The task is to count the most frequent words, which extracts data from dynamic sources.
First, create a web-crawler or scraper with the help of requests module and beautiful soup module, which will extract data from the web-pages and store them in a list. There might be some undesired words or symbols (like special symbols, blank spaces), which can be filtered in order to ease the counts and get the desired results.
After counting each word, we also can have the count of most (say 10 or 20) frequent words.
Modules and Library functions used :
requests : Will allow you to send HTTP/1.1 requests and many more.
beautifulsoup4 : Used for parsing HTML/XML to extract data out of HTML and XML files.
operator : Exports a set of efficient functions corresponding to the intrinsic operators.
collections : Implements high-performance container datatypes.
Below is a implementation of the idea discussed above :
[('to', 10), ('in', 7), ('is', 6), ('language', 6), ('the', 5), ('programming', 5), ('a', 5), ('c', 5), ('you', 5), ('of', 4)]
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course