Prototyping numpy arrays with named axes for data management. Docs are available at URL below
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.. -*- rest -*-
.. vim:syntax=rest

 Datarray: Numpy arrays with named axes

Scientists, engineers, mathematicians and statisticians don't just work with
matrices; they often work with structured data, just like you'd find in a
table. However, functionality for this is missing from Numpy, and there are
efforts to create something to fill the void.  This is one of those efforts.

.. warning::

   This code is currently experimental, and its API *will* change!  It is meant
   to be a place for the community to understand and develop the right
   semantics and have a prototype implementation that will ultimately
   (hopefully) be folded back into Numpy.

Datarray provides a subclass of Numpy ndarrays that support:

- individual dimensions (axes) being labeled with meaningful descriptions
- labeled 'ticks' along each axis
- indexing and slicing by named axis
- indexing on any axis with the tick labels instead of only integers
- reduction operations (like .sum, .mean, etc) support named axis arguments
  instead of only integer indices.

Prior Art

At present, there is no accepted standard solution to dealing with tabular data
such as this. However, based on the following list of ad-hoc and proposal-level
implementations of something such as this, there is *definitely* a demand for
it.  For examples, in no particular order:

* [Tabular]( implements a
  spreadsheet-inspired datatype, with rows/columns, csv/etc. IO, and fancy
  tabular operations.

* [scikits.statsmodels]( sounded as
  though it had some features we'd like to eventually see implemented on top of
  something such as datarray, and [Skipper](
  seemed pretty interested in something like this himself.

* [scikits.timeseries]( also has a
  time-series-specific object that's somewhat reminiscent of labeled arrays.

* [pandas]( is based around a number of
  DataFrame-esque datatypes.

* [pydataframe]( is supposed to be a
  clone of R's data.frame.

* [larry](, or "labeled array," often comes up
  in discussions alongside pandas.

* [divisi]( includes labeled sparse and
  dense arrays.

* [pymvpa]( provides Dataset class
  encapsulating the data together with matching in length sets of
  attributes for the first two (samples and features) dimensions.
  Dataset is not a subclass of numpy array to allow other data
  structures (e.g. sparse matrices).

* [ptsa]( subclasses
  ndarray to provide attributes per dimensions aiming to ease
  slicing/indexing given the values of the axis attributes

Project Goals

1. Get something akin to this in the numpy core.

2. Stick to basic functionality such that projects like scikits.statsmodels and
pandas can use it as a base datatype.

3. Make an interface that allows for simple, pretty manipulation that doesn't
introduce confusion.

4. Oh, and make sure that the base numpy array is still accessible.


You can find our sources and single-click downloads:

* `Main repository`_ on Github.
* Documentation_ for all releases and current development tree.
* Download as a tar/zip file the `current trunk`_.
* Downloads of all `available releases`_.

.. _main repository:
.. _Documentation:
.. _current trunk:
.. _available releases: