Binarizing columns
While numeric values can often be used without any feature engineering, there will be cases when some form of manipulation can be useful. For example on some occasions, you might not care about the magnitude of a value but only care about its direction, or if it exists at all. In these situations, you will want to binarize a column. In the so_survey_df data, you have a large number of survey respondents that are working voluntarily (without pay). You will create a new column titled Paid_Job indicating whether each person is paid (their salary is greater than zero).
This exercise is part of the course
Feature Engineering for Machine Learning in Python
Exercise instructions
- Create a new column called
Paid_Jobfilled with zeros. - Replace all the
Paid_Jobvalues with a 1 where the correspondingConvertedSalaryis greater than 0.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create the Paid_Job column filled with zeros
so_survey_df[____] = ____
# Replace all the Paid_Job values where ConvertedSalary is > 0
so_survey_df.____[____, 'Paid_Job'] = 1
# Print the first five rows of the columns
print(so_survey_df[['Paid_Job', 'ConvertedSalary']].head())