May 4th, 2020
Scraping Yelp data with Python and Beautiful Soup

Today we are going to see how we can scrape Yelp data using Python and BeautifulSoup in a simple and elegant manner.

The aim of this article is to get you started on a real-world problem solving while keeping it super simple so you get familiar and get practical results as fast as possible.

So the first thing we need is to make sure we have Python 3 installed. If not, you can just get Python 3 and get it installed before you proceed.

Then you can install beautiful soup with...

pip3 install beautifulsoup4

We will also need the libraries requests, lxml and soupsieve to fetch data, break it down to XML, and to use CSS selectors. Install them using.

pip3 install requests soupsieve lxml

Once installed open an editor and type in.

# -*- coding: utf-8 -*-
from bs4 import BeautifulSoup
import requests

Now let's go to the Yelp San Fransisco restaurants listing page and inspect the data we can get.

This is how it looks:

Back to our code now. Let's try and get this data by pretending we are a browser like this.

# -*- coding: utf-8 -*-
from bs4 import BeautifulSoup
import requests

headers = {'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/601.3.9 (KHTML, like Gecko) Version/9.0.2 Safari/601.3.9'}
url='https://www.yelp.com/search?cflt=restaurants&find_loc=San Francisco, CA'
response=requests.get(url,headers=headers)

print(response)

Save this as yelp_bs.py.

If you run it.

python3 yelp_bs.py

You will see the whole HTML page

Now, let's use CSS selectors to get to the data we want. To do that let's go back to Chrome and open the inspect tool.

We notice that all the individual rows of data are contained in a

with the class 'container along with other jibberish before and after it. This is good enough for us to scrape it. We can get BeautifulSoup to select the data that has the word inside its class definition anywhere with the * operator like this.
# -*- coding: utf-8 -*-
from bs4 import BeautifulSoup
import requests

headers = {'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/601.3.9 (KHTML, like Gecko) Version/9.0.2 Safari/601.3.9'}
url='https://www.yelp.com/search?cflt=restaurants&find_loc=San Francisco, CA'
response=requests.get(url,headers=headers)


soup=BeautifulSoup(response.content,'lxml')


for item in soup.select('[class*=container]'):
	try:
		print(item)


	except Exception as e:
		raise e
		print('')

This prints all the content in each of the containers that hold the restaurant data.

We now can pick out classes inside these rows that contain the data we want. We notice that the title is inside a tag. We select this but also do all other selections under this protective umbrella. This is because in the selection above the class container might be used to contain other things other than the data we want. So to be sure, we make sure that there is a tag in there before we scrape other pieces of data.

# -*- coding: utf-8 -*-
from bs4 import BeautifulSoup
import requests

headers = {'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/601.3.9 (KHTML, like Gecko) Version/9.0.2 Safari/601.3.9'}
url='https://www.yelp.com/search?cflt=restaurants&find_loc=San Francisco, CA'
response=requests.get(url,headers=headers)


soup=BeautifulSoup(response.content,'lxml')


for item in soup.select('[class*=container]'):
	try:
		#print(item)
		if item.find('h4'):
			name = item.find('h4').get_text()
			print(name)

			print('------------------')


	except Exception as e:
		raise e
		print('')

If you run it it will print out all the names.

Bingo!! we got the names...

Now with the same process, we get the other data like phone, address, review count, ratings, price range etc...
# -*- coding: utf-8 -*-
from bs4 import BeautifulSoup
import requests

headers = {'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/601.3.9 (KHTML, like Gecko) Version/9.0.2 Safari/601.3.9'}
url='https://www.yelp.com/search?cflt=restaurants&find_loc=San Francisco, CA'
response=requests.get(url,headers=headers)


soup=BeautifulSoup(response.content,'lxml')


for item in soup.select('[class*=container]'):
	try:
		#print(item)
		if item.find('h4'):
			name = item.find('h4').get_text()
			print(name)
			print(soup.select('[class*=reviewCount]')[0].get_text())
			print(soup.select('[aria-label*=rating]')[0]['aria-label'])
			print(soup.select('[class*=secondaryAttributes]')[0].get_text())
			print(soup.select('[class*=priceRange]')[0].get_text())
			print(soup.select('[class*=priceCategory]')[0].get_text())
			print('------------------')


	except Exception as e:
		raise e
		print('')

Notice the trickery we employ when we want to get the ratings. We know that the ratings are hidden as a label in this div

So we get it with this line.. it selects the element with the attribute aria-label but only ones that have the word label in them and then goes onto to ask for its aria-label attribute value... loved writing this one.

			print(soup.select('[aria-label*=rating]')[0]['aria-label'])

When we run it it will print out every detail we want like this.

We even added a separator to show where each restaurant detail ends. You can now pass this data into an array or save it to CSV and do whatever you want.

If you want to use this in production and want to scale to thousands of links then you will find that you will get IP blocked easily by Yelp. In this scenario using a rotating proxy service to rotate IPs is almost a must.

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