pandas: A Powerful Python Data Analysis Toolkit

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What is it?

pandas is a Python package that provides 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 towards this goal.

Table of Contents

• Main Features
• Where to get it
• Dependencies
• Installation from sources
• License
• Documentation
• Background
• Getting Help
• Discussion and Development
• Contributing to pandas

Main Features

Here are just a few of the things that pandas does well:

• Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating point data
• Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
• Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
• Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
• Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
• Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
• Intuitive merging and joining data sets
• Flexible reshaping and pivoting of data sets
• Hierarchical labeling of axes (possible to have multiple labels per tick)
• Robust I/O tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
• Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging

Where to get it

The source code is currently hosted on GitHub.

Binary installers for the latest released version are available at the Python Package Index (PyPI) and on conda-forge.

# conda
conda install -c conda-forge pandas

# or PyPI
pip install pandas

The list of changes to pandas between each release can be found in the release notes. For full details, see the GitHub commit logs.

Dependencies

• NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays
• python-dateutil - Provides powerful extensions to the standard datetime module
• tzdata - Provides an IANA time zone database (Only required on Windows/Emscripten)

See the full installation instructions for minimum supported versions of required, recommended and optional dependencies.

Installation from sources

See the full instructions for installing from source.

License

BSD 3

Documentation

The official documentation is hosted on pandas.pydata.org.

Background

Work on pandas started at AQR (a quantitative hedge fund) in 2008 and has been under active development by open source contributors since then.

Getting Help

For usage questions, the best place to go to is Stack Overflow. Further, general questions and discussions can also take place on the pydata mailing list.

Discussion and Development

Most development discussions take place in the GitHub issue tracker.

Further, the pandas-dev mailing list can also be used for specialized discussions or design issues, and a Slack channel is available for quick development related questions.

There are also frequent community meetings for project maintainers open to the community as well as monthly new contributor meetings to help support new contributors.

Additional information on the communication channels can be found on the contributor community page.

Contributing to pandas

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

A detailed overview on how to contribute can be found in the contributing guide.

Feel free to ask questions on the forums described in the discussion and development section.

As contributors and maintainers to this project, you are expected to abide by pandas' contributor code of conduct.

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