pandas: powerful Python data analysis toolkit

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: http://github.com/pydata/pandas

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

http://pypi.python.org/pypi/pandas/

And via easy_install or pip:

easy_install pandas
pip install pandas

Dependencies

NumPy: 1.6.1 or higher

python-dateutil 1.5 or higher

pytz

Needed for time zone support with date_range

Highly Recommended Dependencies

numexpr: to accelerate some expression evaluation operations

also required by PyTables

bottleneck: to accelerate certain numerical operations

Optional dependencies

Cython: Only necessary to build development version. Version 0.17.1 or higher.

SciPy: miscellaneous statistical functions

PyTables: necessary for HDF5-based storage

matplotlib: for plotting

statsmodels

Needed for parts of :mod:`pandas.stats`

openpyxl, xlrd/xlwt

openpyxl version 1.6.1 or higher

Needed for Excel I/O

Installation from sources

To install pandas from source you need cython in addition to the normal dependencies above, which 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 setup.py install

or for installing in development mode:

python setup.py develop

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

pip install -e .

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

python setup.py build --compiler=mingw32
python setup.py install

See http://pandas.pydata.org/ for more information.

License

BSD

Documentation

The official documentation is hosted on PyData.org: http://pandas.pydata.org/

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.

Background

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:

http://groups.google.com/group/pystatsmodels

QuocTran/pandas