Exercise

Dropping rows

When you know that a specific column will be critical to your analysis, and only a small fraction of rows are missing a value in that column, it often makes sense to remove those rows from the dataset.

During this course, the driver_gender column will be critical to many of your analyses. Because only a small fraction of rows are missing driver_gender, we'll drop those rows from the dataset.

Instructions

100 XP
  • Count the number of missing values in each column.
  • Drop all rows that are missing driver_gender by passing the column name to the subset parameter of .dropna().
  • Count the number of missing values in each column again, to verify that none of the remaining rows are missing driver_gender.
  • Examine the DataFrame's .shape to see how many rows and columns remain.