Based on the "Science and Data Analysis" category.
Alternatively, view Pandas alternatives based on common mentions on social networks and blogs.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of Pandas or a related project?
PyPI Latest Release Conda Latest Release DOI Package Status License Coverage OpenSSF Scorecard Downloads Slack Powered by NumFOCUS Code style: black Imports: isort
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:
NaN, NA, or NaT) in floating point as well as non-floating point dataSeries, DataFrame, etc. automatically
align the data for you in computationsThe source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas
Binary installers for the latest released version are available at the Python Package Index (PyPI) and on Conda.
# 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
In the pandas directory (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
or alternatively
python setup.py develop
See the full instructions for installing from source.
[BSD 3](LICENSE)
The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
Work on pandas started at AQR (a quantitative hedge fund) in 2008 and
has been under active development since then.
For usage questions, the best place to go to is StackOverflow. Further, general questions and discussions can also take place on the pydata mailing list.
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 Slack channel is available for quick development related questions.
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 .
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!
Feel free to ask questions on the mailing list or on Slack.
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
*Note that all licence references and agreements mentioned in the Pandas README section above
are relevant to that project's source code only.
Do not miss the trending, packages, news and articles with our weekly report.