How To Create Sunburst Chart in Python

Data visualization is a crucial aspect of data analysis, enabling users to interpret complex datasets effectively. Among various visualization techniques, the sunburst chart stands out for its ability to represent hierarchical data in a visually appealing manner. This article will guide you through the process of creating sunburst charts in Python using the Plotly library. By the end, you will have a comprehensive understanding of how to implement and customize these charts for your data visualization needs.

What is a Sunburst Chart?

A sunburst chart is a type of radial chart that displays hierarchical data. It is composed of concentric circles, with each level of the hierarchy represented as a ring. The innermost circle represents the root node, while subsequent rings represent child nodes. This structure allows viewers to quickly grasp the relationships and proportions within the dataset.

Sunburst charts are particularly useful for visualizing data with multiple levels of categories. For example, they can be used to show organizational structures, product categories, or even website navigation paths. Compared to other chart types like treemaps, sunburst charts provide a more intuitive representation of hierarchical relationships.

Why Use Python for Data Visualization?

Python has emerged as one of the leading programming languages for data science and visualization due to its simplicity and versatility. Here are several reasons why Python is an excellent choice for creating visualizations:

• Rich Libraries: Python offers numerous libraries tailored for data visualization, including Matplotlib, Seaborn, and Plotly.
• User-Friendly Syntax: Python’s syntax is clean and easy to understand, making it accessible for beginners and experienced programmers alike.
• Interactive Visualizations: Libraries like Plotly allow for the creation of interactive charts that enhance user engagement.

This article will focus on using Plotly, particularly its Express module and Graph Objects, to create sunburst charts.

Getting Started with Plotly

To create sunburst charts in Python, you first need to install the Plotly library. Follow these steps to get started:

• • Install Plotly: Open your terminal or command prompt and run the following command:

pip install plotly

• • Import Necessary Modules: In your Python environment (Jupyter Notebook or any IDE), import Plotly Express as follows:

import plotly.express as px

• Prepare Your Environment: Ensure you have a Jupyter Notebook or an IDE set up for running your Python scripts efficiently.

Creating a Basic Sunburst Chart Using Plotly Express

Plotly Express simplifies the process of creating visualizations with concise syntax. Here’s how to create a basic sunburst chart:

1. 1. Import Libraries:

import plotly.express as px
import pandas as pd

1. 1. Prepare Sample Data: You can use a sample dataset provided by Plotly or create your own. For this example, we will use the built-in tips dataset.

df = px.data.tips()

1. 1. Create the Sunburst Chart:

fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill')
fig.show()

This code snippet generates a sunburst chart that categorizes total bills based on sex, day, and time. The `path` parameter defines the hierarchy while `values` specifies what each segment represents.

Advanced Sunburst Chart Creation Using Plotly Graph Objects

If you require more control over your visualizations, consider using Plotly Graph Objects. This approach allows for detailed customization. Here’s how to create a sunburst chart using Graph Objects:

1. 1. Import Libraries:

import plotly.graph_objects as go

1. 1. Create Data Structure: Define your labels and their corresponding parent-child relationships.

labels = ["A", "B", "C", "D"]
parents = ["", "A", "A", "B"]
values = [10, 20, 30, 40]

1. 1. Create the Sunburst Chart:

fig = go.Figure(go.Sunburst(
        labels=labels,
        parents=parents,
        values=values,
        branchvalues="total"
    ))
fig.show()

This example illustrates how to define labels and their respective parents explicitly. The `branchvalues` parameter determines how values are calculated across branches.

Customizing Sunburst Charts

The ability to customize your sunburst charts enhances their effectiveness in conveying information. Here are some customization options you can implement:

• • Changing Colors and Themes: You can specify colors using the `marker` attribute in Graph Objects or use predefined color scales in Plotly Express.

fig.update_traces(marker=dict(colors=['#636EFA', '#EF553B', '#00CC96']))

• • Add Titles and Labels: Titles can be added easily to provide context about what the chart represents.

fig.update_layout(title='Sunburst Chart Example')

• • Adjusting Layout Settings: Modify size and margins to improve readability.

fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))

Common Use Cases for Sunburst Charts

The versatility of sunburst charts makes them suitable for various applications across different fields. Here are some common use cases:

• Business Reports: Visualize sales data by product categories and subcategories.
• Demographic Data Analysis: Display population distributions across age groups and regions.
• Project Structures: Represent project phases and tasks in project management tools.
• Navigational Paths: Illustrate user navigation paths on websites or applications.

The ability to represent complex hierarchical relationships makes sunburst charts an invaluable tool for data analysts and business intelligence professionals.

Troubleshooting Common Issues

• Error: Data Format Issues: If your data is not structured correctly (e.g., missing parent-child relationships), you may receive errors or unexpected results. Ensure that your dataset is clean and follows the expected hierarchy format.
• Error: Parameter Misconfigurations: If you receive errors related to parameters (e.g., incorrect column names), double-check your code against the dataset structure. Ensure that all specified columns exist in your DataFrame.
• No Output Displayed : If running in Jupyter Notebook but not seeing any output, ensure that you have included `fig.show()` at the end of your code block. Additionally, check that your notebook’s kernel is running correctly.