Revamp your matplotlib charts with this easy trick for placing legends outside the figure – plus code snippets for quick implementation

Table of content

1. Introduction
2. Why legends outside the figure are important
3. Traditional approach to plotting legends in matplotlib
4. The easy trick: placing legends outside the figure
5. Step-by-step instructions for implementing the trick
6. Code snippets for quick implementation
7. Examples showcasing the trick
8. Conclusion

Introduction

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Matplotlib is a widely used tool for data visualization and is a staple for many data scientists, researchers, and engineers who work with Python. One of the challenges when creating charts with Matplotlib is finding the best place for the legend. By default, the legend is placed within the axis of the figure, which can sometimes obscure important data. However, with a simple trick, legends can be placed outside the figure, making it easier to read and understand the chart. In this article, we will explore this easy trick to help you revamp your Matplotlib charts with a more effective legend placement. We will provide code snippets that you can easily implement in your own projects to achieve this impressive feature. So, let's dive in!

Why legends outside the figure are important

Legends play an essential role in data visualization. They allow us to quickly interpret and understand the information presented in a chart. When creating charts with Matplotlib, placing the legend inside the figure is the default setting. However, this may not always be the best option, especially when dealing with small figures or multiple charts on the same page. This is where placing legends outside the figure becomes important.

By placing the legend outside the figure, we can free up space for our graphs, make them more readable, and enhance their visual appeal. Moreover, legends placed outside the figure can be helpful when we need to compare data across different charts. By having all the legends in one place, we can easily compare and contrast the information presented.

Placing a legend outside the figure can make our charts more user-friendly, especially when presenting complex data to non-technical audiences. It can also enhance the visual appeal of the chart and make it more aesthetically pleasing. Overall, legends placed outside the figure can be a simple yet powerful tool in creating effective and informative data visualizations.

Traditional approach to plotting legends in matplotlib

The involves placing them inside the figure, which can sometimes add clutter and detract from the visual appeal of the chart. One common method for placing legends within the figure involves calling the legend function and specifying its location using a string parameter such as 'upper left', 'lower right', or 'center'. However, this method can sometimes result in the legend overlapping or concealing important data elements in the chart.

Another approach involves specifying the legend location using numerical coordinates or offset values, which can be more precise, but may be more difficult to implement and adjust. Additionally, this method still places the legend within the figure boundaries and can still contribute to visual clutter.

There are also some third-party packages, such as mpl_toolkits.axes_grid1, which provide more flexibility in placing legends outside the figure, but these packages may not be readily available in all environments and may require additional installation steps.

Luckily, there is a simple and effective trick for placing legends outside the figure in Matplotlib that only requires a few lines of code. By adjusting the bbox_to_anchor parameter when calling the legend function, you can specify the legend location relative to the figure boundaries, even placing it completely outside the figure. This can create a cleaner and more visually appealing chart, with the legend clearly visible without distracting from the main data elements.

Overall, while there are several approaches to plotting legends in Matplotlib, the traditional method of placing them inside the figure can sometimes detract from the visual appeal of the chart. The trick for placing legends outside the figure using bbox_to_anchor offers a simple and effective alternative that can create a cleaner, more readable chart.

The easy trick: placing legends outside the figure

Placing legends outside the figure is a useful technique for creating clean and readable charts in Matplotlib. By default, Matplotlib places legends inside the figure, which can sometimes make the chart appear cluttered or difficult to read. However, with just a few lines of code, you can easily move the legend outside the figure and improve the overall readability of your chart.

To move the legend outside the figure, you'll need to create a new axis specifically for the legend to reside. Here are the steps to get started:

1. Add a new axis to the figure using the add_axes() method. This will create a new axis below or to the right of the existing chart.
2. Define the dimensions for the new axis using the rect parameter, which takes a list or tuple of four values representing the [left, bottom, width, height].
3. Use the legend() method to create the legend on the new axis.

Once you have created the new axis and legend, you can customize the location and styling of the legend as needed using Matplotlib's built-in options.

Here's an example of how to move the legend outside the figure in Matplotlib:

import matplotlib.pyplot as plt

# Create sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create the chart
fig, ax = plt.subplots()
ax.plot(x, y, label="Sample Data")

# Create a new axis for the legend
legend_ax = fig.add_axes([0, -0.2, 1, 0.1])

# Add the legend to the new axis
ax.legend(loc="upper center", ncol=3, bbox_to_anchor=(0.5, -0.15), bbox_transform=fig.transFigure, fontsize="large")

# Show the chart
plt.show()

In this example, we create a new axis below the original chart by setting the bottom value of the rect parameter to a negative number. We then use the loc parameter of the legend() method to specify the location of the legend on the new axis.

With just a few lines of code, we have successfully moved the legend outside the figure and made our chart easier to read.

