Polldaddy Python API Docs | dltHub

Polldaddy is a survey and poll platform that provides a JSON API for managing polls, votes, and results. The REST API base URL is api.polldaddy.com and Requests require a partnerGUID API key and a userCode token obtained via pdInitiate..

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 Polldaddy data in under 10 minutes.

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What data can I load from Polldaddy?

Here are some of the endpoints you can load from Polldaddy:

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How do I authenticate with the Polldaddy API?

Authentication is performed by including the partnerGUID API key and the userCode token inside the top‑level pdRequest object of each JSON POST request. The request must include the header Content-Type: application/json.

1. Get your credentials

1. Log in to your Crowdsignal (Polldaddy) account.
2. Navigate to Account Settings → API Keys (or similar section) and click Create API Key. Copy the generated partnerGUID.
3. Use the pdInitiate method (shown in the API docs) with your partnerGUID, email and password to obtain a userCode token.
4. Store both the partnerGUID and the returned userCode; they will be used in every request.
5. In dlt, place the partnerGUID in the secrets.toml file as the API key.

2. Add them to .dlt/secrets.toml

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[sources.polldaddy_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 Polldaddy 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 polldaddy_pipeline.py

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

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Pipeline polldaddy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset polldaddy_data
The duckdb destination used duckdb:/polldaddy.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 polldaddy_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 get_polls and get_poll_results from the Polldaddy 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 polldaddy_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "api.polldaddy.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "get_polls", "endpoint": {"path": "", "data_selector": "pdResponse.demands.demand.polls.poll"}},
            {"name": "get_poll_results", "endpoint": {"path": "", "data_selector": "pdResponse.demands.demand.result.answers.answer"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="polldaddy_pipeline",
        destination="duckdb",
        dataset_name="polldaddy_data",
    )
    load_info = pipeline.run(polldaddy_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("polldaddy_pipeline").dataset()
sessions_df = data.get_polls.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM polldaddy_data.get_polls LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("polldaddy_pipeline").dataset()
data.get_polls.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 Polldaddy 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

If the partnerGUID or userCode is missing or invalid, the API returns an error indicating authentication failed. Verify that the API key is correct and that you have successfully called pdInitiate to obtain a fresh userCode.

Rate limiting

The documentation does not publish explicit request limits, but excessive calls may result in generic HTTP 429 responses. Implement exponential back‑off and respect any Retry‑After header.

Pagination

Many list endpoints accept a <list> node with start and end attributes to page results. The response includes a total attribute indicating the total number of items available. Use these attributes to request subsequent pages.

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