Name: pandas
Owner: pandas
Description: Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Created: 2010-08-24 01:37:33.0
Updated: 2018-01-17 19:40:13.0
Pushed: 2018-01-17 22:23:18.0
Homepage: http://pandas.pydata.org
Size: 114283
Language: Python
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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 toward this goal.
Here are just a few of the things that pandas does well:
NaN
) in floating point as well as non-floating point dataSeries
, DataFrame
, etc. automatically
align the data for you in computationsThe 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 and on conda.
nda
a install pandas
h
PyPI
install pandas
See the full installation instructions for recommended and optional dependencies.
To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from pypi:
install cython
In the pandas
directory (same one where you found this file after
cloning the git repo), execute:
on setup.py install
or for installing in development mode:
on 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):
install -e .
See the full instructions for installing from source.
The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
Work on pandas
started at AQR (a quantitative hedge fund) in 2008 and
has been under active development since then.
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.
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.
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 Difficulty Novice where you could start out.
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.