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.

Este ejercicio forma parte del curso

Analyzing Marketing Campaigns with pandas

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Instrucciones del ejercicio

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.

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# 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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