Update app.py

app.py

@@ -34,146 +34,66 @@ def init_conn():
 
 gcservice_account = init_conn()
 
-DEM_data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109'
+NHL_data = 'https://docs.google.com/spreadsheets/d/1NmKa-b-2D3w7rRxwMPSchh31GKfJ1XcDI2GU8rXWnHI/edit#gid=811139250'
 
-percentages_format = {'Pts% Boost': '{:.2%}', 'Reb% Boost': '{:.2%}', 'Ast% Boost': '{:.2%}', '3p% Boost': '{:.2%}',
-                      'Stl Boost%': '{:.2%}', 'Blk Boost%': '{:.2%}', 'TOV Boost%': '{:.2%}', 'FPPM Boost': '{:.2%}',
-                      'Team FPPM Boost': '{:.2%}'}
+percentages_format = {'Shots': '{:.2%}', 'HDCF': '{:.2%}', 'Goals': '{:.2%}', 'Assists': '{:.2%}', 'Blocks': '{:.2%}',
+                      'L14_Shots': '{:.2%}', 'L14_HDCF': '{:.2%}', 'L14_Goals': '{:.2%}', 'L14_Assists': '{:.2%}', 'L14_Blocks': '{:.2%}'}
 
 @st.cache_resource(ttl = 600)
 def init_baselines():
-    sh = gcservice_account.open_by_url(DEM_data)
+    sh = gcservice_account.open_by_url(NHL_data)
     
-    worksheet = sh.worksheet('DEM Matchups')
+    worksheet = sh.worksheet('Matchups')
     raw_display = pd.DataFrame(worksheet.get_values())
     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['Var'] != ""]
+    matchups = raw_display[raw_display['Opp'] != ""]
+    data_cols = matchups.columns.drop(['Team', 'Opp'])
+    matchups[data_cols] = matchups[data_cols].apply(pd.to_numeric, errors='coerce')
     matchups_dict = dict(zip(matchups['Team'], matchups['Opp']))
     
-    worksheet = sh.worksheet('PG_DEM_Calc')
+    worksheet = sh.worksheet('Marketshares')
     raw_display = pd.DataFrame(worksheet.get_values())
     raw_display.columns = raw_display.iloc[0]
     raw_display = raw_display[1:]
     raw_display = raw_display.reset_index(drop=True)
-    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
-    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
-    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
-    raw_display['position'] = 'Point Guard'
-    pg_dem = raw_display[raw_display['Acro'] != ""]
+    raw_display = raw_display[raw_display['Line'] != ""]
+    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')
 
-    worksheet = sh.worksheet('SG_DEM_Calc')
-    raw_display = pd.DataFrame(worksheet.get_values())
-    raw_display.columns = raw_display.iloc[0]
-    raw_display = raw_display[1:]
-    raw_display = raw_display.reset_index(drop=True)
-    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
-    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
-    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
-    raw_display['position'] = 'Shooting Guard'
-    sg_dem = raw_display[raw_display['Acro'] != ""]
-    
-    worksheet = sh.worksheet('SF_DEM_Calc')
-    raw_display = pd.DataFrame(worksheet.get_values())
-    raw_display.columns = raw_display.iloc[0]
-    raw_display = raw_display[1:]
-    raw_display = raw_display.reset_index(drop=True)
-    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
-    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
-    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
-    raw_display['position'] = 'Small Forward'
-    sf_dem = raw_display[raw_display['Acro'] != ""]
-    
-    worksheet = sh.worksheet('PF_DEM_Calc')
-    raw_display = pd.DataFrame(worksheet.get_values())
-    raw_display.columns = raw_display.iloc[0]
-    raw_display = raw_display[1:]
-    raw_display = raw_display.reset_index(drop=True)
-    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
-    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
-    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
-    raw_display['position'] = 'Power Forward'
-    pf_dem = raw_display[raw_display['Acro'] != ""]
-    
-    worksheet = sh.worksheet('C_DEM_Calc')
-    raw_display = pd.DataFrame(worksheet.get_values())
-    raw_display.columns = raw_display.iloc[0]
-    raw_display = raw_display[1:]
-    raw_display = raw_display.reset_index(drop=True)
-    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
-    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
-    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
-    raw_display['position'] = 'Center'
-    c_dem = raw_display[raw_display['Acro'] != ""]
-    
-    overall_dem = pd.concat([pg_dem, sg_dem, sf_dem, pf_dem, c_dem])
-    overall_dem = overall_dem[['Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
-                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'position']]
-    overall_dem['Team'] = overall_dem['Acro'] + '-' + overall_dem['position']
-    overall_dem['Team FPPM Boost'] = overall_dem.groupby('Acro', sort=False)['FPPM Boost'].transform('mean')
-    overall_dem = overall_dem.reset_index()
-    
-    
-    export_dem = overall_dem[['Team', 'Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
-                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'Team FPPM Boost', 'position']]
-
-    return export_dem, matchups, matchups_dict
+    return matchups, matchups_dict, overall_ms
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-overall_dem, matchups, matchups_dict = init_baselines()
+matchups, matchups_dict, overall_ms = init_baselines()
 
