Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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.
Here are just a few of the things that pandas does well:
NaT) in floating point as well as non-floating point data
DataFrame, etc. automatically align the data for you in computations
The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas
# conda conda install pandas
# or PyPI pip install pandas
See the full installation instructions for minimum supported versions of required, recommended and optional dependencies.
To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from PyPI:
pip install cython
pandasdirectory (same one where you found this file after cloning the git repo), execute:
python setup.py install
or for installing in development mode:
python -m pip install -e . --no-build-isolation --no-use-pep517
If you have
make, you can also use
make developto run the same command.
python setup.py develop
See the full instructions for installing from source.
The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
pandasstarted at AQR (a quantitative hedge fund) in 2008 and has been under active development since then.
Most development discussions take place on GitHub in this repo. Further, the pandas-dev mailing list can also be used for specialized discussions or design issues, and a Gitter channel is available for quick development related questions.
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
If you are simply looking to start working with the pandas codebase, navigate to the GitHub "issues" tab and start looking through interesting issues. There are a number of issues listed under Docs and good first issue where you could start out.
You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to subscribe to pandas on CodeTriage.
Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!
As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: Contributor Code of Conduct