Store Locator Widgets Python API Docs | dltHub

Store Locator Widgets is a service that provides location management and search APIs for retrieving store location data. The REST API base URL is https://www.storelocatorwidgets.com/api/ and All requests require a Store Locator Widgets Location Management API key..

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 Store Locator Widgets data in under 10 minutes.

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What data can I load from Store Locator Widgets?

Here are some of the endpoints you can load from Store Locator Widgets:

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How do I authenticate with the Store Locator Widgets API?

The API uses a Location Management API key (uid) which is supplied as a query parameter named uid on each request.

1. Get your credentials

1. Log in to your Store Locator Widgets account.
2. Navigate to the Your Details (or Account Details) page.
3. Locate the field labeled Store Locator API key or Location Management API key.
4. Copy the displayed UID value; this is the API key to use in requests.
5. Optionally, store the key securely for use in your DLT secrets.toml file.

2. Add them to .dlt/secrets.toml

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[sources.store_locator_widgets_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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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:

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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 Store Locator Widgets 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:

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python store_locator_widgets_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline store_locator_widgets_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.

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Python pipeline example

This example loads locations and search_by_address from the Store Locator Widgets API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def store_locator_widgets_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.storelocatorwidgets.com/api/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "locations", "endpoint": {"path": "locations", "data_selector": "locations"}},
            {"name": "search_by_address", "endpoint": {"path": "searchByAddress/{address}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="store_locator_widgets_pipeline",
        destination="duckdb",
        dataset_name="store_locator_widgets_data",
    )
    load_info = pipeline.run(store_locator_widgets_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.

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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):

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import dlt

data = dlt.pipeline("store_locator_widgets_pipeline").dataset()
sessions_df = data.locations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM store_locator_widgets_data.locations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("store_locator_widgets_pipeline").dataset()
data.locations.df().head()

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

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What destinations can I load Store Locator Widgets 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.

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Troubleshooting

Authentication Errors

• 401 Unauthorized – The API key (uid) is missing, malformed, or invalid. Verify that the uid query parameter is included and matches the value shown in your account details.

Rate Limiting

• 429 Too Many Requests – The service limits the number of requests per minute. Reduce request frequency or implement exponential back‑off.

Bad Request

• 400 Bad Request – The request URL or parameters are malformed (e.g., missing required path variables). Ensure all placeholders such as {locationid} or {address} are correctly URL‑encoded.

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

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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.

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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