Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Python API for Google Visualization

Project Description
Introduction
============

Gviz Data Table is a simple Python library for converting Python data types
to the Google Visualization Data Table JSON format.
https://developers.google.com/chart/interactive/docs/reference

The Google Visualization Library itself is a Javascript library that provides
interactive charts that work in pretty much any browser. The libraries cover
most use cases including tables as well as charts, so you can have a chart
and a table of the same data.

Gviz Data Table is designed primarily for use with data sources such as
databases. Usage is supposed to be minimal: you provide a schema, that is a
list of columns, and the rows of data. A column must have a name and Python
data type. It can also have a label which will be used for display, otherwise
the name will be used.

Each row is a sequence of cells. Although columns are explicit row names are
always the first cell in a row. Like columns, cells can also have labels.
Gviz Data Table will validate each cell to make sure that data conforms to
type specified in the schema and will map Python types to their JSON
equivalent but it does not coerce any data, i.e. if a column has type `int`
and a cell's data is a string containing numerical characters only this will
still raise an exception.

Gviz Data Table handles data conversion only. You will need to add the
necessary Javascript to an web page in order for any charts or table to be
drawn. Tables, columns and cells can all have options which are just
dictionaries. As there is no further definition of options no validation of
their items occurs. Unknown items will simply be ignored.

Gviz Data Table is composed of: one container class "Table"; two data
classes, Cell and Column and one JSON encoder. Application code should
probably only ever need to use Table and the encoder.

Usage
-----

Tables can be initialised with a schema or these can be added imperatively.
Once one row has been added to a table no more columns can be added. Once all
the rows have been added. The table can be converted into JSON using the
encoder.

Example
*******

Let's say we have data representing the names and salaries of people

====== ======
Name Salary
====== ======
Jim 50
Bob 80
====== ======

This could be coded in Gviz Data Table like this:

.. code-block:: python

from gviz_data_table import Table

table = Table()
table.add_column('name', str, "Name")
table.add_column(salary', int, "Salary")
table.append(["Jim", 50])
table.append(["Bob", 80])

This can be encoded into JSON using the encoder:

.. code-block:: python

from gviz_data_table import encode

encode(table)

It can also be directly encoded

.. code-block:: python

table.encode()

And also used as a static data source for asynchronous loading from Javascript

.. code-block:: python

table.source()


Complete documentation including the API at
http://gviz-data-table.readthedocs.org/en/latest/


1.0.2 (2015-06-29)
------------------

- Support `long`type in Python 2


1.0.1 (2013-03-18)
------------------

- Correct release


1.0.0 (2013-03-18)
------------------

- Python 3 compatibile
- Changed convenience import: `encode` replaces `encoder`
- Added convenience methods to Table to allow direct encoding as JSON and a
Google data source.


0.9.1 (2012-07-26)
------------------

- Changed signature of add_column when I found I used it wrongly myself in
the docs


0.9 (2012-07-25)
----------------

- Initial release
Release History

Release History

This version
History Node

1.0.2

History Node

1.0.1

History Node

1.0.0

History Node

0.9.1

History Node

0.9.0

Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
gviz_data_table-1.0.2.tar.gz (13.3 kB) Copy SHA256 Checksum SHA256 Source Jun 29, 2015

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting