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Python Packages for Data Visualization in 2025
A detailed analysis of ten Python visualization packages, comparing their capabilities, popularity metrics, and maintenance health to help developers choose the right tool for their specific needs. Th
e article provides both quick decision-making guidance through a decision tree and in-depth individual package profiles with practical recommendations.

Reasons to Read -- Learn:

  • how to select the most appropriate Python visualization package based on quantifiable metrics, including PyPI downloads, GitHub stars, and contributor activity, helping you make an informed decision backed by data.
  • detailed profiles of ten major visualization packages with specific use cases and limitations, allowing you to understand when to use Matplotlib for comprehensive static visualizations, Plotly for interactive dashboards, or specialized tools like Datashader for large datasets.
  • maintenance health and community support of different visualization packages through multiple indicators, helping you avoid investing time in potentially abandoned projects while identifying growing alternatives like Altair.
  • 8 min readauthor: Zlatan B
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