在 NumPy 中使用数组时，resize 和 reshape 有什么区别? [英] What is the difference between resize and reshape when using arrays in NumPy?

问题描述

我刚刚开始使用 NumPy.数组的 resize 和 reshape 有什么区别?

解决方案

Reshape 不会改变前面提到的数据 此处.调整大小会更改数据，如此处所示.

以下是一些示例:

<预><代码>>>>numpy.random.rand(2,3)数组([[ 0.6832785, 0.23452056, 0.25131171],[ 0.81549186, 0.64789272, 0.48778127]])>>>ar = numpy.random.rand(2,3)>>>ar.reshape(1,6)数组([[ 0.43968751, 0.95057451, 0.54744355, 0.33887095, 0.95809916,0.88722904]])>>>ar数组([[ 0.43968751, 0.95057451, 0.54744355],[ 0.33887095, 0.95809916, 0.88722904]])

reshape 后数组没有变化，只是输出一个临时数组reshape.

<预><代码>>>>ar.resize(1,6)>>>ar数组([[ 0.43968751, 0.95057451, 0.54744355, 0.33887095, 0.95809916,0.88722904]])

调整大小后，数组改变了形状.

I have just started using NumPy. What is the difference between resize and reshape for arrays?

解决方案

Reshape doesn't change the data as mentioned here. Resize changes the data as can be seen here.

Here are some examples:

>>> numpy.random.rand(2,3)
array([[ 0.6832785 ,  0.23452056,  0.25131171],
       [ 0.81549186,  0.64789272,  0.48778127]])
>>> ar = numpy.random.rand(2,3)
>>> ar.reshape(1,6)
array([[ 0.43968751,  0.95057451,  0.54744355,  0.33887095,  0.95809916,
         0.88722904]])
>>> ar
array([[ 0.43968751,  0.95057451,  0.54744355],
       [ 0.33887095,  0.95809916,  0.88722904]])

After reshape the array didn't change, but only output a temporary array reshape.

>>> ar.resize(1,6)
>>> ar
array([[ 0.43968751,  0.95057451,  0.54744355,  0.33887095,  0.95809916,
         0.88722904]])

After resize the array changed its shape.

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