PayPal Python API Docs | dltHub

PayPal is an online payments platform that provides REST APIs for processing payments, orders, refunds, captures, webhooks, invoicing, and related payment operations. The REST API base URL is https://api-m.paypal.com (live), https://api-m.sandbox.paypal.com (sandbox) and all requests require an OAuth 2.0 Bearer access token for authentication.

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 PayPal data in under 10 minutes.

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What data can I load from PayPal?

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

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How do I authenticate with the PayPal API?

Exchange your REST API app client ID and client secret for an OAuth2 access token via POST /v1/oauth2/token using HTTP Basic auth; include the token in requests with header Authorization: Bearer ACCESS_TOKEN.

1. Get your credentials

1. Sign in to developer.paypal.com. 2. Go to Dashboard > My Apps & Credentials. 3. Under REST API apps, create or select an app. 4. Copy the client ID and client secret for sandbox or live environment. 5. Use them to request an access token at /v1/oauth2/token.

2. Add them to .dlt/secrets.toml

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[sources.paypal_transactions_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_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 PayPal 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 paypal_transactions_pipeline.py

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

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Pipeline paypal_transactions_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset paypal_transactions_data
The duckdb destination used duckdb:/paypal_transactions.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 paypal_transactions_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 payments and orders from the PayPal 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 paypal_transactions_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-m.paypal.com (live), https://api-m.sandbox.paypal.com (sandbox)",
            "auth": {
                "type": "http_basic",
                "token": client_secret,
            },
        },
        "resources": [
            {"name": "payments", "endpoint": {"path": "v1/payments/payment", "data_selector": "payments"}},
            {"name": "orders", "endpoint": {"path": "v2/checkout/orders"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="paypal_transactions_pipeline",
        destination="duckdb",
        dataset_name="paypal_transactions_data",
    )
    load_info = pipeline.run(paypal_transactions_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("paypal_transactions_pipeline").dataset()
sessions_df = data.payments.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM paypal_transactions_data.payments LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("paypal_transactions_pipeline").dataset()
data.payments.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 PayPal 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 invalid_client errors when requesting an access token, verify you are using the correct client_id and client_secret for the environment (sandbox vs live) with HTTP Basic auth to POST /v1/oauth2/token and that the Authorization header for API calls is "Bearer ACCESS_TOKEN". For token requests ensure Content-Type: application/x-www-form-urlencoded and body grant_type=client_credentials.

Rate limits and 429 responses

PayPal may return 429 Too Many Requests when hitting rate limits. Inspect the Retry-After header, back off exponentially, and reduce request rate. Consider using webhooks to avoid polling.

Pagination quirks

Several list endpoints (e.g., GET /v1/payments/payment) return a wrapper object with keys like "payments" plus pagination fields such as "count" and "next_id"; use next_id or start_id/start_index parameters to page. Newer v2 APIs use standard page links in responses.

Error response format

Client errors (4xx) return JSON with fields: name, message, debug_id, information_link, and details (array). Each detail contains field, value, location, issue, and description. Do not rely on the human-readable description for logic; use machine-safe issue codes where available.

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