Update app.py

app.py

@@ -361,13 +361,15 @@ elif app_mode == "Data Cleaning":
         st.warning("Please upload data first")
         st.stop()
 
-    df = st.session_state.raw_data.copy()  # Ensure df is defined in this section
+    # Use cleaned_data as the base dataframe
+    df = st.session_state.cleaned_data.copy()  # Changed line
 
-    # Initialize session state (only if it's not already there)
+    # Initialize session state
     if 'data_versions' not in st.session_state:
         st.session_state.data_versions = [st.session_state.raw_data.copy()]
-    if 'cleaned_data' not in st.session_state: # Added a conditional value
+    if 'cleaned_data' not in st.session_state:
         st.session_state.cleaned_data = st.session_state.raw_data.copy()
+        st.dataframe(st.session_state.cleaned_data.head(), use_container_width=True, height=300)
 
     # --------------------------
     # Data Health Dashboard
@@ -397,7 +399,6 @@ elif app_mode == "Data Cleaning":
             st.session_state.data_versions.pop()  # Remove current version
             st.session_state.cleaned_data = st.session_state.data_versions[-1].copy() # Set data
             st.success("Last action undone!")
-            st.rerun() #Force re-run after undo
 
     # --------------------------
     # Missing Value Handling
@@ -438,8 +439,7 @@ elif app_mode == "Data Cleaning":
                         new_df[cols] = new_df[cols].bfill()
 
                     update_cleaned_data(new_df)
-                    st.rerun() #Force re-run after apply
-
+                    
                 except Exception as e:
                     st.error(f"Error: {str(e)}")
         else:
@@ -480,7 +480,6 @@ elif app_mode == "Data Cleaning":
                     new_df[col_to_convert] = pd.to_datetime(new_df[col_to_convert], format=date_format, errors='coerce')
 
                 update_cleaned_data(new_df)
-                st.rerun() #Force re-run after apply
             except Exception as e:
                 st.error(f"Error: {str(e)}")
 
@@ -496,7 +495,6 @@ elif app_mode == "Data Cleaning":
                 new_df = df.copy()
                 new_df = new_df.drop(columns=columns_to_drop)
                 update_cleaned_data(new_df)
-                st.rerun() #Force re-run after apply
 
     # --------------------------
     # Label Encoding
@@ -513,7 +511,6 @@ elif app_mode == "Data Cleaning":
                     new_df[col] = le.fit_transform(new_df[col].astype(str))
                     label_encoders[col] = le
                 update_cleaned_data(new_df)
-                st.rerun() #Force re-run after apply
 
     # --------------------------
     # StandardScaler
@@ -528,7 +525,6 @@ elif app_mode == "Data Cleaning":
                     scaler = StandardScaler()
                     new_df[scale_cols] = scaler.fit_transform(new_df[scale_cols])
                     update_cleaned_data(new_df)
-                    st.rerun()#Force re-run after apply
                 except Exception as e:
                     st.error(f"Error: {str(e)}")
 
@@ -562,7 +558,6 @@ elif app_mode == "Data Cleaning":
             text_cols = new_df.select_dtypes(include='object').columns
             new_df[text_cols] = new_df[text_cols].apply(lambda x: x.str.strip())
             update_cleaned_data(new_df)
-            st.rerun() #Force re-run after apply
 
     # --------------------------
     # Cleaned Data Preview
@@ -570,10 +565,11 @@ elif app_mode == "Data Cleaning":
     if st.session_state.get("cleaned_data") is not None:
         enhance_section_title("Cleaned Data Preview", "✨")
         with st.expander("✨ Cleaned Data Preview", expanded=True):
-            st.dataframe(st.session_state.cleaned_data.head(), use_container_width=True)
-
-
-
+            st.dataframe(
+                st.session_state.cleaned_data.head(), 
+                use_container_width=True, 
+                height=400  # <-- Add height parameter here
+            )
 
 # --------------------------
 # EDA