Highlighting a Single Segment in a Pie Chart with Python’s Matplotlib

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πŸ’‘ Problem Formulation: When creating a pie chart to represent data, it’s often useful to draw attention to a particular segment to emphasize its importance or relevance. This article describes how to highlight a single pie segment in a pie chart using Python’s Matplotlib library. As input, we have an array of numerical data and the index of the segment we want to highlight. The desired output is a pie chart with one segment visually distinguished from the others.

Method 1: Utilize the ‘explode’ Parameter in Matplotlib

This method involves using the ‘explode’ parameter of the pie() function, which allows you to specify the fraction of the radius with which to offset each wedge of the pie chart. To highlight a single pie, you assign a value greater than zero to the segment you wish to stand out.

Here’s an example:

import matplotlib.pyplot as plt

# Slice data and the slice you want to highlight
sizes = [15, 30, 45, 10]
highlight = [0, 0, 0.1, 0]  # Highlight the third slice

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=highlight, labels=['A', 'B', 'C', 'D'], autopct='%1.1f%%')

plt.show()

The output is a pie chart with four segments, where the third segment (labeled ‘C’) is visually offset from the center of the pie.

In the example, we define the data to be represented in a list called sizes and then create another list named highlight with the same length. The highlight list has zeros for all elements except for the one we want to explode, which has a value greater than zero. Using Matplotlib’s pie() function with these two lists, the specified segment ‘C’ appears to pop out, effectively highlighting it.

Method 2: Change the Properties of the Wedge

Another approach is to modify the individual wedge properties after creating the pie chart. You can adjust the wedge properties such as ‘edgecolor’ and ‘linewidth’ to highlight it.

Here’s an example:

import matplotlib.pyplot as plt

sizes = [15, 30, 45, 10]
colors = ['blue', 'green', 'red', 'purple']
highlight = {'edgecolor': 'black', 'linewidth': 2, 'width': 1}

fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(sizes, colors=colors, autopct='%1.1f%%')

# Highlight the third wedge
wedges[2].set(**highlight)

plt.show()

The output shows a pie chart with distinctive colors for each segment, and the third segment is outlined and highlighted with a thicker black edge.

The code snippet creates a standard pie chart with an array of color values to differentiate the segments. After creating the wedges using the pie() function, the third wedge in the list is accessed and its appearance modified by applying a dictionary of new style properties, making it stand out from the other wedges.

Method 3: Adjusting the Z-order

Changing the z-order of a segment brings it to the forefront without altering its size. This makes it possible to highlight it by placing it on top of the other slices. The z-order value controls the stack order of the wedge.

Here’s an example:

import matplotlib.pyplot as plt

sizes = [15, 30, 45, 10]

fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(sizes, autopct='%1.1f%%')

# Place the third wedge on top
wedges[2].set_zorder(3)

plt.show()

The output is a pie chart in which the third segment is superimposed over the other segments, subtly distinguishing it.

This code creates a simple pie chart and uses the wedges object returned by the pie() function. The desired wedge’s z-order is increased using the set_zorder() method, pushing it above the others visually in the stacking order.

Method 4: Combining Explode with a Color Highlight

A highly effective highlight can be achieved by combining the explode effect with a change in color for the targeted segment. This method uses both visual distancing and distinctive coloring to draw attention to the pie segment.

Here’s an example:

import matplotlib.pyplot as plt

sizes = [15, 30, 45, 10]
colors = ['blue', 'green', 'yellow', 'purple']
explode = [0, 0, 0.1, 0]  # Only explode the third slice

fig, ax = plt.subplots()
ax.pie(sizes, explode=explode, colors=colors, labels=['A', 'B', 'C', 'D'], autopct='%1.1f%%', startangle=90)

plt.show()

The resulting pie chart has a ‘popped out’ third segment with a yellow color that differs markedly from the default color scheme, making it immediately conspicuous.

In this example, we combine the aforementioned use of the explode parameter with an array specifying the color of each slice; particularly, using a striking color for our segment of interest. When rendered, the third segment stands out not only because it is exploded but also because its color is unique compared to the others.

Bonus One-Liner Method 5: Shadow Effect

A quick way to highlight a slice is by applying a shadow effect to just the target slice, creating a subtle three-dimensional look that raises it above the rest.

Here’s an example:

import matplotlib.pyplot as plt

sizes = [15, 30, 45, 10]

fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(sizes, autopct='%1.1f%%')

# Add shadow to the third wedge
wedges[2].set_shadow(True)

plt.show()

This code’s output is a chart with all slices flat except the third one, which casts a slight shadow, appearing more prominent to the viewer.

This succinct approach modifies the shadow property of the desired wedge. However, it’s worth noting that the shadow property might be unsupported or require additional custom implementation as it’s not a built-in feature for individual wedges in Matplotlib. Nonetheless, this illustrates the creative ways you can think about highlighting segments programmatically.

Summary/Discussion

  • Method 1: Explode Parameter. Ensures the segment stands out by increasing its distance from the center. The adjustment is obvious but affects the chart’s layout.
  • Method 2: Wedge Properties. Adjusting edge color and width gives flexibility in design, but visual changes are limited to the border, which might not be as noticeable.
  • Method 3: Z-order. Bringing a segment to the front is a subtle method of highlighting without making the chart seem disproportionate; however, the effect might not be as pronounced.
  • Method 4: Explode with Color. Very visually impactful, utilizing both spacing and color to highlight. May be too aggressive for some presentations and can affect the chart’s aesthetics.
  • Method 5: Shadow Effect. Provides a more subtle, three-dimensional highlight. However, this approach may require custom code, as native support for individual shadowing is limited in Matplotlib.