Selecting specific data types
Often a dataset will contain columns with several different data types (like the one you are working with). The majority of machine learning models require you to have a consistent data type across features. Similarly, most feature engineering techniques are applicable to only one type of data at a time. For these reasons among others, you will often want to be able to access just the columns of certain types when working with a DataFrame.
The DataFrame (so_survey_df) from the previous exercise is available in your workspace.
This exercise is part of the course
Feature Engineering for Machine Learning in Python
Exercise instructions
- Create a subset of
so_survey_dfconsisting of only the numeric (intandfloat) columns. - Print the column names contained in
so_survey_df_num.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create subset of only the numeric columns
so_numeric_df = so_survey_df.____(____=[____])
# Print the column names contained in so_survey_df_num
print(so_numeric_df.____)