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‎README.md

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<picture align="center">
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<source media="(prefers-color-scheme: dark)" srcset="https://pandas.pydata.org/static/img/pandas_white.svg">
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<img alt="Pandas Logo" src="https://pandas.pydata.org/static/img/pandas.svg">
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</picture>
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-----------------
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# pandas: A Powerful Python Data Analysis Toolkit
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| | |
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| --- | --- |
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| 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) |
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| 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) |
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| 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) |
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## What is it?
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**pandas** is a Python package that provides fast, flexible, and expressive data
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structures designed to make working with "relational" or "labeled" data both
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easy and intuitive. It aims to be the fundamental high-level building block for
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doing practical, **real-world** data analysis in Python. Additionally, it has
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the broader goal of becoming **the most powerful and flexible open-source data
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analysis/manipulation tool available in any language**. It is already well on
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its way towards this goal.
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## Table of Contents
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- [Main Features](#main-features)
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- [Where to get it](#where-to-get-it)
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- [Dependencies](#dependencies)
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- [Installation from sources](#installation-from-sources)
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- [License](#license)
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- [Documentation](#documentation)
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- [Background](#background)
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- [Getting Help](#getting-help)
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- [Discussion and Development](#discussion-and-development)
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- [Contributing to pandas](#contributing-to-pandas)
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## Main Features
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Here are just a few of the things that pandas does well:
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- Easy handling of [**missing data**][missing-data] (represented as
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`NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
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- Size mutability: columns can be [**inserted and
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deleted**][insertion-deletion] from DataFrame and higher dimensional
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objects
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- Automatic and explicit [**data alignment**][alignment]: objects can
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be explicitly aligned to a set of labels, or the user can simply
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ignore the labels and let `Series`, `DataFrame`, etc. automatically
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align the data for you in computations
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- Powerful, flexible [**group by**][groupby] functionality to perform
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split-apply-combine operations on data sets, for both aggregating
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and transforming data
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- Make it [**easy to convert**][conversion] ragged,
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differently-indexed data in other Python and NumPy data structures
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into DataFrame objects
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- Intelligent label-based [**slicing**][slicing], [**fancy
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indexing**][fancy-indexing], and [**subsetting**][subsetting] of
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large data sets
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- Intuitive [**merging**][merging] and [**joining**][joining] data
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sets
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- Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
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data sets
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- [**Hierarchical**][mi] labeling of axes (possible to have multiple
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labels per tick)
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- Robust I/O tools for loading data from [**flat files**][flat-files]
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(CSV and delimited), [**Excel files**][excel], [**databases**][db],
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and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
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- [**Time series**][timeseries]-specific functionality: date range
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generation and frequency conversion, moving window statistics,
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date shifting and lagging
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[missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
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[insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
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[alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
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[groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
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[conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
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[slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
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[fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
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[subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
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[merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
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[joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
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[reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
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[pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
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[mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
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[flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
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[excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
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[db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
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[hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
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[timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality
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## Where to get it
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The source code is currently hosted on GitHub at:
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https://github.com/pandas-dev/pandas
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Binary installers for the latest released version are available at the [Python
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Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://anaconda.org/conda-forge/pandas).
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```sh
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# conda
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<p align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://pandas.pydata.org/static/img/pandas_white.svg">
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<img alt="Pandas Logo" src="https://pandas.pydata.org/static/img/pandas.svg" width="300">
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</picture>
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</p>
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---
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# 🐼 pandas: Python Data Analysis Made Easy
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| Category | Badges |
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| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| **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) |
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| **Package** | [![PyPI Version](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 Version](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) |
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| **Meta** | [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg)](https://numfocus.org) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![License](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#community-slack) |
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---
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## 🧐 What is pandas?
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**pandas** is a popular Python library for working with **structured data** (like tables or spreadsheets). It's built to make **data cleaning**, **analysis**, and **manipulation** easy and powerful.
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If you’ve used Excel or SQL, pandas gives you similar tools — right inside Python.
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It helps you:
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* Load data from CSV, Excel, or databases
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* Clean up messy data
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* Analyze it with stats and math
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* Visualize trends and patterns (when used with other libraries like Matplotlib)
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> pandas is widely used in data science, machine learning, finance, and research.
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---
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## 📚 Table of Contents
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* [Main Features](#main-features)
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* [Installation](#installation)
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* [Dependencies](#dependencies)
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* [Installing from Source](#installing-from-source)
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* [Documentation](#documentation)
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* [Getting Help](#getting-help)
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* [Contributing](#contributing)
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---
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## 🚀 Main Features
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Here’s what you can do with pandas:
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* Handle **missing data** easily
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* Add or remove columns in a table (called a *DataFrame*)
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* Automatically align data by labels
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* Group and summarize data with `groupby`
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* Convert lists or dictionaries into a table format
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* Select data by row, column, condition, or labels
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* Merge or join datasets together
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* Reshape data (pivot, stack, unstack)
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* Work with **multi-level indexes** (called *MultiIndex*)
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* Load data from CSV, Excel, SQL, HDF5, and more
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* Work with **date/time data** like a pro
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[📖 Learn more about each feature](https://pandas.pydata.org/docs/user_guide/index.html)
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---
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## 💻 Installation
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### 📦 Using conda (recommended for beginners):
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```bash
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conda install -c conda-forge pandas
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```
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```sh
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# or PyPI
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### 📦 Or using pip:
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```bash
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pip install pandas
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```
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The list of changes to pandas between each release can be found
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[here](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). For full
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details, see the commit logs at https://github.com/pandas-dev/pandas.
