Change Python API Docs | dltHub

Change is a donations-as-a-service platform that lets apps and sites create, list, and manage donations and nonprofit data via a REST API. The REST API base URL is https://api.getchange.io and All requests require HTTP Basic auth with a public and secret key (pk_..., sk_...)..

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 Change data in under 10 minutes.

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What data can I load from Change?

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

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How do I authenticate with the Change API?

Authentication uses HTTP Basic Auth with a public key (username) and secret key (password). Alternatively set an Authorization header with Basic <base64(pk:sk)>.

1. Get your credentials

1. Sign up at https://api.getchange.io/sign_up. 2) In the Change dashboard locate API keys; you will have pk_test/sk_test and pk_live/sk_live pairs. 3) Use test keys for sandbox and live keys in production.

2. Add them to .dlt/secrets.toml

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[sources.change_api_source]
public_key = "pk_test_your_public_key"
secret_key = "sk_test_your_secret_key"

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 Change 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 change_api_pipeline.py

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

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Pipeline change_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset change_api_data
The duckdb destination used duckdb:/change_api.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 change_api_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 donations and nonprofits from the Change 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 change_api_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.getchange.io",
            "auth": {
                "type": "http_basic",
                "public_key, secret_key": credentials,
            },
        },
        "resources": [
            {"name": "donations", "endpoint": {"path": "api/v1/donations", "data_selector": "donations"}},
            {"name": "nonprofits", "endpoint": {"path": "api/v1/nonprofits", "data_selector": "nonprofits"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="change_api_pipeline",
        destination="duckdb",
        dataset_name="change_api_data",
    )
    load_info = pipeline.run(change_api_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("change_api_pipeline").dataset()
sessions_df = data.donations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM change_api_data.donations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("change_api_pipeline").dataset()
data.donations.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 Change 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 get 401/403, verify you are using the correct key pair and that Basic auth is formed correctly (username=public key, password=secret key) or Authorization: Basic <base64(pk:sk)>. Use test keys in sandbox.

Pagination quirks

List endpoints (e.g., GET /api/v1/donations, /api/v1/nonprofits) are paginated. Use page (default 1) and limit parameters where supported. Donations returns up to 30 per page.

Rate limiting and errors

The docs do not document an exact rate limit; handle 429 responses by backing off and retrying. Standard API errors return JSON with an "error" key for not found or other failures (e.g., {"error":"Nonprofit not found"}). Ensure Content-Type: application/json for POSTs and include Accept: application/json for GETs.

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