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Generate Pandas data frames, load and extract data, based on JSON Table Schema descriptors.

Project description


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Generate and load Pandas data frames `Table
Schema <>`__ descriptors.


- implements ``tableschema.Storage`` interface

Getting Started


The package use semantic versioning. It means that major versions could
include breaking changes. It's highly recommended to specify ``package``
version range in your ``setup/requirements`` file e.g.


$ pip install tableschema-pandas


Code examples in this readme requires Python 3.3+ interpreter. You could
see even more example in
`examples <>`__

You can easily load resources from a data package as Pandas data frames
by simply using ``datapackage.push_datapackage`` function:

.. code:: python

>>> import datapackage

>>> data_url = ''
>>> storage = datapackage.push_datapackage(data_url, 'pandas')

>>> storage.buckets

>>> type(storage['data___data'])
<class 'pandas.core.frame.DataFrame'>

>>> storage['data___data'].head()
Name Code
0 Afghanistan AF
1 Åland Islands AX
2 Albania AL
3 Algeria DZ
4 American Samoa AS

Also it is possible to pull your existing data frame into a data

.. code:: python

>>> datapackage.pull_datapackage('/tmp/datapackage.json', 'country_list', 'pandas', tables={
... 'data': storage['data___data'],
... })


The whole public API of this package is described here and follows
semantic versioning rules. Everyting outside of this readme are private
API and could be changed without any notification on any new version.


Package implements `Tabular
Storage <>`__
interface (see full documentation on the link):


This driver provides an additional API:


- ``dataframes (object[])`` - list of storage dataframes

We can get storage this way:

.. code:: python

>>> from tableschema_pandas import Storage

>>> storage = Storage()

Storage works as a container for Pandas data frames. You can define new
data frame inside storage using ``storage.create`` method:

.. code:: python

>>> storage.create('data', {
... 'primaryKey': 'id',
... 'fields': [
... {'name': 'id', 'type': 'integer'},
... {'name': 'comment', 'type': 'string'},
... ]
... })

>>> storage.buckets

>>> storage['data'].shape
(0, 0)

Use ``storage.write`` to populate data frame with data:

.. code:: python

>>> storage.write('data', [(1, 'a'), (2, 'b')])

>>> storage['data']
id comment
1 a
2 b

Also you can use
`tabulator <>`__ to
populate data frame from external data file. As you see, subsequent
writes simply appends new data on top of existing ones:

.. code:: python

>>> import tabulator

>>> with tabulator.Stream('data/comments.csv', headers=1) as stream:
... storage.write('data', stream)

>>> storage['data']
id comment
1 a
2 b
1 good


The project follows the `Open Knowledge International coding
standards <>`__.

| Recommended way to get started is to create and activate a project
virtual environment.
| To install package and development dependencies into active


$ make install

To run tests with linting and coverage:

.. code:: bash

$ make test

| For linting ``pylama`` configured in ``pylama.ini`` is used. On this
stage it's already
| installed into your environment and could be used separately with more
fine-grained control
| as described in documentation -

For example to sort results by error type:

.. code:: bash

$ pylama --sort <path>

| For testing ``tox`` configured in ``tox.ini`` is used.
| It's already installed into your environment and could be used
separately with more fine-grained control as described in documentation

| For example to check subset of tests against Python 2 environment with
increased verbosity.
| All positional arguments and options after ``--`` will be passed to

.. code:: bash

tox -e py27 -- -v tests/<path>

| Under the hood ``tox`` uses ``pytest`` configured in ``pytest.ini``,
| and ``mock`` packages. This packages are available only in tox


Here described only breaking and the most important changes. The full
changelog and documentation for all released versions could be found in
nicely formatted `commit
history <>`__.


Initial driver implementation.

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