# Penguin Random House Publisher Catalog Scraper (`jungle_synthesizer/penguinrandomhouse-publisher-catalog-scraper`) Actor Scrape the official Penguin Random House publisher catalog for book metadata: title, author, ISBN, imprint, format, publication date, price, description, praise blurbs, and more. Search-seeded crawl into detail pages returns primary-source data not available from consumer aggregators. - **URL**: https://apify.com/jungle\_synthesizer/penguinrandomhouse-publisher-catalog-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 ## Penguin Random House Publisher Catalog Scraper Scrape the official Penguin Random House publisher catalog from penguinrandomhouse.com. Extracts authoritative book metadata: title, author, ISBN, imprint, format, publication date, price, description, praise blurbs, and series information — primary-source data not available from consumer review aggregators. ### What data does it collect? Each record is one book edition (one canonical detail page): | Field | Type | Description | |-------|------|-------------| | `prh_id` | string | Penguin Random House work ID (numeric, from URL) | | `title` | string | Book title | | `subtitle` | string | Subtitle, if present | | `author` | string | Primary author name | | `contributors` | string | All contributors as JSON array: `[{"name":"...", "role":"..."}]` | | `imprint` | string | Publisher imprint (e.g. Random House, Crown, Dial Press) | | `format` | string | Format: Hardcover, Paperback, Ebook, or Audiobook | | `isbn` | string | Primary ISBN-13 | | `pages` | integer | Page count | | `publication_date` | string | Publication date (ISO 8601, e.g. `2024-10-01`) | | `price` | number | List price in USD | | `category` | string | Genre/category as JSON array of strings | | `description` | string | Publisher description (about the book) | | `about_the_author` | string | Author biography from the publisher | | `praise` | string | Praise/endorsement blurbs as JSON array of strings | | `series` | string | Series name if the book is part of a series | | `related_titles` | string | Related edition ISBNs as JSON array | | `cover_url` | string | Cover image URL | | `product_url` | string | Full URL of the book detail page | ### How to use it #### Search by keyword Provide one or more search queries. The scraper paginates through search results, visits each book detail page, and saves the metadata. Queries can be genre names, author names, topics, or any other search terms the PRH catalog supports. ```json { "queries": ["mystery", "science fiction"], "maxItems": 50, "sp_intended_usage": "catalog research" } ```` #### Small focused run ```json { "queries": ["romance"], "maxItems": 10, "sp_intended_usage": "spot check" } ``` ### Input parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `queries` | array | Yes | `["fiction"]` | Search terms to scrape. Each query seeds an independent paginated search. | | `maxItems` | integer | Yes | 5 | Maximum total book records to collect across all queries. | ### Notes - Extraction uses the structured JSON-LD `Book` schema embedded on each detail page — the same data the publisher uses for SEO. This gives authoritative `isbn`, `publisherImprint`, `datePublished`, and `offers.price` without scraping fragile HTML. - Praise blurbs, author bios, and categories are extracted from the HTML where JSON-LD does not carry them. - The `contributors`, `category`, `praise`, and `related_titles` fields are serialised as JSON strings so they remain compatible with spreadsheet and CSV exports. - The Penguin Random House catalog covers ~120k+ titles across all imprints (Random House, Crown, Knopf, Bantam, Viking, Penguin, and many more). - No proxy required — the catalog is publicly accessible without bot protection. # 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. ## `queries` (type: `array`): List of search terms to scrape (e.g. \["fiction", "mystery", "science fiction"]). Each query seeds a paginated search through the Penguin Random House catalog. ## `maxItems` (type: `integer`): Maximum number of book records to scrape across all queries ## 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...", "queries": [ "fiction" ], "maxItems": 5 } ``` # 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...", "queries": [ "fiction" ], "maxItems": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/penguinrandomhouse-publisher-catalog-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...", "queries": ["fiction"], "maxItems": 5, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/penguinrandomhouse-publisher-catalog-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...", "queries": [ "fiction" ], "maxItems": 5 }' | apify call jungle_synthesizer/penguinrandomhouse-publisher-catalog-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/penguinrandomhouse-publisher-catalog-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Penguin Random House Publisher Catalog Scraper", "description": "Scrape the official Penguin Random House publisher catalog for book metadata: title, author, ISBN, imprint, format, publication date, price, description, praise blurbs, and more. Search-seeded crawl into detail pages returns primary-source data not available from consumer aggregators.", "version": "0.1", "x-build-id": "PJYpRQearwVyEgLEk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~penguinrandomhouse-publisher-catalog-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-penguinrandomhouse-publisher-catalog-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~penguinrandomhouse-publisher-catalog-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-penguinrandomhouse-publisher-catalog-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~penguinrandomhouse-publisher-catalog-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-penguinrandomhouse-publisher-catalog-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": [ "queries", "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." }, "queries": { "title": "Search Queries", "type": "array", "description": "List of search terms to scrape (e.g. [\"fiction\", \"mystery\", \"science fiction\"]). Each query seeds a paginated search through the Penguin Random House catalog.", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of book records to scrape across all queries" } } }, "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 } } } } } } } } } } ```