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][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#database-style-dataframe-or-named-series-joining-merging
[joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
[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/indexing.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

The 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)](https://pypi.org/project/pandas) and on Conda.

# conda
conda install -c conda-forge pandas

# or PyPI
pip install pandas

The list of changes to pandas between each release can be found
here. For full
details, see the commit logs at https://github.com/pandas-dev/pandas.

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

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:

pip install .

or for installing in development mode:

python -m pip install -ve . --no-build-isolation --config-settings editable-verbose=true

See the full instructions for installing from source.

License

BSD 3

Documentation

The official documentation is hosted on PyData.org.

Background

Work on `pandas` started at AQR (a quantitative hedge fund) in 2008 and
has been under active development 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 on GitHub in this repo, via 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.

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

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