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 = "01a09657-f34b-7eb8-a548-ea86c77ad673"
# 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 |
|---|---|
| action_id | uuidAction |
| baseline | numeric |
| horizon_step | bigint |
| id | uuid |
| median | numeric |
| q10 | numeric |
| q90 | numeric |
| series_id | text |
| target_date | 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.
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.
# 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",
)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)Chart-ready copy of the forecast ledger: all 36 non-void forecast rows (12 ICSA weekly, 12 PCOPPUSDM monthly, 12 UNRATE monthly) with median, 80% interval edges (q10/q90), and seasonal-naive baseline. Refreshed 2026-09-13 from forecast-ledger (dataset 01a09102-1aaa-797d-95b9-f68e6cf53193); the void seed row is excluded.
| Column | Range | Mean | Filled |
|---|---|---|---|
| series_id | 3 distinct | — | 100% |
| target_date | 25 distinct | — | 100% |
| horizon_step | 1–12 | 6.5 | 100% |
| median | 4.047–206,000 | 72,630 | 100% |
| baseline | 4.1–233,000 | 78,920 | 100% |
| q10 | 3.625–197,400 | 68,240 | 100% |
| q90 | 4.221–222,800 | 77,840 | 100% |
36/36 reconciliation with forecast-ledger, checked 2026-09-13.
This dataset now holds exactly the 36 non-void forecast rows (the void seed row in the ledger is excluded by design):
Keys: all 36 (series_id, target_date, horizon_step) triples match the ledger's non-void rows exactly — no missing, no extra.
Counts: ICSA 12, PCOPPUSDM 12, UNRATE 12.
Values: median, q10, q90, and baseline match the ledger for every row to full precision (float columns upgraded from real to numeric; the earlier float32 storage rounded ICSA step-12 median 204377.5 → 204378, now fixed).
Action reference: action_id is populated on all 36 rows and promoted to a native action reference — 12 rows point at the ICSA forecast run (action 01a09849-44ce-7ffd-ab8b-4caffe68f7cb), 12 at the PCOPPUSDM run (01a09094-47a1-7c33-80bc-81b6b274f1f4), 12 at the UNRATE run (01a09094-386a-71ac-ae7b-9675b7056980).
Interval sanity: 0 inverted or zero-width [q10, q90] intervals.
Schema retained per the task: q10, median, q90, baseline, target_date, series_id, horizon_step, action_id.