In my last article, I discussed SQL user-defined functions, but, compared to SQL, Python stands out for its versatility in function design. Based on my experience working in tech companies, it is evident that no data science project can be completed without extensive use of Python functions. Python has become a critical tool for data scientists to efficiently manage and analyze data, tackle complex tasks, and implement product features. With its wide range of functions at its core, Python proves to be a powerful tool in the field of data science. However, with so many types of functions available, it is difficult and impossible for data scientists to get familiar with all of them. Today’s article will cover the top 8 types of functions that are commonly used in real-world data science, explaining the intricate logic and mechanisms behind them that other tutorials rarely mention. This article will also clear the confusion between different types of functions that are often confused with each other. Finally, a mini-project will demonstrate how to effectively implement several of these features in practice.
