How to Scrape Yelp Data in Python: Tools, Steps & Use Cases

Scraping Yelp data can be a helpful tool for businesses to gather information on their competitors, as well as for data analysts looking to study consumer behavior. In this article, we will go over the business advantages, use cases, and the tools that can be used to scrape Yelp.

What Is Yelp and Why Does Its Data Matter

Yelp is a review and local business discovery platform founded in 2004 in San Francisco. As of 2025, it reached 330 million cumulative reviews and around 29 million monthly active users on its mobile app, covering businesses across restaurants, home services, health, beauty, and dozens of other categories.

The data Yelp gathers is commercially relevant in concrete ways. Research from Harvard Business School found that a one-star increase in a Yelp rating leads to a 5–9% increase in restaurant revenue. Approximately 35% of people who search for a business on Yelp visit that establishment within a day, which represents a strong purchase intent behind the platform’s traffic.

For companies monitoring competitors, tracking review sentiment, or building lead lists of local businesses, Yelp is one of the more data-rich public sources available.

Why Scrape Yelp? Business Use Cases

Before setting up a Yelp scraper Python workflow, it’s worth being specific about what the data is for. The most common use cases:

• Competitor monitoring — tracking ratings, review volume, and sentiment trends across competing businesses in a specific city or category.
• Lead generation — extracting business names, phone numbers, addresses, and categories for outreach campaigns targeting local service businesses.
• Market research — mapping the density and quality of businesses in a vertical across geographies to identify underserved markets.
• Reputation analysis — scraping review text for sentiment analysis to understand what customers praise or criticize about a category of businesses.
• Machine learning datasets — building labeled review text collections for training rating prediction models.

Learn more about local businesses data collection here —> Data Scraping Google Maps: Professional Services for Business Growth

Everything You Need to Know Before You Scrape Yelp Python

Before diving into the process, it is important to note that scraping Yelp’s data is against their terms of service. Therefore, using the information obtained through scraping is crucial for lawful purposes only.

The first step in scraping Yelp is determining the specific data you want to collect. Yelp provides a wide range of information, including business names, addresses, phone numbers, ratings, reviews, and more. Once you have identified the data you want to collect, you can use web scraping tools to extract it from Yelp’s website.

There are several approaches to web scraping Yelp Python style. The right one depends on what data you need and whether the page renders it statically or dynamically.

Tools to Scrape Yelp Data in Python

• BeautifulSoup is a Python library that allows for the parsing of HTML and XML documents. It can be used to navigate and search for specific elements within a webpage, making it a useful tool for scraping Yelp.
• Selenium is another library that can be used as web scraping Yelp Python tool. It allows for the automation of web browsers, making it possible to navigate through multiple pages and extract data.
• Scrapy is a web scraping framework for Python that can be used to extract data from websites. It is particularly useful for scraping large amounts of data and can be easily integrated with other tools such as BeautifulSoup and Selenium.
• ParseHub is a web scraping platform that allows users to scrape data without the need for coding. It can be used to extract data from Yelp by creating a template and specifying the data that needs to be extracted.

For most how to scrape Yelp using Python tasks, the combination of Selenium (for rendering) and BeautifulSoup (for parsing the resulting HTML) covers the majority of what Yelp’s structure requires.

Once you have chosen a tool to use, the next step is to create a script or template that will be used to extract the data from Yelp. This will typically involve specifying the specific elements within Yelp’s website that contain the data you want to collect, such as the business name or address. Once the script or template is created, you can run it to extract the data from Yelp.

The data will then be saved in a format that can be easily analyzed, such as a CSV file. It’s important to keep in mind that scraping Yelp can be a time-consuming process, especially if you are looking to collect a large amount of data. Additionally, it’s worth noting that Yelp may change its website structure, which could break your scraping script, so it’s important to stay updated.

Yelp Data Scraping – Step-By-Step Guide

Yelp Scraping Using BeautifulSoup

To scrape Yelp using BeautifulSoup, you will need to do the following:

• Install BeautifulSoup by running “pip install beautifulsoup4” in your command line.
• Import the necessary modules:

from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.chrome.service import Service

• Use Selenium to load the page and get the rendered HTML, then pass it to BeautifulSoup:

DRIVER_PATH = '/path/to/chromedriver'
service = Service(executable_path=DRIVER_PATH)
driver = webdriver.Chrome(service=service)

driver.get("https://www.yelp.com/search?find_desc=restaurants&find_loc=New+York")

soup = BeautifulSoup(driver.page_source, 'html.parser')

