Python 🐍 Put Legend Outside Plot 📈 – Easy Guide

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Are you tired of feeling boxed in by your Python plots and ready to break free from the constraints of traditional legend placement?

In this guide, I’ll show you how to put legends outside your plot for (click to 🦘 jump):

Let’s start with the first! 👇👩‍💻

Matplotlib Put Legend Outside Plot

Let’s start with various ways to position the legend outside for better visualization and presentation.

Matplotlib Set Legend Outside Plot (General)

First, let’s adjust the legend’s position outside the plot in general. To do this, use the bbox_to_anchor parameter in the legend function like this: matplotlib.pyplot.legend(bbox_to_anchor=(x, y)) 😃. Here, adjust the values of x and y to control the legend’s position.

import matplotlib.pyplot as plt

# Sample data for plotting
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [1, 8, 27, 64, 125]

# Create the plot
plt.plot(x, y1, label='y = x^2')
plt.plot(x, y2, label='y = x^3')

# Set the legend's position outside the plot using bbox_to_anchor
plt.legend(bbox_to_anchor=(1.05, 1))

# Add axis labels and title
plt.xlabel('x')
plt.ylabel('y')
plt.title('Plotting with External Legend')

# Display the plot
plt.show()

In this example, the bbox_to_anchor parameter is set to (1.05, 1), which moves the legend slightly to the right of the plot.

💡 Info: The bbox_to_anchor parameter in matplotlib.pyplot.legend() uses a tuple of two values, x and y, to control the position of the legend. x=0/1 controls the left/right and y=0/1 controls the bottom/top legend placement.

Generally, in axes coordinates:

  • (0, 0) represents the left-bottom corner of the axes.
  • (0, 1) represents the left-top corner of the axes.
  • (1, 0) represents the right-bottom corner of the axes.
  • (1, 1) represents the right-top corner of the axes.

However, the values of x and y are not limited to the range [0, 1]. You can use values outside of this range to place the legend beyond the axes’ boundaries.

For example:

  • (1.05, 1) places the legend slightly to the right of the top-right corner of the axes.
  • (0, 1.1) places the legend slightly above the top-left corner of the axes.

Using negative values is also allowed. For example:

  • (-0.3, 0) places the legend to the left of the bottom-left corner of the axes.
  • (1, -0.2) places the legend below the bottom-right corner of the axes.

The range of x and y depends on the desired position of the legend relative to the plot. By adjusting these values, you can fine-tune the legend’s position to create the perfect visualization. 💫

Matplotlib Set Legend Below or Above Plot

To place the legend below the plot, you can set the loc parameter as ‘upper center’ and use bbox_to_anchor like this: plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1)). For placing the legend above the plot, use bbox_to_anchor=(0.5, 1.1) instead 📊.

Matplotlib Set Legend Left of Plot (Upper, Center, Lower Left)

For positioning the legend to the left of the plot, use the following examples:

  • Upper left: plt.legend(loc='center left', bbox_to_anchor=(-0.2, 0.5)) 🌟
  • Center left: plt.legend(loc='center left', bbox_to_anchor=(-0.1, 0.5))
  • Lower left: plt.legend(loc='lower left', bbox_to_anchor=(-0.2, 0))

Matplotlib Set Legend Right of Plot (Upper, Center, Lower Right)

To position the legend to the right of the plot, you can try the following:

  • Upper right: plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1)) 👍
  • Center right: plt.legend(loc='center right', bbox_to_anchor=(1.1, 0.5))
  • Lower right: plt.legend(loc='lower right', bbox_to_anchor=(1.1, 0))

Matplotlib Set Subplots Legend Outside Plot

When working with subplots, you can place a single, unified legend outside the plot by iterating over the axes and using the legend() method on the last axis (source) 😊. Remember to use the bbox_to_anchor parameter to control the legend’s position.

Here’s an example that does two things, i.e., (1) placing the legend on the right and (2) adjusting the layout to accommodate the external legend:

import numpy as np
import matplotlib.pyplot as plt

# Sample data for plotting
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# Create subplots
fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True)

# Plot data on the first subplot
axes[0].plot(x, y1, label='sin(x)')
axes[0].set_title('Sine Wave')

# Plot data on the second subplot
axes[1].plot(x, y2, label='cos(x)', color='orange')
axes[1].set_title('Cosine Wave')

# Set a common x-label for both subplots
fig.text(0.5, 0.04, 'x', ha='center')

# Set y-labels for individual subplots
axes[0].set_ylabel('sin(x)')
axes[1].set_ylabel('cos(x)')

# Create a unified legend for both subplots
handles, labels = axes[-1].get_legend_handles_labels()
for ax in axes[:-1]:
    h, l = ax.get_legend_handles_labels()
    handles += h
    labels += l

# Place the unified legend outside the plot using bbox_to_anchor
fig.legend(handles, labels, loc='upper right', bbox_to_anchor=(1, 0.75))

# Adjust the layout to accommodate the external legend
fig.subplots_adjust(right=0.7)

# Display the subplots
plt.show()

Legend Outside Plot Is Cut Off

If your legend is cut off, you can adjust the saved figure’s dimensions using plt.savefig('filename.ext', bbox_inches='tight'). The bbox_inches parameter with the value ‘tight’ will ensure that the whole legend is visible on the saved figure 🎉.

