GAReport

A simple way to call the Google Analytics (v4) API

Why

Looking around the web, one can find a bunch of code based on Google's initial 2016 sample for v4 of the Google Analytics API. While it all works, pulling multiple reports or changing things on the fly is a pain. A high-level abstraction makes the process easier. Getting the results in a Pandas dataframe makes running calculations, creating visualizations or even exporting to a database a breeze.

So, borrowing the excellent code base from RitwikGA/GoogleAnalytics-Pandas-Sheet this project wraps it into a class, adds a class for API calls and human readable labels.

Getting Started

To get started with this code, follow the instructions at Hello Analytics Reporting API v4; Python quickstart for service accounts. Especially, steps

1. Enable the API

2. Install the client library

3. Setup the code

   1. Copy or download the following source code to where it will run.
   2. Move the previously downloaded client_secrets.json to the same directory as the code.
   3. In GAReport replace the value of the KEY_FILE_LOCATION with the appropriate path to the downloaded client_secrets.json.
   4. In GAPullTest.py replace the value of VIEW_ID. You can use the Account Explorer to find a View ID.

Create a Report

GAPullTest.py is a sample report but this code can easily run from a Jupyter notebook.

1. Start by importing the GAReport class

from GAReport import GAReport

2. Set the dimension(s). These are the first column(s) of the returned table. (Example: Dimension is Page. The metrics, such as Pageviews, then display for each page.)

DIMENSIONS = ["Page", ]

3. Set the metric(s) (i.e. what you want to know about the dimension(s)).

METRICS = ["Pageviews", "Unique Pageviews", "Avg. Time on Page", "Entrances", "Bounce Rate", "% Exit", "Page Value"]

4. Create the report. Access the report via GAReport.df.

report = GAReport(startdate="yesterday", 
	enddate="yesterday", 
	viewID=VIEW_ID, 
	dimensions=DIMENSIONS, 
	metrics=METRICS, 
	filters=FILTERS)
print(report.df.head(3))

The output:

	Page	Pageviews	Unique Pageviews	Avg. Time on Page	Entrances	Bounce Rate	% Exit	Page Value
0	/news	25	23	11.05	0	0	20	33.6957
1	/news/2021/new-food-services-partner	15	15	50.3077	1	100	13.3333	0
2	/news/2021/what-to-expect-for-fall-2021	88	67	76.6923	5	100	26.1364	11.5672