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| HTTP status | PASS | download_http_code = 200 | HTTP 200 |
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| Spam/ban | PASS | fh_dont_index != 1 AND ml_spam_score = 0 | ml_spam_score=0 |
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| Property | Value |
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| URL | https://pypi.org/project/pandas/ |
| Last Crawled | 2026-04-08 08:43:02 (1 day ago) |
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| Meta Title | pandas Ā· PyPI |
| Meta Description | Powerful data structures for data analysis, time series, and statistics |
| Meta Canonical | null |
| Boilerpipe Text | pandas: A 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.
Table of Contents
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 I/O 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
NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays
python-dateutil - Provides powerful extensions to the standard datetime module
tzdata - Provides an IANA time zone database
(Only required on Windows/Emscripten)
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
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.
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 āthis can be improvedā... 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
Go to Top |
| Markdown | [Skip to main content](https://pypi.org/project/pandas/#content)
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# pandas 3.0.2
pip install pandas Copy PIP instructions
[Latest version](https://pypi.org/project/pandas/)
Released: Mar 31, 2026
Powerful data structures for data analysis, time series, and statistics
### Navigation
- [Project description](https://pypi.org/project/pandas/#description)
- [Release history](https://pypi.org/project/pandas/#history)
- [Download files](https://pypi.org/project/pandas/#files)
### Verified details
*These details have been [verified by PyPI](https://docs.pypi.org/project_metadata/#verified-details)*
###### Project links
- [repository](https://github.com/pandas-dev/pandas)
###### GitHub Statistics
- [**Repository**](https://github.com/pandas-dev/pandas)
- [**Stars:** 48369](https://github.com/pandas-dev/pandas/stargazers)
- [**Forks:** 19835](https://github.com/pandas-dev/pandas/network/members)
- [**Open issues:** 3210](https://github.com/pandas-dev/pandas/issues)
- [**Open PRs:** 269](https://github.com/pandas-dev/pandas/pulls)
###### Maintainers
[ jorisvandenbossche](https://pypi.org/user/jorisvandenbossche/) [ jreback](https://pypi.org/user/jreback/) [ jreback1](https://pypi.org/user/jreback1/) [ mroeschke](https://pypi.org/user/mroeschke/)
### Unverified details
*These details have **not** been verified by PyPI*
###### Project links
- [documentation](https://pandas.pydata.org/docs/)
- [homepage](https://pandas.pydata.org/)
###### Meta
- **License:** BSD License (BSD 3-Clause License)
- **Author:** [The Pandas Development Team](mailto:pandas-dev@python.org)
- **Requires:** Python \>=3.11
- **Provides-Extra:** `test` , `pyarrow` , `performance` , `computation` , `fss` , `aws` , `gcp` , `excel` , `parquet` , `feather` , `iceberg` , `hdf5` , `spss` , `postgresql` , `mysql` , `sql-other` , `html` , `xml` , `plot` , `output-formatting` , `clipboard` , `compression` , `timezone` , `all`
###### Classifiers
- **Development Status**
- [5 - Production/Stable](https://pypi.org/search/?c=Development+Status+%3A%3A+5+-+Production%2FStable)
- **Environment**
- [Console](https://pypi.org/search/?c=Environment+%3A%3A+Console)
- **Intended Audience**
- [Science/Research](https://pypi.org/search/?c=Intended+Audience+%3A%3A+Science%2FResearch)
- **License**
- [OSI Approved :: BSD License](https://pypi.org/search/?c=License+%3A%3A+OSI+Approved+%3A%3A+BSD+License)
- **Operating System**
- [OS Independent](https://pypi.org/search/?c=Operating+System+%3A%3A+OS+Independent)
- **Programming Language**
- [Cython](https://pypi.org/search/?c=Programming+Language+%3A%3A+Cython)
- [Python](https://pypi.org/search/?c=Programming+Language+%3A%3A+Python)
- [Python :: 3](https://pypi.org/search/?c=Programming+Language+%3A%3A+Python+%3A%3A+3)
- [Python :: 3 :: Only](https://pypi.org/search/?c=Programming+Language+%3A%3A+Python+%3A%3A+3+%3A%3A+Only)
- [Python :: 3.11](https://pypi.org/search/?c=Programming+Language+%3A%3A+Python+%3A%3A+3.11)
- [Python :: 3.12](https://pypi.org/search/?c=Programming+Language+%3A%3A+Python+%3A%3A+3.12)
- [Python :: 3.13](https://pypi.org/search/?c=Programming+Language+%3A%3A+Python+%3A%3A+3.13)
- [Python :: 3.14](https://pypi.org/search/?c=Programming+Language+%3A%3A+Python+%3A%3A+3.14)
- **Topic**
- [Scientific/Engineering](https://pypi.org/search/?c=Topic+%3A%3A+Scientific%2FEngineering)
[Report project as malware](https://pypi.org/project/pandas/submit-malware-report/)
- [Project description](https://pypi.org/project/pandas/#description)
- [Project details](https://pypi.org/project/pandas/#data)
- [Release history](https://pypi.org/project/pandas/#history)
- [Download files](https://pypi.org/project/pandas/#files)
