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Create a Pie Chart of Pandas Series Values

You can better visualize a pandas series with categorical values via a pie chart of counts. In this tutorial, we will look at how to plot a pie chart of pandas series values.

To plot a pie chart, you first need to create a series of counts of each unique value (use the pandas value_counts() function) and then proceed to plot the resulting series of counts as a pie chart using the pandas series plot() function.

The plot() function plots a line chart of the series values by default but you can specify the type of chart to plot using the kind parameter. To plot a pie chart, pass 'pie' to the kind parameter. The following is the syntax:

# pie chart using pandas series plot()
s.value_counts().plot(kind='pie')

Here, s is the pandas series with categorical values which is converted to a series of counts using the value_counts() function. The pandas series plot() function returns a matplotlib axes object to which you can add additional formatting.

Let’s look at some examples of plotting a series values as a pie chart. First, we’ll create a sample pandas series which we will be using throughout this tutorial.

import pandas as pd

# pandas series Wimbledon winners from 2015 to 2019
wimbledon_winners = pd.Series(index=[2015, 2016, 2017, 2018, 2019],
                   data=['Novak Djokovic', 'Andy Murray', 'Roger Federer', 'Novak Djokovic', 'Novak Djokovic'],
                   name='Winners')

# display the series
print(wimbledon_winners)

Output:

2015    Novak Djokovic
2016       Andy Murray
2017     Roger Federer
2018    Novak Djokovic
2019    Novak Djokovic
Name: Winners, dtype: object

You can see the contents of the series object above. We now have a pandas series containing the name of Wimbledon Winners from 2015 to 2019 with the year as its index.

Let’s create a series of counts of championships won by each player during the period using the value_counts() function. We’ll be using this series to plot our pie chart.

# series of counts
wimbledon_wins_count = wimbledon_winners.value_counts()
# print the counts
print(wimbledon_wins_count)

Output:

Novak Djokovic    3
Andy Murray       1
Roger Federer     1
Name: Winners, dtype: int64

The returned series contains counts of victories by each player in the original series.

To create a pie chart from the series values we’ll pass kind='pie' to the pandas series plot() function. For example, let’s see its usage on the “wimbledon_wins_count” series created above.

wimbledon_wins_count.plot(kind='pie')

Output:

Pie chart of counts of wins by each player.

The above pie chart shows the distribution of Wimbledon victories from 2015 to 2019. You can see that Novak Djokovic has won more than half of the championships during that period. Note that the resulting plot is a matplotlib pie chart.

For more on the pandas series plot() function, refer to its documentation.

You can also customize the formatting of the chart. For instance, you can add the axes labels, chart title, change colors and fonts, etc. Since the returned plot is a matplotlib axes object, you can apply any formatting that would work with matplotlib charts.

Let’s go ahead and add a title to our plot.

# create the pie chart
ax = wimbledon_wins_count.plot(kind='pie')
# set the title
ax.set_title("Distribution of Wimbledon Victories 2015-2019")

Output:

Pie chart with additional formatting.

You can see in the above chart has “Distribution of Wimbledon Victories 2015-2019” as its title.

For more on pie charts and their formatting in matplotlib, refer to our tutorial on matplotlib pie charts.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5


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Author

  • Piyush Raj

    Piyush is a data professional passionate about using data to understand things better and make informed decisions. In the past, he's worked as a Data Scientist for ZS and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.