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<!DOCTYPE html>

<head>

<title>About</title>

</head>

<h1><a href="../">Doing Math with Python</a></h1>

<li class="active"><a href="../pages/about.html">About</a></li>

<li><a href="../pages/software-installation.html">Software Installation</a></li>

<li><a href="../pages/programs.html">Programs</a></li>

<li><a href="../pages/errata.html">Errata</a></li>

<li><a href="../pages/reviews.html">Reviews</a></li>

</ul></nav>

</header>

<h1 class="entry-title">About</h1>

<p><cite>Doing Math with Python</cite> is written by <a class="reference external" href="https://echorand.me">Amit Saha</a> and published by <a class="reference external" href="http://www.nostarch.com/doingmathwithpython">No

Starch Press</a>.

It has been translated to French, Simplified Chinese, Korean and Japanese.</p>

<p>The English version of the book is now available in print and electronic formats (including

Amazon Kindle). Please refer to the <a class="reference external" href="../pages/buy.html">Buy</a>

page. Some readers have taken the time out to post a review of the

book on Amazon and elsewhere. Please refer to the <a class="reference external" href="../pages/reviews.html">Reviews</a> page.</p>

<p>There are seven chapters and 2 Appendices in the book. A detailed

table of contents is available from the <a class="reference external" href="http://www.nostarch.com/download/Doing%20Math%20with%20Python_dTOC.pdf">publisher's website</a>.</p>

<h2>Chapter 1: Numbers</h2>

<p>This chapter starts with the absolute basics of handling numbers in

Python. It discusses integers, floating point numbers, fractions,

complex numbers and performing operations with them.</p>

</div>

<h2>Chapter 2: Visualizing Data with Graphs</h2>

<p>This chapter introduces Matplotlib and shows how to create graphs

representing collections of numbers.</p>

</div>

<h2>Chapter 3: Describing Data with Statistics</h2>

<p>As the title suggests, this chapter is all about the statistical

measures one would first learn in high school – mean, median, mode,

frequency table, range, variance, standard deviation and linear

correlation are discussed.</p>

</div>

<h2>Chapter 4: Algebra and Symbolic Math with SymPy</h2>

<p>The first three chapters are all about number crunching. The fourth

chapter introduces the reader to the basics of manipulating symbolic

expressions using SymPy. Factorizing algebraic expressions, solving

equations, plotting from symbolic expressions are some of the topics

discussed in this chapter.</p>

</div>

<h2>Chapter 5: Sets and Probability</h2>

<p>This chapter starts off with how to create a set and demonstrating the

common set operations. Utility of the different set operations are

demonstrated via simple applications. For example, Cartesian product

is used to write a program to simulate an experiment to calculate the

time period of a simple pendulum of different lengths and at places

with varying gravity. Union and intersection operations are applied to

finding the probability of events.</p>

<p>The chapter then moves onto discussing how to generate uniform and non

uniform random numbers, and using them to simulate scenarios such as a

die roll and a fictional ATM which dispenses dollar bills of different

denominations with varying probability.</p>

</div>

<h2>Chapter 6: Drawing shapes and Fractals</h2>

<p>This chapter is logically divided into two parts. The first part

introduces the reader to matplotlib patches which allows drawing

geometric shapes (circles and polygons), followed by matplotlib’s

animation API which allows drawing animated figures. The trajectory of

a projectile motion discussed elsewhere in various contexts is

animated combining both these things.</p>

<p>The second part of the chapter introduces the concept of geometric

transformation. Combining that with the knowledge of generating random

numbers learned earlier in Chapter 5, the reader will learn how to

draw fractals such as the Barnsley Fern, Sierpinski triangle and

Mandelbrot set.</p>

</div>

<h2>Chapter 7: Solving Calculus Problems</h2>

<p>This chapter discusses solving differentiation and integration tasks

using programs. As applications, the gradient ascent (and descent)

methods and continuous probability are discussed.</p>

</div>

<h2>Appendix A</h2>

<p>This appendix covers the installation of software required to run the

programs in the book on Microsoft Windows, Linux and Mac OS X.</p>

</div>

<h2>Appendix B</h2>

<p>This appendix discusses some Python topics which the reader may not be

familiar with, but are needed to follow some parts of the book. It

also includes topics which provide additional information beyond that

required for the programs in the book.</p>

<h3>Stay in touch</h3>

<p>You can stay connected with the book, its readers and me via the

following channels:</p>

<li><a class="reference external" href="https://www.facebook.com/doingmathwithpython">Facebook page</a></li>

<li><a class="reference external" href="https://plus.google.com/u/0/communities/113121562865298236232">G+ Community</a></li>

<li><a class="reference external" href="https://twitter.com/mathwithpython">Twitter</a></li>

</ul>

<p>You can contact me directly via:</p>

<li>Twitter: <a class="reference external" href="https://twitter.com/mathwithpython">@mathwithpython</a></li>

<li>Email : <a class="reference external" href="mailto:doingmathwithpython@gmail.com">doingmathwithpython@gmail.com</a></li>

</ul>

<p><cite>Page last updated: August 29, 2015</cite></p>

</div>

</section>

<h2>social</h2>

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Proudly powered by <a href="https://getpelican.com/">Pelican</a>, which takes great advantage of <a href="https://www.python.org/">Python</a>.

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<p>The theme is by <a href="https://www.smashingmagazine.com/2009/08/designing-a-html-5-layout-from-scratch/">Smashing Magazine</a>, thanks!</p>

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