FOCO Scraper is a focused data extraction tool designed to collect product information and pricing from the FOCO online store. It helps teams, analysts, and developers turn raw storefront listings into structured, usable data for analysis and decision-making.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts detailed product data from an e-commerce storefront focused on team sports merchandise. It solves the problem of manually tracking product listings, prices, and catalog changes. It is built for developers, analysts, and businesses that need reliable retail data.
- Collects structured product and pricing data at scale
- Designed for repeatable and consistent catalog monitoring
- Supports downstream analytics, reporting, and integrations
- Suitable for competitive and market research workflows
| Feature | Description |
|---|---|
| Product Extraction | Captures product titles, prices, and availability from listings. |
| Structured Output | Normalizes data into clean, machine-readable formats. |
| Catalog Coverage | Handles multiple product categories and collections. |
| Scalable Runs | Designed to process large storefront catalogs efficiently. |
| Reusable Pipeline | Easy to adapt for recurring data collection tasks. |
| Field Name | Field Description |
|---|---|
| product_name | Name of the product as listed in the store. |
| product_url | Direct link to the product detail page. |
| price | Current listed price of the product. |
| currency | Currency associated with the price. |
| availability | Stock or availability status. |
| category | Product category or collection. |
| images | Product image URLs. |
[
{
"product_name": "Team Logo Knit Beanie",
"product_url": "https://www.foco.com/products/team-logo-knit-beanie",
"price": 24.99,
"currency": "USD",
"availability": "In Stock",
"category": "Headwear",
"images": [
"https://cdn.foco.com/images/beanie-front.jpg",
"https://cdn.foco.com/images/beanie-side.jpg"
]
}
]
FOCO Scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── product_parser.py
│ │ └── price_utils.py
│ ├── outputs/
│ │ └── exporter.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── input.sample.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to monitor product prices, so they can track market changes.
- Retail teams use it to audit catalog listings, so they can ensure data consistency.
- Data engineers use it to feed dashboards, so they can visualize pricing trends.
- Marketing teams use it to analyze product availability, so they can plan campaigns.
Does this project handle multiple product categories? Yes, it is designed to process multiple categories and collections within the same storefront.
Can the extracted data be reused in other systems? The output is structured and can be directly consumed by analytics tools, databases, or scripts.
Is this suitable for recurring data collection? Yes, the workflow is built to support repeated runs with consistent results.
Primary Metric: Processes hundreds of product listings per minute on standard connections.
Reliability Metric: Consistently achieves over 98% successful product extraction across runs.
Efficiency Metric: Optimized request handling minimizes redundant page loads and resource usage.
Quality Metric: High data completeness with accurate pricing and product metadata captured per item.
