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

Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator & .

Parameters

x1, x2 : array_like

Only integer and boolean types are handled. 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

out : ndarray or scalar

Result. This is a scalar if both x1 and :None:None:`x2` are scalars.

Compute the bit-wise AND of two arrays element-wise.

See Also

binary_repr

Return the binary representation of the input number as a string.

bitwise_or

bitwise_xor

logical_and

Examples

The number 13 is represented by 00001101 . Likewise, 17 is represented by 00010001 . The bit-wise AND of 13 and 17 is therefore 000000001 , or 1:

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

>>> np.bitwise_and(13, 17)
1

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

>>> np.bitwise_and(14, 13)
12

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

>>> np.binary_repr(12)
'1100'

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

>>> np.bitwise_and([14,3], 13)
array([12,  1])

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

>>> np.bitwise_and([11,7], [4,25])
array([0, 1])

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

>>> np.bitwise_and(np.array([2,5,255]), np.array([3,14,16]))
array([ 2,  4, 16])

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

>>> np.bitwise_and([True, True], [False, True])
array([False,  True])

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

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

>>> x1 = np.array([2, 5, 255])
... x2 = np.array([3, 14, 16])
... x1 & x2
array([ 2,  4, 16])

See :

Back References

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

numpy.ma.core.logical_and numpy.ma.core.bitwise_xor numpy.ma.core.bitwise_or

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