# NYT Best Sellers List Scraper (`jungle_synthesizer/nyt-best-sellers-list-scraper`) Actor Scrapes NYT Best Sellers lists — rank, title, author, publisher, description, ISBN-13, buy links, and weeks on list. Covers all ~20 list categories with full historical archive by week. No NYT API key required. - **URL**: https://apify.com/jungle\_synthesizer/nyt-best-sellers-list-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** News, AI - **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 ## NYT Best Sellers List Scraper Extract ranked book data from the New York Times Best Sellers lists — no API key required. Covers all ~20 weekly list categories with support for the full historical archive (2008–present via dated URLs). ### What it does The NYT Best Sellers lists are updated weekly across categories including Hardcover Fiction, Paperback Nonfiction, Young Adult, Children's, Combined Print & E-Book, and more. This actor reads the free public HTML pages (which embed the complete structured book record) and extracts per-book data including rank movement, sales metadata, and retailer buy links. The official NYT Books API requires a developer key and enforces strict rate quotas — this actor reads the public HTML pages instead, which return full data with no authentication. ### Output fields | Field | Description | |-------|-------------| | `list_name` | Human-readable list name (e.g. "Hardcover Fiction") | | `list_slug` | URL slug (e.g. "hardcover-fiction") | | `bestsellers_date` | Publication date of this ranking (YYYY-MM-DD) | | `published_date` | Full ISO timestamp of publication | | `rank` | Current rank on the list (1 = #1 bestseller) | | `rank_last_week` | Rank the previous week (0 = new entry) | | `weeks_on_list` | Number of weeks on this list | | `title` | Book title | | `author` | Author credit (e.g. "by Jane Smith") | | `publisher` | Publisher name | | `description` | Short description or plot summary | | `isbn13` | Primary ISBN-13 identifier | | `book_image_url` | URL to the book cover image | | `amazon_url` | Amazon buy link | | `buy_links` | All retailer buy links (pipe-separated: "Seller\|url Seller2\|url2") | ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | — | Maximum book records to return. 0 = no limit | | `listSlugs` | array | [] | Filter to specific list slugs. Empty = all lists | | `currentOnly` | boolean | true | When true, only scrape the most recent published week | #### List slugs reference Common list slugs you can use with `listSlugs`: - `hardcover-fiction` - `hardcover-nonfiction` - `paperback-nonfiction` - `trade-fiction-paperback` - `combined-print-and-e-book-fiction` - `combined-print-and-e-book-nonfiction` - `young-adult-hardcover` - `childrens-middle-grade-hardcover` - `picture-books` - `series-books` - `advice-how-to-and-miscellaneous` ### Example runs #### Current week — Hardcover Fiction top 5 ```json { "maxItems": 5, "listSlugs": ["hardcover-fiction"], "currentOnly": true } ```` #### All current week lists ```json { "maxItems": 200, "currentOnly": true } ``` #### Historical archive — all weeks for one list ```json { "listSlugs": ["hardcover-fiction"], "currentOnly": false } ``` ### Sample output record ```json { "list_name": "Hardcover Fiction", "list_slug": "hardcover-fiction", "bestsellers_date": "2026-05-17", "published_date": "2026-05-17T04:00:00.000Z", "rank": 1, "rank_last_week": 1, "weeks_on_list": 4, "title": "YESTERYEAR", "author": "by Caro Claire Burke", "publisher": "Knopf", "description": "Natalie Heller Mills, a privileged tradwife social media influencer, wakes up to find she is living in the comparatively difficult reality of 1855.", "isbn13": "9780593804216", "book_image_url": "https://static01.nyt.com/bestsellers/images/9780593804216.jpg", "amazon_url": "https://www.amazon.com/dp/059380421X?tag=thenewyorktim-20", "buy_links": "Amazon|https://www.amazon.com/dp/059380421X?tag=thenewyorktim-20 Apple Books|https://goto.applebooks.apple/9780593804216?at=10lIEQ Barnes and Noble|... Bookshop.org|..." } ``` ### Use cases - **Publishing analytics** — Track rank movement, weeks on list, and new entries across categories - **Author/PR research** — Monitor a title's performance over time using historical data - **Bookstore merchandising** — Pull current bestseller data to inform inventory and display decisions - **Reading recommendation engines** — Seed with current bestsellers by category - **Competitive analysis** — Compare publisher performance across categories and time periods ### Schedule recommendations The NYT Best Sellers lists publish weekly (Saturday/Sunday). Set a schedule to run every Sunday with `currentOnly: true` to capture each new list as it publishes. ### Notes - Each list page contains 10-15 titles; a full scrape of all current-week lists returns approximately 150-200 records - Historical data is available from approximately 2008 onwards via dated URLs - The actor reads public HTML pages — no NYT developer account or API key required - Buy links include Amazon, Apple Books, Barnes & Noble, Books-A-Million, and Bookshop.org # 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 book records to return across all lists. 0 = no limit. ## `listSlugs` (type: `array`): Filter to specific list slugs (e.g. "hardcover-fiction", "paperback-nonfiction"). Leave empty to scrape all available lists. ## `currentOnly` (type: `boolean`): When true (default), only scrape the most recent published week. When false, scrape all historical weeks in the sitemap. ## 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, "listSlugs": [], "currentOnly": true } ``` # 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, "listSlugs": [], "currentOnly": true }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/nyt-best-sellers-list-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, "listSlugs": [], "currentOnly": True, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/nyt-best-sellers-list-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, "listSlugs": [], "currentOnly": true }' | apify call jungle_synthesizer/nyt-best-sellers-list-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/nyt-best-sellers-list-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "NYT Best Sellers List Scraper", "description": "Scrapes NYT Best Sellers lists — rank, title, author, publisher, description, ISBN-13, buy links, and weeks on list. Covers all ~20 list categories with full historical archive by week. No NYT API key required.", "version": "0.1", "x-build-id": "aZD6hMVo8zeJBfszz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~nyt-best-sellers-list-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-nyt-best-sellers-list-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~nyt-best-sellers-list-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-nyt-best-sellers-list-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~nyt-best-sellers-list-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-nyt-best-sellers-list-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", "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 book records to return across all lists. 0 = no limit." }, "listSlugs": { "title": "List Slugs", "type": "array", "description": "Filter to specific list slugs (e.g. \"hardcover-fiction\", \"paperback-nonfiction\"). Leave empty to scrape all available lists.", "items": { "type": "string" } }, "currentOnly": { "title": "Current Week Only", "type": "boolean", "description": "When true (default), only scrape the most recent published week. When false, scrape all historical weeks in the sitemap.", "default": true } } }, "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 } } } } } } } } } } ```