bebe Scraper is a focused data extraction tool designed to collect product information and pricing from the bebe online store. It helps businesses and analysts turn raw product listings into structured data for smarter decisions in the women’s fashion market.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts structured product and pricing data from bebe’s online catalog and organizes it for easy reuse. It solves the challenge of manually tracking product changes, prices, and availability across a fast-moving e-commerce store. It is built for developers, analysts, and marketers working with women’s clothing and retail data.
- Collects up-to-date product listings from a single fashion brand
- Normalizes pricing and product attributes into clean datasets
- Supports repeatable runs for ongoing market monitoring
- Designed for scalable analysis and reporting workflows
| Feature | Description |
|---|---|
| Product data extraction | Collects names, prices, and product metadata in a structured format. |
| Pricing intelligence | Tracks current pricing to support comparisons and trend analysis. |
| Structured outputs | Produces clean, analysis-ready data suitable for tools and reports. |
| Repeatable runs | Enables consistent data collection for monitoring changes over time. |
| Field Name | Field Description |
|---|---|
| product_name | The displayed name of the clothing item. |
| product_url | Direct link to the product detail page. |
| price | Current listed price of the product. |
| currency | Currency associated with the price. |
| category | Product category or collection. |
| availability | Stock or availability status if present. |
| images | One or more product image URLs. |
bebe Scraper/
├── src/
│ ├── main.py
│ ├── crawler/
│ │ └── product_crawler.py
│ ├── parsers/
│ │ └── product_parser.py
│ └── utils/
│ └── helpers.py
├── data/
│ ├── input.example.json
│ └── output.sample.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to track product pricing, so they can identify trends and changes quickly.
- Fashion retailers use it to monitor competitors, so they can adjust pricing strategies.
- Market researchers use it to collect historical product data, so they can analyze brand positioning.
- Developers use it as a base dataset, so they can integrate fashion data into dashboards or apps.
Is this scraper limited to women’s clothing only? It focuses on the bebe store catalog, which primarily features women’s fashion, ensuring the data remains relevant and consistent.
Can the extracted data be reused in spreadsheets or databases? Yes, the output is structured so it can be easily imported into spreadsheets, databases, or analytics tools.
Does it support repeated data collection? The project is designed for repeatable runs, making it suitable for ongoing monitoring and comparisons over time.
Primary Metric: Processes dozens of product pages per minute under standard conditions.
Reliability Metric: Maintains a high success rate with consistent data capture across runs.
Efficiency Metric: Optimized parsing minimizes unnecessary requests and resource usage.
Quality Metric: Captures complete product records with accurate pricing and metadata.
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