Splitwise Python API Docs | dltHub

Splitwise is a platform and REST API for managing shared expenses, groups, friends, and balances between users. The REST API base URL is https://secure.splitwise.com/api/v3.0 and OAuth1 (and OAuth2) or API key via query/header; OAuth‑based authentication required for most endpoints..

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 Splitwise data in under 10 minutes.

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What data can I load from Splitwise?

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

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How do I authenticate with the Splitwise API?

Splitwise supports OAuth 1.0a (consumer_key, consumer_secret, oauth_token, oauth_token_secret) and OAuth 2 (access_token). Some endpoints also accept an API‑key query parameter for limited access. Tokens are sent via the Authorization header (OAuth1) or Bearer header (OAuth2).

1. Get your credentials

1. Register an app or request API credentials by contacting developers@splitwise.com or via the API docs / GitHub repo.
2. Obtain consumer_key and consumer_secret from Splitwise (self‑serve or developer approval).
3. Use OAuth 1.0a flow: call the authorize URL, exchange for oauth_token and oauth_token_secret. For OAuth 2, redirect the user to the authorize URL and exchange the code for an access token.
4. Store consumer_key, consumer_secret, oauth_token, and oauth_token_secret in your dlt secrets.toml.

2. Add them to .dlt/secrets.toml

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[sources.splitwise_source]
consumer_key = "your_consumer_key"
consumer_secret = "your_consumer_secret"
oauth_token = "your_oauth_token"
oauth_token_secret = "your_oauth_token_secret"

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 Splitwise 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 splitwise_pipeline.py

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

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Pipeline splitwise_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset splitwise_data
The duckdb destination used duckdb:/splitwise.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 splitwise_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 expenses and groups from the Splitwise 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 splitwise_source(consumer_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://secure.splitwise.com/api/v3.0",
            "auth": {
                "type": "oauth1",
                "oauth_token": consumer_key,
            },
        },
        "resources": [
            {"name": "expenses", "endpoint": {"path": "get_expenses", "data_selector": "expenses"}},
            {"name": "groups", "endpoint": {"path": "get_groups", "data_selector": "groups"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="splitwise_pipeline",
        destination="duckdb",
        dataset_name="splitwise_data",
    )
    load_info = pipeline.run(splitwise_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("splitwise_pipeline").dataset()
sessions_df = data.expenses.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM splitwise_data.expenses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("splitwise_pipeline").dataset()
data.expenses.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 Splitwise 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, verify that the OAuth token is valid and that consumer_key/consumer_secret and the token pair are correct. For OAuth1 ensure the oauth_signature is computed correctly; for OAuth2 ensure the Authorization: Bearer <access_token> header is present. Some endpoints also accept an api_key query parameter for limited access.

Rate limits and API Terms

Splitwise enforces conservative rate limits for the self‑serve API and may throttle or block apps that exceed them. Expect 429 Too Many Requests responses; back off and retry with exponential backoff. For higher limits contact developers@splitwise.com.

Pagination

List endpoints such as get_expenses, get_groups, and get_friends are paginated. Use the page and per_page query parameters where supported, and continue requesting pages until an empty result set is returned.

Common error responses

• 200 with an errors object: write endpoints may return success status but include an errors field that needs to be inspected.
• 401 Invalid API key or OAuth access token.
• 403 Forbidden – insufficient permissions.
• 404 Not Found – invalid resource ID.
• 400 Bad Request – malformed parameters.

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