例题1

8*8棋盘矩阵，其中1、3、5、7行&&0、2、4、6列的元素值为1， 1、3、5、7列&&0、2、4、6行的元素值也为1。示例代码：

>>>import numpy as np
>>>z = np.zeros((8, 8), dtype = int)
>>>z[1::2, ::2] = 1
>>>z[::2, 1::2] = 1
>>>z
array([[0, 1, 0, 1, 0, 1, 0, 1],
       [1, 0, 1, 0, 1, 0, 1, 0],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [1, 0, 1, 0, 1, 0, 1, 0],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [1, 0, 1, 0, 1, 0, 1, 0],
       [0, 1, 0, 1, 0, 1, 0, 1],
       [1, 0, 1, 0, 1, 0, 1, 0]])

例题2

归一化，将矩阵规格化到0~1，即最小的变成0，最大的变成1，最小与最大的等比缩放。示例代码：

>>>z = 10 * np.random.rand(5, 5)
>>>z
array([[ 6.07608738,  0.07881604,  2.36949897,  6.5653295 ,  3.48561715],
       [ 9.6485403 ,  1.85315985,  8.38311931,  5.92832551,  7.34598467],
       [ 9.50648487,  8.68362313,  5.71049914,  9.18061123,  1.22047609],
       [ 7.77896973,  7.16390673,  2.50980284,  9.20855074,  6.00073204],
       [ 5.4355661 ,  9.17817416,  2.76074893,  0.65534421,  4.09743307]])
>>>zmin, zmax = z.min(), z.max()
>>>z = (z - zmin)/(zmax - zmin)
>>>z
array([[ 0.62669218,  0.        ,  0.2393677 ,  0.67781613,  0.35599783],
       [ 1.        ,  0.18541222,  0.86776829,  0.61125162,  0.75939164],
       [ 0.98515574,  0.8991698 ,  0.58848959,  0.95110318,  0.11929916],
       [ 0.80463694,  0.74036519,  0.25402893,  0.95402275,  0.61881783],
       [ 0.55976013,  0.95084852,  0.28025185,  0.06024501,  0.41993028]])

例题3

交换矩阵中其中的两行。示例代码：

>>>a = np.arange(25).reshape((5, 5))
>>>a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])
>>>a[[0, 1]] = a[[1, 0]]        #第一行和第二行元素进行对调
>>>a
array([[ 5,  6,  7,  8,  9],
       [ 0,  1,  2,  3,  4],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])

例题4

找出数组中与给定值最接近的数。示例代码：

>>>z = np.arange(8).reshape((2, 4))
>>>z
array([[0, 1, 2, 3],
       [4, 5, 6, 7]])
>>>a = 5.1
>>>np.abs(z - a).argmin()

例题5

判断二维数组中，有没有一整列元素值为0。示例代码：

>>>z = np.random.randint(0, 3, size = (2, 10))
>>>z
array([[1, 2, 2, 2, 2, 0, 0, 1, 2, 2],
       [0, 1, 2, 1, 1, 1, 2, 1, 2, 2]])
>>>z.any(axis = 0)
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,  True], dtype=bool)

说明，通过布尔值判断列值是否都为0，如果都为0，则为False，反之为True。

例题6

生成二维的高斯矩阵。示例代码：

>>>x, y = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))
>>>D = np.sqrt(x**2 + y**2)
>>>sigma, mu = 1, 0        #标准差为1，均值为0
>>>a = np.exp(-(D-mu)**2/(2*sigma**2))
>>>a
array([[ 0.36787944,  0.44822088,  0.51979489,  0.57375342,  0.60279818,
         0.60279818,  0.57375342,  0.51979489,  0.44822088,  0.36787944],
       [ 0.44822088,  0.54610814,  0.63331324,  0.69905581,  0.73444367,
         0.73444367,  0.69905581,  0.63331324,  0.54610814,  0.44822088],
       [ 0.51979489,  0.63331324,  0.73444367,  0.81068432,  0.85172308,
         0.85172308,  0.81068432,  0.73444367,  0.63331324,  0.51979489],
       [ 0.57375342,  0.69905581,  0.81068432,  0.89483932,  0.9401382 ,
         0.9401382 ,  0.89483932,  0.81068432,  0.69905581,  0.57375342],
       [ 0.60279818,  0.73444367,  0.85172308,  0.9401382 ,  0.98773022,
         0.98773022,  0.9401382 ,  0.85172308,  0.73444367,  0.60279818],
       [ 0.60279818,  0.73444367,  0.85172308,  0.9401382 ,  0.98773022,
         0.98773022,  0.9401382 ,  0.85172308,  0.73444367,  0.60279818],
       [ 0.57375342,  0.69905581,  0.81068432,  0.89483932,  0.9401382 ,
         0.9401382 ,  0.89483932,  0.81068432,  0.69905581,  0.57375342],
       [ 0.51979489,  0.63331324,  0.73444367,  0.81068432,  0.85172308,
         0.85172308,  0.81068432,  0.73444367,  0.63331324,  0.51979489],
       [ 0.44822088,  0.54610814,  0.63331324,  0.69905581,  0.73444367,
         0.73444367,  0.69905581,  0.63331324,  0.54610814,  0.44822088],
       [ 0.36787944,  0.44822088,  0.51979489,  0.57375342,  0.60279818,
         0.60279818,  0.57375342,  0.51979489,  0.44822088,  0.36787944]])

7. 读取csv文件，将数组中元素为NAN的值替换成数值0，并且求数组中元素最大值。示例代码：

>>>arr = np.genfromtxt("123.csv", delimiter = ",", dtype = "String", skip_header = 1)
>>>empty_value = np.isnan(arr)
>>>arr[empty_value] = '0'
>>>arr = arr.astype(float)
>>>arr
>>>arr.max()