In this guide, I'll teach you how to scrape Etsy, one of the most popular marketplaces for handmade and vintage items. If you've ever tried scraping Etsy before, you know it's not exactly a walk in the park. The website's anti-bot protections, such as CAPTCHA, IP address flagging, and constant updates, make web scraping Etsy product data a challenge.
That’s why ScrapingBee's Etsy scraper is the best tool to get the job done. It's a reliable web scraper that helps you capture real-time data from Etsy listings. It's built to handle all complex parts with JavaScript rendering and proxy rotation. With our API at hand, you can focus on extracting the data you need: Etsy product titles, prices, shop names, and more.
I started scraping e-commerce sites years ago, and let me tell you, using a dedicated web scraping API like our solution has saved me numerous hours of frustration with blocked requests.
Now, I'll show you exactly how quick and easy it is to scrape Etsy product data.
Quick Answer (TL;DR)
ScrapingBee makes Etsy scraping simple and easy by automatically handling JavaScript rendering and proxy rotation. With just one API call, you can scrape Etsy product data such as titles, shop pages, and seller details without worrying about being blocked.
Our Etsy Scraping API handles browser fingerprinting and CAPTCHA challenges behind the scenes, ensuring that your requests appear to originate from a real user.
Here's the complete code that allows you to launch your Etsy scraper:
import requests
from bs4 import BeautifulSoup
def scrape_etsy_example(api_key):
# Example URL: Etsy search page for "soy wax candle"
search_url = 'https://www.etsy.com/search?q=soy+wax+candle'
# Setup parameters for ScrapingBee API (no JS rendering)
params = {
'api_key': api_key,
'url': search_url,
}
# Make the GET request
response = requests.get('https://app.scrapingbee.com/api/v1/', params=params, timeout=20)
if not response.ok:
print("Failed to fetch page:", response.status_code)
return
html_content = response.text
soup = BeautifulSoup(html_content, 'html.parser')
# Locate one product card—we assume Etsy uses some identifiable structure
product = soup.find('li', {'class': 'wt-list-unstyled'}) # adjust selector as needed
if not product:
print("No product listing found in the static HTML.")
return
title_tag = product.find('h3')
# Price may not be visible without JS, but we'll try to locate it
price_tag = product.find('span', {'class': 'currency-value'}) # example class, may vary
shop_anchor = product.find('a', {'class': 'wt-text-link-no-underline'}) # example class
title = title_tag.get_text(strip=True) if title_tag else "N/A"
price = price_tag.get_text(strip=True) if price_tag else "N/A"
shop_link = shop_anchor['href'] if shop_anchor else "N/A"
print("Title:", title)
print("Price:", price)
print("Shop Link:", shop_link)
if __name__ == '__main__':
API_KEY = 'YOUR_API_KEY' # Replace with your actual key
scrape_etsy_example(API_KEY)
If you prefer following a step-by-step guide on how to build a robust Etsy scraper with ScrapingBee, continue reading.
How to Scrape Etsy with ScrapingBee
Etsy’s dynamic content and anti-bot measures make traditional scraping public data approaches frustrating. If you’ve ever tried to scrape Etsy data manually, you’ll know how quickly things break within days (or even hours) as the web page updates its protection measures.
That's why it's essential that you use a reliable tool. Our API solves technical issues by providing a reliable way to access Etsy data without managing proxies or browser automation yourself. Our service handles the browser fingerprinting, JavaScript rendering, and proxy rotation automatically, making your scraping efforts much more reliable.
Let's walk through the process of setting up and using ScrapingBee to extract valuable data from Etsy.
Set Up Your Environment
To follow this tutorial, you'll need a few essentials in your toolbox. I remember when I first started scraping; I spent way too much time figuring out the right libraries. So, let me save you that trouble.
You'll need:
Python 3.x installed on your machine
The ScrapingBee Python SDK for easy API integration
beautifulsoup4 for parsing the HTML we receive
Getting these dependencies installed is straightforward with pip. Once done, you’ll be ready to follow this tutorial step by step and start writing code that can scrape data reliably:
pip install scrapingbee beautifulsoup4
If you're more of a requirements.txt person, I've found it helpful to pin versions to ensure consistency across environments.
Get Your ScrapingBee API Key
Before diving into code, you'll need to grab your API key from the dashboard:
Sign up or log in at Scrapingbee.com.
Go to your Dashboard.
Copy your API key from the top-right corner.
You'll use this key to authenticate your requests.
I keep my API keys in environment variables rather than hardcoding them. It's a simple habit that's saved me from accidentally pushing credentials to GitHub more times than I'd like to admit.
