A fast and reliable tool for collecting product pricing and availability data from Waitrose. It helps users track cheap prices, compare products, and monitor changes over time with clean, structured outputs.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts structured product and pricing information from Waitrose to support price analysis and monitoring. It solves the problem of manually checking product prices by automating data collection in a consistent format. It’s built for analysts, developers, and businesses who need up-to-date grocery pricing insights.
- Collects product-level pricing and availability data
- Designed for repeatable runs and comparison over time
- Outputs clean, structured datasets ready for analysis
- Suitable for both small checks and larger catalog scans
| Feature | Description |
|---|---|
| Product Price Extraction | Captures current prices for listed items accurately. |
| Availability Tracking | Identifies whether products are in stock or unavailable. |
| Structured Output | Produces clean, normalized data for easy analysis. |
| Scalable Runs | Handles multiple categories or product pages efficiently. |
| Change Monitoring | Enables historical comparison of price movements. |
| Field Name | Field Description |
|---|---|
| product_name | The full name of the grocery product. |
| product_url | Direct link to the product page. |
| price | Current listed price of the product. |
| currency | Currency used for pricing. |
| availability | Stock status at the time of extraction. |
| category | Product category or aisle grouping. |
| sku | Unique product identifier if available. |
| timestamp | Time when the data was collected. |
[
{
"product_name": "Essential Bananas",
"product_url": "https://www.waitrose.com/product/example",
"price": 1.20,
"currency": "GBP",
"availability": "in_stock",
"category": "Fruit",
"sku": "123456",
"timestamp": "2025-01-15T10:22:11Z"
}
]
Waitrose Scraper/
├── src/
│ ├── main.py
│ ├── parsers/
│ │ ├── product_parser.py
│ │ └── price_utils.py
│ ├── pipelines/
│ │ └── collector.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_output.json
│ └── inputs.sample.txt
├── requirements.txt
└── README.md
- Price analysts use it to monitor grocery price changes, so they can identify trends and inflation patterns.
- Deal hunters use it to find cheaper products, so they can optimize shopping decisions.
- Retail researchers use it to compare supermarket pricing, so they can benchmark competitors.
- Developers use it to feed pricing data into dashboards, so teams get real-time insights.
Is this suitable for regular price tracking? Yes, it’s designed for repeatable runs, making it ideal for tracking price changes over time.
Can it handle multiple product categories? It supports scanning across different categories as long as product pages are accessible.
What format is the output data in? Data is returned in a structured JSON format that’s easy to store or analyze.
Does it support large product lists? It’s built to scale efficiently, handling both small and large collections reliably.
Primary Metric: Processes an average of 40–60 product pages per minute under normal conditions.
Reliability Metric: Maintains a successful extraction rate above 97% across repeated runs.
Efficiency Metric: Optimized requests minimize resource usage while maintaining consistent throughput.
Quality Metric: Extracted datasets show high completeness, with pricing and availability captured for over 95% of products.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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