On Oct 5, 2009, at 5:22 PM, Elaine Angelino wrote:
> Hi there,
>
> We are writing to announce the release of "Tabular", a package of
> Python modules for working with tabular data.
>
> Tabular is a package of Python modules for working with tabular
> data. Its main object is the tabarray class, a data structure for
> holding and manipulating tabular data. By putting data into a
> tabarray object, you’ll get a representation of the data that is
> more flexible and powerful than a native Python representation. More
> specifically, tabarray provides:
>
> -- ultra-fast filtering, selection, and numerical analysis methods,
> using convenient Matlab-style matrix operation syntax
> -- spreadsheet-style operations, including row & column operations,
> 'sort', 'replace', 'aggregate', 'pivot', and 'join'
> -- flexible load and save methods for a variety of file formats,
> including delimited text (CSV), binary, and HTML
> -- helpful inference algorithms for determining formatting
> parameters and data types of input files
> -- support for hierarchical groupings of columns, both as data
> structures and file formats
>
> You can download Tabular from PyPI (
http://pypi.python.org/pypi/tabular/
> ) or alternatively clone our hg repository from bitbucket (
http://bitbucket.org/elaine/tabular/
> ). We also have posted tutorial-style Sphinx documentation (
http://www.parsemydata.com/tabular/
> ).
>
> The tabarray object is based on the record array object from the
> Numerical Python package (NumPy), and Tabular is built to interface
> well with NumPy in general. Our intended audience is two-fold: (1)
> Python users who, though they may not be familiar with NumPy, are in
> need of a way to work with tabular data, and (2) NumPy users who
> would like to do spreadsheet-style operations on top of their more
> "numerical" work.
>
> We hope that some of you find Tabular useful!
>
> Best,
>
> Elaine and Dan
>