Don't drop the stack
It's almost time to go home, but first, you need to finish your last task. You have a small dataset containing the total number of calls made by customers.
To perform your analysis, you need to reshape your churn data by stacking different levels. You know this process will generate missing data. You want to check if it is worth keeping the rows that contain all missing values, or if it's better to drop that information.
The churn DataFrame is available for you.
Cet exercice fait partie du cours
Reshaping Data with pandas
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Stack the level type from churn
churn_stack = churn.____(____=____)
# Fill the resulting missing values with zero
churn_fill = churn_stack.____(____)
# Print churn_fill
print(churn_fill)