Learn how to interact with this dataset using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Retrieve dataset
Get dataset metadata including name, visibility, description, and other asset properties.
import osfrom ouro import Ouro# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))dataset_id = "01973888-6d0a-7fef-a00d-b753d223f9dd"# Retrieve dataset metadatadataset = ouro.datasets.retrieve(dataset_id)print(dataset.name, dataset.visibility)print(dataset.metadata)
Read schema
Get column definitions for the underlying table, including column names, data types, and constraints.
# Get column definitions for the underlying tablecolumns = ouro.datasets.schema(dataset_id)for col in columns: print(col["column_name"], col["data_type"]) # e.g., age integer, name text
Query data
Fetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
# Option 1: Query data by dataset ID (returns Pandas DataFrame)df = ouro.datasets.query(dataset_id)print(df.head())# Option 2: Load data by table name (faster for large datasets)table_name = dataset.metadata["table_name"] # e.g., "gold-price-forecast-june-2025"df = ouro.datasets.load(table_name)print(len(df))
Update dataset
Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.
This report offers an in-depth analysis of gold price movements with a 12 month forecast, focusing on the current state, projected trends, and implications for stakeholders.