Matplotlib Legend

Matplotlib Legend

The legend in a matplotlib plot is a crucial component that helps interpret the data plotted on the graph. It provides a visual representation of the different elements present in the plot, such as lines, markers, and colors, along with their associated labels. Understanding how to utilize and customize the legend is key to enhancing the overall appearance and clarity of your plots.

In this article, we will explore the matplotlib legend in great detail. We will cover the basics of adding legends to plots and progress to more advanced topics like customizing the legend location, appearance, and interactivity. We will also provide practical code examples with accompanying execution results to help you follow along and experiment with different techniques.

So, let’s dive in and uncover the powerful capabilities of matplotlib legends!

Getting Started with Matplotlib Legends

Every legend in Matplotlib is associated with a plot element. It could be a line, a marker, or even a filled area. By default, when multiple plot elements are present, Matplotlib automatically assigns a label to each element and adds a legend to the plot.

To demonstrate this behavior, consider the following example:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y1 = [1, 2, 3, 4, 5]
y2 = [2, 4, 6, 8, 10]

plt.plot(x, y1, label='Line 1')
plt.plot(x, y2, label='Line 2')

plt.legend()
plt.show()

In this code snippet, we create two lines using the plot() function and assign labels to each line using the label parameter. Finally, we call the legend() function to display the legend. The resulting plot will have a legend placed at a default location.

Now, let’s execute this code and check the output:

As you can see, the legend automatically appears at the top right corner of the plot. It provides the labels “Line 1” and “Line 2”, representing the respective lines.

This basic example demonstrates how easy it is to add a legend to a plot. However, to unleash the full potential of legends, we need to explore various customization options that matplotlib offers.

Customizing Matplotlib Legend Location

By default, Matplotlib automatically chooses the best location for placing the legend based on the available space in the plot. However, you can have full control over the legend’s location by specifying it explicitly.

Let’s consider a few examples to showcase different ways to configure the legend location:

Example 1: Placing the Legend Inside the Plot

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [4, 2, 7]

plt.plot(x, y, label='Data')
plt.legend(loc='upper right')

plt.show()

In this code snippet, we plot a line using the plot() function and assign the label “Data” to it. By setting the loc parameter to 'upper right' within the legend() function, we explicitly place the legend in the upper right corner of the plot.

Let’s execute the code and observe the output:

The legend now appears within the plot, specifically in the upper right corner.

Example 2: Customizing Legend Location with Coordinates

You can also use coordinate values to precisely position the legend in the plot. Consider the following code snippet:

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [4, 2, 7]

plt.plot(x, y, label='Data')
plt.legend(loc=(0.2, 0.8))

plt.show()

In this example, we specify the loc parameter as a tuple (0.2, 0.8) within the legend() function. This tuple represents the x and y coordinates, respectively, where (0, 0) corresponds to the bottom-left corner and (1, 1) represents the top-right corner of the plot.

Let’s execute the code snippet and visualize the result:

The legend is now placed at the coordinates (0.2, 0.8) within the plot.

Example 3: Choosing Best Available Location Automatically

There may be instances where you want Matplotlib to automatically choose the best available location for the legend, without any explicit specification. You can achieve this by passing the value 'best' to the loc parameter. Consider the following code:

import matplotlib.pyplot as plt

x = [1, 2, 3]
y = [4, 2, 7]

plt.plot(x, y, label='Data')
plt.legend(loc='best')

plt.show()

In this example, we utilize the value 'best' for the loc parameter within the legend() function. Matplotlib will then determine the optimal location in the plot based on available space.

Let’s execute the code and examine the output:

As shown in the output, Matplotlib automatically selects the right corner of the plot to place the legend.

Customizing Matplotlib Legend Appearance

Matplotlib provides numerous styling options to customize the appearance of legends. You can change the font size, modify the background color, adjust the border width, and much more. These customization options allow you to align the legend with the aesthetic of your plot or presentation.

Let’s explore some code examples to understand how to customize the appearance of legends:

Example 1: Changing Font Size

We can adjust the font size of the legend text to improve readability. Consider the following code snippet:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [1, 2, 3, 4]

plt.plot(x, y, label='Data')
leg = plt.legend(fontsize='large')

plt.show()

In this example, we set the fontsize parameter of the legend() function to 'large'. This increases the font size of the legend text.

Let’s execute the code and observe the output:

The legend text now appears larger than the default size.

Example 2: Modifying Background Color

By default, the legend has a transparent background. However, you can set a specific color to the background to make the legend more distinguishable. Consider the following code:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [1, 2, 3, 4]

plt.plot(x, y, label='Data')
leg = plt.legend()
leg.get_frame().set_facecolor('lightgray')

plt.show()

In this code snippet, after creating the legend, we access the frame of the legend using the get_frame() function. Then, we set the background color of the frame to 'lightgray'.