## Project description
***
# pandas: A Powerful Python Data Analysis Toolkit
| | |
|---|---|
| Testing | [](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml) [](https://codecov.io/gh/pandas-dev/pandas) |
| Package | [](https://pypi.org/project/pandas/) [](https://pypi.org/project/pandas/) [](https://anaconda.org/conda-forge/pandas) [](https://anaconda.org/conda-forge/pandas) |
| Meta | [](https://numfocus.org/) [](https://doi.org/10.5281/zenodo.3509134) [](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) [](https://insights.linuxfoundation.org/project/pandas-dev-pandas) |
## 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](https://pypi.org/project/pandas/#main-features)
- [Where to get it](https://pypi.org/project/pandas/#where-to-get-it)
- [Dependencies](https://pypi.org/project/pandas/#dependencies)
- [Installation from sources](https://pypi.org/project/pandas/#installation-from-sources)
- [License](https://pypi.org/project/pandas/#license)
- [Documentation](https://pypi.org/project/pandas/#documentation)
- [Background](https://pypi.org/project/pandas/#background)
- [Getting Help](https://pypi.org/project/pandas/#getting-help)
- [Discussion and Development](https://pypi.org/project/pandas/#discussion-and-development)
- [Contributing to pandas](https://pypi.org/project/pandas/#contributing-to-pandas)
## Main Features
Here are just a few of the things that pandas does well:
- Easy handling of [**missing data**](https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html) (represented as `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
- Size mutability: columns can be [**inserted and deleted**](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion) from DataFrame and higher dimensional objects
- Automatic and explicit [**data alignment**](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures): 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**](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine) functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
- Make it [**easy to convert**](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe) ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
- Intelligent label-based [**slicing**](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges), [**fancy indexing**](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced), and [**subsetting**](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing) of large data sets
- Intuitive [**merging**](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging) and [**joining**](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index) data sets
- Flexible [**reshaping**](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html) and [**pivoting**](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html) of data sets
- [**Hierarchical**](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex) labeling of axes (possible to have multiple labels per tick)
- Robust I/O tools for loading data from [**flat files**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files) (CSV and delimited), [**Excel files**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files), [**databases**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries), and saving/loading data from the ultrafast [**HDF5 format**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables)
- [**Time series**](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality)\-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)](https://pypi.org/project/pandas) and on [Conda](https://anaconda.org/conda-forge/pandas).
```
# 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](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). For full details, see the commit logs at <https://github.com/pandas-dev/pandas>.
## Dependencies
- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org/)
- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
- [tzdata - Provides an IANA time zone database](https://tzdata.readthedocs.io/en/latest/) (Only required on Windows/Emscripten)
See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.
## Installation from sources
To install pandas from source you need [Cython](https://cython.org/) 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](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
```
python -m pip install -ve . --no-build-isolation --config-settings editable-verbose=true
```
See the full instructions for [installing from source](https://pandas.pydata.org/docs/dev/development/contributing_environment.html).
## License
[BSD 3](https://pypi.org/project/pandas/LICENSE)
## Documentation
The official documentation is hosted on [PyData.org](https://pandas.pydata.org/pandas-docs/stable/).
## Background
Work on `pandas` started at [AQR](https://www.aqr.com/) (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](https://stackoverflow.com/questions/tagged/pandas). Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
## Discussion and Development
Most development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https://github.com/pandas-dev/pandas/issues).
Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions.
There are also frequent [community meetings](https://pandas.pydata.org/docs/dev/development/community.html#community-meeting) for project maintainers open to the community as well as monthly [new contributor meetings](https://pandas.pydata.org/docs/dev/development/community.html#new-contributor-meeting) to help support new contributors.
Additional information on the communication channels can be found on the [contributor community](https://pandas.pydata.org/docs/development/community.html) page.
## Contributing to pandas
[](https://www.codetriage.com/pandas-dev/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](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?q=is%3Aissue%20state%3Aopen%20label%3ADocs%20sort%3Aupdated-desc) and [good first issue](https://github.com/pandas-dev/pandas/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22%20sort%3Aupdated-desc) 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](https://www.codetriage.com/pandas-dev/pandas).
Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking āthis can be improvedā... you can do something about it\!
Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-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](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)
***
[Go to Top](https://pypi.org/project/pandas/#table-of-contents)
## Project details
### Verified details
*These details have been [verified by PyPI](https://docs.pypi.org/project_metadata/#verified-details)*
###### Project links
- [repository](https://github.com/pandas-dev/pandas)
###### GitHub Statistics
- [**Repository**](https://github.com/pandas-dev/pandas)
- [**Stars:** 48369](https://github.com/pandas-dev/pandas/stargazers)
- [**Forks:** 19835](https://github.com/pandas-dev/pandas/network/members)
- [**Open issues:** 3210](https://github.com/pandas-dev/pandas/issues)
- [**Open PRs:** 269](https://github.com/pandas-dev/pandas/pulls)
###### Maintainers
[ jorisvandenbossche](https://pypi.org/user/jorisvandenbossche/) [ jreback](https://pypi.org/user/jreback/) [ jreback1](https://pypi.org/user/jreback1/) [ mroeschke](https://pypi.org/user/mroeschke/)
### Unverified details
*These details have **not** been verified by PyPI*
###### Project links
- [documentation](https://pandas.pydata.org/docs/)
- [homepage](https://pandas.pydata.org/)
###### Meta
- **License:** BSD License (BSD 3-Clause License)
- **Author:** [The Pandas Development Team](mailto:pandas-dev@python.org)
- **Requires:** Python \>=3.11
- **Provides-Extra:** `test` , `pyarrow` , `performance` , `computation` , `fss` , `aws` , `gcp` , `excel` , `parquet` , `feather` , `iceberg` , `hdf5` , `spss` , `postgresql` , `mysql` , `sql-other` , `html` , `xml` , `plot` , `output-formatting` , `clipboard` , `compression` , `timezone` , `all`
###### Classifiers
- **Development Status**
- [5 - Production/Stable](https://pypi.org/search/?c=Development+Status+%3A%3A+5+-+Production%2FStable)
