By 2025, Yelp will have grown into a vast, user-generated database of local business data. It’s a goldmine for businesses, market researchers, and data analysts, used for everything from lead generation to sentiment analysis and competitive research. However, obtaining this data is not easy. Scraping Yelp effectively has become increasingly difficult because of Yelp’s increasingly sophisticated content protection.
This guide tackles that challenge head-on. We'll walk through how to scrape Yelp using Python, handle the platform's anti-bot defenses, and explore powerful no-code alternatives. We'll also cover the legal and ethical lines you need to walk, emphasizing the critical role of tools like proxies to keep your operations smooth and responsible.
Why Scrape Yelp?
Yelp listings and reviews offer incredibly valuable insights. Businesses leverage this data to monitor consumer sentiment, identify emerging trends, and benchmark themselves against competitors. For example, a restaurant chain might analyze city-wide reviews to pinpoint popular dishes or common complaints. Sales teams can also generate location-based leads by collecting contact information for companies within a specific category and region.
Yelp attracts tens of millions of visitors each month and has accumulated 308 million reviews, according to its official fast facts page. This scale makes it one of the richest publicly available sources of consumer sentiment and local business insights.
Is It Legal to Scrape Yelp?
In the U.S., the scraping law sits at the intersection of multiple frameworks. The Computer Fraud and Abuse Act (CFAA) has historically been invoked in scraping disputes, though courts have limited its reach for publicly accessible data. Beyond the CFAA, contract law (through Terms of Service), intellectual property, and anti-circumvention rules (like those in the DMCA) also shape what’s legally defensible.
U.S. case law is evolving, and certain rulings have approved scraping of publicly available data, but legal risk remains, especially where (1) you breach platform Terms of Service, (2) you bypass technical protections, or (3) you collect personal data without consent. Consult legal counsel for sensitive or credentialed scraping and prioritize official or consented data channels.
Further reading: What Is Data Retrieval, How It Works, and What Happens During It? and What Is an HTTP Proxy? Definition, Uses & How It Works
How to Scrape Yelp Using Python
Because Yelp employs an advanced bot detection system, traditional scraping approaches no longer work reliably. That’s why this section focuses on practical, updated techniques that continue to work in 2025.
Tools and Setup
First, let's get your environment ready. You'll need a few key libraries that are adept at mimicking real browser behavior.
- Create a Virtual Environment: It's a best practice to keep your project dependencies isolated.
source yelp_env/bin/activate # On Windows: yelp_env\Scripts\activate
2. Install Packages: You'll need curl-cffi to impersonate browser TLS fingerprints, parsel for parsing HTML, and pandas for organizing the data.
pip install curl_cffi parsel pandas requests
For this project, you'll also need a reliable way to rotate your IP address to avoid getting blocked. Proxy services like Live Proxies are essential for this, providing residential IPs that make your requests look like they're coming from genuine users.
1. Scraping the Listing Page
Instead of parsing rendered HTML directly from Yelp's main search page, a more effective strategy is to mimic the backend requests that Yelp's frontend makes. These requests, often found in your browser's DevTools Network tab (e.g., to an endpoint like /search/snippet), are crucial.
For these endpoints to return valid data, specific cookies are essential. These cookies help bypass some of Yelp's bot detection mechanisms. Therefore, a hybrid approach is recommended: generate valid cookies during a browser session and then reuse them in your scraping pipeline. Here, requests should be made using the curl_cffi request module to accurately simulate real browser behavior.
This script shows how to replicate a request to Yelp's search snippet endpoint to get business listing data as a clean JSON object.
