Update app.py

app.py

@@ -1088,43 +1088,44 @@ elif app_mode == "EDA":
             st.subheader("Data Transformation")
             transform_col = st.selectbox("Column to transform", df.select_dtypes(include=[np.number]).columns)
             transform_type = st.selectbox("Transformation", ["Log", "Square Root", "Z-score", "Standardization", "Normalization", "Box-Cox", "Inverse"])
-            
+        
             fig, ax = plt.subplots(1, 2, figsize=(12, 5))
-            
+        
             if transform_col:
                 sns.histplot(df[transform_col], bins=30, kde=True, ax=ax[0])
                 ax[0].set_title('Before Transformation')
-            
-            if transform_type == "Log":
-                df[transform_col] = np.log1p(df[transform_col])
-            elif transform_type == "Square Root":
-                df[transform_col] = np.sqrt(df[transform_col])
-            elif transform_type == "Z-score":
-                df[transform_col] = (df[transform_col] - df[transform_col].mean())/df[transform_col].std()
-            elif transform_type == "Standardization":
-                df[transform_col] = (df[transform_col] - df[transform_col].mean()) / df[transform_col].std()
-            elif transform_type == "Normalization":
-                df[transform_col] = (df[transform_col] - df[transform_col].min()) / (df[transform_col].max() - df[transform_col].min())
-            elif transform_type == "Box-Cox":
-                df[transform_col], _ = boxcox(df[transform_col] + 1)  # Adding 1 to avoid log(0) error
-            elif transform_type == "Inverse":
-                df[transform_col] = 1 / df[transform_col]
-            
-            sns.histplot(df[transform_col], bins=30, kde=True, ax=ax[1])
-            ax[1].set_title('After Transformation')
-            
-            st.pyplot(fig)
+        
+                if transform_type == "Log":
+                    df[transform_col] = np.log1p(df[transform_col])
+                elif transform_type == "Square Root":
+                    df[transform_col] = np.sqrt(df[transform_col])
+                elif transform_type == "Z-score":
+                    df[transform_col] = (df[transform_col] - df[transform_col].mean())/df[transform_col].std()
+                elif transform_type == "Standardization":
+                    df[transform_col] = (df[transform_col] - df[transform_col].mean()) / df[transform_col].std()
+                elif transform_type == "Normalization":
+                    df[transform_col] = (df[transform_col] - df[transform_col].min()) / (df[transform_col].max() - df[transform_col].min())
+                elif transform_type == "Box-Cox":
+                    df[transform_col], _ = boxcox(df[transform_col] + 1)  # Adding 1 to avoid log(0) error
+                elif transform_type == "Inverse":
+                    df[transform_col] = 1 / df[transform_col]
+        
+                sns.histplot(df[transform_col], bins=30, kde=True, ax=ax[1])
+                ax[1].set_title('After Transformation')
+        
+                st.pyplot(fig)
+        
+                # Error handling for invalid transformations
+                try:
+                    if transform_type == "Log" and (df[transform_col] <= 0).any():
+                        st.error("Log transformation is not applicable to non-positive values.")
+                    elif transform_type == "Box-Cox" and (df[transform_col] <= 0).any():
+                        st.error("Box-Cox transformation requires all values to be positive.")
+                except Exception as e:
+                    st.error(f"Transformation failed: {str(e)}")
             else:
-            st.warning("Please select a column for transformation.")
-            
-            # Error handling for invalid transformations
-            try:
-            if transform_type == "Log" and (df[transform_col] <= 0).any():
-                st.error("Log transformation is not applicable to non-positive values.")
-            elif transform_type == "Box-Cox" and (df[transform_col] <= 0).any():
-                st.error("Box-Cox transformation requires all values to be positive.")
-            except Exception as e:
-            st.error(f"Transformation failed: {str(e)}")
+                st.warning("Please select a column for transformation.")
+
 
     enhance_section_title("Export Options", "💾")
     st.subheader("💾 Export Options")