NumPy Binary operations: bitwise_and() function

Last update on April 03 2023 12:31:28 (UTC/GMT +8 hours)

numpy.bitwise_and() function

The bitwise_and() is used to compute the bit-wise AND of two arrays element-wise.

This function is useful in various applications, including:
1. Image Processing: Manipulating the bits of an image to apply masks or filters by isolating specific color channels or adjusting the transparency of an image.
2. Binary Data Processing: When working with data in binary format, bitwise_and() can be used to extract specific bits of data or combine data from multiple sources.
3. Bit Flags: In some cases, you may want to store multiple boolean values in a single integer using bit flags. The bitwise_and() function can be used to check if specific flags are set.
4. Cryptography: Bitwise operations, such as bitwise AND, are often used in cryptographic algorithms for scrambling and unscrambling data.

Syntax:

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

Parameters:

Name	Description	Required / Optional
x1,x2	Only integer and boolean types are handled.	Required
out	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.	Optional
where	Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.	Optional
**kwargs	For other keyword-only arguments.

Return value:

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

Example: Demonstration of NumPy Bitwise AND Operation

>>> import numpy as np
>>> np.bitwise_and(18, 12)
0
>>> np.bitwise_and(18, 17)
16
>>> np.binary_repr(16)
'10000'
>>> np.bitwise_and([18, 7], 17 )
array([16,  1])

The above code snippet demonstrates the usage of the NumPy library's bitwise_and() function for performing bitwise AND operations on integers and arrays. It also shows how to obtain the binary representation of an integer using the binary_repr() function.

Example: Bitwise AND Operation with NumPy Arrays

>>> import numpy as np
>>> np.bitwise_and([15, 9], [3, 22])
array([3, 0])
>>> np.bitwise_and(np.array([3, 7, 356]), np.array([4,15,18]))
array([0, 7, 0])
>>> np.bitwise_and([True, True], [False, True])
array([False,  True], dtype=bool)

The above code demonstrates how to perform bitwise AND operations on NumPy arrays. The numpy.bitwise_and() function is used to calculate the element-wise bitwise AND of two input arrays. It accepts lists or NumPy arrays as input and returns a NumPy array with the result. The code also shows an example of using boolean values as input and outputs a boolean array.

Python Code Editor:

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