Null value operations

While working with missing data, you'll have to store these missing values as an empty type. This way, you will easily be able to identify them, replace them or play with them! This is why we have the None and numpy.nan types. You need to be able to differentiate clearly between the two types.

In this exercise, you will compare the differences between the behavior of None and numpy.nan types on application of arithmetic and logical operations.numpy has already been imported as np. The try and except blocks have been used to avoid errors.