Introduction of Python’s core functions, use cases, scripts, and underlying mechanisms
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I discussed SQL User-Defined Functions in my last article. But compared to SQL, Python stands out with its versatility in function design. From my experience working in tech companies, it’s evident that no data science project can be completed without the extensive use of Python functions. Python has become a fundamental tool for data scientists to efficiently manage and analyze data, tackle complex tasks and deploy product features. With its broad range of functions at the core, Python proves to be a powerful tool in the field of data science. However, with so many types of functions available, it’s hard and impossible for data scientists to be familiar with them all. Today’s article will cover the top 8 types of functions commonly used in real-world data science, explaining the intricate logic and mechanisms behind them that other tutorials rarely mentioned. This article will also clear up the confusion between different types of functions that are often mistaken for one another. Finally, a mini project will demonstrate how to effectively apply several of these functions in practice.