Python for Data Analysis: Step-By-Step with Projects - Tackling Missing Data (Imputing with Statistics) and Missing Indi

This lesson covers methods to impute missing data using statistics like mean, median, and mode. It demonstrates how to use pandas and scikit-learn's SimpleImputer for this purpose, treating numerical and categorical columns differently. The lesson also explains how to mark missing data with indicators, providing a comprehensive guide to handling missing values in datasets.

What is the purpose of imputing missing data with statistics?

How do we treat numerical and categorical columns differently when imputing missing values?

What are the three central measures mentioned for imputing missing values?

Explain how the mean and median can be used to impute missing values in numerical columns.

What method can be used to impute missing values in categorical columns?

How can we check if the imputation was successful?

Describe the process of using the Simple Imputer from Scikit-learn for imputation.

What are the advantages of using the Simple Imputer over the few Anna method?

In what scenarios might you prefer to use the few Anna method instead of the Simple Imputer?

What is the significance of adding an indicator for missing data during imputation?