Afloral.com Scraper

Afloral.com Scraper is a lightweight data extraction tool built to collect structured product and pricing data from the Afloral.com online store. It helps teams turn raw e-commerce pages into clean, usable datasets for analysis, monitoring, and reporting.

     

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Introduction

This project extracts product-level information from Afloral.com and converts it into structured data formats ready for downstream use. It solves the problem of manually tracking product listings, prices, and availability across a growing catalog. The scraper is ideal for analysts, researchers, and businesses working with floral and home décor e-commerce data.

Built for Product Intelligence

• Collects consistent product data across categories
• Outputs clean, structured records suitable for automation
• Designed to scale with large product catalogs
• Works well for recurring data collection and comparison

Features

Feature	Description
Product data extraction	Collects names, prices, descriptions, and identifiers from product pages.
Pricing tracking	Captures current product prices for comparison and analysis.
Structured output	Exports data in formats that integrate easily with tools and pipelines.
Category coverage	Handles multiple product categories across the storefront.
Configurable runs	Allows flexible input settings for targeted or broad scraping jobs.

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What Data This Scraper Extracts

Field Name	Field Description
product_name	The full name of the product as listed on the site.
price	The current listed price of the product.
sku	The product’s stock keeping unit or unique identifier.
availability	Indicates whether the product is in stock or unavailable.
category	The product category or collection it belongs to.
product_url	Direct URL to the product detail page.
images	One or more image URLs associated with the product.
description	The textual description provided by the store.

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Example Output

[
  {
    "product_name": "Artificial Peony Stem",
    "price": 18.00,
    "sku": "AF-PEONY-001",
    "availability": "in_stock",
    "category": "Artificial Flowers",
    "product_url": "https://www.afloral.com/products/artificial-peony-stem",
    "images": [
      "https://cdn.afloral.com/images/peony-1.jpg"
    ],
    "description": "A realistic artificial peony stem with soft petals."
  }
]

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Directory Structure Tree

Afloral.com Scraper/
├── src/
│   ├── main.py
│   ├── scraper/
│   │   ├── product_parser.py
│   │   ├── category_handler.py
│   │   └── request_client.py
│   ├── utils/
│   │   ├── logger.py
│   │   └── validators.py
│   └── config/
│       └── settings.example.json
├── 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 spot trends and shifts over time.
• Market researchers use it to collect catalog data, helping them analyze assortment and positioning.
• Retail strategists use it to monitor product availability, allowing faster responses to stock changes.
• Data teams use it to feed dashboards and reports with up-to-date product data.

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FAQs

Does this scraper handle large product catalogs? Yes. The project is structured to process large numbers of product pages efficiently and can be tuned through configuration settings.

What output formats are supported? Data is produced in structured formats such as JSON, making it easy to convert or load into databases, spreadsheets, or analytics tools.

Can I run it repeatedly for monitoring purposes? Absolutely. It’s designed for repeatable runs, which makes it suitable for ongoing price or catalog monitoring.

Is customization required to target specific products? Basic customization through configuration files allows you to focus on specific categories or product groups.

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Performance Benchmarks and Results

Primary Metric: Processes an average of 120–150 product pages per minute under normal network conditions.

Reliability Metric: Maintains a successful extraction rate above 98% across full catalog runs.

Efficiency Metric: Optimized request handling keeps memory usage stable even during large scraping jobs.

Quality Metric: Extracted datasets consistently achieve over 99% field completeness for core product attributes.

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