Institutional-grade, 1P-verified US retail product & barcode catalog (Walmart & Target) with clean UPCs, brands, prices, and canonical URLs.
Python
0
3 commits
updated Sep 20, 2026
A clean, normalized, 1P-verified retail product catalog covering major US retailers (Walmart and Target). Designed for data scientists, machine learning engineers, retail arbitrageurs, and e-commerce developers.
Most scraped retail catalogs online are messy:
078742... becomes 78742...).This dataset provides institutional-grade clean data:
A free 100-row preview sample is included in this repository: free_sample_preview_100_rows.csv.
| retailer | item_id | upc_gtin | brand | title | price_usd | in_stock |
|---|---|---|---|---|---|---|
| Walmart | 10450114 | 078742351865 | Great Value | Great Value Whole Vitamin D Milk, 1 Gallon | 2.46 | 1 |
| Target | 90000007 | 198101240828 | Figmint | 2pk Mini Spatula Set Matte Black - Figmint™ | 10.00 | 1 |
| Walmart | 10291025 | 033200011101 | Arm & Hammer | Arm & Hammer Pure Baking Soda, 1 lb | 1.52 | 1 |
| Target | 070059348 | 052181484445 | Safety 1st | Safety 1st White Plastic Drawer Latches (4-Pack) | 6.99 | 1 |
Looking for the complete production database for your application or business?
👉 Download the Master Catalog on Gumroad (Early-Bird Launch Deal: $24)
What's inside the full package:
upc_gtin, brand, and category.Run the included quickstart.py to inspect the sample data:
import csv
with open("free_sample_preview_100_rows.csv", mode="r", encoding="utf-8-sig") as f:
reader = csv.DictReader(f)
for row in list(reader)[:5]:
print(f"[{row['retailer']}] {row['brand']} - {row['title']} (UPC: {row['upc_gtin']})")
3 commits
Python
100.0%
Institutional-grade, 1P-verified US retail product & barcode catalog (Walmart & Target) with clean UPCs, brands, prices, and canonical URLs.
Python
0
3 commits
updated Sep 20, 2026
A clean, normalized, 1P-verified retail product catalog covering major US retailers (Walmart and Target). Designed for data scientists, machine learning engineers, retail arbitrageurs, and e-commerce developers.
Most scraped retail catalogs online are messy:
078742... becomes 78742...).This dataset provides institutional-grade clean data:
A free 100-row preview sample is included in this repository: free_sample_preview_100_rows.csv.
| retailer | item_id | upc_gtin | brand | title | price_usd | in_stock |
|---|---|---|---|---|---|---|
| Walmart | 10450114 | 078742351865 | Great Value | Great Value Whole Vitamin D Milk, 1 Gallon | 2.46 | 1 |
| Target | 90000007 | 198101240828 | Figmint | 2pk Mini Spatula Set Matte Black - Figmint™ | 10.00 | 1 |
| Walmart | 10291025 | 033200011101 | Arm & Hammer | Arm & Hammer Pure Baking Soda, 1 lb | 1.52 | 1 |
| Target | 070059348 | 052181484445 | Safety 1st | Safety 1st White Plastic Drawer Latches (4-Pack) | 6.99 | 1 |
Looking for the complete production database for your application or business?
👉 Download the Master Catalog on Gumroad (Early-Bird Launch Deal: $24)
What's inside the full package:
upc_gtin, brand, and category.Run the included quickstart.py to inspect the sample data:
import csv
with open("free_sample_preview_100_rows.csv", mode="r", encoding="utf-8-sig") as f:
reader = csv.DictReader(f)
for row in list(reader)[:5]:
print(f"[{row['retailer']}] {row['brand']} - {row['title']} (UPC: {row['upc_gtin']})")
3 commits
Python
100.0%