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.
Pandas Series as Pie Chart
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 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)
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.
- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science
- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization
- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science
🔎 Find Data Science Programs 👨💻 111,889 already enrolled
Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.
# series of counts wimbledon_wins_count = wimbledon_winners.value_counts() # print the counts print(wimbledon_wins_count)
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.
1. Plot Pie Chart of Series Values
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.
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.
2. Customize the plot formatting
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")
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
Subscribe to our newsletter for more informative guides and tutorials.
We do not spam and you can opt out any time.
Tutorials on pandas series –
- Convert Pandas Series to a DataFrame
- Convert Pandas Series to a List
- Convert Pandas Series to a NumPy Array
- Convert Pandas Series to a Dictionary
- Sort a Pandas Series
- Append Two Pandas Series
- Apply a Function to a Pandas Series
- Pandas – Shift column values up or down
- Plot a Histogram of Pandas Series Values
- Create a Pie Chart of Pandas Series Values
- Plot a Bar Chart of Pandas Series Values
- Create a Boxplot from Pandas Series Values
- Create a Density Plot from Pandas Series Values