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pandas: powerful Python data analysis toolkit

What is it?

undefinedpandas 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.

Main Features

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

• Easy handling of undefinedmissing dataundefined (represented as
  NaN, NA, or NaT) in floating point as well as non-floating point data
• Size mutability: columns can be undefinedinserted and
  deletedundefined from DataFrame and higher dimensional
  objects
• Automatic and explicit undefineddata alignmentundefined: 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 undefinedgroup byundefined functionality to perform
  split-apply-combine operations on data sets, for both aggregating
  and transforming data
• Make it undefinedeasy to convertundefined ragged,
  differently-indexed data in other Python and NumPy data structures
  into DataFrame objects
• Intelligent label-based undefinedslicingundefined, undefinedfancy
  indexingundefined, and undefinedsubsettingundefined of
  large data sets
• Intuitive undefinedmergingundefined and undefinedjoiningundefined data
  sets
• Flexible undefinedreshapingundefined and undefinedpivotingundefined of
  data sets
• undefinedHierarchicalundefined labeling of axes (possible to have multiple
  labels per tick)
• Robust IO tools for loading data from undefinedflat filesundefined
  (CSV and delimited), undefinedExcel filesundefined, undefineddatabasesundefined,
  and saving/loading data from the ultrafast undefinedHDF5 formatundefined
• undefinedTime seriesundefined-specific functionality: date range
  generation and frequency conversion, moving window statistics,
  date shifting and lagging

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) and on Conda.

# conda
conda install pandas

# or PyPI
pip install 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
• pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations

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:

python setup.py install

or for installing in development mode:

python -m pip install -e . --no-build-isolation --no-use-pep517

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

or alternatively

python 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 and
has 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 discussions take 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.

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