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numpy
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‎PythonDemo/numpy/arrayboolean.py‎

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# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 12 16:07:17 2018
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@author: Administrator
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@description： any和all函数
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"""
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import numpy as np
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from numpy.matlib import randn
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print('Boolean values are coerced to 1 (True) and 0 (False) in the above methods. Thus, sum is often used as a means of counting True values in a boolean array:')
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arr = randn(100)
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print("输出生成的随机数数组中数值大于0的个数：")
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print((arr > 0).sum())
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bools = np.array([False, False, True, False])
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print(bools)
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print("只要有一个为true就输出true:")
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print(bools.any()) # true
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print("全部才输出true：")
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print(bools.all()) #false
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bools2 = np.array([ True, True, True, True])
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print(bools2.all()) #true
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#两个数组之间
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# any():a数组与b数组有一个元素对应相等就输出true
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#all():a数组与b数组每个元素都对应相等才输出true
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a=np.array([1,2,3])
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b=np.array([2,2,3])
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boolean=(a==b).all()
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boolean2=(a==b).any()
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print(boolean)#true
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print(boolean2)#true

‎PythonDemo/numpy/arraymethod.py‎

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@description： numpy库操纵数组的一些方法
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"""
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import numpy as np
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from numpy.matlib import randn
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#定义数组类型
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arr3 = np.array([1, 2, 3], dtype=np.float64)
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arr3d[0] = 3 #将第1个二维数组的元素都修改为3
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arr3d[1] = 3 #将第2个二维数组的元素都修改为3
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print(arr3d) #输出全为3的三维数组
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arr3d[0] = old_values
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print(arr3d) # 输出 [[[1 2 3] [4 5 6]] [[3 3 3] [3 3 3]] ]
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names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
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print("输出names数组：")
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print(names)
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print("输出randn(7, 4)生成的7行4列的随机数函数")
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data = randn(7, 4)
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print(data)
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print("判断names数组中的每一个元素是否等于Bob:")
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print(names == 'Bob') #, 输出 [True False False True FalseFalse False]
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print("data[names == 'Bob']输出data中判断为true的行元素:")
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print(data[names == 'Bob'])
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print("data[names == 'Bob', 2:]输出data中判断为true所在的行，及第3列第4列的所有元素:")
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print(data[names == 'Bob', 2:],) #输出data中判断为true所在的行，及第3列第4列的所有元素；
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print("data[names == 'Bob', 3]输出data中判断为true所在的行及第4列的元素 :")
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print(data[names == 'Bob', 3])
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print("输出名字为Bob以及Will所在的行元素 :")
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print(data[((names == 'Bob') | (names == 'Will'))]) #输出名字为Bob以及Will所在的行元素
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‎PythonDemo/numpy/arraymethod2.py‎

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# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 12 15:37:14 2018
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@author: Administrator
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"""
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import numpy as np
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from numpy.matlib import randn
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arr = np.empty((8, 4)) #二维数组
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#range(8)即 [0, 1, 2, 3, 4, 5, 6, 7]
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for i in range(8):
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arr[i] = i #第i行的数字都为i;
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print(arr)
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print(arr[[4, 3, 0, 6]]) #[[ 4. 4. 4. 4.] [ 3. 3. 3. 3.] [ 0. 0. 0. 0.] [ 6. 6. 6. 6.]]
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arr = np.arange(32).reshape((8, 4))
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print("np.arange(32).reshape((8, 4)):")
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print(arr)
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print(arr[[1, 5, 7, 2], [0, 3, 1, 2]])#[ 4 23 29 10]
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print(" 输出矩阵转置: ")
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arr = np.arange(15).reshape((3, 5))
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print(arr.T)
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print("输出np.arange(10)：")
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arr = np.arange(10)
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print(arr)
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print("输出np.sqrt(arr)：")
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print(np.sqrt(arr)) #仍是一维数组，求数组中的每个元素求平方根
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print("输出np.exp(arr))：")
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print(np.exp(arr)) #仍是一维数组，求以e为底，数组中的每个元素作为指数的值
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print("randn(8):")
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x = randn(8) #生成8个随机数
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print(x)
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print("randn(8):")
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y = randn(8)
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print(y)
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print(np.maximum(x, y)) #仍是一维数组，每个元素为数组x和y对应元素最大的那个；
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print("-------------------------------------")
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xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])
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yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])
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cond = np.array([True, False, True, True, False])
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result = np.where(cond, xarr, yarr)
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print(result)#输出：[1.1 2.2 1.3 1.4 2.5]
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print("-------------------------------------")
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arr = randn(4, 4)
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print(arr)
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print(np.where(arr > 0, 2, -2))
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print("-------------------------------------")
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arr = np.random.randn(5, 4) # normally-distributed data
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print(arr)
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print(arr.mean())
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print(np.mean(arr))
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print(arr.sum())
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print(arr.mean(axis=1))
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‎PythonDemo/numpy/arraysort.py‎

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# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 12 16:01:13 2018
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@author: Administrator
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@description： numpy库中的数组排序(sort)函数
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"""
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import numpy as np
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from numpy.matlib import randn
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arr = randn(8)
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print("排序前：")
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print(arr)
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print("排序后：")
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print(np.sort(arr))

‎PythonDemo/numpy/arrayunique.py‎

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# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 12 16:02:47 2018
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@author: Administrator
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@description： numpy库unique函数保证数组中的元素唯一（剔除重复元素）
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"""
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import numpy as np
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names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
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print(np.unique(names))
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ints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4,5])
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print(np.unique(ints))

‎PythonDemo/numpy/creatArray.py‎

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@description： numpy库创建数组的一些方法
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"""
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import numpy as np
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from numpy.matlib import randn
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print("创建一维数组：")
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data1 = [3, 3.3, 9, 5, 6]
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arr1 = np.array(data1)
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print(np.empty((3,6)))
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#用arrange函数创建数组
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print("np.arange(9)创建一维数组：")
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print(np.arange(9))
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print(np.arange(9))
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#用随机函数randn创建二维数组，7行4列
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print("randn(7, 4))创建数字随机的一维数组：")
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data = randn(7, 4)
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print(data)

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