numpy.bitwise_and — NumPy v1.15 Manual (original) (raw)

numpy. bitwise_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, _subok=True_[, signature, _extobj_]) = <ufunc 'bitwise_and'>¶

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

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. 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 Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.
Returns:	out : ndarray or scalar Result. This is a scalar if both x1 and x2 are scalars.

Parameters:

x1, x2 : array_like Only integer and boolean types are handled. 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 Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs.

Returns:

out : ndarray or scalar Result. This is a scalar if both x1 and x2 are scalars.

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:

np.bitwise_and(13, 17) 1

np.bitwise_and(14, 13) 12 np.binary_repr(12) '1100' np.bitwise_and([14,3], 13) array([12, 1])

np.bitwise_and([11,7], [4,25]) array([0, 1]) np.bitwise_and(np.array([2,5,255]), np.array([3,14,16])) array([ 2, 4, 16]) np.bitwise_and([True, True], [False, True]) array([False, True])