In Numpy, the `bitwise_and()`

function is mainly used to perform the `bitwise_and`

operation.

- This function will calculate the bit-wise AND of two arrays, element-wise.
- The
`bitwise_and()`

function calculates the**bit-wise AND of the underlying binary representation**of the integers in the input array.

Let us take a look at the **truth table of AND operation**:

If and only if both the bits are 1 only then the output of the AND result of the two bits is 1 otherwise it will be 0.

### Syntax of `bitwise_and()`

:

The syntax required to use this function is as follows:

`numpy.bitwise_and(x1, x2, /, out, *, where=True, casting='same_kind', order='K', dtype,subok=True[, signature, extobj]) = <ufunc 'bitwise_and'>`

**Parameters:**

Let us now take a look at the parameters of this function:

**x1, x2**

These two are input arrays and with this function only integer and boolean types are handled. If`x1.shape != x2.shape`

, then they must be broadcastable to a common shape (and this shape will become the shape of the output).**out**

This parameter mainly indicates a location in which the result is stored. If this parameter is provided, it must have a shape that the inputs broadcast to. If this parameter is either not provided or it is**None**then a freshly-allocated array is returned.**where**

This parameter is used to indicate a condition that is broadcast over the input. At those locations where the condition is**True**, the*out*array will be set to the b result, else the out array will retain its original value.

**Returned Values:**

This function will return a scalar if both **x1** and **x2** are scalars.

## Example 1:

In the example below, we will illustrate the usage of `bitwise_and()`

function:

```
import numpy as np
num1 = 15
num2 = 20
print ("The Input number1 is :", num1)
print ("The Input number2 is :", num2)
output = np.bitwise_and(num1, num2)
print ("The bitwise_and of 15 and 20 is: ", output)
```

Output:

The input number1 is: 15

The input number2 is: 20

The bitwise_and of 15 and 20 is: 4

## Example 2:

In the example below, we will apply the `bitwise_and()`

function on two arrays:

```
import numpy as np
ar1 = [2, 8, 135]
ar2 = [3, 5, 115]
print ("The Input array1 is : ", ar1)
print ("The Input array2 is : ", ar2)
output_arr = np.bitwise_and(ar1, ar2)
print ("The Output array after bitwise_and: ", output_arr)
```

Output:

The Input array1 is : [2, 8, 135]

The Input array2 is : [3, 5, 115]

The Output array after bitwise_and: [2 0 3]