-    "def subarray_string(data, low, high):\n",
-    "    temp = '   ' * low  # spaces for alignment\n",
-    "    temp += ' '.join(str(item) for item in data[low:high + 1])\n",
-    "    return temp\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Function `main`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    data = np.random.randint(10, 91, 10)\n",
-    "    print(f'Unsorted array: {data}\\n')\n",
-    "    merge_sort(data) \n",
-    "    print(f'\\nSorted array: {data}\\n')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# call main to execute the sort\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_13.02selfcheck-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_13.02selfcheck-checkpoint.ipynb
deleted file mode 100755
index b8af1cb..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_13.02selfcheck-checkpoint.ipynb
+++ /dev/null
@@ -1,64 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 15.13.2 Self Check"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**1. _(Fill-In)_** The efficiency of merge sort is         .\n",
-    " \n",
-    "**Answer:** O(n log n)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_15-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_15-checkpoint.ipynb
deleted file mode 100644
index 04809d4..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_15-checkpoint.ipynb
+++ /dev/null
@@ -1,268 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 15.15 Visualizing Algorithms\n",
-    "\n",
-    "**If you are using JupyterLab, it does not currently support JavaScript output in a notebook, which is required for Matplotlib animations. This example will run in traditional Jupyter, but not JupyterLab.** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# selectionsortanimation.py\n",
-    "\"\"\"Animated selection sort visualization.\"\"\"\n",
-    "from matplotlib import animation\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns\n",
-    "import sys\n",
-    "from ch15soundutilities import play_sound"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `update` Function That Displays Each Animation Frame"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update(frame_data):\n",
-    "    \"\"\"Display bars representing the current state.\"\"\"    \n",
-    "    # unpack info for graph update\n",
-    "    data, colors, swaps, comparisons = frame_data\n",
-    "    plt.cla()  # clear old contents contents of current Figure\n",
-    "\n",
-    "    # create barplot and set its xlabel\n",
-    "    bar_positions = np.arange(len(data))\n",
-    "    axes = sns.barplot(bar_positions, data, palette=colors)  # new bars\n",
-    "    axes.set(xlabel=f'Comparisons: {comparisons}; Swaps: {swaps}',\n",
-    "             xticklabels=data)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `flash_bars` Function That Flashes the Bars About to be Swapped"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def flash_bars(index1, index2, data, colors, swaps, comparisons):\n",
-    "    \"\"\"Flash the bars about to be swapped and play their notes.\"\"\"\n",
-    "    for x in range(0, 2):\n",
-    "        colors[index1], colors[index2] = 'white', 'white'\n",
-    "        yield (data, colors, swaps, comparisons) \n",
-    "        colors[index1], colors[index2] = 'purple', 'purple'\n",
-    "        yield (data, colors, swaps, comparisons) \n",
-    "        play_sound(data[index1], seconds=0.05)\n",
-    "        play_sound(data[index2], seconds=0.05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `selection_sort` as a Generator Function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def selection_sort(data):\n",
-    "    \"\"\"Sort data using the selection sort algorithm and\n",
-    "    yields values that update uses to visualize the algorithm.\"\"\"\n",
-    "    swaps = 0  \n",
-    "    comparisons = 0\n",
-    "    colors = ['lightgray'] * len(data)  # list of bar colors\n",
-    "    \n",
-    "    # display initial bars representing shuffled values\n",
-    "    yield (data, colors, swaps, comparisons)    \n",
-    "    \n",
-    "    # loop over len(data) - 1 elements    \n",
-    "    for index1 in range(0, len(data) - 1):\n",
-    "        print('outerloop')\n",
-    "        smallest = index1\n",
-    "        \n",
-    "        # loop to find index of smallest element's 9/12/2018index   \n",
-    "        for index2 in range(index1 + 1, len(data)):\n",
-    "            print('innerloop')\n",
-    "            comparisons += 1\n",
-    "            colors[smallest] = 'purple'\n",
-    "            colors[index2] = 'red'            \n",
-    "            yield (data, colors, swaps, comparisons) \n",
-    "            play_sound(data[index2], seconds=0.05)\n",
-    "\n",
-    "            # compare elements at positions index and smallest\n",
-    "            if data[index2] < data[smallest]:\n",
-    "                colors[smallest] = 'lightgray'\n",
-    "                smallest = index2\n",
-    "                colors[smallest] = 'purple'\n",
-    "                yield (data, colors, swaps, comparisons) \n",
-    "            else: \n",
-    "                colors[index2] = 'lightgray'\n",
-    "                yield (data, colors, swaps, comparisons) \n",
-    "            \n",
-    "        # ensure that last bar is not red or blue\n",
-    "        colors[-1] = 'lightgray'\n",
-    "        \n",
-    "        # flash the bars about to be swapped\n",
-    "        yield from flash_bars(index1, smallest, data, colors, \n",
-    "                              swaps, comparisons)\n",
-    "\n",
-    "        # swap the elements at positions i and smallest\n",
-    "        swaps += 1\n",
-    "        data[smallest], data[index1] = data[index1], data[smallest]  \n",
-    "        \n",
-    "        # flash the bars that were just swapped\n",
-    "        yield from flash_bars(index1, smallest, data, colors, \n",
-    "                              swaps, comparisons)\n",
-    "        \n",
-    "        # indicate that bar i is now in its final spot\n",
-    "        colors[index1] = 'lightgreen'\n",
-    "        yield (data, colors, swaps, comparisons)    \n",
-    "\n",
-    "    # indicate that last bar is now in its final spot\n",
-    "    colors[-1] = 'lightgreen'\n",
-    "    yield (data, colors, swaps, comparisons)    \n",
-    "    play_sound(data[-1], seconds=0.05)\n",
-    "\n",
-    "    # play each bar's note once and color it darker green\n",
-    "    for index in range(len(data)):\n",
-    "        colors[index] = 'green'\n",
-    "        yield (data, colors, swaps, comparisons)\n",
-    "        play_sound(data[index], seconds=0.05)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### `main` Function That Launches the Animation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    number_of_values = int(sys.argv[1]) if len(sys.argv) == 2 else 10 \n",
-    "\n",
-    "    figure = plt.figure('Selection Sort')  # Figure to display barplot\n",
-    "    numbers = np.arange(1, number_of_values + 1)  # create array \n",
-    "    np.random.shuffle(numbers)  # shuffle the array\n",
-    "\n",
-    "    # start the animation\n",
-    "    anim = animation.FuncAnimation(figure, update, repeat=False,\n",
-    "        frames=selection_sort(numbers), interval=50)\n",
-    "    \n",
-    "    plt.show()  # display the Figure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c9b3daa820044bbdb2f33758177de43e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "FigureCanvasNbAgg()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "started animation\n"
-     ]
-    }
-   ],
-   "source": [
-    "# call main to execute the animation\n",
-    "main()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_Exercises-checkpoint.ipynb b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_Exercises-checkpoint.ipynb
deleted file mode 100644
index a49b3f3..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/15_Exercises-checkpoint.ipynb
+++ /dev/null
@@ -1,149 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 15 Exercises with Code Snippets\n",
-    "\n",
-    "**15.1** What does the following code do?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mystery(a, b):\n",
-    "    if b == 1:\n",
-    "        return a\n",
-    "    else:\n",
-    "        return a + mystery(a, b - 1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mystery(2, 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**15.2** Find the logic error(s) in the following recursive function, and explain how to correct it (them). This function should find the sum of the values from 0 to `n`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sum(n):\n",
-    "    if n == 0:\n",
-    "        return 0\n",
-    "    else: \n",
-    "        return n + sum(n)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "    \n",
-    "\n",
-    "**15.3** What does the following code do?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mystery(a_array, size):\n",
-    "    if size == 1:\n",
-    "        return a_array[0]\n",
-    "    else: \n",
-    "        return a_array[size - 1] + mystery(a_array, size - 1)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numbers = np.arange(1, 11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mystery(numbers, len(numbers))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/ch11soundutilities-checkpoint.py b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/ch11soundutilities-checkpoint.py
deleted file mode 100755
index 177fd23..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/ch11soundutilities-checkpoint.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# ch11soundutilities.py
-"""Functions to play sounds."""
-from pysine import sine
-
-TWELFTH_ROOT_2 = 1.059463094359  # 12th root of 2
-A3 = 220  # hertz frequency for musical note A from third octave 
-
-def play_sound(i, seconds=0.1):
-    """Play a note representing a bar's magnitude. Calculation 
-    based on https://pages.mtu.edu/~suits/NoteFreqCalcs.html."""
-    sine(frequency=(A3 * TWELFTH_ROOT_2 ** i), duration=seconds)
-    
-def play_found_sound(seconds=0.1):
-    """Play sequence of notes indicating a found item."""
-    sine(frequency=523.25, duration=seconds) # C5
-    sine(frequency=698.46, duration=seconds) # F5
-    sine(frequency=783.99, duration=seconds) # G5
-
-def play_not_found_sound(seconds=0.3):
-    """Play a note indicating an item was not found."""
-    sine(frequency=220, duration=seconds) # A3
-       
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/ch11utilities-checkpoint.py b/examples/ch11/snippets_ipynb/.ipynb_checkpoints/ch11utilities-checkpoint.py
deleted file mode 100755
index e20cb4f..0000000
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/ch11utilities-checkpoint.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# ch11utilities.py
-"""Utility function for printing a pass of the 
-insertion_sort and selection_sort algorithms"""
-
-def print_pass(data, pass_number, index): 
-    """Print a pass of the algorithm."""
-    label = f'after pass {pass_number}: '
-    print(label, end='')
-
-    # output elements up to selected item
-    print('  '.join(str(d) for d in data[:index]), 
-        end='  ' if index != 0 else '') 
-
-    print(f'{data[index]}* ', end='')  # indicate swap with *
-
-    # output rest of elements
-    print('  '.join(str(d) for d in data[index + 1:len(data)]))
-
-    # underline elements that are sorted after this pass_number
-    print(f'{" " * len(label)}{"--  " * pass_number}')  
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch11/snippets_ipynb/__pycache__/ch11soundutilities.cpython-37.pyc b/examples/ch11/snippets_ipynb/__pycache__/ch11soundutilities.cpython-37.pyc
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diff --git a/examples/ch11/snippets_ipynb/__pycache__/ch15soundutilities.cpython-36.pyc b/examples/ch11/snippets_ipynb/__pycache__/ch15soundutilities.cpython-36.pyc
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diff --git a/examples/ch11/snippets_ipynb/__pycache__/ch15utilities.cpython-36.pyc b/examples/ch11/snippets_ipynb/__pycache__/ch15utilities.cpython-36.pyc
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diff --git a/examples/ch11/snippets_py/.ipynb_checkpoints/11_02-checkpoint.py b/examples/ch11/snippets_py/.ipynb_checkpoints/11_02-checkpoint.py
deleted file mode 100755
index 73af305..0000000
--- a/examples/ch11/snippets_py/.ipynb_checkpoints/11_02-checkpoint.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Section 11.2 snippets
-factorial = 1
-
-for number in range(5, 0, -1):
-    factorial *= number
-    
-factorial
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch11/snippets_py/.ipynb_checkpoints/11_03-checkpoint.py b/examples/ch11/snippets_py/.ipynb_checkpoints/11_03-checkpoint.py
deleted file mode 100755
index fed4ae0..0000000
--- a/examples/ch11/snippets_py/.ipynb_checkpoints/11_03-checkpoint.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Section 11.3 snippets
-# Note that the self check #5 is included here as it
-# continues the section's IPython session.
-
-def factorial(number):
-    """Return factorial of number."""
-    if number <= 1:
-        return 1
-    return number * factorial(number - 1)  # recursive call
-    
-for i in range(11):
-    print(f'{i}! = {factorial(i)}')
-
-# Self Check Exercise 5
-factorial(50)
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch11/snippets_py/.ipynb_checkpoints/11_04-checkpoint.py b/examples/ch11/snippets_py/.ipynb_checkpoints/11_04-checkpoint.py
deleted file mode 100755
index 69e867c..0000000
--- a/examples/ch11/snippets_py/.ipynb_checkpoints/11_04-checkpoint.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Section 11.4 snippets
-# Note that the self check #3 is included here as it
-# continues the section's IPython session.
-
-# Function fibonacci 
-def fibonacci(n):
-    if n in (0, 1):  # base cases
-        return n
-    else:
-        return fibonacci(n - 1) + fibonacci(n - 2)
-    
-# Testing Function fibonacci
-for n in range(41):
-    print(f'Fibonacci({n}) = {fibonacci(n)}')
-
-# Self Check Exercise 3
-def iterative_fibonacci(n):
-    result = 0
-    temp = 1
-    for j in range(0, n):
-        temp, result = result, result + temp
-    return result
-
-%timeit fibonacci(32)
-
-%timeit iterative_fibonacci(32)
-
-%timeit fibonacci(33)
-
-%timeit iterative_fibonacci(33)
-
-%timeit fibonacci(34)
-
-%timeit iterative_fibonacci(34)
-
-
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch11/snippets_py/.ipynb_checkpoints/11_07-checkpoint.py b/examples/ch11/snippets_py/.ipynb_checkpoints/11_07-checkpoint.py
deleted file mode 100755
index 9594843..0000000
--- a/examples/ch11/snippets_py/.ipynb_checkpoints/11_07-checkpoint.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Section 11.7 snippets
-
-# Linear Search Implementation
-def linear_search(data, search_key):
-    for index, value in enumerate(data):
-        if value == search_key:
-            return index
-    return -1
-
-import numpy as np
-
-np.random.seed(11)
-
-values = np.random.randint(10, 91, 10)
-
-values
-
-linear_search(values, 78)
-
-linear_search(values, 61)
-
-linear_search(values, 66)
-
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch11/snippets_py/.ipynb_checkpoints/11_exercises-checkpoint.py b/examples/ch11/snippets_py/.ipynb_checkpoints/11_exercises-checkpoint.py
deleted file mode 100755
index 7c8f68b..0000000
--- a/examples/ch11/snippets_py/.ipynb_checkpoints/11_exercises-checkpoint.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# 11 Exercise Snippets
-
-# 11.1. What does the following code do?
-def mystery(a, b):
-    if b == 1:
-        return a
-    else:
-        return a + mystery(a, b - 1)
-
-mystery(2, 10)
-
-
-# 11.2. Find the logic error(s) in the following recursive function, 
-# and explain how to correct it (them). This function should find 
-# the sum of the values from 0 to `n`.
-def sum(n):
-    if n == 0:
-        return 0
-    else: 
-        return n + sum(n)
-
-# 11.3. What does the following code do?
-def mystery(a_array, size):
-    if size == 1:
-        return a_array[0]
-    else: 
-        return a_array[size - 1] + mystery(a_array, size - 1)
-    
-import numpy as np
-
-numbers = np.arange(1, 11)
-
-mystery(numbers, len(numbers))
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_13_02-checkpoint.ipynb b/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_13_02-checkpoint.ipynb
deleted file mode 100755
index 73b3afb..0000000
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_13_02-checkpoint.ipynb
+++ /dev/null
@@ -1,178 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 13.13.2 Initiating Stream Processing\n",
-    "### Authenticating"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tweepy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import keys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "auth = tweepy.OAuthHandler(keys.consumer_key, \n",
-    "                           keys.consumer_secret)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "auth.set_access_token(keys.access_token, \n",
-    "                      keys.access_token_secret)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "api = tweepy.API(auth, wait_on_rate_limit=True, \n",
-    "                 wait_on_rate_limit_notify=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a `TweetListener` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tweetlistener import TweetListener"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweet_listener = TweetListener(api)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a `Stream` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweet_stream = tweepy.Stream(auth=api.auth, \n",
-    "                             listener=tweet_listener)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Starting the Tweet Stream\n",
-    "\n",
-    "We removed the is_async argument to ensure that the streamed tweets all appear below this cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweet_stream.filter(track=['Mars Rover'])  #, is_async=True) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Asynchronous vs. Synchronous Streams"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Other filter Method Parameters\n",
-    "### Twitter Restrictions Note"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_15-checkpoint.ipynb b/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_15-checkpoint.ipynb
deleted file mode 100755
index a4401ca..0000000
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_15-checkpoint.ipynb
+++ /dev/null
@@ -1,340 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 13.15.1 Getting and Mapping the Tweets\n",
-    "### Get the `API` Object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tweetutilities import get_API"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "api = get_API()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Collections Required By `LocationListener`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweets = [] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counts = {'total_tweets': 0, 'locations': 0}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating the `LocationListener` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from locationlistener import LocationListener"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "location_listener = LocationListener(api, counts_dict=counts, \n",
-    "    tweets_list=tweets, topic='football', limit=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Configure and Start the Stream of Tweets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tweepy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stream = tweepy.Stream(auth=api.auth, listener=location_listener)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stream.filter(track=['football'], languages=['en'], is_async=False)  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying the Location Statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counts['total_tweets']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "counts['locations']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'{counts[\"locations\"] / counts[\"total_tweets\"]:.1%}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Geocoding the Locations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tweetutilities import get_geocodes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bad_locations = get_geocodes(tweets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying the Bad Location Statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bad_locations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f'{bad_locations / counts[\"locations\"]:.1%}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cleaning the Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(tweets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df.dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a Map with Folium"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import folium"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "usmap = folium.Map(location=[39.8283, -98.5795], \n",
-    "                    zoom_start=5, detect_retina=True)\n",
-    "                    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating Popup Markers for the Tweet Locations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for t in df.itertuples():\n",
-    "     text = ': '.join([t.screen_name, t.text])\n",
-    "     popup = folium.Popup(text, parse_html=True)\n",
-    "     marker = folium.Marker((t.latitude, t.longitude), \n",
-    "                            popup=popup)\n",
-    "     marker.add_to(usmap)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Saving the Map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "usmap.save('tweet_map.html')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_07-11withSelfChecks-checkpoint.ipynb b/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_07-11withSelfChecks-checkpoint.ipynb
deleted file mode 100755
index 01dc47a..0000000
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_07-11withSelfChecks-checkpoint.ipynb
+++ /dev/null
@@ -1,1053 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**_Note: This notebook contains ALL the code for Sections 14.7 through 14.11, including the Self Check snippets because all the snippets in these sections are consecutively numbered in the text._**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.7 Authenticating with Twitter Via Tweepy "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tweepy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import keys"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating and Configuring an `OAuthHandler` to Authenticate with Twitter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "auth = tweepy.OAuthHandler(keys.consumer_key,\n",
-    "                           keys.consumer_secret)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "auth.set_access_token(keys.access_token,\n",
-    "                      keys.access_token_secret)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating an API Object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "api = tweepy.API(auth, wait_on_rate_limit=True, \n",
-    "                 wait_on_rate_limit_notify=True)\n",
-    "              "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.7 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** Authenticating with Twitter via Tweepy involves two steps. First, create an object of the Tweepy module’s `________` class, passing your API key and API secret key to its constructor. \n",
-    "\n",
-    "**Answer:** `OAuthHandler`.\n",
-    "\n",
-    "**2. _(True/False)_** The keyword argument `wait_on_rate_limit_notify=True` to the `tweepy.API` call tells Tweepy to terminate the user because of a rate-limit violation.\n",
-    "\n",
-    "**Answer:** False. The call tells Tweepy that if it needs to wait to avoid rate-limit violations it should display a message at the command line indicating that it’s waiting for the rate limit to replenish."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.8 Getting Information About a Twitter Account"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa = api.get_user('nasa')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting Basic Account Information"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa.id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa.name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa.screen_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa.description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting the Most Recent Status Update"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa.status.text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting the Number of Followers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa.followers_count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting the Number of Friends "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa.friends_count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting Your Own Account’s Information"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.8 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** After authenticating with Twitter, you can use the Tweepy `API` object’s `________` method to get a tweepy.models.User object containing information about a user’s Twitter account.\n",
-    "\n",
-    "**Answer:** get_user\n",
-    "\n",
-    "**2. _(True/False)_** Retweeting often results in truncation because a retweet adds characters that could exceed the character limit.\n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**3. _(IPython Session)_** Use the `api` object to get a `User` object for the `NASAKepler` account, then display its number of followers and most recent tweet.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa_kepler = api.get_user('NASAKepler')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa_kepler.followers_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa_kepler.status.text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.9 Introduction to Tweepy `Cursor`s: Getting an Account’s Followers and Friends\n",
-    "# 14.9.1 Determining an Account’s Followers "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "followers = []"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a Cursor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cursor = tweepy.Cursor(api.followers, screen_name='nasa')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for account in cursor.items(10):\n",
-    "     followers.append(account.screen_name)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Followers:', \n",
-    "       ' '.join(sorted(followers, key=lambda s: s.lower())))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Automatic Paging\n",
-    "### Getting Follower IDs Rather Than Followers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.9.1 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** Each Twitter API method’s documentation discusses the maximum number of items the method can return in one call—this is known as a `________` of results. \n",
-    "\n",
-    "**Answer:** page.\n",
-    "\n",
-    "**2. _(True/False)_** Though you can get complete `User` objects for a maximum of 200 followers at a time, you can get many more Twitter ID numbers by calling the `API` object’s `followers_ids` method.\n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**3. _(IPython Session)_** Use a Cursor to get and display 10 followers of the `NASAKepler` account.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kepler_followers = []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cursor = tweepy.Cursor(api.followers, screen_name='NASAKepler')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for account in cursor.items(10):\n",
-    "     kepler_followers.append(account.screen_name)\n",
-    "     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(' '.join(kepler_followers))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.9.2 Determining Whom an Account Follows "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "friends = []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cursor = tweepy.Cursor(api.friends, screen_name='nasa')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for friend in cursor.items(10):\n",
-    "     friends.append(friend.screen_name)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Friends:', \n",
-    "       ' '.join(sorted(friends, key=lambda s: s.lower())))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.9.2 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** The `API` object’s `friends` method calls the Twitter API’s `________` method to get a list of User objects representing an account’s friends. \n",
-    "\n",
-    "**Answer:** `friends/list`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.9.3 Getting a User’s Recent Tweets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa_tweets = api.user_timeline(screen_name='nasa', count=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for tweet in nasa_tweets:\n",
-    "     print(f'{tweet.user.screen_name}: {tweet.text}\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Grabbing Recent Tweets from Your Own Timeline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.9.3 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** You can call the `API` method `home_timeline` to get tweets from your home timeline, that is, your tweets and tweets from `________`. \n",
-    "\n",
-    "**Answer:** the people you follow.\n",
-    "\n",
-    "**2. _(IPython Session)_** Get and display two tweets from the `NASAKepler` account.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kepler_tweets = api.user_timeline(\n",
-    "     screen_name='NASAKepler', count=2)     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for tweet in kepler_tweets:\n",
-    "     print(f'{tweet.user.screen_name}: {tweet.text}\\n') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.10 Searching Recent Tweets\n",
-    "### Tweet Printer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tweetutilities import print_tweets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Searching for Specific Words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweets = api.search(q='Mars Opportunity Rover', count=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print_tweets(tweets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Searching with Twitter Search Operators"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweets = api.search(q='from:nasa since:2018-09-01', count=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print_tweets(tweets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Searching for a Hashtag"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweets = api.search(q='#collegefootball', count=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print_tweets(tweets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.10 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** The Tweepy `API` method `________` returns tweets that match a query string.\n",
-    "\n",
-    "**Answer:** search.\n",
-    "\n",
-    "**2. _(True/False)_** If you plan to request more results than can be returned by one call to search, you should use an `API` object.\n",
-    "\n",
-    "**Answer:** False. If you plan to request more results than can be returned by one call to `search`, you should use a `Cursor` object.\n",
-    "\n",
-    "**3. _(IPython Session)_** Search for one tweet from the `nasa` account containing `'astronaut'`.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tweets = api.search(q='astronaut from:nasa', count=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print_tweets(tweets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.11 Spotting Trends with the Twitter Trends API\n",
-    "# 14.11.1 Places with Trending Topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trends_available = api.trends_available()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(trends_available)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trends_available[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trends_available[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.11.1 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** If a topic “goes viral,” you could have thousands or even millions of people tweeting about that topic at once. Twitter refers to these as `________` topics.\n",
-    "\n",
-    "**Answer:** trending.\n",
-    "\n",
-    "**2. _(True/False)_** The Twitter Trends API’s `trends/place` method uses Yahoo! Where on Earth IDs (WOEIDs) to look up trending topics. The WOEID `1` represents worldwide. \n",
-    "\n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.11.2 Getting a List of Trending Topics\n",
-    "### Worldwide Trending Topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "world_trends = api.trends_place(id=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trends_list = world_trends[0]['trends']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trends_list[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trends_list = [t for t in trends_list if t['tweet_volume']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from operator import itemgetter "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trends_list.sort(key=itemgetter('tweet_volume'), reverse=True) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for trend in trends_list[:5]:\n",
-    "     print(trend['name'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### New York City Trending Topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc_trends = api.trends_place(id=2459115)  # New York City WOEID"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc_list = nyc_trends[0]['trends']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc_list = [t for t in nyc_list if t['tweet_volume']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc_list.sort(key=itemgetter('tweet_volume'), reverse=True) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for trend in nyc_list[:5]:\n",
-    "     print(trend['name'])\n",
-    "     "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.11.2 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** You also can look up WOEIDs `________` using Yahoo!’s web services via Python libraries like `woeid`.\n",
-    "\n",
-    "**Answer:** programmatically.\n",
-    "\n",
-    "**2. _(True/False)_** The statement `todays_trends = api.trends_place(id=1)` gets today’s U. S. trending topics.\n",
-    "\n",
-    "**Answer:** False. Actually, it gets today’s worldwide trending topics.\n",
-    "\n",
-    "**3. _(IPython Session)_** Display the top 3 trending topics today in the United States.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "us_trends = api.trends_place(id='23424977')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "us_list = us_trends[0]['trends']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "us_list = [t for t in us_list if t['tweet_volume']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "us_list.sort(key=itemgetter('tweet_volume'), reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for trend in us_list[:3]:\n",
-    "     print(trend['name'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 14.11.3 Create a Word Cloud from Trending Topics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topics = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for trend in nyc_list:\n",
-    "     topics[trend['name']] = trend['tweet_volume']\n",
-    "     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from wordcloud import WordCloud"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordcloud = WordCloud(width=1600, height=900,\n",
-    "     prefer_horizontal=0.5, min_font_size=10, colormap='prism', \n",
-    "     background_color='white')\n",
-    "                       "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordcloud = wordcloud.fit_words(topics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordcloud = wordcloud.to_file('TrendingTwitter.png')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.11.3 Self Check\n",
-    "\n",
-    "**1. _(IPython Session)_** Create a word cloud using the `us_list` list from the previous section’s Self Check.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "topics = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for trend in us_list:\n",
-    "     topics[trend['name']] = trend['tweet_volume']\n",
-    "     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordcloud = wordcloud.fit_words(topics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wordcloud = wordcloud.to_file('USTrendingTwitter.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
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diff --git a/examples/ch13/Thumbs.db b/examples/ch13_TwitterV1.1/Thumbs.db
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+++ b/examples/ch13_TwitterV1.1/_READ_ME_FIRST
@@ -0,0 +1,16 @@
+# Upgrading from Twitter v1.1 to Twitter v2
+
+On August 18, 2022, we discovered that **new** Twitter developer accounts cannot access the Twitter version 1.1 APIs on which we based Chapter 13, Data Mining Twitter, and two case studies in Chapter 17, Big Data: Hadoop, Spark, NoSQL and IoT. 
+
+Twitter users who already had Twitter developer accounts can still access the Twitter version 1.1 APIs, but most students and some instructors will not fall into this category.
+ 
+We’ve already rewritten Chapter 13 to the Twitter version 2 APIs and will be rewriting the two Twitter-based Chapter 17 case studies soon. 
+
+The updated chapter is posted on the book's webpage:
+https://deitel.com/intro-to-python-for-computer-science-and-data-science/
+
+Once completed, we’ll post updated versions of:
+* the Chapter 17 Twitter-based case studies 
+* the instructor materials to the Pearson Instructor Resource Center (IRC), which is accessible only to qualified instructors. 
+ 
+Questions? Please email paul@deitel.com.
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rename from examples/ch13/snippets_py/locationlistener.py
rename to examples/ch13_TwitterV1.1/snippets_py/locationlistener.py
diff --git a/examples/ch13/snippets_py/sentimentlistener.py b/examples/ch13_TwitterV1.1/snippets_py/sentimentlistener.py
similarity index 100%
rename from examples/ch13/snippets_py/sentimentlistener.py
rename to examples/ch13_TwitterV1.1/snippets_py/sentimentlistener.py
diff --git a/examples/ch13/snippets_py/tweet_map.html b/examples/ch13_TwitterV1.1/snippets_py/tweet_map.html
similarity index 100%
rename from examples/ch13/snippets_py/tweet_map.html
rename to examples/ch13_TwitterV1.1/snippets_py/tweet_map.html
diff --git a/examples/ch13/snippets_py/tweetlistener.py b/examples/ch13_TwitterV1.1/snippets_py/tweetlistener.py
similarity index 100%
rename from examples/ch13/snippets_py/tweetlistener.py
rename to examples/ch13_TwitterV1.1/snippets_py/tweetlistener.py
diff --git a/examples/ch13/snippets_py/tweetutilities.py b/examples/ch13_TwitterV1.1/snippets_py/tweetutilities.py
similarity index 100%
rename from examples/ch13/snippets_py/tweetutilities.py
rename to examples/ch13_TwitterV1.1/snippets_py/tweetutilities.py
diff --git a/examples/ch13/tweetlistener.py b/examples/ch13_TwitterV1.1/tweetlistener.py
similarity index 100%
rename from examples/ch13/tweetlistener.py
rename to examples/ch13_TwitterV1.1/tweetlistener.py
diff --git a/examples/ch13/tweetutilities.py b/examples/ch13_TwitterV1.1/tweetutilities.py
similarity index 100%
rename from examples/ch13/tweetutilities.py
rename to examples/ch13_TwitterV1.1/tweetutilities.py
diff --git a/examples/ch13_TwitterV2/.ipynb_checkpoints/_READ_ME_FIRST-checkpoint b/examples/ch13_TwitterV2/.ipynb_checkpoints/_READ_ME_FIRST-checkpoint
new file mode 100644
index 0000000..eb2b19f
--- /dev/null
+++ b/examples/ch13_TwitterV2/.ipynb_checkpoints/_READ_ME_FIRST-checkpoint
@@ -0,0 +1,20 @@
+Upgrading from Twitter v1.1 to Twitter v2
+On August 18, 2022, we discovered that new Twitter developer accounts cannot access the Twitter version 1.1 APIs on which we based Chapter 13, Data Mining Twitter, and two case studies in Chapter 17, Big Data: Hadoop, Spark, NoSQL and IoT. 
+
+Twitter users who already had Twitter developer accounts can still access the Twitter version 1.1 APIs, but most students and some instructors will not fall into this category.
+ 
+We’re updating Chapter 13 and the two case studies in Chapter 17 to the Twitter version 2 APIs. We anticipate this could take two to four weeks as we:
+1.	master the new Twitter v2 APIs,
+2.	update our Chapter 13 and 17 code examples,
+3.	rewrite the code explanations in Chapters 13 and 17 to match the new code, 
+4.	update the exercise descriptions,
+5.	update the instructor resources: instructor’s manual solutions, test-item file, and Jupyter Notebooks slides (we use these in lieu of PowerPoint slides for our Python book), and 
+6.	update the student resources: source-code files and Jupyter Notebooks.
+
+Once completed, we’ll:
+•	post an updated version of Chapter 13 and the relevant sections of Chapter 17 to the book’s companion website on https://pearson.com/deitel, 
+•	post an updated version of Chapter 13 and the relevant sections of Chapter 17 to the book’s webpage on https://deitel.com,  
+•	post updated instructor materials to the Pearson Instructor Resource Center (IRC), which is accessible only to qualified instructors, and 
+•	post updated student files to the book’s GitHub repository. 
+ 
+If you have any questions, please email paul@deitel.com.
diff --git a/examples/ch13_TwitterV2/_READ_ME_FIRST b/examples/ch13_TwitterV2/_READ_ME_FIRST
new file mode 100644
index 0000000..d901e2f
--- /dev/null
+++ b/examples/ch13_TwitterV2/_READ_ME_FIRST
@@ -0,0 +1,16 @@
+# Upgrading from Twitter v1.1 to Twitter v2
+
+On August 18, 2022, we discovered that **new** Twitter developer accounts cannot access the Twitter version 1.1 APIs on which we based Chapter 13, Data Mining Twitter, and two case studies in Chapter 17, Big Data: Hadoop, Spark, NoSQL and IoT. 
+
+Twitter users who already had Twitter developer accounts can still access the Twitter version 1.1 APIs, but most students and some instructors will not fall into this category.
+ 
+We’ve already rewritten Chapter 13 to the Twitter version 2 APIs and will be rewriting the two Twitter-based Chapter 17 case studies soon. 
+
+The updated chapter is posted on the book's webpage:
+https://deitel.com/intro-to-python-for-computer-science-and-data-science/
+
+Once completed, we’ll post updated versions of:
+* the Chapter 17 Twitter-based case studies 
+* the instructor materials to the Pearson Instructor Resource Center (IRC), which is accessible only to qualified instructors. 
+ 
+Questions? Please email paul@deitel.com.
diff --git a/examples/ch13_TwitterV2/keys.py b/examples/ch13_TwitterV2/keys.py
new file mode 100755
index 0000000..bf7c356
--- /dev/null
+++ b/examples/ch13_TwitterV2/keys.py
@@ -0,0 +1,2 @@
+bearer_token = 'YourBearerToken'
+mapquest_key = 'YourAPIKey'
\ No newline at end of file
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/locationlistener-checkpoint.py b/examples/ch13_TwitterV2/locationlistener.py
similarity index 61%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/locationlistener-checkpoint.py
rename to examples/ch13_TwitterV2/locationlistener.py
index b029377..f154d61 100755
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/locationlistener-checkpoint.py
+++ b/examples/ch13_TwitterV2/locationlistener.py
@@ -1,47 +1,50 @@
 # locationlistener.py
 """Receives tweets matching a search string and stores a list of
-dictionaries containing each tweet's screen_name/text/location."""
+dictionaries containing each tweet's username/text/location."""
 import tweepy
 from tweetutilities import get_tweet_content
 
-class LocationListener(tweepy.StreamListener):
+class LocationListener(tweepy.StreamingClient):
     """Handles incoming Tweet stream to get location data."""
 
