Electric Scraper extracts structured product information and pricing data from the Electric apparel store with speed and accuracy. It helps businesses and analysts turn raw product pages into clean, usable datasets for e-commerce intelligence and market insights.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Electric Scraper is a specialized data extraction tool designed to collect detailed apparel product data from an online retail store. It solves the challenge of manually tracking product changes, prices, and catalogs by automating data collection in a consistent format. This project is built for developers, analysts, and e-commerce teams who need reliable product data for decision-making.
- Collects structured product and pricing data at scale
- Converts storefront pages into analytics-ready datasets
- Supports ongoing monitoring of catalog and price changes
- Designed for integration with internal tools and dashboards
| Feature | Description |
|---|---|
| Product Data Extraction | Retrieves titles, descriptions, categories, and product URLs accurately. |
| Price Monitoring | Captures current pricing to support tracking and comparison. |
| Image Collection | Extracts high-quality product images for catalogs or analysis. |
| Structured Output | Produces clean, consistent data suitable for reports and databases. |
| Scalable Processing | Handles large product catalogs efficiently. |
| Field Name | Field Description |
|---|---|
| product_name | Name of the apparel product. |
| product_url | Direct link to the product page. |
| price | Current listed price of the product. |
| currency | Currency used for the price. |
| category | Product category or collection. |
| description | Full product description text. |
| images | Array of product image URLs. |
| availability | Stock or availability status. |
Electric Scraper/
├── src/
│ ├── main.py
│ ├── crawler/
│ │ ├── product_collector.py
│ │ └── page_parser.py
│ ├── utils/
│ │ └── helpers.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- E-commerce analysts use it to track product prices, so they can identify trends and pricing opportunities.
- Retail managers use it to monitor catalog changes, helping maintain accurate product listings.
- Market researchers use it to collect apparel data, enabling competitive analysis.
- Developers use it to feed product data into dashboards and internal tools.
Can this scraper handle large product catalogs? Yes, it is designed to scale efficiently and process large numbers of product pages without compromising data consistency.
What formats can the extracted data be used in? The structured output is suitable for databases, spreadsheets, analytics tools, and custom applications.
Is technical setup required to run this project? Basic familiarity with Python and dependency management is sufficient to configure and run the scraper.
Primary Metric: Processes dozens of product pages per minute under standard conditions.
Reliability Metric: Maintains a high success rate with consistent data extraction across runs.
Efficiency Metric: Optimized parsing minimizes resource usage while handling large catalogs.
Quality Metric: Delivers complete product records with accurate pricing and metadata.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
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