It is used extensively in scientific computing, data analysis, and machine learning applications. 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 provides a high-level interface for creating informative and attractive statistical graphics. Plotnine provides a simple and powerful syntax for creating high-quality, customizable plots for data analysis and presentation. Plotnine is a Python data visualization library built on top of the popular ggplot2 library from the R programming language. It allows you to create beautiful and informative visualizations quickly and easily. 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.īokeh is a Python library for creating interactive visualizations for web browsers. Pydeck is a Python library for creating interactive maps and 3D visualizations using WebGL. It supports a wide range of chart types, including line charts, bar charts, pie charts, radar charts, and more. Pygal is a Python module that allows you to create interactive and customizable vector-based graphs and charts. 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. Plotly is a powerful and interactive data visualization library that allows users to create high-quality and interactive graphs, charts, and dashboards. It is built on top of the Leaflet.js library and provides a simple and intuitive way to create maps and overlay various 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.įolium is a Python library used for creating interactive maps and visualizations. AltairĪltair is a Python library used for creating interactive visualizations of data. You can learn more about DEX and its powerful toolkit with this article. Data Explorer (“DEX”) is Noteable’s internal no-code data visualization tooling relying on d3. We also cover dx which is Noteable’s IPython display formatter registration and tabular data formatting for DEX media types. 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. Some of the libraries support interactive plots. These libraries are Matplotlib, Seaborn, Plotnine, Bokeh, Pygal, Plotly, geoplotlib, missingno, Altair, Pydeck, and Folium.

We cover Python data visualization libraries that can be used in various fields.

Folium and bokeh python