# Package overview

`pandas` consists of the following things

> - A set of labeled array data structures, the primary of which are Series and DataFrame
> - Index objects enabling both simple axis indexing and multi-level / hierarchical axis indexing
> - An integrated group by engine for aggregating and transforming data sets
> - Date range generation (date_range) and custom date offsets enabling the implementation of customized frequencies
> - Input/Output tools: loading tabular data from flat files (CSV, delimited, Excel 2003), and saving and loading pandas objects from the fast and efficient PyTables/HDF5 format.
> - Memory-efficient “sparse” versions of the standard data structures for storing data that is mostly missing or mostly constant (some fixed value)
> - Moving window statistics (rolling mean, rolling standard deviation, etc.)
> - Static and moving window linear and panel regression

## Data structures at a glance

Dimensions | Name | Description
--- | --- | ---
1 | Series | 1D labeled homogeneously-typed array
2 | DataFrame | General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns
3 | Panel | General 3D labeled, also size-mutable array

### Why more than 1 data structure?

The best way to think about the pandas data structures is as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container for DataFrame objects. We would like to be able to insert and remove objects from these containers in a dictionary-like fashion.

Also, we would like sensible default behaviors for the common API functions which take into account the typical orientation of time series and cross-sectional data sets. When using ndarrays to store 2- and 3-dimensional data, a burden is placed on the user to consider the orientation of the data set when writing functions; axes are considered more or less equivalent (except when C- or Fortran-contiguousness matters for performance). In pandas, the axes are intended to lend more semantic meaning to the data; i.e., for a particular data set there is likely to be a “right” way to orient the data. The goal, then, is to reduce the amount of mental effort required to code up data transformations in downstream functions.

For example, with tabular data (DataFrame) it is more semantically helpful to think of the index (the rows) and the columns rather than axis 0 and axis 1. And iterating through the columns of the DataFrame thus results in more readable code:

    for col in df.columns:
        series = df[col]
        # do something with series

## Mutability and copying of data

All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. The length of a Series cannot be changed, but, for example, columns can be inserted into a DataFrame. However, the vast majority of methods produce new objects and leave the input data untouched. In general, though, we like to favor immutability where sensible.

## Getting Support

The first stop for pandas issues and ideas is the Github Issue Tracker. If you have a general question, pandas community experts can answer through Stack Overflow.

Longer discussions occur on the developer mailing list, and commercial support inquiries for Lambda Foundry should be sent to: support@lambdafoundry.com

## Credits

pandas development began at AQR Capital Management in April 2008. It was open-sourced at the end of 2009. AQR continued to provide resources for development through the end of 2011, and continues to contribute bug reports today.

Since January 2012, Lambda Foundry, has been providing development resources, as well as commercial support, training, and consulting for pandas.

pandas is only made possible by a group of people around the world like you who have contributed new code, bug reports, fixes, comments and ideas. A complete list can be found on Github.

## Development Team

pandas is a part of the PyData project. The PyData Development Team is a collection of developers focused on the improvement of Python’s data libraries. The core team that coordinates development can be found on Github. If you’re interested in contributing, please visit the project website.

## License

    =======
    License
    =======

    pandas is distributed under a 3-clause ("Simplified" or "New") BSD
    license. Parts of NumPy, SciPy, numpydoc, bottleneck, which all have
    BSD-compatible licenses, are included. Their licenses follow the pandas
    license.

    pandas license
    ==============

    Copyright (c) 2011-2012, Lambda Foundry, Inc. and PyData Development Team
    All rights reserved.

    Copyright (c) 2008-2011 AQR Capital Management, LLC
    All rights reserved.

    Redistribution and use in source and binary forms, with or without
    modification, are permitted provided that the following conditions are
    met:

        * Redistributions of source code must retain the above copyright
           notice, this list of conditions and the following disclaimer.

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           copyright notice, this list of conditions and the following
           disclaimer in the documentation and/or other materials provided
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        * Neither the name of the copyright holder nor the names of any
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    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS
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    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

    About the Copyright Holders
    ===========================

    AQR Capital Management began pandas development in 2008. Development was
    led by Wes McKinney. AQR released the source under this license in 2009.
    Wes is now an employee of Lambda Foundry, and remains the pandas project
    lead.

    The PyData Development Team is the collection of developers of the PyData
    project. This includes all of the PyData sub-projects, including pandas. The
    core team that coordinates development on GitHub can be found here:
    http://github.com/pydata.

    Full credits for pandas contributors can be found in the documentation.

    Our Copyright Policy
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