matplotlib legend outside plot

Guri Bee

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19-10-2024

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Placing Your Legend Outside the Plot: A Guide to Clarity in Matplotlib

Visualizing data effectively is crucial for understanding trends and communicating insights. Matplotlib, Python's powerful plotting library, provides a wealth of options for customizing plots, including the ability to position legends outside the plot area. This can significantly enhance readability, especially when dealing with complex plots with multiple datasets.

Why Move the Legend Outside?

• Avoid Clutter: A legend inside a plot can overlap with data points, especially if you have a large number of lines or markers. Moving it outside ensures a clean and uncluttered visualization.
• Accessibility: Placing the legend outside can make it easier for users with visual impairments to understand the plot by providing a clear and separate key to the data.
• Better Data Visualization: For complex plots with many series, a legend outside can be crucial for visualizing the data effectively. It allows users to easily identify and distinguish between different lines or markers.

How to Position Your Legend Outside

Matplotlib offers several ways to place your legend outside the plot. Here's a breakdown with examples:

1. bbox_to_anchor Keyword:

This powerful keyword within plt.legend() lets you precisely control the legend's position relative to the axes.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

plt.plot(x, y1, label='Sine Wave')
plt.plot(x, y2, label='Cosine Wave')

# Place legend outside the plot, top right corner
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)

plt.show()

• bbox_to_anchor: Defines the legend's position using coordinates relative to the axes (bottom left corner is (0,0) and top right is (1,1)). In the example, (1.05, 1) positions the legend slightly outside the right edge and top of the plot.
• loc: Determines the legend's alignment within the bbox_to_anchor box. 'upper left' places it at the top left corner.
• borderaxespad: Controls the spacing between the legend and the axes.

2. loc='center left' with bbox_to_anchor:

This combination places the legend centered horizontally outside the left side of the plot.

import matplotlib.pyplot as plt
import numpy as np

# ... (code from previous example)

# Center the legend outside the left side
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))

plt.show()

3. Using plt.figlegend():

For legends involving multiple subplots, plt.figlegend() offers a flexible solution.

import matplotlib.pyplot as plt
import numpy as np

fig, axes = plt.subplots(2, 1)

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

axes[0].plot(x, y1, label='Sine Wave')
axes[1].plot(x, y2, label='Cosine Wave')

# Create a legend for all subplots
plt.figlegend(loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=2) 

plt.show()

Example Use Case: Comparing Multiple Datasets

Imagine you're analyzing sales data from different regions. Placing a legend outside the plot will improve clarity and allow you to readily compare sales trends across regions.

import matplotlib.pyplot as plt
import pandas as pd

# Simulate sales data for three regions
data = {
    'Region A': [100, 120, 135, 150, 160],
    'Region B': [80, 95, 105, 115, 130],
    'Region C': [110, 125, 140, 155, 170]
}

df = pd.DataFrame(data)

# Plot the sales data
plt.plot(df.index, df['Region A'], label='Region A')
plt.plot(df.index, df['Region B'], label='Region B')
plt.plot(df.index, df['Region C'], label='Region C')

# Position legend outside the plot
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)

plt.xlabel('Month')
plt.ylabel('Sales')
plt.title('Monthly Sales by Region')

plt.show()

Key Considerations:

• Legend Placement: Experiment with different bbox_to_anchor values and loc options to find the optimal placement for your plot.
• Legend Size: You can adjust the legend's size using prop keyword argument in plt.legend(), setting font size, marker size, and other attributes.
• Number of Entries: For a large number of legend entries, consider using a column layout (ncol) or a figlegend for better organization.

Conclusion

By mastering the art of placing legends outside plots, you can elevate your data visualizations and improve their readability and clarity. With Matplotlib's flexibility and these techniques, you'll be able to create impactful and easily understood data visualizations.