python】numpy库ndarray多维数组的维度变换方法:reshape、resize、swapaxes、flatten

numpy库对多维数组有非常灵巧的处理方式,主要的处理方法有:

  • .reshape(shape) : 不改变数组元素,返回一个shape形状的数组,原数组不变
  • .resize(shape) : 与.reshape()功能一致,但修改原数组
In [22]: a = np.arange(20)
#原数组不变
In [23]: a.reshape([4,5])
Out[23]:
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19]])

In [24]: a
Out[24]:
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19])

#修改原数组
In [25]: a.resize([4,5])

In [26]: a
Out[26]:
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19]])
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • 8
  • 9
  • 10
  • 11
  • 12
  • 13
  • 14
  • 15
  • 16
  • 17
  • 18
  • 19
  • 20
  • 21
  • 22
  • 23
  • 24
  • .swapaxes(ax1,ax2) : 将数组n个维度中两个维度进行调换,不改变原数组
In [27]: a.swapaxes(1,0)
Out[27]:
array([[ 0,  5, 10, 15],
       [ 1,  6, 11, 16],
       [ 2,  7, 12, 17],
       [ 3,  8, 13, 18],
       [ 4,  9, 14, 19]])
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • .flatten() : 对数组进行降维,返回折叠后的一维数组,原数组不变
In [29]: a.flatten()
Out[29]:
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19])

猜你喜欢

转载自blog.csdn.net/mago2015/article/details/81050590
今日推荐