Tabular

Tabular data can be easily represented in Python using the language’s native objects – e.g. by lists of tuples representing the records of the data set.    Though easy to create, these kind of representations typically do not enable important tabular data manipulations, like efficient column selection, matrix mathematics, or spreadsheet-style operations.

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’,  ‘a(chǎn)ggregate’, ‘pivot’, and ‘join’


• flexible load and save methods for a variety of file formats, including delimited text (CSV), binary, and HTML


• sophisticated 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

Note to NumPy Users:  The tabarray object is based on the ndarray object from the Numerical Python package (NumPy), and the Tabular package is built to interface well with NumPy in general.  In particular, users of NumPy can get many of the benefits of Tabular, e.g. the spreadsheet-style operations, without having replace their usual NumPy objects with tabarrays, since most of the useful functional pieces of Tabular are written to work directly on NumPy ndarrays and record arrays (see relationship to NumPy).