Or specify multiple columns within a list: df.drop_duplicates(subset=['column','column2'], inplace=True) One addition above that’s worth calling out is the presence of the inplace parameter.

In this approach, we pass get_dummies() a column in a data frame and it creates a full matrix of zero-one values. In other words, it gives us a matrix with 103 rows (because we have 103 rows in the ...

As the original list of columns is lost in the second case, I have to handle empty data frames differently, or add columns back by myself, both of which are inconvenient. Expected Output. The list of ...