Spotify Python API Docs | dltHub

Spotify Web API is a RESTful API that allows developers to access Spotify's catalog data, user profiles, and playback controls. The REST API base URL is https://api.spotify.com/v1 and All requests require a Bearer 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 Spotify data in under 10 minutes.

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What data can I load from Spotify?

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

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How do I authenticate with the Spotify API?

The Spotify Web API uses OAuth 2.0 for authorization. Client credentials (client ID and client secret) are exchanged for an access token, which must be included in the 'Authorization' header of all API requests as a Bearer token.

1. Get your credentials

1. Go to the Spotify Developer Dashboard and create an application. 2. Once the app is created, you will have access to your client ID and client secret. 3. To obtain an access token, send a POST request to https://accounts.spotify.com/api/token with grant_type=client_credentials, client_id, and client_secret in the request body.

2. Add them to .dlt/secrets.toml

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[sources.spotify_ads_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 Spotify 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 spotify_ads_pipeline.py

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

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Pipeline spotify_ads_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset spotify_ads_data
The duckdb destination used duckdb:/spotify_ads.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 spotify_ads_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 artists and albums from the Spotify 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 spotify_ads_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.spotify.com/v1",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "artists", "endpoint": {"path": "artists/{id}"}},
            {"name": "albums", "endpoint": {"path": "albums/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="spotify_ads_pipeline",
        destination="duckdb",
        dataset_name="spotify_ads_data",
    )
    load_info = pipeline.run(spotify_ads_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("spotify_ads_pipeline").dataset()
sessions_df = data.artists.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM spotify_ads_data.artists LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("spotify_ads_pipeline").dataset()
data.artists.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 Spotify 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

If you receive a 401 Unauthorized status code, it indicates that your request lacks valid authentication credentials. Ensure that your access token is correctly obtained and included in the Authorization: Bearer {access_token} header. Access tokens are valid for 1 hour, so you may need to refresh them.

Rate Limiting

The Spotify Web API implements rate limiting. If you receive a 429 Too Many Requests status code, it means you have exceeded the allowed request rate. You should pause your requests and retry after the time specified in the Retry-After header (if provided), or implement an exponential backoff strategy.

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