Google Analytics Reporting API v4 in Python with pandas

This repo contains a setup to get started with the Google Analytics Reporting API v4 in Python. It takes three steps:

For a step by step guide to setting up a project like this, ready my tutorial on The Marketing Technologist.

1. Create a project

First, create a project in your Google Developer console. I highly recommend using the 17 steps of this post. Add a credentials.py file to your Python project and create variables for the client_id, client_secret and redirect_uri and fill out the corresponding values.

For your Python project, I recommend using Python 3.x over 2.7 because it's better at handling special characters in strings.

2. Connect to the API

To connect to the Google Analytics API, run config.py two times:

1. Your first run prints a URL in the console. Open the URL, grant access to your Google account of choice, copy the &code= parameter value and add an access_code variable with the parameter value in credentials.py.
2. Your second run prints the access token and refresh token. For both values, create a variable (access_token and refresh_token) and set the corresponding values.

• the refresh token is only returned with your first API connection. If the second line says None, revoke your app's access at https://myaccount.google.com/permissions, clear the access_code and reconnect.

All future runs will use the access token and refresh token to connect to the API.

3. Run your report

Lastly, you can run run.py to return a report in a DataFrame. The return_ga_data function returns a pandas DataFarme. The example code is set to return sessions by source:

df = return_ga_data(
  start_date='2017-09-13',
  end_date='2017-09-21',
  view_id='100555616',
  metrics=[{'expression': 'ga:sessions'},],
  dimensions=[{'name': 'ga:source'}],
  split_dates=False,
  group_by=[],
  dimensionFilterClauses=[
      {
          'operator': 'OR',
          'filters': [
              {
                  'dimensionName': 'ga:userType',
                  'not': False,
                  'expressions':[
                    'new visitor'
                  ],
                  'caseSensitive': False
              }
          ],

      }
  ],
  segments=[]
)

A brief description of each parameter:

• start_date & end_date:
• date format in 'YYYY-MM-DD'
• relative date: 'today', 'yesterday', 'NdaysAgo' (where N is the amount of days)
• view_id: the ID of the Google Analytics view you want to import data from.
• metrics: the list of sessions you want to import (max. 10) - full list here.
• dimensions: the list of dimensions you want to import (max. 9) - full list here.
• split_dates: boolean. If true each day in your date range is queries seperately and merged into a data frame later on.
• group_by (optional): if you enable split_dates you can group the data on a dimension of choice. Especially handy when you're not include the date in your export.
• dimensionFilterClauses (optional): filter data based on dimensions if required (see documention here.
• segments (optional): use to apply segments (see example here). Requires the dimension ga:segment in your query.

Export to Excel

As not all of your data analyst friends are as cool as you are, I've added basic DataFrame to Excel export function. Here's how you can use it:

save_df_to_excel(
  df=df,
  path='C:\\Users\\Erik\\Documents\\',
  file_name='test_export',
  sheet_name='data'
)

To do

• Create function to make use of segments easier.