Update app.py

app.py

@@ -26,7 +26,7 @@ gc = gspread.service_account_from_dict(credentials)
 st.set_page_config(layout="wide")
 
 american_format = {'OwnAvg': '{:.2%}'}
-mma_format = {'ML_perc': '{:.2%}', 'Min_%': '{:.2%}', 'Med_%': '{:.2%}', }
+stacks_format = {'Total Own': '{:.2%}'}
 
 @st.cache_resource(ttl = 600)
 def init_baselines():
@@ -119,17 +119,48 @@ def init_baselines():
                 
     flex_frame = flex_frame.sort_values(by='GPP Rank', ascending=False)
     
-    return qb_frame, rb_frame, wr_frame, flex_frame
+    worksheet = sh.worksheet('Stacks')
+    all_values = worksheet.get_all_values()
+    cell_vals = [row[0:30] for row in all_values[1:500]]
+    frame_hold = pd.DataFrame(cell_vals, columns=['Team', 'Opp', 'd1', 'd2', 'Game Stack', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9', 'd10', 'd11', 'd12', 'd13', 'd14', 'Team Stack',
+                                                  '15', '16', '17', '18', '19', '20', '21', 'Total Stack Cost', 'Total Own', 'Total Points', 'Points/$'])
+    frame_hold['Total Points'] = frame_hold['Total Points'].astype(float)
+    frame_hold['Total Own'] = frame_hold['Total Own'].str.replace('%', '').astype(float)/100
+    stack_frame = frame_hold[['Team', 'Opp', 'Game Stack', 'Team Stack', 'Total Stack Cost', 'Total Own', 'Total Points', 'Points/$']]
+    string_cols = ['Team', 'Opp']
+    stack_frame = stack_frame.drop_duplicates(subset='Team')
+    stack_frame = stack_frame.set_index('Team')
+    
+    for col in stack_frame.columns:
+        if col not in string_cols:
+            try:
+                stack_frame[col] = pd.to_numeric(stack_frame[col], errors='coerce')
+            except ValueError:
+                pass  # Ignore columns that cannot be converted
+                
+    stack_frame = stack_frame.sort_values(by='Team Stack', ascending=False)
+    
+    return qb_frame, rb_frame, wr_frame, flex_frame, stack_frame
 
 @st.cache_resource()
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-qb_frame, rb_frame, wr_frame, flex_frame = init_baselines()
+qb_frame, rb_frame, wr_frame, flex_frame, stack_frame = init_baselines()
 
-tab1, tab2, tab3, tab4 = st.tabs(['QB data', 'RB data', 'WR data', 'Flex data'])
+tab1, tab2, tab3, tab4, tab5 = st.tabs(['Stacks data', 'QB data', 'RB data', 'WR data', 'Flex data'])
 
 with tab1:
+    with st.container():
+              st.dataframe(stack_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(stacks_format, precision=2), height = 1000, use_container_width = True)
+              st.download_button(
+                      label="Export Tables",
+                      data=convert_df_to_csv(stack_frame),
+                      file_name='NCAAF_Stacks_model_export.csv',
+                      mime='text/csv',
+              )
+
+with tab2:
     with st.container():
               st.dataframe(qb_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True)
               st.download_button(
@@ -138,7 +169,7 @@ with tab1:
                       file_name='NCAAF_QB_model_export.csv',
                       mime='text/csv',
               )
-with tab2:
+with tab3:
     with st.container():
               st.dataframe(rb_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True)
               st.download_button(
@@ -147,7 +178,7 @@ with tab2:
                       file_name='NCAAF_RB_model_export.csv',
                       mime='text/csv',
               )
-with tab3:
+with tab4:
     with st.container():
               st.dataframe(wr_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True)
               st.download_button(
@@ -156,7 +187,7 @@ with tab3:
                       file_name='NCAAF_WR_model_export.csv',
                       mime='text/csv',
               )
-with tab4:
+with tab5:
     with st.container():
               st.dataframe(flex_frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(american_format, precision=2), height = 1000, use_container_width = True)
               st.download_button(