对numpy.append()里的axis的用法详解

如下所示:

def append(arr, values, axis=None):

"""

Append values to the end of an array.

Parameters

----------

arr : array_like

Values are appended to a copy of this array.

values : array_like

These values are appended to a copy of `arr`. It must be of the

correct shape (the same shape as `arr`, excluding `axis`). If

`axis` is not specified, `values` can be any shape and will be

flattened before use.

axis : int, optional

The axis along which `values` are appended. If `axis` is not

given, both `arr` and `values` are flattened before use.

Returns

-------

append : ndarray

A copy of `arr` with `values` appended to `axis`. Note that

`append` does not occur in-place: a new array is allocated and

filled. If `axis` is None, `out` is a flattened array.

numpy.append(arr, values, axis=None):

简答来说,就是arr和values会重新组合成一个新的数组,做为返回值。而axis是一个可选的值

当axis无定义时,是横向加成,返回总是为一维数组!

Examples

--------

>>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])

array([1, 2, 3, 4, 5, 6, 7, 8, 9])

当axis有定义的时候,分别为0和1的时候。(注意加载的时候,数组要设置好,行数或者列数要相同。不然会有error:all the input array dimensions except for the concatenation axis must match exactly)

当axis为0时,数组是加在下面(列数要相同):

import numpy as np

aa= np.zeros((1,8))

bb=np.ones((3,8))

c = np.append(aa,bb,axis = 0)

print(c)

[[ 0. 0. 0. 0. 0. 0. 0. 0.]

[ 1. 1. 1. 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1. 1. 1. 1.]

[ 1. 1. 1. 1. 1. 1. 1. 1.]]

当axis为1时,数组是加在右边(行数要相同):

import numpy as np

aa= np.zeros((3,8))

bb=np.ones((3,1))

c = np.append(aa,bb,axis = 1)

print(c)

[[ 0. 0. 0. 0. 0. 0. 0. 0. 1.]

[ 0. 0. 0. 0. 0. 0. 0. 0. 1.]

[ 0. 0. 0. 0. 0. 0. 0. 0. 1.]]

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