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Amazon-Bestsellers-Scraper

Amazon-Bestsellers-Scraper

Open source Python
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About Amazon-Bestsellers-Scraper

amazon bestsellers scraper analytics tool

Platforms

Web Self-hosted

Languages

Python

Links

Amazon Bestsellers Scraper – Analytics Tool

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Overview

Amazon Bestsellers Scraper is a Python-based tool to efficiently scrape top-selling products on Amazon.
It collects structured data including product titles, prices, ratings, and URLs, making it perfect for e-commerce analysis, affiliate research, or market insights.

Key Benefits:

  • Extract real-time product data from Amazon Bestsellers
  • Automate multi-category scraping
  • Export results in CSV, JSON, or Excel
  • Mimic human browsing to reduce IP blocks
  • Customizable scraping with CLI or config file

Core Features

Feature Description
Scrape Product Data Extract product name, price, rating, and product link from Amazon Bestsellers pages.
Category Support Scrape multiple categories like Electronics, Books, Toys, and more.
Pagination Handling Automatically scrape multiple pages per category.
Export Data Save scraped data as CSV, JSON, or Excel for analysis.
Proxy & IP Rotation Optional proxy support to prevent IP bans.
Delay & Throttle Requests Randomized delays and throttling to mimic human browsing.
CLI & Configurable Options Choose categories, number of pages, and output format via config file or CLI arguments.
Logging Track successful scrapes, errors, and skipped products.

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Target Audience

  • E-commerce marketers & affiliates
  • Amazon sellers and competitors
  • Data analysts and researchers
  • Python developers learning web scraping

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Installation Instructions

1. Clone the Repository


python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

# Convert example CSV → JSON
python src/cli.py --csv data/examples.csv --out data/examples.json

# Start local practice server
uvicorn src.practice_api:app --reload --port 8000

# Open web UI
# Serve the web/ folder (e.g., with VS Code Live Server) or a quick Python server:
# In another terminal:
#   cd web && python -m http.server 5500
# Then open http://localhost:5500 and upload data/examples.json