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.

Este ejercicio forma parte del curso

Intermediate Predictive Analytics in Python

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Instrucciones del ejercicio

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 function calculate_age has been implemented for you. It takes date_of_birth and reference_date as arguments.
Print the mean age of all donors.

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Prueba este ejercicio completando el código de muestra.

# 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["____"].____()))

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