loongson/pypi/: pandas-1.4.1 metadata and description

Homepage Simple index

Powerful data structures for data analysis, time series, and statistics

author The Pandas Development Team
author_email pandas-dev@python.org
  • Development Status :: 5 - Production/Stable
  • Environment :: Console
  • Intended Audience :: Science/Research
  • License :: OSI Approved :: BSD License
  • Operating System :: OS Independent
  • Programming Language :: Cython
  • Programming Language :: Python
  • Programming Language :: Python :: 3
  • Programming Language :: Python :: 3 :: Only
  • Programming Language :: Python :: 3.8
  • Programming Language :: Python :: 3.9
  • Programming Language :: Python :: 3.10
  • Topic :: Scientific/Engineering
description_content_type text/markdown
license BSD-3-Clause
  • any
  • Bug Tracker, https://github.com/pandas-dev/pandas/issues
  • Documentation, https://pandas.pydata.org/pandas-docs/stable
  • Source Code, https://github.com/pandas-dev/pandas
provides_extras test
  • python-dateutil (>=2.8.1)
  • pytz (>=2020.1)
  • numpy (>=1.18.5) ; platform_machine != "aarch64" and platform_machine != "arm64" and python_version < "3.10"
  • numpy (>=1.19.2) ; platform_machine == "aarch64" and python_version < "3.10"
  • numpy (>=1.20.0) ; platform_machine == "arm64" and python_version < "3.10"
  • numpy (>=1.21.0) ; python_version >= "3.10"
  • hypothesis (>=5.5.3) ; extra == 'test'
  • pytest (>=6.0) ; extra == 'test'
  • pytest-xdist (>=1.31) ; extra == 'test'
requires_python >=3.8

Because this project isn't in the mirror_whitelist, no releases from root/pypi are included.

File Tox results History
43 MB
Python Wheel
  • Replaced 1 time(s)
  • Uploaded to loongson/pypi by loongson 2022-08-26 06:16:04

pandas: powerful Python data analysis toolkit

PyPI Latest Release Conda Latest Release DOI Package Status License Azure Build Status Coverage Downloads Gitter Powered by NumFOCUS Code style: black Imports: isort

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.

Main Features

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

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


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.




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.

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 Open Source Helpers

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