-    def __init__(self, api, counts_dict, tweets_list, topic, limit=10):
+    def __init__(self, bearer_token, counts_dict, 
+                 tweets_list, topic, limit=10):
         """Configure the LocationListener."""
         self.tweets_list = tweets_list
         self.counts_dict = counts_dict
         self.topic = topic
         self.TWEET_LIMIT = limit
-        super().__init__(api)  # call superclass's init
+        super().__init__(bearer_token, wait_on_rate_limit=True) 
 
-    def on_status(self, status):
+    def on_response(self, response):
         """Called when Twitter pushes a new tweet to you."""
-        # get each tweet's screen_name, text and location
-        tweet_data = get_tweet_content(status, location=True)  
 
+        # get tweet's username, text and location
+        tweet_data = get_tweet_content(response)  
+        
         # ignore retweets and tweets that do not contain the topic
         if (tweet_data['text'].startswith('RT') or
             self.topic.lower() not in tweet_data['text'].lower()):
             return
 
-        self.counts_dict['total_tweets'] += 1  # original tweet
+        self.counts_dict['total_tweets'] += 1 # it's an original tweet
 
-        # ignore tweets with no location and non-English tweets
-        if not status.user.location:  
+        # ignore tweets with no location
+        if not tweet_data.get('location'):  
             return
 
-        self.counts_dict['locations'] += 1  # tweet with location
-        self.tweets_list.append(tweet_data)  # store the tweet
-        print(f'{status.user.screen_name}: {tweet_data["text"]}\n')
+        self.counts_dict['locations'] += 1 # user account has location
+        self.tweets_list.append(tweet_data) # store the tweet
+        print(f"{tweet_data['username']}: {tweet_data['text']}\n")
 
-        # if TWEET_LIMIT is reached, return False to terminate streaming
-        return self.counts_dict['locations'] <= self.TWEET_LIMIT
+        # if TWEET_LIMIT is reached, terminate streaming
+        if self.counts_dict['locations'] == self.TWEET_LIMIT:
+            self.disconnect()
 
 
 
