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diff --git a/README.rst b/README.rst deleted file mode 100644 index 96344dc..0000000 --- a/README.rst +++ /dev/null @@ -1,182 +0,0 @@ -Tablib: format-agnostic tabular dataset library -=============================================== - -.. image:: https://jazzband.co/static/img/badge.svg - :target: https://jazzband.co/ - :alt: Jazzband - -.. image:: https://travis-ci.org/jazzband/tablib.svg?branch=master - :target: https://travis-ci.org/jazzband/tablib - -:: - - _____ ______ ___________ ______ - __ /_______ ____ /_ ___ /___(_)___ /_ - _ __/_ __ `/__ __ \__ / __ / __ __ \ - / /_ / /_/ / _ /_/ /_ / _ / _ /_/ / - \__/ \__,_/ /_.___/ /_/ /_/ /_.___/ - - - -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 <https://cash.me/$KennethReitz>`_. 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 <https://pypi.python.org/pypi/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_. - - - -.. _`the repository`: http://github.com/jazzband/tablib -.. _AUTHORS: http://github.com/jazzband/tablib/blob/master/AUTHORS |