add list filtering

appStore/target.py

@@ -96,7 +96,7 @@ def target_display():
         if range_val !=0:
             # collecting some statistics
             count_target = sum(hits['Target Label'] == 'TARGET')
-            count_netzero = sum(hits['Netzero Label'] == 'NET-ZERO')
+            count_netzero = sum(hits['Netzero Label'] == 'NETZERO TARGET')
             count_ghg = sum(hits['GHG Label'] == 'GHG')
             count_transport = sum([True if 'Transport' in x else False 
                               for x in hits['Sector Label']])
@@ -198,7 +198,15 @@ def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:
             left, right = st.columns((1, 20))
             left.write("↳")
             # Treat columns with < 10 unique values as categorical
-            if is_categorical_dtype(df[column]) or df[column].nunique() < 10:
+            if is_list_like(df[column]):
+                list_vals = set(tuple(x) for x in df[column].tolist())
+                user_multi_input = right.multiselect(
+                    f"Values for {column}",
+                    list_vals,
+                    default=list_vals,
+                )      
+                df = df[df[column].str.contains(user_multi_input)]
+            elif is_categorical_dtype(df[column]) or df[column].nunique() < 10:
                 user_cat_input = right.multiselect(
                     f"Values for {column}",
                     df[column].unique(),
@@ -217,14 +225,7 @@ def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:
                     step=step,
                 )
                 df = df[df[column].between(*user_num_input)]
-            elif is_list_like(df[column]):
-                list_vals = set(tuple(x) for x in df[column].tolist())
-                user_multi_input = right.multiselect(
-                    f"Values for {column}",
-                    list_vals,
-                    default=list_vals,
-                )      
-                df = df[df[column].str.contains(user_multi_input)]
+            
                 # df[df[column].between(*user_num_input)]
             # elif is_datetime64_any_dtype(df[column]):
             #     user_date_input = right.date_input(