 ##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
 # Pearson Education, Inc. All Rights Reserved.                           #
 #                                                                        #
 # DISCLAIMER: The authors and publisher of this book have used their     #
diff --git a/examples/ch13_TwitterV2/sentimentlistener.py b/examples/ch13_TwitterV2/sentimentlistener.py
new file mode 100755
index 0000000..2cc45ec
--- /dev/null
+++ b/examples/ch13_TwitterV2/sentimentlistener.py
@@ -0,0 +1,106 @@
+# sentimentlisener.py
+"""Searches for tweets that match a search string and tallies 
+the number of positive, neutral and negative tweets."""
+import keys
+import preprocessor as p 
+import sys
+from textblob import TextBlob
+import tweepy
+
+class SentimentListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream."""
+
+    def __init__(self, bearer_token, sentiment_dict, topic, limit=10):
+        """Configure the SentimentListener."""
+        self.sentiment_dict = sentiment_dict
+        self.tweet_count = 0
+        self.topic = topic
+        self.TWEET_LIMIT = limit
+        
+        # set tweet-preprocessor to remove URLs/reserved words
+        p.set_options(p.OPT.URL, p.OPT.RESERVED) 
+        super().__init__(bearer_token, wait_on_rate_limit=True)
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+
+        # if the tweet is not a retweet
+        if not response.data.text.startswith('RT'):
+            text = p.clean(response.data.text) # clean the tweet
+
+            # ignore tweet if the topic is not in the tweet text
+            if self.topic.lower() not in text.lower():
+                return
+
+            # update self.sentiment_dict with the polarity
+            blob = TextBlob(text)
+            if blob.sentiment.polarity > 0:
+                sentiment = '+'
+                self.sentiment_dict['positive'] += 1 
+            elif blob.sentiment.polarity == 0:
+                sentiment = ' '
+                self.sentiment_dict['neutral'] += 1 
+            else:
+                sentiment = '-'
+                self.sentiment_dict['negative'] += 1 
+
+            # display the tweet
+            username = response.includes['users'][0].username
+            print(f'{sentiment} {username}: {text}\n')
+
+            self.tweet_count += 1 # track number of tweets processed
+
+            # if TWEET_LIMIT is reached, terminate streaming
+            if self.tweet_count == self.TWEET_LIMIT:
+                self.disconnect()
+
+def main():
+    # get search term and number of tweets
+    search_key = sys.argv[1]
+    limit = int(sys.argv[2]) # number of tweets to tally
+    
+    # set up the sentiment dictionary
+    sentiment_dict = {'positive': 0, 'neutral': 0, 'negative': 0}
+
+    # create the StreamingClient subclass object
+    sentiment_listener = SentimentListener(keys.bearer_token, 
+        sentiment_dict, search_key, limit)
+
+    # redirect sys.stderr to sys.stdout
+    sys.stderr = sys.stdout
+
+    # delete existing stream rules
+    rules = sentiment_listener.get_rules().data
+    rule_ids = [rule.id for rule in rules]
+    sentiment_listener.delete_rules(rule_ids)    
+
+    # create stream rule
+    sentiment_listener.add_rules(
+        tweepy.StreamRule(f'{search_key} lang:en'))
+    
+    # start filtering English tweets containing search_key
+    sentiment_listener.filter(expansions=['author_id'])
+
+    print(f'Tweet sentiment for "{search_key}"')
+    print('Positive:', sentiment_dict['positive'])
+    print(' Neutral:', sentiment_dict['neutral'])
+    print('Negative:', sentiment_dict['negative'])
+
+# call main if this file is executed as a script
+if __name__ == '__main__':
+    main()
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.ipynb
new file mode 100755
index 0000000..8fcf2b7
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.ipynb
@@ -0,0 +1,1537 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**_Note: This notebook contains ALL the code for Sections 13.7 through 13.11, including the Self Check snippets because all the snippets in these sections are consecutively numbered in the text._**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.7 Authenticating with Twitter Via Tweepy to Access Twitter v2 APIs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tweepy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a Client Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = tweepy.Client(bearer_token=keys.bearer_token,\n",
+    "                       wait_on_rate_limit=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.8 Getting Information About a Twitter Account"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "nasa = client.get_user(username='NASA', \n",
+    "    user_fields=['description', 'public_metrics'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### tweepy.Response Object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting a User’s Basic Account Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11348282"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa.data.id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NASA'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa.data.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NASA'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa.data.username"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"There's space for everybody. ✨\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa.data.description"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Getting the Number of Accounts That Follow This Account and the Number of Accounts This Account Follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61468657"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa.data.public_metrics['followers_count']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "181"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa.data.public_metrics['following_count']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting Your Own Account’s Information"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.8 Self Check\n",
+    "**2. _(IPython Session)_** Use the `client` object to get information about the `NASAMars` account, then display its ID, name, username, description and number of followers.\n",
+    "\n",
+    "**Answer:** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "nasa_mars = client.get_user(username='NASAMars', \n",
+    "    user_fields=['description', 'public_metrics'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15165502"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa_mars.data.id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NASA Mars'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa_mars.data.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NASAMars'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa_mars.data.username"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'NASA’s official Twitter account for all things Mars. Join us as we explore the Red Planet!'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa_mars.data.description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1228089"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nasa_mars.data.public_metrics['followers_count']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.9 Intro to Tweepy `Paginator`s: Getting More than One Page of Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 13.9.1 Determining an Account’s Followers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "followers = []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a `Paginator`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paginator = tweepy.Paginator(\n",
+    "    client.get_users_followers, nasa.data.id, max_results=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for follower in paginator.flatten(limit=10):\n",
+    "    followers.append(follower.username)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Followers: ANNUBABY4 Ashutos34571752 BlustiTv EichhorstVarda mariam45839172 MeternikBarman4 MeternikBarman4 OlarulD PakpimolChanta1 sujitha_josh\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Followers:', \n",
+    "      ' '.join(sorted(followers, key=lambda s: s.lower())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.9.1 Self Check\n",
+    "**3. _(IPython Session)_** Use a Cursor to get and display 10 followers of the `NASAKepler` account.\n",
+    "\n",
+    "**Answer:** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nasa_mars_followers = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nasa_mars_followers_paginator = tweepy.Paginator(\n",
+    "    client.get_users_followers, nasa_mars.data.id, max_results=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for follower in nasa_mars_followers_paginator.flatten(limit=10):\n",
+    "    nasa_mars_followers.append(follower.username)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Donononon Dheeraj13540205 YJingxian DNice_86 TiannDavis MnFuzail 2f4hkkmprhB N47iD DCCashout1 marker1220\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(' '.join(nasa_mars_followers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.9.2 Determining Whom an Account Follows "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "following = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paginator = tweepy.Paginator(\n",
+    "    client.get_users_following, nasa.data.id, max_results=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for user_followed in paginator.flatten(limit=10):\n",
+    "    following.append(user_followed.username)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Following: Astro_Ayers astro_berrios astro_deniz astro_matthias astro_watkins JimFree NASA_Gateway NASASpaceSci NASASpaceSci v_wyche\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Following:', \n",
+    "      ' '.join(sorted(following, key=lambda s: s.lower())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.9.3 Getting a User’s Recent Tweets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nasa_tweets = client.get_users_tweets(\n",
+    "    id=nasa.data.id, max_results=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NASA: @Lee44356382 @Dailymotion #Artemis I will launch from @NASAKennedy to the northeast over the Atlantic Ocean. This graphic has more details. Click through for a larger version. https://t.co/qIdNR2CB5I\n",
+      "\n",
+      "NASA: @nfloyd58 The launch window opens at 8:33 a.m. EDT, which is 1133 GMT. All of the links to the #Artemis launch events should automatically display the broadcast time in the user's local time zone.\n",
+      "\n",
+      "NASA: @plungerman We feel you. Orbital dynamics don't care about human sleep schedules. You can watch on-demand recordings later in the day, and we'll keep the short recaps coming.\n",
+      "\n",
+      "NASA: And of course, you can always find us on NASA TV. We love sharing space with you! https://t.co/z1RgZwyJyi\n",
+      "\n",
+      "NASA: ¡Vamos a la Luna con #Artemis I y @NASA_es!\n",
+      "\n",
+      "Nuestra transmisión en vivo del lanzamiento en español incluirá entrevistas con miembros hispanos de la misión y comentario en directo durante el despegue.\n",
+      "https://t.co/fgKZg1tQ1I\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for tweet in nasa_tweets.data:\n",
+    "    print(f\"NASA: {tweet.data['text']}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "### Grabbing Recent Tweets from Your Own Timeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.9.3 Self Check\n",
+    "**2. _(IPython Session)_** Get and display two tweets from the `NASAMars` account.\n",
+    "\n",
+    "**Answer:** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nasa_mars_tweets = client.get_users_tweets(\n",
+    "    id=nasa_mars.data.id, max_results=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NASAMars: RT @NASA: It's #InternationalDogDay! While there aren't any dogs on other planets (that we know of), we do have a couple of adorable rovers…\n",
+      "\n",
+      "NASAMars: Rocks of ages: surprising findings help scientists better understand the timeline of an ancient Martian lake. Next up, exploring layers of sedimentary rocks that should reveal more of the story...\n",
+      "\n",
+      "Follow along at https://t.co/POzRmYauHo https://t.co/XFzqJtoL7S\n",
+      "\n",
+      "NASAMars: How to drive a Mars rover: On the latest episode of the \"On a Mission\" podcast, buckle up for a Mars tour with rover driver Vandi Verma.\n",
+      "▶️ https://t.co/CCWmVPwRez https://t.co/zHnDYFWMyw\n",
+      "\n",
+      "NASAMars: RT @NASAInSight: Thanks again for all the kind thoughts you’ve been sending. There’s still time to write me a note for the mission team to…\n",
+      "\n",
+      "NASAMars: We see Martian dust devils (whirlwinds) from the ground, as in this shot from the Opportunity rover in 2016, left. From space, we can see the tracks they leave behind, as in this view of dunes from Mars Reconnaissance Orbiter in 2009, right. More: https://t.co/kd1BNEDBUD https://t.co/RxeKTI5Fv5\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for tweet in nasa_mars_tweets.data:\n",
+    "    print(f\"NASAMars: {tweet.data['text']}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.10 Searching Recent Tweets; Intro to Twitter v2 API Search Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Utility Function `print_tweets` from `tweetutilities.py`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tweetutilities import print_tweets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "def print_tweets(tweets):\n",
+    "    # translator to autodetect source language and return English\n",
+    "    translator = GoogleTranslator(source='auto', target='en')\n",
+    "\n",
+    "    \"\"\"For each tweet in tweets, display the username of the sender\n",
+    "    and tweet text. If the language is not English, translate the text \n",
+    "    with the deep-translator library's GoogleTranslator.\"\"\"\n",
+    "    for tweet, user in zip(tweets.data, tweets.includes['users']):\n",
+    "        print(f'{user.username}:', end=' ')\n",
+    "\n",
+    "        if 'en' in tweet.lang:\n",
+    "            print(f'{tweet.text}\\n')\n",
+    "        elif 'und' not in tweet.lang: # translate to English first\n",
+    "            print(f'\\n  ORIGINAL: {tweet.text}')\n",
+    "            print(f'TRANSLATED: {translator.translate(tweet.text)}\\n')\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching for Specific Words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tweets = client.search_recent_tweets(\n",
+    "    query='Webb Space Telescope', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "esotericfaerie: RT @northstardoll: jupiter shown by the james webb space telescope https://t.co/LpfbQEs0ho\n",
+      "\n",
+      "fIamboyants: RT @northstardoll: jupiter shown by the james webb space telescope https://t.co/LpfbQEs0ho\n",
+      "\n",
+      "Linsey_Li: RT @latestinspace: BREAKING 🚨: James Webb Space Telescope has detected carbon dioxide (CO2) in the atmosphere of a planet outside our solar…\n",
+      "\n",
+      "onlinehellcat: RT @northstardoll: jupiter shown by the james webb space telescope https://t.co/LpfbQEs0ho\n",
+      "\n",
+      "shayarrowood: RT @latestinspace: BREAKING 🚨: James Webb Space Telescope has detected carbon dioxide (CO2) in the atmosphere of a planet outside our solar…\n",
+      "\n",
+      "sirfapcelot: RT @latestinspace: BREAKING 🚨: James Webb Space Telescope has detected carbon dioxide (CO2) in the atmosphere of a planet outside our solar…\n",
+      "\n",
+      "yilmazcihan: RT @latestinspace: BREAKING 🚨: James Webb Space Telescope has detected carbon dioxide (CO2) in the atmosphere of a planet outside our solar…\n",
+      "\n",
+      "AaronLoganMusic: RT @latestinspace: BREAKING 🚨: James Webb Space Telescope has detected carbon dioxide (CO2) in the atmosphere of a planet outside our solar…\n",
+      "\n",
+      "najiwakim1: RT @latestinspace: BREAKING 🚨: James Webb Space Telescope has detected carbon dioxide (CO2) in the atmosphere of a planet outside our solar…\n",
+      "\n",
+      "SenseiQuan: RT @latestinspace: BREAKING 🚨: James Webb Space Telescope has detected carbon dioxide (CO2) in the atmosphere of a planet outside our solar…\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_tweets(tweets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching with Twitter v2 API Search Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching with Twitter v2 API Search Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Operator Documentation and Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching for Tweets From NASA Containing Links"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tweets = client.search_recent_tweets(\n",
+    "    query='from:NASA has:links', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NASA: @Lee44356382 @Dailymotion #Artemis I will launch from @NASAKennedy to the northeast over the Atlantic Ocean. This graphic has more details. Click through for a larger version. https://t.co/qIdNR2CB5I\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_tweets(tweets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching for a Hashtag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tweets = client.search_recent_tweets(query='#metaverse', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ultracig: @Ralvero I’m richer than ever \n",
+      "\n",
+      "AS SEEN ON TV &amp; IN SPACE\n",
+      "400,000 #vikings\n",
+      "- Ecosystem with #DeFi, #NFT #Metaverse, #crypto #education\n",
+      "#floki #flokidao #doge #shib #F1 #huobi #valhalla #eSports @RealflokiInu #FlokiFi #projectL #GME #AMC #BBBY https://t.co/wG7NZobeoY\n",
+      "\n",
+      "ArnobRo20029983: RT @BlockChain_CK: #marsmine is running a massive 7,500TRX #airdrop campaign in  tokens!\n",
+      "\n",
+      "Join our gleam competition:\n",
+      "🔗https://t.co/mHBJoUj…\n",
+      "\n",
+      "Alimeralfinans: \n",
+      "  ORIGINAL: #BRN #Metaverse Bitmart listelemesi dün gerçekleşti.\n",
+      "3 Gün sonra Büyük Bir haber geleceğini ve büyük borsa listelemeler için Ekip Çalışmalarına Devam Ettiğini Duyurdu.  \n",
+      "https://t.co/YqJTkQVcM8\n",
+      "TRANSLATED: The #BRN #Metaverse Bitmart listing took place yesterday.\n",
+      "Announced that big news will come 3 days later and that it continues its teamwork for major stock market listings.\n",
+      "https://t.co/YqJTkQVcM8\n",
+      "\n",
+      "Bristi22599251: RT @UncleCCNFT: 2027 : Crime is happening across the #Metaverse … \n",
+      "Uncle Chop Chop rules the metastreets , walk without fear , protect what…\n",
+      "\n",
+      "Diness70996570: RT @BlockChain_CK: #marsmine is running a massive 7,500TRX #airdrop campaign in  tokens!\n",
+      "\n",
+      "Join our gleam competition:\n",
+      "🔗https://t.co/mHBJoUj…\n",
+      "\n",
+      "BongoBob17: RT @CrebbsMaynard: When FBI=KGB, know that we are in a Bolshevik Revolution\n",
+      "https://t.co/eKC7qbs4KT\n",
+      "#BTC #God #USA #news #bb24 #ETH #art #C…\n",
+      "\n",
+      "konzeth: I just participated in the Xandar Xclusive Airdrop Campaign.\n",
+      "\n",
+      "What are you waiting for? Become a Xandarian now!!\n",
+      "\n",
+      "#Play2Earn #MMORPG #AAA #NFTs #Airdrop #giveaway #P2P #Metaverse #Xandar #Multiverse #Octa-Attributes\n",
+      " – https://t.co/lbF5jvlZnS\n",
+      "\n",
+      "PINGULUX: \n",
+      "  ORIGINAL: @NikolaBench @BlueSparrowETH @VitaInuCoin @BabyDogeCoin @yooshi_official #VINU #VinuSquad #NFTs #Metaverse        #vitainucoin #DAO #cryptocurrency\n",
+      "TRANSLATED: @NikolaBench @BlueSparrowETH @VitaInuCoin @BabyDogeCoin @yooshi_official #VINU #VinuSquad #NFTs #Metaverse #vitainucoin #DAO #cryptocurrency\n",
+      "\n",
+      "harisweetraskal: RT @ketsshoe: 📣Massive #Airdrops🔥\n",
+      "\n",
+      "😱 Reward Pool-\n",
+      "🎁 🏆 BiG Prize $50,000 KETS TOKEN &amp; 👟 3000 Kets Shoebox NFT 👟\n",
+      "\n",
+      "To Enter:\n",
+      "✅Follow @ketsshoe…\n",
+      "\n",
+      "mohus_sam: \n",
+      "  ORIGINAL: @_NFTsPromoter @cybotz_nft @Lifestory_App #NFTCommunity #NFTProjects  #BlueChipNFT #Lifestory #NFTUtilities  #MetaverseNFT #NFTmint #nftutility $LIFC #NFTMetaverse #Metaverse #lifestory_app\n",
+      "\n",
+      "OS: https://t.co/tQ0qlHZKty\n",
+      "📸:https://t.co/QbzA6JE2Ci\n",
+      "🌐:https://t.co/DoeFdhhCRU\n",
+      "Discord: https://t.co/ZRdYTDFElv\n",
+      "@Lifestory_App\n",
+      "TRANSLATED: @_NFTsPromoter @cybotz_nft @Lifestory_App #NFTCommunity #NFTProjects  #BlueChipNFT #Lifestory #NFTUtilities  #MetaverseNFT #NFTmint #nftutility $LIFC #NFTMetaverse #Metaverse #lifestory_app\n",
+      "\n",
+      "OS: https://t.co/tQ0qlHZKty\n",
+      "📸:https://t.co/QbzA6JE2Ci\n",
+      "🌐:https://t.co/DoeFdhhCRU\n",
+      "Discord: https://t.co/ZRdYTDFElv\n",
+      "@Lifestory_App\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_tweets(tweets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.10 Self Check\n",
+    "**3. _(IPython Session)_** Search for one tweet from the `NASA` account containing `'astronaut'`.\n",
+    "\n",
+    "**Answer:** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tweets = client.search_recent_tweets(query='from:nasa astronaut', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NASA: LIVE: Astronaut Frank Rubio discusses his upcoming mission to the @Space_Station, scheduled to launch Sept. 21 from Kazakhstan. https://t.co/xr1tWHMjmQ\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_tweets(tweets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.11 Spotting Trending Topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "auth = tweepy.OAuth2BearerHandler(keys.bearer_token)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api = tweepy.API(auth=auth, wait_on_rate_limit=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.11.1 Places with Trending Topics\n",
+    "**Note: This part of the Twitter APIs has not been migrated from v1.1 to v2 yet and is accessible only to \"Elevated\" and \"Academic Research\" access.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "available_trends = api.available_trends()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "467"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(available_trends)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Worldwide',\n",
+       " 'placeType': {'code': 19, 'name': 'Supername'},\n",
+       " 'url': 'http://where.yahooapis.com/v1/place/1',\n",
+       " 'parentid': 0,\n",
+       " 'country': '',\n",
+       " 'woeid': 1,\n",
+       " 'countryCode': None}"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "available_trends[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Winnipeg',\n",
+       " 'placeType': {'code': 7, 'name': 'Town'},\n",
+       " 'url': 'http://where.yahooapis.com/v1/place/2972',\n",
+       " 'parentid': 23424775,\n",
+       " 'country': 'Canada',\n",
+       " 'woeid': 2972,\n",
+       " 'countryCode': 'CA'}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "available_trends[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.11.2 Getting a List of Trending Topics\n",
+    "### Worldwide Trending Topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "world_trends = api.get_place_trends(id=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trends_list = world_trends[0]['trends']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': '#SOUMUN',\n",
+       " 'url': 'http://twitter.com/search?q=%23SOUMUN',\n",
+       " 'promoted_content': None,\n",
+       " 'query': '%23SOUMUN',\n",
+       " 'tweet_volume': 121659}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trends_list[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trends_list = [t for t in trends_list if t['tweet_volume']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trends_list.sort(key=itemgetter('tweet_volume'), reverse=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONBELLE PHIHNOMENALConcert\n",
+      "Southampton\n",
+      "#SOUMUN\n",
+      "花火大会\n",
+      "アニサマ\n",
+      "KANAWUT\n",
+      "#LetsGULFtoJAPAN\n",
+      "StylipS\n",
+      "#Venue101\n",
+      "nectar\n",
+      "Crystal Palace\n",
+      "アオペラ\n",
+      "Elanga\n",
+      "#LIVBOU\n",
+      "#アモアスマリカ杯\n",
+      "DJ JOHNNY\n",
+      "VadLip\n",
+      "アドニス\n",
+      "McTominay\n",
+      "Tell Your World\n",
+      "キブレハン\n",
+      "Beit\n",
+      "Gallagher\n",
+      "Dalot\n",
+      "Bruno Fernandes\n",
+      "Eriksen\n",
+      "アフリカ支援\n",
+      "Firmino\n",
+      "リンレン\n",
+      "ヒッパレ\n"
+     ]
+    }
+   ],
+   "source": [
+    "for trend in trends_list:\n",
+    "    print(trend['name'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### New York City Trending Topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "nyc_trends = api.get_place_trends(id=2459115) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nyc_list = nyc_trends[0]['trends']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nyc_list = [t for t in nyc_list if t['tweet_volume']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nyc_list.sort(key=itemgetter('tweet_volume'), reverse=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#MUFC\n",
+      "Chelsea\n",
+      "Ronaldo\n",
+      "Nigeria\n",
+      "Southampton\n"
+     ]
+    }
+   ],
+   "source": [
+    "for trend in nyc_list[:5]:\n",
+    "    print(trend['name'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.11.2 Self Check\n",
+    "**3. _(IPython Session)_** Display the top 3 trending topics today in the United States.\n",
+    "\n",
+    "**Answer:** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "us_trends = api.get_place_trends(id='23424977')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "us_list = us_trends[0]['trends']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "us_list = [t for t in us_list if t['tweet_volume']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "us_list.sort(key=itemgetter('tweet_volume'), reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ronaldo\n",
+      "Southampton\n",
+      "#SOUMUN\n"
+     ]
+    }
+   ],
+   "source": [
+    "for trend in us_list[:3]:\n",
+    "    print(trend['name'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.11.3 Create a Word Cloud from Trending Topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "topics = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "for trend in nyc_list:\n",
+    "    topics[trend['name']] = trend['tweet_volume']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from wordcloud import WordCloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wordcloud = WordCloud(width=1600, height=900,\n",
+    "    prefer_horizontal=0.5, min_font_size=10, colormap='prism', \n",
+    "    background_color='white')       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wordcloud = wordcloud.fit_words(topics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wordcloud = wordcloud.to_file('TrendingTwitter.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE: The following code displays the image in a Jupyter Notebook**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {
+      "image/png": {
+       "width": 400
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='TrendingTwitter.png', width=400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.11.3 Self Check\n",
+    "\n",
+    "**1. _(IPython Session)_** Create a word cloud using the `us_list` list from the previous section’s Self Check.\n",
+    "\n",
+    "**Answer:** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topics = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for trend in us_list:\n",
+    "    topics[trend['name']] = trend['tweet_volume']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wordcloud = wordcloud.fit_words(topics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wordcloud = wordcloud.to_file('USTrendingTwitter.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE: The following code displays the image in a Jupyter Notebook**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {
+      "image/png": {
+       "width": 400
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='USTrendingTwitter.png', width=400)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##########################################################################\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
+    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
+    "#                                                                        #\n",
+    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
+    "# best efforts in preparing the book. These efforts include the          #\n",
+    "# development, research, and testing of the theories and programs        #\n",
+    "# to determine their effectiveness. The authors and publisher make       #\n",
+    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
+    "# programs or to the documentation contained in these books. The authors #\n",
+    "# and publisher shall not be liable in any event for incidental or       #\n",
+    "# consequential damages in connection with, or arising out of, the       #\n",
+    "# furnishing, performance, or use of these programs.                     #\n",
+    "##########################################################################\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/ch01/snippets_ipynb/.ipynb_checkpoints/TestDrive-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_12-checkpoint.ipynb
old mode 100644
new mode 100755
similarity index 70%
rename from examples/ch01/snippets_ipynb/.ipynb_checkpoints/TestDrive-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_12-checkpoint.ipynb
index b8f8915..fbce1b0
--- a/examples/ch01/snippets_ipynb/.ipynb_checkpoints/TestDrive-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_12-checkpoint.ipynb
@@ -1,78 +1,60 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "117"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "45 + 72"
+    "# 13.12 Cleaning/Preprocessing Tweets for Analysis\n",
+    "### tweet-preprocessor Library and TextBlob Utility Functions\n",
+    "### Installing tweet-preprocessor\n",
+    "### Cleaning a Tweet"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21.75"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "5 * (12.7 - 4) / 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 1,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# Self Check"
+    "import preprocessor as p"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "**2. _(IPython Session)_** Ensure that JupyterLab is running, then open your `TestDrive.ipynb` notebook. Add and execute two more snippets that evaluate the expression `5 * (3 + 4)` both with and without the parentheses. \n",
-    "\n",
-    "**Answer:** "
+    "p.set_options(p.OPT.URL, p.OPT.RESERVED)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "5 * (3 + 4)"
+    "tweet_text = 'RT A sample retweet with a URL https://nasa.gov'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A sample retweet with a URL'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "5 * 3 + 4"
+    "p.clean(tweet_text)"
    ]
   },
   {
@@ -82,7 +64,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -100,7 +82,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -114,9 +96,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.01selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_12selfcheck-checkpoint.ipynb
similarity index 76%
rename from examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.01selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_12selfcheck-checkpoint.ipynb
index 8e6f704..4344c2f 100755
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_02.01selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_12selfcheck-checkpoint.ipynb
@@ -5,20 +5,16 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.2.1 Self Check"
+    "# 13.12 Self Check"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**1. _(Fill-In)_** Each new class you create becomes a new data type that can be used to create objects. This is one reason why Python is said to be a(n) `________` language.\n",
+    "**1. _(True/False)_** The tweet-preprocessor library can automatically remove URLs, `@`-mentions (like `@nasa`), hashtags (like `#mars`), Twitter reserved words (like, `RT` for retweet and `FAV` for favorite, which is similar to a “like” on other social networks), emojis (all or just smileys) and numbers, or any combination of these. \n",
     "\n",
-    "**Answer:** extensible. \n",
-    "\n",
-    "**2. _(Fill-In)_** A(n) `________` expression creates and initializes an object of a class.\n",
-    "\n",
-    "**Answer:** constructor."
+    "**Answer:** True."
    ]
   },
   {
@@ -28,7 +24,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -46,7 +42,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -60,9 +56,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_13_02-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_13_02-checkpoint.ipynb
new file mode 100755
index 0000000..b635410
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_13_02-checkpoint.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.13.2 Initiating Stream Processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Authenticating\n",
+    "**Don't need to authenticate in advance for streaming. Just pass bearer-token as you create the StreamingClient subclass object.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tweepy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a `TweetListener` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tweetlistener import TweetListener"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tweet_listener = TweetListener(\n",
+    "    bearer_token=keys.bearer_token, limit=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Redirecting Standard Error Stream to Standard Output Stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.stderr = sys.stdout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Deleting Existing Stream Rules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rules = tweet_listener.get_rules().data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rule_ids = [rule.id for rule in rules]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Response(data=None, includes={}, errors=[], meta={'sent': '2022-08-24T14:37:25.398Z', 'summary': {'deleted': 1, 'not_deleted': 0}})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tweet_listener.delete_rules(rule_ids)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating and Adding a Stream Rule"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_rule = tweepy.StreamRule('football')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Response(data=[StreamRule(value='football', tag=None, id='1562449001378619392')], includes={}, errors=[], meta={'sent': '2022-08-24T14:37:26.600Z', 'summary': {'created': 1, 'not_created': 0, 'valid': 1, 'invalid': 0}})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tweet_listener.add_rules(filter_rule)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Starting the Tweet Stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connection successful\n",
+      "\n",
+      "Screen name: JamieAmis\n",
+      "   Language: en\n",
+      " Tweet text: I’ve said it before…I am loving the hunger that JDT is putting into these youngsters. Hats off to TM and Venkys as well for investing the time and trust into the academy too! https://t.co/P2N3X0kwgX\n",
+      "\n",
+      "Screen name: BeachPetey\n",
+      "   Language: en\n",
+      " Tweet text: @miles_commodore We used a Len Dawson football, back in the day.\n",
+      "\n",
+      "Screen name: KingDrewbs69\n",
+      "   Language: en\n",
+      " Tweet text: @SeekNDestroy21 Dusty baller mate 😂 nothing more\n",
+      "\n",
+      "Stream connection closed by Twitter\n"
+     ]
+    }
+   ],
+   "source": [
+    "tweet_listener.filter(\n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Asynchronous vs. Synchronous Streams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##########################################################################\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
+    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
+    "#                                                                        #\n",
+    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
+    "# best efforts in preparing the book. These efforts include the          #\n",
+    "# development, research, and testing of the theories and programs        #\n",
+    "# to determine their effectiveness. The authors and publisher make       #\n",
+    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
+    "# programs or to the documentation contained in these books. The authors #\n",
+    "# and publisher shall not be liable in any event for incidental or       #\n",
+    "# consequential damages in connection with, or arising out of, the       #\n",
+    "# furnishing, performance, or use of these programs.                     #\n",
+    "##########################################################################\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_01-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_01-checkpoint.ipynb
new file mode 100755
index 0000000..49d8650
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_01-checkpoint.ipynb
@@ -0,0 +1,1541 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.15.1 Getting and Mapping the Tweets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Collections Required By `LocationListener`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tweets = [] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "counts = {'total_tweets': 0, 'locations': 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating the `LocationListener` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tweepy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from locationlistener import LocationListener"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "location_listener = LocationListener(\n",
+    "    keys.bearer_token, counts_dict=counts, tweets_list=tweets,\n",
+    "    topic='football', limit=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Redirect sys.stderr to sys.stdout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.stderr = sys.stdout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delete Existing `StreamRule`s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rules = location_listener.get_rules().data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rule_ids = [rule.id for rule in rules]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Response(data=None, includes={}, errors=[], meta={'sent': '2022-08-24T15:35:39.587Z', 'summary': {'deleted': 1, 'not_deleted': 0}})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "location_listener.delete_rules(rule_ids)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a `StreamRule`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Response(data=[StreamRule(value='football lang:en', tag=None, id='1562463657035972608')], includes={}, errors=[], meta={'sent': '2022-08-24T15:35:41.024Z', 'summary': {'created': 1, 'not_created': 0, 'valid': 1, 'invalid': 0}})"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "location_listener.add_rules(\n",
+    "    tweepy.StreamRule('football lang:en'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Configure and Start the Stream of Tweets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "valerieinhoops: Rebel football Saturday- 12:30 pm kick off! 🏈✅🥤\n",
+      "\n",
+      "Breakfast tailgate, drinking Mimosas &amp; Bloody Mary’s  before noon AND Rebel football @AllegiantStadm?!! \n",
+      "\n",
+      "Hell yes! Let’s goooooo! @unlvfootball @joearrigofsm @TheFranchiseLV @UNLVRebellion @UNLVRAF @UNLVathletics @UNLVgirl https://t.co/6IdAqVdL17\n",
+      "\n",
+      "b_lanaux: When people try to say LSU isn’t the greatest atmosphere in college football I just show them this video. https://t.co/A5zAfPyrSF\n",
+      "\n",
+      "cbaginski15: @JoshReynolds24 @Utah_Football Thanks for chance buddy!!\n",
+      "\n",
+      "1Alexthetrainer: Thankyou for the opportunity go @AztecFB @Shawcroft_M  @keithismael  @jmatthews8321  @Daygofootball  @KUSIPPR @KUSINews @BStoneKUSI @Tpstreets @CoachPatArinze @tariq__thompson @calmunson @alexbarrett @DavidWells14 @MP2TheGreat https://t.co/aqsimzgX3a\n",
+      "\n",
+      "orionkwa: @Lk47041027 one football\n",
+      "\n",
+      "franciscotrigo5: @cxrd52 Bro I actually think at the top of elite football hardest most deificou position is the GK \n",
+      "Imagine you need to be more than a shot stopper and if you make a mistake it’s your fault and no one can back you \n",
+      "A 6 has Cb to back you box to box it’s only difficult coz energy\n",
+      "\n",
+      "ILoveCoreyB: I’m so excited for fantasy football\n",
+      "\n",
+      "pcsd: The @PCSD_PHS Red Raiders football squad shut out Notre Dame 14-0 to start the season on the W side of the ledger. Check out the game action and more at: https://t.co/BIbp6M7o0R. #RaiderStrong @PulaskiRedSea https://t.co/TrlEtOHmye\n",
+      "\n",
+      "KingBanter_T: @elonmusk buy Manchester United football club...\n",
+      "\n",
+      "ItsUTOrange75: @smyrnafootball Absolutely amazing game. Great pictures\n",
+      "\n",
+      "Shiv_rants: @Football__Tweet @nasdaily is that you ?!\n",
+      "\n",
+      "CANAMPREP: It's a pretty SAD situation when \n",
+      "SO CALLED FOOTBALL EXPERTS like yourselves\n",
+      "\n",
+      "have to dwell on the topic of LOW ATTENDANCE\n",
+      "just to uphold CFL\n",
+      "INTEGRITY:\n",
+      "\n",
+      "But as I've said all along:\n",
+      "\n",
+      "THE TRUTH HURTS\n",
+      "TIME TO CHANGE CULTURE OF 🇨🇦🏈\n",
+      "\n",
+      "Start with \n",
+      "\n",
+      "ELIMINATING THE ROSTER RATIO https://t.co/Qhn4ohDNJY\n",
+      "\n",
+      "ReporterChrisW: So you want to turn your football stadium into a pre-game basketball show? Will someone in the press box be announcing the starting lineup? https://t.co/vtYZmgwHZu\n",
+      "\n",
+      "mikeb26and352: @Rajulesrirama Good for you listening to your body! Good work today, Raju!\n",
+      "\n",
+      "So where do we sign up for the runner fantasy football league? 🤷‍♂️\n",
+      "\n",
+      "BrandtAWitt: @peterkrupa In France too. People like the image of what American college represents! Same with sports teams. Sometimes Americans wear Euro football team shirts, don’t see a big difference. No one cares about Euro colleges (Oxford/Cambridge Anglo not Euro lol)\n",
+      "Also saw multiple churros /\n",
+      "\n",
+      "Black_Mormon: @CFBHome Most exciting college football player: CJ Spiller\n",
+      "\n",
+      "QB: Tommie Frazier\n",
+      "\n",
+      "RB: Barry Sanders \n",
+      "\n",
+      "WR: Randy Moss\n",
+      "\n",
+      "coach: Tom Osbourne\n",
+      "\n",
+      "Best stadium: Clemson\n",
+      "\n",
+      "Funnest game you’ve ever watched: JMU v App St 2008\n",
+      "\n",
+      "Best National Championship team: Nebraska 1995\n",
+      "\n",
+      "psainz: Have you checked out the NEW Staley strong Football website!?!? The home page has us READY for 7pm kickoff Friday night! Let’s go !!!! @StaleyFootball @SHSFalconClub @N2SportsStaley @TheNestSHS  #staleystrong https://t.co/0Xj099ZHCW https://t.co/vNBL36yUNR\n",
+      "\n",
+      "Ourand_SBJ: Burke Magnus on the importance of college football to ESPN.\n",
+      "\n",
+      "Listen here:\n",
+      "Apple: https://t.co/cCtJ151IJM\n",
+      "Spotify: https://t.co/G7BNgGCwDm\n",
+      "Google: https://t.co/an10IdStCs https://t.co/YxfzZwsdJr\n",
+      "\n",
+      "leed2019: @Football__Tweet Gary Lineker\n",
+      "\n",
+      "dwjones91: The O Dub Football Team is putting in that work for another Championship year with dedication from the players and the coaches\n",
+      "\n",
+      "basicfreshness: Damn ion think nobody from my class going to that brooks reunion 🤣 but they having a football game and I deserve to see that\n",
+      "\n",
+      "Hombrevender: @IDP_Baumer Yeah that's what I'm saying he has RB1 in college football. He'll do plenty of running.\n",
+      "\n",
+      "erniesuggs: On Tuesday, @Morehouse dedicated the newly renovated Edwin C. Moses Track &amp; B.T. Harvey Stadium Football Field, allowing the #HBCU to host track meets for the 1st time in a decade. In today's @ajc PLS RT\n",
+      "@usatf @edwinmoses @ImGailDevers \n",
+      "https://t.co/aZSTw3jAfJ\n",
+      "\n",
+      "be_that_guy11: @CalistudJohn @slmandel a guy who lives in reno should not be talking college football.\n",
+      "\n",
+      "djmeiho63: @miles_commodore Not only a good football player but also a good commentator, which I remember him more as a commentator.  R I P, Len.\n",
+      "\n",
+      "CarlonCarpenter: To use an example:\n",
+      "\n",
+      "The Where Goals Come From project was born out of me trying to answer a basic question: \"What are the best types of passes to score goals in college football?\"\n",
+      "\n",
+      "It morphed into something much greater/profound (with a lot of help) but the start was rudimentary.\n",
+      "\n",
+      "Volquest_Rivals: Tim Banks: \"We have some veterans who have played a lot of football. They will be the first one to tell you, these guys are interchangeable.\"\n",
+      "\n",
+      "ProstatsC: Welcome to the top Ante Milanovic-Litre!\n",
+      "\n",
+      "He was our most productive rusher for #CFL week 11.  \n",
+      "\n",
+      "#ProStatsCanada #Football #Canada #CanadianFootball #AmericanFootball #NFL #Elks #ElksCharge #Edmonton #GoElks @GoElks @CFL https://t.co/E6uIP6OB5y\n",
+      "\n",
+      "MarkARoper1: Jill picked Isobel out @a football camp of 100s girls a few years ago &amp; spotted her for her talent &amp; said if she worked hard she would go far. She remembers that always. \n",
+      "She then gave her, her signed winning shirt ofthe continental cup.\n",
+      "Isobel has it pride of place on her wall.\n",
+      "\n",
+      "stef_oppong: @libz_67 @Benzema  is going down in the history books as one of the greatest immortals of Football after winning the ballon d'or and there's no banter that's stopping that😂😂\n",
+      "\n",
+      "Cyluho: It's quite possible I spent the entire day illustrating a completely unasked-for cover for an essay for uni about football and religion. Shit happens. https://t.co/5P7T3jrNEY\n",
+      "\n",
+      "6thymes: @SimonRi96255549 @OwenFaragher Football existed before your oil money ye little nonce\n",
+      "\n",
+      "AuburnLiveOn3: Asked and Answered: Auburn football questions as fall camp ends, game preparation begins https://t.co/HzCCy8TKlX via @on3sports\n",
+      "\n",
+      "lusa1209: @lucaskagiso @football_papi I was about to say until real Madrid shows up\n",
+      "\n",
+      "BigBlueDart: Two of the three $1k bets on USU to win the national championship were the result of tequila shots at a wedding with former Michigan football players in attendance.\n",
+      "\n",
+      "The story behind the wildest college football bet of the offseason https://t.co/JHAttbizCi\n",
+      "\n",
+      "noahrohlfing: back from northern Michigan and back at work, but here’s a story that dropped on my wedding day and I’m proud of. \n",
+      "\n",
+      "50 Years of the Marshalltown Football League https://t.co/S5hfZNNUNh\n",
+      "\n",
+      "planwithchapman: Fantasy Football Twitter is insufferable this year.\n",
+      "\n",
+      "I follow less than ten accounts and I’ve been made to understand that there are 427 different players who are guaranteed to win me my league this year.\n",
+      "\n",
+      "Seems a tad high.\n",
+      "\n",
+      "JoeDaSamurai: @babygirl_riss Football Season baby 💪🏼🤘🏼\n",
+      "\n",
+      "MaseDenver: There are many directions in which Amazon could have gone for its “Thursday Night Football” theme music, but I would have chosen this one: https://t.co/h7wHK1Xg82\n",
+      "\n",
+      "andylev15: Man u appoint former club legend as their new director of football ;\n",
+      "Webb to become Chief Refereeing Officer at PGMOL #lfc \n",
+      "https://t.co/X6ljZQWcuL\n",
+      "\n",
+      "MichaelDanger19: @hendocfc I understand the \"modern football crest is bad!\" sentiment but I do think this is a massive upgrade\n",
+      "\n",
+      "now to figure out what letters are on the crest\n",
+      "\n",
+      "bigfoots0169: @MPFrazer @steelpanthers72 I don’t give a rat’s ass if he checks down or not Lol, he moves the football, the offense scores TDs isn’t that the goal LOL\n",
+      "\n",
+      "MickGProduction: The Fact I get to see my first College Football game in Ireland with the GF is next level 😎 Bring on Saturday 😍 #GBR https://t.co/GFLQLSEoox\n",
+      "\n",
+      "mcwilsonky: 6 High School Football Players Combine Their Strength to Rescue Injured Woman Trapped in a Wrecked Car \n",
+      "@goodnewsnetwork \n",
+      "https://t.co/Qyinr6FzhJ\n",
+      "\n",
+      "ronpila: @LFCryan__ @LFCLaurie Meanwhile we may not achieve top 4… do you remember what happened after 09/10 season? And football has changed a lot since then, if you can’t win titles and constantly have UCL football you are embedded in a vicious cicle of shit.\n",
+      "\n",
+      "melvinator76: Coming off a nice week 0 win over SE Warren, @WacoWarriors prep for week 1 vs. @OrioleUpdate. While us fans have the luxury of looking ahead, I can assure you @cmedeker and his team are NOT! #iahsfb #FridayNightLights\n",
+      "#football\n",
+      "\n",
+      "https://t.co/ZYbbvJvWka\n",
+      "\n",
+      "BOS_Sportskeeda: The key findings from this year’s #football  #MONEY League report by @Deloitte are:\n",
+      "\n",
+      "Matchday revenue: It was the lowest ever in Money League history.\n",
+      "Broadcast revenue: A €1.4bn increase from 2019/20\n",
+      "Commercial revenue decreased by 6% (€222m)\n",
+      "#Data #DataAnalytics #footballbiz https://t.co/eLM3EU10iA\n",
+      "\n",
+      "slideme: The NFL season is about to kick off, and you’re still not in any fantasy football leagues yet. Time’s running out, but you’ve still got a couple weeks to get in on the action before week 1, and we’ve got you covered with the best fantasy football apps for  https://t.co/6y8tkXiv4N\n",
+      "\n",
+      "mobiquotes: What happens is once you start to understand football, you realise that it's not just about the physical side of the game and chasing after a ball. It's a strategic sport which requires a lot of intelligence. It's a very mental game.\n",
+      "\n",
+      "EricNMoody: Check out the Wednesday edition of the Fantasy Football Daily Notes:\n",
+      "https://t.co/v5ohTxYUGV\n",
+      "\n",
+      "Stream connection closed by Twitter\n"
+     ]
+    }
+   ],
+   "source": [
+    "location_listener.filter(expansions=['author_id'], \n",
+    "    user_fields=['location'], tweet_fields=['lang'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Displaying the Location Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "83"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "counts['total_tweets']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "counts['locations']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'{counts[\"locations\"] / counts[\"total_tweets\"]:.1%}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geocoding the Locations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tweetutilities import get_geocodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Getting coordinates for tweet locations...\n",
+      "Done geocoding\n"
+     ]
+    }
+   ],
+   "source": [
+    "bad_locations = get_geocodes(tweets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Displaying the Bad Location Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bad_locations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18.0%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'{bad_locations / counts[\"locations\"]:.1%}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cleaning the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(tweets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a Map with Folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "usmap = folium.Map(location=[39.8283, -98.5795], \n",
+    "    tiles='Stamen Terrain', zoom_start=5, detect_retina=True)        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Popup Markers for the Tweet Locations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for t in df.itertuples():\n",
+    "     text = ': '.join([t.username, t.text])\n",
+    "     popup = folium.Popup(text, parse_html=True)\n",
+    "     marker = folium.Marker((t.latitude, t.longitude), \n",
+    "                            popup=popup)\n",
+    "     marker.add_to(usmap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving the Map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "usmap.save('tweet_map.html')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE: We added the following cell to display the map in the Jupyter Notebook.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
+       "&lt;head&gt;    \n",
+       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
+       "    \n",
+       "        &lt;script&gt;\n",
+       "            L_NO_TOUCH = false;\n",
+       "            L_DISABLE_3D = false;\n",
+       "        &lt;/script&gt;\n",
+       "    \n",
+       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
+       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
+       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
+       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
+       "    \n",
+       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
+       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
+       "            &lt;style&gt;\n",
+       "                #map_2489621787630edabd1643ad3b7760d1 {\n",
+       "                    position: relative;\n",
+       "                    width: 100.0%;\n",
+       "                    height: 100.0%;\n",
+       "                    left: 0.0%;\n",
+       "                    top: 0.0%;\n",
+       "                }\n",
+       "            &lt;/style&gt;\n",
+       "        \n",
+       "&lt;/head&gt;\n",
+       "&lt;body&gt;    \n",
+       "    \n",
+       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_2489621787630edabd1643ad3b7760d1&quot; &gt;&lt;/div&gt;\n",
+       "        \n",
+       "&lt;/body&gt;\n",
+       "&lt;script&gt;    \n",
+       "    \n",
+       "            var map_2489621787630edabd1643ad3b7760d1 = L.map(\n",
+       "                &quot;map_2489621787630edabd1643ad3b7760d1&quot;,\n",
+       "                {\n",
+       "                    center: [39.8283, -98.5795],\n",
+       "                    crs: L.CRS.EPSG3857,\n",
+       "                    zoom: 5,\n",
+       "                    zoomControl: true,\n",
+       "                    preferCanvas: false,\n",
+       "                    detectRetina: true,\n",
+       "                }\n",
+       "            );\n",
+       "\n",
+       "            \n",
+       "\n",
+       "        \n",
+       "    \n",
+       "            var tile_layer_b7d3252af4b4b0d8cf6672b77fb26e26 = L.tileLayer(\n",
+       "                &quot;https://stamen-tiles-{s}.a.ssl.fastly.net/terrain/{z}/{x}/{y}.jpg&quot;,\n",
+       "                {&quot;attribution&quot;: &quot;Map tiles by \\u003ca href=\\&quot;http://stamen.com\\&quot;\\u003eStamen Design\\u003c/a\\u003e, under \\u003ca href=\\&quot;http://creativecommons.org/licenses/by/3.0\\&quot;\\u003eCC BY 3.0\\u003c/a\\u003e. Data by \\u0026copy; \\u003ca href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca href=\\&quot;http://creativecommons.org/licenses/by-sa/3.0\\&quot;\\u003eCC BY SA\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "            var marker_c9aa6355c0b244bbc8cdcb4af8e5943b = L.marker(\n",
+       "                [36.1662859, -115.149225],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_bde4d83f6117982bc1b40b145de86df1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_aff38ac34976793987d8917ebc18485d = $(`&lt;div id=&quot;html_aff38ac34976793987d8917ebc18485d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;valerieinhoops: Rebel football Saturday- 12:30 pm kick off! 🏈✅🥤  Breakfast tailgate, drinking Mimosas &amp;amp;amp; Bloody Mary’s  before noon AND Rebel football @AllegiantStadm?!!   Hell yes! Let’s goooooo! @unlvfootball @joearrigofsm @TheFranchiseLV @UNLVRebellion @UNLVRAF @UNLVathletics @UNLVgirl https://t.co/6IdAqVdL17&lt;/div&gt;`)[0];\n",
+       "            popup_bde4d83f6117982bc1b40b145de86df1.setContent(html_aff38ac34976793987d8917ebc18485d);\n",
+       "        \n",
+       "\n",
+       "        marker_c9aa6355c0b244bbc8cdcb4af8e5943b.bindPopup(popup_bde4d83f6117982bc1b40b145de86df1)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_75c46199414c40a8a6a07d9673154ab0 = L.marker(\n",
+       "                [29.9499323, -90.0701156],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_241f1b27908d34854633661158a602e1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_5c66e76e858407ce36d4e3ecb0760814 = $(`&lt;div id=&quot;html_5c66e76e858407ce36d4e3ecb0760814&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;b_lanaux: When people try to say LSU isn’t the greatest atmosphere in college football I just show them this video. https://t.co/A5zAfPyrSF&lt;/div&gt;`)[0];\n",
+       "            popup_241f1b27908d34854633661158a602e1.setContent(html_5c66e76e858407ce36d4e3ecb0760814);\n",
+       "        \n",
+       "\n",
+       "        marker_75c46199414c40a8a6a07d9673154ab0.bindPopup(popup_241f1b27908d34854633661158a602e1)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_1a0e41a3cf535efa0e7ce0d2f2b0ae94 = L.marker(\n",
+       "                [41.3809333, 2.12291205923077],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_33e7a1e932f364f88fe5540f15145b95 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_36a2816f8dc3c337152de595107ca759 = $(`&lt;div id=&quot;html_36a2816f8dc3c337152de595107ca759&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;orionkwa: @Lk47041027 one football&lt;/div&gt;`)[0];\n",
+       "            popup_33e7a1e932f364f88fe5540f15145b95.setContent(html_36a2816f8dc3c337152de595107ca759);\n",
+       "        \n",
+       "\n",
+       "        marker_1a0e41a3cf535efa0e7ce0d2f2b0ae94.bindPopup(popup_33e7a1e932f364f88fe5540f15145b95)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_91ded6f6bcc05aeeb9661917ee716c7d = L.marker(\n",
+       "                [51.5073219, -0.1276474],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_b6ce28fce85303a7c2fd71815fb93f91 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_8196276bc4afbed5b7dc46a1c540839a = $(`&lt;div id=&quot;html_8196276bc4afbed5b7dc46a1c540839a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;franciscotrigo5: @cxrd52 Bro I actually think at the top of elite football hardest most deificou position is the GK  Imagine you need to be more than a shot stopper and if you make a mistake it’s your fault and no one can back you  A 6 has Cb to back you box to box it’s only difficult coz energy&lt;/div&gt;`)[0];\n",
+       "            popup_b6ce28fce85303a7c2fd71815fb93f91.setContent(html_8196276bc4afbed5b7dc46a1c540839a);\n",
+       "        \n",
+       "\n",
+       "        marker_91ded6f6bcc05aeeb9661917ee716c7d.bindPopup(popup_b6ce28fce85303a7c2fd71815fb93f91)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_a91b7d951dcf3f9ddcb7a5f63f7fc524 = L.marker(\n",
+       "                [44.6722158, -88.2426026],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_4ceec6f2290c188046d58a46b3728c44 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_a1ca3c1c3f171739c4bd2b491fc84285 = $(`&lt;div id=&quot;html_a1ca3c1c3f171739c4bd2b491fc84285&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;pcsd: The @PCSD_PHS Red Raiders football squad shut out Notre Dame 14-0 to start the season on the W side of the ledger. Check out the game action and more at: https://t.co/BIbp6M7o0R. #RaiderStrong @PulaskiRedSea https://t.co/TrlEtOHmye&lt;/div&gt;`)[0];\n",
+       "            popup_4ceec6f2290c188046d58a46b3728c44.setContent(html_a1ca3c1c3f171739c4bd2b491fc84285);\n",
+       "        \n",
+       "\n",
+       "        marker_a91b7d951dcf3f9ddcb7a5f63f7fc524.bindPopup(popup_4ceec6f2290c188046d58a46b3728c44)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_b156e5b2030d8782d295a879435b91ce = L.marker(\n",
+       "                [52.7954791, -0.540240286617432],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_d23ae33cb7ac33f69b467419ec5adcb0 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_6e59cd23056a1d67028e03f13576c255 = $(`&lt;div id=&quot;html_6e59cd23056a1d67028e03f13576c255&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;KingBanter_T: @elonmusk buy Manchester United football club...&lt;/div&gt;`)[0];\n",
+       "            popup_d23ae33cb7ac33f69b467419ec5adcb0.setContent(html_6e59cd23056a1d67028e03f13576c255);\n",
+       "        \n",
+       "\n",
+       "        marker_b156e5b2030d8782d295a879435b91ce.bindPopup(popup_d23ae33cb7ac33f69b467419ec5adcb0)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_236b93128a2496d7c1a9467fd93f56b5 = L.marker(\n",
+       "                [35.9828413, -86.5186045],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_7a8a62c4f51a26a2c67d5c773b4ae141 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_ad460966195b91989b271e2653470782 = $(`&lt;div id=&quot;html_ad460966195b91989b271e2653470782&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ItsUTOrange75: @smyrnafootball Absolutely amazing game. Great pictures&lt;/div&gt;`)[0];\n",
+       "            popup_7a8a62c4f51a26a2c67d5c773b4ae141.setContent(html_ad460966195b91989b271e2653470782);\n",
+       "        \n",
+       "\n",
+       "        marker_236b93128a2496d7c1a9467fd93f56b5.bindPopup(popup_7a8a62c4f51a26a2c67d5c773b4ae141)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_f607a81095ef09d2c474f7ea4caf7163 = L.marker(\n",
+       "                [13.0801721, 80.2838331],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_0a8178f768652caaf5e6ccb93e9934c5 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_c75878aaf65a09de3aeac3ee52e1e77b = $(`&lt;div id=&quot;html_c75878aaf65a09de3aeac3ee52e1e77b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Shiv_rants: @Football__Tweet @nasdaily is that you ?!&lt;/div&gt;`)[0];\n",
+       "            popup_0a8178f768652caaf5e6ccb93e9934c5.setContent(html_c75878aaf65a09de3aeac3ee52e1e77b);\n",
+       "        \n",
+       "\n",
+       "        marker_f607a81095ef09d2c474f7ea4caf7163.bindPopup(popup_0a8178f768652caaf5e6ccb93e9934c5)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_1a6a73848e18f3b924e82d6501115359 = L.marker(\n",
+       "                [43.255205, -79.868202],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_75b032f7b86efa1ac5b8f998297dc80a = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_23cb2ce507b2e00c786e4cff928d91e0 = $(`&lt;div id=&quot;html_23cb2ce507b2e00c786e4cff928d91e0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;CANAMPREP: It&amp;#39;s a pretty SAD situation when  SO CALLED FOOTBALL EXPERTS like yourselves  have to dwell on the topic of LOW ATTENDANCE just to uphold CFL INTEGRITY:  But as I&amp;#39;ve said all along:  THE TRUTH HURTS TIME TO CHANGE CULTURE OF 🇨🇦🏈  Start with   ELIMINATING THE ROSTER RATIO https://t.co/Qhn4ohDNJY&lt;/div&gt;`)[0];\n",
+       "            popup_75b032f7b86efa1ac5b8f998297dc80a.setContent(html_23cb2ce507b2e00c786e4cff928d91e0);\n",
+       "        \n",
+       "\n",
+       "        marker_1a6a73848e18f3b924e82d6501115359.bindPopup(popup_75b032f7b86efa1ac5b8f998297dc80a)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_47a7c551a0c13c83819a3471991a17d3 = L.marker(\n",
+       "                [39.100105, -94.5781416],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_01866a1c21e133aff79bc45d46ee736e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_e3b3269bd26b3b9930f047640e4a1a01 = $(`&lt;div id=&quot;html_e3b3269bd26b3b9930f047640e4a1a01&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;mikeb26and352: @Rajulesrirama Good for you listening to your body! Good work today, Raju!  So where do we sign up for the runner fantasy football league? 🤷‍♂️&lt;/div&gt;`)[0];\n",
+       "            popup_01866a1c21e133aff79bc45d46ee736e.setContent(html_e3b3269bd26b3b9930f047640e4a1a01);\n",
+       "        \n",
+       "\n",
+       "        marker_47a7c551a0c13c83819a3471991a17d3.bindPopup(popup_01866a1c21e133aff79bc45d46ee736e)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_039021c165aab0edc7d6c1e780087af5 = L.marker(\n",
+       "                [38.8949549, -77.0366456],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_9bbd943b0bd7b3e5045a5d630a7f568d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_ca026fcead71bf606bfe24434f5dc279 = $(`&lt;div id=&quot;html_ca026fcead71bf606bfe24434f5dc279&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BrandtAWitt: @peterkrupa In France too. People like the image of what American college represents! Same with sports teams. Sometimes Americans wear Euro football team shirts, don’t see a big difference. No one cares about Euro colleges (Oxford/Cambridge Anglo not Euro lol) Also saw multiple churros /&lt;/div&gt;`)[0];\n",
+       "            popup_9bbd943b0bd7b3e5045a5d630a7f568d.setContent(html_ca026fcead71bf606bfe24434f5dc279);\n",
+       "        \n",
+       "\n",
+       "        marker_039021c165aab0edc7d6c1e780087af5.bindPopup(popup_9bbd943b0bd7b3e5045a5d630a7f568d)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_20c4cc63bf79a5741086f2f1958ab24b = L.marker(\n",
+       "                [40.2338438, -111.6585337],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_719e383478e783ecdbf4b782024f5ad4 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_8d226c3ebc8e542cf4942862870c371d = $(`&lt;div id=&quot;html_8d226c3ebc8e542cf4942862870c371d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Black_Mormon: @CFBHome Most exciting college football player: CJ Spiller  QB: Tommie Frazier  RB: Barry Sanders   WR: Randy Moss  coach: Tom Osbourne  Best stadium: Clemson  Funnest game you’ve ever watched: JMU v App St 2008  Best National Championship team: Nebraska 1995&lt;/div&gt;`)[0];\n",
+       "            popup_719e383478e783ecdbf4b782024f5ad4.setContent(html_8d226c3ebc8e542cf4942862870c371d);\n",
+       "        \n",
+       "\n",
+       "        marker_20c4cc63bf79a5741086f2f1958ab24b.bindPopup(popup_719e383478e783ecdbf4b782024f5ad4)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_a5132ac4859e99e50d19511e05e7c45f = L.marker(\n",
+       "                [12.9969668, 77.6909566],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_5772d215015d063627d9cb697748b6da = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_44c4cd17703f6ecb1eee1ba791a81161 = $(`&lt;div id=&quot;html_44c4cd17703f6ecb1eee1ba791a81161&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;psainz: Have you checked out the NEW Staley strong Football website!?!? The home page has us READY for 7pm kickoff Friday night! Let’s go !!!! @StaleyFootball @SHSFalconClub @N2SportsStaley @TheNestSHS  #staleystrong https://t.co/0Xj099ZHCW https://t.co/vNBL36yUNR&lt;/div&gt;`)[0];\n",
+       "            popup_5772d215015d063627d9cb697748b6da.setContent(html_44c4cd17703f6ecb1eee1ba791a81161);\n",
+       "        \n",
+       "\n",
+       "        marker_a5132ac4859e99e50d19511e05e7c45f.bindPopup(popup_5772d215015d063627d9cb697748b6da)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_fd2cf5526c181cc51e604fcf84f4d591 = L.marker(\n",
+       "                [38.89366125, -76.987883253882],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_feca2d6be3108a268ecb0a08e07947a5 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_87b42881a30e72967895a4f9d944e485 = $(`&lt;div id=&quot;html_87b42881a30e72967895a4f9d944e485&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Ourand_SBJ: Burke Magnus on the importance of college football to ESPN.  Listen here: Apple: https://t.co/cCtJ151IJM Spotify: https://t.co/G7BNgGCwDm Google: https://t.co/an10IdStCs https://t.co/YxfzZwsdJr&lt;/div&gt;`)[0];\n",
+       "            popup_feca2d6be3108a268ecb0a08e07947a5.setContent(html_87b42881a30e72967895a4f9d944e485);\n",
+       "        \n",
+       "\n",
+       "        marker_fd2cf5526c181cc51e604fcf84f4d591.bindPopup(popup_feca2d6be3108a268ecb0a08e07947a5)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_3df3ac184c58ca30008e02e8e47ba550 = L.marker(\n",
+       "                [51.46426255, -0.651714721140142],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_9db01e144b506dcb99d300aa538089c1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_31066ab18903ac71ab6cdf651d77131d = $(`&lt;div id=&quot;html_31066ab18903ac71ab6cdf651d77131d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;leed2019: @Football__Tweet Gary Lineker&lt;/div&gt;`)[0];\n",
+       "            popup_9db01e144b506dcb99d300aa538089c1.setContent(html_31066ab18903ac71ab6cdf651d77131d);\n",
+       "        \n",
+       "\n",
+       "        marker_3df3ac184c58ca30008e02e8e47ba550.bindPopup(popup_9db01e144b506dcb99d300aa538089c1)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_2e727c9213acb4a7baa9301dfbdaab0f = L.marker(\n",
+       "                [32.7154295, -96.7916608141908],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_abd5784451a476f81a3db17f1df67a45 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_0fc31c17107704e110950d5f1417a492 = $(`&lt;div id=&quot;html_0fc31c17107704e110950d5f1417a492&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;dwjones91: The O Dub Football Team is putting in that work for another Championship year with dedication from the players and the coaches&lt;/div&gt;`)[0];\n",
+       "            popup_abd5784451a476f81a3db17f1df67a45.setContent(html_0fc31c17107704e110950d5f1417a492);\n",
+       "        \n",
+       "\n",
+       "        marker_2e727c9213acb4a7baa9301dfbdaab0f.bindPopup(popup_abd5784451a476f81a3db17f1df67a45)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_828073c1e3da1a002a6ce4830d440f56 = L.marker(\n",
+       "                [33.7490987, -84.3901849],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_e2916b27d10f5f09cae26bbb37ef2403 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_66455be61ba956859433d1dee4107725 = $(`&lt;div id=&quot;html_66455be61ba956859433d1dee4107725&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;erniesuggs: On Tuesday, @Morehouse dedicated the newly renovated Edwin C. Moses Track &amp;amp;amp; B.T. Harvey Stadium Football Field, allowing the #HBCU to host track meets for the 1st time in a decade. In today&amp;#39;s @ajc PLS RT @usatf @edwinmoses @ImGailDevers  https://t.co/aZSTw3jAfJ&lt;/div&gt;`)[0];\n",
+       "            popup_e2916b27d10f5f09cae26bbb37ef2403.setContent(html_66455be61ba956859433d1dee4107725);\n",
+       "        \n",
+       "\n",
+       "        marker_828073c1e3da1a002a6ce4830d440f56.bindPopup(popup_e2916b27d10f5f09cae26bbb37ef2403)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_2478a8b0b9d999b4a76089432f1d1921 = L.marker(\n",
+       "                [30.1734194, -95.504686],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_2c3f15c3110ed2c5fd058e4e79f39fc6 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_eb45612158083d158538c2d361ad369a = $(`&lt;div id=&quot;html_eb45612158083d158538c2d361ad369a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;be_that_guy11: @CalistudJohn @slmandel a guy who lives in reno should not be talking college football.&lt;/div&gt;`)[0];\n",
+       "            popup_2c3f15c3110ed2c5fd058e4e79f39fc6.setContent(html_eb45612158083d158538c2d361ad369a);\n",
+       "        \n",
+       "\n",
+       "        marker_2478a8b0b9d999b4a76089432f1d1921.bindPopup(popup_2c3f15c3110ed2c5fd058e4e79f39fc6)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_e9b89d386aba0e805cde5d9e66adf6cb = L.marker(\n",
+       "                [41.9216734, -93.3122705],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_00b350dd89f471e94deec4108884670f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_880f70455acbd8d2b67a73d477011715 = $(`&lt;div id=&quot;html_880f70455acbd8d2b67a73d477011715&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;djmeiho63: @miles_commodore Not only a good football player but also a good commentator, which I remember him more as a commentator.  R I P, Len.&lt;/div&gt;`)[0];\n",
+       "            popup_00b350dd89f471e94deec4108884670f.setContent(html_880f70455acbd8d2b67a73d477011715);\n",
+       "        \n",
+       "\n",
+       "        marker_e9b89d386aba0e805cde5d9e66adf6cb.bindPopup(popup_00b350dd89f471e94deec4108884670f)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_acf64eff7c161d9236a16e5c4d5ffd20 = L.marker(\n",
+       "                [51.3813864, -2.3596963],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_5c1995f5ba98a87169c2c2b1bf413f46 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_71cc5e5460e933ce224c1cee9a22c2f2 = $(`&lt;div id=&quot;html_71cc5e5460e933ce224c1cee9a22c2f2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;CarlonCarpenter: To use an example:  The Where Goals Come From project was born out of me trying to answer a basic question: &amp;#34;What are the best types of passes to score goals in college football?&amp;#34;  It morphed into something much greater/profound (with a lot of help) but the start was rudimentary.&lt;/div&gt;`)[0];\n",
+       "            popup_5c1995f5ba98a87169c2c2b1bf413f46.setContent(html_71cc5e5460e933ce224c1cee9a22c2f2);\n",
+       "        \n",
+       "\n",
+       "        marker_acf64eff7c161d9236a16e5c4d5ffd20.bindPopup(popup_5c1995f5ba98a87169c2c2b1bf413f46)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_90fd7efb051e41bd7ba5f1d0025c43e5 = L.marker(\n",
+       "                [35.9603948, -83.9210261],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_c1aad35e828de96f555cebef4a83cd7a = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_bb2d18d965a93fdc90e9931e1646ae23 = $(`&lt;div id=&quot;html_bb2d18d965a93fdc90e9931e1646ae23&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Volquest_Rivals: Tim Banks: &amp;#34;We have some veterans who have played a lot of football. They will be the first one to tell you, these guys are interchangeable.&amp;#34;&lt;/div&gt;`)[0];\n",
+       "            popup_c1aad35e828de96f555cebef4a83cd7a.setContent(html_bb2d18d965a93fdc90e9931e1646ae23);\n",
+       "        \n",
+       "\n",
