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• .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]])

.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]])

.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])