For people who are used to working with heterogeneously typed tables in SQL, switching to similar analyses in Python may seem like a daunting task. Fortunately, there are a number of pandas functions that can be combined to yield results similar to those yielded by common SQL queries, using operations such as grouping and joining. There is even a subsection in the pandas documentation (https://pandas.pydata.org/pandas-docs/stable/comparison_with_sql.html) that describes how to perform SQL-like operations with pandas DataFrames. We provide two such examples in this section.