确定结果阵列的形状在numpy的切片后 [英] Determining the shape of result array after slicing in Numpy

问题描述

我有困难的时候认识的合成数组的形状如何numpy的切片后确定。比如我使用下面这个简单的code：

I have difficult time understanding how the shape of resultant array be determined after slicing in numpy. For example I am using the following simple code:

import numpy as np


array=np.arange(27).reshape(3,3,3)

slice1 = array[:,1:2,1]
slice2= array[:,1,1]

print "Content in slice1 is  ", slice1
print "Shape of slice1 is ", slice1.shape
print "Content in slice2 is ",slice2
print "Shape of Slice2 is", slice2.shape

这个输出是：

Content in slice1 is 
 [[ 4]
  [13]
  [22]]
Shape of slice1 is  (3, 1)
Content in slice2 is  [ 4 13 22]
Shape of Slice2 is (3,)

在这两种情况下，内容是一样的（因为它应该是）。但是，他们在不同的形状。那么，如何产生的形状由numpy的决定？

In both of these cases, content is same(as it should be). But they differ in shapes. So, how does the resultant shape is determined by numpy?

推荐答案

这基本上可以归结到这一点 -

It basically boils down to this -

In [118]: a = np.array([1,2,3,4,5])

In [119]: a[1:2]
Out[119]: array([2])

In [120]: a[1]
Out[120]: 2

当你做 A [1：2]。，你所要求的1元素的数组

When you do a[1:2], you are asking for an array with 1 element.

当你做 A [1] 你所要求的该索引处的元素。

When you do a[1] you are asking for the element at that index.

类似的事情发生在你的情况。

Similar thing happens in your case.

当你这样做 - 数组[：，1：2,1] - 这意味着从第一个维度的所有可能的指标，从第二个方面指标的子表（虽然子清单只包含一个元素），并从第三维第一个指数。所以，你回来阵列的阵列 -

When you do - array[:,1:2,1] - it means all possible indexes from first dimension , a sub-list of indexes from second dimension (though the sub-list only contains one element) , and 1st index from third dimension. So you get back a array of arrays -

 [[ 4]
  [13]
  [22]]

当你这样做 - 数组[：1,1] - 这意味着从第一个维度，从第二个维度第一个指数，并从第三个层面第一个指数的所有可能的指标。所以，你会得到一个数组 -

When you do - array[:,1,1] - it means all possible indexes from first dimension, 1st index from second dimension, and 1st index from third dimension. So you get back an array -

[4 13 22]

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