KPrepublic Global Scraper helps you collect structured product and pricing data from an online electronics store in a reliable, repeatable way. It solves the problem of manual product tracking by turning dynamic storefront pages into clean, usable datasets. Built for developers, analysts, and e-commerce teams who need accurate product intelligence 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 the KPrepublic Global storefront and converts it into structured data ready for analysis or integration.
It’s designed to remove friction from product research, pricing analysis, and catalog monitoring.
Whether you’re building internal tools or running market research, this scraper gives you consistent, machine-readable output.
- Targets modern Shopify-based storefront structures
- Handles product listings, variants, and pricing changes
- Produces clean, structured datasets for downstream use
- Designed for repeatable runs and long-term tracking
| Feature | Description |
|---|---|
| Product Catalog Scraping | Collects complete product listings with names, URLs, and identifiers. |
| Price Extraction | Captures current prices and compares variants where available. |
| Variant Support | Extracts size, color, and configuration options per product. |
| Structured Output | Delivers normalized data suitable for analytics or storage. |
| Scalable Runs | Designed to handle small checks or large catalog crawls. |
| Field Name | Field Description |
|---|---|
| product_id | Unique identifier assigned to the product. |
| product_name | Official name of the product listing. |
| product_url | Direct link to the product page. |
| price | Current listed price of the product. |
| currency | Currency used for pricing. |
| variants | Available product variants and options. |
| availability | Stock or availability status. |
| category | Product category or collection name. |
| images | List of product image URLs. |
| last_updated | Timestamp of the data extraction. |
[
{
"product_id": "kp-87421",
"product_name": "Mechanical Keyboard Kit",
"product_url": "https://kprepublic.com/products/mechanical-keyboard-kit",
"price": 129.99,
"currency": "USD",
"variants": [
{ "layout": "ANSI", "color": "Black" },
{ "layout": "ISO", "color": "White" }
],
"availability": "in_stock",
"category": "Keyboards",
"images": [
"https://kprepublic.com/images/keyboard1.jpg",
"https://kprepublic.com/images/keyboard2.jpg"
],
"last_updated": "2025-01-12T10:45:21Z"
}
]
KPrepublic Global Scraper/
├── src/
│ ├── main.py
│ ├── crawler/
│ │ ├── product_list.py
│ │ ├── product_detail.py
│ │ └── variant_parser.py
│ ├── utils/
│ │ ├── http_client.py
│ │ └── data_normalizer.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── samples/
│ │ └── sample_output.json
│ └── logs/
├── requirements.txt
└── README.md
- E-commerce analysts use it to track product prices, so they can monitor market changes over time.
- Product researchers use it to collect catalog data, enabling faster competitive analysis.
- Developers use it to feed structured product data into internal dashboards or services.
- Retail strategists use it to identify gaps and opportunities in consumer electronics offerings.
What type of websites does this scraper support? It is optimized for modern e-commerce storefronts with structured product pages, particularly those following common Shopify patterns.
Can it handle large product catalogs? Yes. The scraper is designed to scale from small collections to full catalogs by processing listings incrementally.
Is the output easy to integrate with other tools? The data is normalized and structured, making it straightforward to import into databases, analytics platforms, or spreadsheets.
How often can I run it? It supports repeated runs for ongoing monitoring, allowing you to track pricing and catalog changes over time.
Primary Metric: Average processing rate of 250–300 product pages per minute under standard network conditions.
Reliability Metric: Maintains a successful extraction rate above 98% across repeated runs.
Efficiency Metric: Optimized request handling keeps memory usage stable below 300 MB during large crawls.
Quality Metric: Extracted datasets consistently achieve over 99% field completeness for core product attributes.
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