numpy.bitwise_and#

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

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

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

Parameters:
x1, x2array_like

Only integer and boolean types are handled.Ifx1.shape!=x2.shape, they must be broadcastable to a commonshape (which becomes the shape of the output).

outndarray, None, or tuple of ndarray and None, optional

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

wherearray_like, optional

This condition is broadcast over the input. At locations where thecondition is True, theout array will be set to the ufunc result.Elsewhere, theout array will retain its original value.Note that if an uninitializedout array is created via the defaultout=None, locations within it where the condition is False willremain uninitialized.

**kwargs

For other keyword-only arguments, see theufunc docs.

Returns:
outndarray or scalar

Result.This is a scalar if bothx1 andx2 are scalars.

See also

logical_and
bitwise_or
bitwise_xor
binary_repr

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

Examples

>>>importnumpyasnp

The number 13 is represented by00001101. Likewise, 17 isrepresented by00010001. The bit-wise AND of 13 and 17 istherefore000000001, 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])

The& operator can be used as a shorthand fornp.bitwise_and onndarrays.

>>>x1=np.array([2,5,255])>>>x2=np.array([3,14,16])>>>x1&x2array([ 2,  4, 16])
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