Managing Plots with Matplotlib: Clearing and Closing Figures

Matplotlib is a powerful plotting library for Python that provides a wide range of tools for creating high-quality 2D and 3D plots. When working with multiple plots, it’s essential to manage them efficiently to avoid clutter and improve performance. In this tutorial, we’ll explore how to clear and close figures in Matplotlib using the cla(), clf(), and close() functions.

Introduction to Figure Hierarchy

Before diving into the clearing and closing functions, let’s understand the figure hierarchy in Matplotlib. A figure is the top-level container that holds all the plot elements, including axes, titles, labels, and plots. An axis is a single plot area within a figure, and it can contain multiple plots.

Clearing Axes: cla()

The cla() function clears the current axis, removing all plot elements, ticks, titles, and labels. This function is useful when you want to reuse an existing axis with new data.

import matplotlib.pyplot as plt

# Create a figure and axis
fig, ax = plt.subplots()

# Plot some data
ax.plot([1, 2, 3])

# Clear the axis
ax.cla()

# Plot new data
ax.plot([4, 5, 6])

Clearing Figures: clf()

The clf() function clears the entire figure, removing all axes and plot elements. This function is useful when you want to start with a clean slate and create a new plot.

import matplotlib.pyplot as plt

# Create a figure and axis
fig, ax = plt.subplots()

# Plot some data
ax.plot([1, 2, 3])

# Clear the figure
plt.clf()

# Create a new axis and plot new data
ax = fig.add_subplot()
ax.plot([4, 5, 6])

Closing Figures: close()

The close() function closes a figure window, freeing up system resources. This function is useful when you’re working with multiple plots and want to close the current figure.

import matplotlib.pyplot as plt

# Create a figure and axis
fig, ax = plt.subplots()

# Plot some data
ax.plot([1, 2, 3])

# Close the figure
plt.close(fig)

Memory Management

When working with multiple plots, it’s essential to manage memory efficiently to avoid performance issues. The clf() and cla() functions can help reduce memory usage by reusing existing figures and axes.

However, if you’re creating many similar plots in a loop, using plt.close() can be more memory-efficient than clearing and reusing the same figure.

import matplotlib.pyplot as plt

# Create multiple plots in a loop
for i in range(10):
    fig = plt.figure()
    ax = fig.add_subplot()
    ax.plot([i, i+1, i+2])
    fig.savefig(f'plot_{i}.png')
    plt.close(fig)

Best Practices

Here are some best practices to keep in mind when working with Matplotlib:

• Use cla() and clf() to clear axes and figures when reusing them.
• Use close() to close figure windows when you’re finished with them.
• Consider using plt.close('all') to close all figure windows at once.
• Be mindful of memory usage when creating multiple plots, and use the most efficient approach for your specific use case.

By following these best practices and understanding how to clear and close figures in Matplotlib, you can create high-quality plots efficiently and effectively.