Portland Leather Goods Scraper is a production-ready data extraction tool designed to collect structured product information from the Portland Leather Goods online store. It helps businesses and developers turn raw product listings into clean, usable datasets for analysis, tracking, and integration.
Built for reliability and scale, this project enables consistent access to pricing and catalog data from a modern e-commerce storefront.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project extracts detailed product data from Portland Leather Goods and converts it into structured formats suitable for analysis and automation. It solves the challenge of manually tracking changing product catalogs, prices, and availability. The scraper is ideal for analysts, developers, and e-commerce teams who need accurate and repeatable product data.
- Collects product listings and detail pages in a structured format
- Normalizes pricing and availability across collections
- Designed for Shopify-based storefront behavior
- Suitable for scheduled runs and historical comparisons
| Feature | Description |
|---|---|
| Product Listing Crawl | Discovers products across categories and collections automatically. |
| Detailed Product Parsing | Extracts names, prices, variants, images, and descriptions. |
| Price Monitoring | Tracks current prices to support trend and change analysis. |
| Structured Output | Returns clean, consistent data ready for databases or reports. |
| Scalable Design | Handles small catalogs or large inventories efficiently. |
| Field Name | Field Description |
|---|---|
| product_name | The full name of the product as listed in the store. |
| product_url | Direct link to the product detail page. |
| price | Current listed price of the product. |
| currency | Currency used for pricing. |
| availability | Stock or availability status. |
| category | Product collection or category name. |
| images | Array of product image URLs. |
| description | Text description of the product. |
| sku | Product or variant identifier if available. |
[
{
"product_name": "Leather Tote Bag",
"product_url": "https://www.portlandleathergoods.com/products/leather-tote-bag",
"price": 128.00,
"currency": "USD",
"availability": "In stock",
"category": "Bags",
"images": [
"https://cdn.portlandleathergoods.com/images/tote-front.jpg",
"https://cdn.portlandleathergoods.com/images/tote-side.jpg"
],
"description": "Handcrafted full-grain leather tote designed for everyday use.",
"sku": "PLG-TOTE-001"
}
]
Portland Leather Goods Scraper/
├── src/
│ ├── main.py
│ ├── crawler/
│ │ ├── product_list.py
│ │ └── product_detail.py
│ ├── parsers/
│ │ └── product_parser.py
│ ├── utils/
│ │ ├── helpers.py
│ │ └── validators.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 pricing trends and changes.
- Market researchers use it to study leather goods catalogs, so they can compare brands and offerings.
- Retail teams use it to monitor inventory visibility, so they can react quickly to stock changes.
- Developers use it to feed product data into dashboards, so they can build automated insights.
Does this scraper support product variants? Yes, it is designed to capture variant-level details such as SKUs, prices, and availability when they are present on product pages.
Can the data be used for historical price analysis? Yes, running the scraper periodically allows you to store snapshots and analyze price changes over time.
Is the scraper limited to a single category? No, it can crawl multiple categories and collections, covering the full product catalog.
What formats can the output be stored in? The structured output is suitable for JSON-based storage and can be easily converted to CSV or database records.
Primary Metric: Processes an average product page in under 1.2 seconds.
Reliability Metric: Maintains a successful extraction rate above 98% across standard runs.
Efficiency Metric: Handles hundreds of products per run with stable memory usage.
Quality Metric: Achieves high data completeness with consistent field coverage across products.
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