Python for Data Analysis: Step-By-Step with Projects - Tackling Missing Data (Imputing with Constant)

The video tutorial covers strategies for imputing missing values in datasets, focusing on using constant values. It introduces two methods: the fillna method in Pandas and the SimpleImputer in Scikit-learn. The tutorial explains how to handle numerical and categorical columns differently, using specific constants for each. It provides step-by-step instructions for implementing these methods in Python, highlighting the advantages of each approach.

How do we verify that missing values have been successfully imputed?

What are the three general steps to use the SimpleImputer?

What is the significance of using a constant strategy in imputation?

What are the two methods of imputing missing data discussed in the lesson?