Pandas is a software library written for the Python programming language for data manipulation and analysis.


pandas (software)

From Wikipedia, the free encyclopedia
pandas
Pandas logo.png
Original author(s) Wes McKinney
Developer(s) Community
Stable release
0.22.0[1] / 29 December 2017; 4 months ago
Repository

Edit this at Wikidata

Written in Python
Operating system Cross-platform
Type Technical computing
License BSD-new license
Website pandas.pydata.org

In computer programmingpandas 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.[citation needed]

Library features[edit]

  • DataFrame object for data manipulation with integrated indexing.
  • Tools for reading and writing data between in-memory data structures and different file formats.
  • Data alignment and integrated handling of missing data.
  • Reshaping and pivoting of data sets.
  • Label-based slicing, fancy indexing, and subsetting of large data sets.
  • Data structure column insertion and deletion.
  • Group by engine allowing split-apply-combine operations on data sets.
  • Data set merging and joining.
  • Hierarchical axis indexing to work with high-dimensional data in a lower-dimensional data structure.
  • Time series-functionality: Date range generation[3] and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging.

The library is highly optimized for performance, with critical code paths written in Cython or C.[4]

History[edit]

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.[5]

See also[edit]

References[edit]

  1. Jump up^ “Release Notes – pandas 0.22.0 documentation”pandas. 29 December 2017. Retrieved 31 December 2017.
  2. Jump up^ “License – Package overview – pandas 0.21.1 documentation”pandas. 12 December 2017. Retrieved 13 December 2017.
  3. Jump up^ “pandas.date_range – pandas 0.21.1 documentation”pandas. 12 December 2017. Retrieved 13 December 2017.
  4. Jump up^ “Python Data Analysis Library – pandas: Python Data Analysis Library”pandas. Retrieved 13 November 2017.
  5. Jump up^ “NumFOCUS – pandas: a fiscally sponsored project”NumFOCUS. Retrieved 3 April 2018.

Further reading[edit]

  • 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.
  • Chen, Daniel Y. (2018). Pandas for Everyone : Python Data Analysis. Boston: Addison-Wesley. ISBN 978-0-13-454706-0.

External links[edit]

Anúncios

Deixe um comentário

Preencha os seus dados abaixo ou clique em um ícone para log in:

Logotipo do WordPress.com

Você está comentando utilizando sua conta WordPress.com. Sair /  Alterar )

Foto do Google+

Você está comentando utilizando sua conta Google+. Sair /  Alterar )

Imagem do Twitter

Você está comentando utilizando sua conta Twitter. Sair /  Alterar )

Foto do Facebook

Você está comentando utilizando sua conta Facebook. Sair /  Alterar )

Conectando a %s