Yesterday, Google released an update to their popular Google Trends tool. There are improvements over the previous version, but the biggest new feature is a new shiny button that lets you download all your data in the format of a CSV. This is a very cool enhancement. Where Google Trends was a geeky toy, it now takes the leap to integrate into analysts' reports and with that, edge its way onto managerial desks.

This python module is a quasi-API to make it easier to authenticate into Google Trends for those who want to squeeze the extra level of functionality out of their data. The advantage of programmatic access is that the data can be automatically trended and merged. It can be snuck into a 9:00 AM daily email to the VP of Marketing so that she knows to ramp up Google Adwords campaigns for some specific keyword. Also, by programatically pulling multiple reports, it is possible to create a wealth of data not visible in a single report. Using one keyword as a benchmark to merge multiple reports, we can do a meaningful comparison on tens or hundreds of relevant keywords.

To use the pyGTrends, the quasi-Google-Trends-API, download the file from our server.

Here is an example of the most basic basic report that you can pull down from Google Trends. The connector function needs authentication info, and download_report needs to be passed a list of keywords.

from pyGTrends import pyGTrends

connector = pyGTrends('google username','google password')
connector.download_report(('keyword1', 'keyword2'))
print connector.csv()

You can, however, use pyGTrends to get any slice of data that you can pull down from Google Trends. To see the exact parameters that you should use, go to Google Trends, and navigate to the specific sufficiently-narrow report that you are interested in. Then, right-click on the CSV download, and save the link location. The different parameters should be discernible from the link. The following code downloads a report for banana, bread, and bakery keywords from April 2008, originating from the magnificent nation of Austria, and scaled using fixed scaling (aka the second download link).

connector.download_report(('banana', 'bread', 'bakery'), 
                          date='2008-4', 
                          geo='AT', 
                          scale=1)

By default, the csv() function downloads the main part of the report, but there are a few additional parts stuck to the bottom of the CSV file. If you are interested in those, pass the section parameter to the csv() function. If you do not want column headers on your data, you can also pass the column_headers parameter as false. The following will return the Language section without any headers.

print connector.csv(section='Language', column_headers=False)

Here is a snapshot from the new Google Trends to add some eye-candy to the post:

This post is the code behind how we mashed external data into Google Analytics.

The first step is to yank reference data from the Google Analytics site to reference against Kampyle's data. We specifically want to gather individual names of websites (index.html, /index2.html), and the current selected daterange. The cell references to the website names in the table can be found using a neat Javascript Shell popular among Greasemonkey and Javascript developers. I will not go into detail about the Javascript Shell, but by checking out the various child nodes for the table object we can track down that document.getElementById('f_table_data').childNodes[3].rows[1].cells[1].textContent points at the text in the first cell of the first row. While the syntax looks long, it is just nested HTML in a more elegant programmatic fashion.

For the date, Google Analytics uses a slightly peculiar hybrid system where the date is drawn initially from the URL, but if the date is modified with the java date tool in the upper right hand corner, it uses that instead. From our end, document.getElementById('f_primaryBegin').value and document.getElementById('f_primaryEnd').value are the java date tool values that only start existing if the date tool is used. Pull these two values if they exist, and simply parse the date from the URL otherwise.

The clickable tab we created is essentially the equivalent of a little Greasemonkey button with a few frills that can be created in the standard Greasemonkey fashion. Wherever possible, I use Google-defined layouts for consistency with the site.

Next, we want to send out our reference data to some external server. Greasemonkey has good functionality for pulling data from other sites and servers through the use of the GM_xmlhttpRequest command. A server-end PHP or Django service might be easiest to implement. In this specific example, Kampyle wanted to use the SOAP protocol. While there is an excellent overall SOAP client for javascript by Matteo Casati, this client does not work in a plug and play fashion with Greasemonkey, and needed some modification. For any devoted SOAPers who want to try Greasemonkey, the revised javascript-soap-client code can be found in the attached file. We use the SHA256 encryption function written by Angel Marin and Paul Johnston, but that is accomplished by just copying and pasting the function into our code.

The result comes back in the form of an xml object describing each row in the table, which we parse using native Javascript/Greasemonkey methods, and pop back into the table in the way that we extracted the individual website names. A neat trick here is to call each individual row individually, and not to wait for the data to come back before calling the next row from the server. Separate listeners can wait and insert the data at their leisure. This allows our page to load up faster, and in case there is an error with one data element, it could potentially allow the rest of the rows to load in peace.

You can play around with my code here. This code is released under the BSD License. You won't be able to run the code verbatim without Kampyle's compliance, since they have changed the API calls on their server. However, much of it should be very portable to other data sources.