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pandas: A Powerful Python Data Analysis Toolkit

| | | | --- | --- | | Testing | | | Package | | | Meta | |

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

Main Features

- Easy handling of [missing data][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**][insertion-deletion] from DataFrame and higher dimensional objects - Automatic and explicit [data alignment][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][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data - Make it [easy to convert][conversion] ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects - Intelligent label-based [slicing][slicing], [**fancy indexing][fancy-indexing], and [subsetting**][subsetting] of large data sets - Intuitive [merging][merging] and [joining][joining] data sets - Flexible [reshaping][reshape] and [pivoting][pivot-table] of data sets - [Hierarchical][mi] labeling of axes (possible to have multiple labels per tick) - Robust I/O tools for loading data from [flat files][flat-files] (CSV and delimited), [Excel files][excel], [databases][db], and saving/loading data from the ultrafast [HDF5 format][hdfstore] - [Time series][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging

[missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merge [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#dataframe-join [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#hierarchical-indexing-multiindex [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality

Where to get it

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)

Installation from sources

See the full instructions for installing from source.

License

Documentation

Background

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

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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