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- [Console](https://pypi.org/search/?c=Environment+%3A%3A+Console)
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- [Science/Research](https://pypi.org/search/?c=Intended+Audience+%3A%3A+Science%2FResearch)
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- [OSI Approved :: BSD License](https://pypi.org/search/?c=License+%3A%3A+OSI+Approved+%3A%3A+BSD+License)
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- [Cython](https://pypi.org/search/?c=Programming+Language+%3A%3A+Cython)
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- **Topic**
- [Scientific/Engineering](https://pypi.org/search/?c=Topic+%3A%3A+Scientific%2FEngineering)
## Release history [Release notifications](https://pypi.org/help/#project-release-notifications) \| [RSS feed](https://pypi.org/rss/project/pandas/releases.xml)
This version
[3\.0.2 Mar 31, 2026](https://pypi.org/project/pandas/3.0.2/)
[3\.0.1 Feb 17, 2026](https://pypi.org/project/pandas/3.0.1/)
[3\.0.0 Jan 21, 2026](https://pypi.org/project/pandas/3.0.0/)
[3\.0.0rc2 pre-release Jan 14, 2026](https://pypi.org/project/pandas/3.0.0rc2/)
[3\.0.0rc1 pre-release Dec 19, 2025](https://pypi.org/project/pandas/3.0.0rc1/)
[3\.0.0rc0 pre-release Dec 3, 2025](https://pypi.org/project/pandas/3.0.0rc0/)
[2\.3.3 Sep 29, 2025](https://pypi.org/project/pandas/2.3.3/)
[2\.3.2 Aug 21, 2025](https://pypi.org/project/pandas/2.3.2/)
[2\.3.1 Jul 7, 2025](https://pypi.org/project/pandas/2.3.1/)
[2\.3.0 Jun 5, 2025](https://pypi.org/project/pandas/2.3.0/)
[2\.2.3 Sep 20, 2024](https://pypi.org/project/pandas/2.2.3/)
[2\.2.2 Apr 10, 2024](https://pypi.org/project/pandas/2.2.2/)
[2\.2.1 Feb 23, 2024](https://pypi.org/project/pandas/2.2.1/)
[2\.2.0 Jan 20, 2024](https://pypi.org/project/pandas/2.2.0/)
[2\.1.4 Dec 8, 2023](https://pypi.org/project/pandas/2.1.4/)
[2\.1.3 Nov 10, 2023](https://pypi.org/project/pandas/2.1.3/)
[2\.1.2 Oct 26, 2023](https://pypi.org/project/pandas/2.1.2/)
[2\.1.1 Sep 20, 2023](https://pypi.org/project/pandas/2.1.1/)
[2\.1.0 Aug 30, 2023](https://pypi.org/project/pandas/2.1.0/)
[2\.0.3 Jun 28, 2023](https://pypi.org/project/pandas/2.0.3/)
[2\.0.2 May 29, 2023](https://pypi.org/project/pandas/2.0.2/)
[2\.0.1 Apr 24, 2023](https://pypi.org/project/pandas/2.0.1/)
[2\.0.0 Apr 3, 2023](https://pypi.org/project/pandas/2.0.0/)
[1\.5.3 Jan 19, 2023](https://pypi.org/project/pandas/1.5.3/)
[1\.5.2 Nov 23, 2022](https://pypi.org/project/pandas/1.5.2/)
[1\.5.1 Oct 19, 2022](https://pypi.org/project/pandas/1.5.1/)
[1\.5.0 Sep 19, 2022](https://pypi.org/project/pandas/1.5.0/)
[1\.4.4 Aug 31, 2022](https://pypi.org/project/pandas/1.4.4/)
[1\.4.3 Jun 23, 2022](https://pypi.org/project/pandas/1.4.3/)
[1\.4.2 Apr 2, 2022](https://pypi.org/project/pandas/1.4.2/)
[1\.4.1 Feb 12, 2022](https://pypi.org/project/pandas/1.4.1/)
[1\.4.0 Jan 22, 2022](https://pypi.org/project/pandas/1.4.0/)
[1\.3.5 Dec 12, 2021](https://pypi.org/project/pandas/1.3.5/)
[1\.3.4 Oct 17, 2021](https://pypi.org/project/pandas/1.3.4/)
