The Google Analytics API provides access to Google Analytics (GA) report information such as pageviews, sessions, traffic source, and bounce rate.
The official Google documents describes that it can be used to:
- Build customized dashboards to show GA information.
- Automate complex reporting jobs.
- Integrate with other applications.
This post will simply cover some of the methods that can be used to gain access to various subsets of data utilizing different metrics and dimensions.
I wish to compose a follow-up guide exploring various ways you can analyze, picture, and combine the information.
Setting Up The API
Developing A Google Service Account
The first step is to develop a task or select one within your Google Service Account.
When this has actually been developed, the next step is to pick the + Develop Service Account button.
Screenshot from Google Cloud, December 2022 You will then be promoted to add some information such as a name, ID, and description.< img src= "// www.w3.org/2000/svg%22%20viewBox=%220%200%201152%201124%22%3E%3C/svg%3E"alt="Service Account Details"width="1152"height=" 1124"data-src="https://cdn.searchenginejournal.com/wp-content/uploads/2022/12/screenshot-2022-12-12-at-20.20.21-639b81474320f-sej.png"/ > Screenshot from Google Cloud, December 2022 Once the service account has been created, navigate to the secret section and add a new secret. Screenshot from Google Cloud, December 2022  This will trigger you to create and download a private secret. In this circumstances, select JSON, and after that develop and
await the file to download. Screenshot from Google Cloud, December 2022
Add To Google Analytics Account
You will likewise wish to take a copy of the email that has actually been produced for the service account– this can be discovered on the primary account page.
Screenshot from Google Cloud, December 2022 The next step is to include that e-mail as a user in Google Analytics with Expert authorizations. Screenshot from Google Analytics, December 2022
Enabling The API The final and perhaps crucial step is guaranteeing you have made it possible for access to the API. To do this, guarantee you are in the right project and follow this link to allow gain access to.
Then, follow the steps to enable it when promoted.
Screenshot from Google Cloud, December 2022 This is required in order to access the API. If you miss this step, you will be prompted to complete it when first running the script. Accessing The Google Analytics API With Python Now everything is established in our service account, we can start writing the script to export the data. I picked Jupyter Notebooks to develop this, however you can also use other integrated designer
environments(IDEs)consisting of PyCharm or VSCode. Setting up Libraries The first step is to install the libraries that are required to run the rest of the code.
Some are special to the analytics API, and others are useful for future areas of the code.! pip install– upgrade google-api-python-client! pip3 set up– upgrade oauth2client from apiclient.discovery import construct from oauth2client.service _ account import ServiceAccountCredentials! pip install connect! pip set up functions import link Note: When using pip in a Jupyter notebook, include the!– if running in the command line or another IDE, the! isn’t required. Developing A Service Build The next action is to set up our scope, which is the read-only analytics API authentication link. This is followed by the customer secrets JSON download that was produced when developing the private secret. This
is used in a similar method to an API secret. To easily access this file within your code, guarantee you
have actually saved the JSON file in the exact same folder as the code file. This can then quickly be called with the KEY_FILE_LOCATION function.
Lastly, include the view ID from the analytics account with which you wish to access the data. Screenshot from author, December 2022 Completely
this will look like the following. We will reference these functions throughout our code.
SCOPES = [‘ https://www.googleapis.com/auth/analytics.readonly’] KEY_FILE_LOCATION=’client_secrets. json’ VIEW_ID=’XXXXX’ Once we have added our personal crucial file, we can include this to the credentials function by calling the file and setting it up through the ServiceAccountCredentials step.
Then, established the build report, calling the analytics reporting API V4, and our already defined qualifications from above.
credentials = ServiceAccountCredentials.from _ json_keyfile_name(KEY_FILE_LOCATION, SCOPES) service = construct(‘analyticsreporting’, ‘v4’, qualifications=credentials)
Composing The Demand Body
As soon as we have everything established and defined, the genuine enjoyable starts.
From the API service build, there is the ability to select the aspects from the action that we wish to access. This is called a ReportRequest things and requires the following as a minimum:
- A legitimate view ID for the viewId field.
- A minimum of one legitimate entry in the dateRanges field.
- A minimum of one legitimate entry in the metrics field.
As discussed, there are a few things that are required throughout this build phase, starting with our viewId. As we have actually already specified formerly, we just need to call that function name (VIEW_ID) instead of adding the whole view ID again.
If you wanted to collect information from a various analytics view in the future, you would simply need to alter the ID in the initial code block instead of both.
Then we can add the date range for the dates that we want to collect the data for. This includes a start date and an end date.
There are a number of methods to compose this within the construct request.
You can pick specified dates, for instance, between 2 dates, by adding the date in a year-month-date format, ‘startDate’: ‘2022-10-27’, ‘endDate’: ‘2022-11-27’.
Or, if you wish to see data from the last 1 month, you can set the start date as ’30daysAgo’ and the end date as ‘today.’
Metrics And Dimensions
The final step of the fundamental response call is setting the metrics and dimensions. Metrics are the quantitative measurements from Google Analytics, such as session count, session duration, and bounce rate.
Dimensions are the attributes of users, their sessions, and their actions. For instance, page course, traffic source, and keywords utilized.
