Adding age with timegap
The basetable of previous exercise is given. The timeline is given below, there is a timegap of one month. In this exercise, you will learn how to add the age of the donors to the basetable, compliant with the timeline.
This exercise is part of the course
Intermediate Predictive Analytics in Python
Exercise instructions
- Fill out the reference date on which the age should be calculated, this is the start date of the timegap.
- Add a column "age" to the
basetable
that is the age of the donor on the reference date. The functioncalculate_age
has been implemented for you. It takesdate_of_birth
andreference_date
as arguments. - Print the mean age of all donors.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Reference date
reference_date = datetime.date(____, ____, ____)
# Add age to the basetable
basetable["age"] = (pd.Series([calculate_age(____, ____)
for date_of_birth in basetable["date_of_birth"]]))
# Calculate mean age
print(round(basetable["____"].____()))