Have you ever scraped or extracted data from any site? If yes, then you are already aware of web scraping. If you don’t, web scraping is the art of extracting a site’s information. Web scraping can be regarded as one of the most regular activities conducted by not only tech people but also the ones involved in search engine optimization, market analysis, and similar activities. It is primarily an automated approach that draws out massive amounts of data from a particular website. The data found on a site is mostly shapeless, and a scraping tool provides a base for that. 

There are certain ways through which a site can be scraped like online services, APIs, or maybe writing your own code. Python is one of the ways through which web scraping can be performed. It is often regarded as the best programming language and has been equally effective as a scraping tool as well. 

Understanding How to Use Python for Web Scraping 

There are certain steps to using a .python web scraper When you are running a code for web scraping, a request is delivered to the stated URL. As an answer, the server delivers the data and permits you to go through the HTML or XML page. Post that, the code parses the HTML or XML page, locates the data, and extracts it. This can be understood by the web scraping of the Flipkart website using Python. 

Libraries Utilized for Web Scraping 

Python offers numerous applications and similarly diverse applications for specific objectives. The following libraries will be used for the following methods:

  1. Selenium: This is a web testing library, and is used for automating browser activities. 
  2. BeautifulSoup: This is a python package for parsing HTML and XML documents. It generates parse trees helpful for extracting the data easily. 
  3. Pandas: This is a library utilized for data handling and analysis. 

Methods to Scrape the Web with Python 

For scraping the Flipkart websites, the following are necessities:

  1. Python 2.x or Python 3.x with Selenium, BeautifulSoup, pandas libraries installed
  2. Google-chrome browser
  3. Ubuntu operating system 

Step 1: Locate the URL you wish to Scrape 

To help understand, Flipkart’s website containing the name, price, and rating of laptops will be scraped. 

Step 2: Examining the Page

The data is generally ingrained in tags. Therefore, we examine the page to see the location of the tag under which the data that we wish to scrape is ingrained. For examining the page, just right-click on the feature and select ‘Inspect’. Upon selection of the ‘Inspect’ tab, you will notice a ‘Browser Inspector Box’ popped open.

Step 3: Look for the Data you Expect to Scrape

Let us draw out the name, rating, and price which is present in the ‘div’ tag respectively. 

Step 4: Pen Down the Code

Begin with generating a Python file. For performing this, open the terminal in Ubuntu and write ‘gedit’ <your file name> followed by the .py extension. Let the file name be ‘web-s’, here goes the command:

  1. Gedit web-s .py

Start by importing all the required libraries:

  1. from selenium import webdriver
  2. from BeautifulSoup import BeautifulSoup
  3. import pandas as pd

For configuring web driver to utilize Chrome browser, you need to make way to the chrome driver:

  1. driver = webdriver.chrome (“/usr/lib/chronium-browser/chromedriver”)

Point out the following code for accessing the URL:

  1. products=[] #List to store name of the product
  2. prices=[] #List to store price of the product
  3. ratings=[] #List to store rating of the product
  4. driver.get(“<a href=”https://www.flipkart.com/laptops/”>https://www.flipkart.com/laptops/</a>~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5g&amp;amp;amp;amp;amp;amp;amp;amp;amp;uniq”)

Once the code is written for opening the URL, start extracting the website nested in <div> tags. Refer to the below code:

  1. content = driver.page_source
  2. soup = BeautifulSoup(content)
  3. for a in soup.findAll(‘a’,href=True, attrs={‘class’:’_31qSD5′}):
  4. name=a.find(‘div’, attrs={‘class’:’_3wU53n’})
  5. price=a.find(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
  6. rating=a.find(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
  7. products.append(name.text)
  8. prices.append(price.text)
  9. ratings.append(rating.text)

Step 5: Conduct the Code and Draw the Data 

For running the code, use the following command:

  1. Python web-s.py

Step 6: Accumulate Data in a Set Format

Once extracting the data, you might wish to reserve it in the correct format depending on the requirement. For instance, for now, the storage will be done in a CSV (Comma separated value) format. For doing this, I will put forward the following lines to the code:

  1. df = pd.DataFrame({‘Product Name’:products,’Price’:prices,’Rating’:ratings})
  2. df.to_csv(‘products.csv’, index=False, encoding=’utf-8′)

Now, run the complete code again. The file would successfully contain all the extracted data. 
There are numerous free API services that can be used if you are a newbie. You can try out the aforementioned steps to easily use python for web scraping.