Many users look to Google Analytics 4 to track user engagement, behavior, and conversions on their websites in real-time. In this tutorial, we will explore how to send data from your Python app to GA4. This involves understanding GA4, setting up your Python environment, creating a GA4 property, authenticating your Python app with GA4, and implementing data collection and analytics reporting for your app.

Understanding Google Analytics 4 and its benefits

Google Analytics 4 is the newest version of the popular web analytics tool by Google. It is a powerful tool that can help you understand your website's performance and user behavior. GA4 is designed to provide a more comprehensive understanding of user behavior by tracking user interactions across multiple platforms and devices.

If you are a site owner or marketer, GA4 can help you optimize your online presence by providing you with valuable insights into user behavior. With GA4, you can track user behavior across your website or mobile app and analyze user interaction data using events and parameters.

What is Google Analytics 4?

Google Analytics 4 is a web analytics tool that allows site owners to track user behavior and interactions across their website or mobile app. GA4 collects user data using events and parameters, allowing for a more comprehensive understanding of user behavior. With GA4, you can track user interactions such as clicks, scrolls, form submissions, and other events.

GA4 provides deeper insights into user behavior by tracking user interactions across multiple platforms and devices. This means that you can analyze user behavior on your website, mobile app, and other platforms, providing you with a more comprehensive understanding of your audience.

Key Features of Google Analytics 4

GA4 comes with several new features that can help you optimize your online presence. These features include:

• Event-driven data collection: GA4 collects data using events and parameters, allowing for a more granular analysis of user behavior.
• Cross-device tracking: GA4 allows you to track user behavior across multiple devices and platforms, providing you with a more comprehensive understanding of your audience.
• Deeper integration with Google Ads: GA4 provides deeper integration with Google Ads, allowing you to analyze the performance of your ads and optimize your campaigns.
• Built-in machine learning models: GA4 comes with built-in machine learning models that can help you identify patterns and trends in your data.
• Enhanced privacy controls: GA4 provides enhanced privacy controls that allow you to control the data you collect and how it is used.

How GA4 Differs from Universal Analytics

GA4 has several differences from its predecessor, Universal Analytics. While Universal Analytics focuses on pageviews and sessions, GA4 focuses on events and parameters. This means that GA4 allows for more granular analysis of user behavior by tracking clicks, scrolls, form submissions, and other interactions. GA4 also provides cross-device tracking, enabling site owners to track user behavior across multiple devices and platforms.

GA4 provides a more comprehensive understanding of user behavior by tracking user interactions across multiple platforms and devices. This means that you can analyze user behavior on your website, mobile app, and other platforms, providing you with a more complete picture of your audience.

Overall, GA4 is a powerful tool that can help you optimize your online presence by providing you with valuable insights into user behavior. If you are a site owner or marketer, GA4 is definitely worth considering.

Setting Up Your Python Environment

Before we start implementing GA4 data collection in Python, we need to set up our environment. This involves installing required Python libraries and configuring our Python project.

Setting up your Python environment is an important step in ensuring that you have all the necessary tools to work with the Google Analytics API. In addition to the google-analytics-data library, there are a number of other libraries that you may find useful when working with GA4 data. These include libraries for data visualization, data manipulation, and machine learning.

We recommend that for this tutorial you use a Jupyter Notebook, Visual Studio Code, or any other editor that supports Python notebooks.

Installing Required Python Libraries

To use GA4 with Python, we will need to install the google-analytics-data library. This library provides a Python wrapper for the GA4 API. We can install this library using pip:

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pip install google-analytics-data

In addition to the google-analytics-data library, there are several other libraries that you may find useful when working with GA4 data. These include:

• pandas - a library for data manipulation and analysis
• matplotlib - a library for data visualization
• seaborn - a library for data visualization
• scikit-learn - a library for machine learning

You can install these libraries using pip as well:

pip install pandas matplotlib seaborn scikit-learn

Configuring Your Python Project

After installing the required libraries, we need to configure our Python project to use the Google Analytics API. This involves creating a new project in the Google Cloud Console, enabling the Analytics API, and creating a credentials file.

The Google Cloud Console is a web-based interface for managing your Google Cloud Platform projects. To create a new project, go to the Google Cloud Console and click the "Create Project" button.

Next, we need to enable the Analytics API for our project. To do this, navigate to the "APIs & Services" section of the Cloud Console and click the "Enable APIs and Services" button. Search for "Google Analytics API" and click the "Enable" button.

Finally, we need to create a credentials file that our Python project can use to authenticate with the Google Analytics API. To do this, navigate to the "Credentials" section of the Cloud Console and click the "Create Credentials" button. Select "Service Account Key" as the credential type, and follow the prompts to create a new service account and download a JSON key file.

Once you have your credentials file, you can use it to authenticate with the Google Analytics API in your Python project. You can find more information on how to do this in the Google Analytics API documentation.

Creating a Google Analytics 4 Property

Before we can start sending data to GA4 from our Python app, we need to create a new GA4 property. Google Analytics 4 (GA4) is the latest version of Google Analytics, and it offers a more advanced and insightful way of tracking user behavior on your website or app. With GA4, you can get a better understanding of your users' interactions with your content and make data-driven decisions to improve your business.

Setting Up a New GA4 Property

To create a new GA4 property, you need to follow a few simple steps:

1. Log in to your Google Analytics account. If you don't have one, you can easily create one by signing up for a free account on the Google Analytics website.
2. Select the "Admin" tab in the left-hand menu. This will take you to the Admin page, where you can manage your Google Analytics account.
3. Under the "Property" section, click "Create Property". This will open a new page where you can set up your new GA4 property.
4. Select "Web" or "App" as the type of property you want to create, depending on whether you want to track a website or an app. If you choose "Web", you will need to enter your website's URL. If you choose "App", you will need to enter your app's name and select the platform (Android or iOS).
5. Follow the prompts to set up your property. You will need to enter some basic information about your website or app, such as the name and time zone.

Once you have created your GA4 property, you can start tracking user behavior on your website or app. However, before you can do that, you need to find your measurement ID.

Finding Your Measurement ID

Your measurement ID is a unique identifier for your GA4 property, and it is required to send data to GA4 using the API. To find your measurement ID, you need to follow these steps:

1. Open the "Data Streams" tab in your GA4 property. This is where you can manage the data streams that are associated with your property.
2. Select the data stream you want to use. If you have multiple data streams, make sure you select the one that corresponds to the website or app you want to track.
3. Copy the measurement ID from the "Measurement ID" field. This is a long string of letters and numbers that uniquely identifies your GA4 property.

Once you have your measurement ID, you can start sending data to GA4 using the API. This will allow you to track user behavior on your website or app and gain valuable insights into how your users interact with your content.

Authenticating Your Python App with Google Analytics 4

Before we can start sending data from our Python app to GA4, we need to authenticate our app with the GA4 API. This involves creating a service account, generating and downloading an API key, and granting permissions to the service account.

Creating a Service Account

To create a service account:

1. Open the Google Cloud Console
2. Select your project
3. Open the "IAM & admin" section
4. Select "Service Accounts"
5. Click "Create Service Account"
6. Follow the prompts to create a new service account

Generating and Downloading API Key for Authentication

To generate and download your API key:

1. Select the service account you just created
2. Open the "Keys" tab
3. Click "Add Key"
4. Select "JSON" as the key type
5. Click "Create" and save the JSON file to your local machine

Granting Permissions to the Service Account

To grant permissions to the service account:

1. Open your GA4 property
2. Select the "Admin" tab
3. Under the "Account" section, click "User Management"
4. Add the email address of your service account and grant it "Edit" permissions

Implementing Data Collection in Your Python App

Now that we've set up our Python environment, created a GA4 property, and authenticated our app with the GA4 API, we can start collecting page and custom events in our Python app. This involves building the data payload and sending events to GA4 measurement Protocol.

The GA4 Measurement Protocol is a Reporting API provided by Google Analytics 4 (GA4) that allows you to query and retrieve data from your GA4 properties programmatically. With this API, you can access the same data that is available in the GA4 user interface and retrieve it in a structured format for further analysis or integration with other systems.

Configure Google Credentials

Now we will need the key and the JSON file we downloaded earlier from the Google Cloud Console and assign the value here. To correctly authenticate, assign the JSON filename to:

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os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'ga4-project-xxxxxx-xxxxxxxxxxxx.json'

Sending Events to Google Analytics 4

The first step in sending data to GA4 is building the data payload. This payload includes the measurement ID, event parameters, and other data that you want to track.

To build the data payload, we can use the MeasurementProtocolBuilder class provided by the google-analytics-data library. Once the payload is built, we will make an api call and pass the payload to the GA4 Measurement protocol.

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from google.analytics.data_v1beta import BetaAnalyticsDataClient

from google.analytics.data_v1beta.types import (

DateRange,

Dimension,

Metric,

RunReportRequest,

)





def sample_run_report(property_id="YOUR-GA4-PROPERTY-ID"):




client = BetaAnalyticsDataClient()




request = RunReportRequest(

property=f"properties/{property_id}",

dimensions=[Dimension(name="city")],

 metrics=[Metric(name="activeUsers")],

date_ranges=[DateRange(start_date="2020-03-31", end_date="today")],

)

response = client.run_report(request)




print("Report result:")

for row in response.rows:

print(row.dimension_values[0].value, row.metric_values[0].value)

You can also view the full Python code in this github quickstart template.

Conclusion

In conclusion, sending data from your Python app to Google Analytics 4 is relatively straightforward. By following the steps outlined in this tutorial, you should now be able to set up your Python environment, create a new GA4 property, authenticate your Python app with the GA4 API, and implement data collection in your app.

By leveraging the power of GA4, you can gain valuable insights into user behavior and engagement on your website or mobile app. Whether you're a site owner or marketer, GA4 can help you optimize your online presence and drive conversions. Check out RudderStack's Python App to Google Analytics 4 integration.