How To Access Google Analytics API Via Python

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[]The Google Analytics API supplies access to Google Analytics (GA) report information such as pageviews, sessions, traffic source, and bounce rate.

[]The main Google documentation discusses that it can be used to:

  • Develop custom dashboards to display GA data.
  • Automate complex reporting jobs.
  • Incorporate with other applications.

[]You can access the API response using a number of different approaches, including Java, PHP, and JavaScript, but this short article, in particular, will concentrate on accessing and exporting data utilizing Python.

[]This post will simply cover some of the methods that can be used to access various subsets of data utilizing various metrics and dimensions.

[]I hope to write a follow-up guide checking out different methods you can evaluate, visualize, and combine the data.

Establishing The API

Developing A Google Service Account

[]The primary step is to produce a project or choose one within your Google Service Account.

[]When this has actually been created, the next step is to pick the + Create 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= "//"alt="Service Account Details"width="1152"height=" 1124"data-src=""/ > Screenshot from Google Cloud, December 2022 Once the service account has actually been produced, navigate to the secret section and add a brand-new secret. Screenshot from Google Cloud, December 2022 [] This will trigger you to develop and download a personal secret. In this circumstances, select JSON, and then produce and

await the file to download. Screenshot from Google Cloud, December 2022

Contribute To Google Analytics Account

[]You will likewise want to take a copy of the email that has actually been created 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 Analyst authorizations. Screenshot from Google Analytics, December 2022

Making it possible for The API The final and arguably most important action is guaranteeing you have made it possible for access to the API. To do this, ensure you are in the right job and follow this link to allow gain access to.

[]Then, follow the actions to allow it when promoted.

Screenshot from Google Cloud, December 2022 This is needed in order to access the API. If you miss this action, you will be triggered to complete it when first running the script. Accessing The Google Analytics API With Python Now everything is set up in our service account, we can start composing the []script to export the information. I chose Jupyter Notebooks to develop this, however you can also utilize other incorporated developer

[]environments(IDEs)consisting of PyCharm or VSCode. Installing Libraries The initial step is to set up the libraries that are required to run the remainder of the code.

Some are special to the analytics API, and others work for future areas of the code.! pip install– upgrade google-api-python-client! pip3 set up– upgrade oauth2client from apiclient.discovery import develop from oauth2client.service _ account import ServiceAccountCredentials! pip install link! pip set up functions import link Note: When using pip in a Jupyter note pad, add the!– if running in the command line or another IDE, the! isn’t required. Producing A Service Construct The next step is to set []up our scope, which is the read-only analytics API authentication link. This is followed by the customer tricks JSON download that was produced when producing the private key. This

[]is utilized in a comparable method to an API key. To quickly access this file within your code, ensure 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, add the view ID from the analytics account with which you wish to access the information. Screenshot from author, December 2022 Altogether

[]this will appear like the following. We will reference these functions throughout our code.

SCOPES = [‘’] KEY_FILE_LOCATION=’client_secrets. json’ VIEW_ID=’XXXXX’ []Once we have added our private crucial file, we can add this to the qualifications operate by calling the file and setting it up through the ServiceAccountCredentials step.

[]Then, set up the develop report, calling the analytics reporting API V4, and our already specified qualifications from above.

qualifications = ServiceAccountCredentials.from _ json_keyfile_name(KEY_FILE_LOCATION, SCOPES) service = build(‘analyticsreporting’, ‘v4’, credentials=qualifications)

Writing The Demand Body

[]When we have whatever established and defined, the real enjoyable starts.

[]From the API service construct, there is the ability to select the components from the response that we wish to gain access to. This is called a ReportRequest object and needs the following as a minimum:

  • A legitimate view ID for the viewId field.
  • At least one valid entry in the dateRanges field.
  • A minimum of one valid entry in the metrics field.

[]View ID

[]As discussed, there are a few things that are needed during this develop stage, beginning with our viewId. As we have already specified formerly, we simply need to call that function name (VIEW_ID) instead of adding the entire view ID again.

[]If you wanted to gather information from a different analytics view in the future, you would just require to alter the ID in the initial code block rather than both.

[]Date Variety

[]Then we can add the date variety for the dates that we wish to collect the information for. This consists of a start date and an end date.

[]There are a couple of methods to write this within the build request.

[]You can choose specified dates, for instance, between 2 dates, by including the date in a year-month-date format, ‘startDate’: ‘2022-10-27’, ‘endDate’: ‘2022-11-27’.

[]Or, if you want to see data from the last 1 month, you can set the start date as ’30daysAgo’ and completion date as ‘today.’

