Best Python Data Visualization Libraries in 2025

Simran Mishra

Matplotlib remains the foundation for Python plots, offering complete control for static and animated visuals.

Seaborn simplifies statistical graphics, creating beautiful plots for data analysis with minimal code.

Plotly powers interactive and web-ready charts, perfect for dashboards and 3D data visualization.

Bokeh creates interactive, real-time web visualizations ideal for dashboards and streaming data.

Altair uses a declarative approach to make clean, interactive charts quickly and efficiently.

Plotnine brings R’s ggplot2 style to Python, using a grammar of graphics for layered plotting.

Pygal generates lightweight, interactive SVG charts, perfect for embedding on websites.

Folium builds interactive maps with Leaflet.js, making it ideal for geospatial data visualization.

Dash combines Plotly charts into full Python dashboards for analysis and reporting.

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