Why, if there is an empty cell in the Pandas column, are integer values in this column converted into floats?
This happens since NumPy (and based on it, Pandas) does not support null integers—every null is a special case of a float. Thus, to keep the datatype consistent across the column, NumPy has to convert all integers into floats.
What is the benefit of plotting missing values?
Often, missing values in a dataset can have a certain pattern—for example, records with a missing value in one column also miss values in others. Having a bird's-eye view allows you to find those patterns and define an appropriate imputation strategy.
What is RegEx? Is it a separate language?
Indeed, Regular Expressions, or regex, is a distinct mini-language for text extraction and search. RegEx is implemented in most programming languages—including Python.
How can...
