Axis scales (linear, log) in Matplotlib

Introduction

Axis scales help us show data clearly. Linear scale shows data evenly. Log scale shows data that changes a lot in a smaller space.

When you want to show normal data that changes evenly.

When your data has very big and very small values together.

When you want to see percentage changes or growth rates clearly.

When you want to compare data that grows exponentially.

When you want to avoid big values hiding small values on the graph.

Syntax

Matplotlib

import matplotlib.pyplot as plt

plt.plot(x, y)
plt.xscale('linear' or 'log')
plt.yscale('linear' or 'log')
plt.show()

Use 'linear' for normal scale and 'log' for logarithmic scale.

You can set scale for x-axis, y-axis, or both.

Examples

Both axes use normal linear scale.

Matplotlib

plt.xscale('linear')
plt.yscale('linear')

X-axis uses log scale, Y-axis uses linear scale.

Matplotlib

plt.xscale('log')
plt.yscale('linear')

Both axes use log scale, good for data with wide range.

Matplotlib

plt.xscale('log')
plt.yscale('log')

Sample Program

This code plots the function y = x² twice. The first plot uses normal linear scales on both axes. The second plot uses logarithmic scales on both axes. This helps see how the data grows quickly and fits better on the graph.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(1, 100, 100)
y = x ** 2

plt.figure(figsize=(10, 4))

plt.subplot(1, 2, 1)
plt.plot(x, y)
plt.title('Linear scale')
plt.xscale('linear')
plt.yscale('linear')
plt.xlabel('x')
plt.ylabel('y = x^2')

plt.subplot(1, 2, 2)
plt.plot(x, y)
plt.title('Log scale')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('x (log scale)')
plt.ylabel('y = x^2 (log scale)')

plt.tight_layout()
plt.show()

Important Notes

Log scale cannot show zero or negative values. Make sure data is positive.

Log scale helps to see multiplicative changes clearly.

You can combine linear and log scales on different axes for better visualization.

Summary

Linear scale shows data evenly spaced.

Log scale shows data on a multiplicative scale, useful for wide ranges.

Use plt.xscale() and plt.yscale() to set axis scales in matplotlib.