Update app.py

app.py

@@ -48,9 +48,11 @@ def init_baselines():
     raw_display.columns = raw_display.iloc[0]
     raw_display = raw_display[1:]
     raw_display = raw_display.reset_index(drop=True)
-    matchups = raw_display[raw_display['Opp'] != ""]
+    raw_display = raw_display[raw_display['Opp'] != ""]
+    matchups = raw_display[['Team', 'Opp', 'FL1$', 'FL2$', 'FL3$', 'Team Total', 'Game Pace', 'SF', 'o_SA', 'SF_m', 'HDCF',
+                              'o_HDCA', 'HDCF_m']]
     data_cols = matchups.columns.drop(['Team', 'Opp'])
-    # matchups[data_cols] = matchups[data_cols].apply(pd.to_numeric, errors='coerce')
+    matchups[data_cols] = matchups[data_cols].apply(pd.to_numeric, errors='coerce')
     
     worksheet = sh.worksheet('Marketshares')
     raw_display = pd.DataFrame(worksheet.get_values())
@@ -61,7 +63,7 @@ def init_baselines():
     overall_ms = raw_display[['Line', 'SK1', 'SK2', 'SK3', 'Cost', 'Team Total', 'Shots', 'HDCF', 'Goals', 'Assists', 'Blocks',
                               'L14_Shots', 'L14_HDCF', 'L14_Goals', 'L14_Assists', 'L14_Blocks']]
     data_cols = overall_ms.columns.drop(['Line', 'SK1', 'SK2', 'SK3'])
-    # overall_ms[data_cols] = overall_ms[data_cols].apply(pd.to_numeric, errors='coerce')
+    overall_ms[data_cols] = overall_ms[data_cols].apply(pd.to_numeric, errors='coerce')
 
     return matchups, overall_ms
 
@@ -80,7 +82,7 @@ with col1:
 with col2:
     if split_var1 == 'Slate Matchups':
         display_table = matchups
-        st.dataframe(display_table, use_container_width = True)
+        st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         st.download_button(
             label="Export Matchups",
             data=convert_df_to_csv(display_table),
@@ -89,7 +91,7 @@ with col2:
         )
     elif split_var1 == 'Line Marketshares':
         display_table = overall_ms
-        st.dataframe(display_table, use_container_width = True)
+        st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         st.download_button(
             label="Export Marketshares",
             data=convert_df_to_csv(display_table),