Sister Jane Japan Scraper

A production-ready tool for extracting structured product information and pricing from the Sister Jane Japan storefront. It helps teams collect reliable women's clothing data for analysis, monitoring, and decision-making using the Sister Jane Japan Scraper.

     

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Introduction

This project extracts product listings, details, and prices from Sister Jane Japan’s online store into clean, structured datasets. It solves the challenge of manually tracking fashion product changes and pricing across collections, and is built for analysts, developers, and e-commerce teams.

Apparel Data Intelligence

• Focused on women’s clothing catalogs and collections
• Converts unstructured product pages into usable datasets
• Designed for repeatable runs and consistent outputs
• Supports downstream analytics, reporting, and automation

Features

Feature	Description
Product Catalog Extraction	Collects complete product listings from categories and collections.
Detailed Product Parsing	Extracts titles, prices, variants, images, and descriptions.
Structured Outputs	Delivers clean, analysis-ready data formats.
Scalable Crawling	Handles large collections with stable performance.
Update-Friendly	Suitable for recurring runs to detect changes over time.

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What Data This Scraper Extracts

Field Name	Field Description
product_id	Unique identifier for the product.
product_name	Official product title as listed.
price	Current listed price of the product.
currency	Currency used for pricing.
availability	Stock or availability status.
category	Product category or collection name.
description	Full product description text.
images	Array of product image URLs.
product_url	Direct link to the product page.

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Example Output

[
    {
        "product_id": "SJ-4821",
        "product_name": "Floral Puff Sleeve Dress",
        "price": 16800,
        "currency": "JPY",
        "availability": "In Stock",
        "category": "Dresses",
        "description": "A lightweight floral dress with signature puff sleeves.",
        "images": [
            "https://example.com/images/4821-1.jpg",
            "https://example.com/images/4821-2.jpg"
        ],
        "product_url": "https://sisterjane.com/products/floral-puff-sleeve-dress"
    }
]

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Directory Structure Tree

sister-jane-japan-scraper/
├── src/
│   ├── main.py
│   ├── crawler/
│   │   ├── collection_crawler.py
│   │   └── product_parser.py
│   ├── utils/
│   │   ├── text_cleaner.py
│   │   └── price_parser.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_output.json
│   └── inputs.example.txt
├── requirements.txt
└── README.md

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Use Cases

• E-commerce analysts use it to track product pricing, so they can identify trends and price shifts.
• Fashion researchers use it to study collections, so they can analyze seasonal design patterns.
• Retail teams use it to monitor availability, so they can react quickly to stock changes.
• Developers use it to feed dashboards, so they can automate apparel data pipelines.

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FAQs

Does this scraper support recurring runs? Yes, it is designed to be run repeatedly, making it suitable for monitoring price or catalog changes over time.

What types of products are supported? It focuses on women’s clothing products available on the Sister Jane Japan storefront, including dresses, tops, and accessories.

Can the data be integrated into other systems? The structured output makes it easy to import into databases, spreadsheets, or analytics tools.

How does it handle large collections? The crawler processes collections incrementally to maintain stability and consistent results.

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Performance Benchmarks and Results

Primary Metric: Average processing speed of ~120 products per minute on standard collections.

Reliability Metric: Successfully completes over 99% of product page requests in typical runs.

Efficiency Metric: Maintains low memory usage through incremental parsing and streaming outputs.

Quality Metric: Consistently captures complete product records, including images and pricing, with high accuracy.

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