# 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

Repository: https://github.com/pandas-dev/pandas
Canonical: https://ross.abutalabs.com/products/pandas
Homepage: https://pandas.pydata.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: data-analysis, pandas, flexible, alignment, python, data-science
Last push: 2026-08-26T22:27:09+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 82, longevity 100
- inputs: {"age_days": 5854, "days_push": 7, "days_rel": 42, "gap_med": 40.5, "n_releases_24m": 11}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 49564, forks 20290 (observed 2026-08-28T04:12:11.641615+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: data-science, etl, csv, json, date-time, math, analytics
- domain: data-science, analytics, data-visualization, machine-learning
- platform: python, cross-platform
- tags: dataframe, data-wrangling, time-series, statistics, tabular-data, excel, sql, data-engineering

## Member repositories
- pandas-dev/pandas (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:11.641615+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:20:11.710750+00:00, confidence not recorded.
  - readme: https://github.com/pandas-dev/pandas (fetched 2026-08-28T04:12:11.641615+00:00, sha d1068938f507)
  - homepage: https://pandas.pydata.org (fetched 2026-08-28T18:13:20.348553+00:00, sha 88270b2c1ae5)
  - site_page: https://pandas.pydata.org/about/ (fetched 2026-08-28T18:13:20.356839+00:00, sha ad0206aa3146)
  - site_page: https://pandas.pydata.org/about/roadmap.html (fetched 2026-08-28T18:13:20.358521+00:00, sha 62c7c090ec45)
  - site_page: https://pandas.pydata.org/about/governance.html (fetched 2026-08-28T18:13:20.360092+00:00, sha b03cffd6cc7f)
  - site_page: https://pandas.pydata.org/about/team.html (fetched 2026-08-28T18:13:20.362354+00:00, sha 1d17de296d88)
  - site_page: https://pandas.pydata.org/about/sponsors.html (fetched 2026-08-28T18:13:20.364867+00:00, sha 32e812b9c11c)
  - site_page: https://pandas.pydata.org/about/citing.html (fetched 2026-08-28T18:13:20.367269+00:00, sha 830cc60c6429)
  - site_page: https://pandas.pydata.org/docs/ (fetched 2026-08-28T18:13:20.369232+00:00, sha 18f16cd13df2)
  - site_page: https://pandas.pydata.org/getting_started.html (fetched 2026-08-28T18:13:20.370880+00:00, sha 1072c0d8e421)
- Data as of 2026-08-30T08:39:29.467469+00:00.