The datasets that you find on the internet from various data sources are either created by companies and organizations or are collected from websites. You must have scraped data from web pages by using the Python libraries, but may have stuck while preparing the scraped data to create a dataset. So in this article, I’m going to walk you through a tutorial on web scraping to create a dataset using Python.
How are Datasets Created by Scraping the Web?
There are so many libraries, frameworks, and tools that are used for the task of web scraping. Some of the most common libraries and modules in Python used for web scraping are:
- Scrapy
- Selenium
- BeautifulSoup
- Urlib.request
All of the above Python libraries and modules are great for scraping data from websites. After scraping the data, the data is prepared so that it can be stored in a CSV file to create a dataset.
Web Scraping to Create a Dataset using Python
Now let’s see how to create a dataset by scraping the web using Python. For this task, I will be using the BeautifulSoup library in Python. Here I am going to search for a random term on Google and then I will collect the data from the very first page that Google shows me.
So, I searched for “comparison of programming languages” on Google and got this article as the first result. Let’s see how we can scrape data from this web page to create a dataset. Below is how we can use the BeautifulSoup library in Python for the task of web scraping to create a dataset:
Also, Read – Python Projects with Source Code.
Summary
The dataset that we have created by scraping the web can be downloaded from here. It looks like the same datasets that we see on various data sources on the internet. I hope you liked this article on a tutorial on web scraping to create a dataset with Python. Feel free to ask your valuable questions in the comments section below.
