MatrixAdapt | Logiciel de gestion d'Entreprise, Création et référencement des sites web

Seo Master present to you: We know it's easier for developers to program in the languages they know. So we updated the Google Analytics API Python Client library with all the new API version 2 features and added reference exampels for both the Account Feed and Data Feed. Now it's easier than ever to automate your analysis workflow using our API.

Taking The Library For a Spin

With the updated library, we thought it would be a great time to highlight the power of the new v2 features. So we created a sample application to do just that. The application uses the new Google Analytics Python client library to retrieve metrics for a series of segments. It then performs some calculations on the data and creates bar charts using the GChartWrapper package, an open source Python wrapper for the Google Charts API. Finally, it uses the Python Imaging Library to add a title and legend, and stitches all the charts together into a single image. We decided to release this application as open source so you can create visualizations with your own data.

Solving Business Problems

With social media all the rage, we wanted to use this new application to help Avinash Kaushik, our Analytics Evangelist, to measure "engagement" on his popular Occam's Razor blog. We also wanted to determine if the time he spends participating in social media sites is valuable and sends new readers to his blog.

First we created segments to pull all the referrals from Facebook and Twitter. Second, we chose five calculations and corresponding metrics to compare the performance of thee two segments. We then compared the segments to each other and, for context, to all the visits to the site as a control.

They say a picture is worth a thousand words, here are the results:



Let's Analyze

Some interesting observations become apparent.

• Far more visits originate from Twitter (3.6x) when compared to Facebook, perhaps not surprising given Avinash's Twitter followers (~16,120)
• Visitors from Twitter tend to be new visitors, a good thing, but they view fewer pages and spend significantly less time on the blog.
• On the other hand Facebook delivers an audience that is loyal. These visitors come back to the site more often and spend a significant time on the blog (compared to Twitter and all other visitors).

The bottom line? Even though social networking sites are all the rage, they actually contribute very little to Avinash's blog. If this blog were a company, it would be wise to ensure the time and effort put into driving traffic from social media is proportionate to the actual volume of traffic and goal conversions from those sites.

Hopefully this example shows how powerful our new features can be.

If you're interested in running this report against your own data, the application is free and open sourced. Additionally, we made it really easy to change the metrics, segments, calculations and all the other visual properties to power your own visualizations. So please download it here and give it a whirl, we would love to hear your feedback.

By Nick Mihailovski, Google Analytics API Team2013, By: Seo Master

Seo Master present to you: By Itai Raz, Google Visualization API Team

Today, we're publicly documenting the Google Visualization API's open-wire protocol, thus dramatically expanding the capabilities of this API beyond what had been available since we first launched in March of this year. Organizations can now expose their server-side data, such as in SQL databases and even in Excel spreadsheets, and display this data through visualizations from our growing directory. This flexibility makes it possible to connect easily almost any data source to a wealth of 40+ visualizations, including standard pie and line charts and complex heat maps and motion charts.

To make it even easier for developers to get started, we have documented an open-source Python library that enables any Python developer to quickly start using the API. What we find particularly cool about this library is that it also runs on Google's AppEngine. You don't even need to be an owner of your own servers to expose your data: You can place it on AppEngine and use the Visualization API to expose your data in meaningful, insightful ways in dashboards and reports. Expect to see additional server-side tools for the Visualization API in the near future.

Moreover, this week at the Dreamforce conference, Salesforce announced they've created tools, including code snippets and API harnesses, to make the Google Visualization API even easier to use. Salesforce customers can now quickly and easily add dashboards and custom reporting applications over their Salesforce data and publish these on any webpage. ISVs and BI firms such as Panorama and Conceptual Clarity, who are already marketing their powerful reporting tools over Google Spreadsheets using the Visualization API, now have access to Salesforce customers. The icing on the cake: since they use the Visualization API, they can address this new market without adding new code to their existing applications.

To learn more about how to implement your data store as a Visualization API data source, by checking out our documentation.

2013, By: Seo Master

Seo Master present to you: It’s been only 7 weeks since we’ve launched the Google Analytics Management API and we’ve heard a lot of great feedback. Thanks!

Since Python is one of our more popular languages, we’ve updated the Google Analytics Python Client Library to access all 5 feeds of the Management API. Now it’s easier than ever to get your configuration data from the API.

To show you how simple it is to use the library, here is an example which returns all the goal names for a profile:

import gdata.analytics.client

APP_NAME = 'goal_names_demo'
my_client = gdata.analytics.client.AnalyticsClient(source=APP_NAME)

# Authorize
my_client.client_login(
    INSERT_USER_NAME,
    INSERT_PASSWORD,
    APP_NAME,
    service='analytics')

# Make a query.
query = gdata.analytics.client.GoalQuery(
      acct_id='INSERT_ACCOUNT_ID',
      web_prop_id='INSERT_WEB_PROP_ID',
      profile_id='INSERT_PROFILE_ID')

# Get and print results.
results = my_client.GetManagementFeed(query)
for entry in results.entry:
  print 'Goal number = %s' % entry.goal.number
  print 'Goal name   = %s' % entry.goal.name
  print 'Goal value  = %s' % entry.goal.value

To get you started, we wrote a reference example which accesses all the important information for each feed. We also added links to the source and PyDoc from the Management API Libraries and Examples page. Have a look and let us know what you think!

By Nick Mihailovski, Google Analytics API Team2013, By: Seo Master

Seo Master present to you: by Adrian Holovaty, Django core developer

More than 200 people around the world devoted their time and brainpower to improving the Django Web framework this weekend, during a scheduled Django coding sprint. On Friday, September 14th, the first day of the sprint, some Django developers gathered at Google's offices in Chicago and Mountain View for the benefits of in-person communication, camaraderie and, yes, free food.

17 people showed up at Google Chicago, which was a sort of ground zero for the sprint, with the project's BDFLs Adrian Holovaty and Jacob Kaplan-Moss in attendance. Another 7 people participated from Google's Mountain View office, which was linked with Chicago via videoconference.

Python creator (and Google employee) Guido van Rossum even stopped by via videoconference to give a pep talk about Django version 1.0 and share some of his experience running a large open-source project.

The sprint was intensely productive, with more than 400 tickets closed in the Django issue-tracking system, 300 new patches/ticket attachments and more than 200 commits to the Django code base. All told, there were more than 2,440 changes, including wiki changes, ticket changes, patch uploads and code check-ins.

Overall, the consensus was: "We should do this more often!"


The Chicago sprinters, hard at work (photo by Jacob Kaplan-Moss)2013, By: Seo Master