5 Best Python Data Visualization Libraries To Use in 2024

Imagine trying to understand a long, complicated story without any pictures or diagrams. It would probably be kind of difficult, right?

That’s where data visualization comes in. It’s like adding pictures to a story about numbers. By using charts and graphs, you can make data easier to understand and spot important things you might miss otherwise.

If you want to tell a compelling story with your data, Python has a number of data visualization libraries to help you create appealing pictures.

In this article, we’ll look at some of the best Python data visualization libraries for making data more appealing and easily digestible. Whether you’re a data expert or you’re just starting out, these tools will help you tell exciting stories with your data.

Table of Contents Table of Contents

1. Key Takeaways
2. 5 Best Python Visualization Libraries to Explore in 2024
3. The Bottom Line
4. FAQs
5. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. 5 Best Python Visualization Libraries to Explore in 2024
3. The Bottom Line
Show Full Guide
4. FAQs
5. References

5 Best Python Visualization Libraries to Explore in 2024

Let’s take a look at five of the best Python libraries for data visualization, all of which are open-source tools.

Top Python Visualization Libraries: Side-By-Side Comparison

5. Altair

Altair is a Python library that aims to make it easy for you to create interactive data visualizations.

Its simple and clear syntax lets you quickly turn data into charts and graphs. Because Altair uses a declarative approach, you only have to describe what you want to visualize, and the tool takes care of the rest.

This makes it easy to use if you want to visualize data without having to deal with complex code. Altair is based on the Vega and Vega-Lite visualization grammars.

Key Features of Altair

4. Bokeh

The Bokeh Python visualization library aims to help you make interactive graphics so you can examine data by zooming, panning, and hovering over different parts of a chart.

It’s often used in data science and analytics to present information in a clear and engaging way.

Bokeh supports a variety of visual formats, from simple line charts to complex statistical plots, which makes it a good choice for beginners and experienced programmers.

Key Features of Bokeh

3. Seaborn

Seaborn is a Python graph library that you can use to make statistical data visualizations. Built on top of Matplotlib, Seaborn makes it easier to create attractive and informative graphs.

Seaborn offers a variety of built-in styles that improve the appearance of your graphs without much customizing.

Seaborn also integrates well with Pandas, another data-handling library in Python, so you can plot data directly from DataFrames.

Key Features of Seaborn

2. Plotly

Plotly, another top Python visualization library, helps you create interactive and attractive charts and graphs.

It’s often used in data science because it supports a variety of charts, including scatter plots, 3D charts, line charts, and maps.

Plotly’s interactivity lets you hover over, zoom in, and click on the charts to get more details, making it useful for web projects and presentations.

Key Features of Plotly

1. Matplotlib

Matplotlib is one of the most popular Python plotting libraries. Matplotlib offers a wide range of tools for creating visualizations, such as graphs, bar charts, or scatter plots, making it easier to understand and share information.

Matplotlib can create high-quality visualizations for a variety of applications, from scientific research to data analysis.

It serves as the foundation for other Python libraries, including Plotly and Seaborn. Matplotlib is one of the best Python libraries for data visualization.

Features of Python Matplotlib

The Bottom Line

Data visualization with Python helps you make your data look interesting by turning it into charts and graphs. Selecting one of the best data visualization libraries for Python depends on your needs.

The Matplotlib and Seaborn libraries work well for making custom plots and charts. Plotly and Bokeh are better for making interactive and dynamic charts. And Altair is good for big data and advanced visualizations.

Knowing which library to use depends on what you need to do and what the library does best.

FAQs