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.
-
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.
-
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
-
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.
-
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 'itemName' (an e-commerce dimension)
# is compatible with 'activeUsers' and 'totalRevenue'.
request = CheckCompatibilityRequest(
property=f"properties/{property_id}",
dimensions=[
Dimension(name="itemName"),
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")