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Web Scraping With Python: Top Whys And Hows

Introduction

“Things get done only if the data we gather can inform and inspire those in a position to make a difference”  - Michael Schmoker, Former School Administrator.

So, how can your data turn into something inspirational? It is through data scraping from the websites. This process helps you pull out the correct data and transform them into insightful information.

Sounds simple? But in actuality, it is not? As the internet has tons of information displayed across websites. 

Can you extract and deploy every single piece of data manually for each of your projects? No human can do this painstaking task.

What can be the possible solution? Few codes to extract data from thousands of pages instead of doing it with your hands. 

Yes, it is the automation technique that many data scientists and developers deploy to procure accurate results in a quick turnaround.

As the subject of discussion is vast, the article has some digestible section that includes the following:

  • Basics of web scraping with Python
  • Why use it?
  • Top 10 reasons to use Python for data extraction
  • Six simple steps to do web scraping with python
  • In-sight into three libraries in Python for website scraping

Without any further adieu, let us dive into our technical discussion and talk about web scraping using python in detail in this article!

Website Scraping Using Python

In simple words, Web scraping with Python is the task of collecting volumes of information from websites, aka web data extraction. What are the applications for web scraping with Python? 

There are several, but few general ones include the following:

  • Market research
  • Price comparisons
  • Collect email addresses
  • Lead generation

All the information you extract through a programming language like Python has one commonality, it helps businesses or individuals like you make clever decisions from public data.

You can now make a hassle-free extract of countless data that does not exist in the manual version of the same.

What Is Web Scraping?

Web scraping is an automation technique to retrieve volumes of data from different websites and convert and store them in a structured format. You can do it via several methods. But here, it is all about scraping with Python.

Basics Of Web Scraping

A brief about the basics helps you comprehend the process with efficacy. 

The web crawler helps the scraper extract the requested data from the internet. That was a crisp intro to crawlers and scrapers, Right? But it has more to it.

So, how do they work? Are they the same or not? Read below to know further.

Web Crawler

Do you know what spiders do? They crawl around the wall and build their webs, right? Yes, web crawlers/spiders are AI that surf the internet to search and index your content through the URL links. They are just like a person who has more time and does not have a to-do list for that day.

In general, any project crawling process precedes scraping. After the website(s) is crawled through and the URLs get figured are handed over to your scraper tool.

Web scraping tools known as web scrapers have the potential to extract data from any website within a blink of an eye. These tools help you to develop data for ML in specific.

The Scraper

It is a customized tool that extracts web data with accuracy and efficiency at rapid rates. The data selectors present in the scrapers detect and extract info from the HTML files.

People synonymously toss web scraping and crawling. But there exist a few differences. They are: 

Web scraping

Web crawling

Downloads the info

Indexes the web page

A Web site visit is not necessary

A Web site is mandatory throughout the entire process

Deployed on for small- and large-scale data

Housed for large-scale data only

 

Finds application in ML, Retail marketing, Equity search, Travel and Tourism, Real estate, Academic research

The areas of application include Price intelligence, Competition research, Brand monitoring, Data-driven portfolio management

Requires both crawl agent and parser for parsing

Crawling needs crawl agent alone

Does not abide robots.txt (most cases)

Not all crawlers abide by robots.txt

So, the next question that pops up in your mind is that does web scraping with Python work? The answer is, Yes!

Why use Web Scraping?

What is the purpose of web scraping with Python? Gather volumes of information from websites. Why collect so much information from these online sources? Insights into the application of web scraping can provide you with an answer.

Price Intelligence

The most significant use case for web scraping with Python is price intelligence. What is price intelligence? Why is it necessary?

The retailers look for the same product prices on other sites and extract them, which helps them better their marketing decisions or costs. In addition, price intelligence helps provide better prices than your competitors.

Collect Email Addresses

What is your marketing methodology? Is it newsletters and email marketing to promote your products or services? Then collecting email addresses is essential to reach your target audience. 

How to gather those addresses? You can use a web scraper tool like Hunter.io to download the information from similar sites of your domain.

Research And Development (R&D)

Do you have to collect vital data and stats for your high-end research project(s)? Then web scraper saves your time on manual copying of voluminous data.

Social Media Web Scraping

Do you want to find out the business trends and methods that make your business stay differentiated from the rest? Then these scraping tactics can help you do it. 

Also, it helps to find out about people’s perspectives of your brand.

Testing

How to determine the efficiency and user-interactiveness of your website? Test your site using a web scraping tool that sends volumes of requests to figure out the response time.

What is the best programming language to deploy for web scraping? Python

Top 10 Reasons Web Scraping With Python Is The Best

Many programming languages help you build a web scraper from scratch. So, why choose Python over them? The following benefits list will persuade you to go for it and also pick it as your career option:

Easy Automation

Do you know that all the websites are not the same and have different formats? So, the scrapping process needs iterations with every new web data entry per second.

But doing manual processes is nerve-wracking? Here is where the fully automated library of Python enables the web scraper to auto-extract data every day.

The write-once and write-few lines of automated Python codings are a time and effort saver to a great extent with faster data extraction rates. 

