Session Ready
Exercise

Extract a function

While you were developing a model to predict the likelihood of a student graduating from college, you wrote this bit of code to get the z-scores of students' yearly GPAs. Now you're ready to turn it into a production-quality system, so you need to do something about the repetition. Writing a function to calculate the z-scores would improve this code.

# Standardize the GPAs for each year
df['y1_z'] = (df.y1_gpa - df.y1_gpa.mean()) / df.y1_gpa.std()
df['y2_z'] = (df.y2_gpa - df.y2_gpa.mean()) / df.y2_gpa.std()
df['y3_z'] = (df.y3_gpa - df.y3_gpa.mean()) / df.y3_gpa.std()
df['y4_z'] = (df.y4_gpa - df.y4_gpa.mean()) / df.y4_gpa.std()

Note: df is a pandas DataFrame where each row is a student with 4 columns of yearly student GPAs: y1_gpa, y2_gpa, y3_gpa, y4_gpa

Instructions
100 XP
  • Finish the function so that it returns the z-scores of a column.
  • Use the function to calculate the z-scores for each year (df['y1_z'], df['y2_z'], etc.) from the raw GPA scores (df.y1_gpa, df.y2_gpa, etc.).
    Loading