Astrology API Python API Docs | dltHub

Astrology API is a REST API providing Vedic and Western astrology, horoscope, tarot and related astrological calculation endpoints. The REST API base URL is https://json.astrologyapi.com/v1/ and All requests require HTTP Basic authentication (User Id as username and API Key as password) 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 Astrology API data in under 10 minutes.

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What data can I load from Astrology API?

Here are some of the endpoints you can load from Astrology API:

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How do I authenticate with the Astrology API API?

The API uses HTTP Basic Auth in the Authorization header. Set Authorization: Basic <base64(userId:apiKey)> on every request; Accept-Language may be provided.

1. Get your credentials

1. Sign up at https://astrologyapi.com/ and log into the dashboard. 2) In the dashboard/API keys or account section locate your User Id and Api Key (API Key is shown as the password). 3) Use those two values as HTTP Basic credentials (User Id = username, Api Key = password).

2. Add them to .dlt/secrets.toml

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[sources.astrology_api_source]
user_id = "your_user_id_here"
api_key = "your_api_key_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 Astrology API 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 astrology_api_pipeline.py

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

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Pipeline astrology_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset astrology_api_data
The duckdb destination used duckdb:/astrology_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 astrology_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 planets and sun_sign_prediction/daily/:zodiacName from the Astrology API 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 astrology_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://json.astrologyapi.com/v1/",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "planets", "endpoint": {"path": "planets"}},
            {"name": "sun_sign_daily", "endpoint": {"path": "sun_sign_prediction/daily/:zodiacName"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="astrology_api_pipeline",
        destination="duckdb",
        dataset_name="astrology_api_data",
    )
    load_info = pipeline.run(astrology_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("astrology_api_pipeline").dataset()
sessions_df = data.planets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM astrology_api_data.planets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("astrology_api_pipeline").dataset()
data.planets.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 Astrology API 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 the API returns 401 Unauthorized, verify the Authorization header is Basic <base64(userId:apiKey)> and that you are using the dashboard User Id and Api Key. Ensure credentials are not expired and that you are calling the JSON base URL.

Rate limits and quotas

The documentation indicates standard HTTP status codes for errors; if you receive 429 Too Many Requests, retry with exponential backoff and consult your plan limits in the dashboard.

Request format and language

Most endpoints expect JSON request bodies (even for lookups) and accept an Accept-Language header to set response language (default en). Ensure Content-Type: application/json and required parameters (day, month, year, hour, min, lat, lon, tzone) for birth/planetary endpoints are supplied.

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