Numpy.select() method **“returns an array drawn from elements in choicelist, depending on conditions.“**

**Syntax**

`numpy.select(condlist, choicelist, default=0)`

**Parameters**

**condlist:**list of bool arrays or boolean conditions. The list of conditions to be evaluated.

**choicelist:**list of arrays. The list of actions (values) to apply for each condition. It must have the same length as the condlist.

**default:**scalar, optional. The value to use for positions where none of the conditions are True. The default is 0.

**Return value**

Depending on conditions, it returns an array drawn from elements in a **choicelist**.

**Example 1: How to Use numpy.select() method**

```
import numpy as np
arr = np.arange(6)
condlist = [arr < 5, arr > 1]
choicelist = [arr, arr**2]
output_arr = np.select(condlist, choicelist)
print(output_arr)
```

**Output**

```
[ 0 1 2 3 4 25]
```

**Example 2: Applying conditions and actions **

```
import numpy as np
# Sample array
arr = np.array([1, 4, 7, 10, 13])
# Define conditions and corresponding actions
conditions = [
(arr < 5),
(arr >= 5) & (arr <= 10),
(arr > 10)
]
actions = [
arr * 2,
arr,
arr - 3
]
# Apply conditions and actions
result = np.select(conditions, actions)
print("Original array:", arr)
print("New array with applied conditions:", result)
```

**Output**

```
Original array: [ 1 4 7 10 13]
New array with applied conditions: [ 2 8 7 10 10]
```

You can see that we created an array and defined three conditions and corresponding actions using lists.

The **numpy.select()** function applies the conditions and actions, creating a new array with the same shape as the input array.

The values of the new array are determined by the first condition, **True,** at a given position. If no condition is True, the corresponding value in the new array will be 0 (the default fill value).

That’s it.

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.