Step-by-step instructions for implementing the trick

To implement the trick of placing legends outside the figure in your matplotlib charts, follow these step-by-step instructions:

1. Import the necessary packages:

import matplotlib.pyplot as plt
import numpy as np

2. Create the plot and set the figure size:

fig, ax = plt.subplots(figsize=(8, 4))

3. Generate some data to plot:

x = np.linspace(0, 10, 100)
y = np.sin(x)

4. Plot the data with a label for the legend:

ax.plot(x, y, label='sin(x)')

5. Move the legend outside the plot using the bbox_to_anchor parameter:

ax.legend(loc='upper right', bbox_to_anchor=(1.15, 1))

The bbox_to_anchor parameter specifies the position of the legend outside the plot. The first value is the horizontal position, with 1 indicating the right edge of the figure, while the second value is the vertical position, with 1 indicating the top edge of the figure. Adjust these values as needed to place the legend where you want it.

By following these simple steps, you can easily revamp your matplotlib charts and improve their readability with the legend outside the figure. Feel free to experiment with different values for the bbox_to_anchor parameter to find the perfect placement for your legend.

Code snippets for quick implementation

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For placing legends outside the figure, you can use the matplotlib function legend() along with the keyword argument bbox_to_anchor. The bbox_to_anchor argument takes a tuple of two values representing the x and y coordinates of the legend box.

Here's an example:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

fig, ax = plt.subplots()

ax.plot(x, y, label='y = x^2')

legend = ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1),
                   fancybox=True, shadow=True, ncol=5)

plt.show()

In this example, the loc parameter is set to 'upper center', which specifies that the legend should be placed at the top of the plot. The bbox_to_anchor parameter is set to (0.5, -0.1), which specifies that the legend box should be placed at the horizontal center of the plot and slightly below the bottom edge of the plot. The other arguments used in this example (fancybox=True, shadow=True, ncol=5) are optional and control the appearance of the legend.

You can customize the size and position of the legend box by adjusting the bbox_to_anchor parameter. For example, to place the legend to the right of the plot, you could use (1.05, 0.5) as the bbox_to_anchor value:

legend = ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5),
                   fancybox=True, shadow=True, ncol=1)

This will place the legend box outside of the plot area to the right, with the center of the box aligned with the vertical center of the plot.

You can also adjust the font size of the legend using the fontsize parameter:

legend = ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5),
                   fancybox=True, shadow=True, ncol=1, fontsize='large')

This will increase the font size of the legend text to 'large'.

Examples showcasing the trick

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If you are struggling to position your legend in your matplotlib charts, fear not! This easy trick for placing legends outside the figure will make your life easier. Here are some :

1. Scatter plot with legend outside the figure

Imagine you have a scatter plot with different groups of points, and you want to show the legend outside the figure. You can do this with a few lines of code:

import matplotlib.pyplot as plt

# create the plot and plot each group of points with a different color
fig, ax = plt.subplots()
ax.scatter(x1, y1, c='red', label='Group 1')
ax.scatter(x2, y2, c='blue', label='Group 2')
ax.scatter(x3, y3, c='green', label='Group 3')

# move the legend outside the figure
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

The bbox_to_anchor parameter controls the position of the legend, while loc controls the alignment. In this case, the legend is placed at the top right corner of the figure.

2. Line plot with legend outside the figure

If you have a line plot with multiple lines, you can use the same trick to move the legend outside the figure:

import matplotlib.pyplot as plt

# create the plot and plot each line with a different label
fig, ax = plt.subplots()
ax.plot(x, y1, label='Line 1')
ax.plot(x, y2, label='Line 2')
ax.plot(x, y3, label='Line 3')

# move the legend outside the figure
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

Again, the legend is placed at the top right corner of the figure.

3. Heatmap with legend outside the figure

If you have a heatmap, you can use the trick to move the colorbar (which serves as the legend) outside the figure:

import matplotlib.pyplot as plt
import numpy as np

# create the heatmap and colorbar
fig, ax = plt.subplots()
im = ax.imshow(np.random.rand(10,10))
cbar = ax.figure.colorbar(im, ax=ax)

# move the colorbar outside the figure
cbar.ax.set_ylabel('Colorbar Label', rotation=270, labelpad=15)
fig.subplots_adjust(right=0.8)

In this case, the colorbar is moved to the right of the figure, with a label rotated by 270 degrees.

These examples illustrate how easy it is to use this trick to position legends outside the figure in matplotlib charts. With a few lines of code, you can improve the readability and aesthetics of your plots.

Conclusion

In , placing legends outside the figure in Matplotlib is a great way to enhance the readability and visual appeal of your charts. By implementing this easy trick, you can improve the accuracy and effectiveness of your data visualization. Using code snippets, you can quickly and easily place your legends outside the figure in just a few steps. With Matplotlib's vast range of customization options, you can customize your legend's location, font size, and color, among other things. This simple modification can help revolutionize the way you communicate your data and insights to your audience. So why not revamp your Matplotlib charts today and take your data visualization to the next level?