2010-08-28 20:47:03 -04:00
2010-08-28 17:14:39 -04:00
2010-07-16 17:23:10 -04:00
2010-08-28 20:47:03 -04:00
2010-07-16 17:23:10 -04:00
2010-07-16 17:23:10 -04:00
2010-08-28 17:14:39 -04:00
2010-07-16 17:23:10 -04:00
2010-08-28 20:47:03 -04:00
MIT
2010-07-13 08:54:26 -04:00
2010-08-28 20:47:03 -04:00
2010-08-28 20:47:03 -04:00
2010-08-28 20:47:03 -04:00
2010-08-28 17:14:39 -04:00

**Tabbed is under active documentation-driven development.**

	_____         ______  ______        _________
	__  /_______ ____  /_ ___  /_ _____ ______  /
	_  __/_  __ `/__  __ \__  __ \_  _ \_  __  / 
	/ /_  / /_/ / _  /_/ /_  /_/ //  __// /_/ /  
	\__/  \__,_/  /_.___/ /_.___/ \___/ \__,_/   


Tabbed is a format-agnostic tabular dataset library, written in Python. 
It is a full python module which doubles as a CLI application for quick
dataset conversions. 

Formats supported:

  - JSON
  - YAML
  - Excel
  - CSV
  - HTML

Please note that tabbed _purposefully_ excludes XML support. It always will.


Features
--------

Convert datafile formats via API:

	tabbed.import(filename='data.csv').export('data.json')


Convert datafile formats via CLI:

	tabbed data.csv data.json
	
Convert data formats via CLI pipe interface:
	
	curl http://domain.dev/dataset.json | tabbed --to excel | gist -p
	
	
Populate fresh data files:
	
	headers = ('first_name', 'last_name', 'gpa')

	data = [
		('John', 'Adams', 4.0),
		('George', 'Washington', 2.6),
		('Henry', 'Ford', 2.3)
	]
	
	data = tabbed.Data(*data, headers=headers)

	# Establish file location and save
	data.save('test.xls')
	

Intelligently add new rows:

	data.addRow('Bob', 'Dylan')
	# >>> Warning: Existing column count is 3
	
	print data.headers
	# >>> ('first_name', 'last_name', 'gpa')
	

Slice rows:	

	print data[0:1]
	# >>> [('John', 'Adams', 4.0), ('George', 'Washington', 2.6)]
	

Slice columns by header:

	print data['first_name']
	# >>> ['John', 'George', 'Henry']
	

Manipulate rows by index:

	data.delRow(0)
	print data[0:1]
	# >>> [('George', 'Washington', 2.6), ('Henry', 'Ford', 2.3)]
	
	# Update saved file
	data.save()
	

Export to various formats:

	# Save copy as CSV
	data.export('backup.csv')
S
Description
Languages
Python 100%