2000charge Python API Docs | dltHub

2000charge is a payment processing platform that provides a REST API for managing transactions, customers, and related financial data. The REST API base URL is https://api.2000charge.com/api and All requests require HTTP Basic Auth with the API key as the username and an empty password..

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 add "dlt[hub]" and start loading 2000charge data in under 10 minutes.

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What data can I load from 2000charge?

Here are some of the endpoints you can load from 2000charge:

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How do I authenticate with the 2000charge API?

Include an Authorization header generated by HTTP Basic Auth where the username is the API key and the password is left blank.

1. Get your credentials

1. Log in to your 2000charge merchant account.
2. Navigate to Settings → API (or Developers → API Keys).
3. Click Create New API Key or Reveal Existing Key.
4. Copy the displayed secret key; this will be used as the username for HTTP Basic Auth.
5. Store the key securely – it will be referenced in the secrets.toml file.

2. Add them to .dlt/secrets.toml

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[sources.api_2000charge_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 init
uv add "dlt[hub]"

1. Install the dlt AI Workbench:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 2000charge 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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uv run python api_2000charge_pipeline.py

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

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Pipeline api_2000charge_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset api_2000charge_data
The duckdb destination used duckdb:/api_2000charge.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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uv run dlthub 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 transactions and customers from the 2000charge 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 api_2000charge_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.2000charge.com/api",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "transactions", "endpoint": {"path": "transactions", "data_selector": "transactions"}},
            {"name": "customers", "endpoint": {"path": "customers", "data_selector": "customers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="api_2000charge_pipeline",
        destination="duckdb",
        dataset_name="api_2000charge_data",
    )
    load_info = pipeline.run(api_2000charge_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("api_2000charge_pipeline").dataset()
sessions_df = data.transactions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM api_2000charge_data.transactions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("api_2000charge_pipeline").dataset()
data.transactions.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 2000charge 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 – Returned when the API key is missing or invalid. Ensure the api_key is correct and supplied as the username in HTTP Basic Auth.
• api_keys_not_provided – Explicit error code indicating no credentials were sent.

Parameter and Request Errors

• 400 Bad Request – Triggered by invalid_object_sent or missing_required_parameter. Verify request payloads and required query parameters.
• api_error – Generic server‑side failure; retry with exponential back‑off.

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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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-runtime

• 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