Back باندا (برمجية) Arabic Pandas Byelorussian পান্ডাস (সফটওয়্যার) Bengali/Bangla Pandas (Software) German Pandas (software) Spanish Pandas Estonian پانداس (نرمافزار) Persian Pandas French Pandas Galician Pandas HE
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| Original author(s) | Wes McKinney |
|---|---|
| Developer(s) | Community |
| Initial release | 11 January 2008 [citation needed] |
| Stable release | 2.2.3[1]
/ 20 September 2024 |
| Preview release | 2.0rc1
/ 15 March 2023 |
| Repository | |
| Written in | Python, Cython, C |
| Operating system | Cross-platform |
| Type | Technical computing |
| License | New BSD License |
| Website | pandas |
Pandas (styled as 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] as well as a play on the phrase "Python data analysis".[4]: 5 Wes McKinney started building what would become Pandas at AQR Capital while he was a researcher there from 2007 to 2010.[5]
The development of Pandas introduced into Python many comparable features of working with DataFrames that were established in the R programming language.[6] The library is built upon another library, NumPy.
- ^ "Release 2.2.3". 20 September 2024. Retrieved 22 September 2024.
- ^ "License – Package overview – pandas 1.0.0 documentation". pandas. 28 January 2020. Archived from the original on 16 September 2018. Retrieved 30 January 2020.
- ^ Wes McKinney (2011). "pandas: a Foundational Python Library for Data Analysis and Statistics" (PDF). Archived (PDF) from the original on 19 February 2018. Retrieved 2 August 2018.
- ^ McKinney, Wes (2014). Python for Data Analysis (First ed.). O'Reilly. ISBN 978-1-449-31979-3.
- ^ Kopf, Dan. "Meet the man behind the most important tool in data science". Quartz. Archived from the original on 9 November 2020. Retrieved 17 November 2020.
- ^ "Comparison with R". pandas Getting started. Retrieved 15 July 2024.
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