+       "        marker_90fd7efb051e41bd7ba5f1d0025c43e5.bindPopup(popup_c1aad35e828de96f555cebef4a83cd7a)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_ff4e2aa5c7e9dabd80553877ef78c4ec = L.marker(\n",
+       "                [43.6858146, -79.7599337],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_f257aec848f2455264b95bd145e134fd = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_385002879cff04a21ec1c5ed6f5a9456 = $(`&lt;div id=&quot;html_385002879cff04a21ec1c5ed6f5a9456&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ProstatsC: Welcome to the top Ante Milanovic-Litre!  He was our most productive rusher for #CFL week 11.    #ProStatsCanada #Football #Canada #CanadianFootball #AmericanFootball #NFL #Elks #ElksCharge #Edmonton #GoElks @GoElks @CFL https://t.co/E6uIP6OB5y&lt;/div&gt;`)[0];\n",
+       "            popup_f257aec848f2455264b95bd145e134fd.setContent(html_385002879cff04a21ec1c5ed6f5a9456);\n",
+       "        \n",
+       "\n",
+       "        marker_ff4e2aa5c7e9dabd80553877ef78c4ec.bindPopup(popup_f257aec848f2455264b95bd145e134fd)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_acf0b874a6ff1b61f30fcbcb7162e0f0 = L.marker(\n",
+       "                [52.4492074, -2.0514646],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_05efc19d50aa4707969756e577fa4136 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_e51da3e09053dc2c70f7c6a7983100cb = $(`&lt;div id=&quot;html_e51da3e09053dc2c70f7c6a7983100cb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MarkARoper1: Jill picked Isobel out @a football camp of 100s girls a few years ago &amp;amp;amp; spotted her for her talent &amp;amp;amp; said if she worked hard she would go far. She remembers that always.  She then gave her, her signed winning shirt ofthe continental cup. Isobel has it pride of place on her wall.&lt;/div&gt;`)[0];\n",
+       "            popup_05efc19d50aa4707969756e577fa4136.setContent(html_e51da3e09053dc2c70f7c6a7983100cb);\n",
+       "        \n",
+       "\n",
+       "        marker_acf0b874a6ff1b61f30fcbcb7162e0f0.bindPopup(popup_05efc19d50aa4707969756e577fa4136)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_fe6a50eb22614febf03c1b4ed1ea6849 = L.marker(\n",
+       "                [5.6596441, -0.0096592],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_71d0658963ffbd9571420afcd674c8ef = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_3864fc84c8ca5fdc65ca2d49f735dd5a = $(`&lt;div id=&quot;html_3864fc84c8ca5fdc65ca2d49f735dd5a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;stef_oppong: @libz_67 @Benzema  is going down in the history books as one of the greatest immortals of Football after winning the ballon d&amp;#39;or and there&amp;#39;s no banter that&amp;#39;s stopping that😂😂&lt;/div&gt;`)[0];\n",
+       "            popup_71d0658963ffbd9571420afcd674c8ef.setContent(html_3864fc84c8ca5fdc65ca2d49f735dd5a);\n",
+       "        \n",
+       "\n",
+       "        marker_fe6a50eb22614febf03c1b4ed1ea6849.bindPopup(popup_71d0658963ffbd9571420afcd674c8ef)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_ec2cf0ac34b81146ad625d15840dbb76 = L.marker(\n",
+       "                [53.0758196, 8.8071646],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_0eb540773ee799afd5b8ad888b1f330d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_b280ba0b7b8f1505467fc67c9b05cc10 = $(`&lt;div id=&quot;html_b280ba0b7b8f1505467fc67c9b05cc10&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Cyluho: It&amp;#39;s quite possible I spent the entire day illustrating a completely unasked-for cover for an essay for uni about football and religion. Shit happens. https://t.co/5P7T3jrNEY&lt;/div&gt;`)[0];\n",
+       "            popup_0eb540773ee799afd5b8ad888b1f330d.setContent(html_b280ba0b7b8f1505467fc67c9b05cc10);\n",
+       "        \n",
+       "\n",
+       "        marker_ec2cf0ac34b81146ad625d15840dbb76.bindPopup(popup_0eb540773ee799afd5b8ad888b1f330d)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_70ef0d067adae9fc1cddff087f312708 = L.marker(\n",
+       "                [53.4054719, -2.9805392],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_fa2b13317d7eca33a64828e666dbd752 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_60667191dfa4b02dfb35753906b1b141 = $(`&lt;div id=&quot;html_60667191dfa4b02dfb35753906b1b141&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;6thymes: @SimonRi96255549 @OwenFaragher Football existed before your oil money ye little nonce&lt;/div&gt;`)[0];\n",
+       "            popup_fa2b13317d7eca33a64828e666dbd752.setContent(html_60667191dfa4b02dfb35753906b1b141);\n",
+       "        \n",
+       "\n",
+       "        marker_70ef0d067adae9fc1cddff087f312708.bindPopup(popup_fa2b13317d7eca33a64828e666dbd752)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_01837588131ad02434163caff5225ddd = L.marker(\n",
+       "                [32.713387, -85.465079034839],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_f05a0ba76f3f539d1b9024b479b5566e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_9c585b28bfeb6f41e130fc9829c17916 = $(`&lt;div id=&quot;html_9c585b28bfeb6f41e130fc9829c17916&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;AuburnLiveOn3: Asked and Answered: Auburn football questions as fall camp ends, game preparation begins https://t.co/HzCCy8TKlX via @on3sports&lt;/div&gt;`)[0];\n",
+       "            popup_f05a0ba76f3f539d1b9024b479b5566e.setContent(html_9c585b28bfeb6f41e130fc9829c17916);\n",
+       "        \n",
+       "\n",
+       "        marker_01837588131ad02434163caff5225ddd.bindPopup(popup_f05a0ba76f3f539d1b9024b479b5566e)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_a9ff83b10cc58382caaf754652b9d6e7 = L.marker(\n",
+       "                [-28.8166236, 24.991639],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_91fc61907f3f42873d8d7020c096a2fb = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_8dfbe81588a0902f7e03febcd340dd5a = $(`&lt;div id=&quot;html_8dfbe81588a0902f7e03febcd340dd5a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;lusa1209: @lucaskagiso @football_papi I was about to say until real Madrid shows up&lt;/div&gt;`)[0];\n",
+       "            popup_91fc61907f3f42873d8d7020c096a2fb.setContent(html_8dfbe81588a0902f7e03febcd340dd5a);\n",
+       "        \n",
+       "\n",
+       "        marker_a9ff83b10cc58382caaf754652b9d6e7.bindPopup(popup_91fc61907f3f42873d8d7020c096a2fb)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_e3bca33e955b400f26e7992150d3bfc6 = L.marker(\n",
+       "                [41.0895938, -112.0638903],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_569aceae64e41049a8ca2e29a0fc648d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_da38e2f97a3545790a4ad6bfbaacccab = $(`&lt;div id=&quot;html_da38e2f97a3545790a4ad6bfbaacccab&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BigBlueDart: Two of the three $1k bets on USU to win the national championship were the result of tequila shots at a wedding with former Michigan football players in attendance.  The story behind the wildest college football bet of the offseason https://t.co/JHAttbizCi&lt;/div&gt;`)[0];\n",
+       "            popup_569aceae64e41049a8ca2e29a0fc648d.setContent(html_da38e2f97a3545790a4ad6bfbaacccab);\n",
+       "        \n",
+       "\n",
+       "        marker_e3bca33e955b400f26e7992150d3bfc6.bindPopup(popup_569aceae64e41049a8ca2e29a0fc648d)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_a256bc10bd23331a684dfef3b64bbd8a = L.marker(\n",
+       "                [51.44602845, 7.26250632987869],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_fb9775778a34a85994dad936fc436dc7 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_c1b47dd4b2bbee07aa1515968bade01a = $(`&lt;div id=&quot;html_c1b47dd4b2bbee07aa1515968bade01a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;noahrohlfing: back from northern Michigan and back at work, but here’s a story that dropped on my wedding day and I’m proud of.   50 Years of the Marshalltown Football League https://t.co/S5hfZNNUNh&lt;/div&gt;`)[0];\n",
+       "            popup_fb9775778a34a85994dad936fc436dc7.setContent(html_c1b47dd4b2bbee07aa1515968bade01a);\n",
+       "        \n",
+       "\n",
+       "        marker_a256bc10bd23331a684dfef3b64bbd8a.bindPopup(popup_fb9775778a34a85994dad936fc436dc7)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_da6d7804892dc39f220b733a070b46be = L.marker(\n",
+       "                [43.653963, -79.387207],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_f20360616b0d0a2af6ddcafb3990bfcd = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_f094363ceede2d99306cccb650b15c91 = $(`&lt;div id=&quot;html_f094363ceede2d99306cccb650b15c91&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;planwithchapman: Fantasy Football Twitter is insufferable this year.  I follow less than ten accounts and I’ve been made to understand that there are 427 different players who are guaranteed to win me my league this year.  Seems a tad high.&lt;/div&gt;`)[0];\n",
+       "            popup_f20360616b0d0a2af6ddcafb3990bfcd.setContent(html_f094363ceede2d99306cccb650b15c91);\n",
+       "        \n",
+       "\n",
+       "        marker_da6d7804892dc39f220b733a070b46be.bindPopup(popup_f20360616b0d0a2af6ddcafb3990bfcd)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_cbdea68f406ff579b81689b937f59847 = L.marker(\n",
+       "                [49.198057, -124.087538],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_4afb59f072bfca01b4a3d6dace6e9ab1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_6afdd67d842667941189e8f4fd27c809 = $(`&lt;div id=&quot;html_6afdd67d842667941189e8f4fd27c809&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;JoeDaSamurai: @babygirl_riss Football Season baby 💪🏼🤘🏼&lt;/div&gt;`)[0];\n",
+       "            popup_4afb59f072bfca01b4a3d6dace6e9ab1.setContent(html_6afdd67d842667941189e8f4fd27c809);\n",
+       "        \n",
+       "\n",
+       "        marker_cbdea68f406ff579b81689b937f59847.bindPopup(popup_4afb59f072bfca01b4a3d6dace6e9ab1)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_0d76c58a923fd2c04ed7af29563eea40 = L.marker(\n",
+       "                [52.7954791, -0.540240286617432],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_f6c650cdd84c977800f2b0721ddf5974 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_c76c6e98ff08ed1d4223281a77c7ebf3 = $(`&lt;div id=&quot;html_c76c6e98ff08ed1d4223281a77c7ebf3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;andylev15: Man u appoint former club legend as their new director of football ; Webb to become Chief Refereeing Officer at PGMOL #lfc  https://t.co/X6ljZQWcuL&lt;/div&gt;`)[0];\n",
+       "            popup_f6c650cdd84c977800f2b0721ddf5974.setContent(html_c76c6e98ff08ed1d4223281a77c7ebf3);\n",
+       "        \n",
+       "\n",
+       "        marker_0d76c58a923fd2c04ed7af29563eea40.bindPopup(popup_f6c650cdd84c977800f2b0721ddf5974)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_58a4716b28769d680ecfa9b5710f9588 = L.marker(\n",
+       "                [0.7191, 120.2419],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_61b13026ac024ccf37d0ebf90618832f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_69d2fb558e7c5b949511602e707a9bcb = $(`&lt;div id=&quot;html_69d2fb558e7c5b949511602e707a9bcb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MichaelDanger19: @hendocfc I understand the &amp;#34;modern football crest is bad!&amp;#34; sentiment but I do think this is a massive upgrade  now to figure out what letters are on the crest&lt;/div&gt;`)[0];\n",
+       "            popup_61b13026ac024ccf37d0ebf90618832f.setContent(html_69d2fb558e7c5b949511602e707a9bcb);\n",
+       "        \n",
+       "\n",
+       "        marker_58a4716b28769d680ecfa9b5710f9588.bindPopup(popup_61b13026ac024ccf37d0ebf90618832f)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_116cd4d8b615d7dab364bbf7c9a6e6d0 = L.marker(\n",
+       "                [52.865196, -7.9794599],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_91363529fef919e42f28ad1399774b73 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_b9003ccaa206f1a7397eb83fee74768d = $(`&lt;div id=&quot;html_b9003ccaa206f1a7397eb83fee74768d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MickGProduction: The Fact I get to see my first College Football game in Ireland with the GF is next level 😎 Bring on Saturday 😍 #GBR https://t.co/GFLQLSEoox&lt;/div&gt;`)[0];\n",
+       "            popup_91363529fef919e42f28ad1399774b73.setContent(html_b9003ccaa206f1a7397eb83fee74768d);\n",
+       "        \n",
+       "\n",
+       "        marker_116cd4d8b615d7dab364bbf7c9a6e6d0.bindPopup(popup_91363529fef919e42f28ad1399774b73)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_fd7673408365eb37c5bae36c50576df7 = L.marker(\n",
+       "                [36.5656153, -88.8111679],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_d98ae1024e9ee2e9bb5be4a8a17541d1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_6def0495e0c49338ee584f29394d23c4 = $(`&lt;div id=&quot;html_6def0495e0c49338ee584f29394d23c4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;mcwilsonky: 6 High School Football Players Combine Their Strength to Rescue Injured Woman Trapped in a Wrecked Car  @goodnewsnetwork  https://t.co/Qyinr6FzhJ&lt;/div&gt;`)[0];\n",
+       "            popup_d98ae1024e9ee2e9bb5be4a8a17541d1.setContent(html_6def0495e0c49338ee584f29394d23c4);\n",
+       "        \n",
+       "\n",
+       "        marker_fd7673408365eb37c5bae36c50576df7.bindPopup(popup_d98ae1024e9ee2e9bb5be4a8a17541d1)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_006ee5a01bfdc117ed76201142d11cd6 = L.marker(\n",
+       "                [16.5904056, -93.027994],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_63c7e1b0f8505e7ddf3cf9d86e21fcf1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_579ca64ea443a54351ce5a55278a0b53 = $(`&lt;div id=&quot;html_579ca64ea443a54351ce5a55278a0b53&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ronpila: @LFCryan__ @LFCLaurie Meanwhile we may not achieve top 4… do you remember what happened after 09/10 season? And football has changed a lot since then, if you can’t win titles and constantly have UCL football you are embedded in a vicious cicle of shit.&lt;/div&gt;`)[0];\n",
+       "            popup_63c7e1b0f8505e7ddf3cf9d86e21fcf1.setContent(html_579ca64ea443a54351ce5a55278a0b53);\n",
+       "        \n",
+       "\n",
+       "        marker_006ee5a01bfdc117ed76201142d11cd6.bindPopup(popup_63c7e1b0f8505e7ddf3cf9d86e21fcf1)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_75ec70c1149c900d9b50b8478e63ccaf = L.marker(\n",
+       "                [41.1477833, -91.661307],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_a231f65893bf87af6f103132c93ce6ab = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_9f680c8874259604d21d13446966136a = $(`&lt;div id=&quot;html_9f680c8874259604d21d13446966136a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;melvinator76: Coming off a nice week 0 win over SE Warren, @WacoWarriors prep for week 1 vs. @OrioleUpdate. While us fans have the luxury of looking ahead, I can assure you @cmedeker and his team are NOT! #iahsfb #FridayNightLights #football  https://t.co/ZYbbvJvWka&lt;/div&gt;`)[0];\n",
+       "            popup_a231f65893bf87af6f103132c93ce6ab.setContent(html_9f680c8874259604d21d13446966136a);\n",
+       "        \n",
+       "\n",
+       "        marker_75ec70c1149c900d9b50b8478e63ccaf.bindPopup(popup_a231f65893bf87af6f103132c93ce6ab)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_1fe2c923d5e3ba19f625df57eb1f904b = L.marker(\n",
+       "                [53.5120412, -2.2434612],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_f1c79e54a5d4f208bfe5fe99cb98c363 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_dbfa52d0b68fd3c889eb2bc9c71fbc7f = $(`&lt;div id=&quot;html_dbfa52d0b68fd3c889eb2bc9c71fbc7f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BOS_Sportskeeda: The key findings from this year’s #football  #MONEY League report by @Deloitte are:  Matchday revenue: It was the lowest ever in Money League history. Broadcast revenue: A €1.4bn increase from 2019/20 Commercial revenue decreased by 6% (€222m) #Data #DataAnalytics #footballbiz https://t.co/eLM3EU10iA&lt;/div&gt;`)[0];\n",
+       "            popup_f1c79e54a5d4f208bfe5fe99cb98c363.setContent(html_dbfa52d0b68fd3c889eb2bc9c71fbc7f);\n",
+       "        \n",
+       "\n",
+       "        marker_1fe2c923d5e3ba19f625df57eb1f904b.bindPopup(popup_f1c79e54a5d4f208bfe5fe99cb98c363)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_2e0d6bb1791c3fe295174b60d4b98e5d = L.marker(\n",
+       "                [47.6038321, -122.3300624],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_462e42605f968cfdf9dcdae6fd9aa5f0 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_e92f0d85ac1e648c3eb7c7ea53167561 = $(`&lt;div id=&quot;html_e92f0d85ac1e648c3eb7c7ea53167561&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;slideme: The NFL season is about to kick off, and you’re still not in any fantasy football leagues yet. Time’s running out, but you’ve still got a couple weeks to get in on the action before week 1, and we’ve got you covered with the best fantasy football apps for  https://t.co/6y8tkXiv4N&lt;/div&gt;`)[0];\n",
+       "            popup_462e42605f968cfdf9dcdae6fd9aa5f0.setContent(html_e92f0d85ac1e648c3eb7c7ea53167561);\n",
+       "        \n",
+       "\n",
+       "        marker_2e0d6bb1791c3fe295174b60d4b98e5d.bindPopup(popup_462e42605f968cfdf9dcdae6fd9aa5f0)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "    \n",
+       "            var marker_0ddb623cc07ff0f93aa128de14bfa012 = L.marker(\n",
+       "                [38.2636612, 22.0576191],\n",
+       "                {}\n",
+       "            ).addTo(map_2489621787630edabd1643ad3b7760d1);\n",
+       "        \n",
+       "    \n",
+       "        var popup_5298e8157bc0ee8fe8049e6bfb8a9dd1 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
+       "\n",
+       "        \n",
+       "            var html_f56da6110a6ac5fdc4b73d11e285b8e2 = $(`&lt;div id=&quot;html_f56da6110a6ac5fdc4b73d11e285b8e2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;mobiquotes: What happens is once you start to understand football, you realise that it&amp;#39;s not just about the physical side of the game and chasing after a ball. It&amp;#39;s a strategic sport which requires a lot of intelligence. It&amp;#39;s a very mental game.&lt;/div&gt;`)[0];\n",
+       "            popup_5298e8157bc0ee8fe8049e6bfb8a9dd1.setContent(html_f56da6110a6ac5fdc4b73d11e285b8e2);\n",
+       "        \n",
+       "\n",
+       "        marker_0ddb623cc07ff0f93aa128de14bfa012.bindPopup(popup_5298e8157bc0ee8fe8049e6bfb8a9dd1)\n",
+       "        ;\n",
+       "\n",
+       "        \n",
+       "    \n",
+       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x10ce1c970>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "usmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##########################################################################\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
+    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
+    "#                                                                        #\n",
+    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
+    "# best efforts in preparing the book. These efforts include the          #\n",
+    "# development, research, and testing of the theories and programs        #\n",
+    "# to determine their effectiveness. The authors and publisher make       #\n",
+    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
+    "# programs or to the documentation contained in these books. The authors #\n",
+    "# and publisher shall not be liable in any event for incidental or       #\n",
+    "# consequential damages in connection with, or arising out of, the       #\n",
+    "# furnishing, performance, or use of these programs.                     #\n",
+    "##########################################################################\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15_01selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_01selfcheck-checkpoint.ipynb
similarity index 92%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15_01selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_01selfcheck-checkpoint.ipynb
index a3c7168..78f2990 100644
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15_01selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_01selfcheck-checkpoint.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.15.1 Self Check"
+    "# 13.15.1 Self Check"
    ]
   },
   {
@@ -26,7 +26,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -44,7 +44,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -58,9 +58,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15_02selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_02selfcheck-checkpoint.ipynb
similarity index 93%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15_02selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_02selfcheck-checkpoint.ipynb
index e5034c6..8a78832 100755
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15_02selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15_02selfcheck-checkpoint.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.15.2 Self Check"
+    "# 13.15.2 Self Check"
    ]
   },
   {
@@ -60,7 +60,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -78,7 +78,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -92,9 +92,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15selfcheck-checkpoint.ipynb
similarity index 92%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15selfcheck-checkpoint.ipynb
index 20c6d6f..7705012 100755
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/13_15selfcheck-checkpoint.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 14.15 Self Check"
+    "# 13.15 Self Check"
    ]
   },
   {
@@ -28,7 +28,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -46,7 +46,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -60,9 +60,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/README-checkpoint.txt b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/README-checkpoint.txt
new file mode 100755
index 0000000..ac3297c
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/.ipynb_checkpoints/README-checkpoint.txt
@@ -0,0 +1,2 @@
+Sections 13.7-11 use one continuous IPython session for the examples 
+and Self Check exercises, so all of these are in one notebook.
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_07-11withSelfChecks.ipynb
similarity index 68%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_07-11withSelfChecks.ipynb
index 23c3707..8149194 100755
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_07-11withSelfChecks.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 13.7 Authenticating with Twitter Via Tweepy "
+    "# 13.7 Authenticating with Twitter Via Tweepy to Access Twitter v2 APIs"
    ]
   },
   {
@@ -36,7 +36,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating and Configuring an `OAuthHandler` to Authenticate with Twitter"
+    "### Creating a Client Object"
    ]
   },
   {
@@ -45,59 +45,41 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "auth = tweepy.OAuthHandler(keys.consumer_key,\n",
-    "                           keys.consumer_secret)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "auth.set_access_token(keys.access_token,\n",
-    "                      keys.access_token_secret)"
+    "client = tweepy.Client(bearer_token=keys.bearer_token,\n",
+    "                       wait_on_rate_limit=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating an API Object"
+    "# 13.8 Getting Information About a Twitter Account"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "api = tweepy.API(auth, wait_on_rate_limit=True, \n",
-    "                 wait_on_rate_limit_notify=True)\n",
-    "              "
+    "nasa = client.get_user(username='NASA', \n",
+    "    user_fields=['description', 'public_metrics'])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 13.8 Getting Information About a Twitter Account"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nasa = api.get_user('nasa')"
+    "### tweepy.Response Object"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Getting Basic Account Information"
+    "### Getting a User’s Basic Account Information"
    ]
   },
   {
@@ -106,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa.id"
+    "nasa.data.id"
    ]
   },
   {
@@ -115,7 +97,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa.name"
+    "nasa.data.name"
    ]
   },
   {
@@ -124,23 +106,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa.screen_name"
+    "nasa.data.username"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "nasa.description"
+    "nasa.data.description"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "### Getting the Most Recent Status Update"
+    "### Getting the Number of Accounts That Follow This Account and the Number of Accounts This Account Follows"
    ]
   },
   {
@@ -149,57 +135,64 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa.status.text"
+    "nasa.data.public_metrics['followers_count']"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Getting the Number of Followers"
+    "nasa.data.public_metrics['following_count']"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "nasa.followers_count"
+    "### Getting Your Own Account’s Information"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Getting the Number of Friends "
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.8 Self Check\n",
+    "**2. _(IPython Session)_** Use the `client` object to get information about the `NASAMars` account, then display its ID, name, username, description and number of followers.\n",
+    "\n",
+    "**Answer:** "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "nasa.friends_count"
+    "nasa_mars = client.get_user(username='NASAMars', \n",
+    "    user_fields=['description', 'public_metrics'])"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Getting Your Own Account’s Information"
+    "nasa_mars.data.id"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 13.8 Self Check\n",
-    "**3. _(IPython Session)_** Use the `api` object to get a `User` object for the `NASAKepler` account, then display its number of followers and most recent tweet.\n",
-    "\n",
-    "**Answer:** "
+    "nasa_mars.data.name"
    ]
   },
   {
@@ -208,7 +201,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa_kepler = api.get_user('NASAKepler')"
+    "nasa_mars.data.username"
    ]
   },
   {
@@ -217,7 +210,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa_kepler.followers_count"
+    "nasa_mars.data.description"
    ]
   },
   {
@@ -226,14 +219,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa_kepler.status.text"
+    "nasa_mars.data.public_metrics['followers_count']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 13.9 Introduction to Tweepy `Cursor`s: Getting an Account’s Followers and Friends\n",
+    "# 13.9 Intro to Tweepy `Paginator`s: Getting More than One Page of Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
     "# 13.9.1 Determining an Account’s Followers "
    ]
   },
@@ -250,7 +251,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating a Cursor"
+    "### Creating a `Paginator`"
    ]
   },
   {
@@ -259,7 +260,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cursor = tweepy.Cursor(api.followers, screen_name='nasa')"
+    "paginator = tweepy.Paginator(\n",
+    "    client.get_users_followers, nasa.data.id, max_results=5)"
    ]
   },
   {
@@ -275,8 +277,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for account in cursor.items(10):\n",
-    "     followers.append(account.screen_name)\n"
+    "for follower in paginator.flatten(limit=10):\n",
+    "    followers.append(follower.username)"
    ]
   },
   {
@@ -286,15 +288,7 @@
    "outputs": [],
    "source": [
     "print('Followers:', \n",
-    "       ' '.join(sorted(followers, key=lambda s: s.lower())))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Automatic Paging\n",
-    "### Getting Follower IDs Rather Than Followers"
+    "      ' '.join(sorted(followers, key=lambda s: s.lower())))"
    ]
   },
   {
@@ -314,7 +308,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kepler_followers = []"
+    "nasa_mars_followers = []"
    ]
   },
   {
@@ -323,7 +317,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cursor = tweepy.Cursor(api.followers, screen_name='NASAKepler')"
+    "nasa_mars_followers_paginator = tweepy.Paginator(\n",
+    "    client.get_users_followers, nasa_mars.data.id, max_results=5)"
    ]
   },
   {
@@ -332,9 +327,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for account in cursor.items(10):\n",
-    "     kepler_followers.append(account.screen_name)\n",
-    "     "
+    "for follower in nasa_mars_followers_paginator.flatten(limit=10):\n",
+    "    nasa_mars_followers.append(follower.username)"
    ]
   },
   {
@@ -343,7 +337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(' '.join(kepler_followers))"
+    "print(' '.join(nasa_mars_followers))"
    ]
   },
   {
@@ -359,7 +353,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "friends = []"
+    "following = []"
    ]
   },
   {
@@ -368,7 +362,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cursor = tweepy.Cursor(api.friends, screen_name='nasa')"
+    "paginator = tweepy.Paginator(\n",
+    "    client.get_users_following, nasa.data.id, max_results=5)"
    ]
   },
   {
@@ -377,9 +372,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for friend in cursor.items(10):\n",
-    "     friends.append(friend.screen_name)\n",
-    "    "
+    "for user_followed in paginator.flatten(limit=10):\n",
+    "    following.append(user_followed.username)"
    ]
   },
   {
@@ -388,8 +382,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print('Friends:', \n",
-    "       ' '.join(sorted(friends, key=lambda s: s.lower())))"
+    "print('Following:', \n",
+    "      ' '.join(sorted(following, key=lambda s: s.lower())))"
    ]
   },
   {
@@ -405,7 +399,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "nasa_tweets = api.user_timeline(screen_name='nasa', count=3)"
+    "nasa_tweets = client.get_users_tweets(\n",
+    "    id=nasa.data.id, max_results=5)"
    ]
   },
   {
@@ -414,14 +409,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for tweet in nasa_tweets:\n",
-    "     print(f'{tweet.user.screen_name}: {tweet.text}\\n')\n",
-    "    "
+    "for tweet in nasa_tweets.data:\n",
+    "    print(f\"NASA: {tweet.data['text']}\\n\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "### Grabbing Recent Tweets from Your Own Timeline"
    ]
@@ -432,7 +429,7 @@
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
     "# 13.9.3 Self Check\n",
-    "**2. _(IPython Session)_** Get and display two tweets from the `NASAKepler` account.\n",
+    "**2. _(IPython Session)_** Get and display two tweets from the `NASAMars` account.\n",
     "\n",
     "**Answer:** "
    ]
@@ -443,8 +440,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kepler_tweets = api.user_timeline(\n",
-    "     screen_name='NASAKepler', count=2)     "
+    "nasa_mars_tweets = client.get_users_tweets(\n",
+    "    id=nasa_mars.data.id, max_results=5)"
    ]
   },
   {
@@ -453,16 +450,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for tweet in kepler_tweets:\n",
-    "     print(f'{tweet.user.screen_name}: {tweet.text}\\n') "
+    "for tweet in nasa_mars_tweets.data:\n",
+    "    print(f\"NASAMars: {tweet.data['text']}\\n\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 13.10 Searching Recent Tweets\n",
-    "### Tweet Printer"
+    "# 13.10 Searching Recent Tweets; Intro to Twitter v2 API Search Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Utility Function `print_tweets` from `tweetutilities.py`"
    ]
   },
   {
@@ -474,6 +477,29 @@
     "from tweetutilities import print_tweets"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "def print_tweets(tweets):\n",
+    "    # translator to autodetect source language and return English\n",
+    "    translator = GoogleTranslator(source='auto', target='en')\n",
+    "\n",
+    "    \"\"\"For each tweet in tweets, display the username of the sender\n",
+    "    and tweet text. If the language is not English, translate the text \n",
+    "    with the deep-translator library's GoogleTranslator.\"\"\"\n",
+    "    for tweet, user in zip(tweets.data, tweets.includes['users']):\n",
+    "        print(f'{user.username}:', end=' ')\n",
+    "\n",
+    "        if 'en' in tweet.lang:\n",
+    "            print(f'{tweet.text}\\n')\n",
+    "        elif 'und' not in tweet.lang: # translate to English first\n",
+    "            print(f'\\n  ORIGINAL: {tweet.text}')\n",
+    "            print(f'TRANSLATED: {translator.translate(tweet.text)}\\n')\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -487,7 +513,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tweets = api.search(q='Mars Opportunity Rover', count=3)"
+    "tweets = client.search_recent_tweets(\n",
+    "    query='Webb Space Telescope', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
    ]
   },
   {
@@ -503,7 +531,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Searching with Twitter Search Operators"
+    "### Searching with Twitter v2 API Search Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching with Twitter v2 API Search Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Operator Documentation and Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Searching for Tweets From NASA Containing Links"
    ]
   },
   {
@@ -512,7 +561,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tweets = api.search(q='from:nasa since:2018-09-01', count=3)"
+    "tweets = client.search_recent_tweets(\n",
+    "    query='from:NASA has:links', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
    ]
   },
   {
@@ -537,7 +588,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tweets = api.search(q='#collegefootball', count=20)"
+    "tweets = client.search_recent_tweets(query='#metaverse', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
    ]
   },
   {
@@ -555,7 +607,7 @@
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
     "# 13.10 Self Check\n",
-    "**3. _(IPython Session)_** Search for one tweet from the `nasa` account containing `'astronaut'`.\n",
+    "**3. _(IPython Session)_** Search for one tweet from the `NASA` account containing `'astronaut'`.\n",
     "\n",
     "**Answer:** "
    ]
@@ -566,7 +618,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tweets = api.search(q='astronaut from:nasa', count=1)"
+    "tweets = client.search_recent_tweets(query='from:nasa astronaut', \n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
    ]
   },
   {
@@ -582,8 +635,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 13.11 Spotting Trends with the Twitter Trends API\n",
-    "# 13.11.1 Places with Trending Topics"
+    "# 13.11 Spotting Trending Topics"
    ]
   },
   {
@@ -592,7 +644,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "trends_available = api.trends_available()"
+    "auth = tweepy.OAuth2BearerHandler(keys.bearer_token)"
    ]
   },
   {
@@ -601,7 +653,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(trends_available)"
+    "api = tweepy.API(auth=auth, wait_on_rate_limit=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 13.11.1 Places with Trending Topics\n",
+    "**Note: This part of the Twitter APIs has not been migrated from v1.1 to v2 yet and is accessible only to \"Elevated\" and \"Academic Research\" access.**"
    ]
   },
   {
@@ -610,7 +670,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "trends_available[0]"
+    "available_trends = api.available_trends()"
    ]
   },
   {
@@ -619,7 +679,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "trends_available[1]"
+    "len(available_trends)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "available_trends[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "available_trends[1]"
    ]
   },
   {
@@ -636,7 +714,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "world_trends = api.trends_place(id=1)"
+    "world_trends = api.get_place_trends(id=1)"
    ]
   },
   {
@@ -690,8 +768,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for trend in trends_list[:5]:\n",
-    "     print(trend['name'])"
+    "for trend in trends_list:\n",
+    "    print(trend['name'])"
    ]
   },
   {
@@ -704,10 +782,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "nyc_trends = api.trends_place(id=2459115)  # New York City WOEID"
+    "nyc_trends = api.get_place_trends(id=2459115) "
    ]
   },
   {
@@ -744,8 +824,7 @@
    "outputs": [],
    "source": [
     "for trend in nyc_list[:5]:\n",
-    "     print(trend['name'])\n",
-    "     "
+    "    print(trend['name'])"
    ]
   },
   {
@@ -765,7 +844,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "us_trends = api.trends_place(id='23424977')"
+    "us_trends = api.get_place_trends(id='23424977')"
    ]
   },
   {
@@ -802,7 +881,7 @@
    "outputs": [],
    "source": [
     "for trend in us_list[:3]:\n",
-    "     print(trend['name'])"
+    "    print(trend['name'])"
    ]
   },
   {
@@ -815,7 +894,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "topics = {}"
@@ -824,12 +905,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "for trend in nyc_list:\n",
-    "     topics[trend['name']] = trend['tweet_volume']\n",
-    "     "
+    "    topics[trend['name']] = trend['tweet_volume']"
    ]
   },
   {
@@ -848,9 +930,8 @@
    "outputs": [],
    "source": [
     "wordcloud = WordCloud(width=1600, height=900,\n",
-    "     prefer_horizontal=0.5, min_font_size=10, colormap='prism', \n",
-    "     background_color='white')\n",
-    "                       "
+    "    prefer_horizontal=0.5, min_font_size=10, colormap='prism', \n",
+    "    background_color='white')       "
    ]
   },
   {
@@ -871,6 +952,23 @@
     "wordcloud = wordcloud.to_file('TrendingTwitter.png')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE: The following code displays the image in a Jupyter Notebook**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='TrendingTwitter.png', width=400)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -899,8 +997,7 @@
    "outputs": [],
    "source": [
     "for trend in us_list:\n",
-    "     topics[trend['name']] = trend['tweet_volume']\n",
-    "     "
+    "    topics[trend['name']] = trend['tweet_volume']"
    ]
   },
   {
@@ -921,6 +1018,23 @@
     "wordcloud = wordcloud.to_file('USTrendingTwitter.png')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE: The following code displays the image in a Jupyter Notebook**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename='USTrendingTwitter.png', width=400)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -928,7 +1042,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -946,7 +1060,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -960,9 +1074,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_02-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_12.ipynb
similarity index 77%
rename from examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_02-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_12.ipynb
index a521551..3842953 100755
--- a/examples/ch11/snippets_ipynb/.ipynb_checkpoints/11_02-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_12.ipynb
@@ -4,14 +4,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 11.2 Factorials"
+    "# 13.12 Cleaning/Preprocessing Tweets for Analysis\n",
+    "### tweet-preprocessor Library and TextBlob Utility Functions\n",
+    "### Installing tweet-preprocessor\n",
+    "### Cleaning a Tweet"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Iterative Factorial Approach"
+    "import preprocessor as p"
    ]
   },
   {
@@ -20,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "factorial = 1"
+    "p.set_options(p.OPT.URL, p.OPT.RESERVED)"
    ]
   },
   {
@@ -29,8 +34,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for number in range(5, 0, -1):\n",
-    "    factorial *= number"
+    "tweet_text = 'RT A sample retweet with a URL https://nasa.gov'"
    ]
   },
   {
@@ -39,7 +43,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "factorial"
+    "p.clean(tweet_text)"
    ]
   },
   {
@@ -49,7 +53,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -67,7 +71,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -81,9 +85,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.02selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_12selfcheck.ipynb
old mode 100644
new mode 100755
similarity index 76%
rename from examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.02selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_12selfcheck.ipynb
index a244258..4344c2f
--- a/examples/ch06/snippets_ipynb/.ipynb_checkpoints/06.02.02selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_12selfcheck.ipynb
@@ -5,16 +5,16 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 6.2.2 Self Check"
+    "# 13.12 Self Check"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**1. _(Fill-In)_** Dictionary method `________` returns each key–value pair as a tuple.\n",
+    "**1. _(True/False)_** The tweet-preprocessor library can automatically remove URLs, `@`-mentions (like `@nasa`), hashtags (like `#mars`), Twitter reserved words (like, `RT` for retweet and `FAV` for favorite, which is similar to a “like” on other social networks), emojis (all or just smileys) and numbers, or any combination of these. \n",
     "\n",
-    "**Answer:** `items`."
+    "**Answer:** True."
    ]
   },
   {
@@ -24,7 +24,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -42,7 +42,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -56,9 +56,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.02-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_13_02.ipynb
old mode 100644
new mode 100755
similarity index 68%
rename from examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.02-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_13_02.ipynb
index 60896fb..e6424d7
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_13.02-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_13_02.ipynb
@@ -4,16 +4,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 10.13.2 Using the `Card` Data Class "
+    "# 13.13.2 Initiating Stream Processing"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from carddataclass import Card"
+    "### Authenticating\n",
+    "**Don't need to authenticate in advance for streaming. Just pass bearer-token as you create the StreamingClient subclass object.**"
    ]
   },
   {
@@ -22,7 +21,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1 = Card(Card.FACES[0], Card.SUITS[3])"
+    "import tweepy"
    ]
   },
   {
@@ -31,16 +30,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1"
+    "import keys"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "print(c1)"
+    "### Creating a `TweetListener` "
    ]
   },
   {
@@ -49,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1.face"
+    "from tweetlistener import TweetListener"
    ]
   },
   {
@@ -58,7 +55,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1.suit"
+    "tweet_listener = TweetListener(\n",
+    "    bearer_token=keys.bearer_token, limit=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Redirecting Standard Error Stream to Standard Output Stream"
    ]
   },
   {
@@ -67,7 +74,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1.image_name"
+    "import sys"
    ]
   },
   {
@@ -76,7 +83,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c2 = Card(Card.FACES[0], Card.SUITS[3])"
+    "sys.stderr = sys.stdout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Deleting Existing Stream Rules"
    ]
   },
   {
@@ -85,7 +101,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c2"
+    "rules = tweet_listener.get_rules().data"
    ]
   },
   {
@@ -94,7 +110,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c3 = Card(Card.FACES[0], Card.SUITS[0])"
+    "rule_ids = [rule.id for rule in rules]"
    ]
   },
   {
@@ -103,16 +119,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c3"
+    "tweet_listener.delete_rules(rule_ids)    "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "c1 == c2"
+    "### Creating and Adding a Stream Rule"
    ]
   },
   {
@@ -121,25 +135,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c1 == c3"
+    "filter_rule = tweepy.StreamRule('football')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "c1 != c3"
+    "tweet_listener.add_rules(filter_rule)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from deck2 import DeckOfCards  # uses Card data class"
+    "### Starting the Tweet Stream"
    ]
   },
   {
@@ -148,16 +162,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "deck_of_cards = DeckOfCards()"
+    "tweet_listener.filter(\n",
+    "    expansions=['author_id'], tweet_fields=['lang'])"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "print(deck_of_cards)"
+    "### Asynchronous vs. Synchronous Streams"
    ]
   },
   {
@@ -167,7 +180,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -185,7 +198,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -199,9 +212,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.05selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_13_02selfcheck.ipynb
similarity index 71%
rename from examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.05selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_13_02selfcheck.ipynb
index 6fd4d9d..1d0fefa 100755
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/09_12.05selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_13_02selfcheck.ipynb
@@ -5,29 +5,20 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 9.12.5 Self Check"
+    "# 13.13.2 Self Check"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**1. _(Fill-In)_** Pandas function `________` loads a CSV dataset from a URL or the local file system into a `DataFrame`.\n",
+    "**1. _(Fill-In)_** Rather than connecting to Twitter on each method call, a stream uses a persistent connection to `________` (that is, send) tweets to your app. \n",
     "\n",
-    "**Answer:** `read_csv`.\n",
+    "**Answer:**  push.\n",
     "\n",
-    "**2. _(IPython Session)_** Load the `grades.csv` file you created in the preceding section’s Self Check into a `DataFrame` then display it.\n",
+    "**2. _(True/False)_** Twitter’s free Streaming API sends to your app randomly selected tweets dynamically as they occur—up to a maximum of ten percent of the tweets per day. \n",
     "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.read_csv('grades.csv', names=['ID', 'Name', 'Grade'])"
+    "**Answer:** False. Twitter’s Streaming API sends to your app randomly selected tweets dynamically as they occur—up to a maximum of one percent of the tweets per day. "
    ]
   },
   {
@@ -37,7 +28,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -55,7 +46,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -69,9 +60,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_14-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_14.ipynb
similarity index 55%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_14-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_14.ipynb
index 9e64b3c..946714f 100644
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_14-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_14.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 14.14 Tweet Sentiment Analysis \n",
+    "# 13.14 Tweet Sentiment Analysis \n",
     "\n",
     "**NOTE This script has been modified from what we presented in the chapter to accomodate executing it in a notebook** "
    ]
@@ -16,65 +16,60 @@
    "outputs": [],
    "source": [
     "# sentimentlisener.py\n",
-    "\"\"\"Script that searches for tweets that match a search string\n",
-    "and tallies the number of positive, neutral and negative tweets.\"\"\"\n",
+    "\"\"\"Searches for tweets that match a search string and tallies \n",
+    "the number of positive, neutral and negative tweets.\"\"\"\n",
     "import keys\n",
     "import preprocessor as p \n",
     "import sys\n",
     "from textblob import TextBlob\n",
     "import tweepy\n",
     "\n",
-    "class SentimentListener(tweepy.StreamListener):\n",
+    "class SentimentListener(tweepy.StreamingClient):\n",
     "    \"\"\"Handles incoming Tweet stream.\"\"\"\n",
     "\n",
-    "    def __init__(self, api, sentiment_dict, topic, limit=10):\n",
+    "    def __init__(self, bearer_token, sentiment_dict, topic, limit=10):\n",
     "        \"\"\"Configure the SentimentListener.\"\"\"\n",
     "        self.sentiment_dict = sentiment_dict\n",
     "        self.tweet_count = 0\n",
     "        self.topic = topic\n",
     "        self.TWEET_LIMIT = limit\n",
-    "\n",
+    "        \n",
     "        # set tweet-preprocessor to remove URLs/reserved words\n",
     "        p.set_options(p.OPT.URL, p.OPT.RESERVED) \n",
-    "        super().__init__(api)  # call superclass's init\n",
+    "        super().__init__(bearer_token, wait_on_rate_limit=True)\n",
     "\n",
-    "    def on_status(self, status):\n",
+    "    def on_response(self, response):\n",
     "        \"\"\"Called when Twitter pushes a new tweet to you.\"\"\"\n",
-    "        # get the tweet's text\n",
-    "        try:  \n",
-    "            tweet_text = status.extended_tweet.full_text\n",
-    "        except: \n",
-    "            tweet_text = status.text\n",
-    "\n",
-    "        # ignore retweets \n",
-    "        if tweet_text.startswith('RT'):\n",
-    "            return\n",
-    "\n",
-    "        tweet_text = p.clean(tweet_text)  # clean the tweet\n",
-    "        \n",
-    "        # ignore tweet if the topic is not in the tweet text\n",
-    "        if self.topic.lower() not in tweet_text.lower():\n",
-    "            return\n",
-    "\n",
-    "        # update self.sentiment_dict with the polarity\n",
-    "        blob = TextBlob(tweet_text)\n",
-    "        if blob.sentiment.polarity > 0:\n",
-    "            sentiment = '+'\n",
-    "            self.sentiment_dict['positive'] += 1 \n",
-    "        elif blob.sentiment.polarity == 0:\n",
-    "            sentiment = ' '\n",
-    "            self.sentiment_dict['neutral'] += 1 \n",
-    "        else:\n",
-    "            sentiment = '-'\n",
-    "            self.sentiment_dict['negative'] += 1 \n",
-    "            \n",
-    "        # display the tweet\n",
-    "        print(f'{sentiment} {status.user.screen_name}: {tweet_text}\\n')\n",
     "        \n",
-    "        self.tweet_count += 1  # track number of tweets processed\n",
-    "\n",
-    "        # if TWEET_LIMIT is reached, return False to terminate streaming\n",
-    "        return self.tweet_count != self.TWEET_LIMIT"
+    "        # if the tweet is not a retweet\n",
+    "        if not response.data.text.startswith('RT'):\n",
+    "            text = p.clean(response.data.text) # clean the tweet\n",
+    "\n",
+    "            # ignore tweet if the topic is not in the tweet text\n",
+    "            if self.topic.lower() not in text.lower():\n",
+    "                return\n",
+    "\n",
+    "            # update self.sentiment_dict with the polarity\n",
+    "            blob = TextBlob(text)\n",
+    "            if blob.sentiment.polarity > 0:\n",
+    "                sentiment = '+'\n",
+    "                self.sentiment_dict['positive'] += 1 \n",
+    "            elif blob.sentiment.polarity == 0:\n",
+    "                sentiment = ' '\n",
+    "                self.sentiment_dict['neutral'] += 1 \n",
+    "            else:\n",
+    "                sentiment = '-'\n",
+    "                self.sentiment_dict['negative'] += 1 \n",
+    "\n",
+    "            # display the tweet\n",
+    "            username = response.includes['users'][0].username\n",
+    "            print(f'{sentiment} {username}: {text}\\n')\n",
+    "\n",
+    "            self.tweet_count += 1 # track number of tweets processed\n",
+    "\n",
+    "            # if TWEET_LIMIT is reached, terminate streaming\n",
+    "            if self.tweet_count == self.TWEET_LIMIT:\n",
+    "                self.disconnect()"
    ]
   },
   {
@@ -84,26 +79,31 @@
    "outputs": [],
    "source": [
     "#def main():\n",
-    "# configure the OAuthHandler\n",
-    "auth = tweepy.OAuthHandler(keys.consumer_key, keys.consumer_secret)\n",
-    "auth.set_access_token(keys.access_token, keys.access_token_secret)\n",
-    "\n",
-    "# get the API object\n",
-    "api = tweepy.API(auth, wait_on_rate_limit=True, \n",
-    "                 wait_on_rate_limit_notify=True)\n",
-    "                 \n",
-    "# create the StreamListener subclass object\n",
+    "# get search term and number of tweets\n",
     "search_key = 'football' #sys.argv[1]\n",
-    "limit = 10 #int(sys.argv[2])  # number of tweets to tally\n",
+    "limit = 10 #int(sys.argv[2]) # number of tweets to tally\n",
+    "\n",
+    "# set up the sentiment dictionary\n",
     "sentiment_dict = {'positive': 0, 'neutral': 0, 'negative': 0}\n",
-    "sentiment_listener = SentimentListener(api, \n",
+    "\n",
+    "# create the StreamingClient subclass object\n",
+    "sentiment_listener = SentimentListener(keys.bearer_token, \n",
     "    sentiment_dict, search_key, limit)\n",
     "\n",
-    "# set up Stream \n",
-    "stream = tweepy.Stream(auth=api.auth, listener=sentiment_listener)\n",
+    "# redirect sys.stderr to sys.stdout\n",
+    "sys.stderr = sys.stdout\n",
+    "\n",
+    "# delete existing stream rules\n",
+    "rules = sentiment_listener.get_rules().data\n",
+    "rule_ids = [rule.id for rule in rules]\n",
+    "sentiment_listener.delete_rules(rule_ids)    \n",
+    "\n",
+    "# create stream rule\n",
+    "sentiment_listener.add_rules(\n",
+    "    tweepy.StreamRule(f'{search_key} lang:en'))\n",
     "\n",
     "# start filtering English tweets containing search_key\n",
-    "stream.filter(track=[search_key], languages=['en'], is_async=False)  \n",
+    "sentiment_listener.filter(expansions=['author_id'])\n",
     "\n",
     "print(f'Tweet sentiment for \"{search_key}\"')\n",
     "print('Positive:', sentiment_dict['positive'])\n",
@@ -114,9 +114,8 @@
     "#if __name__ == '__main__':\n",
     "#    main()\n",
     "\n",
-    "\n",
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -134,7 +133,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -148,9 +147,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_15_01.ipynb
similarity index 75%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_15_01.ipynb
index 8bb47f0..ceeaa02 100755
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/14_15-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_15_01.ipynb
@@ -4,8 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 14.15.1 Getting and Mapping the Tweets\n",
-    "### Get the `API` Object"
+    "# 13.15.1 Getting and Mapping the Tweets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Collections Required By `LocationListener`"
    ]
   },
   {
@@ -14,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tweetutilities import get_API"
+    "tweets = [] "
    ]
   },
   {
@@ -23,14 +29,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "api = get_API()"
+    "counts = {'total_tweets': 0, 'locations': 0}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Collections Required By `LocationListener`"
+    "### Creating the `LocationListener` "
    ]
   },
   {
@@ -39,7 +45,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tweets = [] "
+    "import keys"
    ]
   },
   {
@@ -48,14 +54,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "counts = {'total_tweets': 0, 'locations': 0}"
+    "import tweepy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from locationlistener import LocationListener"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "location_listener = LocationListener(\n",
+    "    keys.bearer_token, counts_dict=counts, tweets_list=tweets,\n",
+    "    topic='football', limit=50)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Creating the `LocationListener` "
+    "### Redirect sys.stderr to sys.stdout"
    ]
   },
   {
@@ -64,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from locationlistener import LocationListener"
+    "import sys"
    ]
   },
   {
@@ -73,15 +99,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "location_listener = LocationListener(api, counts_dict=counts, \n",
-    "    tweets_list=tweets, topic='football', limit=50)"
+    "sys.stderr = sys.stdout"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Configure and Start the Stream of Tweets"
+    "### Delete Existing `StreamRule`s"
    ]
   },
   {
@@ -90,7 +115,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tweepy"
+    "rules = location_listener.get_rules().data"
    ]
   },
   {
@@ -99,7 +124,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stream = tweepy.Stream(auth=api.auth, listener=location_listener)"
+    "rule_ids = [rule.id for rule in rules]"
    ]
   },
   {
@@ -108,14 +133,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stream.filter(track=['football'], languages=['en'], async=False)  "
+    "location_listener.delete_rules(rule_ids)    "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Displaying the Location Statistics"
+    "### Create a `StreamRule`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "location_listener.add_rules(\n",
+    "    tweepy.StreamRule('football lang:en'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Configure and Start the Stream of Tweets"
    ]
   },
   {
@@ -123,6 +167,25 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "location_listener.filter(expansions=['author_id'], \n",
+    "    user_fields=['location'], tweet_fields=['lang'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Displaying the Location Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "counts['total_tweets']"
    ]
@@ -252,8 +315,7 @@
    "outputs": [],
    "source": [
     "usmap = folium.Map(location=[39.8283, -98.5795], \n",
-    "                    zoom_start=5, detect_retina=True)\n",
-    "                    "
+    "    tiles='Stamen Terrain', zoom_start=5, detect_retina=True)        "
    ]
   },
   {
@@ -270,7 +332,7 @@
    "outputs": [],
    "source": [
     "for t in df.itertuples():\n",
-    "     text = ': '.join([t.screen_name, t.text])\n",
+    "     text = ': '.join([t.username, t.text])\n",
     "     popup = folium.Popup(text, parse_html=True)\n",
     "     marker = folium.Marker((t.latitude, t.longitude), \n",
     "                            popup=popup)\n",
@@ -293,6 +355,24 @@
     "usmap.save('tweet_map.html')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE: We added the following cell to display the map in the Jupyter Notebook.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "usmap"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -300,7 +380,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -318,7 +398,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -332,9 +412,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_03selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_15_01selfcheck.ipynb
old mode 100755
new mode 100644
similarity index 76%
rename from examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_03selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_15_01selfcheck.ipynb
index 949c280..78f2990
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_03selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_15_01selfcheck.ipynb
@@ -5,16 +5,18 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.3 Self Check"
+    "# 13.15.1 Self Check"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**1. _(True/False)_** Like most object-oriented programming languages, Python provides capabilities for encapsulating an object’s data attributes so client code cannot access the data directly.\n",
+    "**1. _(Fill-In)_** The folium classes `________` and `________` enable you to mark locations on a map and add text that displays when the user clicks a marked location.\n",
+    "**Answer:** `Marker`, `Popup`.\n",
     "\n",
-    "**Answer:** False. In Python, all data attributes are accessible. You use attribute naming conventions to indicate that attributes should not be accessed directly from client code. \n"
+    "**2. _(Fill-In)_** Pandas DataFrame method `________` creates an iterator for accessing the rows of a DataFrame as tuples.\n",
+    "**Answer:** `itertuples`.\n"
    ]
   },
   {
@@ -24,7 +26,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -42,7 +44,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -56,9 +58,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_15-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_15_02selfcheck.ipynb
old mode 100644
new mode 100755
similarity index 76%
rename from examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_15-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_15_02selfcheck.ipynb
index 340e427..8a78832
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_15-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_15_02selfcheck.ipynb
@@ -4,20 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 10.15 Namespaces and Scopes\n",
-    "### Local Namespace\n",
-    "### Global Namespace\n",
-    "### Built-In Namespace\n",
-    "### Finding Identifiers in Namespaces"
+    "![Self Check Exercises check mark image](files/art/check.png)\n",
+    "# 13.15.2 Self Check"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "z = 'global z'"
+    "**1. _(IPython Session)_** Use an OpenMapQuest geocoding object to get the latitude and Longitude for Chicago, IL.\n",
+    "\n",
+    "**Answer:** "
    ]
   },
   {
@@ -26,10 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def print_variables():\n",
-    "    y = 'local y in print_variables'\n",
-    "    print(y)\n",
-    "    print(z)"
+    "import keys"
    ]
   },
   {
@@ -38,7 +32,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print_variables()"
+    "from geopy import OpenMapQuest"
    ]
   },
   {
@@ -47,7 +41,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y"
+    "geo = OpenMapQuest(api_key=keys.mapquest_key)"
    ]
   },
   {
@@ -56,16 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "z"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Nested Functions\n",
-    "### Class Namespace\n",
-    "### Object Namespace"
+    "geo.geocode('Chicago, IL')"
    ]
   },
   {
@@ -75,7 +60,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -93,7 +78,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -107,9 +92,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.03selfcheck-checkpoint.ipynb b/examples/ch13_TwitterV2/snippets_ipynb/13_15selfcheck.ipynb
old mode 100644
new mode 100755
similarity index 76%
rename from examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.03selfcheck-checkpoint.ipynb
rename to examples/ch13_TwitterV2/snippets_ipynb/13_15selfcheck.ipynb
index d990f47..7705012
--- a/examples/ch10/snippets_ipynb/.ipynb_checkpoints/10_04.03selfcheck-checkpoint.ipynb
+++ b/examples/ch13_TwitterV2/snippets_ipynb/13_15selfcheck.ipynb
@@ -5,21 +5,20 @@
    "metadata": {},
    "source": [
     "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "# 10.4.3 Self Check"
+    "# 13.15 Self Check"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**1. _(Fill-In)_** A class’s `________` is the set of public properties and methods programmers should use to interact with objects of the class.\n",
+    "**1. _(Fill-In)_** The geopy library enables you to translate locations into latitude and longitude coordinates, known as `________`, so you can plot locations on a map. \n",
     "\n",
-    "**Answer:** interface. \n",
+    "**Answer:** geocoding\n",
     "\n",
-    "**2. _(Fill-In)_** A class’s `________` methods are used only inside the class and are not intended to be used by client code. \n",
+    "**2. _(Fill-In)_** The OpenMapQuest Geocoding API converts locations, like Boston, MA into their `________` and `________` for plotting on maps.\n",
     "\n",
-    "**Answer:** utility.\n",
-    "\n"
+    "**Answer:** latitudes, longitudes."
    ]
   },
   {
@@ -29,7 +28,7 @@
    "outputs": [],
    "source": [
     "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
+    "# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #\n",
     "# Pearson Education, Inc. All Rights Reserved.                           #\n",
     "#                                                                        #\n",
     "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
@@ -47,7 +46,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -61,9 +60,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/README.txt b/examples/ch13_TwitterV2/snippets_ipynb/README.txt
new file mode 100755
index 0000000..ac3297c
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/README.txt
@@ -0,0 +1,2 @@
+Sections 13.7-11 use one continuous IPython session for the examples 
+and Self Check exercises, so all of these are in one notebook.
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/files/art/.ipynb_checkpoints/check-checkpoint.png b/examples/ch13_TwitterV2/snippets_ipynb/files/art/.ipynb_checkpoints/check-checkpoint.png
new file mode 100755
index 0000000..eea18cd
Binary files /dev/null and b/examples/ch13_TwitterV2/snippets_ipynb/files/art/.ipynb_checkpoints/check-checkpoint.png differ
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/files/art/check.png b/examples/ch13_TwitterV2/snippets_ipynb/files/art/check.png
new file mode 100755
index 0000000..eea18cd
Binary files /dev/null and b/examples/ch13_TwitterV2/snippets_ipynb/files/art/check.png differ
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/keys.py b/examples/ch13_TwitterV2/snippets_ipynb/keys.py
new file mode 100644
index 0000000..9fcffad
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/keys.py
@@ -0,0 +1,8 @@
+#consumer_key = 'NVqJ06iaqBbyIQDuxUUCiM5gd'
+#consumer_secret = 'fyVLUOxYMH6GSy8x7aA1OKLfhqUeRst4bnRMTImN1ASym0cgI9'
+#access_token = '24867870-4rhn9y3OzIK9OBu57QMBVHQWKZfMxWRxPZTJIIsKX'
+#access_token_secret = '3lv07LC30CKyzOjIEJiK1P5pdk94KIRTduFPqnIBU8LXb'
+
+mapquest_key = 'L0xOBy7HqIWsNgBvZvgVZx9HX1GSYzri'
+#deep_translator_key = 'de06403b5fda5f896b9b7c23df3e7c0a'
+bearer_token = 'AAAAAAAAAAAAAAAAAAAAAKKybwEAAAAAzvQFdKaTNAYKLzB1RX7Pmz25yJg%3DgNB7yVp00rLtwsmETSvoePoc2ymdOYfZb54Mw8A4LqwpYlTC2X'
\ No newline at end of file
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/keys_empty.py b/examples/ch13_TwitterV2/snippets_ipynb/keys_empty.py
new file mode 100755
index 0000000..bf7c356
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/keys_empty.py
@@ -0,0 +1,2 @@
+bearer_token = 'YourBearerToken'
+mapquest_key = 'YourAPIKey'
\ No newline at end of file
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/locationlistener.py b/examples/ch13_TwitterV2/snippets_ipynb/locationlistener.py
new file mode 100755
index 0000000..f154d61
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/locationlistener.py
@@ -0,0 +1,59 @@
+# locationlistener.py
+"""Receives tweets matching a search string and stores a list of
+dictionaries containing each tweet's username/text/location."""
+import tweepy
+from tweetutilities import get_tweet_content
+
+class LocationListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream to get location data."""
+
+    def __init__(self, bearer_token, counts_dict, 
+                 tweets_list, topic, limit=10):
+        """Configure the LocationListener."""
+        self.tweets_list = tweets_list
+        self.counts_dict = counts_dict
+        self.topic = topic
+        self.TWEET_LIMIT = limit
+        super().__init__(bearer_token, wait_on_rate_limit=True) 
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+
+        # get tweet's username, text and location
+        tweet_data = get_tweet_content(response)  
+        
+        # ignore retweets and tweets that do not contain the topic
+        if (tweet_data['text'].startswith('RT') or
+            self.topic.lower() not in tweet_data['text'].lower()):
+            return
+
+        self.counts_dict['total_tweets'] += 1 # it's an original tweet
+
+        # ignore tweets with no location
+        if not tweet_data.get('location'):  
+            return
+
+        self.counts_dict['locations'] += 1 # user account has location
+        self.tweets_list.append(tweet_data) # store the tweet
+        print(f"{tweet_data['username']}: {tweet_data['text']}\n")
+
+        # if TWEET_LIMIT is reached, terminate streaming
+        if self.counts_dict['locations'] == self.TWEET_LIMIT:
+            self.disconnect()
+
+
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/sentimentlistener.py b/examples/ch13_TwitterV2/snippets_ipynb/sentimentlistener.py
new file mode 100755
index 0000000..2cc45ec
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/sentimentlistener.py
@@ -0,0 +1,106 @@
+# sentimentlisener.py
+"""Searches for tweets that match a search string and tallies 
+the number of positive, neutral and negative tweets."""
+import keys
+import preprocessor as p 
+import sys
+from textblob import TextBlob
+import tweepy
+
+class SentimentListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream."""
+
+    def __init__(self, bearer_token, sentiment_dict, topic, limit=10):
+        """Configure the SentimentListener."""
+        self.sentiment_dict = sentiment_dict
+        self.tweet_count = 0
+        self.topic = topic
+        self.TWEET_LIMIT = limit
+        
+        # set tweet-preprocessor to remove URLs/reserved words
+        p.set_options(p.OPT.URL, p.OPT.RESERVED) 
+        super().__init__(bearer_token, wait_on_rate_limit=True)
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+
+        # if the tweet is not a retweet
+        if not response.data.text.startswith('RT'):
+            text = p.clean(response.data.text) # clean the tweet
+
+            # ignore tweet if the topic is not in the tweet text
+            if self.topic.lower() not in text.lower():
+                return
+
+            # update self.sentiment_dict with the polarity
+            blob = TextBlob(text)
+            if blob.sentiment.polarity > 0:
+                sentiment = '+'
+                self.sentiment_dict['positive'] += 1 
+            elif blob.sentiment.polarity == 0:
+                sentiment = ' '
+                self.sentiment_dict['neutral'] += 1 
+            else:
+                sentiment = '-'
+                self.sentiment_dict['negative'] += 1 
+
+            # display the tweet
+            username = response.includes['users'][0].username
+            print(f'{sentiment} {username}: {text}\n')
+
+            self.tweet_count += 1 # track number of tweets processed
+
+            # if TWEET_LIMIT is reached, terminate streaming
+            if self.tweet_count == self.TWEET_LIMIT:
+                self.disconnect()
+
+def main():
+    # get search term and number of tweets
+    search_key = sys.argv[1]
+    limit = int(sys.argv[2]) # number of tweets to tally
+    
+    # set up the sentiment dictionary
+    sentiment_dict = {'positive': 0, 'neutral': 0, 'negative': 0}
+
+    # create the StreamingClient subclass object
+    sentiment_listener = SentimentListener(keys.bearer_token, 
+        sentiment_dict, search_key, limit)
+
+    # redirect sys.stderr to sys.stdout
+    sys.stderr = sys.stdout
+
+    # delete existing stream rules
+    rules = sentiment_listener.get_rules().data
+    rule_ids = [rule.id for rule in rules]
+    sentiment_listener.delete_rules(rule_ids)    
+
+    # create stream rule
+    sentiment_listener.add_rules(
+        tweepy.StreamRule(f'{search_key} lang:en'))
+    
+    # start filtering English tweets containing search_key
+    sentiment_listener.filter(expansions=['author_id'])
+
+    print(f'Tweet sentiment for "{search_key}"')
+    print('Positive:', sentiment_dict['positive'])
+    print(' Neutral:', sentiment_dict['neutral'])
+    print('Negative:', sentiment_dict['negative'])
+
+# call main if this file is executed as a script
+if __name__ == '__main__':
+    main()
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13_TwitterV2/snippets_ipynb/tweetlistener.py b/examples/ch13_TwitterV2/snippets_ipynb/tweetlistener.py
new file mode 100755
index 0000000..b01654d
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_ipynb/tweetlistener.py
@@ -0,0 +1,61 @@
+# tweetlistener.py
+"""StreamListener subclass that processes tweets as they arrive."""
+from deep_translator import GoogleTranslator
+import tweepy
+
+class TweetListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream."""
+
+    def __init__(self, bearer_token, limit=10):
+        """Create instance variables for tracking number of tweets."""
+        self.tweet_count = 0
+        self.TWEET_LIMIT = limit
+        
+        # GoogleTranslator object for translating tweets to English 
+        self.translator = GoogleTranslator(source='auto', target='en')
+
+        super().__init__(bearer_token, wait_on_rate_limit=True)  
+
+    def on_connect(self):
+        """Called when your connection attempt is successful, enabling 
+        you to perform appropriate application tasks at that point."""
+        print('Connection successful\n')
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+        
+        try:
+            # get username of user who sent the tweet
+            username = response.includes['users'][0].username
+            print(f'Screen name: {username}')
+            print(f'   Language: {response.data.lang}')
+            print(f' Tweet text: {response.data.text}')
+
+            if response.data.lang != 'en' and response.data.lang != 'und':
+                english = self.translator.translate(response.data.text)
+                print(f' Translated: {english}')
+
+            print()
+            self.tweet_count += 1 
+        except Exception as e:
+            print(f'Exception occured: {e}')
+            self.disconnect()
+            
+        # if TWEET_LIMIT is reached, terminate streaming
+        if self.tweet_count == self.TWEET_LIMIT:
+            self.disconnect()
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13/snippets_py/.ipynb_checkpoints/tweetutilities-checkpoint.py b/examples/ch13_TwitterV2/snippets_ipynb/tweetutilities.py
similarity index 65%
rename from examples/ch13/snippets_py/.ipynb_checkpoints/tweetutilities-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_ipynb/tweetutilities.py
index eb84c33..f5a12ab 100755
--- a/examples/ch13/snippets_py/.ipynb_checkpoints/tweetutilities-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_ipynb/tweetutilities.py
@@ -1,47 +1,33 @@
 # tweetutilities.py
 """Utility functions for interacting with Tweepy objects."""
+from deep_translator import GoogleTranslator
 from geopy import OpenMapQuest
 import keys
-from textblob import TextBlob 
 import time 
 import tweepy
 
