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.
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
The official documentation is hosted on PyData.org.
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 Stack Overflow.
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, via 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.
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.
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!
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
pandas 是一个 Python 库，提供快速、灵活的数据结构（Series 和 DataFrame），用于处理带标签的关系型数据，目标是成为 Python 实用数据分析的基础构件。