Load csv23 data to DuckDB

csv23 is a Python library that provides a unicode-based API for handling CSV files, ensuring compatibility across Python 2 and 3 environments. Everything needed to build a working csv23 → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your csv23 to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from csv23 to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the csv23 API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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csv23 API at a glance

These values come from the csv23 API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the csv23 API?

The documentation indicates that csv23 is a local Python library for CSV file handling rather than a REST API, and therefore lacks authentication mechanisms; the provided example uses a placeholder URL and auth configuration for demonstration purposes only.

1. Get your credentials

To obtain your API credentials for csv-api: 1. Sign in to your account dashboard at csv-api.com. 2. Navigate to the 'API Keys' or 'Settings' section. 3. Generate a new key: use a public ('pk_') key for read-only access or a private ('sk_') key for read/write operations. 4. Securely store your key immediately upon generation.

2. Add them to .dlt/secrets.toml

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[sources.csv23_source]
api_key = "your_secret_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What csv23 data can I load into DuckDB?

These are the csv23 endpoints dlt can load into DuckDB:

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How do I load only new csv23 records?

The csv23 API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "open_csv", "endpoint": {
    "path": "open_csv",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated csv23 pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v1/datasets/{public_id}/records and /api/v1/datasets/{public_id}/records/{id} from the csv23 API into DuckDB:

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

@dlt.source
def csv23_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://pypi.org/project/csv23/",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "open_csv", "endpoint": {"path": "open_csv"}},
            {"name": "iterrows", "endpoint": {"path": "iterrows"}}
        ],
    }
    yield from rest_api_resources(config)


def load_csv23_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="csv23_pipeline",
        destination="duckdb",
        dataset_name="csv23_data",
    )
    load_info = pipeline.run(csv23_source())
    print(load_info)


if __name__ == "__main__":
    load_csv23_to_duckdb()

Run it with python csv23_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query csv23 data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("csv23_pipeline").dataset()
df = data.open_csv.df()
print(df.head())

SQL:

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SELECT * FROM csv23_data.open_csv LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the csv23 to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw csv23 loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load csv23 data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack