Accessing Google Analytics API with Python
How To Access Google Analytics API Via Python
Are you looking for a way to access the Google Analytics API using Python? Google's analytics platform is a powerful tool, and accessing its data through the API is the most efficient way if you want to combine the data into custom applications and reports. In this guide, we'll show you how to access the Google Analytics API via Python so that you can use the data to make meaningful business decisions.
Step 1: Setup a google analytics project
The first step is to set up a Google Analytics project. This is a simple process that can be done in a few minutes. Go to the Google Developers Console and create a new project, then turn on the Google Analytics API. You should see the Google Analytics service appear under the APIs & Services tab. You will need to create credentials for your project, so click on "Create Credentials" and select OAuth client ID. Select “Web application” as the application type and enter any relevant information that is required.
Step 2: Install the Python Client Library
Once you have created your project, you will need to install the Python client library for Google Analytics. This is the library that will allow you to access the API. The easiest way to do this is install the library from the Python Package Index (PyPI). Open a terminal window and enter the following command:
pip install google-api-python-client
If everything installed correctly, you should now have access to the Google Analytics API.
Step 3: Authenticate with the API
The next step is to authenticate with the API. You will need to use the credentials that you created in the previous step. Use the following code to authenticate with the API:
from oauth2client.service_account import ServiceAccountCredentials
scopes = ['https://www.googleapis.com/auth/analytics.readonly']
credentials = ServiceAccountCredentials.from_json_keyfile_name('your-key-file-name.json', scopes=scopes)
# authorize and initialize the API
http = credentials.authorize(httplib2.Http())
# initialize the API
analytics = build('analytics', 'v3', http=http)
Replace 'your-key-file-name.json' with the name of the credentials file that was created in step 1. Once you have authenticated with the API, you are ready to start making requests.
Step 4: Make Requests
Now you can use the API to make requests for the data that you need. Each request will return a response in the form of a JSON object. You can use the response data to create custom reports and visualizations. Here is an example of a request for pageview data from the last 30 days:
# define the resource
resource = {
'reportRequests': [
{
'viewId': '12345678',
'dateRanges': [{'startDate': '30daysAgo', 'endDate': 'today'}],
'metrics': [{'expression': 'ga:pageviews'}]
}
]
}
# execute the query
response = analytics.reports().batchGet(
body=resource
).execute()
The response should look something like this:
{
'reports': [
{
'columnHeader': {
'metricHeader': {'metricHeaderEntries': [{'name': 'ga:pageviews', 'type': 'INTEGER'}]}
},
'data': {
'rows': [{'metrics': [{'values': [132322]}, {'values': [152030]}]}],
'totals': [{'values: [2743252]}]
}
}
]
}
You can use the response data to create custom visualizations and reports.
Conclusion
In this guide, we showed you how to access the Google Analytics API via Python. We walked through the setup process and showed you how to authenticate with the API and make requests for the data that you need. With this data, you can create custom visualizations and reports to better understand your users and their behavior. Happy coding!