# Walmart Product Detail Ec Spider (`getdataforme/walmart-product-detail-ec-spider`) Actor Walmart Product Detail Ec Spider is a web scraping tool that extracts detailed product information from Walmart's website, including descriptions, prices, and specifications. It offers comprehensive data extraction with customizable limits and supports batch processing through multiple URLs.... - **URL**: https://apify.com/getdataforme/walmart-product-detail-ec-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results 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 --- ## README.md ### Walmart Product Detail Ec Spider #### Introduction Walmart Product Detail Ec Spider is a powerful web scraping tool designed to extract detailed product information from Walmart's website. It efficiently gathers data such as product descriptions, prices, and specifications, providing valuable insights for businesses and researchers. #### Features - **Comprehensive Data Extraction**: Captures extensive product details including titles, descriptions, categories, and pricing. - **High Reliability**: Ensures accurate and up-to-date information with minimal errors. - **Customizable Limits**: Allows users to set item limits per run for tailored data collection. - **Flexible Input Configuration**: Supports multiple URLs for batch processing. - **User-Friendly Interface**: Easy setup and execution through the Apify platform. #### Input Parameters | Parameter | Type | Required | Description | Example | |---------------|---------|----------|-----------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------| | ProductUrls | array | Yes | The product URLs for the spider. | `["https://www.walmart.com/ip/..."]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | #### Example Usage ##### Input JSON ```json { "ProductUrls": [ "https://www.walmart.com/ip/VEVOR-Rolling-Laptop-Bag-Fits-15-6-Inch-Laptop-Wheeled-Briefcase-Roller-Computer-Tote-Bag-2-Fixed-Wheels-Telescoping-Handle-Laptop-Case-Work-Travel-B/17051752609?athAsset=eyJhdGhjcGlkIjoiMTcwNTE3NTI2MDkiLCJhdGhzdGlkIjoiQ1M0NzgiLCJhdGhwb3NiIjoiLTEiLCJhdGhhbmNpZCI6IjE2OTkyOTY2MzgyIiwiYXRocmsiOjAuMH0=&athena=true" ], "item_limit": 10 } ```` ##### Output JSON ```json [ { "productUrl": "https://www.walmart.com/ip/VEVOR-Rolling-Laptop-Bag-Fits-15-6-Inch-Laptop-Wheeled-Briefcase-Roller-Computer-Tote-Bag-2-Fixed-Wheels-Telescoping-Handle-Laptop-Case-Work-Travel-B/17051752609?athAsset=eyJhdGhjcGlkIjoiMTcwNTE3NTI2MDkiLCJhdGhzdGlkIjoiQ1M0NzgiLCJhdGhwb3NiIjoiLTEiLCJhdGhhbmNpZCI6IjE2OTkyOTY2MzgyIiwiYXRocmsiOjAuMH0=&athena=true", "productId": "17051752609", "title": "VEVOR Rolling Laptop Bag, Fits Up to 15.6 Inch Laptop, Wheeled Briefcase, Roller Computer Tote Bag with 2 Fixed Wheels & Telescoping Handle, Laptop Case for Work Travel Business Office, Women Men", "description": "Prodcut Description Rolling Laptop Bag: Your Ideal Business Partner This rolling laptop bag fits 15.6 in laptops and features a 4-height telescopic handle for easy travel...", "category": "Electronics > Computers, Laptops and Tablets > Laptops > Laptop Accessories > Laptop Bags, Cases & Sleeves", "brand": "VEVOR", "sku": "LGSTDNB156INC0DM0V0", "upc": "199412401700", "currency": "USD", "listPrice": 80.45, "salePrice": 64.99, "discountPercentage": 19.22, "isInStock": true, "stockQuantity": null, "images": [ "https://i5.walmartimages.com/seo/VEVOR-Rolling-Laptop-Bag-Fits-15-6-Inch-Laptop-Wheeled-Briefcase-Roller-Computer-Tote-Bag-2-Fixed-Wheels-Telescoping-Handle-Laptop-Case-Work-Travel-B_50270...", "https://i5.walmartimages.com/seo/VEVOR-Rolling-Laptop-Bag-Fits-15-6-Inch-Laptop-Wheeled-Briefcase-Roller-Computer-Tote-Bag-2-Fixed-Wheels-Telescoping-Handle-Laptop-Case-Work-Travel-B_50271..." ], "attributes": { "scraperVersion": "1.0.0", "actor_id": "UQiGVjmDVV5hmnS4q", "run_id": "xYGvJjbqJFaQv6Hes" } } ] ``` #### Use