-def get_API(wait=True, notify=True):
-    """Authenticate with Twitter and return API object."""
-    # configure the OAuthHandler
-    auth = tweepy.OAuthHandler(keys.consumer_key, keys.consumer_secret)
-    auth.set_access_token(keys.access_token, keys.access_token_secret)
-
-    # get the API object
-    return tweepy.API(auth, wait_on_rate_limit=wait, 
-                      wait_on_rate_limit_notify=notify)
-
 def print_tweets(tweets):
-    """For each Tweepy Status object in tweets, display the 
-    user's screen_name and tweet text. If the language is not
-    English, translate the text with TextBlob."""
-    for tweet in tweets:
-        print(f'{tweet.user.screen_name}:', end=' ')
+    # translator to autodetect source language and return English
+    translator = GoogleTranslator(source='auto', target='en')
     
+    """For each tweet in tweets, display the username of the sender
+    and tweet text. If the language is not English, translate the text 
+    with Deep Translator."""
+    for tweet, user in zip(tweets.data, tweets.includes['users']):
+        print(f'{user.username}:', end=' ')
+
         if 'en' in tweet.lang:
             print(f'{tweet.text}\n')
-        elif 'und' not in tweet.lang:  # translate to English first
+        elif 'und' not in tweet.lang: # translate to English first
             print(f'\n  ORIGINAL: {tweet.text}')
-            print(f'TRANSLATED: {TextBlob(tweet.text).translate()}\n')
+            print(f'TRANSLATED: {translator.translate(tweet.text)}\n')
 
