NumPy Logic functions: all() function

Last update on August 19 2022 21:51:10 (UTC/GMT +8 hours)

numpy.all() function

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

Syntax:

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

Version: 1.15.0

Parameter:

Name	Description	Required / Optional
a	Input array or object that can be converted to an array. array_like	Required
axis	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. 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. None or int or tuple of ints	Optional
out	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). ndarray	Optional
keepdims	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. bool	Optional

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

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

NumPy.all() method Example-1:

>>> import numpy as np
>>> np.all([[True,False],[True,True]])

Output:

False

NumPy.all() method Example-2:

>>> import numpy as np
>>> np.all([[True,False],[True,True]], axis=0)

Output:

array([ True, False])

NumPy.all() method Example-3:

>>> import numpy as np
>>> np.all([-2, 3, 5])

Output:

True

NumPy.all() method Example-4:

>>> import numpy as np
>>> np.all([1.0, np.nan])

Output:

True

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