There are a lot of various metrics and dimensions that can be accessed. I will not go through all of them in this short article, however they can all be discovered together with extra info and associates here.
Anything you can access in Google Analytics you can access in the API. This includes objective conversions, starts and values, the web browser gadget used to access the site, landing page, second-page course tracking, and internal search, website speed, and audience metrics.
Both the metrics and dimensions are included a dictionary format, using secret: worth sets. For metrics, the key will be ‘expression’ followed by the colon (:-RRB- and after that the worth of our metric, which will have a specific format.
For instance, if we wanted to get a count of all sessions, we would add ‘expression’: ‘ga: sessions’. Or ‘expression’: ‘ga: newUsers’ if we wished to see a count of all new users.
With dimensions, the key will be ‘name’ followed by the colon once again and the value of the dimension. For example, if we wished to draw out the different page paths, it would be ‘name’: ‘ga: pagePath’.
Or ‘name’: ‘ga: medium’ to see the different traffic source referrals to the website.
Integrating Dimensions And Metrics
The genuine worth remains in integrating metrics and measurements to extract the key insights we are most interested in.
For instance, to see a count of all sessions that have actually been produced from various traffic sources, we can set our metric to be ga: sessions and our measurement to be ga: medium.
action = service.reports(). batchGet( body= ). execute()
Producing A DataFrame
The action we obtain from the API is in the type of a dictionary, with all of the data in secret: worth pairs. To make the information simpler to view and evaluate, we can turn it into a Pandas dataframe.
To turn our reaction into a dataframe, we first require to develop some empty lists, to hold the metrics and measurements.
Then, calling the reaction output, we will append the data from the dimensions into the empty measurements list and a count of the metrics into the metrics list.
This will draw out the data and add it to our previously empty lists.
dim =  metric =  for report in response.get(‘reports’, : columnHeader = report.get(‘columnHeader’, ) dimensionHeaders = columnHeader.get(‘dimensions’,  metricHeaders = columnHeader.get(‘metricHeader’, ). get(‘metricHeaderEntries’,  rows = report.get(‘information’, ). get(‘rows’,  for row in rows: dimensions = row.get(‘dimensions’,  dateRangeValues = row.get(‘metrics’,  for header, measurement in zip(dimensionHeaders, measurements): dim.append(measurement) for i, values in enumerate(dateRangeValues): for metricHeader, worth in zip(metricHeaders, values.get(‘worths’)): metric.append(int(worth)) Including The Reaction Data
When the data remains in those lists, we can easily turn them into a dataframe by specifying the column names, in square brackets, and assigning the list worths to each column.
df = pd.DataFrame() df [” Sessions”] = metric df [” Medium”] = dim df= df [[ “Medium”,”Sessions”]] df.head()
< img src= "https://cdn.searchenginejournal.com/wp-content/uploads/2022/12/screenshot-2022-12-13-at-20.30.15-639b817e87a2c-sej.png" alt="DataFrame Example"/ > More Response Request Examples Several Metrics There is also the ability to integrate numerous metrics, with each pair added in curly brackets and separated by a comma. ‘metrics’: [“expression”: “ga: pageviews”, ] Filtering You can also ask for the API action just returns metrics that return particular requirements by adding metric filters. It uses the following format:
if metricName operator comparisonValue return the metric For instance, if you only wanted to draw out pageviews with more than ten views.
reaction = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [‘startDate’: ’30daysAgo’, ‘endDate’: ‘today’], ‘metrics’: [‘expression’: ‘ga: pageviews’], ‘measurements’: , “metricFilterClauses”: [“filters”: [“metricName”: “ga: pageviews”, “operator”: “GREATER_THAN”, “comparisonValue”: “10”]]] ). execute() Filters also work for dimensions in a comparable method, but the filter expressions will be slightly different due to the particular nature of measurements.
For instance, if you only want to extract pageviews from users who have actually visited the site utilizing the Chrome web browser, you can set an EXTRACT operator and usage ‘Chrome’ as the expression.
response = service.reports(). batchGet( body= ‘reportRequests’:  ). carry out()
As metrics are quantitative steps, there is likewise the capability to compose expressions, which work likewise to computed metrics.
This involves specifying an alias to represent the expression and completing a mathematical function on 2 metrics.
For example, you can determine conclusions per user by dividing the variety of completions by the variety of users.
action = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [‘startDate’: ’30daysAgo’, ‘endDate’: ‘today’], “metrics”: [ga: users”, “alias”: “completions per user”]] ). perform()
The API likewise lets you pail measurements with an integer (numeric) value into ranges utilizing pie chart containers.
For example, bucketing the sessions count measurement into four pails of 1-9, 10-99, 100-199, and 200-399, you can use the HISTOGRAM_BUCKET order type and specify the ranges in histogramBuckets.
reaction = service.reports(). batchGet( body= ). perform() Screenshot from author, December 2022 In Conclusion I hope this has actually supplied you with a fundamental guide to accessing the Google Analytics API, writing some different requests, and gathering some meaningful insights in an easy-to-view format. I have added the develop and ask for code, and the bits shared to this GitHub file. I will enjoy to hear if you try any of these and your plans for exploring the data further. More resources: Included Image: BestForBest/Best SMM Panel