Create a nested pie chart using matplotlib Python.

by kanika Updated: May 9, 2023

Solution Kit

A nested pie chart is a type of pie chart that uses many layers of nested rings to visualize and analyze data. It shows the relationship between parts of a whole or the composition of a particular group. The innermost circle represents the total sum of the data and each subsequent circle. It shows the proportion of the whole that each part contributes. For example, a nested pie chart can show the proportion of different types of fruit in a basket. It can also tell the proportion of students in a school by grade level.

We can visualize the different types of data with a nested pie chart are:

Numerical Data:

• Population by Age Group
• Expenditure by Category
• Budget Allocation by Department
• Annual Revenue by Region
• Cost of Living by City

Categorical Data:

• Brand Preferences by Gender
• Voter Turnout by Political Party
• Employee Satisfaction by Role
• Education Level by Country
• Job Satisfaction by Industry

Nested pie charts display hierarchical relationships between data in a visual form. The chart contains nested circles giving a circular statistical plot. It's where we can represent the plot from a level in the hierarchy. A different color represents each hierarchy level; the innermost circle is the highest.

Nested pie charts can create bar, pie, and line charts. The bar chart uses a hierarchical structure to compare many data points. It displays the relative proportions of each data point within the hierarchy. The line chart displays trends over time.

• X-Axis: The x-axis measures the categories, or groups, of data in a nested pie chart. It runs along the bottom of the chart and displays the labels for each data group.
• Y-Axis: The y-axis measures the size of each data group in a nested pie chart. It runs from the left side of the chart and displays the numerical values for each data group.
• Scale Axis: The scale axis helps measure each data group's relative size in a nested pie chart. It runs along the top or right side of the chart and displays the numerical values for each data group. Remembering that the scale axis should be consistent across all charts is important.

We can use different types of labels with a nested pie chart.

• Title Label: The title label identifies the chart and provides context for the data. It should explain the chart and give the reader an understanding of the data.
• Data Labels: Data labels identify the individual sections of the pie chart. These labels can be numerical values, percentages, or even words. The words that describe the values.
• Legend Labels: The legend labels identify the pie chart's different sections. These labels should explain what each section of the chart represents. They can be color-coded to identify the sections further.

Different types of layout options are available for a nested pie chart:

Stacked Layout:

The stacked layout shows the segments of the outer pie chart stacked on top. It offers a representation of the relative subcategory sizes within each main category.

Grouped Layout:

The grouped layout for a nested pie chart shows the segments of the outer pie chart grouped. It is useful for identifying the relationships between the subcategories as groupings. It makes comparing the relative subcategory sizes within each main category easier.

Nested Layout:

The nested layout for a nested pie chart shows the segments of the outer pie chart nested within each other. The nested segments make it easier to identify the size of each main category relative to the others. It is useful for identifying the relationships between the main and the subcategories.

For creating a nested pie chart:

Choose the right data type:

Gather the data needed to create the nested pie chart. This data should include the categories of information. It should also include the number of items in each category and the percentages of each category.

Design the chart correctly:

Once we gather the data and use a graphing program or software to create the chart, we set up the chart correctly, ensuring we nest the categories and label the data properly.

Add labels and axes:

Finally, add labels and axes to the chart to make it easier to understand. Be sure to label the category names, the numbers, and the percentages. Also, be sure to add a legend to the chart to explain the meanings of the colors.

We can use a nested pie chart to visualize data by following some points:

Determine the data you want to visualize and the most appropriate chart type. Nested pie charts are great for comparing categories within a whole. So, consider your research question when selecting the chart type. Choose a layout that conveys the data. Avoid using too many pies in one chart, as it can be hard to read. Instead, consider using many charts to differentiate the categories better. Add labels to each pie chart and the data points to identify the category or point in the chart. Make sure to add a title, legend, and other helpful information to the chart to make it easier to interpret. Use colors to differentiate the categories within the chart. Use a consistent color scheme throughout the chart and darker colors for categories. Consider adding a call-out box. It explains the differences between the categories within the chart. This will make it easier for viewers to understand the data.

A nested pie chart visualizes data. It allows the viewer to compare proportions and relationships. By nesting the pie charts, the viewer can identify if one variable is more or less important than another. This makes it quick to identify correlations and trends in the data. Additionally, the visual nature of the chart makes it easier to explain complex data sets.

Fig1: Preview of the Code.

Fig2: Preview of the output.

