Web Table Scraper
The scraper retrieved data from an openly accessible source, which in this case was a table within a Wikipedia article. The scraping was done in Jupyter Notebook, with imported libraries being beautifulsoup, requests, and pandas. The scraped data can then be transformed and loaded, or loaded to a warehouse and then transformed for analytics. The visualization chart was done with the help of Microsoft Power BI.
Chart
You can download the .csv file of scraped data from right underneath.