pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.[2] The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals.[3] Its name is a play on the phrase "Python data analysis" itself.[4] Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.[5]


Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.[6] Pandas allows various data manipulation operations such as merging,[7] reshaping,[8] selecting,[9] as well as data cleaning, and data wrangling features. The development of pandas introduced into Python many comparable features of working with DataFrames that were established in the R programming language. The pandas library is built upon another library, NumPy, which is oriented to efficiently working with arrays instead of the features of working on DataFrames.


Developer Wes McKinney started working on pandas in 2008 while at AQR Capital Management out of the need for a high performance, flexible tool to perform quantitative analysis on financial data. Before leaving AQR he was able to convince management to allow him to open source the library.

Another AQR employee, Chang She, joined the effort in 2012 as the second major contributor to the library.

In 2015, pandas signed on as a fiscally sponsored project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States.[10]

See also


  1. ^ "Release 2.1.0". 30 August 2023. Retrieved 18 September 2023.
  2. ^ "License – Package overview – pandas 1.0.0 documentation". pandas. 28 January 2020. Retrieved 30 January 2020.
  3. ^ Wes McKinney (2011). "pandas: a Foundational Python Library for Data Analysis and Statistics" (PDF). Retrieved 2 August 2018.
  4. ^ McKinney, Wes (2017). Python for Data Analysis, Second Edition. O'Reilly Media. p. 5. ISBN 9781491957660.
  5. ^ Kopf, Dan. "Meet the man behind the most important tool in data science". Quartz. Retrieved 17 November 2020.
  6. ^ "IO tools (Text, CSV, HDF5, …) — pandas 1.4.1 documentation".
  7. ^ "Merge, join, concatenate and compare — pandas 1.4.1 documentation".
  8. ^ "Reshaping and pivot tables — pandas 1.4.1 documentation".
  9. ^ "Indexing and selecting data — pandas 1.4.1 documentation".
  10. ^ "NumFOCUS – pandas: a fiscally sponsored project". NumFOCUS. Retrieved 3 April 2018.

Further reading

  • McKinney, Wes (2017). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (2nd ed.). Sebastopol: O'Reilly. ISBN 978-1-4919-5766-0.
  • Molin, Stefanie (2019). Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python. Packt. ISBN 978-1-7896-1532-6.
  • Chen, Daniel Y. (2018). Pandas for Everyone : Python Data Analysis. Boston: Addison-Wesley. ISBN 978-0-13-454693-3.
  • VanderPlas, Jake (2016). "Data Manipulations with Pandas". Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly. pp. 97–216. ISBN 978-1-4919-1205-8.
  • Pathak, Chankey (2018). Pandas Cookbook. pp. 1–8.