How to Scrape Prices from Websites? Robust Guide in 2024

Therefore, It is necessary to adapt to the latest trends in order to survive and thrive in this ultra-competitive atmosphere. If you are a market player, the first step in this direction is to analyze your competitors. One key component of this analysis is the price. Comparing the prices of products across competitors will help you quote the most competitive price in the market. 

Also, if you are an end-user, you will be able to discover the lowest prices for any product. But, the real challenge here is that many e-commerce websites are available online. It is impossible to manually go to each website and check every product’s price. This is where computer coding comes into play. With the help of Python code, we can extract information from the websites. This makes scraping prices from websites a walk in the park. 

This article will discuss how to scrape prices from websites from an eCommerce website using Python as an example. 

Feel free to check the section that you want to know the most.

Before we deal with scraping prices from websites, we must discuss the definition and legal factors behind web scraping.

Web scraping, also known as web data extraction, uses bots to crawl through a target website and collect necessary data. When you hear the term “web scraping,” the first question that may come to mind is whether web scraping is legal or not.

In short, it is always better to practice extracting data from websites that do not affect the original owner of the data. Another thing to keep in mind is to scrape only what you need. Scraping tons of data from the website will likely affect the bandwidth or performance of the website. It is important to keep an eye on that factor. 

If you are not clear on how to check if the website allows web scraping or not, there are ways to do that:

Now, you should have a basic understanding of web scraping and the legal factors behind web scraping. Let us see how we can build a simple web scraper to find the prices of laptops from an eCommerce website. Python language, along with the Jupyter notebook, is used to build the scraper.

from bs4 import BeautifuSoup
import requests
import pandas as pd
import urllib.parse

For better practice, in this example, the website name is not made visible. Following the web scraping legal guidelines mentioned above and the following steps will provide you with the result. Once you obtain the website address, you can save it in a variable and check if the request is accepted or not. For extracting data, follow the python code which is shown below:

seed_url = 'example.com/laptops'
response = requests.get(seed_url) #Checking whether the request is accepted or not
response.status_code #200 is the code refer to OK Status, which means request is accepted
200

Parsing is the process of converting one format to another format. In this case, HTML parsing is carried out, converting the data (HTML) into an internal format (python) so the environment can run the data. The following image shows the python code to carry out the process of parsing data using the BeautifulSoup library:

soup = BeautifulSoup(response.content, 'html.parser')

By parsing the web pages, python gets all the data like names, tags, prices, image details, and page layout details.

As mentioned above, our objective is to find the prices of the laptop on an e-commerce website. The necessary pieces of information for this example are the name of the laptop and its price. In order to find that, visit the web page you want to scrap. Right-click on the webpage and select the “inspect option.” You will see a terminal like this:

results = soup.find_all('div', {'class':'item-container'})
len(results)

results[1]

#NECESSARY DATA:
#Name and Price of the item
Name_of_the_Item= soup.find('a', {'class':'item-title'}).get_text()
print(Name_of_the_Item)

price=soup.find('li', {'class':'price-current'}).get_text()
print(price)

Now you’ve got the price for a single laptop. What if you need 10 laptops? It is possible by using the same code in for loop. Python code for executing a for loop is shown below.

Name_of_the_item = []
Price_of_the_item = []
for soup in results:
    try:
        Name_of_the_item.append(soup.find('a', {'class':'item-title'}).get_text()) 
    except:
        Name_of_the_item.append('n/a')
    try:
        Price_of_the_item.append(soup.find('li', {'class':'price-current'}).get_text())
    except:
        Price_of_the_item.append('n/a')
print(Name_of_the_item)
print(Price_of_the_item)

Now that all the steps for web scraping, let us see what the output looks like.

This is not in a readable format. To convert this into a readable format, preferably a table (dataframe) format, you can use pandas library. The python code is shown below on how to carry out this step.

#Creating a dataframe
product_details=pd.DataFrame({'Name': Name_of_the_item, 'Price':Price_of_the_item})
product_details.head(10)

Now it looks readable. The last step is to save this dataframe in a CSV file for analysis. The python code to save the dataframe to CSV format is shown below.

product_details.to_csv("Web-scraping.csv")

With this, you can perform simple competitive analysis, focusing on the prices of the products. Instead of doing it manually, automated web scraping using python is an efficient way and saves you a lot of time. 

As discussed above on how to check if the website allows web scraping, proxies will help you clear the problem.

This article hopes to have given enough information to answer the question, “how to scrape prices from websites?” But the reality is that there is no definite way to scrape prices from websites. You can use either dedicated web scraping tools to scrape prices from websites or create your own python scripts to scrape prices from websites. Either way, you can save time and collect much data without difficulty.