A focused scraping tool designed to collect structured product and pricing data from the official BAPE online store. This project helps turn fast-moving apparel listings into usable datasets for analysis, tracking, and decision-making. Built for reliability, it simplifies large-scale product data collection around the A BATHING APE BAPE ltd brand.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts product information from the BAPE online store and converts it into clean, structured data. It solves the challenge of manually tracking apparel listings, prices, and availability across a dynamic e-commerce site. It’s ideal for analysts, developers, and businesses working with fashion retail data.
- Collects up-to-date product listings directly from the storefront
- Normalizes raw product pages into structured datasets
- Supports repeated runs for ongoing tracking and monitoring
- Designed to scale with growing catalog sizes
| Feature | Description |
|---|---|
| Product crawling | Automatically gathers product listings across categories. |
| Price extraction | Captures current pricing for each apparel item. |
| Structured output | Delivers clean, machine-readable datasets. |
| Repeatable runs | Enables consistent data refresh for monitoring. |
| Lightweight setup | Simple configuration without complex dependencies. |
| Field Name | Field Description |
|---|---|
| product_name | Official name of the apparel item. |
| product_url | Direct link to the product page. |
| price | Current listed price of the item. |
| currency | Currency used for the price. |
| availability | Stock or availability status. |
| category | Product category or collection. |
| images | URLs of product images. |
| description | Product description text. |
[
{
"product_name": "BAPE Shark Full Zip Hoodie",
"product_url": "https://bape.com/products/shark-full-zip-hoodie",
"price": 399,
"currency": "USD",
"availability": "in_stock",
"category": "Hoodies",
"images": [
"https://bape.com/images/shark-hoodie-front.jpg"
],
"description": "Iconic full zip hoodie featuring the classic Shark design."
}
]
A BATHING APE BAPE ltd Scraper/
├── src/
│ ├── main.py
│ ├── crawler/
│ │ └── product_collector.py
│ ├── parsers/
│ │ └── product_parser.py
│ ├── utils/
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_output.json
│ └── inputs.example.txt
├── requirements.txt
└── README.md
- Market analysts use it to track BAPE product pricing, so they can spot trends and shifts in demand.
- Retail researchers use it to collect apparel data, so they can compare product positioning across brands.
- Developers use it to feed structured product data into dashboards, enabling faster insights.
- E-commerce teams use it to monitor stock availability, helping with planning and forecasting.
Does this scraper support repeated runs for monitoring? Yes, it’s designed to be run multiple times, allowing consistent tracking of product changes over time.
What format is the extracted data provided in? The output is structured in a clean, JSON-based format that’s easy to integrate into tools, spreadsheets, or applications.
Can it handle large product catalogs? The architecture supports scaling across many product listings without significant performance degradation.
Is this limited to apparel products only? It focuses on apparel-related listings available on the store, aligned with the brand’s catalog structure.
Primary Metric: Average processing speed of several hundred product pages per minute under standard conditions.
Reliability Metric: Consistent successful extraction rate above 99% across repeated runs.
Efficiency Metric: Low memory footprint with stable CPU usage during extended scraping sessions.
Quality Metric: High data completeness with accurate field extraction across product listings.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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