pandas: A Powerful Python Data Analysis Toolkit
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
Here are just a few of the things that pandas does well:
- Easy handling of 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 from DataFrame and higher dimensional
objects
- Automatic and explicit data 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 functionality to perform
split-apply-combine operations on data sets, for both aggregating
and transforming data
- Make it easy to convert ragged,
differently-indexed data in other Python and NumPy data structures
into DataFrame objects
- Intelligent label-based slicing, fancy
indexing, and subsetting of
large data sets
- Intuitive merging and data
sets
Where to get it
The source code is currently hosted on GitHub.
Binary installers for the latest released version are available at the Python
Package Index (PyPI) and on conda-forge.
# conda
conda install -c conda-forge pandas
# or PyPI
pip install pandas
The list of changes to pandas between each release can be found in the
release notes. For full
details, see the GitHub commit logs.
Dependencies
See the full installation instructions for minimum supported versions of required, recommended and optional dependencies.
Installation from sources
See the full instructions for installing from source.
License
BSD 3
Documentation
The official documentation is hosted on pandas.pydata.org.
Background
Work on pandas started at AQR (a quantitative hedge fund) in 2008 and
has been under active development by open source contributors 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 in 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.
Feel free to ask questions on the forums described in the discussion and development section.
As contributors and maintainers to this project, you are expected to abide by pandas' contributor code of conduct.
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