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Salman Yousaf

The NumPy `bitwise_and()`

method is used to evaluate bitwise logical AND operators between two arrays. It performs the bitwise logical AND operation of underlying binary representations.

numpy.bitwise_and(arr1, arr2, out=None, where=True, **kwargs)

It takes the following argument values:

`arr1`

: This is an array-like object. It only handles integer and boolean-type arrays.`arr2`

: This is an array-like object. It only handles integer and boolean-type arrays.`out`

: This is the memory location in which the result will be stored. It will be of the same dimension as`arr1`

or`arr2`

. If it is set to`None`

, a new array of the same dimensions will be initialized.

`where`

: This is an optional parameter. If its value is`True`

, then the result of the function will be replaced with the`ufunc`

result. If set to False, the original value will be returned as it is. Its default value is`True`

.

`**kwargs`

: These are additional arguments.

It returns `ndarray`

or `scalar`

type objects.

In this code, we'll take a look at `numpy.bitwise_and()`

with different argument values.

# importing numpy library import numpy as np # performing logical AND between 12, 16 print("12 AND 16:", end=" ") print(np.bitwise_and(12, 16)) # logical AND between a Python list [3,13] and 12 print("[3,13] AND 12:", end=" ") print(np.bitwise_and([3,13], 12)) # logical AND between two lists of same size print("[9,7] AND [8,35]:", end=" ") print(np.bitwise_and([9,7], [8,35])) # performing logical AND between two numpy arrays print("np.array([2,7,255]) AND np.array([6,12,18]):", end=" ") print(np.bitwise_and(np.array([2,7,255]), np.array([6,12,18])))

- Lines 4–5: We perform bitwise AND between 12 (0000 1100) and 16 (0001 0000). This will return 0.

- Lines 7–8: We perform bitwise AND between a list [3,13]=>(0000 0011, 0000 1101) and 12 (0000 1100). This will return a list.
- Lines 10–11: We perform bitwise AND between 2 lists [9,7] and [8,35].
- Lines 13–14: We perform bitwise AND between two Numpy arrays
`np.array([2,7,255])`

and`np.array([6,12,18]`

.

`ufunc`

Numpy also supports universal functions (`ufunc`

) that implement C or core Python operator `&`

.

# importing numpy library import numpy as np # creating two numpy arrays x1 = np.array([4, 7, 255]) x2 = np.array([8,13,19]) # performing logical AND using conventional & operator print(x1 & x2)

- Line 4: We create a NumPy array
`x1`

containing [4, 7, 255].

- Line 5: We create a NumPy array
`x2`

containing [8,13,19]. - Line 7: We perform logical AND by using a conventional
`&`

operator.

RELATED TAGS

bitwise_and

numpy

python

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