The Forbes Global 2000 is an annual list produced by Forbes that ranks the world’s largest public companies based on a variety of metrics such as market capitalization, revenues and profits. It is seen as a great resource for investors and businesses alike, to determine the health and worth of many of the world’s biggest companies.
Each year, this Forbes list offers insight into relative success or failure between countries and industries, giving investors a unique perspective on their potential investments or partnerships. While it can take many interpretations, one thing is certain: these companies are at the top of their business game.
Python is an incredibly versatile language that has increasingly been used to analyze Forbes magazine rankings. Its powerful data analysis tools allow it to quickly and easily manipulate large datasets of parameters, allowing for more accurate and precise conclusions about which companies and people make up Forbes’ lists every year.
Python enables easier access to the company’s underlying criteria, facets and analytics, making it perfect for developing sophisticated models which are used to measure the impact of any set of metrics influencing annual rankings. Python is great at analyzing both current and historical ranking trends, so analysts can use the same models developed in Python to study proposed changes and see how they will affect Forbes’s future lists before they come out.
In today’s post, I’m going to tell you how I did my research on the top 2,000 companies in the world according to Forbes.
