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對(duì)numpy.append()里的axis的用法詳解

 更新時(shí)間:2018年06月28日 14:33:37   作者:我愛(ài)阿鑫  
今天小編就為大家分享一篇對(duì)numpy.append()里的axis的用法詳解,具有很好的參考價(jià)值,希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

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):

簡(jiǎn)答來(lái)說(shuō),就是arr和values會(huì)重新組合成一個(gè)新的數(shù)組,做為返回值。而axis是一個(gè)可選的值

當(dāng)axis無(wú)定義時(shí),是橫向加成,返回總是為一維數(shù)組!

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

當(dāng)axis有定義的時(shí)候,分別為0和1的時(shí)候。(注意加載的時(shí)候,數(shù)組要設(shè)置好,行數(shù)或者列數(shù)要相同。不然會(huì)有error:all the input array dimensions except for the concatenation axis must match exactly)

當(dāng)axis為0時(shí),數(shù)組是加在下面(列數(shù)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.]]

當(dāng)axis為1時(shí),數(shù)組是加在右邊(行數(shù)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.]]

以上這篇對(duì)numpy.append()里的axis的用法詳解就是小編分享給大家的全部?jī)?nèi)容了,希望能給大家一個(gè)參考,也希望大家多多支持腳本之家。

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