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()
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Marketing Analytics: Predicting Customer Churn in Python
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# Count the number of churners and non-churners by State
print(telco.____('____')['____'].____())