Matplotlib-Jupyter Notebook中如何添加颜色说明侧边小窗口？

Absolutely, you can add that "side window"—it’s called a legend in Matplotlib, and it’s built specifically to explain what different colors, markers, or styles represent. Here’s how to implement it in your Jupyter Notebook:

Basic Inline Legend #

If you’re plotting your red and green data directly, just add a label parameter to each plot call, then use plt.legend() to render the legend:

import matplotlib.pyplot as plt
import numpy as np

# Sample data for demonstration
x = np.linspace(0, 10, 100)
red_data = np.sin(x)
green_data = np.cos(x)

# Plot with descriptive labels
plt.plot(x, red_data, color='red', label='Red Data: Sine Wave')
plt.plot(x, green_data, color='green', label='Green Data: Cosine Wave')

# Add legend (defaults to a sensible position like upper right)
plt.legend()

plt.show()

Positioning the Legend Outside the Plot (Side Panel) #

If you want the legend to sit outside the main plot area (like a dedicated side window), use the bbox_to_anchor parameter to adjust its position. For example, placing it to the right of the plot:

# Reuse the same data as above
plt.plot(x, red_data, color='red', label='Red Data: Sine Wave')
plt.plot(x, green_data, color='green', label='Green Data: Cosine Wave')

# Place legend outside the plot (right side)
plt.legend(loc='upper left', bbox_to_anchor=(1.02, 1), borderaxespad=0)

# Adjust layout to prevent legend from being cut off
plt.tight_layout()

plt.show()

For Scatter Plots or Other Plot Types #

The same logic applies to any plot type—just add the label parameter to your scatter, bar, or histogram calls:

# Sample scatter data
red_points = np.random.rand(50, 2)
green_points = np.random.rand(50, 2) + 1

plt.scatter(red_points[:,0], red_points[:,1], color='red', label='Red Group: Cluster 1')
plt.scatter(green_points[:,0], green_points[:,1], color='green', label='Green Group: Cluster 2')

plt.legend(loc='lower right')
plt.show()

Key Tips: #

• The loc parameter sets where the legend is anchored relative to its own box (e.g., 'upper right', 'center left').
• bbox_to_anchor lets you place the legend anywhere on the figure using coordinates relative to the plot axes.
• plt.tight_layout() ensures the legend doesn’t get clipped when placed outside the plot bounds.

This should give you exactly the explanatory side window you’re looking for!

内容的提问来源于stack exchange，提问作者Aren Mark Boghozian