-def get_tweet_content(tweet, location=False):
-    """Return dictionary with data from tweet (a Status object)."""
+def get_tweet_content(response):
+    """Return dictionary with data from tweet."""
     fields = {}
-    fields['screen_name'] = tweet.user.screen_name
-
-    # get the tweet's text
-    try:  
-        fields['text'] = tweet.extended_tweet.full_text
-    except: 
-        fields['text'] = tweet.text
-
-    if location:
-        fields['location'] = tweet.user.location
+    fields['username'] = response.includes['users'][0].username
+    fields['text'] = response.data.text
+    fields['location'] = response.includes['users'][0].location
 
     return fields
 
@@ -75,7 +61,7 @@ def get_geocodes(tweet_list):
 
 
 ##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
 # Pearson Education, Inc. All Rights Reserved.                           #
 #                                                                        #
 # DISCLAIMER: The authors and publisher of this book have used their     #
diff --git a/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.py
new file mode 100755
index 0000000..81ee54d
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_07-11withSelfChecks-checkpoint.py
@@ -0,0 +1,246 @@
+# Section 13.7-13.11 snippets with Self Checks
+# because those sections are one running IPython session
+
+# 13.7 Authenticating with Twitter Via Tweepy to Access Twitter v2 APIs
+import tweepy
+import keys
+
+# Creating a Client Object
+client = tweepy.Client(bearer_token=keys.bearer_token,
+                       wait_on_rate_limit=True)
+
+#13.8 Getting Information About a Twitter Account
+nasa = client.get_user(username='NASA', 
+    user_fields=['description', 'public_metrics'])
+
+# tweepy.Response Object
+# Getting a User’s Basic Account Information
+nasa.data.id
+
+nasa.data.name
+
+nasa.data.username
+
+nasa.data.description
+
+# Getting the Number of Accounts That Follow This Account and the Number of Accounts This Account Follows
+nasa.data.public_metrics['followers_count']
+
+nasa.data.public_metrics['following_count']
+
+# Getting Your Own Account’s Information
+
+# 13.8 Self Check
+# Exercise 2
+nasa_mars = client.get_user(username='NASAMars', 
+    user_fields=['description', 'public_metrics'])
+
+nasa_mars.data.id
+
+nasa_mars.data.name
+
+nasa_mars.data.username
+
+nasa_mars.data.description
+
+nasa_mars.data.public_metrics['followers_count']
+
+# 13.9 Intro to Tweepy Paginators: Getting More than One Page of Results
+# 13.9.1 Determining an Account’s Followers
+followers = []
+
+# Creating a Paginator
+paginator = tweepy.Paginator(
+    client.get_users_followers, nasa.data.id, max_results=5)
+
+# Getting Results
+for follower in paginator.flatten(limit=10):
+    followers.append(follower.username)
+    
+print('Followers:', 
+      ' '.join(sorted(followers, key=lambda s: s.lower())))
+
+# 13.9.1 Self Check
+# Exercise 3. 
+nasa_mars_followers = []
+
+nasa_mars_followers_paginator = tweepy.Paginator(
+    client.get_users_followers, nasa_mars.data.id, max_results=5)
+
+for follower in nasa_mars_followers_paginator.flatten(limit=10):
+    nasa_mars_followers.append(follower.username)
+    
+print(' '.join(nasa_mars_followers))
+
+# 13.9.2 Determining Whom an Account Follows
+following = []
+
+paginator = tweepy.Paginator(
+    client.get_users_following, nasa.data.id, max_results=5)
+
+for user_followed in paginator.flatten(limit=10):
+    following.append(user_followed.username)
+    
+print('Following:', 
+      ' '.join(sorted(following, key=lambda s: s.lower())))
+
+
+# 13.9.3 Getting a User’s Recent Tweets
+nasa_tweets = client.get_users_tweets(
+    id=nasa.data.id, max_results=5)
+
+for tweet in nasa_tweets.data:
+    print(f"NASA: {tweet.data['text']}\n")
+
+# Grabbing Recent Tweets from Your Own Timeline
+# 13.9.3 Self Check
+# Exercise 2
+nasa_mars_tweets = client.get_users_tweets(
+    id=nasa_mars.data.id, max_results=5)
+
+for tweet in nasa_mars_tweets.data:
+    print(f"NASAMars: {tweet.data['text']}\n")
+
+# 13.10 Searching Recent Tweets; Intro to Twitter v2 API Search Operators
+# Utility Function print_tweets from tweetutilities.py
+from tweetutilities import print_tweets
+
+# Searching for Specific Words
+tweets = client.search_recent_tweets(
+    query='Webb Space Telescope', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# Searching with Twitter v2 API Search Operators
+
+# Operator Documentation and Tutorial
+
+# Searching for Tweets From NASA Containing Links
+tweets = client.search_recent_tweets(
+    query='from:NASA has:links', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# Searching for a Hashtag
+tweets = client.search_recent_tweets(query='#metaverse', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# 13.10 Self Check
+# Exercise 
+tweets = client.search_recent_tweets(query='from:nasa astronaut', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# 13.11 Spotting Trending Topics
+auth = tweepy.OAuth2BearerHandler(keys.bearer_token)
+
+api = tweepy.API(auth=auth, wait_on_rate_limit=True)
+
+# 13.11.1 Places with Trending Topics
+# Note: This part of the Twitter APIs has not been migrated from v1.1 to v2
+# yet and is accessible only to "Elevated" and "Academic Research" access.
+available_trends = api.available_trends()
+
+len(available_trends)
+
+available_trends[0]
+
+available_trends[1]
+
+# 13.11.2 Getting a List of Trending Topics
+# Worldwide Trending Topics
+world_trends = api.get_place_trends(id=1)
+
+trends_list = world_trends[0]['trends']
+
+trends_list[0]
+
+trends_list = [t for t in trends_list if t['tweet_volume']]
+
+from operator import itemgetter 
+trends_list.sort(key=itemgetter('tweet_volume'), reverse=True) 
+
+for trend in trends_list:
+    print(trend['name'])
+
+# New York City Trending Topics
+nyc_trends = api.get_place_trends(id=2459115) 
+
+nyc_list = nyc_trends[0]['trends']
+
+nyc_list = [t for t in nyc_list if t['tweet_volume']]
+
+nyc_list.sort(key=itemgetter('tweet_volume'), reverse=True) 
+
+for trend in nyc_list[:5]:
+    print(trend['name'])
+
+# 13.11.2 Self Check
+Exercise 3
+us_trends = api.get_place_trends(id='23424977')
+
+us_list = us_trends[0]['trends']
+
+us_list = [t for t in us_list if t['tweet_volume']]
+
+us_list.sort(key=itemgetter('tweet_volume'), reverse=True)
+
+for trend in us_list[:3]:
+    print(trend['name'])
+
+# 13.11.3 Create a Word Cloud from Trending Topics
+topics = {}
+
+for trend in nyc_list:
+    topics[trend['name']] = trend['tweet_volume']
+
+from wordcloud import WordCloud
+
+wordcloud = WordCloud(width=1600, height=900,
+    prefer_horizontal=0.5, min_font_size=10, colormap='prism', 
+    background_color='white')       
+
+wordcloud = wordcloud.fit_words(topics)
+
+wordcloud = wordcloud.to_file('TrendingTwitter.png')
+
+
+# 13.11.3 Self Check
+# Exercise 1
+topics = {}
+
+for trend in us_list:
+    topics[trend['name']] = trend['tweet_volume']
+        
+wordcloud = wordcloud.fit_words(topics)
+
+wordcloud = wordcloud.to_file('USTrendingTwitter.png')
+
+
+
+
+
+
+NOTE: The following code displays the image in a Jupyter Notebook
+
+
+
+##########################################################################
+# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_07-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_12-checkpoint.py
similarity index 85%
rename from examples/ch02/snippets_py/.ipynb_checkpoints/02_07-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_12-checkpoint.py
index 12da2a8..1178cdc 100755
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_07-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_12-checkpoint.py
@@ -1,19 +1,16 @@
-# Section 2.7 snippets
+# Section 13.12 snippets
 
-7 > 4
+import preprocessor as p 
 
-7 < 4
+p.set_options(p.OPT.URL, p.OPT.RESERVED)
 
-7 > = 4
+tweet_text = 'RT A sample retweet with a URL https://nasa.gov'
+
+p.clean(tweet_text)
 
-# Chaining Comparisons
-x = 3
 
-1 <= x <= 5
 
-x = 10
 
-1 <= x <= 5
 
 ##########################################################################
 # (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
diff --git a/examples/ch08/snippets_py/.ipynb_checkpoints/08_09-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_13_02-checkpoint.py
similarity index 54%
rename from examples/ch08/snippets_py/.ipynb_checkpoints/08_09-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_13_02-checkpoint.py
index e8c6022..4845a69 100755
--- a/examples/ch08/snippets_py/.ipynb_checkpoints/08_09-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_13_02-checkpoint.py
@@ -1,46 +1,43 @@
-# Section 8.9 snippets
+# 13.13.2 Initiating Stream Processing
 
-# Splitting Strings
-letters = 'A, B, C, D'
+# Authenticating
+import tweepy
 
-letters.split(', ')
+import keys
 
-letters.split(', ', 2)
+# Creating a TweetListener
+from tweetlistener import TweetListener
 
-# Joining Strings
-letters_list = ['A', 'B', 'C', 'D']
+tweet_listener = TweetListener(
+    bearer_token=keys.bearer_token, limit=3)
 
-','.join(letters_list)
+# Redirecting Standard Error Stream to Standard Output Stream
+import sys
 
-','.join([str(i) for i in range(10)])
+sys.stderr = sys.stdout
 
-# String Methods partition and rpartition 
-'Amanda: 89, 97, 92'.partition(': ')
+# Deleting Existing Stream Rules
+rules = tweet_listener.get_rules().data
 
-url = 'http://www.deitel.com/books/PyCDS/table_of_contents.html'
+rule_ids = [rule.id for rule in rules]
 
-rest_of_url, separator, document = url.rpartition('/')
+tweet_listener.delete_rules(rule_ids)    
 
-document
+# Creating and Adding a Stream Rule
+filter_rule = tweepy.StreamRule('football')
 
-rest_of_url
+tweet_listener.add_rules(filter_rule)
 
-# String Method splitlines 
-lines = """This is line 1
-This is line2
-This is line3"""
+# Starting the Tweet Stream
+tweet_listener.filter(
+    expansions=['author_id'], tweet_fields=['lang'])
 
-lines
+# Stream connection closed by Twitter
 
-lines.splitlines()
-
-lines.splitlines(True)
-
-
-    
+# Asynchronous vs. Synchronous Streams
 
 ##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
 # Pearson Education, Inc. All Rights Reserved.                           #
 #                                                                        #
 # DISCLAIMER: The authors and publisher of this book have used their     #
diff --git a/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_15_01-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_15_01-checkpoint.py
new file mode 100755
index 0000000..b192797
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_15_01-checkpoint.py
@@ -0,0 +1,103 @@
+# 13.15.1 Getting and Mapping the Tweets¶
+
+# Collections Required By LocationListener
+tweets = [] 
+
+counts = {'total_tweets': 0, 'locations': 0}
+
+# Creating the LocationListener
+import keys
+
+import tweepy
+
+from locationlistener import LocationListener
+
+location_listener = LocationListener(
+    keys.bearer_token, counts_dict=counts, tweets_list=tweets,
+    topic='football', limit=50)
+
+# Redirect sys.stderr to sys.stdout
+import sys
+
+sys.stderr = sys.stdout
+
+# Delete Existing StreamRules
+rules = location_listener.get_rules().data
+
+rule_ids = [rule.id for rule in rules]
+
+location_listener.delete_rules(rule_ids)    
+
+# Create a StreamRule
+location_listener.add_rules(
+    tweepy.StreamRule('football lang:en'))
+
+# Configure and Start the Stream of Tweets
+location_listener.filter(expansions=['author_id'], 
+    user_fields=['location'], tweet_fields=['lang'])
+
+# Displaying the Location Statistics
+counts['total_tweets']
+
+counts['locations']
+
+print(f'{counts["locations"] / counts["total_tweets"]:.1%}')
+
+# Geocoding the Locations
+from tweetutilities import get_geocodes
+
+bad_locations = get_geocodes(tweets)
+
+# Displaying the Bad Location Statistics
+bad_locations
+
+print(f'{bad_locations / counts["locations"]:.1%}')
+
+# Cleaning the Data
+import pandas as pd
+
+df = pd.DataFrame(tweets)
+
+df = df.dropna()
+
+# Creating a Map with Folium
+import folium
+
+usmap = folium.Map(location=[39.8283, -98.5795], 
+    tiles='Stamen Terrain', zoom_start=5, detect_retina=True)        
+
+# Creating Popup Markers for the Tweet Locations
+for t in df.itertuples():
+     text = ': '.join([t.username, t.text])
+     popup = folium.Popup(text, parse_html=True)
+     marker = folium.Marker((t.latitude, t.longitude), 
+                            popup=popup)
+     marker.add_to(usmap)
+
+# Saving the Map
+usmap.save('tweet_map.html')
+
+
+
+
+
+
+
+
+
+
+
+##########################################################################
+# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.02selfcheck-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_15_02selfcheck-checkpoint.py
similarity index 87%
rename from examples/ch10/snippets_py/.ipynb_checkpoints/10_13.02selfcheck-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_15_02selfcheck-checkpoint.py
index d9c0ff2..a6adaaf 100755
--- a/examples/ch10/snippets_py/.ipynb_checkpoints/10_13.02selfcheck-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/13_15_02selfcheck-checkpoint.py
@@ -1,20 +1,13 @@
-# Section 10.13.2 Self Check snippets
+# Section 15.2 Self Check snippets
 
 # Exercise 1
-from carddataclass import Card
+import keys
 
-c = Card('Ace', 'Spades')
+from geopy import OpenMapQuest
 
-c
-
-type(c.face)
-
-c.face = 100
-
-c
-
-type(c.face)
+geo = OpenMapQuest(api_key=keys.mapquest_key)
 
+geo.geocode('Chicago, IL')
 
 
 ##########################################################################
diff --git a/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/README-checkpoint.txt b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/README-checkpoint.txt
new file mode 100755
index 0000000..3fe48c8
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/README-checkpoint.txt
@@ -0,0 +1,3 @@
+Sections 13.7-11 use one continuous IPython session for the 
+examples and Self Check exercises, so all of these are in one
+snippet file.
diff --git a/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/keys-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/keys-checkpoint.py
new file mode 100755
index 0000000..4f26031
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/keys-checkpoint.py
@@ -0,0 +1,6 @@
+consumer_key = 'YourConsumerKey'
+consumer_secret = 'YourConsumerSecret'
+access_token = 'YourAccessToken'
+access_token_secret = 'YourAccessTokenSecret'
+
+mapquest_key = 'YourAPIKey'
\ No newline at end of file
diff --git a/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/tweetlistener-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/tweetlistener-checkpoint.py
new file mode 100755
index 0000000..b01654d
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/.ipynb_checkpoints/tweetlistener-checkpoint.py
@@ -0,0 +1,61 @@
+# tweetlistener.py
+"""StreamListener subclass that processes tweets as they arrive."""
+from deep_translator import GoogleTranslator
+import tweepy
+
+class TweetListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream."""
+
+    def __init__(self, bearer_token, limit=10):
+        """Create instance variables for tracking number of tweets."""
+        self.tweet_count = 0
+        self.TWEET_LIMIT = limit
+        
+        # GoogleTranslator object for translating tweets to English 
+        self.translator = GoogleTranslator(source='auto', target='en')
+
+        super().__init__(bearer_token, wait_on_rate_limit=True)  
+
+    def on_connect(self):
+        """Called when your connection attempt is successful, enabling 
+        you to perform appropriate application tasks at that point."""
+        print('Connection successful\n')
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+        
+        try:
+            # get username of user who sent the tweet
+            username = response.includes['users'][0].username
+            print(f'Screen name: {username}')
+            print(f'   Language: {response.data.lang}')
+            print(f' Tweet text: {response.data.text}')
+
+            if response.data.lang != 'en' and response.data.lang != 'und':
+                english = self.translator.translate(response.data.text)
+                print(f' Translated: {english}')
+
+            print()
+            self.tweet_count += 1 
+        except Exception as e:
+            print(f'Exception occured: {e}')
+            self.disconnect()
+            
+        # if TWEET_LIMIT is reached, terminate streaming
+        if self.tweet_count == self.TWEET_LIMIT:
+            self.disconnect()
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13_TwitterV2/snippets_py/13_07-11withSelfChecks.py b/examples/ch13_TwitterV2/snippets_py/13_07-11withSelfChecks.py
new file mode 100755
index 0000000..81ee54d
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/13_07-11withSelfChecks.py
@@ -0,0 +1,246 @@
+# Section 13.7-13.11 snippets with Self Checks
+# because those sections are one running IPython session
+
+# 13.7 Authenticating with Twitter Via Tweepy to Access Twitter v2 APIs
+import tweepy
+import keys
+
+# Creating a Client Object
+client = tweepy.Client(bearer_token=keys.bearer_token,
+                       wait_on_rate_limit=True)
+
+#13.8 Getting Information About a Twitter Account
+nasa = client.get_user(username='NASA', 
+    user_fields=['description', 'public_metrics'])
+
+# tweepy.Response Object
+# Getting a User’s Basic Account Information
+nasa.data.id
+
+nasa.data.name
+
+nasa.data.username
+
+nasa.data.description
+
+# Getting the Number of Accounts That Follow This Account and the Number of Accounts This Account Follows
+nasa.data.public_metrics['followers_count']
+
+nasa.data.public_metrics['following_count']
+
+# Getting Your Own Account’s Information
+
+# 13.8 Self Check
+# Exercise 2
+nasa_mars = client.get_user(username='NASAMars', 
+    user_fields=['description', 'public_metrics'])
+
+nasa_mars.data.id
+
+nasa_mars.data.name
+
+nasa_mars.data.username
+
+nasa_mars.data.description
+
+nasa_mars.data.public_metrics['followers_count']
+
+# 13.9 Intro to Tweepy Paginators: Getting More than One Page of Results
+# 13.9.1 Determining an Account’s Followers
+followers = []
+
+# Creating a Paginator
+paginator = tweepy.Paginator(
+    client.get_users_followers, nasa.data.id, max_results=5)
+
+# Getting Results
+for follower in paginator.flatten(limit=10):
+    followers.append(follower.username)
+    
+print('Followers:', 
+      ' '.join(sorted(followers, key=lambda s: s.lower())))
+
+# 13.9.1 Self Check
+# Exercise 3. 
+nasa_mars_followers = []
+
+nasa_mars_followers_paginator = tweepy.Paginator(
+    client.get_users_followers, nasa_mars.data.id, max_results=5)
+
+for follower in nasa_mars_followers_paginator.flatten(limit=10):
+    nasa_mars_followers.append(follower.username)
+    
+print(' '.join(nasa_mars_followers))
+
+# 13.9.2 Determining Whom an Account Follows
+following = []
+
+paginator = tweepy.Paginator(
+    client.get_users_following, nasa.data.id, max_results=5)
+
+for user_followed in paginator.flatten(limit=10):
+    following.append(user_followed.username)
+    
+print('Following:', 
+      ' '.join(sorted(following, key=lambda s: s.lower())))
+
+
+# 13.9.3 Getting a User’s Recent Tweets
+nasa_tweets = client.get_users_tweets(
+    id=nasa.data.id, max_results=5)
+
+for tweet in nasa_tweets.data:
+    print(f"NASA: {tweet.data['text']}\n")
+
+# Grabbing Recent Tweets from Your Own Timeline
+# 13.9.3 Self Check
+# Exercise 2
+nasa_mars_tweets = client.get_users_tweets(
+    id=nasa_mars.data.id, max_results=5)
+
+for tweet in nasa_mars_tweets.data:
+    print(f"NASAMars: {tweet.data['text']}\n")
+
+# 13.10 Searching Recent Tweets; Intro to Twitter v2 API Search Operators
+# Utility Function print_tweets from tweetutilities.py
+from tweetutilities import print_tweets
+
+# Searching for Specific Words
+tweets = client.search_recent_tweets(
+    query='Webb Space Telescope', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# Searching with Twitter v2 API Search Operators
+
+# Operator Documentation and Tutorial
+
+# Searching for Tweets From NASA Containing Links
+tweets = client.search_recent_tweets(
+    query='from:NASA has:links', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# Searching for a Hashtag
+tweets = client.search_recent_tweets(query='#metaverse', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# 13.10 Self Check
+# Exercise 
+tweets = client.search_recent_tweets(query='from:nasa astronaut', 
+    expansions=['author_id'], tweet_fields=['lang'])
+
+print_tweets(tweets)
+
+# 13.11 Spotting Trending Topics
+auth = tweepy.OAuth2BearerHandler(keys.bearer_token)
+
+api = tweepy.API(auth=auth, wait_on_rate_limit=True)
+
+# 13.11.1 Places with Trending Topics
+# Note: This part of the Twitter APIs has not been migrated from v1.1 to v2
+# yet and is accessible only to "Elevated" and "Academic Research" access.
+available_trends = api.available_trends()
+
+len(available_trends)
+
+available_trends[0]
+
+available_trends[1]
+
+# 13.11.2 Getting a List of Trending Topics
+# Worldwide Trending Topics
+world_trends = api.get_place_trends(id=1)
+
+trends_list = world_trends[0]['trends']
+
+trends_list[0]
+
+trends_list = [t for t in trends_list if t['tweet_volume']]
+
+from operator import itemgetter 
+trends_list.sort(key=itemgetter('tweet_volume'), reverse=True) 
+
+for trend in trends_list:
+    print(trend['name'])
+
+# New York City Trending Topics
+nyc_trends = api.get_place_trends(id=2459115) 
+
+nyc_list = nyc_trends[0]['trends']
+
+nyc_list = [t for t in nyc_list if t['tweet_volume']]
+
+nyc_list.sort(key=itemgetter('tweet_volume'), reverse=True) 
+
+for trend in nyc_list[:5]:
+    print(trend['name'])
+
+# 13.11.2 Self Check
+Exercise 3
+us_trends = api.get_place_trends(id='23424977')
+
+us_list = us_trends[0]['trends']
+
+us_list = [t for t in us_list if t['tweet_volume']]
+
+us_list.sort(key=itemgetter('tweet_volume'), reverse=True)
+
+for trend in us_list[:3]:
+    print(trend['name'])
+
+# 13.11.3 Create a Word Cloud from Trending Topics
+topics = {}
+
+for trend in nyc_list:
+    topics[trend['name']] = trend['tweet_volume']
+
+from wordcloud import WordCloud
+
+wordcloud = WordCloud(width=1600, height=900,
+    prefer_horizontal=0.5, min_font_size=10, colormap='prism', 
+    background_color='white')       
+
+wordcloud = wordcloud.fit_words(topics)
+
+wordcloud = wordcloud.to_file('TrendingTwitter.png')
+
+
+# 13.11.3 Self Check
+# Exercise 1
+topics = {}
+
+for trend in us_list:
+    topics[trend['name']] = trend['tweet_volume']
+        
+wordcloud = wordcloud.fit_words(topics)
+
+wordcloud = wordcloud.to_file('USTrendingTwitter.png')
+
+
+
+
+
+
+NOTE: The following code displays the image in a Jupyter Notebook
+
+
+
+##########################################################################
+# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch03/snippets_py/.ipynb_checkpoints/03_17selfcheck-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/13_12.py
similarity index 85%
rename from examples/ch03/snippets_py/.ipynb_checkpoints/03_17selfcheck-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_py/13_12.py
index 14ea8b8..1178cdc 100755
--- a/examples/ch03/snippets_py/.ipynb_checkpoints/03_17selfcheck-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_py/13_12.py
@@ -1,14 +1,15 @@
-# Section 3.17 Self Check snippets
+# Section 13.12 snippets
 
-# Exercise 4
-import statistics
-values = [47, 95, 88, 73, 88, 84]
+import preprocessor as p 
+
+p.set_options(p.OPT.URL, p.OPT.RESERVED)
+
+tweet_text = 'RT A sample retweet with a URL https://nasa.gov'
+
+p.clean(tweet_text)
 
-statistics.mean(values)
 
-statistics.median(values)
 
-statistics.mode(values)
 
 
 ##########################################################################
diff --git a/examples/ch02/snippets_py/.ipynb_checkpoints/02_06-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/13_13_02.py
similarity index 54%
rename from examples/ch02/snippets_py/.ipynb_checkpoints/02_06-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_py/13_13_02.py
index 361c33d..4845a69 100755
--- a/examples/ch02/snippets_py/.ipynb_checkpoints/02_06-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_py/13_13_02.py
@@ -1,43 +1,43 @@
-# Section 2.6 snippets
-name = input("What's your name? ")
+# 13.13.2 Initiating Stream Processing
 
-name
+# Authenticating
+import tweepy
 
-print(name)
+import keys
 
-name = input("What's your name? ")
+# Creating a TweetListener
+from tweetlistener import TweetListener
 
-name
+tweet_listener = TweetListener(
+    bearer_token=keys.bearer_token, limit=3)
 
-print(name)
+# Redirecting Standard Error Stream to Standard Output Stream
+import sys
 
-# Function input Always Returns a String
-value1 = input('Enter first number: ')
+sys.stderr = sys.stdout
 
-value2 = input('Enter second number: ')
+# Deleting Existing Stream Rules
+rules = tweet_listener.get_rules().data
 
-value1 + value2
+rule_ids = [rule.id for rule in rules]
 
-# Getting an Integer from the User
-value = input('Enter an integer: ')
+tweet_listener.delete_rules(rule_ids)    
 
-value = int(value)
+# Creating and Adding a Stream Rule
+filter_rule = tweepy.StreamRule('football')
 
-value
+tweet_listener.add_rules(filter_rule)
 
-another_value = int(input('Enter another integer: '))
+# Starting the Tweet Stream
+tweet_listener.filter(
+    expansions=['author_id'], tweet_fields=['lang'])
 
+# Stream connection closed by Twitter
 
-another_value
-
-value + another_value
-
-bad_value = int(input('Enter another integer: '))
-
-int(10.5)
+# Asynchronous vs. Synchronous Streams
 
 ##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
 # Pearson Education, Inc. All Rights Reserved.                           #
 #                                                                        #
 # DISCLAIMER: The authors and publisher of this book have used their     #
diff --git a/examples/ch13_TwitterV2/snippets_py/13_15_01.py b/examples/ch13_TwitterV2/snippets_py/13_15_01.py
new file mode 100755
index 0000000..b192797
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/13_15_01.py
@@ -0,0 +1,103 @@
+# 13.15.1 Getting and Mapping the Tweets¶
+
+# Collections Required By LocationListener
+tweets = [] 
+
+counts = {'total_tweets': 0, 'locations': 0}
+
+# Creating the LocationListener
+import keys
+
+import tweepy
+
+from locationlistener import LocationListener
+
+location_listener = LocationListener(
+    keys.bearer_token, counts_dict=counts, tweets_list=tweets,
+    topic='football', limit=50)
+
+# Redirect sys.stderr to sys.stdout
+import sys
+
+sys.stderr = sys.stdout
+
+# Delete Existing StreamRules
+rules = location_listener.get_rules().data
+
+rule_ids = [rule.id for rule in rules]
+
+location_listener.delete_rules(rule_ids)    
+
+# Create a StreamRule
+location_listener.add_rules(
+    tweepy.StreamRule('football lang:en'))
+
+# Configure and Start the Stream of Tweets
+location_listener.filter(expansions=['author_id'], 
+    user_fields=['location'], tweet_fields=['lang'])
+
+# Displaying the Location Statistics
+counts['total_tweets']
+
+counts['locations']
+
+print(f'{counts["locations"] / counts["total_tweets"]:.1%}')
+
+# Geocoding the Locations
+from tweetutilities import get_geocodes
+
+bad_locations = get_geocodes(tweets)
+
+# Displaying the Bad Location Statistics
+bad_locations
+
+print(f'{bad_locations / counts["locations"]:.1%}')
+
+# Cleaning the Data
+import pandas as pd
+
+df = pd.DataFrame(tweets)
+
+df = df.dropna()
+
+# Creating a Map with Folium
+import folium
+
+usmap = folium.Map(location=[39.8283, -98.5795], 
+    tiles='Stamen Terrain', zoom_start=5, detect_retina=True)        
+
+# Creating Popup Markers for the Tweet Locations
+for t in df.itertuples():
+     text = ': '.join([t.username, t.text])
+     popup = folium.Popup(text, parse_html=True)
+     marker = folium.Marker((t.latitude, t.longitude), 
+                            popup=popup)
+     marker.add_to(usmap)
+
+# Saving the Map
+usmap.save('tweet_map.html')
+
+
+
+
+
+
+
+
+
+
+
+##########################################################################
+# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch04/snippets_py/.ipynb_checkpoints/04_17-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/13_15_02selfcheck.py
old mode 100644
new mode 100755
similarity index 86%
rename from examples/ch04/snippets_py/.ipynb_checkpoints/04_17-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_py/13_15_02selfcheck.py
index b62c01a..a6adaaf
--- a/examples/ch04/snippets_py/.ipynb_checkpoints/04_17-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_py/13_15_02selfcheck.py
@@ -1,16 +1,15 @@
-# Section 4.17 snippets
+# Section 15.2 Self Check snippets
 
-values = [1, 2, 3]
+# Exercise 1
+import keys
 
-sum(values)
+from geopy import OpenMapQuest
 
-sum(values)  # same call always returns same result
+geo = OpenMapQuest(api_key=keys.mapquest_key)
 
-values
+geo.geocode('Chicago, IL')
 