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[Release Notes & Updates](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html)
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---
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## 🔗 Where to Find pandas
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* **Source Code**: [GitHub - pandas-dev/pandas](https://github.com/pandas-dev/pandas)
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* **PyPI Package**: [pandas on PyPI](https://pypi.org/project/pandas/)
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* **Conda Package**: [pandas on conda-forge](https://anaconda.org/conda-forge/pandas)
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---
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## ⚙️ Dependencies
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To use pandas, you’ll need:
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* [NumPy](https://www.numpy.org): For fast numerical operations
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* [python-dateutil](https://dateutil.readthedocs.io): For working with dates
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* [tzdata](https://tzdata.readthedocs.io): Timezone support
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## Dependencies
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- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
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- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
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- [tzdata - Provides an IANA time zone database](https://tzdata.readthedocs.io/en/latest/)
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📄 [See full dependency info here](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies)
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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.
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---
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## Installation from sources
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To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
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dependencies above. Cython can be installed from PyPI:
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## 🛠️ Installing from Source
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```sh
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If you want to install the latest development version:
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### 1. Clone the repo:
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```bash
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git clone https://github.com/pandas-dev/pandas.git
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cd pandas
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```
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### 2. Install Cython (required):
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```bash
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pip install cython
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```
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In the `pandas` directory (same one where you found this file after
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cloning the git repo), execute:
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### 3. Install pandas from source:
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```sh
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```bash
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pip install .
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```
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or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
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### Or, for development mode:
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```sh
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```bash
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python -m pip install -ve . --no-build-isolation --config-settings editable-verbose=true
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```
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See the full instructions for [installing from source](https://pandas.pydata.org/docs/dev/development/contributing_environment.html).
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📄 [Full developer setup instructions](https://pandas.pydata.org/docs/dev/development/contributing_environment.html)
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---
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## 📖 Documentation
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* Official docs: [pandas.pydata.org/docs](https://pandas.pydata.org/pandas-docs/stable/)
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* Tutorials: [User Guide](https://pandas.pydata.org/docs/user_guide/index.html)
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---
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## License
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[BSD 3](LICENSE)
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## ❓ Getting Help
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## Documentation
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The official documentation is hosted on [PyData.org](https://pandas.pydata.org/pandas-docs/stable/).
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If you’re stuck, here are good places to ask questions:
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## Background
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Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
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has been under active development since then.
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* [Stack Overflow](https://stackoverflow.com/questions/tagged/pandas)
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* [pandas Slack](https://pandas.pydata.org/docs/dev/development/community.html#community-slack)
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* [PyData mailing list](https://groups.google.com/forum/#!forum/pydata)
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## Getting Help
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---
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For usage questions, the best place to go to is [Stack Overflow](https://stackoverflow.com/questions/tagged/pandas).
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Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
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## 🤝 Contributing to pandas
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## Discussion and Development
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Most development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https://github.com/pandas-dev/pandas/issues).
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We welcome **everyone** to help make pandas better!
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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.
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### Ways you can contribute:
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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.
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* Fix bugs
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* Improve documentation
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* Add new features
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* Review pull requests
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* Help answer questions
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Additional information on the communication channels can be found on the [contributor community](https://pandas.pydata.org/docs/development/community.html) page.
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🧭 Start by checking out:
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## Contributing to pandas
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* [Good First Issues](https://github.com/pandas-dev/pandas/issues?q=is%3Aissue+label%3A%22good+first+issue%22)
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* [Contribution Guide](https://pandas.pydata.org/docs/dev/development/contributing.html)
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[![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas)
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You can also [subscribe on CodeTriage](https://www.codetriage.com/pandas-dev/pandas) to help triage issues.
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All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
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📜 [Code of Conduct](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)
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A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
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---
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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.
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## 🏁 Background
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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).
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pandas was created in 2008 by Wes McKinney while working at [AQR Capital](https://www.aqr.com/) and has grown into one of the most widely-used tools in data science and analytics.
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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!
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---
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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).
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## 🧭 Want to Learn More?
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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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* [pandas Docs](https://pandas.pydata.org/pandas-docs/stable/)
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* [pandas YouTube Tutorials](https://www.youtube.com/results?search_query=pandas+python+tutorial)
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<hr>
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---
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[Go to Top](#table-of-contents)
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🔝 [Back to Top](#-pandas-python-data-analysis-made-easy)

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