Plt.subplots with rows and columns in Matplotlib

Introduction

We use plt.subplots to create multiple plots in one figure easily. It helps us compare data side by side.

When you want to show different charts together for comparison.

When you have several related datasets to visualize at once.

When you want to organize plots in a grid layout.

When you want to save space by combining plots in one image.

Syntax

Matplotlib

fig, axes = plt.subplots(nrows=number_of_rows, ncols=number_of_columns)

fig is the whole figure (the window or image).

axes is an array of plots arranged in rows and columns.

Examples

This creates a 2 by 2 grid of plots (4 plots total).

Matplotlib

fig, axes = plt.subplots(nrows=2, ncols=2)

This creates 1 row and 3 columns of plots (3 plots in a row).

Matplotlib

fig, axes = plt.subplots(1, 3)

This creates a single plot (1 row, 1 column by default).

Matplotlib

fig, axes = plt.subplots()

Sample Program

This code creates a 2x2 grid of plots showing different math functions. Each plot has a title. The tight_layout() makes sure plots don't overlap.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)

fig, axes = plt.subplots(nrows=2, ncols=2)

axes[0, 0].plot(x, np.sin(x))
axes[0, 0].set_title('sin(x)')

axes[0, 1].plot(x, np.cos(x), 'r')
axes[0, 1].set_title('cos(x)')

axes[1, 0].plot(x, np.tan(x), 'g')
axes[1, 0].set_title('tan(x)')
axes[1, 0].set_ylim(-10, 10)  # limit y to avoid extreme values

axes[1, 1].plot(x, np.log1p(x), 'm')
axes[1, 1].set_title('log1p(x)')

plt.tight_layout()
plt.show()

Important Notes

If you create only one plot, axes is not an array but a single object.

You can access each plot by axes[row, column] when you have multiple rows and columns.

Use plt.tight_layout() to avoid overlapping labels and titles.

Summary

plt.subplots helps create multiple plots in a grid.

You get a figure and an array of axes to draw on.

Use rows and columns to organize your plots clearly.