How To Access Google Analytics API Via Python

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

[]The official Google paperwork discusses that it can be used to:

  • Construct custom control panels to show GA data.
  • Automate complex reporting tasks.
  • Integrate with other applications.

[]You can access the API reaction using a number of various approaches, consisting of Java, PHP, and JavaScript, however this article, in specific, will concentrate on accessing and exporting information using Python.

[]This article will just cover a few of the techniques that can be used to gain access to different subsets of data utilizing different metrics and measurements.

[]I wish to compose a follow-up guide exploring different ways you can evaluate, imagine, and combine the data.

Setting Up The API

Producing A Google Service Account

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

[]As soon as this has been produced, 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 Particulars"width="1152"height=" 1124"data-src=""/ > Screenshot from Google Cloud, December 2022 Once the service account has been produced, navigate to the secret section and add a brand-new key. Screenshot from Google Cloud, December 2022 [] This will trigger you to produce and download a personal key. In this circumstances, select JSON, and after that create and

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

Add To Google Analytics Account

[]You will also want to take a copy of the e-mail that has actually been created for the service account– this can be found on the primary account page.

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

Enabling The API The final and perhaps most important step is guaranteeing you have enabled access to the API. To do this, ensure you remain in the correct job and follow this link to enable gain access to.

[]Then, follow the steps 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 prompted to complete it when very first running the script. Accessing The Google Analytics API With Python Now whatever is established in our service account, we can begin composing the []script to export the data. I chose Jupyter Notebooks to produce this, but you can also use other integrated designer

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

Some are special to the analytics API, and others are useful for future sections of the code.! pip install– upgrade google-api-python-client! pip3 install– upgrade oauth2client from apiclient.discovery import develop from oauth2client.service _ account import ServiceAccountCredentials! pip set up link! pip install functions import link Note: When utilizing pip in a Jupyter note pad, include the!– if running in the command line or another IDE, the! isn’t required. Creating 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 customer secrets JSON download that was created when developing the private key. This

[]is utilized in a similar way to an API key. To quickly access this file within your code, guarantee you

[]have saved the JSON file in the exact 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 Entirely

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

[]Then, established the build report, calling the analytics reporting API V4, and our already defined credentials from above.

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

Composing The Demand Body

[]Once we have whatever set up and specified, the genuine enjoyable begins.

[]From the API service construct, there is the ability to pick the elements from the action that we want to access. This is called a ReportRequest item 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 valid entry in the metrics field.

[]View ID

[]As discussed, there are a few things that are needed during this build stage, starting with our viewId. As we have already specified previously, we simply need to call that function name (VIEW_ID) rather than adding the entire view ID again.

[]If you wished to collect information from a different analytics view in the future, you would simply need to alter the ID in the initial code block rather than both.

[]Date Range

[]Then we can include 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 develop request.

[]You can choose specified dates, for example, between 2 dates, by adding 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 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 reaction call is setting the metrics and measurements. 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 example, page path, traffic source, and keywords utilized.

[]There are a great deal of different metrics and measurements that can be accessed. I will not go through all of them in this article, however they can all be discovered together with additional info and attributes here.

[]Anything you can access in Google Analytics you can access in the API. This includes objective conversions, begins and values, the web browser device utilized to access the website, landing page, second-page path tracking, and internal search, website speed, and audience metrics.

[]Both the metrics and measurements are added in a dictionary format, utilizing key: value pairs. For metrics, the key will be ‘expression’ followed by the colon (:-RRB- and then the worth of our metric, which will have a specific 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 brand-new users.

[]With measurements, the key will be ‘name’ followed by the colon again and the worth of the dimension. 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 different traffic source recommendations to the site.

[]Combining Dimensions And Metrics

[]The real value remains in combining metrics and dimensions to draw out the key insights we are most thinking about.

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

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

Producing A DataFrame

[]The reaction we obtain from the API is in the type of a dictionary, with all of the information in key: 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 require to create some empty lists, to hold the metrics and dimensions.

[]Then, calling the action output, we will append the information 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 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: measurements = row.get(‘measurements’, [] dateRangeValues = row.get(‘metrics’, [] for header, dimension 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)) []Adding The Response Data

[]When the information is in those lists, we can easily turn them into a dataframe by defining 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 Action Demand Examples Numerous Metrics There is also the ability to integrate several metrics, with each pair added in curly brackets and separated by a comma. ‘metrics’: [, “expression”: “ga: sessions”] Filtering []You can also ask for the API response just returns metrics that return certain requirements by including metric filters. It utilizes the following format:

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

reaction = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [], ‘metrics’: [‘expression’: ‘ga: pageviews’], ‘dimensions’: [], “metricFilterClauses”: [“filters”: [“metricName”: “ga: pageviews”, “operator”: “GREATER_THAN”, “comparisonValue”: “10”]]] ). perform() []Filters likewise work for measurements in a comparable method, but the filter expressions will be slightly different due to the characteristic nature of dimensions.

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

action = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [], ‘metrics’: [‘expression’: ‘ga: pageviews’], “measurements”: [], “dimensionFilterClauses”: [“filters”: [“dimensionName”: “ga: web browser”, “operator”: “EXACT”, “expressions”: [” Chrome”]]]] ). carry out()


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

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

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

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


[]The API also lets you container measurements with an integer (numeric) value into ranges using histogram pails.

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

action = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [‘startDate’: ’30daysAgo’, ‘endDate’: ‘today’], “metrics”: [“expression”: “ga: sessions”], “dimensions”: [], “orderBys”: [“fieldName”: “ga: sessionCount”, “orderType”: “HISTOGRAM_BUCKET”]] ). execute() Screenshot from author, December 2022 In Conclusion I hope this has actually provided you with a basic guide to accessing the Google Analytics API, composing some various demands, and gathering some meaningful insights in an easy-to-view format. I have actually included the develop and ask for code, and the bits shared to this GitHub file. I will enjoy to hear if you attempt any of these and your plans for exploring []the information further. More resources: Included Image: BestForBest/Best SMM Panel