How To Gain Access To Google Analytics API Via Python

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[]The Google Analytics API offers 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:

  • Construct custom dashboards to display GA data.
  • Automate complex reporting tasks.
  • Incorporate with other applications.

[]You can access the API response using a number of different techniques, consisting of Java, PHP, and JavaScript, but this post, in specific, will focus on accessing and exporting information utilizing Python.

[]This post will just cover some of the methods that can be used to access various subsets of information using different metrics and measurements.

[]I wish to compose a follow-up guide exploring different methods you can evaluate, imagine, and integrate the information.

Setting Up The API

Creating A Google Service Account

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

[]Once this has been produced, the next step is to pick the + Produce Service Account button.

Screenshot from Google Cloud, December 2022 You will then be promoted to include 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, navigate to the KEYS area and add a new key. Screenshot from Google Cloud, December 2022 [] This will trigger you to create and download a personal secret. In this instance, select JSON, and after that produce and

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

Contribute To Google Analytics Account

[]You will likewise wish to take a copy of the e-mail that has actually been generated for the service account– this can be found on the main account page.

Screenshot from Google Cloud, December 2022 The next action is to add that email []as a user in Google Analytics with Analyst approvals. Screenshot from Google Analytics, December 2022

Enabling The API The last and perhaps crucial step is guaranteeing you have actually enabled access to the API. To do this, ensure you remain in the appropriate job and follow this link to allow access.

[]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 action, 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 begin composing the []script to export the data. I selected Jupyter Notebooks to produce this, however you can also utilize other incorporated developer

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

Some are distinct to the analytics API, and others work for future areas of the code.! pip set up– upgrade google-api-python-client! pip3 install– upgrade oauth2client from apiclient.discovery import construct from oauth2client.service _ account import ServiceAccountCredentials! pip install connect! pip set up functions import connect Note: When using pip in a Jupyter notebook, include the!– if running in the command line or another IDE, the! isn’t needed. Developing A Service Develop The next step 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 generated when producing the personal key. This

[]is used in a comparable way to an API key. To easily access this file within your code, guarantee you

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

[]Finally, add the view ID from the analytics account with which you want to access the information. Screenshot from author, December 2022 Altogether

[]this will look 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 added our personal crucial file, we can include this to the qualifications function by calling the file and setting it up through the ServiceAccountCredentials action.

[]Then, set up the build report, calling the analytics reporting API V4, and our currently defined credentials from above.

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

Composing The Request Body

[]When we have everything set up and specified, the genuine enjoyable starts.

[]From the API service build, there is the ability to choose the elements from the response that we wish to access. This is called a ReportRequest things and needs the following as a minimum:

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

[]View ID

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

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

[]Date Variety

[]Then we can include the date range for the dates that we want to collect the data for. This consists of a start date and an end date.

[]There are a number of ways to compose this within the develop demand.

[]You can choose defined 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 view information from the last thirty days, you can set the start date as ’30daysAgo’ and the end date as ‘today.’

[]Metrics And Dimensions

[]The final action of the standard response call is setting the metrics and measurements. Metrics are the quantitative measurements from Google Analytics, such as session count, session period, and bounce rate.

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

[]There are a great deal of different metrics and measurements that can be accessed. I won’t go through all of them in this post, but they can all be discovered together with extra info and attributes here.

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

[]Both the metrics and dimensions are added in a dictionary format, using key: worth sets. For metrics, the secret will be ‘expression’ followed by the colon (:-RRB- and after that the value of our metric, which will have a particular 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 wished to see a count of all brand-new users.

[]With dimensions, the secret will be ‘name’ followed by the colon again and the worth of the dimension. For example, if we wanted to draw out the various page paths, it would be ‘name’: ‘ga: pagePath’.

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

[]Combining Dimensions And Metrics

[]The genuine worth remains in combining metrics and measurements to extract the crucial insights we are most interested in.

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

reaction = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [‘startDate’: ’30daysAgo’, ‘endDate’: ‘today’], ‘metrics’: [], ‘dimensions’: []] ). perform()

Developing A DataFrame

[]The action we obtain from the API remains in the type of a dictionary, with all of the information in secret: worth sets. To make the information much easier to see and analyze, we can turn it into a Pandas dataframe.

[]To turn our action into a dataframe, we first need to create some empty lists, to hold the metrics and measurements.

[]Then, calling the response output, we will append the data from the measurements 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(‘measurements’, [] 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, measurements): dim.append(dimension) for i, worths in enumerate(dateRangeValues): for metricHeader, value in zip(metricHeaders, values.get(‘values’)): metric.append(int(worth)) []Including The Response 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 appointing 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 Demand Examples Numerous Metrics There is likewise the capability 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 also request the API response just returns metrics that return particular criteria by including metric filters. It uses the following format:

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

action = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [‘startDate’: ’30daysAgo’, ‘endDate’: ‘today’], ‘metrics’: [], ‘dimensions’: [], “metricFilterClauses”: []] ). execute() []Filters also work for measurements in a comparable method, however the filter expressions will be a little various due to the characteristic nature of dimensions.

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

reaction = service.reports(). batchGet( body= ). carry out()


[]As metrics are quantitative measures, there is also the capability to write expressions, which work likewise to computed metrics.

[]This involves defining an alias to represent the expression and finishing a mathematical function on 2 metrics.

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

action = service.reports(). batchGet( body= ). execute()


[]The API also lets you container dimensions with an integer (numerical) value into varieties using histogram buckets.

[]For example, bucketing the sessions count dimension into four buckets of 1-9, 10-99, 100-199, and 200-399, you can use the HISTOGRAM_BUCKET order type and specify the ranges in histogramBuckets.

action = service.reports(). batchGet( body= ). execute() Screenshot from author, December 2022 In Conclusion I hope this has supplied you with a fundamental guide to accessing the Google Analytics API, writing some various requests, and gathering some meaningful insights in an easy-to-view format. I have actually included 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 plans for exploring []the information even more. More resources: Included Image: BestForBest/Best SMM Panel