matplotlib subplot size

Breiz Heurope

3 min read

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10-03-2025

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Matplotlib is a powerful Python library for creating static, interactive, and animated visualizations. A common task is arranging multiple plots within a single figure using subplots. However, controlling the size and aspect ratio of these subplots can be tricky. This guide will equip you with the knowledge and techniques to master Matplotlib subplot size, creating visually appealing and informative figures.

Understanding Matplotlib's Figure and Subplot Structure

Before diving into size manipulation, let's establish a foundational understanding. A Matplotlib figure acts as a container holding one or more subplots. Each subplot represents an individual plot within the figure. The matplotlib.pyplot.subplots() function is your primary tool for creating this structure.

import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=2) # Creates a 2x2 grid of subplots

# Access individual subplots via axes[row, col]
axes[0, 0].plot([1, 2, 3, 4], [5, 6, 7, 8]) 
axes[0, 1].scatter([1, 2, 3, 4], [5, 6, 7, 8])

plt.show()

This code creates a 2x2 grid of subplots. axes is a NumPy array allowing access to individual subplots using their row and column indices.

Controlling Subplot Size: The figsize Argument

The most straightforward method for influencing subplot size is using the figsize argument within plt.subplots(). figsize accepts a tuple specifying the width and height of the entire figure in inches.

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 6)) # 10 inches wide, 6 inches tall

# ... your plotting code ...

plt.show()

Increasing figsize enlarges the entire figure, proportionally increasing subplot size. However, this method doesn't offer granular control over individual subplot dimensions.

Adjusting Subplot Parameters: gridspec_kw

For more precise control, leverage the gridspec_kw argument within plt.subplots(). This allows manipulation of the subplot grid's structure using GridSpec parameters. This is particularly useful for creating subplots with unequal sizes or unusual arrangements.

import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=2, gridspec_kw={'height_ratios': [2, 1], 'width_ratios': [1, 2]})

#The first subplot row is twice as tall as the second, the second column is twice as wide as the first

axes[0, 0].plot([1, 2, 3], [4, 5, 6])
axes[0, 1].plot([1, 2, 3], [4, 5, 6])
axes[1, 0].plot([1, 2, 3], [4, 5, 6])
axes[1, 1].plot([1, 2, 3], [4, 5, 6])
plt.show()

height_ratios and width_ratios control the relative height and width of rows and columns respectively.

Fine-Tuning with subplots_adjust()

The plt.subplots_adjust() function provides even more granular control over subplot spacing and positioning. It allows you to adjust parameters like:

• left: Distance from the left edge of the figure to the left edge of the subplots.
• right: Distance from the right edge of the figure to the right edge of the subplots.
• bottom: Distance from the bottom edge of the figure to the bottom edge of the subplots.
• top: Distance from the top edge of the figure to the top edge of the subplots.
• wspace: Horizontal space between subplots.
• hspace: Vertical space between subplots.

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 6))

# ... your plotting code ...

plt.subplots_adjust(wspace=0.4, hspace=0.3) # Adjust spacing between subplots

plt.show()

Experiment with these values to achieve the desired subplot arrangement and spacing.

Aspect Ratio Considerations

Maintaining a consistent aspect ratio across subplots is crucial for accurate visual representation. You can enforce aspect ratios using the aspect argument within the individual subplot's methods (e.g., axes[0,0].set_aspect('equal')).

Using GridSpec Directly for Complex Layouts

For complex layouts beyond simple grids, using matplotlib.gridspec.GridSpec directly gives maximum flexibility. This allows for completely custom subplot arrangements.

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

fig = plt.figure(figsize=(10, 6))
gs = gridspec.GridSpec(nrows=2, ncols=2, width_ratios=[2, 1], height_ratios=[1, 2])

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[1, 0])
ax4 = fig.add_subplot(gs[1, 1])


# ... your plotting code ...

plt.show()

This offers ultimate control, enabling non-rectangular grids and varying subplot sizes.

Conclusion

Mastering Matplotlib subplot size involves a combination of techniques. Starting with figsize provides a basic approach, while gridspec_kw and subplots_adjust() allow for increasingly fine-grained control. For the most complex layouts, using GridSpec directly offers unparalleled flexibility. By understanding these methods, you can create publication-quality figures with precisely sized and arranged subplots. Remember to always consider the overall aesthetic and clarity when adjusting subplot sizes to ensure your visualizations effectively communicate information.