Scraping Home Depot’s product data requires handling JavaScript rendering and potential anti-bot measures. With ScrapingBee’s API, you can extract product information from Home Depot without managing headless browsers, proxies, or CAPTCHAs
Simply set up a request with JavaScript rendering enabled, target the correct URLs, and extract structured data using your preferred HTML parser. Our API handles all the complex parts of web scraping, letting you focus on using the data. In this guide, we will explain how you can do the same, working with Python and our prolific ScrapingBee API!
Set Up Your ScrapingBee Request
Getting started with ScrapingBee to scrape Home Depot can we tricky without prior coding knowledge. In my experience, the most important part is setting up your initial request correctly to handle Home Depot’s dynamic content, but we need to lay the foundation first.
Let’s break down each component to ensure you’re set up for success. If you want to learn more specifics on extracting Home depot product data, check out our Home Depot Scraping API.
Get Your API Key
First, let's retrieve your API key. Connect to your ScrapingBee account to retrieve the key from the dashboard. Your API key is your passport to our services. To get one:
Create a ScrapingBee account at scrapingbee.com
Navigate to your dashboard
Find your API key in the upper right corner
Not ready to commit? No worries! We offer a free trial with 1,000 credits so you can test the service before purchasing a plan.
Enable JavaScript Rendering
Home Depot, like many modern e-commerce sites, uses JavaScript to load product information dynamically and present information in a comfortable format for the consumer. This means a simple HTTP request won’t capture all the data you need - you need a solution that can execute JavaScript.
This is where our API shines and outperforms competitors. JavaScript rendering is enabled by default in the render_Js parameter without requiring you to set up or manage headless browsers. In my experience, this parameter is essential for successfully scraping Home Depot URLs.
Together with JavaScript configuration, the ScrapingBee API has tons of other configurations that we attach before extracting data with the GET API call. Make sure to check them out in our documentation section.
Scrape Product Data from Home Depot
Now let’s get to the exciting part - extracting valuable product data from Home Depot. With ScrapingBee, you can easily capture product titles, prices, ratings, availability, images, and more.
Note: The CSS selectors that define product data often change on retailer platforms like Home Depot. Make sure to maintain your scraping script to adapt to any updates on the webpage which may affect your browsing experience. For more information, check out our blog on how to scrape e-commerce websites.
Build Your Request URL
Before sending your request, you need to identify the correct Home Depot URLs to target. For example, if you want to scrape search results for “power tools”, your URL might look like:
https://www.homedepot.com/s/drilling+machines
For category pages or specific products, the URL structure will differ. It’s worth exploring the site manually first to understand the URL patterns for the data you need.
Extract HTML with ScrapingBee
Once you have your target URL, it’s time to send your request using our robust API! Let's focus on extracting HTML data with Python, the biggest coding with extensive support for tools and libraries that help scrape Home Depot data.
Let's begin by installing and checking the Python version on your system. Make make sure you have Python 3.6+ installed on your system with this line in Command Prompt or Terminal window.
python --version
Next, install the necessary libraries. For this tutorial, all we need is the integration of ScrapingBee API. Most scrapers would not work without the "requests" library, and our package includes this dependency, plus additional parameters for configuring the API call.
pip install scrapingbee
After downloading the necessary packages, start your scraping script by including these libraries. We've also included a "pprint" library, a part of Python's base libraries. Import them and initialize the our client, tying it to the "client" variable:
from scrapingbee import ScrapingBeeClient
from pprint import pprint
client = ScrapingBeeClient(api_key="YOUR_API_KEY")
Note: Don't forget to add your assigned API key from our dashboard.
After that, let's encompass our entire scraper logic by defining a new function in the script.
def homedepot_shopping_search(search_query):
In it, we will start defining rules for the GET API call. Below is a dictionary of rules that will extract data, specifically product cards from the rendered Home Depot page.
extract_rules = {
"total_results": ".results-applied__primary-filter-label",
"products": {
"selector": "sui-top-0 sui-left-0 sui-absolute sui-size-full sui-z-10",
"type": "list",
"output": {
"brand_name": "[data-testid=attribute-brandname-above]",
"product_name": "[data-testid=product-header] span.sui-text-primary",
"current_price": "[data-testid=price-simple] span.sui-font-display.sui-text-4xl",
"model": ".sui-flex.sui-text-xs.sui-mb-1.sui-mr-1",
"rating": "[name=simple-rating]@aria-label",
"reviews": "[name=simple-rating] + span",
"link": "[data-testid=product-header] > a@href",
"product_id": "[data-testid=product-header] > a@product-id",
},
},
}
The "selector" arguments highlights individual product cards, and each object is identified by CSS elements that define them. In this example, we have a ScrapingBee client taking care of HTML parsing, and seeks out data points based on their class, definition, and position within the HTML document.
