experimental/cuda-ubi9/: pandas-2.2.2 metadata and description

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

author_email The Pandas Development Team <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 :: 3.9
  • Programming Language :: Python :: 3.10
  • Programming Language :: Python :: 3.11
  • Programming Language :: Python :: 3.12
  • Topic :: Scientific/Engineering
description_content_type text/markdown
license BSD 3-Clause License Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team All rights reserved. Copyright (c) 2011-2023, Open source contributors. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  • Homepage, https://pandas.pydata.org
  • Documentation, https://pandas.pydata.org/docs/
  • Repository, https://github.com/pandas-dev/pandas
provides_extras all
  • numpy>=1.22.4; python_version < "3.11"
  • numpy>=1.23.2; python_version == "3.11"
  • numpy>=1.26.0; python_version >= "3.12"
  • python-dateutil>=2.8.2
  • pytz>=2020.1
  • tzdata>=2022.7
  • hypothesis>=6.46.1; extra == "test"
  • pytest>=7.3.2; extra == "test"
  • pytest-xdist>=2.2.0; extra == "test"
  • pyarrow>=10.0.1; extra == "pyarrow"
  • bottleneck>=1.3.6; extra == "performance"
  • numba>=0.56.4; extra == "performance"
  • numexpr>=2.8.4; extra == "performance"
  • scipy>=1.10.0; extra == "computation"
  • xarray>=2022.12.0; extra == "computation"
  • fsspec>=2022.11.0; extra == "fss"
  • s3fs>=2022.11.0; extra == "aws"
  • gcsfs>=2022.11.0; extra == "gcp"
  • pandas-gbq>=0.19.0; extra == "gcp"
  • odfpy>=1.4.1; extra == "excel"
  • openpyxl>=3.1.0; extra == "excel"
  • python-calamine>=0.1.7; extra == "excel"
  • pyxlsb>=1.0.10; extra == "excel"
  • xlrd>=2.0.1; extra == "excel"
  • xlsxwriter>=3.0.5; extra == "excel"
  • pyarrow>=10.0.1; extra == "parquet"
  • pyarrow>=10.0.1; extra == "feather"
  • tables>=3.8.0; extra == "hdf5"
  • pyreadstat>=1.2.0; extra == "spss"
  • SQLAlchemy>=2.0.0; extra == "postgresql"
  • psycopg2>=2.9.6; extra == "postgresql"
  • adbc-driver-postgresql>=0.8.0; extra == "postgresql"
  • SQLAlchemy>=2.0.0; extra == "mysql"
  • pymysql>=1.0.2; extra == "mysql"
  • SQLAlchemy>=2.0.0; extra == "sql-other"
  • adbc-driver-postgresql>=0.8.0; extra == "sql-other"
  • adbc-driver-sqlite>=0.8.0; extra == "sql-other"
  • beautifulsoup4>=4.11.2; extra == "html"
  • html5lib>=1.1; extra == "html"
  • lxml>=4.9.2; extra == "html"
  • lxml>=4.9.2; extra == "xml"
  • matplotlib>=3.6.3; extra == "plot"
  • jinja2>=3.1.2; extra == "output-formatting"
  • tabulate>=0.9.0; extra == "output-formatting"
  • PyQt5>=5.15.9; extra == "clipboard"
  • qtpy>=2.3.0; extra == "clipboard"
  • zstandard>=0.19.0; extra == "compression"
  • dataframe-api-compat>=0.1.7; extra == "consortium-standard"
  • adbc-driver-postgresql>=0.8.0; extra == "all"
  • adbc-driver-sqlite>=0.8.0; extra == "all"
  • beautifulsoup4>=4.11.2; extra == "all"
  • bottleneck>=1.3.6; extra == "all"
  • dataframe-api-compat>=0.1.7; extra == "all"
  • fastparquet>=2022.12.0; extra == "all"
  • fsspec>=2022.11.0; extra == "all"
  • gcsfs>=2022.11.0; extra == "all"
  • html5lib>=1.1; extra == "all"
  • hypothesis>=6.46.1; extra == "all"
  • jinja2>=3.1.2; extra == "all"
  • lxml>=4.9.2; extra == "all"
  • matplotlib>=3.6.3; extra == "all"
  • numba>=0.56.4; extra == "all"
  • numexpr>=2.8.4; extra == "all"
  • odfpy>=1.4.1; extra == "all"
  • openpyxl>=3.1.0; extra == "all"
  • pandas-gbq>=0.19.0; extra == "all"
  • psycopg2>=2.9.6; extra == "all"
  • pyarrow>=10.0.1; extra == "all"
  • pymysql>=1.0.2; extra == "all"
  • PyQt5>=5.15.9; extra == "all"
  • pyreadstat>=1.2.0; extra == "all"
  • pytest>=7.3.2; extra == "all"
  • pytest-xdist>=2.2.0; extra == "all"
  • python-calamine>=0.1.7; extra == "all"
  • pyxlsb>=1.0.10; extra == "all"
  • qtpy>=2.3.0; extra == "all"
  • scipy>=1.10.0; extra == "all"
  • s3fs>=2022.11.0; extra == "all"
  • SQLAlchemy>=2.0.0; extra == "all"
  • tables>=3.8.0; extra == "all"
  • tabulate>=0.9.0; extra == "all"
  • xarray>=2022.12.0; extra == "all"
  • xlrd>=2.0.1; extra == "all"
  • xlsxwriter>=3.0.5; extra == "all"
  • zstandard>=0.19.0; extra == "all"
requires_python >=3.9
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Python Wheel

pandas: powerful Python data analysis toolkit

Testing CI - Test Coverage
Package PyPI Latest Release PyPI Downloads Conda Latest Release Conda Downloads
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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:

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 -c conda-forge pandas
# or PyPI
pip install pandas

The list of changes to pandas between each release can be found here. For full details, see the commit logs at https://github.com/pandas-dev/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:

pip install .

or for installing in development mode:

python -m pip install -ve . --no-build-isolation --config-settings=editable-verbose=true

See the full instructions for installing from source.




The official documentation is hosted on PyData.org.


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, 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.

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

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 Slack.

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

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