Churn by State
When dealing with customer data, geographic regions may play an important part in determining whether a customer will cancel their service or not. You may have noticed that there is a 'State' column in the dataset. In this exercise, you'll group 'State' and 'Churn' to count the number of churners and non-churners by state. For example, if you wanted to group by x and aggregate by y, you could use .groupby() as follows:
df.groupby('x')['y'].value_counts()
This exercise is part of the course
Marketing Analytics: Predicting Customer Churn in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Count the number of churners and non-churners by State
print(telco.____('____')['____'].____())