The Ultimate Pic Chart Guide for Matplotlib

What will you learn?

Pie charts are one of the most powerful visualizations when presenting them. With a few tricks you can make them look professional with a free tool like Matplotlib.

In the end of this tutorial you will know how to make pie charts and customize it even further.

Basic Pie Chart

First you need to make a basic Pie chart with matplotlib.

import matplotlib.pyplot as plt

v = [2, 5, 3, 1, 4]
labels = ["A", "B", "C", "D", "E"]

plt.pie(v, labels=labels)
plt.show()

This will create a chart based on the values in v with the labels in labels.

Based on the above Pie Chart we can continue to build further understanding of how to create more advanced charts.

Exploding Segment in Pie Charts

An exploding segment in a pie chart is simply moving segments of the pie chart out.

The following example will demonstrate it.

import matplotlib.pyplot as plt

v = [2, 5, 3, 1, 4]
labels = ["A", "B", "C", "D", "E"]
explode = [0, 0.1, 0, 0.2, 0]

plt.pie(v, labels=labels, explode=explode)
plt.show()

Though not very pretty, it shows you how to control each segment.

Now let’s learn a bit more about how to style it.

Styling Pie Charts

The following list sets the most used parameters for the pie chart.

  • labels The labels.
  • colors The colors.
  • explode Indicates offset of each segment.
  • startangle Angle to start from.
  • counterclock Default True and sets direction.
  • shadow Enables shadow effect.
  • wedgeprops Example {"edgecolor":"k",'linewidth': 1}.
  • autopct Format indicating percentage labels "%1.1f%%".
  • pctdistance Controls the position of percentage labels.

We already know the labels from above. But let’s add some more to see the effect.

import matplotlib.pyplot as plt

v = [2, 5, 3, 1, 4]
labels = ["A", "B", "C", "D", "E"]
colors = ["blue", "red", "orange", "purple", "brown"]
explode = [0, 0, 0.1, 0, 0]
wedge_properties = {"edgecolor":"k",'linewidth': 1}

plt.pie(v, labels=labels, explode=explode, colors=colors, startangle=30,
           counterclock=False, shadow=True, wedgeprops=wedge_properties,
           autopct="%1.1f%%", pctdistance=0.7)
plt.title("Color pie chart")
plt.show()

This does a decent job.

Donut Chart

A great chart to play with is the Donut chart.

Actually, pretty simple by setting wedgeprops as this example shows.

import matplotlib.pyplot as plt

v1 = [2, 5, 3, 1, 4]
labels1 = ["A", "B", "C", "D", "E"]
width = 0.3
wedge_properties = {"width":width}

plt.pie(v1, labels=labels1, wedgeprops=wedge_properties)
plt.show()

The width is taken from outside and in.

Legends on Pie Chart

You can add a legend, which uses the labels. Also, notice that you can set the placement (loc) of the legend.

import matplotlib.pyplot as plt

labels = 'Dalmatians', 'Beagles', 'Labradors', 'German Shepherds'
sizes = [6, 5, 20, 9]
fig, ax = plt.subplots()
ax.pie(sizes, labels=labels, autopct='%.1f%%')
ax.legend(labels, loc='lower left')
plt.show()

Nested Donut Pie Chart

This one is needed in any situation to show a bit off.

import matplotlib.pyplot as plt

v1 = [2, 5, 3, 1, 4]
labels1 = ["A", "B", "C", "D", "E"]
v2 = [4, 1, 3, 4, 1]
labels2 = ["V", "W", "X", "Y", "Z"]
width = 0.3
wedge_properties = {"width":width, "edgecolor":"w",'linewidth': 2}

plt.pie(v1, labels=labels1, labeldistance=0.85,
        wedgeprops=wedge_properties)
plt.pie(v2, labels=labels2, labeldistance=0.75,
        radius=1-width, wedgeprops=wedge_properties)
plt.show()

Want to learn more?

Actually Data Visualization is an important skill to understand and present data.

This is a key skill in Data Science. If you like to learn more then check my free Expert Data Science Blueprint course with the following resources.

  • 15 video lessons – covers the Data Science Workflow and concepts, demonstrates everything on real data, introduce projects and shows a solution (YouTube video).
  • 30 JuPyter Notebooks – with the full code and explanation from the lectures and projects (GitHub).
  • 15 projects – structured with the Data Science Workflow and a solution explained in the end of video lessons (GitHub).
Data Science

Leave a Reply Cancel reply

Exit mobile version