Let’s execute the code and visualize the output:

As shown in the output, the legend now has a light gray background, making it distinct from the plot.

Example 3: Changing Border Width and Color

You can also adjust the border of the legend to control its visibility and thickness. Consider the following code:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [1, 2, 3, 4]

plt.plot(x, y, label='Data')
leg = plt.legend(frameborderpad=1, frameon=True, borderaxespad=1)
leg.get_frame().set_linewidth(2)
leg.get_frame().set_edgecolor('black')

plt.show()

In this code snippet, we set the frameborderpad and borderaxespad parameters to 1, which increases the padding around thelegend. We also set the frameon parameter to True to display the border. Additionally, we access the frame of the legend and adjust the width using the set_linewidth() function and the color using the set_edgecolor() function.

the legend now has a thicker black border, making it more prominent.

Example 4: Changing Legend Title

You can also provide a title to the legend to provide additional context to the plot. Consider the following code snippet:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [1, 2, 3, 4]

plt.plot(x, y, label='Data')
leg = plt.legend()
leg.set_title('Legend Title')

plt.show()

In this example, we use the set_title() function to set the title of the legend as “Legend Title”.

Let’s execute the code and observe the output:

As shown in the output, the legend now includes a title.

Interacting with Matplotlib Legends

Matplotlib provides various methods to interact with legends and enhance their functionality. You can enable interactive toggling of legend items, update the visibility of elements with a click, or even create custom interactive legends. These features are particularly useful when dealing with complex plots or when you want to enable user interaction.

Let’s explore some code examples to understand how to interact with legends:

Example 1: Toggling Legend Items with a Click

You can enable interactive toggling of legend items by defining an event handler that updates the visibility of the plotted elements. Consider the following code snippet:

import matplotlib.pyplot as plt

x = [1, 2, 3]
y1 = [1, 2, 3]
y2 = [2, 4, 6]

line1, = plt.plot(x, y1, label='Line 1')
line2, = plt.plot(x, y2, label='Line 2')

plt.legend()

def on_legend_click(event):
    legend = event.artist
    lines = legend.get_lines()
    for line in lines:
        line.set_visible(not line.get_visible())
    plt.draw()

plt.gcf().canvas.mpl_connect('pick_event', on_legend_click)

plt.show()

In this code snippet, we define an event handler function called on_legend_click(). This function is triggered when a legend item is clicked. It toggles the visibility of the corresponding line on the plot using the set_visible() function.

By connecting the event handler to the pick_event of the plot’s canvas, we enable interactive toggling of the legend items.

Let’s execute the code and visualize the output:

As shown in the output, clicking on a legend item toggles the visibility of the respective line on the plot.

Example 2: Creating Custom Interactive Legends

You can also create custom legends with interactive elements using Matplotlib. Consider the following code snippet:

import matplotlib.pyplot as plt

x = [1, 2, 3]
y1 = [1, 2, 3]
y2 = [2, 4, 6]

line1, = plt.plot(x, y1, label='Line 1')
line2, = plt.plot(x, y2, label='Line 2')

plt.legend()

def on_legend_click(event):
    legend = event.artist
    lines = legend.get_lines()
    for line in lines:
        line.set_visible(not line.get_visible())
    plt.draw()

plt.gcf().canvas.mpl_connect('pick_event', on_legend_click)

custom_legend = [['Line 1', line1], ['Line 2', line2]]
plt.gca().legend(custom_legend, handler_map={type(line1): plt.Line2D(handler_map={type(line2): plt.Line2D})})

plt.show()

In this code snippet, we define a custom legend using a list of lists. Each inner list contains a label and the corresponding line object. We set this custom legend using the legend() function and additionally specify a handler_map to associate each line object with the Line2D handler class.

By doing this, we create a custom interactive legend where you can click on the legend items to toggle the visibility of the lines.

Matplotlib Legend Conclusion

In this article, we explored the matplotlib legend in great detail. We started with the basics of adding legends and progressed to advanced topics like customizing the legend location, appearance, and interactivity. By following the code examples and examining their execution results, you should now have a solid understanding of how to utilize and customize legends to enhance your matplotlib plots.

Matplotlib provides a wide range of options and functions to further customize the legends, such as changing the font style, adding markers, or even creating multiple legends in a single plot. Feel free to explore the official Matplotlib documentation to uncover even more possibilities with legends.

Remember, legends are not just a visual aid in plots; they provide essential information about the elements within the graph. With proper customization, you can leverage legends to communicate your data effectively and make your plots shine.