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

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Project description

pandas: powerful Python data analysis toolkit


Testing
Package
Meta

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
Where to get it
Dependencies
Installation from sources
License
Documentation
Background
Getting Help
Discussion and Development
Contributing to pandas

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 joining data sets
Flexible reshaping and pivoting of data sets
Hierarchical labeling of axes (possible to have multiple labels per tick)
Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging

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.

Dependencies

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.

License

BSD 3

Documentation

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 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

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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Project details

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This version

2.3.3

Sep 29, 2025

2.3.2

Aug 21, 2025

2.3.1

Jul 7, 2025

2.3.0

Jun 5, 2025

2.2.3

Sep 20, 2024

2.2.2

Apr 10, 2024

2.2.1

Feb 23, 2024

2.2.0

Jan 20, 2024

2.2.0rc0 pre-release

Dec 22, 2023

2.1.4

Dec 8, 2023

2.1.3

Nov 10, 2023

2.1.2

Oct 26, 2023

2.1.1

Sep 20, 2023

2.1.0

Aug 30, 2023

2.1.0rc0 pre-release

Aug 11, 2023

2.0.3

Jun 28, 2023

2.0.2

May 29, 2023

2.0.1

Apr 24, 2023

2.0.0

Apr 3, 2023

2.0.0rc1 pre-release

Mar 16, 2023

2.0.0rc0 pre-release

Feb 20, 2023

1.5.3

Jan 19, 2023

1.5.2

Nov 23, 2022

1.5.1

Oct 19, 2022

1.5.0

Sep 19, 2022

1.5.0rc0 pre-release

Aug 24, 2022

1.4.4

Aug 31, 2022

1.4.3

Jun 23, 2022

1.4.2

Apr 2, 2022

1.4.1

Feb 12, 2022

1.4.0

Jan 22, 2022

1.4.0rc0 pre-release

Jan 6, 2022

1.3.5

Dec 12, 2021

1.3.4

Oct 17, 2021

1.3.3

Sep 12, 2021

1.3.2

Aug 15, 2021

1.3.1

Jul 25, 2021

1.3.0

Jul 2, 2021

1.2.5

Jun 22, 2021

1.2.4

Apr 12, 2021

1.2.3

Mar 2, 2021

1.2.2

Feb 9, 2021

1.2.1

Jan 20, 2021

1.2.0

Dec 26, 2020

1.1.5

Dec 7, 2020

1.1.4

Oct 30, 2020

1.1.3

Oct 5, 2020

1.1.2

Sep 8, 2020

1.1.1

Aug 20, 2020

1.1.0

Jul 28, 2020

1.0.5

Jun 18, 2020

1.0.4

May 29, 2020

1.0.3

Mar 18, 2020

1.0.2

Mar 15, 2020

1.0.1

Feb 5, 2020

1.0.0

Jan 30, 2020

0.25.3

Nov 1, 2019

0.25.2

Oct 19, 2019

0.25.1

Aug 22, 2019

0.25.0

Jul 19, 2019

0.24.2

Mar 14, 2019

0.24.1

Feb 4, 2019

0.24.0

Jan 25, 2019

0.23.4

Aug 4, 2018

0.23.3

Jul 7, 2018

0.23.2

Jul 6, 2018

0.23.1

Jun 13, 2018

0.23.0

May 16, 2018

0.22.0

Dec 31, 2017

0.21.1

Dec 13, 2017

0.21.0

Oct 28, 2017

0.20.3

Jul 7, 2017

0.20.2

Jun 5, 2017

0.20.1

May 5, 2017

0.20.0

May 5, 2017

0.19.2

Dec 24, 2016

0.19.1

Nov 3, 2016

0.19.0

Oct 2, 2016

0.18.1

May 5, 2016

0.18.0

Mar 12, 2016

0.17.1

Nov 20, 2015

0.17.0

Oct 9, 2015

0.16.2

Jun 13, 2015

0.16.1

May 11, 2015

0.16.0

Mar 22, 2015

0.15.2

Dec 11, 2014

0.15.1

Nov 8, 2014

0.15.0

Oct 19, 2014

0.14.1

Jul 10, 2014

0.14.0

May 30, 2014

0.13.1

Feb 9, 2014

0.13.0

Jan 14, 2014

0.12.0

Jul 24, 2013

0.11.0

Apr 23, 2013

0.10.1

Jan 22, 2013

0.10.0

Dec 17, 2012

0.9.1

Nov 15, 2012

0.9.0

Oct 8, 2012

0.8.1

Jul 22, 2012

0.8.0

Jun 29, 2012

0.7.3

Apr 12, 2012

0.7.2

Mar 16, 2012

0.7.1

Feb 29, 2012

0.7.0

Feb 9, 2012

0.6.1

Dec 13, 2011

0.6.0

Nov 26, 2011

0.5.0

Oct 25, 2011

0.4.3

Oct 9, 2011

0.4.2

Oct 3, 2011

0.4.1

Sep 26, 2011

0.4.0

Sep 12, 2011

0.3.0

Feb 20, 2011

0.2

May 18, 2010

0.1

Dec 25, 2009

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SHA256	`c46467899aaa4da076d5abc11084634e2d197e9460643dd455ac3db5856b24d6`
MD5	`febeeebe184efa8402b27fcfd676ff1f`
BLAKE2b-256	`c533dd70400631b62b9b29c3c93d2feee1d0964dc2bae2e5ad7a6c73a7f25325`

Algorithm	Hash digest
SHA256	`ee67acbbf05014ea6c763beb097e03cd629961c8a632075eeb34247120abcb4b`
MD5	`933e80b5bad9a8dbd8f394f2c478c1c1`
BLAKE2b-256	`15b20e62f78c0c5ba7e3d2c5945a82456f4fac76c480940f805e0b97fcbc2f65`

Algorithm	Hash digest
SHA256	`6d2cefc361461662ac48810cb14365a365ce864afe85ef1f447ff5a1e99ea81c`
MD5	`c9163ae6ab3a67993a2dc99031c4d1f0`
BLAKE2b-256	`ca05d01ef80a7a3a12b2f8bbf16daba1e17c98a2f039cbc8e2f77a2c5a63d382`

Algorithm	Hash digest
SHA256	`1611aedd912e1ff81ff41c745822980c49ce4a7907537be8692c8dbc31924593`
MD5	`dd6ae15f98f35a08820a349297390c0e`
BLAKE2b-256	`2100266d6b357ad5e6d3ad55093a7e8efc7dd245f5a842b584db9f30b0f0a287`

Algorithm	Hash digest
SHA256	`ee15f284898e7b246df8087fc82b87b01686f98ee67d85a17b7ab44143a3a9a0`
MD5	`2a981cfcb2400785e3fc99ae27e1ddcc`
BLAKE2b-256	`04fd74903979833db8390b73b3a8a7d30d146d710bd32703724dd9083950386f`

Algorithm	Hash digest
SHA256	`c4fc4c21971a1a9f4bdb4c73978c7f7256caa3e62b323f70d6cb80db583350bc`
MD5	`f7ae8dd412a8383b7fd3119338806496`
BLAKE2b-256	`bd17e756653095a083d8a37cbd816cb87148debcfcd920129b25f99dd8d04271`

Algorithm	Hash digest
SHA256	`a45c765238e2ed7d7c608fc5bc4a6f88b642f2f01e70c0c23d2224dd21829d86`
MD5	`2cfbfcc4bc373ec8e8aa661f66104ff9`
BLAKE2b-256	`53dad10013df5e6aaef6b425aa0c32e1fc1f3e431e4bcabd420517dceadce354`

Algorithm	Hash digest
SHA256	`900f47d8f20860de523a1ac881c4c36d65efcb2eb850e6948140fa781736e110`
MD5	`67b9b83a8c39b2f0ec7d71845110ecf1`
BLAKE2b-256	`442378d645adc35d94d1ac4f2a3c4112ab6f5b8999f4898b8cdf01252f8df4a9`

Algorithm	Hash digest
SHA256	`6435cb949cb34ec11cc9860246ccb2fdc9ecd742c12d3304989017d53f039a78`
MD5	`811ee858692153f7f16e1cca6e8a276e`
BLAKE2b-256	`46b185331edfc591208c9d1a63a06baa67b21d332e63b7a591a5ba42a10bb507`

Algorithm	Hash digest
SHA256	`74ecdf1d301e812db96a465a525952f4dde225fdb6d8e5a521d47e1f42041e21`
MD5	`ac2644491b392c566ec3233949c22ca7`
BLAKE2b-256	`0e5af43efec3e8c0cc92c4663ccad372dbdff72b60bdb56b2749f04aa1d07d7e`

