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/http://pypi.python.org/pypi/tabular/) or alternatively clone our hg repository from bitbucket ( http://bitbucket.org/elaine/tabular/ 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 arrayhttp://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html?high...object from the Numerical Python package ( NumPy http://numpy.scipy.org/), 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