Google Geocoding API Python API Docs | dltHub

Google Geocoding API is a service that converts addresses into geographic coordinates (latitude and longitude) and vice versa. The REST API base URL is https://maps.googleapis.com/maps/api/geocode/outputFormat and https://geocode.googleapis.com and All requests require an API key for authentication..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Google Geocoding API data in under 10 minutes.

---

What data can I load from Google Geocoding API?

Here are some of the endpoints you can load from Google Geocoding API:

---

How do I authenticate with the Google Geocoding API API?

Authentication requires an API key, which must be included as a key=<API_KEY> query parameter in all requests.

1. Get your credentials

1. Enable the Geocoding API in your Google Cloud project.
2. Obtain an API key from the Google Cloud Console.
3. Ensure billing is enabled for your Google Maps Platform project.

2. Add them to .dlt/secrets.toml

Copy
[sources.google_geocoding_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Google Geocoding API API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python google_geocoding_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline google_geocoding_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_geocoding_data
The duckdb destination used duckdb:/google_geocoding.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline google_geocoding_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads geocode and geocode_address from the Google Geocoding API API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def google_geocoding_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://maps.googleapis.com/maps/api/geocode/outputFormat and https://geocode.googleapis.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "geocode", "endpoint": {"path": "maps/api/geocode/json", "data_selector": "results"}},
            {"name": "geocode_address", "endpoint": {"path": "v4beta/geocode/address", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_geocoding_pipeline",
        destination="duckdb",
        dataset_name="google_geocoding_data",
    )
    load_info = pipeline.run(google_geocoding_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("google_geocoding_pipeline").dataset()
sessions_df = data.geocode.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM google_geocoding_data.geocode LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("google_geocoding_pipeline").dataset()
data.geocode.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Google Geocoding API data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Troubleshooting

Common Status Codes and Errors

The Google Geocoding API returns a status field in its JSON response, indicating the outcome of the request. Understanding these status codes is crucial for troubleshooting.

• OK: Indicates that no errors occurred, and at least one geocode result was returned.
• ZERO_RESULTS: Signifies that the geocode was successful but returned no results. This may occur if a non-existent address is provided.
• OVER_DAILY_LIMIT: This status can indicate several issues:
  • The API key is missing or invalid.
  • Billing has not been enabled on your Google Cloud account for the Maps Platform.
  • A self-imposed usage cap has been exceeded.
• OVER_QUERY_LIMIT: Indicates that you are over your quota for the API. This can be due to sending requests too quickly.
• REQUEST_DENIED: Your request was denied, possibly due to incorrect API key restrictions or other security settings.
• INVALID_REQUEST: Generally means that a required parameter (such as address, components, or latlng) is missing from the request.
• UNKNOWN_ERROR: Indicates a server-side error that could be temporary. Retrying the request might resolve the issue.

Component and Country Restrictions

When restricting results, use the components parameter with component:value pairs separated by | to fully restrict results. While bounds, region, and language parameters can bias results, only components provides full restriction. For country filtering, use country:ISO_CODE within the components parameter.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack