Intake driver for Google Analytics Queries
conda install -c defusco -c conda-forge intake-google-analytics
All queries require
- the
view_idfor your Google Analytics account - a start date and end date
- at least one metric to compute
- Service account credentials JSON file
The easiest way to get the view_id is to use the
Analytics Explorer.
To authenticate to the Core Reporting API v4 you will need a service account and JSON credentials file.
Follow the steps here to prepare your account and download the credentials. Typically, this
file is called client_secrets.json.
To compute the total number of visitors between 5 days ago and yesterday the following query is constructed.
import intake
ds = intake.open_google_analytics_query(
view_id='<view_id>',
start_date='5DaysAgo',
end_date='yesterday',
metrics=['ga:users'],
credentials_path='client_secrets.json'
)
df = ds.read()
print(df)The output is
ga:users
0 301012
This driver accepts a number of formats for both start_date and end_date
- Specific string values:
'yesterday','today','<N>DaysAgo' - Datetime-parsable strings in format
'YYYY-MM-DD' pd.TimeStamp,datetime.date, ordatetime.datetimeobjects
These data types can be mixed. Here's a few examples
GA strings only:
ds = intake.open_google_analytics_query(
view_id='<view_id>',
start_date='30DaysAgo',
end_date='yesterday',
metrics=['ga:users'],
credentials_path='client_secrets.json'
)Datetime objects and GA strings:
import pandas as pd
ds = intake.open_google_analytics_query(
view_id='<view_id>',
start_date=pd.to_datetime('March 19, 2020'),
end_date='30DaysAgo',
metrics=['ga:users'],
credentials_path='client_secrets.json'
)Datetime objects with differencing:
import pandas as pd
import datetime as dt
ds = intake.open_google_analytics_query(
view_id='<view_id>',
start_date=dt.datetime.now() - pd.DateOffset(months=6),
end_date=pd.to_datetime('today') - dt.timedelta(days=1),
metrics=['ga:users'],
credentials_path='client_secrets.json'
)The easiest way to learn how to build queries and gather the required information is to use the Query Explorer. Refer to the UA Dimensions and Metrics explorer for documentation on all available fields.
Metrics are specified as a list of strings or dictionaries and required for all queries.
The output DataFrame will have columns that match the metric expressions.
When using the dictionary format the 'expression' key is required and the 'alias' key is
optional to change the column name in the DataFrame.
GA metric expressions
are supported for both string and dictionary inputs. Here's an example
of the supported types of metrics.
ds = intake.open_google_analytics_query(
view_id='<view_id>',
start_date='5DaysAgo',
end_date='yesterday',
metrics=[
'ga:users', # simple string value
'ga:goal1completions/ga:users', # metric expression as string
{'expression': 'ga:sessions'}, # the expression key is required for dicts
{
'expression': 'ga:sessionDuration/ga:sessions', # expression
'alias': 'time-per-session' # rename the column
}
],
credentials_path='client_secrets.json'
)In addition to metrics an optional list of fields can be provided as dimensions. See the UA Dimensions and Metrics Explorer for the full list of available dimensions. Dimensions are provided as a list of strings. Unlike metrics dimensions do not support aliasing.
ds = intake.open_google_analytics_query(
view_id='<view_id>',
start_date='5DaysAgo',
end_date='yesterday',
metrics=[
'ga:users',
{
'expression': 'ga:sessionDuration/ga:sessions',
'alias': 'time-per-session'
}
],
dimensions = ['ga:userType', 'ga:browser']
credentials_path='client_secrets.json'
)Filters can be applied on metrics or dimensions. See the filters documentation for more details.