• Inspect the page structure in your browser’s DevTools to locate the elements containing the data you want. Then extract them:

business_cards = soup.find_all('div', attrs={'data-testid': 'serp-ia-card'})
names, ratings, addresses = [], [], []
for card in business_cards:
try:
name = card.find('a', class_='css-19v1rkv').text
except:
name = ""
try:
rating = card.find('div', attrs={'aria-label': True})['aria-label']
except:
rating = ""
try:
address = card.find('span', class_='raw__09f24__T4Ezm').text
except:
address = ""
names.append(name) ratings.append(rating) addresses.append(address)

Tip: Yelp periodically updates its HTML structure. Always inspect the live page in DevTools before running the scraper. data-testid and aria-label attributes tend to be more stable than class names.

Store the results in a pandas DataFrame and export:

import pandas as pd

df = pd.DataFrame({
'Name': names,
'Rating': ratings,
'Address': addresses
})

df.to_csv('yelp_businesses.csv', index=False)

Scrape Yelp Python Method: Using Selenium

Selenium is a powerful tool for web scraping, and it can be used to extract information from Yelp’s website. Here is an example of how to use Selenium to scrape Yelp’s search results for a specific keyword:

1. Install Selenium

First, you need to install Selenium. You can do this by running the following command in your command prompt: pip install Selenium.

2. Download the ChromeDriver

Starting from Selenium 4, the driver is initialized through an Service object. Make sure ChromeDriver matches your installed Chrome version.

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

DRIVER_PATH = '/path/to/chromedriver'
service = Service(executable_path=DRIVER_PATH)
driver = webdriver.Chrome(service=service)

3. Navigate to Yelp Search Results

keyword = "pizza"
location = "Chicago"
url = f"https://www.yelp.com/search?find_desc={keyword.replace(' ', '+')}&find_loc={location.replace(' ', '+')}"
driver.get(url)

4. Wait for Results to Load and Extract Data

`wait = WebDriverWait(driver, 10)
wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "h3.css-foyide")))

results = driver.find_elements(By.CSS_SELECTOR, "div[data-testid='serp-ia-card']")

for result in results:
try:
title = result.find_element(By.CSS_SELECTOR, "a.css-19v1rkv").text
print(title)
except Exception:
pass`

5. Handle Pagination

Yelp paginates search results with a start parameter in the URL, incrementing by 10 per page. Loop over pages to collect results across multiple pages:

import time
all_titles = []
for page in range(0, 100, 10): # first 10 pages
url = f"https://www.yelp.com/search?find_desc={keyword}&find_loc={location}&start={page}"
driver.get(url)
time.sleep(2)
cards = driver.find_elements(By.CSS_SELECTOR, "div[data-testid='serp-ia-card']") for card in cards: try: title = card.find_element(By.CSS_SELECTOR, "a.css-19v1rkv").text all_titles.append(title) except Exception: pass

6. Close the Driver

driver.quit()

Scraping Yelp Reviews with BeautifulSoup: Example

Once you have a business URL, extracting individual reviews follows the same pattern — navigate to the page, wait for reviews to render, and parse the relevant elements:

driver.get("https://www.yelp.com/biz/your-target-business")
time.sleep(2)

soup = BeautifulSoup(driver.page_source, 'html.parser')
review_blocks = soup.find_all('li', attrs={'data-testid': 'review-list-item'})

for block in review_blocks:
try:
reviewer = block.find('a', class_='css-19v1rkv').text
except:
reviewer = ""
try:
rating = block.find('div', attrs={'aria-label': True})['aria-label']
except:
rating = ""
try:
text = block.find('span', class_='raw__09f24__T4Ezm').text
except:
text = ""
print(reviewer, rating, text)

Get Yelp Data at Scale with DataOx

The steps above cover the fundamentals of how to scrape Yelp using Python for targeted, smaller-scale tasks: to extract data from Yelp and analyze it for useful insights. However, extracting large amounts of web data requires preparation: data quality validation, and understanding of how website protection works: rate limiting, proxy rotation, session management, selector maintenance when Yelp updates its HTML.

DataOx handles scrape data from Yelp Python projects end to end, from scoping what data fields you need and in what format, through to delivering a cleaned, structured dataset. Our team knows how to work with large websites or databases and manages anti-bot handling. Our specialists have extensive experience in extracting data in the most efficient way for our clients. If you have a Yelp data collection task to scope out, schedule a free consultation with our expert.