Bodega Aurrera Scraper

A focused data extraction tool built to collect detailed product information from Bodega Aurrera. It helps teams gather structured product data quickly, making large-scale catalog analysis, pricing checks, and research far more efficient.

     

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

This project extracts rich product data from Bodega Aurrera product listings, category pages, search results, and individual product pages. It removes the manual effort involved in tracking product details and provides clean, structured output for downstream use. It’s designed for developers, analysts, and e-commerce professionals who need reliable access to up-to-date product information.

Product Data Collection at Scale

• Supports category, search, and direct product URLs as input
• Collects structured product-level data in a consistent format
• Handles large product lists efficiently
• Designed for repeatable and automated data collection

Features

Feature	Description
Multi-URL support	Accepts category pages, search results, and direct product URLs.
Rich product details	Extracts titles, descriptions, prices, images, and metadata.
Structured output	Returns clean, machine-readable data ready for analysis.
Scalable processing	Handles large product catalogs efficiently.
Maintainable design	Built with clear structure for easy updates and extensions.

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What Data This Scraper Extracts

Field Name	Field Description
product_id	Unique identifier for the product.
title	Product name as listed on the site.
description	Full product description text.
price	Current listed product price.
currency	Currency used for the product price.
images	Array of product image URLs.
category	Product category or breadcrumb path.
product_url	Direct URL to the product page.
availability	Stock or availability status if shown.

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Example Output

[
  {
    "product_id": "123456",
    "title": "Laundry Detergent 2L",
    "description": "High-efficiency liquid detergent for everyday use.",
    "price": 89.00,
    "currency": "MXN",
    "images": [
      "https://example.com/image1.jpg",
      "https://example.com/image2.jpg"
    ],
    "category": "Cleaning Supplies",
    "product_url": "https://www.bodegaaurrera.com.mx/product/123456",
    "availability": "In stock"
  }
]

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Directory Structure Tree

Bodega Aurrera Scraper/
├── src/
│   ├── main.py
│   ├── scraper/
│   │   ├── category_parser.py
│   │   ├── product_parser.py
│   │   └── search_parser.py
│   ├── utils/
│   │   ├── http_client.py
│   │   └── data_cleaner.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input_urls.txt
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• E-commerce analysts use it to monitor product prices, so they can track market changes accurately.
• Retail researchers use it to collect product catalogs, so they can analyze assortment trends.
• Data teams use it to build datasets, so they can power dashboards and reports.
• Developers use it to automate data collection, so they can save time and reduce manual work.

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FAQs

Is this scraper limited to one type of page? No. It supports category pages, search result pages, and direct product URLs, giving you flexibility in how you collect data.

What kind of data format does it return? The output is structured and consistent, making it easy to store, analyze, or integrate into other systems.

Can it handle large numbers of products? Yes. The scraper is designed to scale and process large product lists efficiently.

Is the extracted data publicly accessible? It works with publicly available product information commonly visible on the website.

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Performance Benchmarks and Results

Primary Metric: Processes several hundred product pages per minute under normal network conditions.

Reliability Metric: Maintains a high success rate with stable extraction across repeated runs.

Efficiency Metric: Optimized request handling minimizes unnecessary network calls and resource usage.

Quality Metric: Delivers consistently complete product records with minimal missing fields.

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