numpy.select() function – Python

Last Updated : 07 Apr, 2025

The numpy.select() function is used to construct an array by selecting elements from a list of choices based on multiple conditions. It is particularly useful when dealing with conditional replacements or transformations in NumPy arrays.

Example:

Python

import numpy as np arr = np.array([10, 20, 30, 40]) conditions = [arr < 20, arr > 30] choices = [100, 200] result = np.select(conditions, choices, default=0) print(result)

Syntax:

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

Parameters:

condlist : list of bool ndarrays
A list of boolean NumPy arrays that determine from which array in choicelist the output elements are selected. If multiple conditions are True, the first one encountered is used.
choicelist : list of ndarrays
A list of arrays from which the output elements are chosen. It must have the same length as condlist.
default : scalar, optional (default=0)
The value inserted in the output array when none of the conditions are met.

Return Value:

ndarray : An array with elements chosen from choicelist based on the conditions in condlist.

Code Implementation

1. Basic Usage of `numpy.select()`

Here:

If arr < 3, the corresponding element is taken from arr.
If arr > 4, the corresponding element is taken from arr**3.
Otherwise, the default value 0 is used.

Python

import numpy as np arr = np.arange(8) condlist = [arr < 3, arr > 4] choicelist = [arr, arr**3] result = np.select(condlist, choicelist) print(result)

Output :

[ 0 1 2 0 0 125 216 343]

2. Using a Different Default Value

Here:

Values where arr < 4 are taken from arr.
Values where arr > 6 are taken from arr**2.
All other values are replaced with -1 (instead of 0).

Python

arr = np.arange(8) condlist = [arr < 4, arr > 6] choicelist = [arr, arr**2]  # Custom default value (e.g., -1) result = np.select(condlist, choicelist, default=-1) print(result)

Output:

0 1 2 3 -1 -1 -1 49]

3. Handling Multiple Conditions

Here :

If arr is even (arr % 2 == 0), it is multiplied by 10.
If arr is divisible by 3 (arr % 3 == 0), it is negated.
If neither condition is met, the default value 100 is used.

Python

arr = np.arange(10) condlist = [arr % 2 == 0, arr % 3 == 0] choicelist = [arr * 10, arr * -1]  result = np.select(condlist, choicelist, default=100) print(result)

Output:

[ 0 100 20 -3 40 100 60 100 80 -9]

Why Use `numpy.select()`?

More flexible than numpy.where() when dealing with multiple conditions.
Helps avoid complex nested if-else conditions in array transformations.
Efficient and concise for applying different transformations to an array based on conditions.

Comparison with `numpy.where()`

Feature	`numpy.select()`	`numpy.where()`
Multiple Conditions	Yes	No (only two conditions: True/False)
Custom Default Value	Yes	No
Simplicity	Better for multiple rules	Better for simple if-else

The numpy.select() function is a powerful tool for conditional selection and transformation of array elements. It is especially useful when handling multiple conditions efficiently in a structured way. Mastering its usage will help simplify complex array operations in Python.