 
-   
-
 ##########################################################################
 # (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
 # Pearson Education, Inc. All Rights Reserved.                           #
diff --git a/examples/ch13_TwitterV2/snippets_py/README.txt b/examples/ch13_TwitterV2/snippets_py/README.txt
new file mode 100755
index 0000000..3fe48c8
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/README.txt
@@ -0,0 +1,3 @@
+Sections 13.7-11 use one continuous IPython session for the 
+examples and Self Check exercises, so all of these are in one
+snippet file.
diff --git a/examples/ch13_TwitterV2/snippets_py/keys.py b/examples/ch13_TwitterV2/snippets_py/keys.py
new file mode 100755
index 0000000..bf7c356
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/keys.py
@@ -0,0 +1,2 @@
+bearer_token = 'YourBearerToken'
+mapquest_key = 'YourAPIKey'
\ No newline at end of file
diff --git a/examples/ch13_TwitterV2/snippets_py/locationlistener.py b/examples/ch13_TwitterV2/snippets_py/locationlistener.py
new file mode 100755
index 0000000..f154d61
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/locationlistener.py
@@ -0,0 +1,59 @@
+# locationlistener.py
+"""Receives tweets matching a search string and stores a list of
+dictionaries containing each tweet's username/text/location."""
+import tweepy
+from tweetutilities import get_tweet_content
+
+class LocationListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream to get location data."""
+
+    def __init__(self, bearer_token, counts_dict, 
+                 tweets_list, topic, limit=10):
+        """Configure the LocationListener."""
+        self.tweets_list = tweets_list
+        self.counts_dict = counts_dict
+        self.topic = topic
+        self.TWEET_LIMIT = limit
+        super().__init__(bearer_token, wait_on_rate_limit=True) 
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+
+        # get tweet's username, text and location
+        tweet_data = get_tweet_content(response)  
+        
+        # ignore retweets and tweets that do not contain the topic
+        if (tweet_data['text'].startswith('RT') or
+            self.topic.lower() not in tweet_data['text'].lower()):
+            return
+
+        self.counts_dict['total_tweets'] += 1 # it's an original tweet
+
+        # ignore tweets with no location
+        if not tweet_data.get('location'):  
+            return
+
+        self.counts_dict['locations'] += 1 # user account has location
+        self.tweets_list.append(tweet_data) # store the tweet
+        print(f"{tweet_data['username']}: {tweet_data['text']}\n")
+
+        # if TWEET_LIMIT is reached, terminate streaming
+        if self.counts_dict['locations'] == self.TWEET_LIMIT:
+            self.disconnect()
+
+
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13_TwitterV2/snippets_py/sentimentlistener.py b/examples/ch13_TwitterV2/snippets_py/sentimentlistener.py
new file mode 100755
index 0000000..2cc45ec
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/sentimentlistener.py
@@ -0,0 +1,106 @@
+# sentimentlisener.py
+"""Searches for tweets that match a search string and tallies 
+the number of positive, neutral and negative tweets."""
+import keys
+import preprocessor as p 
+import sys
+from textblob import TextBlob
+import tweepy
+
+class SentimentListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream."""
+
+    def __init__(self, bearer_token, sentiment_dict, topic, limit=10):
+        """Configure the SentimentListener."""
+        self.sentiment_dict = sentiment_dict
+        self.tweet_count = 0
+        self.topic = topic
+        self.TWEET_LIMIT = limit
+        
+        # set tweet-preprocessor to remove URLs/reserved words
+        p.set_options(p.OPT.URL, p.OPT.RESERVED) 
+        super().__init__(bearer_token, wait_on_rate_limit=True)
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+
+        # if the tweet is not a retweet
+        if not response.data.text.startswith('RT'):
+            text = p.clean(response.data.text) # clean the tweet
+
+            # ignore tweet if the topic is not in the tweet text
+            if self.topic.lower() not in text.lower():
+                return
+
+            # update self.sentiment_dict with the polarity
+            blob = TextBlob(text)
+            if blob.sentiment.polarity > 0:
+                sentiment = '+'
+                self.sentiment_dict['positive'] += 1 
+            elif blob.sentiment.polarity == 0:
+                sentiment = ' '
+                self.sentiment_dict['neutral'] += 1 
+            else:
+                sentiment = '-'
+                self.sentiment_dict['negative'] += 1 
+
+            # display the tweet
+            username = response.includes['users'][0].username
+            print(f'{sentiment} {username}: {text}\n')
+
+            self.tweet_count += 1 # track number of tweets processed
+
+            # if TWEET_LIMIT is reached, terminate streaming
+            if self.tweet_count == self.TWEET_LIMIT:
+                self.disconnect()
+
+def main():
+    # get search term and number of tweets
+    search_key = sys.argv[1]
+    limit = int(sys.argv[2]) # number of tweets to tally
+    
+    # set up the sentiment dictionary
+    sentiment_dict = {'positive': 0, 'neutral': 0, 'negative': 0}
+
+    # create the StreamingClient subclass object
+    sentiment_listener = SentimentListener(keys.bearer_token, 
+        sentiment_dict, search_key, limit)
+
+    # redirect sys.stderr to sys.stdout
+    sys.stderr = sys.stdout
+
+    # delete existing stream rules
+    rules = sentiment_listener.get_rules().data
+    rule_ids = [rule.id for rule in rules]
+    sentiment_listener.delete_rules(rule_ids)    
+
+    # create stream rule
+    sentiment_listener.add_rules(
+        tweepy.StreamRule(f'{search_key} lang:en'))
+    
+    # start filtering English tweets containing search_key
+    sentiment_listener.filter(expansions=['author_id'])
+
+    print(f'Tweet sentiment for "{search_key}"')
+    print('Positive:', sentiment_dict['positive'])
+    print(' Neutral:', sentiment_dict['neutral'])
+    print('Negative:', sentiment_dict['negative'])
+
+# call main if this file is executed as a script
+if __name__ == '__main__':
+    main()
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13_TwitterV2/snippets_py/tweetlistener.py b/examples/ch13_TwitterV2/snippets_py/tweetlistener.py
new file mode 100755
index 0000000..b01654d
--- /dev/null
+++ b/examples/ch13_TwitterV2/snippets_py/tweetlistener.py
@@ -0,0 +1,61 @@
+# tweetlistener.py
+"""StreamListener subclass that processes tweets as they arrive."""
+from deep_translator import GoogleTranslator
+import tweepy
+
+class TweetListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream."""
+
+    def __init__(self, bearer_token, limit=10):
+        """Create instance variables for tracking number of tweets."""
+        self.tweet_count = 0
+        self.TWEET_LIMIT = limit
+        
+        # GoogleTranslator object for translating tweets to English 
+        self.translator = GoogleTranslator(source='auto', target='en')
+
+        super().__init__(bearer_token, wait_on_rate_limit=True)  
+
+    def on_connect(self):
+        """Called when your connection attempt is successful, enabling 
+        you to perform appropriate application tasks at that point."""
+        print('Connection successful\n')
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+        
+        try:
+            # get username of user who sent the tweet
+            username = response.includes['users'][0].username
+            print(f'Screen name: {username}')
+            print(f'   Language: {response.data.lang}')
+            print(f' Tweet text: {response.data.text}')
+
+            if response.data.lang != 'en' and response.data.lang != 'und':
+                english = self.translator.translate(response.data.text)
+                print(f' Translated: {english}')
+
+            print()
+            self.tweet_count += 1 
+        except Exception as e:
+            print(f'Exception occured: {e}')
+            self.disconnect()
+            
+        # if TWEET_LIMIT is reached, terminate streaming
+        if self.tweet_count == self.TWEET_LIMIT:
+            self.disconnect()
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/tweetutilities-checkpoint.py b/examples/ch13_TwitterV2/snippets_py/tweetutilities.py
similarity index 65%
rename from examples/ch13/snippets_ipynb/.ipynb_checkpoints/tweetutilities-checkpoint.py
rename to examples/ch13_TwitterV2/snippets_py/tweetutilities.py
index 25456b9..f5a12ab 100755
--- a/examples/ch13/snippets_ipynb/.ipynb_checkpoints/tweetutilities-checkpoint.py
+++ b/examples/ch13_TwitterV2/snippets_py/tweetutilities.py
@@ -1,47 +1,33 @@
 # tweetutilities.py
 """Utility functions for interacting with Tweepy objects."""
+from deep_translator import GoogleTranslator
 from geopy import OpenMapQuest
 import keys
-from textblob import TextBlob 
 import time 
 import tweepy
 
-def get_API(wait=True, notify=True):
-    """Authenticate with Twitter and return API object."""
-    # configure the OAuthHandler
-    auth = tweepy.OAuthHandler(keys.consumer_key, keys.consumer_secret)
-    auth.set_access_token(keys.access_token, keys.access_token_secret)
-
-    # get the API object
-    return tweepy.API(auth, wait_on_rate_limit=wait, 
-                      wait_on_rate_limit_notify=notify)
-
 def print_tweets(tweets):
-    """For each Tweepy Status object in tweets, display the 
-    user's screen_name and tweet text. If the language is not
-    English, translate the text with TextBlob."""
-    for tweet in tweets:
-        print(f'{tweet.user.screen_name}:', end=' ')
+    # translator to autodetect source language and return English
+    translator = GoogleTranslator(source='auto', target='en')
+    
+    """For each tweet in tweets, display the username of the sender
+    and tweet text. If the language is not English, translate the text 
+    with Deep Translator."""
+    for tweet, user in zip(tweets.data, tweets.includes['users']):
+        print(f'{user.username}:', end=' ')
 
         if 'en' in tweet.lang:
             print(f'{tweet.text}\n')
-        elif 'und' not in tweet.lang:  # translate to English first
+        elif 'und' not in tweet.lang: # translate to English first
             print(f'\n  ORIGINAL: {tweet.text}')
-            print(f'TRANSLATED: {TextBlob(tweet.text).translate()}\n')
+            print(f'TRANSLATED: {translator.translate(tweet.text)}\n')
 
-def get_tweet_content(tweet, location=False):
-    """Return dictionary with data from tweet (a Status object)."""
+def get_tweet_content(response):
+    """Return dictionary with data from tweet."""
     fields = {}
-    fields['screen_name'] = tweet.user.screen_name
-
-    # get the tweet's text
-    try:  
-        fields['text'] = tweet.extended_tweet.full_text
-    except: 
-        fields['text'] = tweet.text
-
-    if location:
-        fields['location'] = tweet.user.location
+    fields['username'] = response.includes['users'][0].username
+    fields['text'] = response.data.text
+    fields['location'] = response.includes['users'][0].location
 
     return fields
 
@@ -75,7 +61,7 @@ def get_geocodes(tweet_list):
 
