Setting up conversion indexes

Now that you've determined that language is, in fact, the issue with House Ads conversion, stakeholders need to know how many subscribers they lost as a result of this bug.

In this exercise, you will index non-English language conversion rates against English conversion rates in the time period before the language bug arose.

This exercise is part of the course

Analyzing Marketing Campaigns with pandas

View Course

Exercise instructions

Create a new DataFrame, house_ads_bug, that contains only the rows from house_ads with 'date_served' prior to '2018-01-11'.
Use your conversion_rate() function on the house_ads_bug DataFrame and the 'language_displayed' column.
Using the appropriate row, divide the Spanish, Arabic and German columns of lang_conv by the English column.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate pre-error conversion rate
house_ads_bug = ____[____['____'] < '____']
lang_conv = ____

# Index other language conversion rate against English
spanish_index = ____/____
arabic_index = ____/____
german_index = ____/____

print("Spanish index:", spanish_index)
print("Arabic index:", arabic_index)
print("German index:", german_index)

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