Product Hunt data at the product level rather than the launch level. Almost every other Product Hunt dataset is a feed of posts: one row per day something hit the leaderboard, which means a product that launched three times appears three times and a product that never launched doesn't appear at all. This is one row per product — the durable entity behind those posts — with its slug, tagline, rating, review count, follower count, pricing type, leaderboard ranks, launch dates and badges.
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 = "01a07cd7-92b1-71e8-ab5f-0906130a7b2e"
# 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 |
|---|---|
| badges | text |
| comments_count | real |
| created_at | timestamp with time zone |
| daily_rank | text |
| description | text |
| first_launched_at | timestamp with time zone |
| followers_count | real |
| id | text |
| is_no_longer_online | bigint |
| latest_launched_at | timestamp with time zone |
| logo_url | text |
| name | text |
| price_currency | text |
| pricing_type | text |
| product_url | text |
| rating | real |
| reviews_count | real |
| shoutouts_count | text |
| slug | text |
| tagline | text |
| updated_at | timestamp with time zone |
| votes_count | real |
| website_url | text |
| weekly_rank | 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.
# 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: 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)