 col1, col2 = st.columns([1, 9])
 with col1:
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              overall_dem, matchups, matchups_dict = init_baselines()
-    split_var1 = st.radio("View all teams or just this main slate's matchups?", ('Slate Matchups', 'All'), key='split_var1')
-    if split_var1 == 'Slate Matchups':
-        view_var1 = matchups.Opp.values.tolist()
-        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
-        if split_var2 == 'Specific Teams':
-            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
-        elif split_var2 == 'All':
-            team_var1 = view_var1
-        split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
-        if split_var3 == 'Specific Positions':
-            pos_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['position'].unique(), key='pos_var1')
-        elif split_var3 == 'All':
-            pos_var1 = overall_dem.position.values.tolist()
-    if split_var1 == 'All':
-        split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
-        if split_var2 == 'Specific Teams':
-            team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['Acro'].unique(), key='team_var1')
-        elif split_var2 == 'All':
-            team_var1 = overall_dem.Acro.values.tolist()
-        split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
-        if split_var3 == 'Specific Positions':
-            pos_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['position'].unique(), key='pos_var1')
-        elif split_var3 == 'All':
-            pos_var1 = overall_dem.position.values.tolist()
+              matchups, matchups_dict, overall_ms = init_baselines()
+    split_var1 = st.radio("View matchups or line marketshares?", ('Slate Matchups', 'Line Marketshares'), key='split_var1')
+    
 with col2:
     if split_var1 == 'Slate Matchups':
-        dem_display = overall_dem[overall_dem['Acro'].isin(view_var1)]
-        dem_display['Team (Getting Boost)'] = dem_display['Acro'].map(matchups_dict)
-        dem_display.rename(columns={"Acro": "Opp (Giving Boost)"}, inplace = True)
-        dem_display = dem_display[['Team (Getting Boost)', 'Opp (Giving Boost)', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
-                                   'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'Team FPPM Boost', 'position']]
-        dem_display = dem_display[dem_display['Team (Getting Boost)'].isin(team_var1)]
-        dem_display = dem_display[dem_display['position'].isin(pos_var1)]
-        dem_display = dem_display.sort_values(by='FPPM Boost', ascending=False)
-    elif split_var1 == 'All':
-        dem_display = overall_dem[overall_dem['Acro'].isin(team_var1)]
-        dem_display = dem_display[dem_display['position'].isin(pos_var1)]
-        dem_display = dem_display.sort_values(by='FPPM Boost', ascending=False)
-        dem_display.rename(columns={"Team": "Team (Giving Boost)"}, inplace = True)
-        dem_display = dem_display.set_index('Team (Giving Boost)')
-    st.dataframe(dem_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
-    st.download_button(
-        label="Export DEM Numbers",
-        data=convert_df_to_csv(overall_dem),
-        file_name='DEM_export.csv',
-        mime='text/csv',
-    )
+        display_table = matchups
+        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),
+            file_name='Matchups_export.csv',
+            mime='text/csv',
+        )
+    elif split_var1 == 'Line Marketshares':
+        display_table = overall_ms
+        st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+        st.download_button(
+            label="Export Marketshares",
+            data=convert_df_to_csv(display_table),
+            file_name='Marketshares_export.csv',
+            mime='text/csv',
+        )