developer-roadmap/roadmaps/python-data-analysis/content/dropping-vs-imputing@xIS0cJ8ui0siBoZJr3-B_.md

Dropping vs. Imputing

When handling missing values, dropping removes rows or columns with dropna(), while imputing fills them with a substitute value using fillna() or SimpleImputer from Scikit-learn. Dropping is appropriate when missing data is rare or random. Imputing is preferred when data is valuable or missing systematically, using the mean, median, mode, or a predicted value.

Visit the following resources to learn more:

• @official@pandas.DataFrame.fillna
• @article@Pandas DataFrame fillna() Method
• @video@Python Pandas Tutorial 5: Handle Missing Data: fillna, dropna, interpolate