Python Data Science Handbook, 2nd Edition

Chapter 29. Customizing Plot Legends

Plot legends give meaning to a visualization, assigning meaning to the various plot elements. We previously saw how to create a simple legend; here we’ll take a look at customizing the placement and aesthetics of the legend in Matplotlib.

The simplest legend can be created with the plt.legend command, which automatically creates a legend for any labeled plot elements (see Figure 29-1).

In [1]: import matplotlib.pyplot as plt
        plt.style.use('seaborn-whitegrid')

In [2]: %matplotlib inline
        import numpy as np

In [3]: x = np.linspace(0, 10, 1000)
        fig, ax = plt.subplots()
        ax.plot(x, np.sin(x), '-b', label='Sine')
        ax.plot(x, np.cos(x), '--r', label='Cosine')
        ax.axis('equal')
        leg = ax.legend()

Figure 29-1. A default plot legend

But there are many ways we might want to customize such a legend. For example, we can specify the location and turn on the frame (see Figure 29-2).

In [4]: ax.legend(loc='upper left', frameon=True)
        fig

Figure 29-2. A customized plot legend

We can use the ncol command to specify the number of columns in the legend, as shown in Figure 29-3.

In [5]: ax.legend(loc='lower center', ncol=2)
        fig

Figure 29-3. A two-column plot legend ...