Algorithm	Hash digest
SHA256	`75ea25f9529fdec2d2e93a42c523962261e567d250b0013b16210e1d40d7c2e5`
MD5	`a01f7ecdb2a1b52ee9bbb2f516dfddc1`
BLAKE2b-256	`f9ca3f8d4f49740799189e1395812f3bf23b5e8fc7c190827d55a610da72ce55`

Algorithm	Hash digest
SHA256	`f8bfc0e12dc78f777f323f55c58649591b2cd0c43534e8355c51d3fede5f4dee`
MD5	`eadc6d00235b79c22b76f4edd7912f15`
BLAKE2b-256	`4fc7e54682c96a895d0c808453269e0b5928a07a127a15704fedb643e9b0a4c8`

pandas 2.3.3

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pandas: powerful Python data analysis toolkit

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Algorithm	Hash digest
SHA256	`93c2d9ab0fc11822b5eece72ec9587e172f63cff87c00b062f6e37448ced4493`
MD5	`303a938156178d857dee67cf7f9cd972`
BLAKE2b-256	`8d0fb4d4ae743a83742f1153464cf1a8ecfafc3ac59722a0b5c8602310cb7158`

Algorithm	Hash digest
SHA256	`4e0a175408804d566144e170d0476b15d78458795bb18f1304fb94160cabf40c`
MD5	`72ee59a3a33a50e4023e8e4509023053`
BLAKE2b-256	`3381a3afc88fca4aa925804a27d2676d22dcd2031c2ebe08aabd0ae55b9ff282`

Algorithm	Hash digest
SHA256	`318d77e0e42a628c04dc56bcef4b40de67918f7041c2b061af1da41dcff670ac`
MD5	`4d60ea1e94ec268ba0daab994f5311f3`
BLAKE2b-256	`1507284f757f63f8a8d69ed4472bfd85122bd086e637bf4ed09de572d575a693`

Algorithm	Hash digest
SHA256	`e32e7cc9af0f1cc15548288a51a3b681cc2a219faa838e995f7dc53dbab1062d`
MD5	`0288a1d12cfec18c840fa945798a5ffd`
BLAKE2b-256	`16879472cf4a487d848476865321de18cc8c920b8cab98453ab79dbbc98db63a`

Algorithm	Hash digest
SHA256	`bdcd9d1167f4885211e401b3036c0c8d9e274eee67ea8d0758a256d60704cfe8`
MD5	`88e1e5326def2c3d0cbf507ca282de4b`
BLAKE2b-256	`319472fac03573102779920099bcac1c3b05975c2cb5f01eac609faf34bed1ca`

Algorithm	Hash digest
SHA256	`56851a737e3470de7fa88e6131f41281ed440d29a9268dcbf0002da5ac366713`
MD5	`60aa5eb671aa114ae1a799ce81c434b1`
BLAKE2b-256	`cd4b18b035ee18f97c1040d94debd8f2e737000ad70ccc8f5513f4eefad75f4b`

Algorithm	Hash digest
SHA256	`a16dcec078a01eeef8ee61bf64074b4e524a2a3f4b3be9326420cabe59c4778b`
MD5	`0604cb4f181a1cbda62d2e2642878a89`
BLAKE2b-256	`8641585a168330ff063014880a80d744219dbf1dd7a1c706e75ab3425a987384`

Algorithm	Hash digest
SHA256	`371a4ab48e950033bcf52b6527eccb564f52dc826c02afd9a1bc0ab731bba084`
MD5	`4b196c27c25afb71c487a3867e229d92`
BLAKE2b-256	`872184072af3187a677c5893b170ba2c8fbe450a6ff911234916da889b698220`

Algorithm	Hash digest
SHA256	`a68e15f780eddf2b07d242e17a04aa187a7ee12b40b930bfdd78070556550e98`
MD5	`83c40d1d5bcaa3b79c76d2f44ba9bece`
BLAKE2b-256	`a6de8b1895b107277d52f2b42d3a6806e69cfef0d5cf1d0ba343470b9d8e0a04`

Algorithm	Hash digest
SHA256	`b3d11d2fda7eb164ef27ffc14b4fcab16a80e1ce67e9f57e19ec0afaf715ba89`
MD5	`ab4c3fb1d7784f3be4419063c69fc6c1`
BLAKE2b-256	`e563cd7d615331b328e287d8233ba9fdf191a9c2d11b6af0c7a59cfcec23de68`