Pricing

Pay per event

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, rating, and review count across hundreds of book sub-categories with timestamped snapshots for trend analysis.

Pricing

Pay per event

Rating

0.0

(0)

Developer

BowTiedRaccoon

Actor stats

Bookmarked

Total users

Monthly active users

2 days ago

Last modified

What it does

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

Fetches the chart page for the selected category
Extracts all 50 entries (pages 1 and 2) with rank, title, author, ASIN, price, rating, and format
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

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