How To Do Web Scraping With Python?

How to do Web Scraping using Python?

Just imagine with your eyes closed that if you had the task of pulling a huge amount of data from websites and you have not much time in your favor then how can you comprehend such a huge responsibility? It is technically impossible to manually go on each website page and collect data and out of it decide what works for you and what doesn’t can leave you all stressed and behind your schedule. Web Scraping is the answer to all your big data problems!

Web Scraping is largely used to dig out information from different websites which is intended. It is used to extract important or intended piece of information that is available on the Internet. Information is increasing day by day with new discoveries and research going on with every passing second. It becomes really tedious to spend hours in looking for the right fit of information for your task. Such data extraction task can take hours without Web crawlers.

Web Scraping Example

Let us suppose that we need to scrape up an Amazon website for data then in the case our Pre-requisites are going to be Python 2.x or Python 3.x with SeleniumBeautifulSoup, pandas libraries, Ubuntu Operating System and Google-chrome browser Ubuntu Operating System for scraping out the right information for our projects.

Why is Python good for Web Scraping?

Here you are an incredible list of why python s more suitable for web scraping than any other software:

Easy to use:

The number one advantage of python is that it is way easier to employ than any other technical software. There is no use of semi-colons (;) or brackets {} to complicate things further.

Various Libraries: 

Such a vast variety of libraries makes it more accessible than any other software; also it can also be utilized to manipulate data further for achieving a higher level of data accuracy. Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas, etc.

Dynamically typed:

In Python, one doesn’t have to spend time on having to define data types for different variables, there is the liberty to directly use the variables wherever it is required. This use eventually saves your time to be invested in more important business tasks to benefit your company.

Easily Understandable Syntax: 

Python Syntax is understood very easily by everyone. It is just like a phrase or a sentence likewise written in the English Language. The indentation used in Python also helps the user to differentiate between different blocks in the code. 

Small code, large task:

 Web scraping is intended to save your time for other data compilation tasks but what is the point for writing elongated codes? Python favors you with writing comparatively smaller codes and does the work in less time.


What is you can’t think of anything at a point about what to do next? You don’t have to worry. Python community has one of the biggest and most active communities, which offer professional help with regard to data scraping operational activities.

How Do You Scrape Data From A Website?

When you are running the web scraping code in python language, a request is sent to a URL that you mention of any specific page. The server as a response operator send sin the data and makes the data visible in the form of an HTML or XML page.

The code then decodes the HTML or XML and makes the page visible to you.

To extract data using web scraping with python, you need to follow primary steps:

  1. Find the URL that is essential to be scraped for data.
  2. Inspect the page.
  3. Find the right data chunk that you want to scrape from the website.
  4. Write down the code into it.
  5. Now run the code and write down what data it shows or save it in an accessible portable format.

Steps to Scrape a Website using Python

The entire Internet is invented to b able to be read by humans. Hence, Web Scrapers act as essential handy tools that can load the entire HTML Code into one page for the incorporated question. Let us assume that you want a detailed product description for your designed company product. So, you wills search through Amazon to get some detail about a similar product that will fill up your product description gap. Web Scraping can not only be used essentially for merely data collection but also serves the purpose of marketing, finance, and sales. Following are the essential steps to scrape a website using python in detail:

Step 1: Find the URL that you want to scrape

To begin with, let us consider a website to scrape to gather data. Let it be Amazon or Flipkart Websites to extract product information such as price lists, name as well as ratings of laptops.

Step 2: Inspecting the Page

Data is not free to be used for everybody. It is surrounded by tags. Here we inspect what tag the data which is intended is nested in. Click right on the element and press inspect option. After which a “Browser Inspector Box” opens.

Step 3: Find the data you want to extract

After knowing what the tag is, the next step involves scraping of data such as the Price, Name, and Rating, which say reside under the ‘div’ tag.

Step 4: Write the code

This step requires the creation of a python file. It is done by opening the terminal in Ubuntu and by typing code in the above format:

gedit <your file name> with .py extension.

Let us name the file for our convenience as “web-s”

The command for initiating the code is: gedit

To configure the webdriver, we need to use Chrome browser for this purpose and set it as:“/usr/lib/chromium-browser/chromedriver”)

After inserting the code in the URL, we are all set to extract the data we want to extract is nested in <div> tags. content+driver.page_source

These are the important codes to extract data from the website:

  1. soup=BeautifulSoup (content)
  2. for a in soup.findAll (‘a’, href=True, attrs={‘class’:’_31qSD5’}):
  3. name=a.find(‘div’, attrs={‘class’:’_3wU53n’})
  4. price=a.find(‘div’, attrs={‘class’:’_1vC4OE_2rQ-NK’})
  5. rating=a.find(‘div’,attrs={‘class’:’hGSR34_2beYZw’})
  6. products.append(name.text)
  7. ratings.append(ratng.text)

Step 5: Run the code and extract the data

To run the code, use the below command:

  1. Python

Step 6: Store the data in a required format

Extracted data can be stored in any desirable format. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. Following lines will be added to the code:

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

Just run the code one more time and the file product.csv is created to hold the extracted information successfully.

How ITS Can Help You With Web Scraping Service?

Information Transformation Service (ITS) includes a variety of Professional Web Scraping Services catered by experienced crew members and technical software. ITS, is an ISO-Certified company that addresses all of your big and reliable data concerns. For the record, ITS served millions of established and struggling businesses making them achieve their mark at the most affordable price tag. For acquiring our Professional Web Scraping Services ask for a free quote!


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