from curl_cffi import requests
import pprint
def yelp_listing_scrape(search_term, location):
cookies = {
# Add the pre-generated cookies
}
headers = {
'accept': '*/*',
'accept-language': 'en-IN,en;q=0.9',
'referer': f'https://www.yelp.com/search?find_desc={search_term.replace(" ", "+")}&find_loc={location.replace(" ", "+")}',
'sec-ch-ua': '"Google Chrome";v="135", "Not-A.Brand";v="8", "Chromium";v="135"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Linux"',
'sec-fetch-dest': 'empty',
'sec-fetch-mode': 'cors',
'sec-fetch-site': 'same-origin',
'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36',
}
params = {
'find_desc': search_term,
'find_loc': location,
'sortby': 'rating',
'request_origin': 'user',
}
response = requests.get(
'https://www.yelp.com/search/snippet',
params=params,
cookies=cookies,
headers=headers,
impersonate="chrome110"
)
if response.status_code == 200:
response_json = response.json()
components = response_json.get("searchPageProps", {}).get("mainContentComponentsListProps", [])
results = []
for component in components:
biz = component.get("searchResultBusiness")
if biz:
result_data = {
"name": biz.get("name"),
"rating": biz.get("rating"),
"review_count": biz.get("reviewCount"),
"categories": [c.get("title") for c in biz.get("categories", [])],
"phone": biz.get("phone"),
}
results.append(result_data)
return results
else:
print(f"Failed to scrape listing page. Status: {response.status_code}")
print(response.text)
return []
if __name__ == '__main__':
search_term = 'Coffee Shops'
location = 'Austin, TX, United States'
search_results = yelp_listing_scrape(search_term, location)
pprint.pprint(search_results)
This modern approach is far more reliable than parsing HTML directly, as it retrieves the same structured data that the Yelp website uses itself.
Output:
The output data will be a list of JSON objects, with each object detailing a coffee shop.
2. Scraping a Business Page
To get detailed information from a specific business page, you can send a direct request to its URL. Again, valid cookies are mandatory, and you can often reuse the same ones from the listing page scrape. Using curl-cffi with the right headers remains crucial.
Below are the screenshots, highlights CSS selector/xpath for data points like - Name, Rating, Reviews, and Categories.
This script scrapes the main details from the "Bellissima Austin" restaurant page by parsing the returned HTML with parsel.
from parsel import Selector
from curl_cffi import requests
def extract_business_details(url):
cookies = {
# Add pre-generated cookies here
}
headers = {
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
'accept-language': 'en-IN,en;q=0.9',
'sec-ch-ua': '"Google Chrome";v="110", "Not-A.Brand";v="8", "Chromium";v="110"',
'sec-ch-ua-mobile': '?0',
'sec-ch-ua-platform': '"Linux"',
'sec-fetch-dest': 'document',
'sec-fetch-mode': 'navigate',
'sec-fetch-site': 'none',
'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36',
}
response = requests.get(url=url, cookies=cookies, headers=headers, impersonate="chrome110")
if response.status_code == 200:
sel = Selector(text=response.text)
business_data = {
"name": sel.xpath('//div[contains(@class, "photo-header-content")]//h1/text()').get(),
"rating": sel.xpath('//div[contains(@class, "photo-header-content")]//div[@role="img" and contains(@aria-label, "star rating")]/@aria-label').get(),
"reviews": sel.xpath('//div[@data-testid="BizHeaderReviewCount"]//a[contains(@href, "#reviews")]/text()').get(),
"categories": sel.xpath('//span[@data-testid="BizHeaderCategory"]/a/text()').getall(),
"hours": sel.xpath('//div[contains(@class,"y-css-4dn0rh")]//span[@data-font-weight="semibold"]/text()').getall(),
"last_updated": sel.xpath('//div[@aria-label="Info"]//p[@data-font-weight="semibold"]/text()').get()
}
return business_data
else:
print(f"Failed to scrape business page. Status: {response.status_code}")
return None
if __name__ == "__main__":
business_data = extract_business_details('https://www.yelp.com/biz/bellissima-austin?osq=Italian')
print(business_data)
By sending a request with valid browser cookies and headers, you can successfully retrieve the page's HTML and parse it for the necessary business details.
Output:
The output will be provided in a JSON format, as illustrated below.
3. Scraping Reviews
Reviews are fetched via a different mechanism: a GraphQL API endpoint. While these requests don't require cookies, they do need a specific JSON payload containing a business identifier (encBizId, document_id). You must extract these from the business page's HTML first.
Note:
Yelp business listings are public, but reviews often contain personal data (names, locations, opinions) that may be classified as such in many jurisdictions. If collecting this data, handle it responsibly: avoid redistributing raw personal identifiers, anonymize when possible, and comply with privacy regulations like GDPR or CCPA. Ethical data handling ensures compliance and builds trust.
Pagination is handled using an after parameter, which is a Base64-encoded string representing the review offset. By updating the offset value inside the JSON, encoding it, and making a new request, you can paginate through all available reviews.
