Paystack Python API Docs | dltHub

Paystack is a payments platform that provides APIs to accept and manage online payments, customers, payment pages, and related settlement/transfer operations. The REST API base URL is https://api.paystack.co and all requests require a Bearer secret key in the Authorization header.

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 Paystack data in under 10 minutes.

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What data can I load from Paystack?

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

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How do I authenticate with the Paystack API?

Paystack uses API keys. Every request must include your secret key in the Authorization header using the Bearer scheme: Authorization: Bearer YOUR_SECRET_KEY.

1. Get your credentials

1. Sign in to your Paystack Dashboard (https://dashboard.paystack.com).
2. Navigate to Settings → API Keys & Webhooks (or Developers → API Keys & Webhooks).
3. Copy the Secret Key (sk_...) for the desired environment (test or live).
4. Store the secret key securely and rotate it if compromised.

2. Add them to .dlt/secrets.toml

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[sources.paystack_source]
api_key = "sk_test_xxx"

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 Paystack 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 paystack_pipeline.py

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

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Pipeline paystack_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset paystack_data
The duckdb destination used duckdb:/paystack.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 paystack_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 transactions and customers from the Paystack 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 paystack_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.paystack.co",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "transactions", "endpoint": {"path": "transactions", "data_selector": "data"}},
            {"name": "customers", "endpoint": {"path": "customer", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM paystack_data.transactions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("paystack_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 Paystack 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 failures

If you receive 401 Unauthorized or 403 Forbidden, verify your Authorization header is present and uses a valid secret key (starts with sk_test_ or sk_live_ depending on environment). Ensure you are using secret keys (not public keys) for backend API calls.

Rate limiting and 429 responses

Paystack enforces rate limits. If you receive HTTP 429 Too Many Requests, back off and retry after a delay. Implement exponential backoff and respect Retry-After header if provided.

Pagination quirks

Most list endpoints return paginated results under the data key and include pagination metadata in a meta key. Paystack supports offset and cursor pagination depending on the endpoint — check the endpoint docs and the Pagination page for details; pass page/perPage or cursor params as documented.

Common API error formats

Errors are returned with appropriate HTTP status codes and JSON bodies. Authentication and permission errors use 401/403; validation errors use 400 with message details. The response body typically contains fields such as status and message and (for successful list responses) data and meta.

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