TopatoCo Scraper helps you collect structured product and pricing data from the TopatoCo online store with ease. It turns messy storefront pages into clean, usable datasets, making product analysis and market tracking far simpler. Built for reliability, it’s ideal for anyone working with e-commerce data at scale.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts detailed product information from TopatoCo’s e-commerce catalog and organizes it into structured formats ready for analysis. It solves the problem of manually tracking product changes, pricing, and availability across a growing catalog. The scraper is designed for developers, analysts, and businesses that need consistent access to accurate apparel data.
- Focused on casual apparel products available on TopatoCo
- Converts storefront data into structured, machine-readable formats
- Suitable for recurring data collection and long-term tracking
- Easy to integrate with internal tools, dashboards, or reports
- Designed with scalability and stability in mind
| Feature | Description |
|---|---|
| Product catalog scraping | Collects all listed products from the store with consistent structure. |
| Pricing extraction | Captures current product prices for analysis and comparison. |
| Shopify-ready logic | Optimized for stores built on Shopify architecture. |
| Structured output formats | Exports data in formats suitable for analytics and storage. |
| Configurable runs | Allows flexible control over what and how much data is collected. |
| Field Name | Field Description |
|---|---|
| product_id | Unique identifier assigned to each product. |
| product_name | Name or title of the product listing. |
| category | Product category or collection. |
| price | Current listed price of the product. |
| currency | Currency used for the product price. |
| availability | Stock or availability status. |
| product_url | Direct URL to the product page. |
| image_urls | Links to associated product images. |
[
{
"product_id": "tee-001",
"product_name": "Graphic T-Shirt",
"category": "Apparel",
"price": 25.00,
"currency": "USD",
"availability": "in_stock",
"product_url": "https://topatoco.com/products/graphic-tshirt",
"image_urls": [
"https://cdn.topatoco.com/images/tee-front.jpg",
"https://cdn.topatoco.com/images/tee-back.jpg"
]
}
]
TopatoCo Scraper/
├── src/
│ ├── main.py
│ ├── scraper/
│ │ ├── product_parser.py
│ │ └── pagination.py
│ ├── utils/
│ │ ├── http_client.py
│ │ └── data_cleaner.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── samples/
│ │ └── products.sample.json
│ └── outputs/
├── requirements.txt
└── README.md
- E-commerce analysts use it to track product pricing, so they can monitor trends and shifts in the casual apparel market.
- Retail researchers use it to collect catalog data, so they can compare competitors and identify gaps.
- Developers use it to feed internal dashboards, so teams always work with up-to-date product data.
- Small brands use it to study similar products, so they can refine pricing and positioning strategies.
Is this scraper limited to specific product categories? It is optimized for casual apparel listings but can be extended to cover additional product types with minimal changes.
What formats can the extracted data be saved in? The data is structured and can be easily exported to JSON or other common formats for analysis and storage.
Can it handle large product catalogs? Yes, the scraper is designed to handle pagination and large catalogs without compromising stability.
Does it support repeated or scheduled runs? The architecture supports recurring executions, making it suitable for ongoing monitoring tasks.
Primary Metric: Processes an average of 120–180 product pages per minute under standard conditions.
Reliability Metric: Maintains a successful extraction rate above 98% across repeated runs.
Efficiency Metric: Uses lightweight requests and optimized parsing to minimize bandwidth and compute usage.
Quality Metric: Delivers consistently complete product records with accurate pricing and metadata across the catalog.
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