Next, the "js_scenario" dictionary targets browsing instructions for the headless browser within the client. It has a list of instructions that define JavaScript actions for the scraper to execute:
js_scenario = {
"instructions": [
{"infinite_scroll": {"max_count": 0, "delay": 1000}},
{"wait": 2000},
]
}
Let's take a brief look at these definitions:
"infinite_scroll": – Tells the scraper to scroll down the page repeatedly.
"max_count": 0 – Means "keep scrolling until no more new content loads" (auto-detect end).
"delay": 1000 – Wait 1000 milliseconds (1 second) between each scroll. This gives the page time to render any new items (e.g., new products, posts, or reviews).
"wait": 2000 – Adds a 2-second pause (2000 milliseconds) after scrolling ends.
The following line of code is a quick addition that transforms the search query into a proper format to attach to the URL.
q = search_query.replace(" ", "+")
For example, calling a function with a variable "drilling machines" will add "drilling+machines" to the URL.
Now it is time to get back to our client. After defining more complex rules like "js_scenario" and "extract_rules", we can finally retrieve the first API call, which will be stored in the "response" variable:
response = client.get(
f"https://www.homedepot.com/s/{q}",
params={
"render_js": "true",
"extract_rules": extract_rules,
"js_scenario": js_scenario,
"premium_proxy": "true",
'country_code':'us'
},
retries=2,
headers=False,
)
Here’s what each key in the params dictionary means:
"render_js": "true" – enables JavaScript rendering to make sure our API waits for HTML to load content before extracting it.
"extract_rules": extract_rules –provides the CSS selectors and structure to extract data from the page, as defined in your extract_rules dictionary.
"js_scenario": js_scenario – runs a set of JavaScript automation instructions (like scrolling or clicking) to simulate user interaction.
"premium_proxy": "true" – uses residential proxies to avoid blocks or captchas, especially on sites with strong bot protection.
"country_code": "us" – ensures the request is sent from a US-based IP, so localized content like prices and availability are correctly shown.
Now all what is left is to retrieve the key data points. We've also included an if statement to stop the script from delivering unknown errors due to accidental mistakes, like forgetting to include the API key. The inner function "get_info()" checks the HTTP response after the connection request, and specifies if any products have been retrieved.
if response.text.startswith('{"message":"Invalid api key:'):
return f"Oops! It seems you may have missed adding your API KEY or you are using an incorrect key.\nYou can obtain your API KEY for by visiting this page: https://app.scrapingbee.com/account/manage/api_key"
else:
def get_info():
if len(response.json()["products"]) == 0:
return "FAILED TO RETRIEVE PRODUCTS"
else:
return "SUCCESS"
return {
"total_results": response.json()["total_results"].replace("\u200c", ""),
"count": len(response.json()["products"]),
"products": response.json()["products"],
"info": f"{response.status_code} {get_info()}",
}
Note: For those who prefer using curl, you can also send the GET request with the following command, but it will not include additional extract rules:
curl -X GET "https://app.scrapingbee.com/api/v1/?api_key=YOUR_API_KEY&url=https://www.homedepot.com/s/power%20tools&render_js=true"
If you want to learn more about the essentials of scraping retailers or other platforms, check out our tutorial on web scraping with Python. By the end, you should end up with a script that looks something like this:
from scrapingbee import ScrapingBeeClient
from pprint import pprint
client = ScrapingBeeClient(api_key="YOUR_API_KEY")
def homedepot_shopping_search(search_query):
extract_rules = {
"total_results": ".results-applied__primary-filter-label",
"products": {
"selector": "sui-top-0 sui-left-0 sui-absolute sui-size-full sui-z-10",
"type": "list",
"output": {
"brand_name": "[data-testid=attribute-brandname-above]",
"product_name": "[data-testid=product-header] span.sui-text-primary",
"current_price": "[data-testid=price-simple] span.sui-font-display.sui-text-4xl",
"model": ".sui-flex.sui-text-xs.sui-mb-1.sui-mr-1",
"rating": "[name=simple-rating]@aria-label",
"reviews": "[name=simple-rating] + span",
"link": "[data-testid=product-header] > a@href",
"product_id": "[data-testid=product-header] > a@product-id",
},
},
}
js_scenario = {
"instructions": [
{"infinite_scroll": {"max_count": 0, "delay": 1000}},
{"wait": 2000},
]
}
q = search_query.replace(" ", "+")
response = client.get(
f"https://www.homedepot.com/s/{q}",
params={
"render_js": "true",
"extract_rules": extract_rules,
"js_scenario": js_scenario,
"premium_proxy": "true",
'country_code':'us'
},
retries=2,
headers=False,
)