Cases - **Market Research and Analysis**: Gain insights into product trends and consumer preferences. - **Competitive Intelligence**: Monitor competitor pricing and offerings. - **Price Monitoring**: Track price changes over time for strategic decision-making. - **Content Aggregation**: Collect data for content creation and marketing strategies. - **Academic Research**: Support studies in e-commerce, consumer behavior, or economics. - **Business Automation**: Integrate with CRM systems for automated updates. #### Installation and Usage 1. Search for "Walmart Product Detail Ec Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). #### Output Format The output is a JSON array containing objects with detailed product information. Key fields include: - `productUrl`: The URL of the product page. - `productId`: Unique identifier for the product. - `title`: Product title. - `description`: Detailed description of the product. - `category`: Category path on Walmart's site. - `brand`, `sku`, `upc`: Brand name, SKU, and UPC codes. - `currency`, `listPrice`, `salePrice`, `discountPercentage`: Pricing details. - `isInStock`: Stock availability status. - `images`: List of image URLs. - `attributes`: Metadata including scraper version, actor ID, and run ID. #### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Get Data For Me](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `ProductUrls` (type: `array`): The product urls for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "ProductUrls": [ "https://www.walmart.com/ip/VEVOR-Rolling-Laptop-Bag-Fits-15-6-Inch-Laptop-Wheeled-Briefcase-Roller-Computer-Tote-Bag-2-Fixed-Wheels-Telescoping-Handle-Laptop-Case-Work-Travel-B/17051752609?athAsset=eyJhdGhjcGlkIjoiMTcwNTE3NTI2MDkiLCJhdGhzdGlkIjoiQ1M0NzgiLCJhdGhwb3NiIjoiLTEiLCJhdGhhbmNpZCI6IjE2OTkyOTY2MzgyIiwiYXRocmsiOjAuMH0=&athena=true" ], "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/walmart-product-detail-ec-spider").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/walmart-product-detail-ec-spider").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 '{}' | apify call getdataforme/walmart-product-detail-ec-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/walmart-product-detail-ec-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Walmart Product Detail Ec Spider", "description": "Walmart Product Detail Ec Spider is a web scraping tool that extracts detailed product information from Walmart's website, including descriptions, prices, and specifications. It offers comprehensive data extraction with customizable limits and supports batch processing through multiple URLs....", "version": "0.0", "x-build-id": "saiv4JoncP6vAPCAa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~walmart-product-detail-ec-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-walmart-product-detail-ec-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~walmart-product-detail-ec-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-walmart-product-detail-ec-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~walmart-product-detail-ec-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-walmart-product-detail-ec-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "ProductUrls": { "title": "Product Urls", "minItems": 1, "type": "array", "description": "The product urls for the spider.", "default": [ "https://www.walmart.com/ip/VEVOR-Rolling-Laptop-Bag-Fits-15-6-Inch-Laptop-Wheeled-Briefcase-Roller-Computer-Tote-Bag-2-Fixed-Wheels-Telescoping-Handle-Laptop-Case-Work-Travel-B/17051752609?athAsset=eyJhdGhjcGlkIjoiMTcwNTE3NTI2MDkiLCJhdGhzdGlkIjoiQ1M0NzgiLCJhdGhwb3NiIjoiLTEiLCJhdGhhbmNpZCI6IjE2OTkyOTY2MzgyIiwiYXRocmsiOjAuMH0=&athena=true" ], "items": { "type": "string" } }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```