Exploring data sets and understanding its structure, content, and relationships is a routine and core process for any Data Scientist. Multiple tools exist for performing such analysis, and we take a deep dive into the benefits and different approaches of two important tools, SQL and Pandas.

Both of these tools are important to not only data scientists but also to those in similar positions like data analytics and business intelligence. With that being said, when should data scientists specifically use pandas over SQL and vice versa? In some situations, you can get away with just using SQL, and some other times, pandas is much easier to use, especially for data scientists who focus on research in a Jupyter Notebook setting. Below, I will discuss when you should use SQL and when you should use pandas. Keep in mind that both of these tools have specific use cases, but there are many times where their functionality overlap, and that is what I will be comparing below as well.

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