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Summary

The web content provides a guide on how to set the legend position in Matplotlib to enhance the readability and aesthetics of plots.

Abstract

The article titled "How to Set Legend Position in Matplotlib" offers a comprehensive overview of customizing legend positions within Matplotlib plots. It emphasizes the importance of proper legend placement to avoid obscuring data, thereby improving the plot's readability and visual appeal. The guide covers the default legend location chosen by Matplotlib to minimize overlap, as well as methods to customize the legend's position both inside and outside the plot area. Inside the plot, common locations include "upper right," "upper left," "lower left," "lower right," "center," "center left," and "center right," which can be set using the loc parameter in the legend() function. For legends outside the plot, the bbox_to_anchor parameter is used in conjunction with loc to define the legend's bounding box and anchor point, ensuring it is placed appropriately without interfering with the data.

Opinions

  • The article suggests that the default legend location in Matplotlib is designed to minimize overlap with the plotted data, indicating a user-friendly approach by the library.
  • It is implied that customizing the legend position is not only about aesthetics but also about ensuring the plot remains informative and functional.
  • The use of code examples alongside explanations conveys an opinion that practical demonstrations are crucial for understanding and applying the concepts discussed.
  • The article's focus on various legend locations and methods to place them indicates a belief that flexibility in legend placement is a key feature for creating effective visualizations.

How to Set Legend Position in Matplotlib

Table of Contents

Introduction

Adjusting the legend position in matplotlib plots enhances readability and aesthetics. In some cases, the default legend location may overlap with the data, making it difficult to interpret the plot. By customizing the legend position, you can ensure it does not obstruct crucial information. Depending on the plot’s purpose, you may want to place the legend inside or outside the plot area. Choosing an appropriate legend location is crucial for creating informative and visually appealing plots, whether for presentations, reports, or sharing with colleagues.

Default legend location

Use the legend() function without arguments to create a legend with the default location. The default legend location is determined by Matplotlib to minimize overlap with the plotted data.

This is the code example.

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")

plt.legend()

plt.show()

Custom legend location inside the plot

You can specify the location of the legend inside the plot using the loc parameter in the legend() function. The loc parameter accepts a string or an integer code.

Here are some common legend locations:

"best"
"upper right"
"upper left"
"lower left"
"lower right"
"center"
"center left"
"center right"

For example, if we want to move the legend to the upper right corner, we can use the following code.

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 the legend in the upper right corner
plt.legend(loc="upper right")

plt.show()

Legend outside the plot

To place the legend outside the plot, you can use the bbox_to_anchor parameter. It defines the bounding box of the legend in the axes coordinate system. The loc parameter is used in combination with bbox_to_anchor to specify the anchor point of the legend.

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 the legend outside the plot, to the right
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))

plt.show()
Matplotlib
Data Science
Python
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