Python tool for Amazon Bestseller Analysis using SellerMagnet API

Building a Python Tool for Amazon Bestseller Category Analysis

By Alex Rodriguez | July 10, 2025

Python Web Scraping Amazon Ecommerce

Building a Python Tool for Amazon Bestseller Category Analysis

For Amazon businesses and market analysts, staying ahead of the curve requires robust tools for competitive analysis, inventory management, and market research. This blog post demonstrates how to build a Python tool leveraging SellerMagnet’s API to analyze Amazon bestseller categories. This tool will provide insights into top-selling products, helping you make data-driven decisions.

Why Analyze Amazon Bestseller Categories?

Understanding which products dominate specific categories on Amazon can inform various business strategies:

  • Competitive Analysis: Identify top competitors and their product offerings.
  • Inventory Management: Discover potential products to add to your inventory.
  • Market Research: Understand market trends and consumer preferences.

Getting Started with SellerMagnet’s API

First, you'll need an API key from SellerMagnet’s API. You can obtain one by signing up for a free trial on our Try Free page. SellerMagnet provides enterprise-grade Amazon data via a robust and reliable API. We will use Python and the `requests` library to interact with the API.

Prerequisites

  • Python 3.6+
  • `requests` library (install using: `pip install requests`)

Step-by-Step Guide to Building Your Tool

1. Setting up the Environment

Create a new Python file (e.g., `amazon_bestseller_analyzer.py`) and import the necessary libraries:


import requests
import json

2. Fetching Amazon Bestsellers

We’ll use the Amazon Bestsellers endpoint to retrieve top-selling products in a specific category. This endpoint requires a `category_id`, `marketplaceId`, and your `api_key`.

First, we need to find category Ids. Use Amazon Categories to find category Ids. 


API_KEY = 'YOUR_API_KEY'
MARKETPLACE_ID = 'ATVPDKIKX0DER'  # Amazon.com
CATEGORY_ID = 'electronics'

def get_amazon_bestsellers(category_id, marketplace_id, api_key):
    url = 'https://sellermagnet-api.com/amazon-bestsellers'
    params = {
        'category_id': category_id,
        'marketplaceId': marketplace_id,
        'api_key': api_key,
        'count': 50  # Retrieve the maximum allowed number of bestsellers
    }

    response = requests.get(url, params=params)
    response.raise_for_status()  # Raise an exception for HTTP errors
    return response.json()

bestsellers_data = get_amazon_bestsellers(CATEGORY_ID, MARKETPLACE_ID, API_KEY)
print(json.dumps(bestsellers_data, indent=4))

Example Request:


curl -G 'https://sellermagnet-api.com/amazon-bestsellers' \
--data-urlencode 'category_id=electronics' \
--data-urlencode 'marketplaceId=ATVPDKIKX0DER' \
--data-urlencode 'api_key=YOUR_API_KEY' \
--data-urlencode 'count=50'

Example Response:


{
  "category": "electronics",
  "bestsellers": [
    {
      "asin": "B08N5WRWNW",
      "rank": 1,
      "title": "Top Product"
    },
    {
      "asin": "B07XJ8C8F5",
      "rank": 2,
      "title": "Second Top Product"
    },
    ...
  ]
}

3. Analyzing Product Statistics

Once you have the ASINs of the top-selling products, you can use the Amazon Product Statistics endpoint to gather more detailed information about each product, such as sales rank history, pricing trends, and review counts. This information is crucial for making informed decisions about potential product offerings.


def get_product_statistics(asin, marketplace_id, api_key):
    url = 'https://sellermagnet-api.com/amazon-product-statistics'
    params = {
        'asin': asin,
        'marketplaceId': marketplace_id,
        'api_key': api_key
    }
    response = requests.get(url, params=params)
    response.raise_for_status()
    return response.json()

if bestsellers_data and bestsellers_data['bestsellers']:
    first_asin = bestsellers_data['bestsellers'][0]['asin']
    product_stats = get_product_statistics(first_asin, MARKETPLACE_ID, API_KEY)
    print(json.dumps(product_stats, indent=4))
else:
    print("No bestsellers found in the data.")

Example Request:


curl -G 'https://sellermagnet-api.com/amazon-product-statistics' \
--data-urlencode 'asin=B08N5WRWNW' \
--data-urlencode 'marketplaceId=ATVPDKIKX0DER' \
--data-urlencode 'api_key=YOUR_API_KEY'

Example Response:


{
    "success": true,
    "data": {
        "asin": "B0CLTBHXWQ",
        "productTitle": "Playstation 5 Console Edizione Digital Slim",
        "buyBoxPrice": 41800,
        "buyBoxFulfillment": "FBM",
        "buyBoxSellerIdHistory": [
          [
            "2025-06-14 17:08:00",
            "A2I59UVTUWUFH0"
          ]
        ],
        ...
    }
}

4. Enhancing Your Analysis with Other SellerMagnet APIs

To further enrich your analysis, consider using other SellerMagnet APIs:

5. Visualizing the Data

Consider integrating visualization libraries like Matplotlib or Seaborn to create charts and graphs that represent the data in an understandable format. This can help identify trends and patterns more easily.

Practical Use Cases

  • Identifying Market Gaps: By analyzing bestseller categories, you can identify products with high demand but low competition.
  • Optimizing Product Listings: Use insights from top-performing products to improve your own product listings and increase visibility.
  • Monitoring Competitor Performance: Track the sales rank and review counts of competitor products to gauge their market performance.

Best Practices

  • Error Handling: Implement robust error handling to manage API request failures gracefully.
  • Rate Limiting: Adhere to SellerMagnet’s API rate limits to avoid being throttled.
  • Data Storage: Store fetched data in a structured format (e.g., CSV, database) for historical analysis.

Legal Compliance

Ensure that your data collection and analysis practices are legally compliant. Avoid scraping copyrighted content or engaging in unethical practices. Always respect Amazon's terms of service.

Conclusion

Building a Python tool for Amazon bestseller category analysis using SellerMagnet’s API can provide invaluable insights for your business. By leveraging the API's robust data, you can make informed decisions to optimize your product offerings, monitor competitors, and stay ahead in the competitive Amazon marketplace. Start building your tool today and unlock the power of Amazon data with SellerMagnet!

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