Scrapes and Parses

In general, every scraping involves two processes that are,

  • Scraping the essential data in an unstructured form
  • Parse it in a structured format

Many web scraping tools handle only the former process and not the latter one. But Python does scraping and parsing, and it saves the data extracted and visualizes it with Matplotlib.

Easy-to-read Syntax

The vital benefit of deploying Python tools for the scrapping process is that all its syntaxes are easy-to-read. Even amateurs can write the scraping codes without much effort.

Easy-to-write Codes

Do you know the reason behind Python’s popularity? It has the easiest-to-write codes for generating scraping scripts as well. You can write down a few coding lines in a matter of minutes without any hassle.

Efficient Parsers

Do you want to develop web scrapers that deliver high-performance? Scrapy and Beautiful Soup are some fast and efficient web scraper tools from Python that help you do it.

Dynamically Typed

With Python, you don’t have to define your variables and enter them directly wherever you need. It is a time-saver benefit.

Multi-function Web Scrapers

Python’s web scraper tools can do much more than data extraction, and they include:

  • Parsing and imports of structured data
  • Better visualization than other competitive programming languages

Auto Re-use Of Codes

The convenience of Python scraping codes is write-and-execute once. Later, the scraper tool automatically collects volumes of data every day that saves your energy and time.

Extensive Library Collection

There are several libraries in Python that makes a developer/programmers life a breeze, and it includes:

  • Matplotlib
  • TensorFlow
  • Numpy
  • Scrapy
  • Pandas 

Large Community

Due to its popularity, the Python community is expanding every day. So, if you face any issues while coding, you can get help from the experts in the community.  

Basics of web scraping with Python, Check! Reasons to scrap using Python, Check!. Now, let us look at web scraping with a python tutorial.

How To Scrape A Website Using Python?

The following is the step-by-step guide to do web scraping with Python:

Select The Page You Wish To Scrape

In our example, we are scraping data for Adidas shoes from Myntra, and the link would be https://www.myntra.com/adidas-shoes. We are focusing on Adidas sports shoes, and the URL would be: https://www.myntra.com/sports-shoes/adidas/adidas-men-navy--red-woven-design-magnificeo-running-shoes/14782120/buy

Note: For web scraping with Python, you have to store the data in the CSV format for later use.

Inspect The Site Code

You can find the data spaced between the nested tags, and for retrieving that info, you have to inspect the page. Follow these steps:

how to scrape a website using python -coding

 

Now, right-click your mouse on the image and select the inspect option from it.

After clicking the inspect option, you will get a window pop up open like the below:

 

how to scrape a website using python image 2

Locate The Data You Planned To Extract

Now, locate your data embedded between the tag. Now you can see the product details here. The tag and class name may vary from different sites or images.

Download Libraries

The next step in web scraping with Python is to create the workspace. For this, you need to download and install Python.

Choose from the several IDE that suits your needs.

Next, install the following libraries for your workspace:

  • Selenium
  • Pandas
  • BeautifulSoup

Use this code in your command line:

python -m pip install selenium pandas beautifulsoup4

You have to import these installed libraries for usage in your Chrome browser by setting the path to chromedriver. You need not worry about the location here if the path is correct. 

Remember to add the pathname at the end along with the location.

Make sure you declare the variables and set the site URL you plan to scrape:

models = [ ]

prices = [ ]

driver.get(site URL comes here)

Finally, you need to extract the information embedded between the

tags and store its class and name in the above-declared variables.

Run The Code

Click paste the following code in your command line to run the code:

Python main.py

Store It In Your Preferred Format

Now store your extracted data in a format that is suitable for you. For instance, you can keep it in a CSV format that helps with easy import.

df = pd.DataFrame({ attributes of

})

df.to_csv('file name', index=False, encoding='utf-8')

Now when you rerun the code, the file name is created.  

This web scraping with python tutorial is a simple one that is effective for single-page data scraping.  

Last but not least, a brief look into the top libraries in Python would deliver a perfect ending to our discussion of web scraping with Python.

Look At Three Best Libraries For Web Scraping In Python

First, let us start with the popular and beautiful one.

Beautiful Soup

  • Easy interface and less complex than Scrapy
  • Suitable for developers who want functionalities like screen scraping
  • The scraper tool is compatible with Mac, Linux, Windows, and BSD

Selenium

  • Selenium is a Javascript web scraper that helps developers test web applications and sites
  • It is a powerful and scalable scraper when combined with Python
  • You can easily use this tool on any social media platform or other websites

Pandas

  • This open-source library offers operations for numerical data 
  • It helps in easy data analysis
  • This package delivers the users high-performance and speed

Conclusion

Web scraping with Python is a technically challenging task that requires more knowledge, practice, hands-on experience to get your career started. Well, web scraping with a python tutorial is beneficial to a great extent. But online Python certifications pave the strong foundation for your bright data scientist career. Choose the best one today!


    Janbask Training

    A dynamic, highly professional, and a global online training course provider committed to propelling the next generation of technology learners with a whole new way of training experience.


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