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.
This exercise is part of the course
Dealing with Missing Data in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
try:
# Print the sum of two None's
print("Add operation output of 'None': ", ___)
except TypeError:
# Print if error
print("'None' does not support Arithmetic Operations!!")