Handling missing data in pandas involves using isnull() and notnull() to identify NaN values introduced during Series alignment. Methods like dropna() remove missing entries, while fillna() replaces them. Introducing NaN changes integer dtypes to float64, but pandas nullable types (e.g., Int64) preserve missing values without upcasting.