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  3. Analyzing Marketing Campaigns with pandas
  • 1

    Pandas

    Free

    In this chapter, you will review pandas basics including importing datasets, exploratory analysis, and basic plotting.

  • 2

    Exploratory Analysis & Summary Statistics

    In this chapter, you will learn about common marketing metrics and how to calculate them using pandas. You will also visualize your results and practice user segmentation.

  • 3

    Conversion Attribution

    In this chapter, you will build functions to automate common marketing analysis and determine why certain marketing channels saw lower than usual conversion rates during late January.

  • 4

    Personalization A/B Test

    In this chapter, you will analyze an A/B test and learn about the importance of segmentation when interpreting the results of a test.

Initializing

Exercise

Creating a DataFrame based on indexes

Now that you've created an index to compare English conversion rates against all other languages, you will build out a DataFrame that will estimate what daily conversion rates should have been if users were being served the correct language.

An expected conversion DataFrame named converted has been created for you grouping house_ads by date and preferred language. It contains a count of unique users as well as the number of conversions for each language, each day.

For example, you can access the number of Spanish-speaking users who received house ads using converted[('user_id','Spanish')].

Instructions

100 XP
  • Use .loc to create the column english_conv_rate in converted with the English conversion rate between '2018-01-11' and '2018-01-31'.
  • Create expected conversion columns for each language by multiplying english_conv_rate by each language index (spanish_index, arabic_index or german_index).
  • Multiply each language's expected conversion rate by the number of users who should have received house ads.