Creating a missing value dummy

Given a basetable that has a predictive variable "total_donations" that has the total number of donations a donor ever made. This variable can have missing values, indicating that this donor never made a donation before. This is important information on its own, so it is appropriate to create a variable "no_donations" that indicates whether "total_donations" is missing.

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Intermediate Predictive Analytics in Python

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Anleitung zur Übung

Create a new column "no_donations" in basetable that has value 1 if total_donations is missing and 0 otherwise.
Calculate the number of missing values in total_donations and assign it to number_na.
Print the percentage of missing values.

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# Create dummy indicating missing values
basetable["____"] = pd.Series([____ if b else ____ for b in basetable["total_donations"].isna()])

# Calculate number of missing values
number_na = sum(____["no_donations"] == ____)

# Calculate percentage of missing values
print(round(____ / ____(____), 2))

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