This script demonstrates how to fetch reviews by sending a POST request to Yelp's GraphQL endpoint.
import pprint
import json
import requests
import base64
def get_pagination_string(offset=0):
"""Encodes the pagination offset into a Base64 string for the API."""
data = {"version": 1, "type": "offset", "offset": offset}
json_str = json.dumps(data, separators=(',', ':'))
encoded = base64.b64encode(json_str.encode("utf-8"))
return encoded.decode("utf-8")
def extract_review_data(enc_biz_id, document_id, offset=0):
headers = {
'accept': '*/*',
'content-type': 'application/json',
'origin': 'https://www.yelp.com',
'referer': f'https://www.yelp.com/biz/{enc_biz_id}',
'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36',
'x-apollo-operation-name': 'GetBusinessReviewFeed',
}
json_data = [{
'operationName': 'GetBusinessReviewFeed',
'variables': {
'encBizId': enc_biz_id,
'reviewsPerPage': 10, # Fetch 10 reviews per page
'sortBy': 'DATE_DESC',
'languageCode': 'en',
'after': get_pagination_string(offset),
},
'extensions': {
'operationType': 'query',
'documentId': document_id
},
}]
response = requests.post('https://www.yelp.com/gql/batch', headers=headers, json=json_data)
if response.status_code != 200:
print(f"Failed to fetch reviews. Status: {response.status_code}")
return []
reviews = []
data = response.json()[0].get("data", {})
business = data.get("business", {})
reviews_block = business.get("reviews", {})
edges = reviews_block.get("edges", [])
for edge in edges:
node = edge.get("node", {})
author = node.get("author", {})
text = node.get("text", {})
review = {
'reviewer_name': author.get("displayName"),
'text': text.get("full"),
'location': author.get("displayLocation")
}
reviews.append(review)
return reviews
if __name__ == "__main__":
# Example IDs (replace with real scraped ones)
document_id = "9b3b35faf4b03df98fb625c8e3f856852e4919935b03c0e94dc71bd3c75beea1"
business_id = "dGMoiigc_InxK_wERGL4RQ"
# Get the first page of reviews (offset 0)
reviews_page_1 = extract_review_data(business_id, document_id=document_id, offset=0)
print("--- REVIEWS PAGE 1 ---")
pprint.pprint(reviews_page_1)
# Get the second page of reviews (offset 10)
reviews_page_2 = extract_review_data(business_id, document_id=document_id, offset=10)
print("\n--- REVIEWS PAGE 2 ---")
pprint.pprint(reviews_page_2)
Tapping into the GraphQL API is the most efficient way to get review data, as it bypasses the need for browser automation and provides clean, structured JSON directly.
Output:
The output data will be a list of JSON objects, with each object containing the details of a review, including the location, reviewer name, and the review text.
Can You Scrape Yelp Without Code?
Absolutely. If you're not a developer or just need data quickly, a variety of no-code tools and APIs are available.
No-Code Scraper Tools (Octoparse, Apify)
These are visual tools that let you build a scraper by simply clicking on the data you want to extract from a webpage. They are great for non-programmers but offer less flexibility than a custom script. These tools can also help scrape Yelp reviews.
- Octoparse: A powerful and user-friendly tool perfect for business users and marketers. It features a point-and-click interface to build scrapers, handles complex scenarios like infinite scrolling and logins, and allows you to schedule jobs to run in the cloud.
2. Apify: A more developer-centric platform that offers a marketplace of pre-built scrapers called "Actors." You can use these ready-made tools for common tasks (like scraping Yelp) or build your own using Python or JavaScript, giving you a mix of no-code convenience and low-code power. Apify offers multiple ways to scrape Yelp data, covering both reviews and business listings.
APIs and Scraper-as-a-Service (Bright Data, SerpApi)
These services handle all the hard work for you. You make a simple API call with a Yelp URL and your desired search query, and they return the structured data in a clean JSON format. They manage proxies, solve CAPTCHA, and keep their parsers updated. This is the easiest and most reliable method, but it comes at a cost.
- Bright Data: An enterprise-grade data collection platform that offers a massive network of proxies and a powerful scraper API. It's designed for large-scale, mission-critical scraping projects and handles all aspects of bot detection and bypassing.
2. SerpApi: Real-time SERP API that can return structured Yelp search data in some cases. While its core strength is Google and other major engines, Yelp coverage may vary by region and query type.. It's known for its speed, reliability, and ease of use, making it great for applications that need quick, clean data without building a full scraper.
