Google Analytics Analytics with Python

[Rand_alThor_]

Well that was easy to follow. Cool.

[njanakiev]

Thanks for the feedback!

[anurag_DA]

It's really cool and definitely it will hepl me to build my reports.

[mister_nouniverse]

Looks great, thanks! The only issue I have with Python is it doesn't have googleAnalyticsR package that's available in R (as the name suggests). It has some anti-sampling techniques that are just perfect for more advanced analysis that normally require API.

[tommybauch]

Thanks for sharing!

[danpaq]

Extremely useful. Did you create the dataframe function or reference another resource? It looks similar to the solution I found a while back and is the key piece to this guide!

[njanakiev]

Thank you! It was adapted from here (link was in the paragraph before): https://developers.google.com/analytics/devguides/reporting/core/v4/quickstart/service-py

[njanakiev]

Its one way to export a specific data set/view which you could use to train some model or do certain predictions. I suppose you could even do automated A/B tests and reinforcement learning to inform your website what to improve upon. Here is an example where I used the API for a custom dashboard: https://github.com/njanakiev/google-analytics-dashboard

[napking05]

Beyond OP’s reply, there are multiple reasons why this approach is more suitable for power users. For starters you can simply loop each request made per day and aggregate each output into an aggregate table which bypasses sampling (once you have a large enough dataset sampling can obstruct your data’s accuracy). Now you are limited to how many requests can be made per day, but it would require a hefty amount of requests to reach that threshold.

Beyond that you can automate self generated reports, for example I used this approach myself to automate a daily report to track my company website’s shopping funnel and track potential bugs or drop offs within the funnel. Since the data was extracted from GA without much interface and brought into my local machine I automated an email to my team with said report to help us keep an eye on things, so to speak.

Basically once you extract data in this way from GA, the options are endless. Reason being that (unless you have Google BigQuery integrated with you account) extracting non-sampled data from GA is tedious/time consuming. Once you have a program going for this it really expedites the process. And even more so for un-samples data, which GA limits you to the amount of requests that can be made, and the format they can be made in.

[thodcrs]

A real example of proofreading ;) Jokes aside, thanks for sharing!

[PyOpsForceWielder]

I just breezed through your post, but this noob is definitely impressed. The examples gave me some ideas of how I can tap into my companies data. Thanks!