Format agnostic tabular data library (XLS, JSON, YAML, CSV)
Project description
Tablib: format-agnostic tabular dataset library
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Tablib is a format-agnostic tabular dataset library, written in Python.
Output formats supported:
Excel (Sets + Books)
JSON (Sets + Books)
YAML (Sets + Books)
CSV (Sets)
Import formats supported:
JSON (Sets + Books)
YAML (Sets + Books)
CSV (Sets)
Note that tablib purposefully excludes XML support. It always will.
Overview
- tablib.Dataset()
A Dataset is a table of tabular data. It may or may not have a header row. They can be build and maniuplated as raw Python datatypes (Lists of tuples|dictonaries). Datasets can be imported from JSON, YAML, and CSV; they can be exported to Excel (XLS), JSON, YAML, and CSV.
- tablib.Databook()
A Databook is a set of Datasets. The most common form of a Databook is an Excel file with multiple spreadsheets. Databooks can be imported from JSON and YAML; they can be exported to Excel (XLS), JSON, and YAML.
Usage
Populate fresh data files:
headers = ('first_name', 'last_name')
data = [
('John', 'Adams'),
('George', 'Washington')
]
data = tablib.Dataset(*data, headers=headers)
Intelligently add new rows:
>>> data.append(('Henry', 'Ford'))
Intelligently add new columns:
>>> data.append(col=('age', 90, 67, 83))
Slice rows:
>>> print data[:2]
[('John', 'Adams', 90), ('George', 'Washington', 67)]
Slice columns by header:
>>> print data['first_name'] ['John', 'George', 'Henry']
Easily delete rows:
>>> del data[1]
Exports
Drumroll please………..
JSON!
>>> print data.json
[
{
"last_name": "Adams",
"age": 90,
"first_name": "John"
},
{
"last_name": "Ford",
"age": 83,
"first_name": "Henry"
}
]
YAML!
>>> print data.yaml
- {age: 90, first_name: John, last_name: Adams}
- {age: 83, first_name: Henry, last_name: Ford}
CSV…
>>> print data.csv first_name,last_name,age John,Adams,90 Henry,Ford,83
EXCEL!
>>> open('people.xls', 'wb').write(data.xls)
It’s that easy.
Imports!
JSON
>>> data.json = '[{"last_name": "Adams","age": 90,"first_name": "John"}]'
>>> print data[0]
('John', 'Adams', 90)
YAML
>>> data.yaml = '- {age: 90, first_name: John, last_name: Adams}'
>>> print data[0]
('John', 'Adams', 90)
CSV
>>> data.yaml = 'age, first_name, last_name\n90, John, Adams'
>>> print data[0]
('John', 'Adams', 90)
>>> print data.yaml
- {age: 90, first_name: John, last_name: Adams}
Installation
To install tablib, simply:
$ pip install tablib
Or, if you absolutely must:
$ easy_install tablib
Contribute
If you’d like to contribute, simply fork the repository, commit your changes to the develop branch (or branch off of it), and send a pull request. Make sure you add yourself to AUTHORS.
Roadmap
Release CLI Interface
Auto-detect import format
Add possible other exports (SQL?)
Ability to assign types to rows (set, regex=, &c.)
History
0.8.0 (2010-09-25)
New format plugin system!
Imports! ELEGANT Imports!
Tests. Lots of tests.
0.7.1 (2010-09-20)
Reverting methods back to properties.
Windows bug compenated in documentation.
0.7.0 (2010-09-20)
Renamed DataBook Databook for consistiency.
Export properties changed to methods (XLS filename / StringIO bug).
Optional Dataset.xls(path=’filename’) support (for writing on windows).
Added utf-8 on the worksheet level.
0.6.4 (2010-09-19)
Updated unicode export for XLS.
More exhaustive unit tests.
0.6.3 (2010-09-14)
Added Dataset.append() support for columns.
0.6.2 (2010-09-13)
Fixed Dataset.append() error on empty dataset.
Updated Dataset.headers property w/ validation.
Added Testing Fixtures.
0.6.1 (2010-09-12)
Packaging hotfixes.
0.6.0 (2010-09-11)
Public Release.
Export Support for XLS, JSON, YAML, and CSV.
DataBook Export for XLS, JSON, and YAML.
Python Dict Property Support.
