adding summary table

app.py

@@ -73,9 +73,30 @@ nonprofit_color =  "#D77031" #orange
 
 from functools import reduce
 
+def get_summary(ca, combined_filter, column, colors=None):
+    df = (ca
+        .filter(combined_filter)
+        .group_by(*column)  # unpack the list for grouping
+        .aggregate(percent_protected=100 * _.Acres.sum() / ca_area_acres)
+        .mutate(percent_protected=_.percent_protected.round(1))
+    )
+    
+    if colors is not None and not colors.empty: # df used for chart. 
+        df = (df #add color column + only gaps 1 and 2. 
+              .filter(_.reGAP <3) 
+              .inner_join(colors, column)       
+             ) 
+        # df = df.inner_join(colors, column)
+    
+    df = df.cast({col: "string" for col in column})  
+    df = df.to_pandas()
+    return df
+
+
 def summary_table(column, colors, filter_cols, filter_vals):
-    filters = [_.reGAP < 3] #only compute with gap codes 1 and 2
     
+    filters = [] 
+
     if filter_cols and filter_vals: #if a filter is selected, add to list of filters 
         for filter_col, filter_val in zip(filter_cols, filter_vals):
             if len(filter_val) > 1:
@@ -85,16 +106,11 @@ def summary_table(column, colors, filter_cols, filter_vals):
                 
     combined_filter = reduce(lambda x, y: x & y, filters)
     
-    df = (ca
-        .filter(combined_filter)
-        .group_by(column)
-        .aggregate(percent_protected=100 * _.Acres.sum() / ca_area_acres)
-        .mutate(percent_protected=_.percent_protected.round(1))
-        .inner_join(colors, column)
-    )
-    df = df.to_pandas()
-    df[column] = df[column].astype(str)
-    return df
+    df = get_summary(ca, combined_filter, [column], colors) # df used for charts 
+    df_tab = get_summary(ca, combined_filter, filter_cols, colors = None) #df used for printed table
+    
+    return df, df_tab
+
 
 
 
@@ -316,12 +332,13 @@ with main:
     map_col, stats_col = st.columns([2,1])
     with map_col:
         to_streamlit(m, height=700)
-    df = summary_table(column, colors, filter_cols, filter_vals)
+    df,df_tab = summary_table(column, colors, filter_cols, filter_vals)
     total_percent = df.percent_protected.sum().round(1)
     with stats_col:
         with st.container():
             f"{total_percent}% CA Covered"
             st.altair_chart(area_plot(df, column), use_container_width=True)
+            st.dataframe(df_tab)