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https://github.com/pandas-dev/pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
https://github.com/pandas-dev/pandas

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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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README 

        

  

  Pandas Logo



-----------------



# pandas: 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](#main-features)

- [Where to get it](#where-to-get-it)

- [Dependencies](#dependencies)

- [Installation from sources](#installation-from-sources)

- [License](#license)

- [Documentation](#documentation)

- [Background](#background)

- [Getting Help](#getting-help)

- [Discussion and Development](#discussion-and-development)

- [Contributing to pandas](#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 IO 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](https://anaconda.org/conda-forge/pandas).



```sh

# conda

conda install -c conda-forge pandas

```



```sh

# or PyPI

pip install pandas

```



The list of changes to pandas between each release can be found

[here](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). 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](https://www.numpy.org)

- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)

- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)



See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.



## Installation from sources

To install pandas from source you need [Cython](https://cython.org/) in addition to the normal

dependencies above. Cython can be installed from PyPI:



```sh

pip install cython

```



In the `pandas` directory (same one where you found this file after

cloning the git repo), execute:



```sh

pip install .

```



or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):



```sh

python -m pip install -ve . --no-build-isolation -Ceditable-verbose=true

```



See the full instructions for [installing from source](https://pandas.pydata.org/docs/dev/development/contributing_environment.html).



## License

[BSD 3](LICENSE)



## Documentation

The official documentation is hosted on [PyData.org](https://pandas.pydata.org/pandas-docs/stable/).



## Background

Work on ``pandas`` started at [AQR](https://www.aqr.com/) (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 [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).

Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).



## Discussion and Development

Most development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https://github.com/pandas-dev/pandas/issues).



Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions.



There are also frequent [community meetings](https://pandas.pydata.org/docs/dev/development/community.html#community-meeting) for project maintainers open to the community as well as monthly [new contributor meetings](https://pandas.pydata.org/docs/dev/development/community.html#new-contributor-meeting) to help support new contributors.



Additional information on the communication channels can be found on the [contributor community](https://pandas.pydata.org/docs/development/community.html) page.



## Contributing to pandas



[![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/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](https://pandas.pydata.org/docs/dev/development/contributing.html)**.



If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) 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](https://www.codetriage.com/pandas-dev/pandas).



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](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-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](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)






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