3. Live Proxies: If you’d rather manage scraping yourself, rotating residential proxies are the backbone of most reliable setups. Unlike public/free proxies, Live Proxies provides real home and mobile IPs through partnered peers, making them much harder to detect and block.
Why Live Proxies?
- Private allocation of IPs: Each customer gets a unique pool of proxies, so the same IPs aren’t reused on identical targets.
- High quality & speed: Designed for demanding tasks like e-commerce scraping and limited-item drops where milliseconds matter.
- Flexible proxy types: Rotating Residential, Rotating Mobile, and Static Residential options depending on your use case.
Rotating vs. Sticky Sessions
- Rotating sessions: Every request cycles through a fresh IP from your pool.
- Sticky sessions: Hold the same IP for up to 60 minutes (useful for login sessions, cart actions, or multi-bot setups).
This flexibility makes Live Proxies a strong choice when you need both scale and stability without building a full scraping infrastructure from scratch.
Which Tool Fits Your Use Case?
Octoparse is best for non-developers who want visual scraping, but it’s harder to master and not the easiest to scale. Apify strikes a balance with ready-made scrapers and strong scalability at a medium cost. Bright Data is the enterprise workhorse for very large collections—powerful, but pricey. SerpApi is the go-to if you only need clean, real-time Google SERP data, with high scalability and an easy start. Live Proxies is the budget-friendly network layer for custom scrapers, high scalability, and easy to use, but you’ll bring your own crawler/code.
| Tool | Best Use Case | Cost | Scalability | Beginner Friendly |
|---|---|---|---|---|
| Octoparse | Visual scraping for non-developers | Medium | Yes | Hard |
| Apify | Pre-built scrapers & low-code platform | Medium | High | Medium |
| Bright Data | Large-scale, enterprise data collection | High | High | Medium |
| SerpApi | Real-time, structured SERP data | Medium-High | High | Easy |
| Live Proxies | Rotating residential & mobile proxies for custom scraping setups | Low | High | Easy |
Anti-Scraping Measures and Solutions
Yelp doesn't make scraping easy. In 2025, you'll face a battery of defenses designed to detect and block automated bots.
How Yelp Blocks Bots
- IP Blocks: The most common defense. Sending too many requests from a single IP address will get you temporarily or permanently banned.
- Browser Fingerprinting: Yelp analyzes details about your browser (like version, screen resolution, and plugins) to see if you look like a real user or a bot.
- TLS Fingerprint: The unique characteristics of a client’s TLS/SSL handshake, including cipher suites, extensions, and their order. Requests that don’t match typical browser fingerprints are more likely to be flagged as bots.
- CAPTCHAs: The classic "I'm not a robot" tests that stop automated scripts in their tracks.
- Honeypots: Hidden links that are invisible to human users but that scrapers might follow, immediately flagging them as bots.
How to Avoid Blocks
Your best defense is to mimic human behavior as closely as possible.
- Rotating Proxies: This is non-negotiable. Using a large pool of rotating residential proxies from a provider like Live Proxies is the most effective way to avoid IP-based blocks. It makes your traffic appear to come from thousands of different real users.
import random
# Your list of proxies from your provider
proxy_list = [
'http://user:[email protected]:12345',
'http://user:[email protected]:12345',
'http://user:[email protected]:12345',
]
# Select a random proxy for each request
random_proxy = random.choice(proxy_list)
proxies = {
'http': random_proxy,
'https': random_proxy,
}
# response = requests.get(url, proxies=proxies)
Note: Sticky sessions, rather than rotating proxies, are sometimes needed for logins, carts, or multi-step flows. Most proxy providers allow sticky sessions via username format.
2. Custom Headers & User-Agents: Always set a realistic User-Agent string to identify your scraper as a standard web browser. Rotating through a list of common user agents is even better.
import random
user_agent_list = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4472.124 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:89.0) Gecko/20100101 Firefox/89.0',
]
# Select a random User-Agent for each request
headers = {'User-Agent': random.choice(user_agent_list)}
# response = requests.get(url, headers=headers)
3. Intelligent Delays: Add random delays between your requests to avoid hitting the server in a predictable, robotic pattern.
import time
import random
# Wait for a random duration between 2 and 5 seconds
time.sleep(random.uniform(2, 5))
How to Structure and Store Yelp Data
Getting the data is only half the battle. You also need to organize and clean it so it’s actually useful for analysis.
