Amazon Books Bestseller Charts Scraper

Scrape Amazon book bestseller charts including Best Sellers, Hot New Releases, Movers and Shakers, and Most Wished For. Collects rank, title, author, ASIN, format, price, star rating, and review count across book sub-categories with timestamped snapshots for trend analysis.

What it does

For each chart type and category node you specify, the actor:

1. Fetches the chart page for the selected category
2. Extracts all 50 entries (pages 1 and 2) with rank, title, author, ASIN, price, rating, and format
3. Saves each entry as a dataset record with a captured_at timestamp

This makes it ideal for building rank-over-time datasets — schedule it hourly to track how books move up and down the charts.

Input

Field	Type	Default	Description
chartTypes	array	["best_sellers"]	Chart types to scrape. Options: best_sellers, hot_new_releases, movers_and_shakers, most_wished_for
categoryNodes	array	[""] (root Books)	Amazon category node IDs. Leave empty string for root Books. Examples: "3" = Business & Money, "4" = Children's, "5" = Computers
maxItems	integer	10	Maximum number of records per run

Example input

{
  "chartTypes": ["best_sellers", "hot_new_releases"],
  "categoryNodes": ["3", "6"],
  "maxItems": 200
}

Output

Each record contains:

Field	Type	Description
chart_type	string	Chart type: best_sellers, hot_new_releases, etc.
category	string	Category name from the page
category_node	string	Amazon category node ID
rank	integer	Current rank on the chart (1–100)
title	string	Book title
author	string	Author name
asin	string	Amazon Standard Identification Number
format	string	Format: Paperback, Kindle, Hardcover, Audible
price	number	Current price in USD
list_price	number	Original list price (null — requires detail page)
star_rating	number	Average star rating (1.0–5.0)
review_count	integer	Total number of customer reviews
cover_url	string	Product cover image URL
product_url	string	Amazon product page URL
captured_at	string	ISO-8601 timestamp of the snapshot

Sample output record

{
  "chart_type": "best_sellers",
  "category": "Amazon Best Sellers",
  "category_node": "3",
  "rank": 5,
  "title": "Unreasonable Hospitality",
  "author": "Will Guidara",
  "asin": "0593418573",
  "format": "Hardcover",
  "price": 16.00,
  "star_rating": 4.8,
  "review_count": 6638,
  "cover_url": "https://images-na.ssl-images-amazon.com/...",
  "product_url": "https://www.amazon.com/dp/0593418573",
  "captured_at": "2026-05-23T04:19:54.245Z"
}

Common category node IDs

Node ID	Category
(empty)	All Books (root)
1	Arts & Photography
2	Biographies & Memoirs
3	Business & Money
4	Children's Books
5	Computers & Technology
6	Cookbooks, Food & Wine
12290	Christian Books & Bibles
4366	Comics & Graphic Novels

Use cases

• KDP analytics: Track how your own book climbs (or falls) across categories
• Publisher research: Monitor competitor titles and pricing
• Trend datasets: Build hourly rank-history time series for ML/analytics
• Retail intelligence: Price monitoring and format mix analysis

Scheduling

This actor is designed for recurring use. Schedule it hourly via Apify's scheduler to capture rank snapshots over time and build a rank-over-time dataset.

Notes

• Amazon's bestseller charts update hourly
• The actor fetches both pages per category (top 100 = pages 1–2 of 50 each)
• Residential proxy rotation is used to bypass Amazon's bot detection