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.
Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| address | text |
| category | text |
text | |
| email_sended | text |
| id | uuid |
| name | text |
| number | text |
| profile_url | text |
| review_count | integer |
| title | text |
| url | text |
| website | text |
Fetch 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.
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 = "ef9312b4-67ed-4e66-869c-83515b41402e"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)# 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 text# 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., "food_beverages_tobacco"
df = ouro.datasets.load(table_name)
print(len(df))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 contains structured information collected from Trustpilot under the “Food Beverages Tobacco” category. It includes publicly available details from company profile pages to help with analysis, lead research, market insights, or automation workflows.
Company Name – Official name listed on Trustpilot
Website URL – Link to the company’s homepage (if available)
TrustScore – Overall rating based on customer reviews
Total Reviews – Number of reviews submitted by users
Categories / Sub-categories – Classification under the “Public Local Service” sector
Location Information – Country / address if publicly displayed
Contact Presence – Indicators of email, phone, or social links if visible
Profile URL – Direct link to the Trustpilot company profile
This dataset is suitable for:
Market research and competitor analysis
Lead generation and outreach targeting local services
Data-driven insights for automation or RPA workflows
Trend analysis based on public ratings and reviews
All data is sourced from publicly accessible pages on Trustpilot.
No private or login-restricted information is included.
Dataset accuracy depends on the availability and completeness of the company’s public profile.