dask.array.bitwise_and

dask.array.bitwise_and¶

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

This docstring was copied from numpy.bitwise_and.

Some inconsistencies with the Dask version may exist.

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, x2array_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).
outndarray, 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.
wherearray_like, 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.

Returns

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

See also

logical_and
bitwise_or
bitwise_xor
binary_repr: Return the binary representation of the input number as a string.

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])

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

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

dask.array.bincount

dask.array.bitwise_not