if response.text.startswith('{"message":"Invalid api key:'):
return f"Oops! It seems you may have missed adding your API KEY or you are using an incorrect key.\nYou can obtain your API KEY for by visiting this page: https://app.scrapingbee.com/account/manage/api_key"
else:
def get_info():
if len(response.json()["products"]) == 0:
return "FAILED TO RETRIEVE PRODUCTS"
else:
return "SUCCESS"
return {
"total_results": response.json()["total_results"].replace("\u200c", ""),
"count": len(response.json()["products"]),
"products": response.json()["products"],
"info": f"{response.status_code} {get_info()}",
}
pprint(homedepot_shopping_search(search_query="drilling+machines"))
What You Can Extract from Home Depot
Home Depot’s website is a treasure trove of valuable product data. Using our API, you can extract:
• Product titles and descriptions
• Current prices and discounts
• Average ratings and review counts
• Product availability (in stock, limited stock, out of stock)
• Product images and videos
• SKU and model numbers
• Product specifications and features
• Related products
• Store availability information
The best part? We handle rotating proxies and JavaScript rendering automatically, so you don’t have to worry about IP blocks or complex browser automation.
Scraping Options Overview
When it comes to scraping Home Depot, you have several options:
DIY approach: Set up your own proxies, manage headless browsers, handle CAPTCHAs, and deal with IP rotation. This gives you full control but requires significant technical expertise and ongoing maintenance.
ScrapingBee solution: Let our API handle all the complex infrastructure while you focus on data extraction. With our tools, you get:
• No need to manage proxies or IP rotation
• Built-in JavaScript rendering
• CAPTCHA handling
• Simple, predictable billing
• High success rates with minimal code
In my experience, what might take days to set up with a DIY approach can be accomplished in minutes with our API and its customization parameters defined in the documentation, making it the go-to choice for developers and businesses that need reliable data without building an unmanageable scraper.
Build a Home Depot Scraper with Pagination
Many Home Depot searches return multiple pages of results. To scrape them all, you’ll need to handle pagination. To access all products, additional API calls are delivered to a new URL. For example, here is a second page URL for home depot drilling machines.
https://www.homedepot.com/s/drilling+machines?Nao=24
Now we know how URLs are structured – the second page begins with the 25th product on the list. By adding a for loop to send API calls to a specific set of pages, we can manipulate the URL generation:
for page in range(max_pages):
start = page * 24 # Nao offset
In this case, the new definition of the response variable has additional elements in the provided URL, that use the "start" variable to target all the product pages:
response = client.get(
f"https://www.homedepot.com/s/{q}?Nao={start}",
....
This approach lets you collect data across multiple pages while ScrapingBee handles all the heavy lifting with JavaScript rendering and proxy rotation.
Start Scraping Home Depot with ScrapingBee
Ready to start extracting valuable data from Home Depot? With ScrapingBee, you can be up and running in minutes, avoiding all the complex issues that typically come with web scraping.
Register today and start with 1,000 free API calls to test your Home Depot scraping project. Our documentation and support team are available to help you get the most out of our service.
Frequently Asked Questions (FAQs)
Is scraping Home Depot allowed?
Web scraping for personal use is generally permitted, but make sure to review Home Depot’s Terms of Service. For commercial use, consider their robots.txt file and rate limits. Our API helps you maintain respectful scraping practices with built-in rate limiting.
Can I scrape product prices and images?
Yes, ScrapingBee can help you extract product prices, images, and other product details from Home Depot. Our JavaScript rendering capability ensures you can access all dynamically loaded content, including prices that may load after the initial page render.
How often can I scrape Home Depot with ScrapingBee?
With ScrapingBee, you can scrape as often as your plan allows. Our system automatically handles request spacing and proxy rotation to avoid detection. For large-scale scraping, consider our Business or Enterprise plans for higher API call limits.
Does Home Depot use JavaScript to load content?
Yes, Home Depot heavily relies on JavaScript to load product information, prices, and availability. The render_js=true parameter is essential for successfully scraping the site, as it ensures all JavaScript-loaded content is captured.
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.