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Have you ever started your data analysis and ended up with repetitive code? Repetitive code is a sign that functions are needed. Functions help keep our code flexible, maintainable, and interpretable.
Our colleague Brenda, a product analyst, has written a script to pull Net Promotor Score (NPS) survey data from multiple sources to calculate the NPS score. This code works well, but it violates coding best practices, including Don't Repeat Yourself (DRY). Let's take a look at her code and write some functions for Brenda! To complete this project, you need to know how to write functions in Python and how to use pandas for DataFrame manipulation.
- 1DRY: Don't repeat yourself
- 2Verifying the files with the "with" keyword
- 3Putting it together with nested functions
- 4Detractors, Passives, and Promoters
- 5Applying our function to a DataFrame
- 6Calculating the Net Promoter Score
- 7Breaking down NPS by source
- 8Adding docstrings
Workspace Architect at DataCamp
Lis holds a Master's degree in Computer Science from McGill University with a focus on computer science education research and applied machine learning. She's passionate about teaching all things related to data and improving the accessibility of these topics.