transpose() for swapping axes in NumPy

Introduction

We use transpose() to flip or swap the rows and columns of data. It helps us see data from a different angle.

When you want to switch rows and columns in a table of numbers.

When preparing data for math operations that need a certain shape.

When you want to rotate or flip images stored as arrays.

When you want to change the order of dimensions in multi-dimensional data.

Syntax

NumPy

numpy.transpose(a, axes=None)

a is the array you want to transpose.

axes is optional. It lets you choose how to reorder the dimensions.

Examples

Transpose a 2x2 matrix to swap rows and columns.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
transposed = np.transpose(arr)
print(transposed)

Transpose a 3D array by swapping the first two axes.

NumPy

import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
transposed = np.transpose(arr, axes=(1, 0, 2))
print(transposed)

Sample Program

This program creates a 2-row, 3-column array and then swaps rows and columns using transpose(). The output shows the original and transposed arrays.

NumPy

import numpy as np

# Create a 2D array
array_2d = np.array([[10, 20, 30], [40, 50, 60]])
print("Original array:")
print(array_2d)

# Transpose the array
transposed_array = np.transpose(array_2d)
print("\nTransposed array:")
print(transposed_array)

Important Notes

For 2D arrays, transpose() swaps rows and columns.

For arrays with more dimensions, use the axes parameter to control how dimensions are reordered.

Transposing does not change the data, only how it is viewed or accessed.

Summary

transpose() flips or swaps the axes of an array.

It is useful to change the shape or orientation of data.

Use the axes parameter to customize the swap for multi-dimensional arrays.