----------------- # pandas: powerful Python data analysis toolkit

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## What is it? **pandas** is a Python package providing 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. ## Main Features Here are just a few of the things that pandas does well: - Easy handling of [**missing data**][missing-data] (represented as `NaN`) 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, moving window linear regressions, date shifting and lagging, etc. [missing-data]: https://pandas.pydata.org/pandas-docs/stable/missing_data.html#working-with-missing-data [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#column-selection-addition-deletion [alignment]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html?highlight=alignment#intro-to-data-structures [groupby]: https://pandas.pydata.org/pandas-docs/stable/groupby.html#group-by-split-apply-combine [conversion]: https://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe [slicing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#slicing-ranges [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-ix [subsetting]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing [merging]: https://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging [joining]: https://pandas.pydata.org/pandas-docs/stable/merging.html#joining-on-index [reshape]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-and-pivot-tables [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/reshaping.html#pivot-tables-and-cross-tabulations [mi]: https://pandas.pydata.org/pandas-docs/stable/indexing.html#hierarchical-indexing-multiindex [flat-files]: https://pandas.pydata.org/pandas-docs/stable/io.html#csv-text-files [excel]: https://pandas.pydata.org/pandas-docs/stable/io.html#excel-files [db]: https://pandas.pydata.org/pandas-docs/stable/io.html#sql-queries [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/io.html#hdf5-pytables [timeseries]: https://pandas.pydata.org/pandas-docs/stable/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](https://pypi.org/project/pandas) and on conda. ```sh # conda conda install pandas ``` ```sh # or PyPI pip install pandas ``` ## Dependencies - [NumPy](https://www.numpy.org): 1.12.0 or higher - [python-dateutil](https://labix.org/python-dateutil): 2.5.0 or higher - [pytz](https://pythonhosted.org/pytz): 2011k or higher See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for 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: ```sh pip install cython ``` In the `pandas` directory (same one where you found this file after cloning the git repo), execute: ```sh python setup.py install ``` or for installing in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs): ```sh python setup.py develop ``` Alternatively, you can use `pip` if you want all the dependencies pulled in automatically (the `-e` option is for installing it in [development mode](https://pip.pypa.io/en/latest/reference/pip_install.html#editable-installs)): ```sh pip install -e . ``` See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/install.html#installing-from-source). ## 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 (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 discussion is taking place on github in this repo. 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 [Gitter channel](https://gitter.im/pydata/pandas) is available for quick development related questions. ## 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-docs.github.io/pandas-docs-travis/contributing.html)**. There is also an [overview](.github/CONTRIBUTING.md) on GitHub. 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 [Gitter](https://gitter.im/pydata/pandas).