Web Scraping in Python using BeautifulSoup and Selenium
There are a lot of Python libraries you can utilize for data scraping as well as many online tutorials are available on how to start.
Today, we will discuss about scraping e-commerce products data from dynamic pages and concentrate on how you could do it with BeautifulSoup and Selenium.
Usually, e-commerce product list pages are dynamic so, various product details is produced for various users — for example, airline price change depending on users’ locations or products getting ranked by significance based on perusing behaviour. The product information is generally populated using Javascript in-browser. That is where Selenium has a role to play. It could programmatically load as well as interact with the web pages within a browser. Then, we can use BeautifulSoup for parsing the page resource and scrape required product data from the HTML elements.
This blog will show how you could automatically recover products data from pages like these…
…for a clean and useable format for use and analysis.
Why to do this? Knowing your competitors, price comparision across different retailers as well as analyzing the market trends are only some practical applications.
Installation
This blog will utilize Pandas, BeautifulSoup, and Selenium. Non-compulsory for more superior progressions include Re, Requests, as well as Time. In case, you don’t have all the things installed, the best way is installation through pip.
pip install selenium pip install beautifulsoup4 pip install requests
We will need to install the web driver. For instance, for Chrome, you need to download the ChromeDriver. Position the executable file in among the directories within PATH variable.
Page Scraping
For demo, we will scrape books.toscrape.com, a fiction book store. Its pages are not dynamic, or static, however, its functionality might be similar.
import pandas as pd from selenium import webdriver from bs4 import BeautifulSoup import re import requests import time url = 'https://books.toscrape.com/catalogue/page-1.html' driver = webdriver.Chrome() driver.implicitly_wait(30) driver.get(url) soup = BeautifulSoup(driver.page_source,'lxml') driver.quit()
The beyond might load the URL within a Chrome browser as well as wait for elements to load, pass the page resources to BeautifulSoup as welll as end a browser session. For the pages, which take long time for loading, you might need to mess around with waiting time (in seconds).
Our soup looks like this. It’s time to start scraping useful elements!
Scraping Elements
To get an element, we could filter through its tag names or attribute name as well as attribute value.
For scraping all product names at the initial page of the fictional book store, let’s recognize which elements they got stored in. This looks like the text is reliably stored in <h3> tag.
soup.find() get the initial element, which matches with our filter: the tag name matches ‘h3’.
Adding .string returns the element texts only.
soup.find_all() gets all the elements, which match with our filter as well as returns them within the list. Note: soup.find_all() as well as soup() would function similar in cse, you’re a brevity fan.
Finally, looping through.string in the list comprehension returns the elements’ texts. Now, we have got the list of 20 products’ names!
The similar can be made with all the product details. To find all product prices, we have filtered through attribute name called ‘class’ as well as attribute value called ‘price_color’.
You can stop here as wella s focus on lists of various product details and it might work very well for the websites having clean HTML. However, e-commerce websites are not always clear as well as troubleshooting for the exceptions could be the most time-consuming part of the procedure.
Missing Elements
It is the most general exception we have encountered.
What occurs when elements are lost for certain products? For instance, if any product is provisionally unavailable as well as there are no tags having prices for the product. Rather than having null values in a list, we might get the price list, which is shorter than list of different product names as well as run risks of getting incorrect pricing against the products.
To avoid that, we found it best for first filtering to the outer elements, which contain all the product data then within every outer element get particular inner elements like product’s name, pricing, etc. We could include the condition for returning the null value in case, the inner elements are missing from the product tiles. It will make sure all the product data is in same order within our lists.
