对numpy.append()里的axis的用法详解

如下所示：defappend(arr,values,axis=None):"""Appendvaluestotheendofanarray.Parameters----------arr:array_likeValues

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.]]

以上这篇对numpy.append()里的axis的用法详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脚本之家。

numpy append axis