Web Scraping using Python - A Beginner's Guide

By StartxLabs
Date 12-05-21
Web Scraping using Python - A Beginner's Guide
" Web Scraping using Python - A Beginner's Guide"


Python helps in doing a number of things nowadays. Likewise, it establishes its importance in web scrapping. Web scrapping deals with extracting data from the web, manipulating and cleaning the data. This article will help you in learning about Web scrapping and how it is done using Python libraries.


What is Web Scraping?


An automated method that extracts an enormous amount of data from websites called Web Scraping. The data available on the websites are usually in the form of unstructured data. The web scraping method collects them and stores them as structured data. There are many ways to perform web scraping. One of the most popular ways is by using Python.



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Why Web Scraping?


We have seen that collecting a large amount of data from websites is called web scraping. However, why do we have to collect them? What is the purpose of web scraping? Let's answer these questions below. Here are some of the applications of web scraping:


  • Social media analysis: Social media like Facebook, Twitter, and Instagram handle loads and loads of trending data every day. Web scraping these social media websites reveals data about the latest information such as trending topics, sentiments, and more.

  • Price Comparison on eCommerce: eCommerce sellers usually have different prices at different marketplaces. Using web scraping they can compare pricing on multiple platforms and analyze the places for getting higher profit.

  • Real Estate Investment: Web scraping helps real estate investors to analyze different marketplaces by getting valuable information from websites such as high-rated areas, attractive renting options, and more.

  • Machine Learning: Machine Learning needs enormous data to learn and improve. Web scraping helps in collecting many texts, images, and data points quickly and that can be used in improving reliability and accuracy in machine learning.

  • Email address collection: Email marketing is one of the most used forms of marketing by many companies. Using scraping, they collect a large number of email addresses and use them for sending bulk emails for marketing purposes.

  • Job listings: Job update information such as job openings, interviews, and dates are collected from various websites and listed in a single place. Users can easily browse all information about job updates in one place.




Is Python Good for Web Scraping?


Among all, Python is good for scraping websites to collect an enormous amount of data. Here are the reasons below.


  • Simplicity: As we know, Python is the easiest and simplest language to code. No need to use curly braces, semicolons and no need to declare data types which makes the code appear clearer and saves time.

  • Huge libraries: There are plenty of libraries available in Python such as Panda, Numpy, Matplotlib, and more. Due to the large availability of libraries, web scraping is easy for data extraction and manipulation using Python.

  • Easy syntax: Since Python is a high-level language, it is easily readable and the syntax is easily understandable. You can write smaller code for performing larger tasks.

  • Larger Community: Python community is one of the largest and you can seek help from it whenever you face issues while writing code.


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Steps used in Web Scraping:


Developing the code for performing web scraping depends on your style. However, the process while executing the code remains the same for all. Once you run your code, the URL of the website you wanted to scrap receives a request. As a response to it, the server sends back the data and lets you read the website's HTML or XML code. The HTML code then gets parsed and you can find the data and extract it. Extracting the website's data involves the following steps:


  • You have to find the exact URL of the website you wanted to scrap.

  • Inspect the page.

  • Find the data you wanted to scrap from the page.

  • Write the code accordingly.

  • Now, run the code and scrap the data.

  • Store the extracted data in the format you require.


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