nums.numpy.bitwise_and

nums.numpy.bitwise_and(x1: nums.core.array.blockarray.BlockArray, x2: nums.core.array.blockarray.BlockArray, out: Optional[nums.core.array.blockarray.BlockArray] = None, where=True, **kwargs) → nums.core.array.blockarray.BlockArray

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

This docstring was copied from numpy.bitwise_and.

Some inconsistencies with the NumS version may exist.

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

x1, x2BlockArray

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).

outBlockArray, None, or 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.

whereBlockArray, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized 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.

outBlockArray or scalar

Result.

logical_and bitwise_or bitwise_xor binary_repr :

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

The The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

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:

>>> nps.bitwise_and(nps.array(13), nps.array(17)).get()  
array(1)
>>> nps.bitwise_and(nps.array([2,5,255]), nps.array([3,14,16])).get()  
array([ 2,  4, 16])
>>> nps.bitwise_and(nps.array([True, True]), nps.array([False, True])).get()  
array([False,  True])