Code

In this solution, we are creating a nested pie chart using matplotlib.

Instructions

Follow the steps carefully to get the output easily.

1. Install Jupyter Notebook on your computer.
2. Open terminal and install the required libraries with following commands.
3. Install numpy - pip install numpy.
4. Install pandas - pip install pandas.
5. Install matplotlib - pip install matplotlib.
6. Copy the code using the "Copy" button above and paste it into your IDE's Python file.
7. Remove the text from line number 17 to 28.
8. Run the file.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "Create a nested pie chart using matplotlib python" in kandi. You can try any such use case!

Dependent Libraries

matplotlibby matplotlib

Python 17559 Version:v3.7.1
License: No License (null)

matplotlib: plotting with Python

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matplotlibby matplotlib

Python 17559 Version:v3.7.1 License: No License

matplotlib: plotting with Python
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numpyby numpy

Python 23755 Version:v1.25.0rc1
License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.

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numpyby numpy

Python 23755 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.
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pandasby pandas-dev

Python 38689 Version:v2.0.2
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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pandasby pandas-dev

Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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If you do not have matplotlib or numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the respective page in kandi.

You can search for any dependent library on kandi like matplotlib

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python 3.9.6
2. The solution is tested on matplotlib version 3.5.0
3. The solution is tested on numpy version 1.21.4
4. The solution is tested on pandas version 1.5.1

Using this solution, we are able to create a nested pie chart with matplotlib.

FAQ

What is a nested pie chart, and what are its applications?

A nested pie chart is a type of chart that uses many layers of concentric circles. It helps represent the relative value of different categories of data. It displays hierarchical data and compares parts of a whole. It can compare a variety of data sets. It can include the relative proportions of countries and the relative product sizes. Or it can include the relative components of an income.

How does a circular statistical plot differ from other kinds of plots?

A circular statistical plot is a circular graph showing relationships between variables. It differs from other plots because it uses angles instead of the typical x and y axes to display the data. This allows for efficient use of space and a more intuitive way of displaying the data. A circular statistical plot can show relationships between variables with a single graph.

Is it possible to create a donut chart using Python?

Yes, it is possible to create a donut chart using Python. Python offers various libraries, like Matplotlib, Seaborn, and Plotly. Additionally, several online resources help you create a donut chart. We can create a donut chart.

When should you use a bar chart over a nested pie chart for data visualization?

Bar is over nested pie charts when comparing values or emphasizing their differences. Bar charts make it easier to compare individual values or groups of values. They also enable viewers to see the data's range of values and trends.

What is the data intensity ratio when plotting with nested pie charts?

When plotting with nested pie charts, the data intensity ratio is 4:1. The inner circle should represent approximately 25% of the total data. It will be when the outer circle should represent the remaining 75%.

Are there any special libraries in Python that can help plot these charts?

Yes, several libraries in Python can help plot charts. Examples include Matplotlib, Plotly, Seaborn, Bokeh, and Pygal.

How do you create an outer circle when making a nested pie chart in Python?

To create an outer circle when making a nested pie chart in Python, you can use the Matplotlib library. You can use matplotlib.pyplot.pie() function and set the radius parameter to a value greater than 1. This will create an outer circle around the nested pie chart.

What tools can help Analyzing Data represented by Nested Pie Charts in Python?

• Matplotlib: Matplotlib helps create static, animated, and interactive visualizations. It is well-suited for analyzing data represented by nested pie charts. It allows users to customize their charts and add extra information.
• Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides an interface for creating interactive and publication-quality figures. It is useful for analyzing data from nested pie charts.
• Plotly: Plotly is an interactive and open-source data visualization library for Python. It provides an intuitive interface and powerful tools for creating and customizing figures. It is particularly well-suited for analyzing data represented by nested pie charts.

How do you use given data to create a Nested Pie Chart using Python?

We can create a nested Pie Chart with the help of the Matplotlib library. Here is an example of creating a Nested Pie Chart using the Matplotlib library:

• First, import the necessary libraries.
• Create the Nested Pie Chart using the Pie chart function.
• Load the data into a Pandas data frame.
• Finally, add a title and display the Nested Pie Chart.

Can I customize the ggplot2 library while making Nested Pie Chart in Python?

Customizing the ggplot2 library while making Nested Pie Charts in Python is possible. You can customize your charts to fit your needs using the customizing options. You can customize the underlying data structure. It can create custom functions to make your charts unique. It can be like labels, colors, sizes, and shapes.

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