 
 ##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
 # Pearson Education, Inc. All Rights Reserved.                           #
 #                                                                        #
 # DISCLAIMER: The authors and publisher of this book have used their     #
diff --git a/examples/ch13_TwitterV2/tweetlistener.py b/examples/ch13_TwitterV2/tweetlistener.py
new file mode 100755
index 0000000..b01654d
--- /dev/null
+++ b/examples/ch13_TwitterV2/tweetlistener.py
@@ -0,0 +1,61 @@
+# tweetlistener.py
+"""StreamListener subclass that processes tweets as they arrive."""
+from deep_translator import GoogleTranslator
+import tweepy
+
+class TweetListener(tweepy.StreamingClient):
+    """Handles incoming Tweet stream."""
+
+    def __init__(self, bearer_token, limit=10):
+        """Create instance variables for tracking number of tweets."""
+        self.tweet_count = 0
+        self.TWEET_LIMIT = limit
+        
+        # GoogleTranslator object for translating tweets to English 
+        self.translator = GoogleTranslator(source='auto', target='en')
+
+        super().__init__(bearer_token, wait_on_rate_limit=True)  
+
+    def on_connect(self):
+        """Called when your connection attempt is successful, enabling 
+        you to perform appropriate application tasks at that point."""
+        print('Connection successful\n')
+
+    def on_response(self, response):
+        """Called when Twitter pushes a new tweet to you."""
+        
+        try:
+            # get username of user who sent the tweet
+            username = response.includes['users'][0].username
+            print(f'Screen name: {username}')
+            print(f'   Language: {response.data.lang}')
+            print(f' Tweet text: {response.data.text}')
+
+            if response.data.lang != 'en' and response.data.lang != 'und':
+                english = self.translator.translate(response.data.text)
+                print(f' Translated: {english}')
+
+            print()
+            self.tweet_count += 1 
+        except Exception as e:
+            print(f'Exception occured: {e}')
+            self.disconnect()
+            
+        # if TWEET_LIMIT is reached, terminate streaming
+        if self.tweet_count == self.TWEET_LIMIT:
+            self.disconnect()
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch13_TwitterV2/tweetutilities.py b/examples/ch13_TwitterV2/tweetutilities.py
new file mode 100755
index 0000000..f5a12ab
--- /dev/null
+++ b/examples/ch13_TwitterV2/tweetutilities.py
@@ -0,0 +1,76 @@
+# tweetutilities.py
+"""Utility functions for interacting with Tweepy objects."""
+from deep_translator import GoogleTranslator
+from geopy import OpenMapQuest
+import keys
+import time 
+import tweepy
+
+def print_tweets(tweets):
+    # translator to autodetect source language and return English
+    translator = GoogleTranslator(source='auto', target='en')
+    
+    """For each tweet in tweets, display the username of the sender
+    and tweet text. If the language is not English, translate the text 
+    with Deep Translator."""
+    for tweet, user in zip(tweets.data, tweets.includes['users']):
+        print(f'{user.username}:', end=' ')
+
+        if 'en' in tweet.lang:
+            print(f'{tweet.text}\n')
+        elif 'und' not in tweet.lang: # translate to English first
+            print(f'\n  ORIGINAL: {tweet.text}')
+            print(f'TRANSLATED: {translator.translate(tweet.text)}\n')
+
+def get_tweet_content(response):
+    """Return dictionary with data from tweet."""
+    fields = {}
+    fields['username'] = response.includes['users'][0].username
+    fields['text'] = response.data.text
+    fields['location'] = response.includes['users'][0].location
+
+    return fields
+
+def get_geocodes(tweet_list):
+    """Get the latitude and longitude for each tweet's location.
+    Returns the number of tweets with invalid location data."""
+    print('Getting coordinates for tweet locations...')
+    geo = OpenMapQuest(api_key=keys.mapquest_key)  # geocoder
+    bad_locations = 0  
+
+    for tweet in tweet_list:
+        processed = False
+        delay = .1  # used if OpenMapQuest times out to delay next call
+        while not processed:
+            try:  # get coordinates for tweet['location']
+                geo_location = geo.geocode(tweet['location'])
+                processed = True
+            except:  # timed out, so wait before trying again
+                print('OpenMapQuest service timed out. Waiting.')
+                time.sleep(delay)
+                delay += .1
+
+        if geo_location:  
+            tweet['latitude'] = geo_location.latitude
+            tweet['longitude'] = geo_location.longitude
+        else:  
+            bad_locations += 1  # tweet['location'] was invalid
+    
+    print('Done geocoding')
+    return bad_locations
+
+
+##########################################################################
+# (C) Copyright 2022 by Deitel & Associates, Inc. and                    #
+# Pearson Education, Inc. All Rights Reserved.                           #
+#                                                                        #
+# DISCLAIMER: The authors and publisher of this book have used their     #
+# best efforts in preparing the book. These efforts include the          #
+# development, research, and testing of the theories and programs        #
+# to determine their effectiveness. The authors and publisher make       #
+# no warranty of any kind, expressed or implied, with regard to these    #
+# programs or to the documentation contained in these books. The authors #
+# and publisher shall not be liable in any event for incidental or       #
+# consequential damages in connection with, or arising out of, the       #
+# furnishing, performance, or use of these programs.                     #
+##########################################################################
diff --git a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_02-03-checkpoint.ipynb b/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_02-03-checkpoint.ipynb
deleted file mode 100644
index 7414fb2..0000000
--- a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_02-03-checkpoint.ipynb
+++ /dev/null
@@ -1,1341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 16.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1\n",
-    "\n",
-    "**This file contains Sections 16.2 and 16.3 and all of their subsections and Self Check exercises**\n",
-    "\n",
-    "### Classification Problems\n",
-    "### Our Approach"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.2 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** `________` classification divides samples into two distinct classes, and `________`-classification divides samples into many distinct classes.\n",
-    "\n",
-    "**Answer:** binary, multi. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.2.1 k-Nearest Neighbors Algorithm\n",
-    "### Hyperparameters and Hyperparameter Tuning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.2.1 Self Check\n",
-    "**1. _(True/False)_** In machine learning, a model implements a machine-learning algorithm. In scikit-learn, models are called estimators.\n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**2. _(Fill-In)_** The process of choosing the best value of *k* for the k-nearest neighbors algorithm is called `________`\n",
-    "\n",
-    "**Answer:** hyperparameter tuning."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.2.2 Loading the Dataset\n",
-    "\n",
-    "**We added `%matplotlib inline` to enable Matplotlib in this notebook.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn.datasets import load_digits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = load_digits()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying the Description"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".. _digits_dataset:\n",
-      "\n",
-      "Optical recognition of handwritten digits dataset\n",
-      "--------------------------------------------------\n",
-      "\n",
-      "**Data Set Characteristics:**\n",
-      "\n",
-      "    :Number of Instances: 5620\n",
-      "    :Number of Attributes: 64\n",
-      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
-      "    :Missing Attribute Values: None\n",
-      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
-      "    :Date: July; 1998\n",
-      "\n",
-      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
-      "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
-      "\n",
-      "The data set contains images of hand-written digits: 10 classes where\n",
-      "each class refers to a digit.\n",
-      "\n",
-      "Preprocessing programs made available by NIST were used to extract\n",
-      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
-      "total of 43 people, 30 contributed to the training set and different 13\n",
-      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
-      "4x4 and the number of on pixels are counted in each block. This generates\n",
-      "an input matrix of 8x8 where each element is an integer in the range\n",
-      "0..16. This reduces dimensionality and gives invariance to small\n",
-      "distortions.\n",
-      "\n",
-      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
-      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
-      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
-      "1994.\n",
-      "\n",
-      ".. topic:: References\n",
-      "\n",
-      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
-      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
-      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
-      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
-      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
-      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
-      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
-      "    2005.\n",
-      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
-      "    Algorithm. NIPS. 2000.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(digits.DESCR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Checking the Sample and Target Sizes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 4, 1, 7, 4, 8, 2, 2, 4, 4, 1, 9, 7, 3, 2, 1, 2, 5])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "digits.target[::100]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1797, 64)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "digits.data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1797,)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "digits.target.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### A Sample Digit Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  2.,  9., 15., 14.,  9.,  3.,  0.],\n",
-       "       [ 0.,  4., 13.,  8.,  9., 16.,  8.,  0.],\n",
-       "       [ 0.,  0.,  0.,  6., 14., 15.,  3.,  0.],\n",
-       "       [ 0.,  0.,  0., 11., 14.,  2.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  2., 15., 11.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  0.,  2., 15.,  4.,  0.],\n",
-       "       [ 0.,  1.,  5.,  6., 13., 16.,  6.,  0.],\n",
-       "       [ 0.,  2., 12., 12., 13., 11.,  0.,  0.]])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "digits.images[13]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Preparing the Data for Use with Scikit-Learn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.,  2.,  9., 15., 14.,  9.,  3.,  0.,  0.,  4., 13.,  8.,  9.,\n",
-       "       16.,  8.,  0.,  0.,  0.,  0.,  6., 14., 15.,  3.,  0.,  0.,  0.,\n",
-       "        0., 11., 14.,  2.,  0.,  0.,  0.,  0.,  0.,  2., 15., 11.,  0.,\n",
-       "        0.,  0.,  0.,  0.,  0.,  2., 15.,  4.,  0.,  0.,  1.,  5.,  6.,\n",
-       "       13., 16.,  6.,  0.,  0.,  2., 12., 12., 13., 11.,  0.,  0.])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "digits.data[13]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.2.2 Self Check\n",
-    "\n",
-    "**1. _(Fill-In)_** A `Bunch` object’s  `________` and `________` attributes are NumPy arrays containing the dataset’s samples and labels, respectively.\n",
-    "\n",
-    "**Answer:**  `data`, `target`.\n",
-    "\n",
-    "**2. _(True/False)_** A scikit-learn `Bunch` object contains only a dataset’s data.\n",
-    "\n",
-    "**Answer:** False. A scikit-learn `Bunch` object contains a dataset’s data and information about the dataset (called metadata), available through the `DESCR` attribute.\n",
-    "\n",
-    "**3. _(IPython Session)_** For sample number `22` in the Digits dataset, display the 8-by-8 image data and numeric value of the digit the image represents.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  0.,  8., 16.,  5.,  0.,  0.,  0.],\n",
-       "       [ 0.,  1., 13., 11., 16.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0., 10.,  0., 13.,  3.,  0.,  0.],\n",
-       "       [ 0.,  0.,  3.,  1., 16.,  1.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.,  9., 12.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0.,  3., 15.,  5.,  0.,  0.,  0.],\n",
-       "       [ 0.,  0., 14., 15.,  8.,  8.,  3.,  0.],\n",
-       "       [ 0.,  0.,  7., 12., 12., 12., 13.,  1.]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "digits.images[22]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "digits.target[22]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.2.3 Visualizing the Data\n",
-    "### Creating the Diagram "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 24 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "figure, axes = plt.subplots(nrows=4, ncols=6, figsize=(6, 4))\n",
-    "\n",
-    "### Displaying Each Image and Removing the Axes Labels \n",
-    "\n",
-    "for item in zip(axes.ravel(), digits.images, digits.target):\n",
-    "    axes, image, target = item\n",
-    "    axes.imshow(image, cmap=plt.cm.gray_r)\n",
-    "    axes.set_xticks([])  # remove x-axis tick marks\n",
-    "    axes.set_yticks([])  # remove y-axis tick marks\n",
-    "    axes.set_title(target)\n",
-    "plt.tight_layout()     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This placeholder cell was added because we had to combine \n",
-    "# the sections snippets 12-13 for the visualization to work in Jupyter\n",
-    "# and want the subsequent snippet numbers to match the book"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.2.3 Self Check\n",
-    "**1. _(Fill-In)_** The process of familiarizing yourself with your data is called `________`.\n",
-    "\n",
-    "**Answer:**  data exploration.\n",
-    "\n",
-    "**2. _(IPython Session)_** Display the image for sample number `22` of the Digits dataset. \n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "axes = plt.subplot()\n",
-    "\n",
-    "image = plt.imshow(digits.images[22], cmap=plt.cm.gray_r)\n",
-    "\n",
-    "xticks = axes.set_xticks([])\n",
-    "\n",
-    "yticks = axes.set_yticks([])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# placeholder due to merge of prior cells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# placeholder due to merge of prior cells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# placeholder due to merge of prior cells"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.2.4 Splitting the Data for Training and Testing "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "     digits.data, digits.target, random_state=11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training and Testing Set Sizes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1347, 64)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(450, 64)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.2.4 Self Check\n",
-    "**1. _(True/False)_** You should typically use all of a dataset’s data to train a model.\n",
-    "\n",
-    "**Answer:** False. It’s important to set aside a portion of your data for testing, so you can evaluate a model’s performance using data that the model has not yet seen. \n",
-    "\n",
-    "**2. _(Discussion)_** For the Digits dataset, what numbers of samples would the following statement reserve for training and testing purposes? \n",
-    "\n",
-    "```python\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    digits.data, digits.target, test_size=0.40)\n",
-    "```\n",
-    "**Answer:** 1078 and 719."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.2.5 Creating the Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.neighbors import KNeighborsClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn = KNeighborsClassifier()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.2.6 Training the Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
-       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
-       "           weights='uniform')"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "knn.fit(X=X_train, y=y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.2.6 Self Check\n",
-    "**1. _(Fill-In)_** The `KNeighborsClassifier` is said to be `________` because its work is performed only when you use it to make predictions.\n",
-    "\n",
-    "**Answer:** lazy.\n",
-    "\n",
-    "**2. _(True/False)_** Each scikit-learn estimator’s `fit` method simply loads a dataset.\n",
-    "\n",
-    "**Answer:** False. For most, scikit-learn estimators, the `fit` method loads the data into the estimator then uses that data to perform complex calculations behind the scenes that learn from the data and train the model. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.2.7 Predicting Digit Classes "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predicted = knn.predict(X=X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expected = y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 4, 9, 9, 3, 1, 4, 1, 5, 0, 4, 9, 4, 1, 5, 3, 3, 8, 5, 6])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 4, 9, 9, 3, 1, 4, 1, 5, 0, 4, 9, 4, 1, 5, 3, 3, 8, 3, 6])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrong = [(p, e) for (p, e) in zip(predicted, expected) if p != e]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(5, 3),\n",
-       " (8, 9),\n",
-       " (4, 9),\n",
-       " (7, 3),\n",
-       " (7, 4),\n",
-       " (2, 8),\n",
-       " (9, 8),\n",
-       " (3, 8),\n",
-       " (3, 8),\n",
-       " (1, 8)]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.2.7 Self Check\n",
-    "**1. _(IPython Session)_** Using the `predicted` and `expected` arrays, calculate and display the prediction accuracy percentage.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "97.78%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'{(len(expected) - len(wrong)) / len(expected):.2%}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**2. _(IPython Session)_** Rewrite the list comprehension in snippet `[29]` using a for loop. Which coding style do you prefer?\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrong = []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p, e in zip(predicted, expected):\n",
-    "      if p != e:\n",
-    "          wrong.append((p, e))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(5, 3),\n",
-       " (8, 9),\n",
-       " (4, 9),\n",
-       " (7, 3),\n",
-       " (7, 4),\n",
-       " (2, 8),\n",
-       " (9, 8),\n",
-       " (3, 8),\n",
-       " (3, 8),\n",
-       " (1, 8)]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 16.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2\n",
-    "## 16.3.1 Metrics for Model Accuracy \n",
-    "### Estimator Method `score`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "97.78%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'{knn.score(X_test, y_test):.2%}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Confusion Matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import confusion_matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion = confusion_matrix(y_true=expected, y_pred=predicted)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[45,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
-       "       [ 0, 45,  0,  0,  0,  0,  0,  0,  0,  0],\n",
-       "       [ 0,  0, 54,  0,  0,  0,  0,  0,  0,  0],\n",
-       "       [ 0,  0,  0, 42,  0,  1,  0,  1,  0,  0],\n",
-       "       [ 0,  0,  0,  0, 49,  0,  0,  1,  0,  0],\n",
-       "       [ 0,  0,  0,  0,  0, 38,  0,  0,  0,  0],\n",
-       "       [ 0,  0,  0,  0,  0,  0, 42,  0,  0,  0],\n",
-       "       [ 0,  0,  0,  0,  0,  0,  0, 45,  0,  0],\n",
-       "       [ 0,  1,  1,  2,  0,  0,  0,  0, 39,  1],\n",
-       "       [ 0,  0,  0,  0,  1,  0,  0,  0,  1, 41]])"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "confusion"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Classification Report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "names = [str(digit) for digit in digits.target_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      1.00      1.00        45\n",
-      "           1       0.98      1.00      0.99        45\n",
-      "           2       0.98      1.00      0.99        54\n",
-      "           3       0.95      0.95      0.95        44\n",
-      "           4       0.98      0.98      0.98        50\n",
-      "           5       0.97      1.00      0.99        38\n",
-      "           6       1.00      1.00      1.00        42\n",
-      "           7       0.96      1.00      0.98        45\n",
-      "           8       0.97      0.89      0.93        44\n",
-      "           9       0.98      0.95      0.96        43\n",
-      "\n",
-      "   micro avg       0.98      0.98      0.98       450\n",
-      "   macro avg       0.98      0.98      0.98       450\n",
-      "weighted avg       0.98      0.98      0.98       450\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(classification_report(expected, predicted, \n",
-    "       target_names=names))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the Confusion Matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "confusion_df = pd.DataFrame(confusion, index=range(10),\n",
-    "     columns=range(10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "axes = sns.heatmap(confusion_df, annot=True, \n",
-    "                    cmap='nipy_spectral_r')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.3.1 Self Check\n",
-    "**1. _(Fill-In)_** A Seaborn `________` displays values as colors, often with values of higher magnitude displayed as more intense colors.\n",
-    "\n",
-    "**Answer:** heat map.\n",
-    "\n",
-    "**2. _(True/False)_** In a classification report, the precision specifies the total number of correct predictions for a class divided by the total number of samples for that class. \n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**3. _(Discussion)_** Explain row 3 of the confusion matrix presented in this section:\n",
-    "\n",
-    "```\n",
-    "[ 0,  0,  0, 42,  0,  1,  0,  1,  0,  0]\n",
-    "```\n",
-    "**Answer:** The number `42` in column index 3 indicates that 42 `3`s were correctly predicted as 3s. The number `1` at column indices 5 and 7 indicates that one `3` was incorrectly classified as a `5` and one was incorrectly classified as a `7`. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.3.2 K-Fold Cross-Validation\n",
-    "### KFold Class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import KFold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kfold = KFold(n_splits=10, random_state=11, shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using the `KFold` Object with Function `cross_val_score` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import cross_val_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = cross_val_score(estimator=knn, X=digits.data, \n",
-    "     y=digits.target, cv=kfold)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.97777778, 0.99444444, 0.98888889, 0.97777778, 0.98888889,\n",
-       "       0.99444444, 0.97777778, 0.98882682, 1.        , 0.98324022])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean accuracy: 98.72%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'Mean accuracy: {scores.mean():.2%}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy standard deviation: 0.75%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'Accuracy standard deviation: {scores.std():.2%}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.3.2 Self Check\n",
-    "**1.  _(True/False)_** Randomizing the data by shuffling it before splitting it into folds is particularly important if the samples might be ordered or grouped. \n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**2. _(True/False)_** When you call `cross_val_score` to peform k-fold cross-validation, the function returns the best score produced while testing the model with each fold.\n",
-    "\n",
-    "**Answer:** False. The function returns an array containing the scores for each fold. The mean of those scores is the estimator’s overall score. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.3.3 Running Multiple Models to Find the Best One "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.svm import SVC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.naive_bayes import GaussianNB"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimators = {\n",
-    "     'KNeighborsClassifier': knn, \n",
-    "     'SVC': SVC(gamma='scale'),\n",
-    "     'GaussianNB': GaussianNB()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "KNeighborsClassifier: mean accuracy=98.72%; standard deviation=0.75%\n",
-      "                 SVC: mean accuracy=99.00%; standard deviation=0.85%\n",
-      "          GaussianNB: mean accuracy=84.48%; standard deviation=3.47%\n"
-     ]
-    }
-   ],
-   "source": [
-    "for estimator_name, estimator_object in estimators.items():\n",
-    "     kfold = KFold(n_splits=10, random_state=11, shuffle=True)\n",
-    "     scores = cross_val_score(estimator=estimator_object, \n",
-    "         X=digits.data, y=digits.target, cv=kfold)\n",
-    "     print(f'{estimator_name:>20}: ' + \n",
-    "           f'mean accuracy={scores.mean():.2%}; ' +\n",
-    "           f'standard deviation={scores.std():.2%}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scikit-Learn Estimator Diagram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.3.3 Self Check\n",
-    "**1. _(True/False)_** You should choose the best estimator before performing your machine learning study.\n",
-    "\n",
-    "**Answer:** False. It’s difficult to know in advance which machine learning model(s) will perform best for a given dataset, especially when they hide the details of how they operate from their users. For this reason, you should run multiple models to determine which is the best for your study. \n",
-    "\n",
-    "**2. _(Discussion)_** How would you modify the code in this section to so that it would also test a `LinearSVC` estimator?\n",
-    "\n",
-    "**Answer:** You’d import the `LinearSVC` class, add a key–value pair to the `estimators` dictionary (`'LinearSVC': LinearSVC()`), then execute the `for` loop, which tests every estimator in the dictionary."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.3.4 Hyperparameter Tuning "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "k=1 ; mean accuracy=98.83%; standard deviation=0.58%\n",
-      "k=3 ; mean accuracy=98.78%; standard deviation=0.78%\n",
-      "k=5 ; mean accuracy=98.72%; standard deviation=0.75%\n",
-      "k=7 ; mean accuracy=98.44%; standard deviation=0.96%\n",
-      "k=9 ; mean accuracy=98.39%; standard deviation=0.80%\n",
-      "k=11; mean accuracy=98.39%; standard deviation=0.80%\n",
-      "k=13; mean accuracy=97.89%; standard deviation=0.89%\n",
-      "k=15; mean accuracy=97.89%; standard deviation=1.02%\n",
-      "k=17; mean accuracy=97.50%; standard deviation=1.00%\n",
-      "k=19; mean accuracy=97.66%; standard deviation=0.96%\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k in range(1, 20, 2):\n",
-    "     kfold = KFold(n_splits=10, random_state=11, shuffle=True)\n",
-    "     knn = KNeighborsClassifier(n_neighbors=k)\n",
-    "     scores = cross_val_score(estimator=knn, \n",
-    "         X=digits.data, y=digits.target, cv=kfold)\n",
-    "     print(f'k={k:<2}; mean accuracy={scores.mean():.2%}; ' +\n",
-    "           f'standard deviation={scores.std():.2%}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.3.4 Self Check\n",
-    "**1. _(True/False)_** When you create an estimator object, the default hyperparameter values that scikit-learn uses are generally the best ones for every machine learning study. \n",
-    "\n",
-    "**Answer:** False. The default hyperparameter values make it easy for you to test estimators quickly. In real-world machine learning studies, you’ll want to use hyperparameter tuning to choose hyperparameter values that produce the best possible predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_04-checkpoint.ipynb b/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_04-checkpoint.ipynb
deleted file mode 100755
index 04682c7..0000000
--- a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_04-checkpoint.ipynb
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 16.4 Case Study: Time Series and Simple Linear Regression \n",
-    "### Loading the Average High Temperatures into a `DataFrame` \n",
-    "\n",
-    "**We added `%matplotlib inline` to enable Matplotlib in this notebook.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc = pd.read_csv('ave_hi_nyc_jan_1895-2018.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.columns = ['Date', 'Temperature', 'Anomaly']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.Date = nyc.Date.floordiv(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nyc.head(3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Splitting the Data for Training and Testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "     nyc.Date.values.reshape(-1, 1), nyc.Temperature.values, \n",
-    "     random_state=11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training the Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression = LinearRegression()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression.fit(X=X_train, y=y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression.coef_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression.intercept_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predicted = linear_regression.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expected = y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p, e in zip(predicted[::5], expected[::5]):\n",
-    "     print(f'predicted: {p:.2f}, expected: {e:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Predicting Future Temperatures and Estimating Past Temperatures "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predict = (lambda x: linear_regression.coef_ * x + \n",
-    "                      linear_regression.intercept_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predict(2019)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predict(1890)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the Dataset with the Regression Line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = sns.scatterplot(data=nyc, x='Date', y='Temperature',\n",
-    "     hue='Temperature', palette='winter', legend=False)\n",
-    "\n",
-    "axes.set_ylim(10, 70)\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "x = np.array([min(nyc.Date.values), max(nyc.Date.values)])\n",
-    "\n",
-    "y = predict(x)\n",
-    "\n",
-    "import matplotlib.pyplot as plt \n",
-    "\n",
-    "line = plt.plot(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This placeholder cell was added because we had to combine \n",
-    "# the sections snippets 22-28 for the visualization to work in Jupyter\n",
-    "# and want the subsequent snippet numbers to match the book"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.4 Self Check\n",
-    "**1. _(Fill-In)_** A `LinearRegression` object’s `________` and `________` attributes can be used as _m_ and _b_, respectively, in the equation _y = mx + b_ to make predictions. \n",
-    "\n",
-    "**Answer:** `coeff_`, `intercept_`.\n",
-    "\n",
-    "**2. _(True/False)_** By default, the `LinearRegression` estimator performs simple linear regression.\n",
-    "\n",
-    "**Answer:** False. By default, the `LinearRegression` estimator uses all the numeric features in a dataset, performing a multiple linear regression.\n",
-    "\n",
-    "**3. _(IPython Session)_** Use the predict lambda to estimate what the average January high temperature was in `1800` and to predict what it will be in `2100`.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predict(1800)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predict(2100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_05-checkpoint.ipynb b/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_05-checkpoint.ipynb
deleted file mode 100755
index b4612b7..0000000
--- a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_05-checkpoint.ipynb
+++ /dev/null
@@ -1,1232 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 16.5 Case Study: Multiple Linear Regression with the California Housing Dataset\n",
-    "## 16.5.1 Loading the Dataset\n",
-    "### Loading the Data\n",
-    "**We added `%matplotlib inline` to enable Matplotlib in this notebook.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn.datasets import fetch_california_housing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "california = fetch_california_housing()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying the Dataset’s Description"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".. _california_housing_dataset:\n",
-      "\n",
-      "California Housing dataset\n",
-      "--------------------------\n",
-      "\n",
-      "**Data Set Characteristics:**\n",
-      "\n",
-      "    :Number of Instances: 20640\n",
-      "\n",
-      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
-      "\n",
-      "    :Attribute Information:\n",
-      "        - MedInc        median income in block\n",
-      "        - HouseAge      median house age in block\n",
-      "        - AveRooms      average number of rooms\n",
-      "        - AveBedrms     average number of bedrooms\n",
-      "        - Population    block population\n",
-      "        - AveOccup      average house occupancy\n",
-      "        - Latitude      house block latitude\n",
-      "        - Longitude     house block longitude\n",
-      "\n",
-      "    :Missing Attribute Values: None\n",
-      "\n",
-      "This dataset was obtained from the StatLib repository.\n",
-      "http://lib.stat.cmu.edu/datasets/\n",
-      "\n",
-      "The target variable is the median house value for California districts.\n",
-      "\n",
-      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
-      "block group. A block group is the smallest geographical unit for which the U.S.\n",
-      "Census Bureau publishes sample data (a block group typically has a population\n",
-      "of 600 to 3,000 people).\n",
-      "\n",
-      "It can be downloaded/loaded using the\n",
-      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
-      "\n",
-      ".. topic:: References\n",
-      "\n",
-      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
-      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(california.DESCR)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20640, 8)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "california.data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20640,)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "california.target.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['MedInc',\n",
-       " 'HouseAge',\n",
-       " 'AveRooms',\n",
-       " 'AveBedrms',\n",
-       " 'Population',\n",
-       " 'AveOccup',\n",
-       " 'Latitude',\n",
-       " 'Longitude']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "california.feature_names"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.2 Exploring the Data with Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('precision', 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('max_columns', 9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.width', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "california_df = pd.DataFrame(california.data, \n",
-    "                              columns=california.feature_names)\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "california_df['MedHouseValue'] = pd.Series(california.target)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MedInc</th>\n",
-       "      <th>HouseAge</th>\n",
-       "      <th>AveRooms</th>\n",
-       "      <th>AveBedrms</th>\n",
-       "      <th>Population</th>\n",
-       "      <th>AveOccup</th>\n",
-       "      <th>Latitude</th>\n",
-       "      <th>Longitude</th>\n",
-       "      <th>MedHouseValue</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>8.3252</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>6.9841</td>\n",
-       "      <td>1.0238</td>\n",
-       "      <td>322.0</td>\n",
-       "      <td>2.5556</td>\n",
-       "      <td>37.88</td>\n",
-       "      <td>-122.23</td>\n",
-       "      <td>4.526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>8.3014</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>6.2381</td>\n",
-       "      <td>0.9719</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>2.1098</td>\n",
-       "      <td>37.86</td>\n",
-       "      <td>-122.22</td>\n",
-       "      <td>3.585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7.2574</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>8.2881</td>\n",
-       "      <td>1.0734</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>2.8023</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.24</td>\n",
-       "      <td>3.521</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.6431</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>5.8174</td>\n",
-       "      <td>1.0731</td>\n",
-       "      <td>558.0</td>\n",
-       "      <td>2.5479</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>3.413</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3.8462</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>6.2819</td>\n",
-       "      <td>1.0811</td>\n",
-       "      <td>565.0</td>\n",
-       "      <td>2.1815</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>3.422</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
-       "0  8.3252      41.0    6.9841     1.0238       322.0    2.5556     37.88   \n",
-       "1  8.3014      21.0    6.2381     0.9719      2401.0    2.1098     37.86   \n",
-       "2  7.2574      52.0    8.2881     1.0734       496.0    2.8023     37.85   \n",
-       "3  5.6431      52.0    5.8174     1.0731       558.0    2.5479     37.85   \n",
-       "4  3.8462      52.0    6.2819     1.0811       565.0    2.1815     37.85   \n",
-       "\n",
-       "   Longitude  MedHouseValue  \n",
-       "0    -122.23          4.526  \n",
-       "1    -122.22          3.585  \n",
-       "2    -122.24          3.521  \n",
-       "3    -122.25          3.413  \n",
-       "4    -122.25          3.422  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "california_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MedInc</th>\n",
-       "      <th>HouseAge</th>\n",
-       "      <th>AveRooms</th>\n",
-       "      <th>AveBedrms</th>\n",
-       "      <th>Population</th>\n",
-       "      <th>AveOccup</th>\n",
-       "      <th>Latitude</th>\n",
-       "      <th>Longitude</th>\n",
-       "      <th>MedHouseValue</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "      <td>20640.0000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>3.8707</td>\n",
-       "      <td>28.6395</td>\n",
-       "      <td>5.4290</td>\n",
-       "      <td>1.0967</td>\n",
-       "      <td>1425.4767</td>\n",
-       "      <td>3.0707</td>\n",
-       "      <td>35.6319</td>\n",
-       "      <td>-119.5697</td>\n",
-       "      <td>2.0686</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>1.8998</td>\n",
-       "      <td>12.5856</td>\n",
-       "      <td>2.4742</td>\n",
-       "      <td>0.4739</td>\n",
-       "      <td>1132.4621</td>\n",
-       "      <td>10.3860</td>\n",
-       "      <td>2.1360</td>\n",
-       "      <td>2.0035</td>\n",
-       "      <td>1.1540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.4999</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.8462</td>\n",
-       "      <td>0.3333</td>\n",
-       "      <td>3.0000</td>\n",
-       "      <td>0.6923</td>\n",
-       "      <td>32.5400</td>\n",
-       "      <td>-124.3500</td>\n",
-       "      <td>0.1500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.5634</td>\n",
-       "      <td>18.0000</td>\n",
-       "      <td>4.4407</td>\n",
-       "      <td>1.0061</td>\n",
-       "      <td>787.0000</td>\n",
-       "      <td>2.4297</td>\n",
-       "      <td>33.9300</td>\n",
-       "      <td>-121.8000</td>\n",
-       "      <td>1.1960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.5348</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>5.2291</td>\n",
-       "      <td>1.0488</td>\n",
-       "      <td>1166.0000</td>\n",
-       "      <td>2.8181</td>\n",
-       "      <td>34.2600</td>\n",
-       "      <td>-118.4900</td>\n",
-       "      <td>1.7970</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>4.7432</td>\n",
-       "      <td>37.0000</td>\n",
-       "      <td>6.0524</td>\n",
-       "      <td>1.0995</td>\n",
-       "      <td>1725.0000</td>\n",
-       "      <td>3.2823</td>\n",
-       "      <td>37.7100</td>\n",
-       "      <td>-118.0100</td>\n",
-       "      <td>2.6472</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>15.0001</td>\n",
-       "      <td>52.0000</td>\n",
-       "      <td>141.9091</td>\n",
-       "      <td>34.0667</td>\n",
-       "      <td>35682.0000</td>\n",
-       "      <td>1243.3333</td>\n",
-       "      <td>41.9500</td>\n",
-       "      <td>-114.3100</td>\n",
-       "      <td>5.0000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           MedInc    HouseAge    AveRooms   AveBedrms  Population    AveOccup  \\\n",
-       "count  20640.0000  20640.0000  20640.0000  20640.0000  20640.0000  20640.0000   \n",
-       "mean       3.8707     28.6395      5.4290      1.0967   1425.4767      3.0707   \n",
-       "std        1.8998     12.5856      2.4742      0.4739   1132.4621     10.3860   \n",
-       "min        0.4999      1.0000      0.8462      0.3333      3.0000      0.6923   \n",
-       "25%        2.5634     18.0000      4.4407      1.0061    787.0000      2.4297   \n",
-       "50%        3.5348     29.0000      5.2291      1.0488   1166.0000      2.8181   \n",
-       "75%        4.7432     37.0000      6.0524      1.0995   1725.0000      3.2823   \n",
-       "max       15.0001     52.0000    141.9091     34.0667  35682.0000   1243.3333   \n",
-       "\n",
-       "         Latitude   Longitude  MedHouseValue  \n",
-       "count  20640.0000  20640.0000     20640.0000  \n",
-       "mean      35.6319   -119.5697         2.0686  \n",
-       "std        2.1360      2.0035         1.1540  \n",
-       "min       32.5400   -124.3500         0.1500  \n",
-       "25%       33.9300   -121.8000         1.1960  \n",
-       "50%       34.2600   -118.4900         1.7970  \n",
-       "75%       37.7100   -118.0100         2.6472  \n",
-       "max       41.9500   -114.3100         5.0000  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "california_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.5.2 Self Check\n",
-    "**1. _(Discussion)_** Based on the `DataFrame`’s summary statistics, what was the average median household income across all block groups for California in 1990?\n",
-    "\n",
-    "**Answer:** $38,707 (`3.8707 * 10000`—recall that the datasets median income is expressed in tens of thousands)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.3 Visualizing the Features "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_df = california_df.sample(frac=0.1, random_state=17)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set(font_scale=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set_style('whitegrid')                                    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for feature in california.feature_names:\n",
-    "     plt.figure(figsize=(16, 9))\n",
-    "     sns.scatterplot(data=sample_df, x=feature, \n",
-    "                     y='MedHouseValue', hue='MedHouseValue', \n",
-    "                     palette='cool', legend=False)\n",
-    "         "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.5.3 Self Check\n",
-    "**1. _(Fill-In)_** `DataFrame` method `________` returns a randomly selected subset of the `DataFrame`’s rows.\n",
-    "\n",
-    "**Answer:** `sample`.\n",
-    "\n",
-    "**2. _(Discussion)_** Why would it be useful in a scatter plot to plot a randomly selected subset of a dataset’s samples?\n",
-    "\n",
-    "**Answer:** When you are getting to know your data for a large dataset, there could be too many samples to get a sense of how they are truly distributed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.4 Splitting the Data for Training and Testing "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "     california.data, california.target, random_state=11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(15480, 8)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5160, 8)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_test.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.5 Training the Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linear_regression = LinearRegression()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
-       "         normalize=False)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "linear_regression.fit(X=X_train, y=y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    MedInc: 0.4377030215382206\n",
-      "  HouseAge: 0.009216834565797713\n",
-      "  AveRooms: -0.10732526637360985\n",
-      " AveBedrms: 0.611713307391811\n",
-      "Population: -5.756822009298454e-06\n",
-      "  AveOccup: -0.0033845664657163703\n",
-      "  Latitude: -0.419481860964907\n",
-      " Longitude: -0.4337713349874016\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i, name in enumerate(california.feature_names):\n",
-    "     print(f'{name:>10}: {linear_regression.coef_[i]}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-36.88295065605547"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "linear_regression.intercept_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.5.5 Self Check\n",
-    "**1. _(True/False)_** By default, a `LinearRegression` estimator uses all the features in the dataset to perform a multiple linear regression. \n",
-    "\n",
-    "**Answer:** False. By default, a `LinearRegression` estimator uses all the numeric features in the dataset to perform a multiple linear regression. An error occurs if any of the features are categorical rather than numeric. Categorical features must be preprocessed into numerical ones or must be excluded from the training process."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.6 Testing the Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predicted = linear_regression.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expected = y_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.25396876, 2.34693107, 2.03794745, 1.8701254 , 2.53608339])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predicted[:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.762, 1.732, 1.125, 1.37 , 1.856])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expected[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.7 Visualizing the Expected vs. Predicted Prices "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Expected'] = pd.Series(expected)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Predicted'] = pd.Series(predicted)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "figure = plt.figure(figsize=(9, 9))\n",
-    "\n",
-    "axes = sns.scatterplot(data=df, x='Expected', y='Predicted', \n",
-    "     hue='Predicted', palette='cool', legend=False)\n",
-    "\n",
-    "start = min(expected.min(), predicted.min())\n",
-    "\n",
-    "end = max(expected.max(), predicted.max())\n",
-    "\n",
-    "axes.set_xlim(start, end)\n",
-    "\n",
-    "axes.set_ylim(start, end)\n",
-    "\n",
-    "line = plt.plot([start, end], [start, end], 'k--')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This placeholder cell was added because we had to combine \n",
-    "# the sections snippets 37-43 for the visualization to work in Jupyter\n",
-    "# and want the subsequent snippet numbers to match the book"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.8 Regression Model Metrics \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn import metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6008983115964333"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metrics.r2_score(expected, predicted)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5350149774449119"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metrics.mean_squared_error(expected, predicted)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.5.8 Self Check\n",
-    "**1. _(Fill-In)_** An R2 score of `________` indicates that an estimator perfectly predicts the dependent variable’s value, given the independent variable(s) value(s). \n",
-    "\n",
-    "**Answer:** 1.0.\n",
-    "\n",
-    "**2. _(True/False)_** When comparing estimators, the one with the mean squared error value closest to 0 is the estimator that best fits your data. \n",
-    "\n",
-    "**Answer:** True. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.5.9 Choosing the Best Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import ElasticNet, Lasso, Ridge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimators = {\n",
-    "    'LinearRegression': linear_regression,\n",
-    "    'ElasticNet': ElasticNet(),\n",
-    "    'Lasso': Lasso(),\n",
-    "    'Ridge': Ridge()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import KFold, cross_val_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LinearRegression: mean of r2 scores=0.599\n",
-      "      ElasticNet: mean of r2 scores=0.423\n",
-      "           Lasso: mean of r2 scores=0.285\n",
-      "           Ridge: mean of r2 scores=0.599\n"
-     ]
-    }
-   ],
-   "source": [
-    "for estimator_name, estimator_object in estimators.items():\n",
-    "     kfold = KFold(n_splits=10, random_state=11, shuffle=True)\n",
-    "     scores = cross_val_score(estimator=estimator_object, \n",
-    "         X=california.data, y=california.target, cv=kfold,\n",
-    "         scoring='r2')\n",
-    "     print(f'{estimator_name:>16}: ' + \n",
-    "           f'mean of r2 scores={scores.mean():.3f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_06-checkpoint.ipynb b/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_06-checkpoint.ipynb
deleted file mode 100755
index 0c48022..0000000
--- a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_06-checkpoint.ipynb
+++ /dev/null
@@ -1,197 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 16.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction \n",
-    "### Loading the Digits Dataset\n",
-    "\n",
-    "**We added `%matplotlib inline` to enable Matplotlib in this notebook.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn.datasets import load_digits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = load_digits()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a `TSNE` Estimator for Dimensionality Reduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.manifold import TSNE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tsne = TSNE(n_components=2, random_state=11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transforming the Digits Dataset’s Features into Two Dimensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reduced_data = tsne.fit_transform(digits.data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reduced_data.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the Reduced Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1],\n",
-    "                    c='black')\n",
-    "                    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the Reduced Data with Different Colors for Each Digit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1],\n",
-    "     c=digits.target, cmap=plt.cm.get_cmap('nipy_spectral_r', 10))\n",
-    "\n",
-    "colorbar = plt.colorbar(dots)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This placeholder cell was added because we had to combine \n",
-    "# the sections snippets 9-10 for the visualization to work in Jupyter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.6 Self Check\n",
-    "**1. _(Fill-In)_** With dimensionality reduction training the estimator, then using the estimator to transform the data into the specified number of dimensions can be performed separately with the TSNE methods `________` and `________`, or in one statement using the `fit_transform` method.\n",
-    "\n",
-    "**Answer:** `fit`, `transform`.\n",
-    "\n",
-    "**2. _(True/False)_** Unsupervised machine learning and visualization can help you get to know your data by finding patterns and relationships among unlabeled samples. \n",
-    "\n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_07-checkpoint.ipynb b/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_07-checkpoint.ipynb
deleted file mode 100755
index 158fb2f..0000000
--- a/examples/ch15/snippets_ipynb/.ipynb_checkpoints/16_07-checkpoint.ipynb
+++ /dev/null
@@ -1,639 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 16.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering\n",
-    "### Iris Dataset\n",
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.7 Self Check\n",
-    "**1. _(Fill-In)_** Each cluster of samples is grouped around a `________`—the cluster’s center point. \n",
-    "\n",
-    "**Answer:** centroid.\n",
-    "\n",
-    "**2. _(True/False)_** The k-means clustering algorithm studies the dataset then automatically determines the appropriate number of clusters. \n",
-    "\n",
-    "**Answer:** False. The algorithm organizes samples into the number of clusters you specify in advance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.7.1 Loading the Iris Dataset\n",
-    "**We added `%matplotlib inline` to enable Matplotlib in this notebook.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from sklearn.datasets import load_iris"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris = load_iris()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(iris.DESCR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Checking the Numbers of Samples, Features and Targets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris.data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris.target.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris.target_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris.feature_names"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.7.2 Exploring the Iris Dataset: Descriptive Statistics with Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('max_columns', 5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.width', None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_df['species'] = [iris.target_names[i] for i in iris.target]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('precision', 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_df['species'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.7.3 Visualizing the Dataset with a Seaborn `pairplot` "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set(font_scale=1.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sns.set_style('whitegrid')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid = sns.pairplot(data=iris_df, vars=iris_df.columns[0:4],\n",
-    "     hue='species')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying the pairplot in One Color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid = sns.pairplot(data=iris_df, vars=iris_df.columns[0:4])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.7.3 Self Check\n",
-    "**1. _(Fill-In)_** Seaborn’s `________` function creates a grid of scatter plots showing features against one another.\n",
-    "\n",
-    "**Answer:** `pairplot`.\n",
-    "\n",
-    "**2. _(True/False)_** A plot of a feature’s distribution shows the feature’s range of values (left-to-right) and the number of samples with those values (top-to-bottom). \n",
-    "\n",
-    "**Answer:** True."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.7.4 Using a `KMeans` Estimator\n",
-    "### Creating the Estimator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import KMeans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kmeans = KMeans(n_clusters=3, random_state=11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fitting the Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kmeans.fit(iris.data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Comparing the Computer Cluster Labels to the Iris Dataset’s Target Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(kmeans.labels_[0:50])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(kmeans.labels_[50:100])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(kmeans.labels_[100:150])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.7.4 Self Check\n",
-    "**1. _(IPython Session)_** Try k-means clustering on the Iris dataset with two clusters, then display the first 50 and the last 100 elements of the estimator’s `labels_` array.\n",
-    "\n",
-    "**Answer:** "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kmeans2 = KMeans(n_clusters=2, random_state=11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kmeans2.fit(iris.data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(kmeans2.labels_[0:50])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(kmeans2.labels_[50:150])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.7.5 Dimensionality Reduction with Principal Component Analysis\n",
-    "### Creating the PCA Object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.decomposition import PCA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca = PCA(n_components=2, random_state=11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transforming the Iris Dataset’s Features into Two Dimensions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca.fit(iris.data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_pca = pca.transform(iris.data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_pca.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualizing the Reduced Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_pca_df = pd.DataFrame(iris_pca, \n",
-    "                            columns=['Component1', 'Component2'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iris_pca_df['species'] = iris_df.species"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "axes = sns.scatterplot(data=iris_pca_df, x='Component1', \n",
-    "     y='Component2', hue='species', legend='brief') \n",
-    "\n",
-    "iris_centers = pca.transform(kmeans.cluster_centers_)\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "dots = plt.scatter(iris_centers[:,0], iris_centers[:,1], \n",
-    "                    s=100, c='k')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This placeholder cell was added because we had to combine \n",
-    "# the sections snippets 39-42 for the visualization to work in Jupyter\n",
-    "# and we wanted the subsequent snippet numbers to match the book"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# placeholder cell "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Self Check Exercises check mark image](files/art/check.png)\n",
-    "## 16.7.6 Self Check\n",
-    "**1. _(True/False)_** Each centroid in a `KMeans` object’s `cluster_centers_` array has the same number of features as the original dataset.\n",
-    "\n",
-    "**Answer:** True.\n",
-    "\n",
-    "**2. _(Discussion)_** What is the purpose of the following statement?\n",
-    "```python\n",
-    "iris_centers = pca.transform(kmeans.cluster_centers_)\n",
-    "```\n",
-    "\n",
-    "**Answer:** This statement reduces the centroids to the number of dimensions specified when the pca object was created. In the Iris case study, we were able to plot the reduced centroids in two dimensions at the centers of their corresponding clusters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 16.7.6 Choosing the Best Clustering Estimator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import DBSCAN, MeanShift,\\\n",
-    "     SpectralClustering, AgglomerativeClustering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimators = {\n",
-    "    'KMeans': kmeans,\n",
-    "    'DBSCAN': DBSCAN(),\n",
-    "    'MeanShift': MeanShift(),\n",
-    "    'SpectralClustering': SpectralClustering(n_clusters=3),\n",
-    "    'AgglomerativeClustering': \n",
-    "        AgglomerativeClustering(n_clusters=3)\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for name, estimator in estimators.items():\n",
-    "     estimator.fit(iris.data)\n",
-    "     print(f'\\n{name}:')\n",
-    "     for i in range(0, 101, 50):\n",
-    "         labels, counts = np.unique(\n",
-    "             estimator.labels_[i:i+50], return_counts=True)\n",
-    "         print(f'{i}-{i+50}:')\n",
-    "         for label, count in zip(labels, counts):\n",
-    "             print(f'   label={label}, count={count}')\n",
-    "             "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "##########################################################################\n",
-    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
-    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
-    "#                                                                        #\n",
-    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
-    "# best efforts in preparing the book. These efforts include the          #\n",
-    "# development, research, and testing of the theories and programs        #\n",
-    "# to determine their effectiveness. The authors and publisher make       #\n",
-    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
-    "# programs or to the documentation contained in these books. The authors #\n",
-    "# and publisher shall not be liable in any event for incidental or       #\n",
-    "# consequential damages in connection with, or arising out of, the       #\n",
-    "# furnishing, performance, or use of these programs.                     #\n",
-    "##########################################################################\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/ch15/snippets_py/.ipynb_checkpoints/16_02-03-checkpoint.py b/examples/ch15/snippets_py/.ipynb_checkpoints/16_02-03-checkpoint.py
deleted file mode 100755
index 4744bfe..0000000
--- a/examples/ch15/snippets_py/.ipynb_checkpoints/16_02-03-checkpoint.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# This file contains Sections 16.2 and 16.3 and all of their subsections and Self Check exercises
-
-# 16.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1
-
-# 16.2.2 Loading the Dataset
-from sklearn.datasets import load_digits
-
-digits = load_digits()
-
-# Displaying the Description
-print(digits.DESCR)
-
-# Checking the Sample and Target Sizes
-digits.target[::100]
-
-digits.data.shape
-
-digits.target.shape
-
-# A Sample Digit Image
-digits.images[13]
-
-# Preparing the Data for Use with Scikit-Learn
-digits.data[13]
-
-# 16.2.2 Self Check
-# Exercise 3
-digits.images[22]
-
-digits.target[22]
-
-# 16.2.3 Visualizing the Data
-
-# Creating the Diagram
-import matplotlib.pyplot as plt
-
-figure, axes = plt.subplots(nrows=4, ncols=6, figsize=(6, 4))
-
-# Displaying Each Image and Removing the Axes Labels 
-
-for item in zip(axes.ravel(), digits.images, digits.target):
-    axes, image, target = item
-    axes.imshow(image, cmap=plt.cm.gray_r)
-    axes.set_xticks([])  # remove x-axis tick marks
-    axes.set_yticks([])  # remove y-axis tick marks
-    axes.set_title(target)
-plt.tight_layout()     
-
-# 16.2.3 Self Check
-# Exercise 2
-axes = plt.subplot()
-
-image = plt.imshow(digits.images[22], cmap=plt.cm.gray_r)
-
-xticks = axes.set_xticks([])
-
-yticks = axes.set_yticks([])
-
-# 16.2.4 Splitting the Data for Training and Testing
-from sklearn.model_selection import train_test_split
-
-X_train, X_test, y_train, y_test = train_test_split(
-     digits.data, digits.target, random_state=11)
-
-# Training and Testing Set Sizes
-X_train.shape
-
-X_test.shape
-
-# 16.2.5 Creating the Model
-from sklearn.neighbors import KNeighborsClassifier
-
-knn = KNeighborsClassifier()
-
-# 16.2.6 Training the Model
-knn.fit(X=X_train, y=y_train)
-
-# 16.2.7 Predicting Digit Classes
-predicted = knn.predict(X=X_test)
-
-expected = y_test
-
-predicted[:20]
-
-expected[:20]
-
-wrong = [(p, e) for (p, e) in zip(predicted, expected) if p != e]
-
-wrong
-
-# 16.2.7 Self Check
-# Exercise 1
-print(f'{(len(expected) - len(wrong)) / len(expected):.2%}')
-
-# Exercise 2
-wrong = []
-
-for p, e in zip(predicted, expected):
-      if p != e:
-          wrong.append((p, e))
-
-wrong
-
-# 16.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2
-
-# 16.3.1 Metrics for Model Accuracy
-
-# Estimator Method score
-print(f'{knn.score(X_test, y_test):.2%}')
-
-# Confusion Matrix
-from sklearn.metrics import confusion_matrix
-
-confusion = confusion_matrix(y_true=expected, y_pred=predicted)
-
-confusion
-
-# Classification Report
-from sklearn.metrics import classification_report
-
-names = [str(digit) for digit in digits.target_names]
-
-print(classification_report(expected, predicted, 
-       target_names=names))
-
-# Visualizing the Confusion Matrix
-import pandas as pd
-
-confusion_df = pd.DataFrame(confusion, index=range(10),
-     columns=range(10))
-
-import seaborn as sns
-
-axes = sns.heatmap(confusion_df, annot=True, 
-                    cmap='nipy_spectral_r')
-
-# 16.3.2 K-Fold Cross-Validation
-
-# KFold Class
-from sklearn.model_selection import KFold
-
-kfold = KFold(n_splits=10, random_state=11, shuffle=True)
-
-# Using the KFold Object with Function cross_val_score
-from sklearn.model_selection import cross_val_score
-
-scores = cross_val_score(estimator=knn, X=digits.data, 
-     y=digits.target, cv=kfold)
-
-scores
-
-print(f'Mean accuracy: {scores.mean():.2%}')
-
-print(f'Accuracy standard deviation: {scores.std():.2%}')
-
-# 16.3.3 Running Multiple Models to Find the Best One
-from sklearn.svm import SVC
-
-from sklearn.naive_bayes import GaussianNB
-
-estimators = {
-     'KNeighborsClassifier': knn, 
-     'SVC': SVC(gamma='scale'),
-     'GaussianNB': GaussianNB()}
-
-for estimator_name, estimator_object in estimators.items():
-     kfold = KFold(n_splits=10, random_state=11, shuffle=True)
-     scores = cross_val_score(estimator=estimator_object, 
-         X=digits.data, y=digits.target, cv=kfold)
-     print(f'{estimator_name:>20}: ' + 
-           f'mean accuracy={scores.mean():.2%}; ' +
-           f'standard deviation={scores.std():.2%}')
-
-# 16.3.4 Hyperparameter Tuning
-for k in range(1, 20, 2):
-     kfold = KFold(n_splits=10, random_state=11, shuffle=True)
-     knn = KNeighborsClassifier(n_neighbors=k)
-     scores = cross_val_score(estimator=knn, 
-         X=digits.data, y=digits.target, cv=kfold)
-     print(f'k={k:<2}; mean accuracy={scores.mean():.2%}; ' +
-           f'standard deviation={scores.std():.2%}')
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch15/snippets_py/.ipynb_checkpoints/16_04-checkpoint.py b/examples/ch15/snippets_py/.ipynb_checkpoints/16_04-checkpoint.py
deleted file mode 100755
index c1ed59a..0000000
--- a/examples/ch15/snippets_py/.ipynb_checkpoints/16_04-checkpoint.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# 16.4 Case Study: Time Series and Simple Linear Regression
-
-# Loading the Average High Temperatures into a DataFrame
-import pandas as pd
-
-nyc = pd.read_csv('ave_hi_nyc_jan_1895-2018.csv')
-
-nyc.columns = ['Date', 'Temperature', 'Anomaly']
-
-nyc.Date = nyc.Date.floordiv(100)
-
-nyc.head(3)
-
-# Splitting the Data for Training and Testing
-from sklearn.model_selection import train_test_split
-
-X_train, X_test, y_train, y_test = train_test_split(
-     nyc.Date.values.reshape(-1, 1), nyc.Temperature.values, 
-     random_state=11)
-
-X_train.shape
-
-X_test.shape
-
-# Training the Model
-from sklearn.linear_model import LinearRegression
-
-linear_regression = LinearRegression()
-
-linear_regression.fit(X=X_train, y=y_train)
-
-linear_regression.coef_
-
-linear_regression.intercept_
-
-# Testing the Model
-predicted = linear_regression.predict(X_test)
-
-expected = y_test
-
-for p, e in zip(predicted[::5], expected[::5]):
-     print(f'predicted: {p:.2f}, expected: {e:.2f}')
-
-# Predicting Future Temperatures and Estimating Past Temperatures
-
-predict = (lambda x: linear_regression.coef_ * x + 
-                      linear_regression.intercept_)
-
-predict(2019)
-
-predict(1890)
-
-# Visualizing the Dataset with the Regression Line
-import seaborn as sns
-
-axes = sns.scatterplot(data=nyc, x='Date', y='Temperature',
-     hue='Temperature', palette='winter', legend=False)
-
-axes.set_ylim(10, 70)
-
-import numpy as np
-
-x = np.array([min(nyc.Date.values), max(nyc.Date.values)])
-
-y = predict(x)
-
-import matplotlib.pyplot as plt 
-
-line = plt.plot(x, y)
-
-# 16.4 Self Check
-# Exercise 3
-predict(1889)
-
-predict(2020)
-
-
-
-##########################################################################
-# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #
-# Pearson Education, Inc. All Rights Reserved.                           #
-#                                                                        #
-# DISCLAIMER: The authors and publisher of this book have used their     #
-# best efforts in preparing the book. These efforts include the          #
-# development, research, and testing of the theories and programs        #
-# to determine their effectiveness. The authors and publisher make       #
-# no warranty of any kind, expressed or implied, with regard to these    #
-# programs or to the documentation contained in these books. The authors #
-# and publisher shall not be liable in any event for incidental or       #
-# consequential damages in connection with, or arising out of, the       #
-# furnishing, performance, or use of these programs.                     #
-##########################################################################
diff --git a/examples/ch15/snippets_py/.ipynb_checkpoints/16_07-checkpoint.py b/examples/ch15/snippets_py/.ipynb_checkpoints/16_07-checkpoint.py
deleted file mode 100755
index 4bca02a..0000000
--- a/examples/ch15/snippets_py/.ipynb_checkpoints/16_07-checkpoint.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# 16.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering
-# Iris Dataset
-
-# 16.7.1 Loading the Iris Dataset
-from sklearn.datasets import load_iris
-
-iris = load_iris()
-
-print(iris.DESCR)
-
-# Checking the Numbers of Samples, Features and Targets
-iris.data.shape
-
-iris.target.shape
-
-iris.target_names
-
-iris.feature_names
-
-# 16.7.2 Exploring the Iris Dataset: Descriptive Statistics with Pandas
-import pandas as pd
-
-pd.set_option('max_columns', 5)
-
-pd.set_option('display.width', None)
-
-iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
-
-iris_df['species'] = [iris.target_names[i] for i in iris.target]
-
-iris_df.head()
-
-pd.set_option('precision', 2)
-
-iris_df.describe()
-
-iris_df['species'].describe()
-
-# 16.7.3 Visualizing the Dataset with a Seaborn pairplot
-import seaborn as sns
-
-sns.set(font_scale=1.1)
-
-sns.set_style('whitegrid')
-
-grid = sns.pairplot(data=iris_df, vars=iris_df.columns[0:4],
-     hue='species')
-
-# Displaying the pairplot in One Color
-grid = sns.pairplot(data=iris_df, vars=iris_df.columns[0:4])
-
-# 16.7.4 Using a KMeans Estimator
-
-# Creating the Estimator
-from sklearn.cluster import KMeans
-
-kmeans = KMeans(n_clusters=3, random_state=11)
-
-# Fitting the Model
-kmeans.fit(iris.data)
-
-# Comparing the Computer Cluster Labels to the Iris Dataset’s Target Values
-print(kmeans.labels_[0:50])
-
-print(kmeans.labels_[50:100])
-
-print(kmeans.labels_[100:150])
-
-# 16.7.4 Self Check
-kmeans2 = KMeans(n_clusters=2)
-
-kmeans2.fit(iris.data)
-
-print(kmeans2.labels_[0:50])
-
-print(kmeans2.labels_[50:150])
-
-# 16.7.5 Dimensionality Reduction with Principal Component Analysis
-# Creating the PCA Object
-from sklearn.decomposition import PCA
-
-pca = PCA(n_components=2, random_state=11)
-
-# Transforming the Iris Dataset’s Features into Two Dimensions
-pca.fit(iris.data)
-
-iris_pca = pca.transform(iris.data)
-
-iris_pca.shape
-
-# Visualizing the Reduced Data
-iris_pca_df = pd.DataFrame(iris_pca, 
-                            columns=['Component1', 'Component2'])
-
-iris_pca_df['species'] = iris_df.species
-
-axes = sns.scatterplot(data=iris_pca_df, x='Component1', 
-     y='Component2', hue='species', legend='brief', 
-     palette='cool')
-
-iris_centers = pca.transform(kmeans.cluster_centers_)
-
-import matplotlib.pyplot as plt
-
-dots = plt.scatter(iris_centers[:,0], iris_centers[:,1], 
-                    s=100, c='k')
-
-# 16.7.6 Choosing the Best Clustering Estimator
-from sklearn.cluster import DBSCAN, MeanShift,\
-     SpectralClustering, AgglomerativeClustering
-
-estimators = {
-    'KMeans': kmeans,
-    'DBSCAN': DBSCAN(),
-    'MeanShift': MeanShift(),
-    'SpectralClustering': SpectralClustering(n_clusters=3),
-    'AgglomerativeClustering': 
-        AgglomerativeClustering(n_clusters=3)
-}
-
-import numpy as np
-
-for name, estimator in estimators.items():
-     estimator.fit(iris.data)
-     print(f'\n{name}:')
-     for i in range(0, 101, 50):
-         labels, counts = np.unique(
-             estimator.labels_[i:i+50], return_counts=True)
-         print(f'{i}-{i+50}:')
-         for label, count in zip(labels, counts):
-             print(f'   label={label}, count={count}')
-             
\ No newline at end of file