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 explains 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 action utilizing a number of different techniques, consisting of Java, PHP, and JavaScript, however this short article, in specific, will concentrate on accessing and exporting information using Python.

[]This short article will just cover a few of the techniques that can be utilized to gain access to various subsets of data using various metrics and measurements.

[]I wish to write a follow-up guide checking out various ways you can evaluate, visualize, and combine the information.

Establishing The API

Developing A Google Service Account

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

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

wait on 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 email that has been produced 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 Analyst authorizations. Screenshot from Google Analytics, December 2022

Enabling The API The last and perhaps crucial step is guaranteeing you have made it possible for access to the API. To do this, guarantee you remain in the correct project 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 required in order to access the API. If you miss this action, you will be prompted to finish it when 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 information. I selected Jupyter Notebooks to produce this, but you can likewise use other integrated developer

[]environments(IDEs)including PyCharm or VSCode. Installing Libraries The primary step is to install the libraries that are needed to run the rest of the code.

Some are distinct 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 build from oauth2client.service _ account import ServiceAccountCredentials! pip install connect! pip set up functions import link Note: When utilizing pip in a Jupyter notebook, add the!– if running in the command line or another IDE, the! isn’t required. Developing A Service Build The next action 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 generated when creating the personal key. This

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

[]have conserved the JSON file in the 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 wish to access the information. Screenshot from author, December 2022 Entirely

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

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

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

Composing The Demand Body

[]Once we have whatever set up and defined, the real enjoyable starts.

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

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

[]View ID

[]As discussed, there are a few things that are needed throughout this build phase, beginning with our viewId. As we have actually already specified formerly, we just need to call that function name (VIEW_ID) rather than including the whole view ID once again.

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

[]Date Range

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

[]There are a couple of ways to compose this within the build demand.

[]You can choose specified dates, for example, 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 30 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 characteristics of users, their sessions, and their actions. For example, page course, 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 short article, but 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 objective conversions, begins and values, the internet browser device utilized to access the website, 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 secret: value sets. 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 instance, 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 measurements, the secret will be ‘name’ followed by the colon once again and the worth of the measurement. For example, if we wanted to draw out the different page paths, 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 worth is in combining metrics and dimensions to extract the crucial insights we are most thinking about.

[]For example, to see a count of all sessions that have actually been produced 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’: [], ‘metrics’: [], ‘dimensions’: []] ). perform()

Producing A DataFrame

[]The action we obtain from the API is in the kind of a dictionary, with all of the information in key: worth pairs. To make the data simpler to see and examine, 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 append the data from the dimensions into the empty measurements list and a count of the metrics into the metrics list.

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

[]As soon as the information is in those lists, we can quickly turn them into a dataframe by specifying the column names, in square brackets, and designating 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 Action Request Examples Several Metrics There is likewise the capability to integrate numerous metrics, with each pair added in curly brackets and separated by a comma. ‘metrics’: [“expression”: “ga: pageviews”, ] Filtering []You can likewise ask for the API response just returns metrics that return particular requirements by adding metric filters. It uses the following format:

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

reaction = service.reports(). batchGet( body= ‘reportRequests’: [‘viewId’: VIEW_ID, ‘dateRanges’: [], ‘metrics’: [], ‘measurements’: [‘name’: ‘ga: pagePath’], “metricFilterClauses”: []] ). carry out() []Filters likewise work for dimensions in a comparable way, but the filter expressions will be slightly various due to the particular nature of dimensions.

[]For instance, if you only wish to draw out pageviews from users who have actually checked out the website using the Chrome web 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’], “measurements”: [“name”: “ga: browser”], “dimensionFilterClauses”: [“filters”: []]] ). perform()

Expressions

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

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

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

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

Histograms

[]The API also lets you pail dimensions with an integer (numerical) worth into ranges utilizing histogram containers.

[]For example, bucketing the sessions count dimension into four 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.

reaction = service.reports(). batchGet( body= ‘reportRequests’: [] ). carry out() Screenshot from author, December 2022 In Conclusion I hope this has supplied you with a basic guide to accessing the Google Analytics API, writing some various demands, and collecting some meaningful insights in an easy-to-view format. I have actually added the construct and request code, and the snippets shared to this GitHub file. I will enjoy to hear if you attempt any of these and your plans for checking out []the information even more. More resources: Included Image: BestForBest/Best SMM Panel