numpy的用法们-边用边记，不断更新

野性酷女 2022-07-15 00:08 180阅读 0赞

1.数组均分

numpy.array\_split()

>>> x = np.arange(8.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]

2.数组叠加合并

列合并/扩展：np.column\_stack()

行合并/扩展：np.row\_stack()

>>> a
    array([[0, 1, 2],
           [3, 4, 5],
           [6, 7, 8]])
    >>> top = np.column_stack((a, np.zeros((3,3))))
    >>> top
    array([[ 0.,  1.,  2.,  0.,  0.,  0.],
           [ 3.,  4.,  5.,  0.,  0.,  0.],
           [ 6.,  7.,  8.,  0.,  0.,  0.]])

3.删除某个元素

np.delete()

numpy. delete ( arr, obj, axis=None ) [\[source\]][source]

Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by *arr\[obj\]*.

<table style="max-width:100%; background-color:transparent; border-collapse:collapse; border-spacing:5px; margin-bottom:9.5px; border-top-width:0px; border-right-width:0px; border-bottom-width:0px; border-left-width:5px!important; border-left-style:solid!important; border-left-color:rgb(238,238,238)!important; margin-left:1px"> 
 <tbody> 
 <tr> 
 <th style="padding:1px 8px 1px 5px; display:inline-block; white-space:nowrap; background-color:rgb(238,238,238); border:0px!important"> Parameters:</th> 
 <td style="padding:1px 8px 1px 5px; border-top-width:0px!important; border-left-style:none!important; border-right-width:0px!important; border-bottom-width:0px!important"> arr&nbsp;: array_like 
 <blockquote style="padding:0px 0px 0px 30px; margin:0px; border-left-style:none"> 
 Input array. 
 </blockquote> obj&nbsp;: slice, int or array of ints 
 <blockquote style="padding:0px 0px 0px 30px; margin:0px; border-left-style:none"> 
 Indicate which sub-arrays to remove. 
 </blockquote> axis&nbsp;: int, optional 
 <blockquote style="padding:0px 0px 0px 30px; margin:0px; border-left-style:none"> 
 The axis along which to delete the subarray defined by&nbsp;obj. If&nbsp;axis&nbsp;is None,&nbsp;obj&nbsp;is applied to the flattened array. 
 </blockquote> </td> 
 </tr> 
 <tr> 
 <th style="padding:1px 8px 1px 5px; display:inline-block; white-space:nowrap; background-color:rgb(238,238,238); border:0px!important"> Returns:</th> 
 <td style="padding:1px 8px 1px 5px; border-top-width:0px!important; border-left-style:none!important; border-right-width:0px!important; border-bottom-width:0px!important"> out&nbsp;: ndarray 
 <blockquote style="padding:0px 0px 0px 30px; margin:0px; border-left-style:none"> 
 A copy of&nbsp;arr&nbsp;with the elements specified by&nbsp;obj&nbsp;removed. Note that&nbsp;<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.delete.html#numpy.delete" title="numpy.delete" style="color:rgb(0,136,204); text-decoration:none" rel="nofollow">delete</a>does not occur in-place. If&nbsp;axis&nbsp;is None,&nbsp;out&nbsp;is a flattened array. 
 </blockquote> </td> 
 </tr> 
 </tbody> 
</table>

>>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
    >>> arr
    array([[ 1,  2,  3,  4],
           [ 5,  6,  7,  8],
           [ 9, 10, 11, 12]])
    >>> np.delete(arr, 1, 0)
    array([[ 1,  2,  3,  4],
           [ 9, 10, 11, 12]])

>>>

>>> np.delete(arr, np.s_[::2], 1)
    array([[ 2,  4],
           [ 6,  8],
           [10, 12]])
    >>> np.delete(arr, [1,3,5], None)
    array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])

3.数据遍历

np.delete()

a = np.arange(9).reshape(3,3)

it = np.nditer(a,flags=\['multi\_index'\], op\_flags=\['readwrite'\])

while not it.finished:

print it.multi\_index

it.iternext()

(0,0)

(0,1)

...

(2,2)

https://docs.scipy.org/doc/numpy/reference/generated/numpy.nditer.html