Gviz Data Table¶

Author:	Charlie Clark <https://clark-consulting.eu>_
Source code:	https://foss.heptapod.net/openpyxl/gviz-data-table
Issues:	https://foss.heptapod.net/openpyxl/gviz-data-table/-/issues
Generated:	Jun 24, 2022
License:	BSD
Version:	2.0.0

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:

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:

from gviz_data_table import encode

encode(table)

It can also be directly encoded

table.encode()

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

table.source()

Sample HTML Page with Chart and Table¶

import os
import sqlite3
from string import Template

from gviz_data_table import Table, encoder


def data():
    folder = os.path.split(__file__)[0]
    db = sqlite3.connect(os.path.join(folder, "sample.db"))
    c = db.cursor()
    c.execute("SELECT name, salary FROM employees")
    cols = [dict(id=col[0], label=col[0].capitalize(), type=col[1])
            for col in c.description]
    # sqlite3 unfortunately does not provide type information
    cols[0]['type'] = str
    cols[1]['type'] = float

    t = Table(cols)
    for r in c.fetchall():
        name, value = r
        label = f"${value}"
        t.append([name, (value, label)])

    return encoder.encode(t)


template = Template("""
<html>

<head>
<script src="http://www.google.com/jsapi" type="text/javascript"></script>
</head>

<body>
<script>
      google.load("visualization", "1", {packages:["corechart", "table"]});

      var data = $data // our data table

      google.setOnLoadCallback(drawChart);
      function drawChart() {

      var chart_data = new google.visualization.DataTable(data);
      var chart = new google.visualization.ColumnChart(document.getElementById('chart'));
      chart.draw(chart_data);
      }

      google.setOnLoadCallback(drawTable);
      function drawTable () {
      var table_data = new google.visualization.DataTable(data);
      var table = new google.visualization.Table(document.getElementById('table'));
      table.draw(table_data);
      }
</script>

<h2>Sample Chart</h2>

<div id="chart"></div>

<h2>Sample Table</h2>
<div id="table"></div>

</body>

</html>
""")

def save():
    with open("chart.html", "w") as f:
        f.write(template.safe_substitute(data=data()))

if __name__ == "__main__":
    save()

This produces a chart and table like this:

Sample Chart

Sample Table

API¶

gviz_data_table package¶

Google Visualisation for Python

Convert Python data structures to JSON suitable for the Google Visualisation Library

Submodules¶

gviz_data_table.cell module¶

class gviz_data_table.cell.Cell(typ, value, label=None, options=None)[source]¶

Bases: object

Cells have values which must conform to their types. They can also have labels

Cell attributes (python:javascript) mapping

{‘value’:’v, ‘label’:’f’, ‘options’:’p’}

label¶

options¶

type¶

validate(value)[source]¶: Check that a value conforms to the column type. Or is None.

value¶

gviz_data_table.column module¶

class gviz_data_table.column.Column(id, type, label=None, options=None)[source]¶

Bases: object

A column is a type definition

id¶

label¶

options¶

type¶

gviz_data_table.encoder module¶

class gviz_data_table.encoder.Encoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)[source]¶

Bases: json.encoder.JSONEncoder

JSON encoder for utility classes.

Also maps datetime/date and time objects to the relevant Google Visualization pseudo Date() constructor (month = month -1). Times are lists of [h, m, s] mapped to timeofday

default(obj)[source]¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

formats = {<class 'datetime.date'>: 'Date({0}, {1}, {2})', <class 'datetime.datetime'>: 'Date({0}, {1}, {2}, {3}, {4}, {5})'}¶

gviz_data_table.encoder.encode(obj)[source]¶

gviz_data_table.table module¶

class gviz_data_table.table.Table(schema=None, options=None)[source]¶

Bases: object

Tables are two-dimensional arrays with fixed schemas.

Columns are ordered dictionaries of id, label and data type.

Rows are ordered dictionaries mirroring columns.

add_column(id, type, label=None, options=None)[source]¶

Add a new column

Columns cannot be added to tables which already contain data.

append(row)[source]¶

Add a row.

Rows are either sequences of values, or sequences of (value, label, options) tuples, or sequences of cell dictionaries. Dictionaries are the most flexible but also the most verbose. Tuples do not have to be complete but will be exhausted in order, i.e. you can’t have just a value and options.

encode()[source]¶: Convenience method for encoding tables

extend(rows)[source]¶: Add multiple rows of data

options¶

source()[source]¶: Convenience method for encoding a table as a static JSON data source. This only wraps the table in the API.

Index¶

Index

Release Notes¶

2.0.0 (2022-06-24)¶

Python 3 release

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