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Google analytics data api basics

Skill hamzabellouch/agent-skills/AI API and Agent Platform/google-analytics-data-api-basics

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Manages Google Analytics reporting data, enables the Analytics Data API via the Cloud CLI, and creates reports using the Google Analytics Data API (v1beta). Use when you need to interact with Google Analytics properties, run customized analytics reports, query metrics (like activeUsers, screenPageViews) and dimensions (like city, date), check metrics and dimensions compatibility, or verify API enablement.

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Getting Started with Google Analytics Data API

The Google Analytics Data API v1beta provides programmatic access to Google Analytics report data. It allows you to build customized dashboards, automate reporting workflows, and integrate Google Analytics data into your enterprise applications.

Enabling the API via Cloud CLI

Before making API calls, ensure the Google Analytics Data API is enabled in your Google Cloud project.

If gcloud is not found, prompt the user to install the Google Cloud CLI before running these commands.

  1. Enable the API: Use the Cloud CLI (gcloud) to enable analyticsdata.googleapis.com.

    gcloud services enable analyticsdata.googleapis.com --quiet

    Why: Enabling the API ensures your Cloud project has the necessary quota and permissions allocated for running Google Analytics reports.

  2. Verify API Enablement:

    gcloud services list --enabled --filter="analyticsdata.googleapis.com"

Authentication

To authenticate your API requests, you must generate Application Default Credentials (ADC) and give your account the necessary scopes. Run the following command in your terminal:

gcloud auth application-default login --scopes="https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/analytics.readonly"

Why: This configures ADC in your local environment with the required Cloud Platform and Google Analytics read-only scopes, allowing the client library to automatically authenticate your requests.

Creating a Data API Report (v1beta)

To create a report, use the official Google Analytics Data client library. Always prefer the v1beta version of the API for stability and access to current Google Analytics reporting capabilities.

[!IMPORTANT] Mandatory Agent Directive: When the user selects or requires a specific programming language, read the corresponding client library setup reference guide in references/ listed below.

Supported Client Libraries

Python

If you need to install or set up the Google Analytics Data API client library for Python, read the setup guide:

Java

If you need to install or set up the Google Analytics Data API client library for Java, read the setup guide:

PHP

If you need to install or set up the Google Analytics Data API client library for PHP, read the setup guide:

Node.js

If you need to install or set up the Google Analytics Data API client library for Node.js, read the setup guide:

Go

If you need to install or set up the Google Analytics Data API client library for Go, read the setup guide:

.NET

If you need to install or set up the Google Analytics Data API client library for .NET / C#, read the setup guide:

Ruby

If you need to install or set up the Google Analytics Data API client library for Ruby, read the setup guide:

[!NOTE] Additional Resources: For further examples of calling the Data API with Java, PHP, Node.js, .NET, Python and REST, as well as hints on authentication with a service account, refer to the official Data API Quickstart.

Python Quick Start

  1. Install the Client Library:

    pip install google-analytics-data

    If pip is not available, prompt the user to install pip before installing the client library.

  2. Run a Report Request: Below is a complete example demonstrating how to query a Google Analytics property for active users and sessions grouped by city and date. Replace YOUR-PROPERTY-ID with your actual Google Analytics property ID (e.g., 1234567).

    from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import DateRange, Dimension, Metric, RunReportRequest

def sample_run_report(property_id: str):
    # Initialize the client.
    # Assumes Application Default Credentials (ADC) are configured in your environment.
    client = BetaAnalyticsDataClient()

    request = RunReportRequest(
        property=f"properties/{property_id}",
        dimensions=[
            Dimension(name="city"),
            Dimension(name="date")
        ],
        metrics=[
            Metric(name="activeUsers"),
            Metric(name="sessions")
        ],
        date_ranges=[
            DateRange(start_date="2026-05-01", end_date="today")
        ],
    )

    response = client.run_report(request)

    print(f"Report result for property {property_id}:")
    for row in response.rows:
        print(
            f"City: {row.dimension_values[0].value}, "
            f"Date: {row.dimension_values[1].value}, "
            f"Active Users: {row.metric_values[0].value}, "
            f"Sessions: {row.metric_values[1].value}"
        )

if __name__ == "__main__":
    sample_run_report("YOUR-PROPERTY-ID")

    Why: Using BetaAnalyticsDataClient and RunReportRequest ensures compatibility with the v1beta endpoint and strongly typed request validation.

Metrics and Dimensions Schema

When constructing your RunReportRequest, you must use valid API names for dimensions and metrics. Refer to the official Data API Schema documentation for the complete, authoritative list of available fields.

Commonly Used Dimensions

Dimensions represent categorical attributes of your data.

  • city: The town or city of the user.
  • country: The country of the user.
  • date: The date of the event, formatted as YYYYMMDD.
  • deviceCategory: The category of mobile device (e.g., desktop, mobile, tablet).
  • eventName: The name of the triggered event.
  • pageTitle: The title of the web page.

Commonly Used Metrics

Metrics represent quantitative measurements.

  • activeUsers: The number of active users.
  • eventCount: The total count of events.
  • sessions: The total number of sessions.
  • screenPageViews: The number of app screens or web pages viewed.
  • totalRevenue: The total revenue from purchases, subscriptions, and advertising.

Metrics and Dimensions Compatibility Check

Some dimensions and metrics cannot be queried together in the same report request. If you encounter an INVALID_ARGUMENT error regarding incompatible fields, verify your field combinations For programmatic access to the Data API schema, use getMetadata(). To programmatically check the compatibility of specific dimension and metric combinations before running a report, use the checkCompatibility() method.

from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import CheckCompatibilityRequest, Compatibility, Dimension, Metric

def sample_check_compatibility(property_id: str):
    client = BetaAnalyticsDataClient()

    # Define the dimensions and metrics you want to query together.
    # For example, checking if 'itemDescription' (an e-commerce dimension)
    # is compatible with 'activeUsers' and 'totalRevenue'.
    request = CheckCompatibilityRequest(
        property=f"properties/{property_id}",
        dimensions=[
            Dimension(name="itemDescription"),
            Dimension(name="date")
        ],
        metrics=[
            Metric(name="activeUsers"),
            Metric(name="totalRevenue")
        ],
    )
    response = client.check_compatibility(request)

    print(f"Compatibility check for property {property_id}:")
    for dim in response.dimension_compatibilities:
        is_compatible = dim.compatibility == Compatibility.COMPATIBLE
        print(f"Dimension '{dim.dimension_metadata.api_name}' is compatible: {is_compatible}")

    for metric in response.metric_compatibilities:
        is_compatible = metric.compatibility == Compatibility.COMPATIBLE
        print(f"Metric '{metric.metric_metadata.api_name}' is compatible: {is_compatible}")

if __name__ == "__main__":
    sample_check_compatibility("YOUR-PROPERTY-ID")

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