Subplots are essential for visualizing multiple datasets in a single figure. Here's a quick guide to get started with creating subplots using Matplotlib in Python:

1. Basic Subplot Creation

Use plt.subplots() to generate a grid of subplots:

import matplotlib.pyplot as plt

fig, axs = plt.subplots(2, 2)  # 2x2 grid
axs[0, 0].plot([1,2,3], [4,5,1])
axs[0, 1].plot([1,2,3], [4,5,1])
axs[1, 0].plot([1,2,3], [4,5,1])
axs[1, 1].plot([1,2,3], [4,5,1])
plt.show()
Matplotlib Subplots

2. Customizing Layouts

  • Row/Column arrangement: Use nrows and ncols parameters
  • Figure size: Adjust with figsize=(width, height)
  • Shared axes: Enable via sharex=True or sharey=True

Example:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
ax1.plot([1,2,3], [4,5,1])
ax2.plot([1,2,3], [4,5,1])
Subplot Layout

3. Advanced Features

  • Subplot spacing: Control with plt.subplots_adjust()
  • Grid of subplots: Use plt.GridSpec() for complex arrangements
  • Image reference: For more details on subplot configurations, visit our Plotting Guide

4. Common Use Cases

  • Comparing datasets side-by-side
  • Creating multi-panel figures for analysis
  • Visualizing different aspects of the same data

For a visual example of subplot applications, check out:

Subplot Examples

Let me know if you'd like to explore specific subplot configurations or advanced customization techniques!