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社区首页 >专栏 >numpy.all

numpy.all

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狼啸风云
修改2022-09-03 21:40:55
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修改2022-09-03 21:40:55
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文章被收录于专栏:计算机视觉理论及其实现

numpy.all(a, axis=None, out=None, keepdims=<no value>)[source]

Test whether all array elements along a given axis evaluate to True.

Parameters:

a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical AND reduction is performed. The default (axis = None) is to perform a logical AND over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis. New in version 1.7.0. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if dtype(out) is float, the result will consist of 0.0’s and 1.0’s). See doc.ufuncs (Section “Output arguments”) for more details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the all method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

Returns:

all : ndarray, bool A new boolean or array is returned unless out is specified, in which case a reference to out is returned.

See also

ndarray.all

equivalent method

any

Test whether any element along a given axis evaluates to True.

Notes

Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero.

Examples

代码语言:javascript
复制
>>>

>>> np.all([[True,False],[True,True]])
False

>>>

>>> np.all([[True,False],[True,True]], axis=0)
array([ True, False])

>>>

>>> np.all([-1, 4, 5])
True

>>>

>>> np.all([1.0, np.nan])
True

>>>

>>> o=np.array(False)
>>> z=np.all([-1, 4, 5], out=o)
>>> id(z), id(o), z
(28293632, 28293632, array(True)) # may vary
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原始发表:2019年09月24日,如有侵权请联系 cloudcommunity@tencent.com 删除

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