Wrightson Parser Spider

Wrightson Parser Spider Scraper extracts structured product data from Wrightson store pages in a clean, reliable format. It helps businesses and analysts turn raw product pages into usable data for pricing, cataloging, and monitoring workflows. Built for accuracy and scale, it simplifies product data extraction from Wrightson listings.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
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Introduction

This project collects detailed product information from Wrightson product pages and transforms it into structured, analysis-ready data. It solves the problem of manually collecting and normalizing product prices, descriptions, and identifiers. It is ideal for e-commerce teams, data analysts, and developers building pricing or catalog intelligence systems.

Product Data Extraction for Retail Intelligence

• Processes individual product URLs and extracts normalized fields
• Detects current and previous pricing automatically
• Captures tax labels, currency, and availability context
• Produces consistent output suitable for downstream systems

Features

Feature	Description
Product detail parsing	Extracts product ID, name, description, and URLs accurately.
Price intelligence	Captures current price, old price, and display labels.
Tax and currency detection	Identifies tax inclusion details and currency codes.
Cart URL extraction	Provides direct add-to-cart links for automation workflows.
Structured output	Returns clean, predictable JSON for easy integration.

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What Data This Scraper Extracts

Field Name	Field Description
product_id	Unique identifier assigned to the product.
product_name	Human-readable product name or title.
price	Current listed product price.
old_price	Previous price if available.
price_label	Display label such as “From” or similar.
currency	Currency code used for pricing.
tax_info	Tax inclusion or exclusion information.
description	Full textual product description.
add_to_cart_url	Direct URL to add the product to cart.
product_url	Original source product page URL.

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Example Output

[
      {
            "product_id": "3158",
            "product_name": "rxp-ldpe-pipe",
            "price": "79.99",
            "old_price": null,
            "price_label": "From",
            "currency": "NZD",
            "tax_info": "Incl GST",
            "description": "RX Plastic’s Low-Density SN6 Pipe is tough and durable, offering flexibility in cool conditions.",
            "add_to_cart_url": "https://store.pggwrightson.co.nz/checkout/cart/add/...",
            "product_url": "https://store.pggwrightson.co.nz/rxp-ldpe-pipe-00192"
      }
]

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Directory Structure Tree

Wrightson Parser Spider/
├── src/
│   ├── main.py
│   ├── parser/
│   │   ├── product_parser.py
│   │   └── price_utils.py
│   ├── config/
│   │   └── settings.json
│   └── outputs/
│       └── formatter.py
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• E-commerce analysts use it to track product pricing, so they can monitor market changes accurately.
• Retail teams use it to build internal catalogs, so they can centralize product data efficiently.
• Data engineers use it to feed pricing pipelines, so they can automate reporting and analytics.
• Market researchers use it to compare products, so they can identify pricing trends over time.

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FAQs

Does this scraper handle price changes automatically? Yes, it detects both current and previous prices when available and includes them as separate fields.

Can it be used for bulk product monitoring? It is designed to process multiple product URLs consistently, making it suitable for batch data collection workflows.

Is the output easy to integrate with other systems? The structured JSON format is designed for direct use in databases, dashboards, or analytics tools.

Does it include tax and currency details? Yes, tax labels and currency codes are extracted alongside pricing information.

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Performance Benchmarks and Results

Primary Metric: Processes individual product pages in under one second on average.

Reliability Metric: Maintains a high success rate across repeated runs with consistent page structures.

Efficiency Metric: Lightweight parsing logic minimizes memory usage while maintaining throughput.

Quality Metric: High data completeness with all key product fields captured when present on the page.

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