numpy.compress() in Python

Introduction

In numerical computing with Python, efficiently selecting specific elements from arrays is crucial. The numpy.compress() function provides a powerful mechanism to extract elements from an array based on a condition, offering flexibility in data manipulation tasks.

What is numpy.compress()?

The numpy.compress() function returns selected slices of an array along a specified axis that satisfy a given condition. It operates by evaluating the condition against the array and extracting the elements where the condition is True.

Syntax

numpy.compress(condition, array, axis=None, out=None)

Parameters:

• condition: A 1-D boolean array or list indicating which entries to return.
• array: The input array from which to extract elements.
• axis: (Optional) The axis along which to take slices. By default, the array is flattened before applying the condition.
• out: (Optional) A pre-allocated array into which the result is placed. It must have the appropriate shape to hold the output.

Examples

Example 1: Basic Usage

import numpy as np

array = np.arange(10).reshape(5, 2)
print("Original array:\n", array)

result = np.compress([False, True], array, axis=0)
print("\nCompressed array:\n", result)

Output:

Original array:
 [[0 1]
 [2 3]
 [4 5]
 [6 7]
 [8 9]]

Compressed array:
 [[2 3]
 [4 5]]

In this example, we extract rows where the condition is True, resulting in a new array with the selected rows.

Example 2: Using Axis Parameter

result = np.compress([True, False], array, axis=1)
print("\nCompressed array along axis 1:\n", result)

Output:

Compressed array along axis 1:
 [[1]
 [3]
 [5]
 [7]
 [9]]

Here, we apply the condition along the columns (axis 1), selecting elements where the condition is True.

Example 3: Flattened Array

result = np.compress([True, False, True, False, True, False, True, False, True, False], array)
print("\nCompressed flattened array:\n", result)

Output:

Compressed flattened array:
 [0 2 4 6 8]

In this case, the array is flattened, and the condition is applied to select elements accordingly.

Use Cases

The numpy.compress() function is particularly useful in scenarios where:

• Filtering data based on specific conditions.
• Extracting subsets of data along a particular axis.
• Performing operations on selected elements of an array.

Conclusion

Mastering the numpy.compress() function enhances your ability to manipulate and analyze data efficiently in NumPy. By understanding its syntax and applications, you can leverage this function to perform advanced data selection and transformation tasks with ease.