[1\.3.3 Sep 12, 2021](https://pypi.org/project/pandas/1.3.3/)
[1\.3.2 Aug 15, 2021](https://pypi.org/project/pandas/1.3.2/)
[1\.3.1 Jul 25, 2021](https://pypi.org/project/pandas/1.3.1/)
[1\.3.0 Jul 2, 2021](https://pypi.org/project/pandas/1.3.0/)
[1\.2.5 Jun 22, 2021](https://pypi.org/project/pandas/1.2.5/)
[1\.2.4 Apr 12, 2021](https://pypi.org/project/pandas/1.2.4/)
[1\.2.3 Mar 2, 2021](https://pypi.org/project/pandas/1.2.3/)
[1\.2.2 Feb 9, 2021](https://pypi.org/project/pandas/1.2.2/)
[1\.2.1 Jan 20, 2021](https://pypi.org/project/pandas/1.2.1/)
[1\.2.0 Dec 26, 2020](https://pypi.org/project/pandas/1.2.0/)
[1\.1.5 Dec 7, 2020](https://pypi.org/project/pandas/1.1.5/)
[1\.1.4 Oct 30, 2020](https://pypi.org/project/pandas/1.1.4/)
[1\.1.3 Oct 5, 2020](https://pypi.org/project/pandas/1.1.3/)
[1\.1.2 Sep 8, 2020](https://pypi.org/project/pandas/1.1.2/)
[1\.1.1 Aug 20, 2020](https://pypi.org/project/pandas/1.1.1/)
[1\.1.0 Jul 28, 2020](https://pypi.org/project/pandas/1.1.0/)
[1\.0.5 Jun 18, 2020](https://pypi.org/project/pandas/1.0.5/)
[1\.0.4 May 29, 2020](https://pypi.org/project/pandas/1.0.4/)
[1\.0.3 Mar 18, 2020](https://pypi.org/project/pandas/1.0.3/)
[1\.0.2 Mar 15, 2020](https://pypi.org/project/pandas/1.0.2/)
[1\.0.1 Feb 5, 2020](https://pypi.org/project/pandas/1.0.1/)
[1\.0.0 Jan 30, 2020](https://pypi.org/project/pandas/1.0.0/)
[0\.25.3 Nov 1, 2019](https://pypi.org/project/pandas/0.25.3/)
[0\.25.2 Oct 19, 2019](https://pypi.org/project/pandas/0.25.2/)
[0\.25.1 Aug 22, 2019](https://pypi.org/project/pandas/0.25.1/)
[0\.25.0 Jul 19, 2019](https://pypi.org/project/pandas/0.25.0/)
[0\.24.2 Mar 14, 2019](https://pypi.org/project/pandas/0.24.2/)
[0\.24.1 Feb 4, 2019](https://pypi.org/project/pandas/0.24.1/)
[0\.24.0 Jan 25, 2019](https://pypi.org/project/pandas/0.24.0/)
[0\.23.4 Aug 4, 2018](https://pypi.org/project/pandas/0.23.4/)
[0\.23.3 Jul 7, 2018](https://pypi.org/project/pandas/0.23.3/)
[0\.23.2 Jul 6, 2018](https://pypi.org/project/pandas/0.23.2/)
[0\.23.1 Jun 13, 2018](https://pypi.org/project/pandas/0.23.1/)
[0\.23.0 May 16, 2018](https://pypi.org/project/pandas/0.23.0/)
[0\.22.0 Dec 31, 2017](https://pypi.org/project/pandas/0.22.0/)
[0\.21.1 Dec 13, 2017](https://pypi.org/project/pandas/0.21.1/)
[0\.21.0 Oct 28, 2017](https://pypi.org/project/pandas/0.21.0/)
[0\.20.3 Jul 7, 2017](https://pypi.org/project/pandas/0.20.3/)
[0\.20.2 Jun 5, 2017](https://pypi.org/project/pandas/0.20.2/)
[0\.20.1 May 5, 2017](https://pypi.org/project/pandas/0.20.1/)
[0\.20.0 May 5, 2017](https://pypi.org/project/pandas/0.20.0/)
[0\.19.2 Dec 24, 2016](https://pypi.org/project/pandas/0.19.2/)
[0\.19.1 Nov 3, 2016](https://pypi.org/project/pandas/0.19.1/)
[0\.19.0 Oct 2, 2016](https://pypi.org/project/pandas/0.19.0/)
[0\.18.1 May 5, 2016](https://pypi.org/project/pandas/0.18.1/)
[0\.18.0 Mar 12, 2016](https://pypi.org/project/pandas/0.18.0/)
[0\.17.1 Nov 20, 2015](https://pypi.org/project/pandas/0.17.1/)
[0\.17.0 Oct 9, 2015](https://pypi.org/project/pandas/0.17.0/)
[0\.16.2 Jun 13, 2015](https://pypi.org/project/pandas/0.16.2/)
[0\.16.1 May 11, 2015](https://pypi.org/project/pandas/0.16.1/)
[0\.16.0 Mar 22, 2015](https://pypi.org/project/pandas/0.16.0/)
[0\.15.2 Dec 11, 2014](https://pypi.org/project/pandas/0.15.2/)
[0\.15.1 Nov 8, 2014](https://pypi.org/project/pandas/0.15.1/)
[0\.15.0 Oct 19, 2014](https://pypi.org/project/pandas/0.15.0/)
[0\.14.1 Jul 10, 2014](https://pypi.org/project/pandas/0.14.1/)
[0\.14.0 May 30, 2014](https://pypi.org/project/pandas/0.14.0/)
[0\.13.1 Feb 9, 2014](https://pypi.org/project/pandas/0.13.1/)
[0\.13.0 Jan 14, 2014](https://pypi.org/project/pandas/0.13.0/)
[0\.12.0 Jul 24, 2013](https://pypi.org/project/pandas/0.12.0/)
[0\.11.0 Apr 23, 2013](https://pypi.org/project/pandas/0.11.0/)
[0\.10.1 Jan 22, 2013](https://pypi.org/project/pandas/0.10.1/)
[0\.10.0 Dec 17, 2012](https://pypi.org/project/pandas/0.10.0/)
[0\.9.1 Nov 15, 2012](https://pypi.org/project/pandas/0.9.1/)
[0\.9.0 Oct 8, 2012](https://pypi.org/project/pandas/0.9.0/)
[0\.8.1 Jul 22, 2012](https://pypi.org/project/pandas/0.8.1/)
[0\.8.0 Jun 29, 2012](https://pypi.org/project/pandas/0.8.0/)
[0\.7.3 Apr 12, 2012](https://pypi.org/project/pandas/0.7.3/)
[0\.7.2 Mar 16, 2012](https://pypi.org/project/pandas/0.7.2/)
[0\.7.1 Feb 29, 2012](https://pypi.org/project/pandas/0.7.1/)
[0\.7.0 Feb 9, 2012](https://pypi.org/project/pandas/0.7.0/)
[0\.6.1 Dec 13, 2011](https://pypi.org/project/pandas/0.6.1/)
[0\.6.0 Nov 26, 2011](https://pypi.org/project/pandas/0.6.0/)
[0\.5.0 Oct 25, 2011](https://pypi.org/project/pandas/0.5.0/)
[0\.4.3 Oct 9, 2011](https://pypi.org/project/pandas/0.4.3/)
[0\.4.2 Oct 3, 2011](https://pypi.org/project/pandas/0.4.2/)
[0\.4.1 Sep 26, 2011](https://pypi.org/project/pandas/0.4.1/)
[0\.4.0 Sep 12, 2011](https://pypi.org/project/pandas/0.4.0/)
[0\.3.0 Feb 20, 2011](https://pypi.org/project/pandas/0.3.0/)
[0\.2 May 18, 2010](https://pypi.org/project/pandas/0.2/)
[0\.1 Dec 25, 2009](https://pypi.org/project/pandas/0.1/)
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Details for the file `pandas-3.0.2.tar.gz`.
### File metadata
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- Upload date:
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[See more details on using hashes here.](https://pip.pypa.io/en/stable/topics/secure-installs/#hash-checking-mode "External link")
### Provenance
The following attestation bundles were made for `pandas-3.0.2.tar.gz`:
Publisher: [`wheels.yml` on pandas-dev/pandas](https://github.com/pandas-dev/pandas/blob/HEAD/.github/workflows/wheels.yml)
Attestations:
*Values shown here reflect the state when the release was signed and may no longer be current.*
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Source repository:
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- Branch / Tag: [`refs/tags/v3.0.2`](https://github.com/pandas-dev/pandas/tree/refs/tags/v3.0.2)
- Owner: <https://github.com/pandas-dev>
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Details for the file `pandas-3.0.2-cp314-cp314t-win_arm64.whl`.
