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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Merge pull request #6867 from jorisvandenbossche/sql-api

API: update SQL functional api (GH6300)
Octocat-spinner-32 LICENSES ENH: support for msgpack serialization/deserialization October 01, 2013
Octocat-spinner-32 bench DEPR: Deprecate DateRange [fix #6816] April 08, 2014
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Octocat-spinner-32 pandas TST: test new read_sql api April 18, 2014
Octocat-spinner-32 scripts DEPR: Deprecate DateRange [fix #6816] April 08, 2014
Octocat-spinner-32 vb_suite ENH: Use Float64HashTable for Float64Index backend April 13, 2014
Octocat-spinner-32 .coveragerc misc documentation, some work on rpy2 interface. near git migration September 19, 2010
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Octocat-spinner-32 TST: and should skip network tests October 19, 2013
Octocat-spinner-32 BLD: make work on OSX too September 09, 2013
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pandas: powerful Python data analysis toolkit

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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


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 pystatsmodels mailing list / Google group, where scikits.statsmodels and other libraries will also be discussed:

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