Replacing missing values with constants
While removing missing data entirely maybe a correct approach in many situations, this may result in a lot of information being omitted from your models.
You may find categorical columns where the missing value is a valid piece of information in itself, such as someone refusing to answer a question in a survey. In these cases, you can fill all missing values with a new category entirely, for example 'No response given'.
This exercise is part of the course
Feature Engineering for Machine Learning in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print the count of occurrences
print(so_survey_df['Gender']____)