- Create a virtual environment:
conda create -n py_ga4 python=3.10 - Change dirctory to site-packages:
cd ~/opt/anaconda3/envs/py_ga4/lib/python3.10/site-packages - Clone the repo:
git clone https://github.com/ckoutavas/GoogleAnalytics4 - Change directory to GoogleAnalytics4:
cd GoogleAnalytics4 - Activate your new enviorment:
conda activate py_ga4 - Install requirements.txt:
pip install -r requirements.txt
After you clone the repo, make sure that you have enabled the Google Analytics Data API v1 in a service account and created a credentials.json. Once you have the credentials.json file, set the path as the following environment variable GOOGLE_APPLICATION_CREDENTIALS
If you do not want to use an enviorment variable, you can use the optional param creds_path, which is the path to your credentials.json file
from GoogleAnalytics4 import GA4
report = GA4.BuildReport(property_id='123456789',
ga_dimensions=['pagePath', 'pageTitle'],
ga_metrics=['screenPageViews', 'activeUsers', 'averageSessionDuration'],
start_date='2023-02-01',
end_date='today',
creds_path='/path/to/credentials.json')
Below are links to the GA4 dimensions and metrics API names:
If you have used UA dimensions and metrics in the past, you can review the Universal Analytics to Google Analytics 4 dimensions and metrics equivalence
from GoogleAnalytics4 import GA4
report = GA4.BuildReport(property_id='123456789',
ga_dimensions=['pagePath', 'pageTitle'],
ga_metrics=['screenPageViews', 'activeUsers', 'averageSessionDuration'],
start_date='2023-02-01',
end_date='today')
df = report.run_report()
The match type of a string filter:
- MATCH_TYPE_UNSPECIFIED = 0
- EXACT = 1
- BEGINS_WITH = 2
- ENDS_WITH = 3
- CONTAINS = 4
- FULL_REGEXP = 5
- PARTIAL_REGEXP = 6
The match type can be added using:
Filter.StringFilter.MatchType.EXACTFilter.StringFilter.MatchType(1)
from GoogleAnalytics4 import GA4
from google.analytics.data_v1beta.types import Filter
report = GA4.BuildReport(property_id='123456789',
ga_dimensions=['pagePath', 'pageTitle'],
ga_metrics=['screenPageViews', 'activeUsers', 'averageSessionDuration'],
start_date='2023-02-01',
end_date='today')
report.add_filter(filter_type='string_filter',
filter_dimension=True,
field_name='pagePath',
match_type=Filter.StringFilter.MatchType.EXACT,
filter_values='/Page/1',
filter_case=True)
df = report.run_report()
from GoogleAnalytics4 import GA4
report = GA4.BuildReport(property_id='123456789',
ga_dimensions=['pagePath', 'pageTitle'],
ga_metrics=['screenPageViews', 'activeUsers', 'averageSessionDuration'],
start_date='2023-02-01',
end_date='today')
report.add_filter(filter_type='in_list_filter',
filter_dimension=True,
field_name='pagePath',
filter_values=['/', '/Page/1', '/Page/2'],
filter_case=False)
df = report.run_report()
The operation applied to a numeric filter:
- OPERATION_UNSPECIFIED = 0
- EQUAL = 1
- LESS_THAN = 2
- LESS_THAN_OR_EQUAL = 3
- GREATER_THAN = 4
- GREATER_THAN_OR_EQUAL = 5
The operation can be added using:
Filter.NumericFilter.Operation.GREATER_THAN_OR_EQUALFilter.NumericFilter.Operation(5)
from GoogleAnalytics4 import GA4
from google.analytics.data_v1beta.types import Filter, NumericValue
report = GA4.BuildReport(property_id='123456789',
ga_dimensions=['pagePath', 'pageTitle'],
ga_metrics=['screenPageViews', 'activeUsers', 'averageSessionDuration'],
start_date='2023-02-01',
end_date='today')
report.add_filter(filter_type='numeric_filter',
filter_dimension=True,
field_name='dayOfWeek',
operation=Filter.NumericFilter.Operation.GREATER_THAN_OR_EQUAL,
filter_values=NumericValue({'int64_value': '5'}))
df = report.run_report()
Can only be used with a GA4 Metrics, not dimensions
from GoogleAnalytics4 import GA4
from google.analytics.data_v1beta.types import Filter, NumericValue
report = GA4.BuildReport(property_id='123456789',
ga_dimensions=['pagePath', 'pageTitle'],
ga_metrics=['screenPageViews', 'activeUsers', 'averageSessionDuration'],
start_date='2023-02-01',
end_date='today')
report.add_filter(filter_type='between_filter',
filter_dimension=False,
field_name='screenPageViews',
from_value=NumericValue({'int64_value': '100'}),
to_value=NumericValue({'int64_value': '200'}))
df = report.run_report()