Add Legend to Figure in Matplotlib
Matplotlib is one of the most widely used data visualization libraries in Python. Typically, when visualizing more than one variable, you'll want to add a legend to the plot, explaining what each variable represents. In this article, we'll take a look at how to add a legend to a Matplotlib plot.
Creating a Plot
Let's first create a simple plot with two variables:
import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() x = np.arange(0, 10, 0.1) y = np.sin(x) z = np.cos(x) ax.plot(y, color='blue') ax.plot(z, color='black') plt.show()
Here, we've plotted a sine function, starting at
0 and ending at
10 with a step of
0.1, as well as a cosine function in the same interval and step. Running this code yields:
Now, it would be very useful to label these and add a legend so that someone who didn't write this code can more easily discern which is which.
Add Legend to a Figure in Matplotlib
Let's add a legend to this plot. Firstly, we'll want to
label these variables, so that we can refer to those labels in the legend. Then, we can simply call
legend() on the
ax object for the legend to be added:
import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() x = np.arange(0, 10, 0.1) y = np.sin(x) z = np.cos(x) ax.plot(y, color='blue', label='Sine wave') ax.plot(z, color='black', label='Cosine wave') leg = ax.legend() plt.show()
Now, if we run the code, the plot will have a legend:
Notice how the legend was automatically placed in the only free space where the waves won't run over it.
Customize Legend in Matplotlib
The legend is added, but it's a little bit cluttered. Let's remove the border around it and move it to another location, as well as change the plot's size:
import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots(figsize=(12, 6)) x = np.arange(0, 10, 0.1) y = np.sin(x) z = np.cos(x) ax.plot(y, color='blue', label='Sine wave') ax.plot(z, color='black', label='Cosine wave') leg = ax.legend(loc='upper right', frameon=False) plt.show()
This results in:
Here, we've used the
loc argument to specify that we'd like to put the legend in the top right corner. Other values that are accepted are
center left and
Additionally, you can use
center to put it in the dead center, or
best to place the legend at the "best" free spot so that it doesn't overlap with any of the other elements. By default,
best is selected.
Add Legend Outside of Axes
Sometimes, it's tricky to place the legend within the border box of a plot. Perhaps, there are many elements going on and the entire box is filled with important data.
In such cases, you can place the legend outside of the axes, and away from the elements that constitute it. This is done via the
bbox_to_anchor argument, which specifies where we want to anchor the legend to:
import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots(figsize=(12, 6)) x = np.arange(0, 10, 0.1) y = np.sin(x) z = np.cos(x) ax.plot(y, color='blue', label='Sine wave') ax.plot(z, color='black', label='Cosine wave') leg = ax.legend(loc='center', bbox_to_anchor=(0.5, -0.10), shadow=False, ncol=2) plt.show()
This results in:
bbox_to_anchor argument accepts a few arguments itself. Firstly, it accepts a tuple, which allows up to 4 elements. Here, we can specify the
height of the legend.
We've only set the
y values, to displace it
-0.10 below the axes, and
0.5 from the left side (
0 being the lefthand of the box and
1 the righthand side).
By tweaking these, you can set the legend at any place. Within or outside of the box.
Then, we've set the
False. This is used to specify whether we want a small shadow rendered below the legend or not.
Finally, we've set the
ncol argument to
2. This specifies the number of labels in a column. Since we have two labels and want them to be in one column, we've set it to
2. If we changed this argument to
1, they'd be placed one above the other:
bbox_to_anchor argument is used alongside the
loc argument. The
loc argument will put the legend based on the
bbox_to_anchor. In our case, we've put it in the
center of the new, displaced, location of the border box.
In this tutorial, we've gone over how to add a legend to your Matplotlib plots. Firstly, we've let Matplotlib figure out where the legend should be located, after which we've used the
bbox_to_anchor argument to specify our own location, outside of the axes.
Data Visualization in Python with Matplotlib and Pandas is a book designed to take absolute beginners to Pandas and Matplotlib, with basic Python knowledge, and allow them to build a strong foundation for advanced work with theses libraries - from simple plots to animated 3D plots with interactive buttons.
It serves as an in-depth, guide that'll teach you everything you need to know about Pandas and Matplotlib, including how to construct plot types that aren't built into the library itself.
Data Visualization in Python, a book for beginner to intermediate Python developers, guides you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair. More specifically, over the span of 11 chapters this book covers 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy.
It serves as a unique, practical guide to Data Visualization, in a plethora of tools you might use in your career.Reference: stackabuse.com