chore: columns refactor

app.py

@@ -42,13 +42,17 @@ with col1:
             df_base["len_pupil_dilation"] = df_base.pupil_dilation.map(lambda l: len(l))
             df_base["len_baseline"] = df_base.baseline.map(lambda l: len(l))
             st.info(f"number of files: {len(file_names)}")
-            st.markdown("Your original data with some extra information about the length of the time-series fields")
-            st.dataframe(df_base)
+            if 'df_base' not in st.session_state:
+                st.session_state['df_base'] = df_base
     else:
         st.caption("Upload your data using the sidebar to start :sunglasses:")
 
+if 'df_base' in st.session_state:
+    st.markdown("Your original data with some extra information about the length of the time-series fields")
+    st.dataframe(st.session_state.df_base)
+    
 # Cleaning starts
-with col2:
+with col1:
     if not df_base.empty:
         st.markdown("**Cleaning actions**")
         detect_blinking = st.button("I want to clean my data 🤗")
@@ -84,25 +88,24 @@ with col2:
             st.caption("No blinking values were found in your data! ")
 
 # Add calculated fields
-with col2:
-    if 'df' in list(st.session_state.keys()):
-        df_right = st.session_state.df.copy(deep=True)
-        if "baseline" in list(df_right.keys()):
-            st.markdown(f"A **baseline** feature has been found on your data, do you want to merge it with any of the other features in a new calculated field?")
-            option = st.multiselect('Select a feature to create relative calculated feature ➕', [k for k in list(df_right.keys()) if k != 'baseline'], [[k for k in list(df_right.keys()) if k != 'baseline'][-4]])
-            relative_key = f"relative_{option[0]}"
-            add_relative = st.button(f"Add {relative_key}")
-            if add_relative:
-                baseline_mean = [sum(s)/len(s) for s in df_right['baseline']]
-                df_right[relative_key] = [[field_value - baseline_mean[i] for field_value in df_right[option[0]][i]] for i in range(len(df_right))]
-                st.markdown("After adding calculated fields")
-                st.dataframe(df_right)
-                csv = convert_df(df_right)
+if 'df' in list(st.session_state.keys()):
+    df_right = st.session_state.df.copy(deep=True)
+    if "baseline" in list(df_right.keys()):
+        st.markdown(f"A **baseline** feature has been found on your data, do you want to merge it with any of the other features in a new calculated field?")
+        option = st.multiselect('Select a feature to create relative calculated feature ➕', [k for k in list(df_right.keys()) if k != 'baseline'], [[k for k in list(df_right.keys()) if k != 'baseline'][-4]])
+        relative_key = f"relative_{option[0]}"
+        add_relative = st.button(f"Add {relative_key}")
+        if add_relative:
+            baseline_mean = [sum(s)/len(s) for s in df_right['baseline']]
+            df_right[relative_key] = [[field_value - baseline_mean[i] for field_value in df_right[option[0]][i]] for i in range(len(df_right))]
+            st.markdown("After adding calculated fields")
+            st.dataframe(df_right)
+            csv = convert_df(df_right)
 
-                # Save transformations to disk
-                downl = st.download_button("Download CSV 💾", csv, "file.csv", "text/csv", key='download-csv')
-                if downl:
-                    st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.")
+            # Save transformations to disk
+            downl = st.download_button("Download CSV 💾", csv, "file.csv", "text/csv", key='download-csv')
+            if downl:
+                st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.")
 
 if not df_base.empty:
     with col1: