Update app.py

app.py

@@ -360,16 +360,23 @@ elif app_mode == "Smart Cleaning":
                     if missing_value_method == "Drop Missing":
                         df = df.dropna(subset=cols) # Drop rows with missing values in selected columns
                         cleaning_actions.append(f"Dropped missing values in selected columns")
-                    elif missing_value_method == "Mean/Median/Mode":
-                       # Imputation logic here, added to perform the imputation in multiple columns
+                   elif missing_value_method == "Mean/Median/Mode":
+                        # Allow the user to select the specific imputation method
+                        imputation_choice = st.radio("Select Imputation Method", ["Mean", "Median", "Mode"], horizontal=True)
+                    
+                        # Imputation logic here, added to perform the imputation in multiple columns
                         for col in cols:
                             if df[col].isnull().any():  # Check if missing values exist before imputing
                                 if pd.api.types.is_numeric_dtype(df[col]):
-                                    df[col] = df[col].fillna(df[col].mean())
-                                else: # Impute strings with mode
+                                    if imputation_choice == "Mean":
+                                        df[col] = df[col].fillna(df[col].mean())
+                                    elif imputation_choice == "Median":
+                                        df[col] = df[col].fillna(df[col].median())
+                                    elif imputation_choice == "Mode":
+                                        df[col] = df[col].fillna(df[col].mode()[0])
+                                else:  # Impute strings with mode
                                     df[col] = df[col].fillna(df[col].mode()[0])
-
-                        cleaning_actions.append(f"Applied Mean/Median/Mode imputation on {cols}")
+                                cleaning_actions.append(f"Applied Mean/Median/Mode imputation on {cols}")
 
                     elif missing_value_method == "KNN Imputation":
                         from sklearn.impute import KNNImputer