Add Legend Outside a Scatter Plot

For a scatter plot, you can use the same approach as mentioned earlier by adding the loc and bbox_to_anchor parameters to position the legend outside the plot. For instance, plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1)) will place the legend in the upper right corner outside the scatter plot 💡.

If your legend is cut off when placing it outside the plot in Python’s Matplotlib, you can adjust the layout and save or display the entire figure, including the external legend, by following these steps:

  1. Use the bbox_to_anchor parameter in the legend() function to control the position of the legend.
  2. Adjust the layout of the figure using the subplots_adjust() or tight_layout() function to make room for the legend.
  3. Save or display the entire figure, including the external legend.

Here’s an example demonstrating these steps:

import matplotlib.pyplot as plt

# Sample data for plotting
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [1, 8, 27, 64, 125]

# Create the plot
plt.plot(x, y1, label='y = x^2')
plt.plot(x, y2, label='y = x^3')

# Set the legend's position outside the plot using bbox_to_anchor
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

# Add axis labels and title
plt.xlabel('x')
plt.ylabel('y')
plt.title('Plotting with External Legend')

# Adjust the layout to accommodate the external legend
plt.subplots_adjust(right=0.7)

# Display the plot
plt.show()

In this example, you use the subplots_adjust() function to adjust the layout of the figure and make room for the legend.

You can also use the tight_layout() function, which automatically adjusts the layout based on the elements in the figure:

# ...
# Set the legend's position outside the plot using bbox_to_anchor
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

# Add axis labels and title
plt.xlabel('x')
plt.ylabel('y')
plt.title('Plotting with External Legend')

# Automatically adjust the layout to accommodate the external legend
plt.tight_layout(rect=[0, 0, 0.7, 1])

# Display the plot
plt.show()

In this case, the rect parameter is a list [left, bottom, right, top], which specifies the normalized figure coordinates of the new bounding box for the subplots. Adjust the values in the rect parameter as needed to ensure the legend is not cut off.

Additional Legend Configurations

In this section, you’ll learn about some additional ways to customize the legend for your plots in Python using Matplotlib. This will help you create more meaningful and visually appealing visualizations. However, feel free to skip ahead to the following sections on plotting the legend outside the figure in Seaborn, Pandas, and Bokeh.

Python Set Legend Position

As you already know by now, you can place the legend at a specific position in the plot by using the bbox_to_anchor parameter. For example, you could place the legend outside the plot to the right by passing (1.05, 0.5) as the argument:

import matplotlib.pyplot as plt
plt.legend(bbox_to_anchor=(1.05, 0.5))

This will place the legend slightly outside the right border of the plot, with its vertical center aligned with the plot center.

Python Set Legend Location

You can easily change the location of the legend by using the loc parameter. Matplotlib allows you to use predefined locations like 'upper right', 'lower left', etc., or use a specific coordinate by passing a tuple:

plt.legend(loc='upper left')

This will place the legend in the upper-left corner of the plot.📍

Python Set Legend Font Size

To change the font size of the legend, you can use the fontsize parameter. You can pass a numeric value or a string like 'small', 'medium', or 'large' to set the font size:

plt.legend(fontsize='large')

This will increase the font size of the legend text.😃

Python Set Legend Title

If you want to add a title to your legend, you can use the title parameter. Just pass a string as the argument to set the title:

plt.legend(title='My Legend Title')

This will add the title “My Legend Title” above your legend.👍

Python Set Legend Labels

If you’d like to customize the legend labels, you can pass the labels parameter. It takes a list of strings as the argument:

plt.legend(labels=['Label 1', 'Label 2'])

Your legend will now display the custom labels “Label 1” and “Label 2” for the corresponding plot elements.🏷️

Python Set Legend Color

Changing the color of the legend text and lines can be achieved by using the labelcolor parameter. Just pass a color string or a list of colors:

plt.legend(labelcolor='red')

This will change the color of the legend text and lines to red.🔴

Seaborn Put Legend Outside Plot

In this section, I’ll show you how to move the legend outside the plot using Seaborn. Let’s dive into various ways of setting the legend position, like below, above, left or right of the plot.👨‍💻

Sns Set Legend Outside Plot (General)

First, let’s talk about the general approach:

You can use the legend() function and the bbox_to_anchor parameter from Matplotlib to move the legend. You can combine this with the loc parameter to fine-tune the legend’s position.📈

Here’s a quick example:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data for plotting
tips = sns.load_dataset("tips")

# Create a Seaborn plot with axis variable 'ax'
fig, ax = plt.subplots()
sns_plot = sns.scatterplot(x="total_bill", y="tip", hue="day", data=tips, ax=ax)

# Set the legend's position outside the plot using bbox_to_anchor on 'ax'
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)

# Add axis labels and title
ax.set_xlabel('Total Bill')
ax.set_ylabel('Tip')
ax.set_title('Tips by Total Bill and Day')

# Display the plot
plt.show()

Sns Set Legend Below or Above Plot

Now let’s move the legend below or above the plot.💡 Simply adjust the bbox_to_anchor parameter accordingly. For example, to place the legend below the plot, you can use bbox_to_anchor=(0.5, -0.1):

ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1))

And to place the legend above the plot, use bbox_to_anchor=(0.5, 1.1):

ax.legend(loc='lower center', bbox_to_anchor=(0.5, 1.1))

Sns Set Legend Left of Plot (Upper, Center, Lower Left)

Similarly, to position the legend on the left side of the plot, you can use the following code snippets:🎨

Upper left:

ax.legend(loc='upper right', bbox_to_anchor=(-0.15, 1))

Center left:

ax.legend(loc='center right', bbox_to_anchor=(-0.15, 0.5))

Lower left:

ax.legend(loc='lower right', bbox_to_anchor=(-0.15, 0))

Sns Set Legend Right of Plot (Upper, Center, Lower Right)

Lastly, to place the legend on the right side of the plot, adjust the bbox_to_anchor parameter like so:🚀

Upper right:

ax.legend(loc='upper left', bbox_to_anchor=(1.05, 1))

Center right:

ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5))

Lower right:

ax.legend(loc='lower left', bbox_to_anchor=(1.05, 0))

With these techniques, you can easily position the legend outside the plot using Seaborn! Happy plotting!👩‍🔬

Pandas Put Legend Outside Plot

When working with Pandas and Matplotlib, you often want to move the legend outside the plot 🖼️ to improve readability. No worries! You can do this by taking advantage of the fact that .plot() returns a Matplotlib axis, enabling you to add .legend(bbox_to_anchor=(x, y)) to your code.

Here’s how:

First, import the necessary libraries like Pandas and Matplotlib:

import pandas as pd
import matplotlib.pyplot as plt

Create your DataFrame and plot it using the plot() function, like this:

data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
ax = df.plot()

Next, you’ll want to place the legend outside the plot. Adjust the coordinates parameter, bbox_to_anchor, to position it according to your preferences. For example, if you want to place the legend to the right of the plot, use:

ax.legend(bbox_to_anchor=(1.05, 1))

plt.tight_layout()
plt.show()

This code will place the legend to the right of the plot, at the top 🚀.

Bokeh Put Legend Outside Plot

Placing a legend outside the plot in Bokeh can be easily done by using the add_layout method. You’ll need to create a Legend object manually and then add it to your plot using add_layout. This gives you the flexibility to position the legend anywhere on your plot, which is particularly helpful when you have many curves that might be obscured by an overlapping legend. 😊

Here’s a short example to help you move the legend outside the plot in Bokeh:

from bokeh.plotting import figure, show
from bokeh.models import Legend, LegendItem
from bokeh.io import output_notebook
output_notebook()

# Example data
x = list(range(10))
y1 = [i**2 for i in x]
y2 = [i**0.5 for i in x]

# Create a figure
p = figure(title="Example Plot")

# Add line glyphs and store their renderer
r1 = p.line(x, y1, line_color="blue", legend_label="Line 1")
r2 = p.line(x, y2, line_color="red",  legend_label="Line 2")

# Create Legend object
legend = Legend(items=[
    LegendItem(label="Line 1", renderers=[r1]),
    LegendItem(label="Line 2", renderers=[r2])
], location="top_left")

# Add legend to plot
p.add_layout(legend, 'right')

# Show plot
show(p)

To ensure your plot is as clear as possible, we recommend experimenting with different legend positions and layouts. By customizing your plot, you can maintain a clean and organized display of your curves even when there’s a lot of information to convey.

With Bokeh, you have full control over your plot’s appearance, so enjoy exploring different options to find the perfect fit for your data! 📈🎉

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