You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. There are a number of issues listed under Docs and good first issue where you could start out. If you are simply looking to start working with the pandas codebase, navigate to the GitHub "issues" tab and start looking through interesting issues.
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Contributing to pandasĪll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.Ī detailed overview on how to contribute can be found in the contributing guide. 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.Īdditional information on the communication channels can be found on the contributor community page. Most development discussions take place on GitHub in this repo, via the GitHub issue tracker.įurther, 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. Getting Helpįor usage questions, the best place to go to is StackOverflow.įurther, general questions and discussions can also take place on the pydata mailing list. Has been under active development since then. Work on pandas started at AQR (a quantitative hedge fund) in 2008 and The official documentation is hosted on : Background
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See the full instructions for installing from source.
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The source code is currently hosted on GitHub at:īinary installers for the latest released version are available at the Python
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Hierarchical labeling of axes (possible to have multiple.Split-apply-combine operations on data sets, for both aggregatingĭifferently-indexed data in other Python and NumPy data structures Powerful, flexible group by functionality to perform.Ignore the labels and let Series, DataFrame, etc. Automatic and explicit data alignment: objects canīe explicitly aligned to a set of labels, or the user can simply.Size mutability: columns can be inserted andĭeleted from DataFrame and higher dimensional.NaN, NA, or NaT) in floating point as well as non-floating point data Easy handling of missing data (represented as.Here are just a few of the things that pandas does well: The broader goal of becoming the most powerful and flexible open source dataĪnalysis / manipulation tool available in any language. It aims to be the fundamental high-level building block forĭoing practical, real world data analysis in Python. Structures designed to make working with "relational" or "labeled" data bothĮasy and intuitive. Pandas is a Python package that provides fast, flexible, and expressive data Pandas: powerful Python data analysis toolkit
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