Exporting and Displaying Scraperwiki Datasets Using the Google Visualisation API

In Visualising Networks in Gephi via a Scraperwiki Exported GEXF File I gave an example of how we can publish arbitrary serialised output file formats from Scraperwiki using the GEXF XML file format as a specific example. Of more general use, however, may be the ability to export Scraperwiki data using the Google visualisation API DataTable format. Muddling around the Google site last night, I noticed the Google Data Source Python Library that makes it easy to generate appropriately formatted JSON data that can be consumed by the (client side) Google visualisation library. (This library provides support for generating line charts, bar charts, sortable tables, etc, as well as interactive dashboards.) A tweet to @frabcus questioning whether the gviz_api Python library was available as a third party library on Scraperwiki resulted in him installing it (thanks, Francis:-), so this post is by way of thanks…

Anyway, here are a couple of examples of how to use the library. The first is a self-contained example (using code pinched from here) that transforms the data into the Google format and then drops it into an HTML page template that can consume the data, in this case displaying it as a sortable table (GViz API on scraperwiki – self-contained sortable table view [code]):

Of possibly more use in the general case is a JSONP exporter (example JSON output (code)):

Here’s the code for the JSON feed example:

import scraperwiki
import gviz_api

#Example of:
## how to use the Google gviz Python library to cast Scraperwiki data into the Gviz format and export it as JSON

#Based on the code example at:
#http://code.google.com/apis/chart/interactive/docs/dev/gviz_api_lib.html

scraperwiki.sqlite.attach( 'openlearn-units' )
q = 'parentCourseCode,name,topic,unitcode FROM "swdata" LIMIT 20'
data = scraperwiki.sqlite.select(q)

description = {"parentCourseCode": ("string", "Parent Course"),"name": ("string", "Unit name"),"unitcode": ("string", "Unit Code"),"topic":("string","Topic")}

data_table = gviz_api.DataTable(description)
data_table.LoadData(data)

json = data_table.ToJSon(columns_order=("unitcode","name", "topic","parentCourseCode" ),order_by="unitcode")

scraperwiki.utils.httpresponseheader("Content-Type", "application/json")
print 'ousefulHack('+json+')'

I hardcoded the wraparound function name (ousefulHack), which then got me wondering: is there a safe/trusted/approved way of grabbing arguments out of the URL in Scraperwiki so this could be set via a calling URL?

Anyway, what this shows (hopefully) is an easy way of getting data from Scraperwiki into the Google visualisation API data format and then consuming either via a Scraperwiki view using an HTML page template, or publishing it as a Google visualisation API JSONP feed that can be consumed by an arbitrary web page and used direclty to drive Google visualisation API chart widgets.

PS as well as noting that the gviz python library “can be used to create a google.visualization.DataTable usable by visualizations built on the Google Visualization API” (gviz_api.py sourcecode), it seems that we can also use it to generate a range of output formats: Google viz API JSON (.ToJSon), as a simple JSON Response (. ToJSonResponse), as Javascript (“JS Code”) (.ToJSCode), as CSV (.ToCsv), as TSV (.ToTsvExcel) or as an HTML table (.ToHtml). A ToResponse method (ToResponse(self, columns_order=None, order_by=(), tqx=””)) can also be used to select the output response type based on the tqx parameter value (out:json, out:csv, out:html, out:tsv-excel).

PPS looking at eg https://spreadsheets.google.com/tq?key=rYQm6lTXPH8dHA6XGhJVFsA&pub=1 which can be pulled into a javascript google.visualization.Query(), it seems we get the following returned:
google.visualization.Query.setResponse({"version":"0.6","status":"ok","sig":"1664774139","table":{ "cols":[ ... ], "rows":[ ... ] }})
I think google.visualization.Query.setResponse can be a user defined callback function name; maybe worth trying to implement this one day?

5 comments

  1. Pingback: Visualising Networks in Gephi via a Scraperwiki Exported GEXF File « OUseful.Info, the blog…
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