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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pandas: powerful Python data analysis toolkit

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What is it?

pandas is a Python package providing fast, flexible, and expressive datastructures designed to make working with "relational" or "labeled" data botheasy and intuitive. It aims to be the fundamental high-level building block fordoing practical, real world data analysis in Python. Additionally, it hasthe broader goal of becoming the most powerful and flexible open source dataanalysis / manipulation tool available in any language. It is already well onits way towards this goal.

Main Features

Here are just a few of the things that pandas does well:

• Easy handling of missing data (represented asNaN) in floating point as well as non-floating point data
• Size mutability: columns can be inserted anddeleted from DataFrame and higher dimensionalobjects
• Automatic and explicit data alignment: objects canbe explicitly aligned to a set of labels, or the user can simplyignore the labels and let Series, DataFrame, etc. automaticallyalign the data for you in computations
• Powerful, flexible group by functionality to performsplit-apply-combine operations on data sets, for both aggregatingand transforming data
• Make it easy to convert ragged,differently-indexed data in other Python and NumPy data structuresinto DataFrame objects
• Intelligent label-based slicing, fancyindexing, and subsetting oflarge data sets
• Intuitive merging and joining datasets
• Flexible reshaping and pivoting ofdata sets
• Hierarchical labeling of axes (possible to have multiplelabels per tick)
• Robust IO tools for loading data from flat files(CSV and delimited), Excel files, databases,and saving/loading data from the ultrafast HDF5 format
• Time series-specific functionality: date rangegeneration and frequency conversion, moving window statistics,moving window linear regressions, date shifting and lagging, etc.

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 Pythonpackage index and on conda.

```sh

conda

conda install pandas```

```sh

or PyPI

pip install pandas```

Dependencies

• NumPy: 1.13.3 or higher
• python-dateutil: 2.5.0 or higher
• pytz: 2015.4 or higher

See the full installation instructionsfor recommended and optional dependencies.

Installation from sources

To install pandas from source you need Cython in addition to the normaldependencies above. Cython can be installed from pypi:

shpip install cython

In the pandas directory (same one where you found this file aftercloning the git repo), execute:

shpython setup.py install

or for installing in development mode:

shpython -m pip install --no-build-isolation -e .

If you have make, you can also use make develop to run the same command.

or alternatively

shpython setup.py develop

See the full instructions for installing from source.

License

BSD 3

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 andhas been under active development since then.

Getting Help

For usage questions, the best place to go to is StackOverflow.Further, general questions and discussions can also take place on the pydata mailing list.

Discussion and Development

Most development discussion is taking place on github in this repo. Further, the pandas-dev mailing list can also be used for specialized discussions or design issues, and a Gitter channel is available for quick development related questions.

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. There is also an overview on GitHub.

If you are simply looking to start working with the pandas codebase, navigate to the GitHub "issues" tab and start looking through interesting issues. There are a number of issues listed under Docs and good first issue 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.

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 Gitter.

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

To restore the repository download the bundle

wget https://archive.org/download/github.com-pandas-dev-pandas_-_2019-10-03_07-31-46/pandas-dev-pandas_-_2019-10-03_07-31-46.bundle

and run:

 git clone pandas-dev-pandas_-_2019-10-03_07-31-46.bundle 

Source: https://github.com/pandas-dev/pandas
Uploader: pandas-dev
Upload date: 2019-10-03

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