Today we are going to see how we can scrape Amazon Best Seller Products 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 Amazon Best Seller Products 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.amazon.in/gp/bestsellers/garden/ref=zg_bs_nav_0/258-0752277-9771203'

response=requests.get(url,headers=headers)


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

Save this as scrapeAmazonBS.py.

If you run it.

python3 scrapeAmazonBS.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 product data are contained in a with the class 'zg-item-immersion'. We can extract this with the CSS selector '.zg-item-immersion' easily. So here is how the code looks then.

# -*- 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.amazon.in/gp/bestsellers/garden/ref=zg_bs_nav_0/258-0752277-9771203'

response=requests.get(url,headers=headers)


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


for item in soup.select('.zg-item-immersion'):
	try:
		print('----------------------------------------')
		print(item)



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

This will print all the content in each of the elements that hold the product data.

We now can pick out classes inside these rows that contain the data we want.

# -*- 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.amazon.in/gp/bestsellers/garden/ref=zg_bs_nav_0/258-0752277-9771203'

response=requests.get(url,headers=headers)


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


for item in soup.select('.zg-item-immersion'):
	try:
		print('----------------------------------------')
		print(item)
		print(item.select('.p13n-sc-truncate')[0].get_text().strip())
		print(item.select('.p13n-sc-price')[0].get_text().strip())
		print(item.select('.a-icon-row i')[0].get_text().strip())
		print(item.select('.a-icon-row a')[1].get_text().strip())
		print(item.select('.a-icon-row a')[1]['href'])
		print(item.select('img')[0]['src'])


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

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

Bingo!! we got them all...

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 Amazon. In this scenario using a rotating proxy service to rotate IPs is almost a must. You can use a service like Proxies API to route your calls through a pool of millions of residential proxies.

If you want to scale the crawling speed and dont want to set up your own infrastructure, you can use our Cloud base crawler crawltohell.com to easily crawl thousands of URLs at high speed from our network of crawlers.