JavaScript Object Notation - JSON
Ideal for nested data structures like businesses and their associated reviews. It's flexible and easy to work with in most programming languages.
import json
# Sample data
data = [
{"name": "Coffee Spot A", "rating": 4.5, "reviews": 150},
{"name": "Cafe B", "rating": 4.0, "reviews": 88}
]
# Save to JSON
with open("yelp_data.json", "w") as f:
json.dump(data, f, indent=2)
Comma-Separated Values - CSV
Best for flat, tabular data. You might have one CSV for businesses and another for reviews, linking them with a business ID.
import csv
# Sample data
data = [
{"name": "Coffee Spot A", "rating": 4.5, "reviews": 150},
{"name": "Cafe B", "rating": 4.0, "reviews": 88}
]
# Save to CSV
with open("yelp_data.csv", "w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=["name", "rating", "reviews"])
writer.writeheader()
writer.writerows(data)
Data Cleaning Tips
Raw scraped data is almost always messy. You'll find extra whitespace, inconsistent formats, and duplicate entries. Using the pandas library in Python is the standard way to clean this up.
This script takes a list of scraped business data (as dictionaries), loads it into a pandas DataFrame, and performs several cleaning operations to prepare it for analysis.
import pandas as pd
import numpy as np
# Sample raw data - imagine this came from your scraper
raw_data = [
{'name': ' Coffee Spot A ', 'rating': '4.5 stars', 'review_count': '150 reviews'},
{'name': 'Cafe B', 'rating': '4.0 stars', 'review_count': '88 reviews'},
{'name': ' Coffee Spot A ', 'rating': '4.5 stars', 'review_count': '150 reviews'}, # Duplicate
{'name': 'Espresso Bar C', 'rating': None, 'review_count': '32 reviews'} # Missing data
]
# 1. Load data into a DataFrame
df = pd.DataFrame(raw_data)
print("Original Data:\n", df)
# 2. Strip leading/trailing whitespace from names
df['name'] = df['name'].str.strip()
# 3. Clean and convert data types
# Use .str.extract() to get only the numbers and convert to numeric types
df['rating'] = pd.to_numeric(df['rating'].str.extract(r'(\d+\.?\d*)')[0], errors='coerce')
df['review_count'] = pd.to_numeric(df['review_count'].str.extract(r'(\d+)')[0], errors='coerce')
# 4. Handle missing values
# Keep missing ratings as NaN, or impute with mean/median if needed
# Example: fill with mean rating instead of 0
df['rating'] = df['rating'].fillna(df['rating'].mean())
# 5. Remove duplicate rows
df.drop_duplicates(inplace=True)
print("\nCleaned Data:\n", df)
Cleaning your data with pandas is a critical step. It ensures data quality and consistency, making your subsequent analysis accurate and reliable.
Output:
The cleaned data output is shown below as a dataframe format.
Further reading: How to Scrape Google Search Results Using Python in 2025: Code and No-Code Solutions and What Is a Proxy Server? Definition, How It Works, and Setup Guide (2025).
Conclusion
We've seen that while Yelp presents some serious scraping challenges in 2025, they are not insurmountable. Whether you choose the control of a custom Python script with requests and curl-cffi or the convenience of a no-code tool, success hinges on a smart and respectful approach.
To effectively scrape Yelp data, it's crucial to simulate human browsing patterns. This involves using a high-quality rotating proxy to conceal your IP address, configuring realistic request headers, and managing your request frequency. Adhering to these ethical practices will enable you to access valuable Yelp data for market research and lead generation.
FAQs
How often can I scrape Yelp safely?
There's no magic number, but slower is always safer. A good starting point is one request every 3-5 seconds, with randomized delays. If you're scraping at scale with a large proxy pool, you can increase this rate, but always monitor for errors and back off if you start getting blocked.
Can I extract emails from Yelp?
Generally, no. Yelp does not publicly display email addresses for businesses. Scraping for emails is not only against their ToS but often ineffective. A better approach for lead generation is to scrape the business's website URL from their Yelp page and then look for a contact email there.
Can I scrape Yelp for multiple cities?
Yes, you can build a scraper to loop through cities and search terms. Scaling is the main challenge here; to avoid blocks when scraping many cities concurrently, you'll need a robust proxy infrastructure with geo-targeting to route requests from relevant regional IPs.
