1 import numpy as np

维度变换

1 a = np.arange(24)
2 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])

reshape(),视图，不修改原数组

1 a.reshape(4,6)

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]])

1 a.reshape(2,3,4)

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]]])

1 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])

resize() 修改原数组

1 a.resize(2,3,4)

1 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]]])

对数组降维，返回折叠后的一维数组，修改视图

1 a.flatten()

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])

类型变换

1 b = np.array([True,20,177.7])
2 b,b.dtype

(array([  1. ,  20. , 177.7]), dtype(‘float64‘))

1 # 定义数组时修改类型
2 np.array([True,20,177.7],dtype=np.int)

array([  1,  20, 177])

1 #调用数组时修改类型  #并不改变原数组
2 b
3 b.astype(np.int) #改变视图

array([  1,  20, 177])

1 b.astype(np.unicode_)

array([‘1.0‘, ‘20.0‘, ‘177.7‘], dtype=‘<U32‘)

1 b

array([  1. ,  20. , 177.7])

原文地址：https://www.cnblogs.com/doitjust/p/9302635.html