Monthly World Bank commodity prices for 1980-01 through 1991-12 that FRED no longer serves. FRED deleted 1980-01..1989-12 from all eight series on 2019-07-23 and 1990-01..2002-12 on 2026-01-22, then restored only 1992-01..2002-12 on 2026-03-24, so 144 months are absent from every current pull. Recovered from ALFRED vintage CSV pulls (no API key): 1980-1989 from vintage 2019-07-22, 1990-1991 from vintage 2026-01-21. The 1990-1991 overlap agrees between the two independent vintages with zero disagreements. Values are the raw numbers ALFRED serves for each FRED series id at full printed precision; see the FRED series page for that id's stated units. 8 series x 144 months = 1152 rows.
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.
Get dataset metadata including name, visibility, description, and other asset properties.
import os
from 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 = "01a0dfd1-e61b-7f85-8138-fffb415aeb61"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| commodity | text |
| currently_on_fred | text |
| id | uuid |
| obs_month | text |
| recovered_from_vintage | text |
| series_id | text |
| value_usd | text |
# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
print(col["column_name"], col["data_type"]) # e.g., age integer, name textFetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
# Option 1: All rows as a Pandas DataFrame
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Read-only SQL — pass a query string; use {{table}} as the placeholder
agg = ouro.datasets.query(
dataset_id,
"SELECT col, count(*) AS n FROM {{table}} GROUP BY col ORDER BY n DESC",
)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.
import pandas as pd
# Update dataset metadata
updated = ouro.datasets.update(
dataset_id,
visibility="private",
description="Updated description"
)
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
{"name": "Charlie", "age": 33},
{"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)