Tablib: format-agnostic tabular dataset library

https://travis-ci.org/kennethreitz/tablib.svg?branch=develop

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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)
HTML (Sets)
TSV (Sets)
OSD (Sets)
CSV (Sets)
DBF (Sets)

Note that tablib purposefully excludes XML support. It always will. (Note: This is a joke. Pull requests are welcome.)

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 manipulated as raw Python datatypes (Lists of tuples|dictionaries). Datasets can be imported from JSON, YAML, DBF, and CSV; they can be exported to XLSX, XLS, ODS, JSON, YAML, DBF, CSV, TSV, and HTML.
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 XLSX, XLS, ODS, 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((90, 67, 83), header='age')

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!

>>> with open('people.xls', 'wb') as f:
...     f.write(data.xls)

DBF!

>>> with open('people.dbf', 'wb') as f:
...     f.write(data.dbf)

It's that easy.

Installation

To install tablib, simply:

$ pip install tablib

Make sure to check out Tablib on PyPi!

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.

dylanroy/tablib