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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