A utility library that provides a consistent interface for reading tabular data.
Project description
tabulator-py
============
`|Travis| <https://travis-ci.org/frictionlessdata/tabulator-py>`_
`|Coveralls| <https://coveralls.io/r/frictionlessdata/tabulator-py?branch=master>`_
`|PyPi| <https://pypi-hypernode.com/pypi/tabulator>`_
`|Gitter| <https://gitter.im/frictionlessdata/chat>`_
A utility library that provides a consistent interface for reading
tabular data.
Getting Started
---------------
Installation
~~~~~~~~~~~~
To get started (under development):
::
$ pip install tabulator
Simple interface
~~~~~~~~~~~~~~~~
Fast access to the table with ``topen`` (stands for ``table open``)
function:
::
from tabulator import topen, processors
with topen('path.csv', with_headers=True) as table:
for row in table:
print(row)
print(row.get('header'))
For the most use cases ``topen`` function is enough. It takes the
``source`` argument:
``<scheme>://path/to/file.<format>`` and uses corresponding ``Loader``
and ``Parser`` to open and start to iterate over the table. Also user
can pass ``scheme`` and ``format`` explicitly as function arguments. The
last ``topen`` argument is ``encoding`` - user can force Tabulator to
use encoding of choice to open the table.
Read more about ``topen`` -
`documentation <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/topen.py>`_.
Function ``topen`` returns ``Table`` instance. We use context manager to
call ``table.open()`` on enter and ``table.close()`` when we exit: -
table can be iterated like file-like object returning row by row - table
can be read row by bow using ``readrow`` method (it returns row tuple) -
table can be read into memory using ``read`` function (return list or
row tuples) with ``limit`` of output rows as parameter. - headers can be
accessed via ``headers`` property - table pointer can be set to start
via ``reset`` method.
Read more about ``Table`` -
`documentation <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/table.py>`_.
In the example above we use ``processors.Headers`` to extract headers
from the table (via ``with_headers=True`` shortcut). Processors is a
powerfull Tabulator concept. Parsed data goes thru pipeline of
processors to be updated before returning as table row.
Read more about ``Processor`` -
`documentation <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/processors/api.py>`_.
Read a processors tutorial -
`tutorial <https://github.com/frictionlessdata/tabulator-py/blob/master/docs/processors.md>`_.
Advanced interface
~~~~~~~~~~~~~~~~~~
To get full control over the process you can use more parameters. Below
all parts of Tabulator are presented:
::
from tabulator import topen, processors, loaders, parsers
table = topen('path.csv',
loader_options={'encondig': 'utf-8'},
parser_options={'delimeter': ',', quotechar: '|'},
loader_class=loaders.File,
parser_class=parsers.CSV,
iterator_class=CustomIterator,
table_class=CustomTable)
table.add_processor(processors.Headers(skip=1))
headers = table.headers
contents = table.read(limit=10)
print(headers, contents)
table.close()
Also ``Table`` class can be instantiated by user (see documentation).
But there is no difference between it and ``topen`` call with extended
list of parameters except ``topen`` also calls the ``table.open()``
method.
Design Overview
---------------
Tabulator uses modular architecture to be fully extensible and flexible.
It uses loosely coupled modules like ``Loader``, ``Parser`` and
``Processor`` to provide clear data flow.
.. figure:: docs/diagram.png
:align: center
:alt: diagram
diagram
Documentation
-------------
API documentation is presented as docstrings: - High-level: -
`topen <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/topen.py>`_
- Core elements: -
`Row <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/row.py>`_
-
`Table <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/table.py>`_
-
`Iterator <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/iterator.py>`_
- Plugin elements: - `Loader
API <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/loaders/api.py>`_
- `Parser
API <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/parsers/api.py>`_
- `Processor
API <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/processors/api.py>`_
Contributing
------------
Please read the contribution guideline:
`How to Contribute <CONTRIBUTING.md>`_
Thanks!
