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any(a, axis=None, out=None, keepdims=<no value>, *, where=<no value>)

Returns single boolean unless :None:None:`axis` is not None

Notes

Not a Number (NaN), positive infinity and negative infinity evaluate to :None:None:`True` because these are not equal to zero.

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 OR reduction is performed. The default ( axis=None ) is to perform a logical OR over all the dimensions of the input array. :None:None:`axis` may be negative, in which case it counts from the last to the first axis.

versionadded

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 it is of type float, then it will remain so, returning 1.0 for True and 0.0 for False, regardless of the type of a). See ufuncs-output-type 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 :None:None:`keepdims` will not be passed through to the any method of sub-classes of ndarray , however any non-default value will be. If the sub-class' method does not implement :None:None:`keepdims` any exceptions will be raised.

where : array_like of bool, optional

Elements to include in checking for any :None:None:`True` values. See :None:None:`~numpy.ufunc.reduce` for details.

versionadded

Returns

any : bool or ndarray: A new boolean or ndarray is returned unless :None:None:`out` is specified, in which case a reference to :None:None:`out` is returned.

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

Examples

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

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

>>> np.any([-1, 0, 5])
True

>>> np.any(np.nan)
True

>>> np.any([[True, False], [False, False]], where=[[False], [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
... z is o
True

>>> id(z), id(o) # identity of z and o              # doctest: +SKIP
(191614240, 191614240)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.core.any pandas.core.arrays.sparse.array.SparseArray.any numpy.allclose pandas.core.generic.NDFrame._add_numeric_operations.<locals>.any numpy.sometrue numpy.ma.core.allequal dask.array.reductions.any numpy.ma.core.allclose numpy.any numpy.matrixlib.defmatrix.matrix.any numpy.all pandas.core.arrays.masked.BaseMaskedArray.any numpy.ma.core.MaskedArray.any dask.array.core.Array.any

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