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Monday, February 27 2023

The Most Common Python Libraries for Data Visualization

Data visualization is important because it helps us understand complex data, communicate information effectively, improve decision-making, provide insights, and save time.

We cover Python data visualization libraries that can be used in various fields. These libraries are Matplotlib, Seaborn, Plotnine, Bokeh, Pygal, Plotly, geoplotlib, missingno, Altair, Pydeck, and Folium. Some of the libraries support interactive plots. Some of them are built on top of Matplotlib, others can be used independently or rely on Vega such as Altair, and some are good for creating interactive data apps or complex dashboards. We also cover dx which is Noteable’s IPython display formatter registration and tabular data formatting for DEX media types. Data Explorer (“DEX”) is Noteable’s internal no-code data visualization tooling relying on d3. You can learn more about DEX and its powerful toolkit with this article.


Altair is a Python library used for creating interactive visualizations of data. It is built on top of the Vega-Lite visualization grammar, which makes it easy to create complex and layered visualizations with concise, declarative syntax.

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Get started with Altair


Folium is a Python library used for creating interactive maps and visualizations. It is built on top of the Leaflet.js library and provides a simple and intuitive way to create maps and overlay various data.


Get started with Folium


Plotly is a powerful and interactive data visualization library that allows users to create high-quality and interactive graphs, charts, and dashboards. Plotly is available in several programming languages, including Python, R, and JavaScript, and it offers a wide range of visualization options, from basic scatter plots to complex heatmaps and 3D visualizations.

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Get started with Plotly


Pygal is a Python module that allows you to create interactive and customizable vector-based graphs and charts. It supports a wide range of chart types, including line charts, bar charts, pie charts, radar charts, and more.

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Get started with Pygal


Pydeck is a Python library for creating interactive maps and 3D visualizations using WebGL. It is built on top of the popular deck.gl library and provides a high-level API for creating maps and visualizations with minimal code.


Get started with Pydeck


Bokeh is a Python library for creating interactive visualizations for web browsers. It allows you to create beautiful and informative visualizations quickly and easily.


Get started with Bokeh


Plotnine is a Python data visualization library built on top of the popular ggplot2 library from the R programming language. Plotnine provides a simple and powerful syntax for creating high-quality, customizable plots for data analysis and presentation.

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Get started with Plotnine


Seaborn is built on top of matplotlib. It provides a high-level interface for creating informative and attractive statistical graphics.

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Get started with Seaborn


Matplotlib is a popular data visualization library in Python that allows users to create a wide range of high-quality 2D and 3D plots, charts, and graphs. It is used extensively in scientific computing, data analysis, and machine learning applications.

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Get started with Matplotplib


GeoPlotlib is an open-source Python library for creating dynamic and interactive geographical data visualizations. It is designed to work with a wide range of geographical data, including point data, line data, and polygon data. GeoPlotlib provides a simple interface for creating maps, which can be customized with various styles, colors, and markers.


Get started with GeoPlotlib


Vega is an open-source data visualization grammar that allows you to create interactive and customizable visualizations using a declarative language. Vega is developed by the University of Washington Interactive Data Lab and is designed to work with a wide range of data types and formats.


Get started with Vega


Missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset.


Get started with missingo


Ready for your next Data Project?

Noteable also makes it easy to go from a dataset to rich, beautiful data visualizations thanks to Data Explorer. Give it a try with this interactive online jupyter notebook.