Why 'axis=1' denote different axis in different cases?
In `df.apply(f, axis=1)` , it means applying `f` on rows. However, In `df.drop([column_name], axis=1)`, it means dropping columns. 
So, what is the trick to determine in which cases `axis=1` means functioning on rows while in other cases it means functioning on columns ?