.. |Travis| image:: https://img.shields.io/travis/frictionlessdata/tabulator-py/master.svg
.. |Coveralls| image:: http://img.shields.io/coveralls/frictionlessdata/tabulator-py.svg?branch=master
.. |PyPi| image:: https://img.shields.io/pypi/v/tabulator.svg
.. |Gitter| image:: https://img.shields.io/gitter/room/frictionlessdata/chat.svg
============
`|Travis| <https://travis-ci.org/frictionlessdata/tabulator-py>`_
`|Coveralls| <https://coveralls.io/r/frictionlessdata/tabulator-py?branch=master>`_
`|PyPi| <https://pypi-hypernode.com/pypi/tabulator>`_
`|Gitter| <https://gitter.im/frictionlessdata/chat>`_
A utility library that provides a consistent interface for reading
tabular data.
Getting Started
---------------
Installation
~~~~~~~~~~~~
To get started (under development):
::
$ pip install tabulator
Simple interface
~~~~~~~~~~~~~~~~
Fast access to the table with ``topen`` (stands for ``table open``)
function:
::
from tabulator import topen, processors
with topen('path.csv', with_headers=True) as table:
for row in table:
print(row)
print(row.get('header'))
For the most use cases ``topen`` function is enough. It takes the
``source`` argument:
``<scheme>://path/to/file.<format>`` and uses corresponding ``Loader``
and ``Parser`` to open and start to iterate over the table. Also user
can pass ``scheme`` and ``format`` explicitly as function arguments. The
last ``topen`` argument is ``encoding`` - user can force Tabulator to
use encoding of choice to open the table.
Read more about ``topen`` -
`documentation <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/topen.py>`_.
Function ``topen`` returns ``Table`` instance. We use context manager to
call ``table.open()`` on enter and ``table.close()`` when we exit: -
table can be iterated like file-like object returning row by row - table
can be read row by bow using ``readrow`` method (it returns row tuple) -
table can be read into memory using ``read`` function (return list or
row tuples) with ``limit`` of output rows as parameter. - headers can be
accessed via ``headers`` property - table pointer can be set to start
via ``reset`` method.
Read more about ``Table`` -
`documentation <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/table.py>`_.
In the example above we use ``processors.Headers`` to extract headers
from the table (via ``with_headers=True`` shortcut). Processors is a
powerfull Tabulator concept. Parsed data goes thru pipeline of
processors to be updated before returning as table row.
Read more about ``Processor`` -
`documentation <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/processors/api.py>`_.
Read a processors tutorial -
`tutorial <https://github.com/frictionlessdata/tabulator-py/blob/master/docs/processors.md>`_.
Advanced interface
~~~~~~~~~~~~~~~~~~
To get full control over the process you can use more parameters. Below
all parts of Tabulator are presented:
::
from tabulator import topen, processors, loaders, parsers
table = topen('path.csv',
loader_options={'encondig': 'utf-8'},
parser_options={'delimeter': ',', quotechar: '|'},
loader_class=loaders.File,
parser_class=parsers.CSV,
iterator_class=CustomIterator,
table_class=CustomTable)
table.add_processor(processors.Headers(skip=1))
headers = table.headers
contents = table.read(limit=10)
print(headers, contents)
table.close()
Also ``Table`` class can be instantiated by user (see documentation).
But there is no difference between it and ``topen`` call with extended
list of parameters except ``topen`` also calls the ``table.open()``
method.
Design Overview
---------------
Tabulator uses modular architecture to be fully extensible and flexible.
It uses loosely coupled modules like ``Loader``, ``Parser`` and
``Processor`` to provide clear data flow.
.. figure:: docs/diagram.png
:align: center
:alt: diagram
diagram
Documentation
-------------
API documentation is presented as docstrings: - High-level: -
`topen <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/topen.py>`_
- Core elements: -
`Row <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/row.py>`_
-
`Table <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/table.py>`_
-
`Iterator <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/iterator.py>`_
- Plugin elements: - `Loader
API <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/loaders/api.py>`_
- `Parser
API <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/parsers/api.py>`_
- `Processor
API <https://github.com/frictionlessdata/tabulator-py/blob/master/tabulator/processors/api.py>`_
Contributing
------------
Please read the contribution guideline:
`How to Contribute <CONTRIBUTING.md>`_
Thanks!
.. |Travis| image:: https://img.shields.io/travis/frictionlessdata/tabulator-py/master.svg
.. |Coveralls| image:: http://img.shields.io/coveralls/frictionlessdata/tabulator-py.svg?branch=master
.. |PyPi| image:: https://img.shields.io/pypi/v/tabulator.svg
.. |Gitter| image:: https://img.shields.io/gitter/room/frictionlessdata/chat.svg
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