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)
• Pandas DataFrames (Sets)
• HTML (Sets)
• Jira (Sets)
• TSV (Sets)
• ODS (Sets)
• CSV (Sets)
• DBF (Sets)

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

If you're interested in financially supporting Kenneth Reitz open source, consider visiting this link. Your support helps tremendously with sustainability of motivation, as Open Source is no longer part of my day job.

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.export('json'))
[
  {
    "last_name": "Adams",
    "age": 90,
    "first_name": "John"
  },
  {
    "last_name": "Ford",
    "age": 83,
    "first_name": "Henry"
  }
]

YAML!

>>> print(data.export('yaml'))
- {age: 90, first_name: John, last_name: Adams}
- {age: 83, first_name: Henry, last_name: Ford}

CSV...

>>> print(data.export('csv'))
first_name,last_name,age
John,Adams,90
Henry,Ford,83

EXCEL!

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

DBF!

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

Pandas DataFrame!

>>> print(data.export('df')):
      first_name last_name  age
0       John     Adams   90
1      Henry      Ford   83

It's that easy.

Installation

To install tablib, simply:

$ pip install tablib[pandas]

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.