README

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 pandas: A Powerful Python Data Analysis Toolkit

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 What is it?

pandas is a Python package that provides fast, flexible, and expressive data
structures designed to make working with "relational" or "labeled" data both
easy and intuitive. It aims to be the fundamental high-level building block for
doing practical, real-world data analysis in Python. Additionally, it has
the broader goal of becoming the most powerful and flexible open-source data
analysis/manipulation tool available in any language. It is already well on
its way towards this goal.

 Table of Contents

- Main Features
- 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][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.htmlcolumn-selection-addition-deletion
   [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignmentintro-to-data-structures
   [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.htmlgroup-by-split-apply-combine
   [conversion]: https://pandas.pydata.o

Top issues

Discussions

Latest release

We are pleased to announce the release of pandas 3.1.1.
This is a patch release in the 3.1.x series and includes some regression fixes and bug fixes. We recommend that all users of the 3.1.x series upgrade to this version.

See the full whatsnew for a list of all the changes.

The release can be installed from PyPI:

    python -m pip install --upgrade pandas==3.1.

Or from conda-forge

    conda install -c conda-forge pandas=3.1

Please report any issues with the release on the pandas issue tracker.

Thanks to all the contributors who made this release possible.