Skip to content
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python HTML C Shell C++ R Other
Latest commit 70f79ce @jreback jreback CI: increase clone depth
Failed to load latest commit information.
LICENSES ENH: support for msgpack serialization/deserialization
asv_bench PERF: more flexible iso8601 parsing
bench CLN: cleanup up platform / python version checks. fix GB10151
ci STYLE: final flake8 fixes, add back check for travis-ci
conda.recipe CI: fixup windows builds
doc DOC: whatsnew updates
pandas DOC: Added deprecation to convert_objects docstring#12052
scripts Fix issue with script
vb_suite API: add DatetimeBlockTZ #8260
.binstar.yml update conda recipe to make import only tests
.coveragerc misc documentation, some work on rpy2 interface. near git migration
.gitattributes CI: use versioneer, for PEP440 version strings #9518
.gitignore PERF: add in numexpr to asv
.travis.yml CI: increase clone depth DOC: Linguistic edit to Contributing
LICENSE RLS: Version 0.10.0 final CI: use versioneer, for PEP440 version strings #9518
Makefile BLD: spring cleaning on Makefile CI: update appveyor to build conda packages as artifacts DOC: update to point to stable whatsnew
appveyor.yml CI: add in 3.4 build include all tags in add args to
setup.cfg PEP: pandas/core round 2 with yapf and add to setup.cfg REF: reorganize pandas/tests/
test.bat TST: add windows test.bat micro + nanosecond time support TST: and should skip network tests TST: and should skip network tests BLD: make work on OSX too TST: pass cmd line args to test scripts so can append -v etc
tox.ini COMPAT: drop suppport for python 2.6, #7718 CI: use versioneer, for PEP440 version strings #9518

pandas: powerful Python data analysis toolkit

Latest Release latest release
latest release
Package Status status
License license
Build Status travis build status
appveyor build status
Conda conda downloads
PyPI pypi downloads

What is it

pandas is a Python package providing 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 toward this goal.

Main Features

Here are just a few of the things that pandas does well:

  • Easy handling of missing data (represented as NaN) 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, moving window linear regressions, date shifting and lagging, etc.

Where to get it

The source code is currently hosted on GitHub at:

Binary installers for the latest released version are available at the Python package index

And via easy_install:

easy_install pandas

or pip:

pip install pandas

or conda:

conda install pandas


Highly Recommended Dependencies

  • numexpr
    • Needed to accelerate some expression evaluation operations
    • Required by PyTables
  • bottleneck
    • Needed to accelerate certain numerical operations

Optional dependencies

Notes about HTML parsing libraries

  • If you install BeautifulSoup4 you must install either lxml or html5lib or both. pandas.read_html will not work with only BeautifulSoup4 installed.
  • You are strongly encouraged to read HTML reading gotchas. It explains issues surrounding the installation and usage of the above three libraries.
  • You may need to install an older version of BeautifulSoup4:
    • Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and 32-bit Ubuntu/Debian
  • Additionally, if you're using Anaconda you should definitely read the gotchas about HTML parsing libraries
  • If you're on a system with apt-get you can do

    sudo apt-get build-dep python-lxml

    to get the necessary dependencies for installation of lxml. This will prevent further headaches down the line.

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:

python install

or for installing in development mode:

python develop

Alternatively, you can use pip if you want all the dependencies pulled in automatically (the -e option is for installing it in development mode):

pip install -e .

On Windows, you will need to install MinGW and execute:

python build --compiler=mingw32
python install

See for more information.




The official documentation is hosted on

The Sphinx documentation should provide a good starting point for learning how to use the library. Expect the docs to continue to expand as time goes on.


Work on pandas started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then.

Discussion and Development

Since pandas development is related to a number of other scientific Python projects, questions are welcome on the scipy-user mailing list. Specialized discussions or design issues should take place on the PyData mailing list / Google group:!forum/pydata

Something went wrong with that request. Please try again.