Installation
PyGoogalytics can be installed using pip:
pip install -U pygoogalytics
Google Analytics API wrapper
client.py defines the class Client which builds googleapiclient.discovery resources for accessing
Google Search Console and Google Analytics 3 (UA) data from the respective APIs,
and also google.analytics.data_v1beta.BetaAnalyticsDataClient resource for accessing GA4 data, although
currently this is only for testing.
Before using this python package you must create a service account and download a JSON key file following the instructions from Google. This process will also give you a service account email address, you need to add this email address to the user list for the Analytics or Search Console account for which you want to obtain data — you only need to give the service account "read" access.
A Client class can create a GoogalyticsWrapper object which has methods to access data
the Google services and return a pandas dataframe.
A typical implementation will look like:
from pygoogalytics.client import Client
googalytics_client = Client(key_file_path='<path-to-your-key-file>')
g_wrapper = googalytics_client.wrapper(
sc_domain='<search-console-domain>',
view_id='<ga3-view-id>',
ga4_property_id='<ga4-property-id>'
)
ga3_dataframe = g_wrapper.get_df(
result='GA3',
start_date='2023-01-01',
end_date='2023-01-07',
metrics=['ga:itemRevenue', 'ga:itemQuantity', 'ga:users'],
dimensions=['ga:dateHourMinute', 'ga:landingPagePath', 'ga:sourceMedium', 'ga:countryIsoCode']
)
The get_df method accepts the following values for the result argument:
- "GSC": for Google Search Console data
- "GA3": for Google Analytics 3 (UA) data
- "URL": for Google Search Console URL inspection data
- "GA4": for Google Analytics 4 data (note, this is not yet available in production)
Advantages of PyGoogalytics
- When using the REST API for GA3 (UA) data one is allowed to request only 10 metrics at once. The PyGoogalytics wrapper allows the passing of any number of compatible dimensions and metrics, when more than ten metrics are passed, the list is partitioned into sub-lists of length 10 and separate API calls are made for each partition, the resulting dataframes are then joined on the dimensions to create a seamless dataframe with all requested metrics.
- Both the Search Console and GA3 APIs can return a maximum of 100k rows of data. The PyGoogalytics wrapper automatically paginates the requests and concatenates the results to return a dataframe of arbitrary length.
Google Pandas
googlepandas.py provides the classes GADataFrame and GSCDataFrame,
both children of pandas DataFrame specific to storing
GA and GSC data with additional metadata fields
(e.g. dimensions and metrics) and methods for adding columns
and filtering by particular metrics.
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Metadata
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Release files for pygoogalytics 0.1.4
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Total release size: 46.7 kB
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