pandas 0.16.0

Powerful data structures for data analysis, time series,and statistics


Platform: Pypi

Language: Python

License: BSD-3-Clause

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pandas: powerful Python data analysis toolkit

Travis-CI Build Status

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

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GitHub Repository

pydata/pandas pydata/pandas

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

Language: Python

Created: August 24, 2010 01:37

Last updated: April 01, 2015 05:46

Last pushed: March 31, 2015 21:06

Size: 124 MB

Stars: 4,210

Forks: 1,505

Watchers: 359

Open issues: 1,361

Top Contributors

Wes McKinney jreback y-p Phillip Cloud jorisvandenbossche Adam Klein Jeff Tratner Chang She Andy Hayden Tom Augspurger Sinhrks orbitfold Skipper Seabold Wouter Overmeire Thomas Kluyver Stephan Hoyer Kieran O'Mahony bwignall behzadnouri immerrr again


  • 0.16.0 - March 22, 2015 13:45
  • 0.15.2 - January 23, 2015 11:30
  • 0.15.2 - December 11, 2014 17:13
  • 0.15.1 - November 08, 2014 22:31
  • 0.15.0 - October 19, 2014 12:27
  • 0.14.1 - July 28, 2014 12:49
  • 0.14.0 - May 30, 2014 19:46
  • 0.13.1 - February 09, 2014 22:21
  • 0.13.0 - January 16, 2014 23:10
  • 0.12.0 - July 24, 2013 20:23
See all 38 releases

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