For a more detailed explanation and troubleshooting options, refer to ScrapingBee's documentation.
Make a ScrapingBee API Request to Etsy
Now, let the scraping of your first e-commerce product page begin! When I first scraped Etsy, I spent days tweaking headers and proxies, but now the process is much more straightforward.
Here's how to scrape Etsy search results (e.g., handmade candles):
from scrapingbee import ScrapingBeeClient
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
response = client.get(
'https://www.etsy.com/search?q=handmade+candles'
)
html_content = response.content
It’s like crafting things from scratch. Sure, you get a deep appreciation for the nuts and bolts of handling HTTP requests, user agents, and rotating IP addresses, but let’s be honest: using a dedicated service like ScrapingBee is much simpler.
To scrape a different search query, just update the URL:
'https://www.etsy.com/search?q=custom+jewelry'
You can also scrape a specific product page:
'https://www.etsy.com/listing/123456789/product-title'
While you can scrape without JavaScript rendering, Etsy's site is increasingly dynamic. The good news is that it's enabled by default, so you don't need to set any additional parameters.
Parse the Results
Now that we have our HTML content, it’s like having a book in a foreign language – we need to extract the meaningful product details.
Data parsing is where the real value of web scraping comes to life. Remember that our API returns the fully rendered HTML, which means all those JavaScript-generated elements are accessible. This is a huge advantage when working with modern websites like Etsy.
Use this snippet for parsing:
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')
# Look for product listing containers
products = soup.select('li.wt-list-unstyled div.v2-listing-card')
for product in products:
title_tag = product.select_one('h3')
link_tag = product.select_one('a.listing-link')
print("Title:", title_tag.get_text(strip=True) if title_tag else "N/A")
print("Link:", link_tag['href'] if link_tag else "N/A")
print('-' * 40)
I've found that Etsy occasionally updates its HTML structure, so don't be surprised if you need to adjust these selectors. It's a bit like a cat-and-mouse game; they change, you adapt.
Exporting Results for Analysis
Scraping is only half the job. Once you’ve managed to scrape Etsy product data successfully, you’ll want to export that valuable data into a structured format. This makes it easier to analyze trends, store results, or share with teammates. One of the most common methods in web scraping is saving the product data into a CSV file.
A CSV file works well because it’s lightweight, easy to open in Excel or Google Sheets, and simple to parse in Python. Whether you’re working with search results or product pages, exporting to CSV ensures your extracted data can be reused for further analysis.
Here’s a code showing how you can extend your scraper to save results into a CSV file:
import csv
import requests
from bs4 import BeautifulSoup
def scrape_and_export(api_key):
target_url = 'https://www.etsy.com/search?q=soy+wax+candle'
params = {
'api_key': api_key,
'url': target_url,
}
response = requests.get('https://app.scrapingbee.com/api/v1/', params=params, timeout=20)
if not response.ok:
print("Failed to fetch page:", response.status_code)
return
html_content = response.text
soup = BeautifulSoup(html_content, 'html.parser')
# Collect all the products from the search results
products = soup.select('li.wt-list-unstyled div.v2-listing-card')
rows = []
for product in products:
title_tag = product.select_one('h3')
price_tag = product.select_one('span.currency-value')
link_tag = product.select_one('a.listing-link')
title = title_tag.get_text(strip=True) if title_tag else "N/A"
price = price_tag.get_text(strip=True) if price_tag else "N/A"
link = link_tag['href'] if link_tag else "N/A"
rows.append([title, price, link])
# Export to CSV file
with open("etsy_results.csv", "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(["Title", "Price", "Product Page"])
writer.writerows(rows)
print("Data exported to etsy_results.csv")
if __name__ == '__main__':
API_KEY = 'YOUR_API_KEY'
scrape_and_export(API_KEY)
Why Exporting Matters in Web Scraping
Scalability: Saving data to a file lets you build large-scale datasets from https://www.etsy.com search results, product pages, or even entire category URLs.
Flexibility: Once the data is stored, you can create reports, dashboards, or connect it to other services for automation.
Reusability: A CSV makes it easy to revisit all the products you scraped without running repeated HTTP requests or hitting anti-bot limits.
With just a few lines of code, you can turn your scraper into a pipeline that not only collects Etsy product data but also organizes it for further analysis or integration with other websites and methods. Whether you’re building a small project or working at a large scale, such as e-commerce giants, exporting ensures that your scraper delivers long-term value.
Example: Extract Title, Price, and Shop Info
Let's put everything together with a concrete sample. I found that having specific extraction rules makes your code more maintainable and your data more consistent.
import requests
from bs4 import BeautifulSoup
def scrape_etsy_example(api_key):
# Example URL: Etsy search page for "soy wax candle"
search_url = 'https://www.etsy.com/search?q=soy+wax+candle'
# Setup parameters for ScrapingBee API (no JS rendering)
params = {
'api_key': api_key,
'url': search_url,
}
# Make the GET request
response = requests.get('https://app.scrapingbee.com/api/v1/', params=params, timeout=20)
if not response.ok:
print("Failed to fetch page:", response.status_code)
return
html_content = response.text
soup = BeautifulSoup(html_content, 'html.parser')
# Locate one product card—we assume Etsy uses some identifiable structure
product = soup.find('li', {'class': 'wt-list-unstyled'}) # adjust selector as needed
if not product:
print("No product listing found in the static HTML.")
return
title_tag = product.find('h3')
# Price may not be visible without JS, but we'll try to locate it
price_tag = product.find('span', {'class': 'currency-value'}) # example class, may vary
shop_anchor = product.find('a', {'class': 'wt-text-link-no-underline'}) # example class
title = title_tag.get_text(strip=True) if title_tag else "N/A"
price = price_tag.get_text(strip=True) if price_tag else "N/A"
shop_link = shop_anchor['href'] if shop_anchor else "N/A"
print("Title:", title)
print("Price:", price)
print("Shop Link:", shop_link)
if __name__ == '__main__':
API_KEY = 'YOUR_API_KEY' # Replace with your actual key
scrape_etsy_example(API_KEY)
When you run this code with your API key, you'll get something like:
Title: Soy Wax Candle in Glass Jar
Price: $14.99
Shop Link: https://www.etsy.com/shop/WaxAndWickCo
This snippet demonstrates how easily you can scrape Etsy product information at scale. Once you have all the data, you can export it into a CSV file for further analysis, making it easy to track trends across Etsy product listings and categories.
Let's break down what's happening:
Requests setup: We call the ScrapingBee API endpoint, passing our API key and the Etsy URL we want to scrape.
HTML parsing: We use BeautifulSoup to navigate the HTML structure, targeting specific elements containing the product information.
Data extraction: We extract the title, price, and shop link from the HTML elements, with fallbacks in case the structure changes.
I've learned that robust error handling is essential. Websites change their structure all the time, and your code needs to handle those changes gracefully.
Scraping Etsy Listings with ScrapingBee
Scraping Etsy can seem daunting with its dynamic content, anti-bot measures, and ever-changing site structure. But with the right tools, like ScrapingBee’s API, you can reliably extract valuable Etsy product data without worrying about CAPTCHAs, IP blocks, or browser fingerprinting.
By combining the web scraper with Python libraries like BeautifulSoup, you can create a scalable and efficient workflow for collecting Etsy data, including shop pages and entire category URLs. Whether you’re working on small projects or building datasets at the scale of e-commerce giants, the platform allows you to scrape all the information you need.
In short: ScrapingBee makes it possible to build an Etsy scraper to gather listings at scale, with minimal friction and maximum reliability. With just a few lines of code and your API key, you’ll be able to capture real-time data from one of the world’s largest e-commerce marketplaces for unique items.
Frequently Asked Questions (FAQs)
Can I scrape Etsy without being blocked?
Scraping Etsy without getting blocked is challenging due to its sophisticated anti-bot handling measures. They use browser fingerprinting, behavioral analysis, and rate limiting to detect automated traffic. I've found that using ScrapingBee's proxy rotation and browser rendering capabilities drastically reduces blocking issues compared to DIY approaches.
Is it legal to scrape Etsy?
The legality of scraping Etsy depends on how you use the data and how you scrape. Generally, scraping publicly available data for non-commercial research, competitive analysis, or personal use is often considered legal. From my experience, it's essential to scrape respectfully: avoid overloading their servers with requests, refrain from scraping private information, and always adhere to their robots.txt guidelines.
Can I use ScrapingBee for Etsy shop reviews?
Yes, ScrapingBee works well for scraping Etsy shop reviews. The JavaScript scenario feature is particularly useful here, as reviews often load dynamically when scrolling down the page. I've used this feature to collect comprehensive review data by simulating scrolling and waiting for content to load. You can configure the JavaScript scenario to scroll down multiple times, wait for elements to appear, and then extract the fully loaded review content.
How often can I scrape Etsy?
I recommend spacing out requests and focusing on the data you genuinely need rather than blindly scraping everything. A good practice I've adopted is to implement incremental scraping – only fetch new or updated listings rather than repeatedly scraping the same data. This approach is both more efficient and less likely to trigger rate limits.
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.