pandas-dev/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 observed · 2026-08-28
Health v2 · maintenance only
93/100
- Activity 99
- Release rhythm 82
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 40.5
- age_days: 5854
- days_rel: 42
- days_push: 7
- n_releases_24m: 11
Adoption not part of the score
49564 stars · 20290 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
pandas is a fast, flexible Python library providing labeled data structures like the DataFrame for practical, real-world data analysis and manipulation. It offers tools for reading/writing many formats, missing-data handling, group-by aggregation, merging/joining, reshaping, and rich time series functionality.
Use cases
- analyze csv data in python
- clean and transform messy tabular data
- group by and aggregate a dataset
- work with time series data in python
- read excel files into a dataframe
- merge and join multiple datasets
- handle missing values in a dataset
When to choose
- you need labeled, relational-style data structures in Python similar to R data.frames
- you are doing exploratory data analysis, cleaning, or preparation for machine learning
- you need robust time series handling, pivoting, and group-by operations
- you need to read and write CSV, Excel, SQL, HDF5, and other formats
When to avoid
- you need distributed or out-of-core processing of datasets larger than memory (consider Dask, Spark, or Polars)
- you need strict type safety or maximum raw performance for simple numeric arrays (NumPy may suffice)
- you are building a production data pipeline where a database or dataframe engine like Polars/DuckDB fits better
Facets
library · maturity stable
data-science etl csv json date-time math analytics data-science analytics data-visualization machine-learning python cross-platform dataframe data-wrangling time-series statistics tabular-data excel sql data-engineering
10 sources
- readme: https://github.com/pandas-dev/pandas · fetched 2026-08-28 · d1068938f507
- homepage: https://pandas.pydata.org · fetched 2026-08-28 · 88270b2c1ae5
- site_page: https://pandas.pydata.org/about/ · fetched 2026-08-28 · ad0206aa3146
- site_page: https://pandas.pydata.org/about/roadmap.html · fetched 2026-08-28 · 62c7c090ec45
- site_page: https://pandas.pydata.org/about/governance.html · fetched 2026-08-28 · b03cffd6cc7f
- site_page: https://pandas.pydata.org/about/team.html · fetched 2026-08-28 · 1d17de296d88
- site_page: https://pandas.pydata.org/about/sponsors.html · fetched 2026-08-28 · 32e812b9c11c
- site_page: https://pandas.pydata.org/about/citing.html · fetched 2026-08-28 · 830cc60c6429
- site_page: https://pandas.pydata.org/docs/ · fetched 2026-08-28 · 18f16cd13df2
- site_page: https://pandas.pydata.org/getting_started.html · fetched 2026-08-28 · 1072c0d8e421
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| pandas-dev/pandas | main | 93 |
For agents
markdown · JSON · MCP: product_card(name="pandas-dev/pandas")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem