How To Gain Access To Google Analytics API Via Python

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

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

  • Build customized dashboards to show GA data.
  • Automate complex reporting tasks.
  • Incorporate with other applications.

[]You can access the API response using several different methods, consisting of Java, PHP, and JavaScript, however this article, in particular, will concentrate on accessing and exporting information utilizing Python.

[]This article will simply cover a few of the methods that can be utilized to access various subsets of information using various metrics and measurements.

[]I wish to write a follow-up guide exploring various methods you can analyze, visualize, and combine the data.

Setting Up The API

Developing A Google Service Account

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

[]When this has actually been developed, the next action is to choose the + Produce 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 been produced, browse to the KEYS section and include a brand-new key. Screenshot from Google Cloud, December 2022 [] This will trigger you to develop and download a personal secret. In this instance, select JSON, and after that develop and

wait for 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 been produced for the service account– this can be found on the main account page.

Screenshot from Google Cloud, December 2022 The next step is to include that email []as a user in Google Analytics with Analyst permissions. Screenshot from Google Analytics, December 2022

Making it possible for The API The final and probably crucial action is ensuring you have actually made it possible for access to the API. To do this, guarantee you remain in the proper task and follow this link to make it possible for gain access to.

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

Screenshot from Google Cloud, December 2022 This is needed in order to access the API. If you miss this step, you will be prompted to complete 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 start composing the []script to export the data. I chose Jupyter Notebooks to produce this, however you can also utilize other integrated developer

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

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

[]is utilized in a similar method to an API secret. 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.

[]Finally, include the view ID from the analytics account with which you want to access the data. Screenshot from author, December 2022 Entirely

[]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 actually included our personal essential file, we can include this to the credentials 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 currently specified credentials from above.

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

Composing The Request Body

[]As soon as we have everything set up and specified, the genuine enjoyable starts.

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

  • A legitimate view ID for the viewId field.
  • At least 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 develop phase, beginning with our viewId. As we have actually already specified formerly, we simply require to call that function name (VIEW_ID) rather than adding the entire view ID once again.

[]If you wanted to collect data from a different analytics see in the future, you would simply need to alter the ID in the initial code block instead of both.

[]Date Range

[]Then we can add the date variety for the dates that we wish to gather the data for. This includes a start date and an end date.

[]There are a number of ways to compose this within the build request.

[]You can select specified dates, for instance, in between two 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 information from the last 1 month, you can set the start date as ’30daysAgo’ and the end date as ‘today.’

[]Metrics And Measurements

[]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 period, and bounce rate.

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

[]There are a great deal of various metrics and dimensions that can be accessed. I will not go through all of them in this post, however they can all be found together with extra information and associates here.

[]Anything you can access in Google Analytics you can access in the API. This includes goal conversions, begins and values, the internet 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, utilizing key: worth pairs. 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 example, 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 secret will be ‘name’ followed by the colon again and the value of the measurement. For instance, if we wanted to draw out the different 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 genuine value remains in combining metrics and dimensions to draw out the key insights we are most thinking about.

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

reaction = service.reports(). batchGet( body= ‘reportRequests’: [] ). carry out()

Creating A DataFrame

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

[]To turn our reaction into a dataframe, we initially require to develop some empty lists, to hold the metrics and measurements.

[]Then, calling the response output, we will add the data from the measurements into the empty measurements list and a count of the metrics into the metrics list.

[]This will extract 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(‘measurements’, [] metricHeaders = columnHeader.get(‘metricHeader’, ). get(‘metricHeaderEntries’, [] rows = report.get(‘data’, ). get(‘rows’, [] for row in rows: dimensions = row.get(‘measurements’, [] dateRangeValues = row.get(‘metrics’, [] for header, measurement in zip(dimensionHeaders, measurements): dim.append(dimension) for i, worths in enumerate(dateRangeValues): for metricHeader, worth in zip(metricHeaders, values.get(‘worths’)): metric.append(int(value)) []Adding The Action Data

[]As soon as the data is 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= "" alt="DataFrame Example"/ > More Action Request Examples Numerous Metrics There is also the ability to combine several metrics, with each set added in curly brackets and separated by a comma. ‘metrics’: [“expression”: “ga: pageviews”, “expression”: “ga: sessions”] Filtering []You can likewise ask for the API response only returns metrics that return certain criteria by including metric filters. It uses the following format:

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

response = service.reports(). batchGet( body= ). carry out() []Filters likewise work for measurements in a similar method, however the filter expressions will be a little different due to the particular nature of dimensions.

[]For instance, if you only wish to extract pageviews from users who have gone to the website using the Chrome 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 procedures, there is likewise the capability to compose expressions, which work likewise to determined metrics.

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

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

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


[]The API likewise lets you pail dimensions with an integer (numerical) value into varieties utilizing pie chart buckets.

[]For example, bucketing the sessions count dimension into four pails of 1-9, 10-99, 100-199, and 200-399, you can utilize the HISTOGRAM_BUCKET order type and specify the varieties 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, composing some various demands, and collecting some significant insights in an easy-to-view format. I have actually added the develop and request code, and the snippets shared to this GitHub file. I will enjoy to hear if you attempt any of these and your prepare for checking out []the data further. More resources: Included Image: BestForBest/Best SMM Panel