How To Gain Access To Google Analytics API Via Python

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

[]The official Google documents describes that it can be utilized to:

  • Develop customized dashboards to show GA information.
  • Automate complex reporting tasks.
  • Incorporate with other applications.

[]You can access the API action utilizing several various approaches, including Java, PHP, and JavaScript, but this short article, in specific, will concentrate on accessing and exporting information utilizing Python.

[]This short article will just cover some of the techniques that can be used to gain access to various subsets of data using various metrics and dimensions.

[]I intend to write a follow-up guide exploring various methods you can analyze, picture, and integrate the data.

Establishing The API

Developing A Google Service Account

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

[]When this has been produced, the next action is to choose the + Develop Service Account button.

Screenshot from Google Cloud, December 2022 You will then be promoted to include some details 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 Particulars"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 actually been produced, navigate to the KEYS section and add a brand-new secret. Screenshot from Google Cloud, December 2022 [] This will trigger you to develop and download a private secret. In this circumstances, choose JSON, and after that create 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 main account page.

Screenshot from Google Cloud, December 2022 The next action is to include that e-mail []as a user in Google Analytics with Expert authorizations. Screenshot from Google Analytics, December 2022

Allowing The API The last and perhaps essential step is guaranteeing you have actually enabled access to the API. To do this, guarantee you are in the proper task and follow this link to make it possible for access.

[]Then, follow the actions to allow 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 triggered to finish it when very first running the script. Accessing The Google Analytics API With Python Now whatever is set up in our service account, we can begin composing the []script to export the information. I selected Jupyter Notebooks to develop this, however you can likewise use other incorporated designer

[]environments(IDEs)including PyCharm or VSCode. Putting up Libraries The first step is to set up the libraries that are required to run the rest of the code.

Some are unique to the analytics API, and others are useful for future areas of the code.! pip set up– upgrade google-api-python-client! pip3 set up– upgrade oauth2client from apiclient.discovery import develop from oauth2client.service _ account import ServiceAccountCredentials! pip install connect! pip install functions import connect Note: When using pip in a Jupyter note pad, add the!– if running in the command line or another IDE, the! isn’t required. Creating A Service Build The next step 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 created when creating the private key. This

[]is used in a similar method to an API secret. To easily access this file within your code, ensure you

[]have actually saved the JSON file in the very same folder as the code file. This can then easily be called with the KEY_FILE_LOCATION function.

[]Finally, add the view ID from the analytics account with which you want 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 actually added our personal key file, we can include this to the credentials operate by calling the file and setting it up through the ServiceAccountCredentials step.

[]Then, established the construct 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 = build(‘analyticsreporting’, ‘v4’, credentials=qualifications)

Composing The Demand Body

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

[]From the API service build, there is the capability to pick the components from the reaction that we wish to access. This is called a ReportRequest item and needs the following as a minimum:

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

[]View ID

[]As discussed, there are a couple of things that are required during this build stage, starting with our viewId. As we have actually already defined formerly, we just need to call that function name (VIEW_ID) rather than including the entire view ID again.

[]If you wanted to gather data from a different analytics see in the future, you would just require to change the ID in the initial code block instead of both.

[]Date Range

[]Then we can include the date variety 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 develop demand.

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

[]Or, if you wish to view 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 standard reaction call is setting the metrics and dimensions. Metrics are the quantitative measurements from Google Analytics, such as session count, session duration, and bounce rate.

[]Measurements are the characteristics of users, their sessions, and their actions. For example, page path, traffic source, and keywords used.

[]There are a great deal of various metrics and dimensions that can be accessed. I won’t go through all of them in this post, 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 includes goal conversions, begins and values, the browser device used to access the website, landing page, second-page path tracking, and internal search, site speed, and audience metrics.

[]Both the metrics and measurements are added in a dictionary format, using key: worth pairs. For metrics, the secret will be ‘expression’ followed by the colon (:-RRB- and after that the worth of our metric, which will have a specific format.

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

[]With measurements, the secret will be ‘name’ followed by the colon once again and the value of the measurement. For example, if we wished to draw out the different page courses, it would be ‘name’: ‘ga: pagePath’.

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

[]Combining Dimensions And Metrics

[]The genuine worth is in integrating metrics and measurements to extract the key insights we are most thinking about.

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

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

Creating A DataFrame

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

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

[]Then, calling the reaction output, we will add the information from the dimensions into the empty dimensions list and a count of the metrics into the metrics list.

[]This will draw out the information 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(‘data’, ). get(‘rows’, [] for row in rows: dimensions = row.get(‘dimensions’, [] dateRangeValues = row.get(‘metrics’, [] for header, measurement in zip(dimensionHeaders, dimensions): dim.append(measurement) for i, values in enumerate(dateRangeValues): for metricHeader, value in zip(metricHeaders, values.get(‘values’)): metric.append(int(value)) []Adding The Action Data

[]As soon as the data is in those lists, we can quickly turn them into a dataframe by specifying the column names, in square brackets, and assigning the list values 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 Reaction Demand Examples Numerous Metrics There is also the capability to combine several metrics, with each set included curly brackets and separated by a comma. ‘metrics’: [, ] Filtering []You can likewise request the API reaction just returns metrics that return specific requirements by including metric filters. It utilizes the following format:

if metricName operator comparisonValue return the metric []For instance, if you only wished to extract pageviews with more than 10 views.

action = service.reports(). batchGet( body= ). execute() []Filters also work for dimensions in a comparable method, but the filter expressions will be slightly different due to the characteristic nature of dimensions.

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

reaction = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [], ‘metrics’: [‘expression’: ‘ga: pageviews’], “dimensions”: [], “dimensionFilterClauses”: []] ). perform()

Expressions

[]As metrics are quantitative measures, there is likewise the ability to compose expressions, which work similarly to computed metrics.

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

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

response = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [], “metrics”: [ga: users”, “alias”: “completions per user”]] ). perform()

Histograms

[]The API likewise lets you bucket dimensions with an integer (numeric) value into ranges utilizing histogram pails.

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

action = service.reports(). batchGet( body= ‘reportRequests’: [] ). perform() Screenshot from author, December 2022 In Conclusion I hope this has provided you with a standard guide to accessing the Google Analytics API, composing some various demands, and gathering some meaningful insights in an easy-to-view format. I have added the build and request code, and the snippets shared to this GitHub file. I will like to hear if you attempt any of these and your prepare for checking out []the information further. More resources: Featured Image: BestForBest/Best SMM Panel