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.
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:
And via easy_install or pip:
easy_install pandas pip install pandas
NumPy: 1.6.1 or higher
python-dateutil 1.5 or higher
- Needed for time zone support with date_range
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
- Needed for parts of :mod:`pandas.stats`
Installation from sources
In the pandas directory (same one where you found this file), execute:
python setup.py install
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.
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.
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: