Mastering Figure and Subplot Sizing in Matplotlib

Introduction

When creating visualizations with Python’s Matplotlib library, controlling the figure size is a fundamental aspect that can enhance readability and presentation quality. This tutorial explores various techniques for adjusting the overall figure size as well as individual subplot sizes within a single figure.

Understanding Figure Size in Matplotlib

The default size of figures in Matplotlib might not be suitable for all contexts or display requirements. Adjusting the size ensures that visualizations are clear, appropriately scaled, and aesthetically pleasing.

Methods to Change Figure Size

Using figsize in plt.subplots():
The simplest way to set the figure size when creating subplots is by using the figsize parameter directly within plt.subplots(). This approach sets both width and height of the entire figure.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y)
ax.set_title('Example Plot with Custom Figure Size')
plt.show()

Using set_size_inches():
After creating a figure using plt.subplots(), you can adjust its size by calling set_size_inches() on the returned Figure object.
```
fig, ax = plt.subplots()
fig.set_size_inches(15, 10)
```
Using set_figwidth() and set_figheight():
Alternatively, specify width and height separately using these methods.
```
fig, ax = plt.subplots()
fig.set_figwidth(12)
fig.set_figheight(8)
```

Adjusting Subplot Sizes

When dealing with multiple subplots, controlling the relative sizes of each subplot can be essential for emphasizing specific data or maintaining a consistent layout.

Using gridspec_kw:
When creating subplots, you can use the gridspec_kw argument to control size ratios.

fig, axs = plt.subplots(2, 1, figsize=(10, 8), gridspec_kw={'height_ratios': [1, 2]})
axs[0].plot(x, y)

Creating Subplots Manually:
You can create a figure manually and add subplots using add_subplot(), which provides more flexibility with layout management.
```
f = plt.figure(figsize=(10, 5))
ax1 = f.add_subplot(121)
ax2 = f.add_subplot(122)
```

Handling Multiple Subplots in Loops

For scenarios requiring dynamic subplot creation (such as iterating over data), you can use loops to add subplots.

plt.figure(figsize=(16, 8))
for i in range(1, 7):
    plt.subplot(2, 3, i)
    plt.title(f'Histogram of {i}')
    # Sample histogram code
    # Assuming 'data' is a numpy array with shape (n_samples, n_features)
    # plt.hist(data[:, i-1], bins=60)

Best Practices

Consistent Scaling: Ensure your figures are scaled consistently across different plots for comparative analysis.
Adjusting Layouts: Utilize plt.tight_layout() to automatically adjust subplot parameters to give specified padding.
Customization and Aesthetics: Always consider the target audience when choosing figure dimensions. Larger sizes may be preferable for presentations or reports.

Conclusion

Adjusting figure size in Matplotlib is a versatile skill that enhances your ability to present data clearly and effectively. By mastering these techniques, you can create visually appealing plots tailored to your specific needs. Experiment with different sizing methods to find the best fit for your visualization tasks.