Learn how to interact with this dataset using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings ā 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 = "8d3dbaf3-dfec-4786-855e-270ab0453124"
# 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 |
|---|---|
| id | integer |
| name | text |
| price | real |
| price_next_week | real |
| price_next_week_2 | text |
| url | 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: 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., "fashionday_ro"
df = ouro.datasets.load(table_name)
print(len(df))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)This dataset is designed to capture short-term market liquidity and volatility by comparing the initial price of an item to its price exactly one week later. It is a powerful tool for analyzing pricing strategies, predicting short-term market trends, and identifying products with significant price instability.
Interpretation:
A positive percentage (e.g., ) indicates the item has experienced appreciation (price increase) over the week, potentially signaling rising demand or reduced supply.
A negative percentage (e.g., ) indicates depreciation (price decrease), possibly due to overstocking, new competition, or sales events.
A value close to signifies strong price stability.
The simple structure of this data facilitates a wide range of analytical tasks:
Market Timing: Determine the optimal days or conditions under which prices are most likely to drop (or surge) over a 7-day window.
Product Segmentation: Group products by their volatility index to understand which market segments are most sensitive to change.
Pricing Strategy Audit: Assess the effectiveness of promotional campaigns by observing the seven-day rebound or stabilization after an initial price change.
Risk Assessment: Use the change percentage as a risk indicator for inventory management or investment decisions.
First time time it was scraped at 02-12-2025 and next time at 09-12-2025.