Counting Leading NaN Values in Original Columns and Non-NaN Values in Extra Columns with Pandas DataFrames

Here's a more detailed explanation of how to achieve this using Python:

Counting NaN Rows in a Pandas DataFrame with 'Unnamed' Column

Splitting and Combining Pandas Columns into Separate Rows Using str.split() and explode()