## Numpy `where`

function with multiple conditions

Numpy is a popular Python library used for numerical computing. One of the useful functions that Numpy provides is the `where`

function, which allows us to perform conditional operations on arrays. In this article, we will explore how to use the `where`

function with multiple conditions.

## Syntax of the `where`

function

The syntax of the `where`

function in Numpy is as follows:

```
numpy.where(condition, x, y)
```

`condition`

: This is the condition we want to apply on the array.`x`

: This is the value to be used if the condition is True.`y`

: This is the value to be used if the condition is False.

## Applying multiple conditions using the `where`

function

We can apply multiple conditions using the `logical_and`

and `logical_or`

functions in Numpy. Let’s see some examples to understand how to use the `where`

function with multiple conditions.

## Example 1

```
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
result = np.where((arr > 2) & (arr < 5), arr, 0)
print(result)
```

Output:

## Example 2

```
import numpy as np
arr = np.array([10, 20, 30, 40, 50, 60])
result = np.where((arr % 10 == 0) | (arr < 40), arr, -1)
print(result)
```

Output:

## Example 3

```
import numpy as np
arr = np.array([50, 60, 70, 80, 90, 100])
result = np.where((arr > 70) & (arr % 10 == 0), arr, 0)
print(result)
```

Output:

## Example 4

```
import numpy as np
arr = np.array([5, 15, 25, 35, 45, 55])
result = np.where((arr < 10) | (arr > 40), arr, 0)
print(result)
```

Output:

## Example 5

```
import numpy as np
arr = np.array([2, 12, 22, 32, 42, 52])
result = np.where((arr % 10 == 2) & (arr > 25), arr, -1)
print(result)
```

Output:

## Example 6

```
import numpy as np
arr = np.array([3, 13, 23, 33, 43, 53])
result = np.where((arr % 10 == 3) | (arr < 20), arr, 0)
print(result)
```

Output:

## Example 7

```
import numpy as np
arr = np.array([4, 14, 24, 34, 44, 54])
result = np.where((arr % 10 == 4) & (arr > 40), arr, 0)
print(result)
```

Output:

## Example 8

```
import numpy as np
arr = np.array([1, 11, 21, 31, 41, 51])
result = np.where((arr < 15) | (arr > 45), arr, 0)
print(result)
```

Output:

## Example 9

```
import numpy as np
arr = np.array([6, 16, 26, 36, 46, 56])
result = np.where((arr % 10 == 6) & (arr < 30), arr, -1)
print(result)
```

Output:

## Example 10

```
import numpy as np
arr = np.array([7, 17, 27, 37, 47, 57])
result = np.where((arr % 10 == 7) & (arr > 35), arr, 0)
print(result)
```

Output:

## Conclusion of Numpy `where`

function

In conclusion, the `where`

function in Numpy is a powerful tool for performing conditional operations on arrays. By using logical operators like `logical_and`

and `logical_or`

, we can apply multiple conditions to filter and modify arrays efficiently.