# Amazon Books Bestseller Charts Scraper (`jungle_synthesizer/amazon-books-bestseller-charts-scraper`) Actor 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. - **URL**: https://apify.com/jungle\_synthesizer/amazon-books-bestseller-charts-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 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 ```json { "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 ```json { "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 # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of records to scrape ## `chartTypes` (type: `array`): Chart types to scrape. Options: best\_sellers, hot\_new\_releases, movers\_and\_shakers, most\_wished\_for. Defaults to best\_sellers. ## `categoryNodes` (type: `array`): Specific Amazon category node IDs to scrape (e.g., '3' for Business). Leave empty to scrape all top-level categories. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "chartTypes": [ "best_sellers" ], "categoryNodes": [ "3" ] } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "chartTypes": [ "best_sellers" ], "categoryNodes": [ "3" ] }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/amazon-books-bestseller-charts-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "chartTypes": ["best_sellers"], "categoryNodes": ["3"], } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/amazon-books-bestseller-charts-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, "chartTypes": [ "best_sellers" ], "categoryNodes": [ "3" ] }' | apify call jungle_synthesizer/amazon-books-bestseller-charts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/amazon-books-bestseller-charts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Books Bestseller Charts Scraper", "description": "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.", "version": "0.1", "x-build-id": "P0s89Nyb8emVouFbZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~amazon-books-bestseller-charts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-amazon-books-bestseller-charts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~amazon-books-bestseller-charts-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-amazon-books-bestseller-charts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~amazon-books-bestseller-charts-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-amazon-books-bestseller-charts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape", "default": 10 }, "chartTypes": { "title": "Chart Types", "type": "array", "description": "Chart types to scrape. Options: best_sellers, hot_new_releases, movers_and_shakers, most_wished_for. Defaults to best_sellers.", "items": { "type": "string" } }, "categoryNodes": { "title": "Category Nodes", "type": "array", "description": "Specific Amazon category node IDs to scrape (e.g., '3' for Business). Leave empty to scrape all top-level categories.", "items": { "type": "string" } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```