When dealing with missing values in a Pandas DataFrame, the best approach depends on your data and the type of analysis you plan to do. Common strategies include:







Removing rows or columns with missing values if they are few and won’t affect your results significantly.







Filling missing values with the mean, median, or mode for numerical data to preserve the dataset size.







Interpolation for data that follows a sequence or time series.







Advanced imputation methods like KNN or iterative imputers from scikit-learn for machine learning tasks or datasets with many missing values.







Choose the method that makes sense for each column to ensure your analysis remains accurate and meaningful.