logical_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Some inconsistencies with the Dask version may exist.

Compute the truth value of x1 AND x2 element-wise.

Parameters

x1, x2 : array_like

Input arrays. If x1.shape != x2.shape , they must be broadcastable to a common shape (which becomes the shape of the output).

out : ndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

where : array_like, optional

This condition is broadcast over the input. At locations where the condition is True, the :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`out` array is created via the default out=None , locations within it where the condition is False will remain uninitialized.

**kwargs :

For other keyword-only arguments, see the ufunc docs <ufuncs.kwargs> .

Returns

y : ndarray or bool

Boolean result of the logical AND operation applied to the elements of :None:None:`x1` and :None:None:`x2`; the shape is determined by broadcasting. This is a scalar if both :None:None:`x1` and :None:None:`x2` are scalars.

This docstring was copied from numpy.logical_and.

See Also

bitwise_and

logical_not

logical_or

logical_xor

Examples

This example is valid syntax, but we were not able to check execution

>>> np.logical_and(True, False)  # doctest: +SKIP
False

This example is valid syntax, but we were not able to check execution

>>> np.logical_and([True, False], [False, False])  # doctest: +SKIP
array([False, False])

This example is valid syntax, but we were not able to check execution

>>> x = np.arange(5)  # doctest: +SKIP
... np.logical_and(x>1, x<4)  # doctest: +SKIP
array([False, False,  True,  True, False])

The & operator can be used as a shorthand for np.logical_and on boolean ndarrays.

This example is valid syntax, but we were not able to check execution

>>> a = np.array([True, False])  # doctest: +SKIP
... b = np.array([False, False])  # doctest: +SKIP
... a & b  # doctest: +SKIP
array([False, False])

See :

Back References

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

dask.array.ufunc.logical_not dask.array.ufunc.logical_xor dask.array.ufunc.bitwise_and dask.array.ufunc.logical_or

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