数组的切片与索引

一维NumPy数组的切片操作与Python列表的切片一样

1. 通过下标选择该数组3-7的数

   In [1]: import numpy as np
   
   In [2]: a = np.arange(9)
   
   In [3]: a[3:7]
   Out[3]: array([3, 4, 5, 6])
   

2. 下标范围0-7，下标每次递增2

   In [6]: a[:7:2]
   Out[6]: array([0, 2, 4, 6])
   

3. 通过负值翻转数组

   In [7]: a[::-1]
   Out[7]: array([8, 7, 6, 5, 4, 3, 2, 1, 0])
   

处理数组形状

In [8]: b = np.arange(24).reshape(2,3,4)

In [9]: b
Out[9]: 
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]]])

• 拆解(ravel)

  可以利用ravel()函数将多维数组变成一维数组

    In [10]: b.ravel()
    Out[10]: 
    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])
  

• 拉直（Flatten）

  其功能与ravel()相同,不同的是flatten()返回的是真实的数组,需要分配新的空间;而ravel()返回的只是数组的视图

    In [11]: b.flatten()
    Out[11]: 
    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])
  

• 用元祖指定数组形状

  除reshape()函数外,还可以用元祖来定义数组的形状

    In [13]: b.shape = (6,4)
  
    In [14]: b
    Out[14]: 
    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]])
  

• 转置(transpose)

  转置是一种数据变换方法;对于二维表而言,转置就意味着行变成列,列变成行

   In [16]: b.transpose()
   Out[16]: 
   array([[ 0,  4,  8, 12, 16, 20],
          [ 1,  5,  9, 13, 17, 21],
          [ 2,  6, 10, 14, 18, 22],
          [ 3,  7, 11, 15, 19, 23]])
  

• 调整大小(resize)

  函数resize()的作用类似于reshape(),但是会改薄案所作用的数组

   In [18]: b
   Out[18]: 
   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]])
  

结束语

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