A not-insignificant part of my time at Curology was spent wrangling SurveyMonkey data into a visualizable format, and then visualizing the aggregated answers to every question that might be of interest from a user-acquisition and consumer-insights perspective.
This post breaks down how to deal with survey data in
pandas, including how to rename columns, deal with missing data, preserve important metadata, convert numeric data to categorical, and apply custom functions. This tutorial is great for if you are looking to start using
pandas to automate some of the data analysis you previously performed manually in Tableau or another more hands-on tool.
Find it here!