Date: Dec 24, 2016 Version: 0.19.2
Binary Installers: http://pypi.python.org/pypi/pandas
Source Repository: http://github.com/pydata/pandas
Issues & Ideas: https://github.com/pydata/pandas/issues
Q&A Support: http://stackoverflow.com/questions/tagged/pandas
Developer Mailing List: http://groups.google.com/group/pydata
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
pandas is well suited for many different kinds of data:
The two primary data structures of pandas,
Series (1-dimensional) and
DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering. For R users,
DataFrame provides everything that R’s
data.frame provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.
Here are just a few of the things that pandas does well:
DataFrame, etc. automatically align the data for you in computations
Many of these principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these tasks.
Some other notes
This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first.
See the package overview for more detail about what’s in the library.
© 2011–2012 Lambda Foundry, Inc. and PyData Development Team
© 2008–2011 AQR Capital Management, LLC
© 2008–2014 the pandas development team
Licensed under the 3-clause BSD License.