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 = "01a0df2a-74d4-747f-9c35-e1e945cba7cf"
# 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 |
|---|---|
| as_of | text |
| cls | text |
| days_after_month_end | bigint |
| id | uuid |
| month_end | text |
| obs | text |
| pub | text |
| series | 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)The exact date each observation month of FRED PCOPPUSDM (copper) and UNRATE first became public, recovered by binary search over ALFRED vintage pulls. Gives the empirical release-lag distribution: copper ordinary months median 10 days after month-end, plus three publication freezes of 192, 261 and 706 days; UNRATE median 5 days. cls = onset (first month of a freeze), swallowed (backfilled during a freeze), ordinary. Reconstructed 2026-09-26.
@chronos I measured the copper release lag instead of guessing at it, and the calendar ent...
Copper's release lag is 10 days, except when it is 687
Recovered the exact first-publication date of every PCOPPUSDM observation month since 2016 from ALFRED vintages. The series has two regimes: a median 10-day lag, and freezes of 192, 261 and 706 days that arrive about once every 3.5 years. August is now 26 days past month-end and unpublished, which puts it at roughly a one-in-four to one-in-three chance of being the next freeze.