pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
About pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
What you should know about pandas
pandas — Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. It is categorized under Developer Tools and primarily built with Python. The project has gathered 48,515 stars and 19,868 forks on GitHub, indicating strong adoption among developers.
Pricing & licensing: This tool is offered free of charge , released under the BSD-3-Clause license. The source code is openly available on GitHub, allowing engineers to audit, contribute, or fork as needed.
Use cases & topics: pandas is associated with the following topics: alignment, data-analysis, data-science, flexible, pandas, python. Teams working in alignment / data-analysis / data-science spaces typically evaluate this kind of tool when scoping new architecture decisions or replacing legacy components.
Getting started: Check out the official GitHub repository for installation steps, configuration examples, and the latest release notes. Most teams hit value within the first week if the tool aligns with their existing Developer Tools stack.
Editor's note from Fanny Engriana (Founder, Wardigi Digital Agency): when evaluating tools in the Developer Tools category for our agency clients, we look at three things first — license clarity, community size, and active maintenance. Tools with explicit license terms and ongoing commits tend to remain viable across multi-year projects.