
Tablib: Pythonic Tabular Datasets
*********************************

Release v0.9.11. (*Installation*)

Tablib is an *MIT Licensed* format-agnostic tabular dataset library,
written in Python. It allows you to import, export, and manipulate
tabular data sets. Advanced features include, segregation, dynamic
columns, tags & filtering, and seamless format import & export.

   >>> data = tablib.Dataset(headers=['First Name', 'Last Name', 'Age'])
   >>> map(data.append, [('Kenneth', 'Reitz', 22), ('Bessie', 'Monke', 21)])

   >>> data.json
   [{"Last Name": "Reitz", "First Name": "Kenneth", "Age": 22}, {"Last Name": "Monke", "First Name": "Bessie", "Age": 21}]

   >>> data.yaml
   - {Age: 22, First Name: Kenneth, Last Name: Reitz}
   - {Age: 21, First Name: Bessie, Last Name: Monke}

   >>> data.xlsx
   <censored binary data>


Testimonials
============

National Geographic, Digg, Inc, Northrop Grumman, Discovery Channel,
and The Sunlight Foundation use Tablib internally.

**Greg Thorton**
   Tablib by @kennethreitz saved my life. I had to consolidate like 5
   huge poorly maintained lists of domains and data. It was a breeze!

**Dave Coutts**
   It's turning into one of my most used modules of 2010. You really
   hit a sweat spot for managing tabular data with a minimal amount of
   code and effort.

**Joshua Ourisman**
   Tablib has made it so much easier to deal with the inevitable 'I
   want an Excel file!' requests from clients...

**Brad Montgomery**
   I think you nailed the "Python Zen" with tablib. Thanks again for
   an awesome lib!


User's Guide
============

This part of the documentation, which is mostly prose, begins with
some background information about Tablib, then focuses on step-by-step
instructions for getting the most out of your datasets.

* Introduction
  * Philosphy
  * MIT License
  * Tablib License
  * Pythons Supported
* Installation
  * Installing Tablib
  * Download the Source
* Quickstart
  * Creating a Dataset
  * Adding Rows
  * Adding Headers
  * Adding Columns
  * Exporting Data
  * Selecting Rows & Columns
  * Removing Rows & Columns
* Advanced Usage
  * Dynamic Columns
  * Filtering Datasets with Tags
  * Separators
* Development
  * Design Considerations
  * Source Control
  * Adding New Formats
  * Testing Tablib
  * Continuous Integration
  * Building the Docs

API Reference
=============

If you are looking for information on a specific function, class or
method, this part of the documentation is for you.

* API
  * Dataset Object
  * Databook Object
  * Functions
  * Exceptions