I acquired knowledge in webscraping through supervised projects on YouTube videos. These projects encompassed:
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Table Extraction projects: I learned from the esteemed data analysts @Alextheanalyst, specifically through their YouTube video at https://youtu.be/8dTpNajxaH0. With their guidance, I undertook my first webscraping mini-project, which involved extracting a table from the Wikipedia website that represented the largest companies in the United States by revenue. It was a delightful experience as I delved into the fascinating world of webscraping. Throughout this project, I extensively utilized modules like Pandas, BeautifulSoup, and requests, with a specific focus on leveraging the find_all() method. Extracting the desired table proved to be challenging due to the presence of multiple tables on the webpage. However, with determination and perseverance, I successfully accomplished the task, resulting in a rewarding outcome. This experience was both fruitful and enlightening, leading to the creation of a CSV file named 'companies'.
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Scraping Popular Github Topics projects: I had the opportunity to further enhance my skills by participating in a live session on the YouTube channel @JOVIAN (https://www.youtube.com/live/RKsLLG-bzEY?feature=share). During this session, I built a pandas dataframe from scratch, which included popular topic titles, descriptions, and URLs. The most demanding aspect of this project was retrieving the URLs for each topic. Ultimately, I saved the resulting dataframe as a CSV file named 'github_topics'.