dpnp.logical_and

dpnp.logical_and(x1, x2, /, out=None, *, order='K', where=True, dtype=None, subok=True, **kwargs)[source]

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

For full documentation refer to numpy.logical_and.

Returns:: out – Boolean result of the logical AND operation applied to the elements of x1 and x2; the shape is determined by broadcasting.
Return type:: dpnp.ndarray

Limitations

Parameters x1 and x2 are supported as either scalar, dpnp.ndarray or dpctl.tensor.usm_ndarray, but both x1 and x2 can not be scalars at the same time. Parameters where, dtype and subok are supported with their default values. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP Data types.

See also

dpnp.logical_or: Compute the truth value of x1 OR x2 element-wise.
dpnp.logical_not: Compute the truth value of NOT x element-wise.
dpnp.logical_xor: Compute the truth value of x1 XOR x2, element-wise.
dpnp.bitwise_and: Compute the bit-wise AND of two arrays element-wise.

Examples

>>> import dpnp as np
>>> x1 = np.array([True, False])
>>> x2 = np.array([False, False])
>>> np.logical_and(x1, x2)
array([False, False])

>>> x = np.arange(5)
>>> np.logical_and(x > 1, x < 4)
array([False, False,  True,  True, False])

The & operator can be used as a shorthand for logical_and on boolean dpnp.ndarray.

>>> a = np.array([True, False])
>>> b = np.array([False, False])
>>> a & b
array([False, False])