Pandas

pandas is a Python package that provides 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 towards this goal. Main Features Here are just a few of the things that pandas does well: Easy handling of [ missing data ][missing-data] (represented as NaN , NA , or NaT ) in floating point as well as non-floating point data Size mutability: columns can be [ inserted and deleted ][insertion-deletion] from DataFrame and higher dimensional objects Automatic and explicit [ data alignment ][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 ][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data Make it [ easy to convert ][conversion] ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects Intelligent label-based [ slicing ][slicing], [ fancy indexing ][fancy-indexing], and [ subsetting ][subsetting] of large data sets Intuitive [ merging ][merging] and [ joining ][joining] data sets Flexible [ reshaping ][reshape] and [ pivoting ][pivot-table] of data sets [ Hierarchical ][mi] labeling of axes (possible to have multiple labels per tick) Robust I/O tools for loading data from [ flat files ][flat-files] (CSV and delimited), [ Excel files ][excel], [ databases ][db], and saving/loading data from the ultrafast [ HDF5 format ][hdfstore] [ Time series ][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging Binary installers for the latest released version are available at the Python Package Index (PyPI) and on Conda. The list of changes to pandas between each release can be found here. For full details, see the commit logs at See the full installation instructions for minimum supported versions of required, recommended and optional dependencies. Installation from sources To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from PyPI: Background Work on pandas started at AQR (a quantitative hedge fund) in 2008 and has been under active development since then. For usage questions, the best place to go to is Stack Overflow. Further, general questions and discussions can also take place on the pydata mailing list. Discussion and Development Most development discussions take place on GitHub in this repo, via the GitHub issue tracker. Further, the pandas-dev mailing list can also be used for specialized discussions or design issues, and a Slack channel is available for quick development related questions. There are also frequent community meetings for project maintainers open to the community as well as monthly new contributor meetings to help support new contributors. Additional information on the communication channels can be found on the contributor community page. All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’... you can do something about it!