### File metadata
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Mar 31, 2026
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[See more details on using hashes here.](https://pip.pypa.io/en/stable/topics/secure-installs/#hash-checking-mode "External link")
### Provenance
The following attestation bundles were made for `pandas-3.0.2-cp314-cp314t-win_arm64.whl`:
Publisher: [`wheels.yml` on pandas-dev/pandas](https://github.com/pandas-dev/pandas/blob/HEAD/.github/workflows/wheels.yml)
Attestations:
*Values shown here reflect the state when the release was signed and may no longer be current.*
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Mar 31, 2026, 6:45:52 AM
Source repository:
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- Branch / Tag: [`refs/tags/v3.0.2`](https://github.com/pandas-dev/pandas/tree/refs/tags/v3.0.2)
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### Provenance
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### Provenance
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### Provenance
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### Provenance
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### Provenance
The following attestation bundles were made for `pandas-3.0.2-cp312-cp312-win_arm64.whl`:
Publisher: [`wheels.yml` on pandas-dev/pandas](https://github.com/pandas-dev/pandas/blob/HEAD/.github/workflows/wheels.yml)
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### Provenance
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### Provenance
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### Provenance
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Source repository:
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***
## pandas: A Powerful Python Data Analysis Toolkit
| | |
|---|---|
| Testing | [](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml) [](https://codecov.io/gh/pandas-dev/pandas) |
| Package | [](https://pypi.org/project/pandas/) [](https://pypi.org/project/pandas/) [](https://anaconda.org/conda-forge/pandas) [](https://anaconda.org/conda-forge/pandas) |
| Meta | [](https://numfocus.org/) [](https://doi.org/10.5281/zenodo.3509134) [](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) [](https://insights.linuxfoundation.org/project/pandas-dev-pandas) |
## 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](https://pypi.org/project/pandas/#main-features)
- [Where to get it](https://pypi.org/project/pandas/#where-to-get-it)
- [Dependencies](https://pypi.org/project/pandas/#dependencies)
- [Installation from sources](https://pypi.org/project/pandas/#installation-from-sources)
- [License](https://pypi.org/project/pandas/#license)
- [Documentation](https://pypi.org/project/pandas/#documentation)
- [Background](https://pypi.org/project/pandas/#background)
- [Getting Help](https://pypi.org/project/pandas/#getting-help)
- [Discussion and Development](https://pypi.org/project/pandas/#discussion-and-development)
- [Contributing to pandas](https://pypi.org/project/pandas/#contributing-to-pandas)
## Main Features
Here are just a few of the things that pandas does well:
- Easy handling of [**missing data**](https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html) (represented as `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
- Size mutability: columns can be [**inserted and deleted**](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion) from DataFrame and higher dimensional objects
- Automatic and explicit [**data alignment**](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures): 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**](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine) functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
- Make it [**easy to convert**](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe) ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
- Intelligent label-based [**slicing**](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges), [**fancy indexing**](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced), and [**subsetting**](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing) of large data sets
- Intuitive [**merging**](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging) and [**joining**](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index) data sets
- Flexible [**reshaping**](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html) and [**pivoting**](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html) of data sets
- [**Hierarchical**](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex) labeling of axes (possible to have multiple labels per tick)
- Robust I/O tools for loading data from [**flat files**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files) (CSV and delimited), [**Excel files**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files), [**databases**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries), and saving/loading data from the ultrafast [**HDF5 format**](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables)
- [**Time series**](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality)\-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)](https://pypi.org/project/pandas) and on [Conda](https://anaconda.org/conda-forge/pandas).
```
# 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](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). For full details, see the commit logs at <https://github.com/pandas-dev/pandas>.
## Dependencies
- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org/)
- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
- [tzdata - Provides an IANA time zone database](https://tzdata.readthedocs.io/en/latest/) (Only required on Windows/Emscripten)
See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.
## Installation from sources
To install pandas from source you need [Cython](https://cython.org/) 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](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
```
python -m pip install -ve . --no-build-isolation --config-settings editable-verbose=true
```
See the full instructions for [installing from source](https://pandas.pydata.org/docs/dev/development/contributing_environment.html).
## License
[BSD 3](https://pypi.org/project/pandas/LICENSE)
## Documentation
The official documentation is hosted on [PyData.org](https://pandas.pydata.org/pandas-docs/stable/).
## Background
Work on `pandas` started at [AQR](https://www.aqr.com/) (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](https://stackoverflow.com/questions/tagged/pandas). Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
## Discussion and Development
Most development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https://github.com/pandas-dev/pandas/issues).
Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions.
There are also frequent [community meetings](https://pandas.pydata.org/docs/dev/development/community.html#community-meeting) for project maintainers open to the community as well as monthly [new contributor meetings](https://pandas.pydata.org/docs/dev/development/community.html#new-contributor-meeting) to help support new contributors.
Additional information on the communication channels can be found on the [contributor community](https://pandas.pydata.org/docs/development/community.html) page.
## Contributing to pandas
[](https://www.codetriage.com/pandas-dev/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](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?q=is%3Aissue%20state%3Aopen%20label%3ADocs%20sort%3Aupdated-desc) and [good first issue](https://github.com/pandas-dev/pandas/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22%20sort%3Aupdated-desc) 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](https://www.codetriage.com/pandas-dev/pandas).
Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking āthis can be improvedā... you can do something about it\!
Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-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](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)
***
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