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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

Setting up our data to visualize daily conversion

When you want to understand how your campaign performed, it is essential to look at how key metrics changed throughout the campaign. Your key metrics can help you catch problems that may have happened during the campaign, such as a bug in the checkout system that led to a dip in conversion toward the end of your campaign. Metrics over time can also surface trends like gaining more subscribers over the weekends or on specific holidays.

In this exercise, you will build upon the daily conversion rate Series daily_conversion_rates you built in a previous exercise. Before you can begin visualizing, you need to transform your data into an easier format to use with pandas and matplotlib.

Instructions

100 XP
  • Reset the index of the daily_conversion_rates Series and use pd.DataFrame() to convert the results into a DataFrame named daily_conversion_rate.
  • Rename the columns in the new DataFrame daily_conversion_rate to be 'date_served' and 'conversion_rate'.