# pandas > Powerful data structures for data analysis, time series, and statistics - **URL**: https://www.freshcrate.ai/projects/pandas - **Author**: pypi - **Category**: Frameworks - **Latest version**: `v3.0.3` (2026-05-11) - **License**: non-standard - **Source**: https://github.com/pandas-dev/pandas - **Homepage**: https://pypi.org/project/pandas/ - **Language**: Python - **GitHub**: 48,542 stars, 19,863 forks - **Registry**: pypi (`pandas`) - **Tags**: `pypi` ## Description Pandas Logo ----------------- # pandas: A Powerful Python Data Analysis Toolkit | | | | --- | --- | | Testing | [![CI - Test](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml) [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) | | Package | [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) [![PyPI Downloads](https://img.shields.io/pypi/dm/pandas.svg?label=PyPI%20downloads)](https://pypi.org/project/pandas/) [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/conda-forge/pandas) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pandas.svg?label=Conda%20downloads)](https://anaconda.org/conda-forge/pandas) | | Meta | [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![License - BSD 3-Clause](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [![Slack](https://img.shields.io/badge/join_Slack-information-brightgreen.svg?logo=slack)](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) [![LFX Health Score](https://insights.linuxfoundation.org/api/badge/health-score?project=pandas-dev-pandas)](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](#main-features) - [Where to get it](#where-to-get-it) - [Dependencies](#dependencies) - [Installation from sources](#installation-from-sources) - [License](#license) - [Documentation](#documentation) - [Background](#background) - [Getting Help](#getting-help) - [Discussion and Development](#discussion-and-development) - [Contributing to pandas](#contributing-to-pandas) ## Main Features Here are just a few of the things that pandas does well: - Easy handling of [**missing data**][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**][insertion-deletion] from DataFrame and higher dimensional objects - Automatic and explicit [**data alignment**][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**][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data - Make it [**easy to convert**][conversion] ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects - Intelligent label-based [**slicing**][slicing], [**fancy indexing**][fancy-indexing], and [**subsetting**][subsetting] of large data sets - Intuitive [**merging**][merging] and [**joining**][joining] data sets - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of data sets - [**Hierarchical**][mi] labeling of axes (possible to have multiple labels per tick) - Robust I/O tools for loading data from [**flat files**][flat-files] (CSV and delimited), [**Excel files**][excel], [**databases**][db], and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] - [**Time series**][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-c ## Recent releases | Version | Date | Urgency | Changes | | --- | --- | --- | --- | | `v3.0.3` | 2026-05-11 | High | We are pleased to announce the release of pandas 3.0.3. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/whatsnew/v3.0.3.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-forge | | `3.0.2` | 2026-04-21 | Low | Imported from PyPI (3.0.2) | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | | `v3.0.2` | 2026-03-30 | Medium | We are pleased to announce the release of pandas 3.0.2. This is a patch release in the 3.0.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.0.x series upgrade to this version. See the [full whatsnew](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.2.html) for a list of all the changes. Pandas 3.0 supports Python 3.11 and higher. The release can be installed from PyPI: python -m pip install --upgrade pandas==3.0.* Or from conda-fo | ## Citation - HTML: https://www.freshcrate.ai/projects/pandas - Markdown: https://www.freshcrate.ai/projects/pandas.md - Dependencies JSON: https://www.freshcrate.ai/api/projects/pandas/deps _Generated by freshcrate.ai. Indexes pypi releases for AI-agent ecosystem packages._