numpy.any#

numpy.any(a,axis=None,out=None,keepdims=<novalue>,*,where=<novalue>)[source]#

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

Returns single boolean ifaxis isNone

Parameters:
aarray_like

Input array or object that can be converted to an array.

axisNone or int or tuple of ints, optional

Axis or axes along which a logical OR reduction is performed.The default (axis=None) is to perform a logical OR over allthe dimensions of the input array.axis may be negative, inwhich case it counts from the last to the first axis. If thisis a tuple of ints, a reduction is performed on multipleaxes, instead of a single axis or all the axes as before.

outndarray, optional

Alternate output array in which to place the result. It must havethe same shape as the expected output and its type is preserved(e.g., if it is of type float, then it will remain so, returning1.0 for True and 0.0 for False, regardless of the type ofa).SeeOutput type determination for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are leftin 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, thenkeepdims will not bepassed through to theany method of sub-classes ofndarray, however any non-default value will be. If thesub-class’ method does not implementkeepdims anyexceptions will be raised.

wherearray_like of bool, optional

Elements to include in checking for anyTrue values.Seereduce for details.

New in version 1.20.0.

Returns:
anybool or ndarray

A new boolean orndarray is returned unlessout is specified,in which case a reference toout is returned.

See also

ndarray.any

equivalent method

all

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

Notes

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

Changed in version 2.0:Before NumPy 2.0,any did not return booleans for object dtypeinput arrays.This behavior is still available vianp.logical_or.reduce.

Examples

>>>importnumpyasnp>>>np.any([[True,False],[True,True]])True
>>>np.any([[True,False,True],...[False,False,False]],axis=0)array([ True, False, True])
>>>np.any([-1,0,5])True
>>>np.any([[np.nan],[np.inf]],axis=1,keepdims=True)array([[ True],       [ True]])
>>>np.any([[True,False],[False,False]],where=[[False],[True]])False
>>>a=np.array([[1,0,0],...[0,0,1],...[0,0,0]])>>>np.any(a,axis=0)array([ True, False,  True])>>>np.any(a,axis=1)array([ True,  True, False])
>>>o=np.array(False)>>>z=np.any([-1,4,5],out=o)>>>z,o(array(True), array(True))>>># Check now that z is a reference to o>>>zisoTrue>>>id(z),id(o)# identity of z and o(191614240, 191614240)
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