Beyond Matplotlib: 10 Python Libraries for Advanced Data Visualization

Discovering Powerful Tools for Creating Interactive and Dynamic Visualizations in Python

Here are 10 Python libraries that are useful in data visualization but not commonly used in data science, along with their GitHub links:

1. Plotly Express:

Plotly Express is a high-level data visualization library that allows users to create interactive charts and graphs with just a few lines of code. It includes a range of chart types, including scatter plots, line charts, and bar charts.

GitHub link: https://github.com/plotly/plotly_express

2. Altair:

Altair is a declarative data visualization library that allows users to create interactive charts and graphs with a simple and intuitive API. It includes a range of chart types, including scatter plots, line charts, and bar charts.

GitHub link: https://github.com/altair-viz/altair

3. Seaborn:

Seaborn is a Python library for data visualization based on matplotlib. It provides a range of high-level interfaces for creating statistical graphics, making it easy to create complex visualizations.

GitHub link: https://github.com/mwaskom/seaborn

4. Bokeh:

Bokeh is a Python library for creating interactive visualizations for the web. It includes a range of chart types, including scatter plots, line charts, and bar charts, and supports a wide range of data sources.

GitHub link: https://github.com/bokeh/bokeh

5. VisPy :

VisPy is a Python library for interactive scientific visualization that leverages the power of modern graphics processing units (GPUs). It includes a range of high-level interfaces for creating complex 3D visualizations.

GitHub link: https://github.com/vispy/vispy

6. Mayavi :

Mayavi is a Python library for scientific data visualization in 3D. It includes a range of high-level interfaces for creating complex visualizations of volumetric data, including scalar fields, vector fields, and more.

GitHub link: https://github.com/enthought/mayavi

7. D3.js :

D3.js is a JavaScript library for creating interactive data visualizations for the web. It includes a range of chart types, including scatter plots, line charts, and bar charts, and supports a wide range of data sources.

GitHub link: https://github.com/d3/d3

8. NetworkX :

NetworkX is a Python library for studying the structure, dynamics, and function of complex networks. It includes a range of algorithms for analyzing network data and visualizing network structures.

GitHub link: https://github.com/networkx/networkx

9.Pygal :

Pygal is a Python library for creating interactive SVG charts and graphs. It includes a range of chart types, including line charts, bar charts, and pie charts, and supports a wide range of data sources.

GitHub link: https://github.com/Kozea/pygal

10. Leather:

Leather is a Python library for creating custom data visualizations using a simple and intuitive API. It includes a range of chart types, including line charts, scatter plots, and histograms, and supports a wide range of data sources.

GitHub link: https://github.com/wireservice/leather

These libraries are just a few examples of the many useful Python libraries available for data visualization. Whether you’re creating interactive web visualizations or exploring complex network data, Python has a library to help you get the job done.