Numpy中转置transpose、T和swapaxes的实例讲解
利用Python进行数据分析时,Numpy是最常用的库,经常用来对数组、矩阵等进行转置等,有时候用来做数据的存储。
在numpy中,转置transpose和轴对换是很基本的操作,下面分别详细讲述一下,以免自己忘记。
In [1]: import numpy as np In [2]: arr=np.arange(16).reshape(2,2,4) In [3]: arr Out[3]: array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7]], [[ 8, 9, 10, 11], [12, 13, 14, 15]]])
如上图所示,将0-15放在一个2 2 4 的矩阵当中,得到结果如上。
现在要进行装置transpose操作,比如
In [4]: arr.transpose(1,0,2) Out[4]: array([[[ 0, 1, 2, 3], [ 8, 9, 10, 11]], [[ 4, 5, 6, 7], [12, 13, 14, 15]]])
结果是如何得到的呢?
每一个元素都分析一下,0位置在[0,0,0],转置为[1,0,2],相当于把原来位置在[0,1,2]的转置到[1,0,2],对0来说,位置转置后为[0,0,0],同理,对1 [0,0,1]来说,转置后为[0,0,1],同理我们写出所有如下:
其中第一列是值,第二列是转置前位置,第三列是转置后,看到转置后位置,再看如上的结果,是不是就豁然开朗了?
0 [0,0,0] [0,0,0] 1 [0,0,1] [0,0,1] 2 [0,0,2] [0,0,2] 3 [0,0,3] [0,0,3] 4 [0,1,0] [1,0,0] 5 [0,1,1] [1,0,1] 6 [0,1,2] [1,0,2] 7 [0,1,3] [1,0,3] 8 [1,0,0] [0,1,0] 9 [1,0,1] [0,1,1] 10 [1,0,2] [0,1,2] 11 [1,0,3] [0,1,3] 12 [1,1,0] [1,1,0] 13 [1,1,1] [1,1,1] 14 [1,1,2] [1,1,2] 15 [1,1,3] [1,1,3]
再看另一个结果:
In [20]: arr.T Out[20]: array([[[ 0, 8], [ 4, 12]], [[ 1, 9], [ 5, 13]], [[ 2, 10], [ 6, 14]], [[ 3, 11], [ 7, 15]]]) In [21]: arr.transpose(2,1,0) Out[21]: array([[[ 0, 8], [ 4, 12]], [[ 1, 9], [ 5, 13]], [[ 2, 10], [ 6, 14]], [[ 3, 11], [ 7, 15]]])
再对比转置前后的图看一下:
0 [0,0,0] [0,0,0] 1 [0,0,1] [1,0,0] 2 [0,0,2] [2,0,0] 3 [0,0,3] [3,0,0] 4 [0,1,0] [0,1,0] 5 [0,1,1] [1,1,0] 6 [0,1,2] [2,1,0] 7 [0,1,3] [3,1,0] 8 [1,0,0] [0,0,1] 9 [1,0,1] [1,0,1] 10 [1,0,2] [2,0,1] 11 [1,0,3] [3,0,1] 12 [1,1,0] [0,1,1] 13 [1,1,1] [1,1,1] 14 [1,1,2] [2,1,1] 15 [1,1,3] [3,1,1]
瞬间就明白转置了吧!其实只要动手写写,都很容易明白的。另外T其实就是把顺序全部颠倒过来,如下:
In [22]: arr3=np.arange(16).reshape(2,2,2,2) In [23]: arr3 Out[23]: array([[[[ 0, 1], [ 2, 3]], [[ 4, 5], [ 6, 7]]], [[[ 8, 9], [10, 11]], [[12, 13], [14, 15]]]]) In [24]: arr3.T Out[24]: array([[[[ 0, 8], [ 4, 12]], [[ 2, 10], [ 6, 14]]], [[[ 1, 9], [ 5, 13]], [[ 3, 11], [ 7, 15]]]]) In [25]: arr3.transpose(3,2,1,0) Out[25]: array([[[[ 0, 8], [ 4, 12]], [[ 2, 10], [ 6, 14]]], [[[ 1, 9], [ 5, 13]], [[ 3, 11], [ 7, 15]]]])
转置就是这样子,具体上面aar3转置前后的位置,就不写了。
下面说说swapaxes,轴对称。
话不多,上结果
In [27]: arr.swapaxes(1,2) Out[27]: array([[[ 0, 4], [ 1, 5], [ 2, 6], [ 3, 7]], [[ 8, 12], [ 9, 13], [10, 14], [11, 15]]]) In [28]: arr.transpose(0,2,1) Out[28]: array([[[ 0, 4], [ 1, 5], [ 2, 6], [ 3, 7]], [[ 8, 12], [ 9, 13], [10, 14], [11, 15]]])
发现了吧,其实swapaxes其实就是把矩阵中某两个轴对换一下,不信再看一个:
In [29]: arr3 Out[29]: array([[[[ 0, 1], [ 2, 3]], [[ 4, 5], [ 6, 7]]], [[[ 8, 9], [10, 11]], [[12, 13], [14, 15]]]]) In [30]: arr3.swapaxes(1,3) Out[30]: array([[[[ 0, 4], [ 2, 6]], [[ 1, 5], [ 3, 7]]], [[[ 8, 12], [10, 14]], [[ 9, 13], [11, 15]]]]) In [31]: arr3.transpose(0,3,2,1) Out[31]: array([[[[ 0, 4], [ 2, 6]], [[ 1, 5], [ 3, 7]]], [[[ 8, 12], [10, 14]], [[ 9, 13], [11, 15]]]])
哈哈,只要动手做做,会发现其实没有那么困难,不能只看。
纸上得来终觉浅,绝知此事要躬行!共勉!
以上这篇Numpy中转置transpose、T和swapaxes的实例讲解就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持【听图阁-专注于Python设计】。