[]Metrics And Dimensions

[]The final step of the standard action 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 characteristics of users, their sessions, and their actions. For instance, page course, traffic source, and keywords used.

[]There are a great deal of different metrics and dimensions that can be accessed. I won’t go through all of them in this article, however they can all be found together with extra details and attributes here.

[]Anything you can access in Google Analytics you can access in the API. This consists of goal conversions, begins and values, the web browser device used to access the website, landing page, second-page course tracking, and internal search, website speed, and audience metrics.

[]Both the metrics and dimensions are added in a dictionary format, using secret: value sets. For metrics, the secret will be ‘expression’ followed by the colon (:-RRB- and then the worth of our metric, which will have a particular format.

[]For instance, if we wished to get a count of all sessions, we would include ‘expression’: ‘ga: sessions’. Or ‘expression’: ‘ga: newUsers’ if we wished to see a count of all new users.

[]With measurements, the secret will be ‘name’ followed by the colon again and the value of the measurement. For instance, if we wanted to extract the various page courses, it would be ‘name’: ‘ga: pagePath’.

[]Or ‘name’: ‘ga: medium’ to see the various traffic source recommendations to the site.

[]Combining Measurements And Metrics

[]The real worth remains in integrating metrics and dimensions to draw out the key insights we are most interested in.

[]For example, to see a count of all sessions that have actually been produced from different traffic sources, we can set our metric to be ga: sessions and our dimension to be ga: medium.

response = service.reports(). batchGet( body= ‘reportRequests’: [] ). execute()

Creating A DataFrame

[]The action we obtain from the API is in the kind of a dictionary, with all of the information in key: value sets. To make the information much easier to view and evaluate, we can turn it into a Pandas dataframe.

[]To turn our action into a dataframe, we initially need to produce some empty lists, to hold the metrics and dimensions.

[]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 extract the data and include it to our formerly 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(‘measurements’, [] dateRangeValues = row.get(‘metrics’, [] for header, measurement in zip(dimensionHeaders, measurements): dim.append(measurement) for i, worths in enumerate(dateRangeValues): for metricHeader, worth in zip(metricHeaders, values.get(‘values’)): metric.append(int(worth)) []Including The Response Data

[]As soon as the information remains in those lists, we can quickly turn them into a dataframe by specifying the column names, in square brackets, and designating the list worths to each column.

df = pd.DataFrame() df [” Sessions”] = metric df [” Medium”] = dim df= df [[ “Medium”,”Sessions”]] df.head()

< img src= "" alt="DataFrame Example"/ > More Reaction Request Examples Multiple Metrics There is likewise 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 likewise request the API response only returns metrics that return particular criteria by including metric filters. It utilizes the following format:

if return the metric []For example, if you only wanted to draw out pageviews with more than 10 views.

action = service.reports(). batchGet( body= ‘reportRequests’: [] ). carry out() []Filters also work for measurements in a comparable way, but the filter expressions will be somewhat various due to the characteristic nature of dimensions.

[]For instance, if you just wish to extract pageviews from users who have actually visited the site using the Chrome internet browser, you can set an EXTRACT operator and usage ‘Chrome’ as the expression.

action = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [‘startDate’: ’30daysAgo’, ‘endDate’: ‘today’], ‘metrics’: [‘expression’: ‘ga: pageviews’], “measurements”: [“name”: “ga: internet browser”], “dimensionFilterClauses”: []] ). execute()


[]As metrics are quantitative procedures, there is likewise the ability to write expressions, which work similarly to calculated metrics.

[]This includes specifying an alias to represent the expression and completing a mathematical function on two metrics.

[]For example, you can compute completions per user by dividing the number of conclusions by the variety of users.

response = service.reports(). batchGet( body= ). perform()


[]The API also lets you bucket dimensions with an integer (numeric) worth into ranges using histogram containers.

[]For instance, bucketing the sessions count dimension into 4 containers of 1-9, 10-99, 100-199, and 200-399, you can use the HISTOGRAM_BUCKET order type and specify the varieties in histogramBuckets.

action = service.reports(). batchGet( body= ‘reportRequests’: [] ). execute() Screenshot from author, December 2022 In Conclusion I hope this has actually offered you with a fundamental guide to accessing the Google Analytics API, writing some various demands, and collecting some meaningful insights in an easy-to-view format. I have included the construct and request code, and the bits shared to this GitHub file. I will like to hear if you attempt any of these and your prepare for checking out []the data further. More resources: Featured Image: BestForBest/SMM Panel