Update app.py

app.py

@@ -130,6 +130,46 @@ def hitter_seasonlong_build(data_sample):
 
     return season_long_table
 
+@st.cache_data(show_spinner=False)
+def hitter_team_build(data_sample):
+    season_long_table = data_sample[['Team']]
+    season_long_table['G'] = data_sample.groupby(['Team'], sort=False)['G'].transform('sum').astype(int)
+    season_long_table['AB'] = data_sample.groupby(['Team'], sort=False)['AB'].transform('sum').astype(int)
+    season_long_table['PA'] = data_sample.groupby(['Team'], sort=False)['PA'].transform('sum').astype(int)
+    season_long_table['H'] = data_sample.groupby(['Team'], sort=False)['H'].transform('sum').astype(int)
+    season_long_table['1B'] = data_sample.groupby(['Team'], sort=False)['1B'].transform('sum').astype(int)
+    season_long_table['2B'] = data_sample.groupby(['Team'], sort=False)['2B'].transform('sum').astype(int)
+    season_long_table['3B'] = data_sample.groupby(['Team'], sort=False)['3B'].transform('sum').astype(int)
+    season_long_table['HR'] = data_sample.groupby(['Team'], sort=False)['HR'].transform('sum').astype(int)
+    season_long_table['R'] = data_sample.groupby(['Team'], sort=False)['R'].transform('sum').astype(int)
+    season_long_table['RBI'] = data_sample.groupby(['Team'], sort=False)['RBI'].transform('sum').astype(int)
+    season_long_table['BB'] = data_sample.groupby(['Team'], sort=False)['BB'].transform('sum').astype(int)
+    season_long_table['IBB'] = data_sample.groupby(['Team'], sort=False)['IBB'].transform('sum').astype(int)
+    season_long_table['SO'] = data_sample.groupby(['Team'], sort=False)['SO'].transform('sum').astype(int)
+    season_long_table['HBP'] = data_sample.groupby(['Team'], sort=False)['HBP'].transform('sum').astype(int)
+    season_long_table['SF'] = data_sample.groupby(['Team'], sort=False)['SF'].transform('sum').astype(int)
+    season_long_table['SH'] = data_sample.groupby(['Team'], sort=False)['SH'].transform('sum').astype(int)
+    season_long_table['GDP'] = data_sample.groupby(['Team'], sort=False)['GDP'].transform('sum').astype(int)
+    season_long_table['SB'] = data_sample.groupby(['Team'], sort=False)['SB'].transform('sum').astype(int)
+    season_long_table['CS'] = data_sample.groupby(['Team'], sort=False)['CS'].transform('sum').astype(int)
+    season_long_table['Avg AVG'] = data_sample.groupby(['Team'], sort=False)['AVG'].transform('mean').astype(float)
+    season_long_table['Avg SLG'] = data_sample.groupby(['Team'], sort=False)['SLG'].transform('mean').astype(float)
+    season_long_table['Avg wRC+'] = data_sample.groupby(['Team'], sort=False)['wRC+'].transform('mean').astype(float)
+    season_long_table['Avg LD%'] = data_sample.groupby(['Team'], sort=False)['LD%'].transform('mean').astype(float)
+    season_long_table['Avg GB%'] = data_sample.groupby(['Team'], sort=False)['GB%'].transform('mean').astype(float)
+    season_long_table['Avg FB%'] = data_sample.groupby(['Team'], sort=False)['FB%'].transform('mean').astype(float)
+    season_long_table['Avg Hard%'] = data_sample.groupby(['Team'], sort=False)['Hard%'].transform('mean').astype(float)
+    season_long_table['Barrels'] = data_sample.groupby(['Team'], sort=False)['Barrels'].transform('sum').astype(int)
+    season_long_table['Avg Barrel%'] = data_sample.groupby(['Team'], sort=False)['Barrel%'].transform('mean').astype(float)
+    season_long_table = season_long_table.drop_duplicates(subset='Team')
+
+    season_long_table = season_long_table.sort_values(by='Avg wRC+', ascending=False)
+    
+    season_long_table = season_long_table.set_axis(['Team', 'G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SF', 'SH',
+                                                    'GDP', 'SB', 'CS', 'Avg AVG', 'Avg SLG', 'Avg wRC+', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg Hard%', 'Barrels', 'Avg Barrel%'], axis=1)
+
+    return season_long_table
+
 @st.cache_data(show_spinner=False)
 def pitcher_seasonlong_build(data_sample):
     season_long_table = data_sample[['Player', 'Team']]
@@ -197,6 +237,73 @@ def pitcher_seasonlong_build(data_sample):
 
     return season_long_table
 
+@st.cache_data(show_spinner=False)
+def pitcher_team_build(data_sample):
+    season_long_table = data_sample[['Team']]
+    season_long_table['G'] = data_sample.groupby(['Team'], sort=False)['G'].transform('sum').astype(int)
+    season_long_table['GS'] = data_sample.groupby(['Team'], sort=False)['GS'].transform('sum').astype(int)
+    season_long_table['CG'] = data_sample.groupby(['Team'], sort=False)['CG'].transform('sum').astype(int)
+    season_long_table['W'] = data_sample.groupby(['Team'], sort=False)['W'].transform('sum').astype(int)
+    season_long_table['L'] = data_sample.groupby(['Team'], sort=False)['L'].transform('sum').astype(int)
+    season_long_table['Avg ERA'] = data_sample.groupby(['Team'], sort=False)['ERA'].transform('mean').astype(float)
+    season_long_table['ShO'] = data_sample.groupby(['Team'], sort=False)['ShO'].transform('sum').astype(int)
+    season_long_table['SV'] = data_sample.groupby(['Team'], sort=False)['SV'].transform('sum').astype(int)
+    season_long_table['HLD'] = data_sample.groupby(['Team'], sort=False)['HLD'].transform('sum').astype(int)
+    season_long_table['BS'] = data_sample.groupby(['Team'], sort=False)['BS'].transform('sum').astype(int)
+    season_long_table['IP'] = data_sample.groupby(['Team'], sort=False)['IP'].transform('sum').astype(int)
+    season_long_table['TBF'] = data_sample.groupby(['Team'], sort=False)['TBF'].transform('sum').astype(int)
+    season_long_table['H'] = data_sample.groupby(['Team'], sort=False)['H'].transform('sum').astype(int)
+    season_long_table['R'] = data_sample.groupby(['Team'], sort=False)['R'].transform('sum').astype(int)
+    season_long_table['ER'] = data_sample.groupby(['Team'], sort=False)['ER'].transform('sum').astype(int)
+    season_long_table['HR'] = data_sample.groupby(['Team'], sort=False)['HR'].transform('sum').astype(int)
+    season_long_table['BB'] = data_sample.groupby(['Team'], sort=False)['BB'].transform('sum').astype(int)
+    season_long_table['IBB'] = data_sample.groupby(['Team'], sort=False)['IBB'].transform('sum').astype(int)
+    season_long_table['HBP'] = data_sample.groupby(['Team'], sort=False)['HBP'].transform('sum').astype(int)
+    season_long_table['WP'] = data_sample.groupby(['Team'], sort=False)['WP'].transform('sum').astype(int)
+    season_long_table['BK'] = data_sample.groupby(['Team'], sort=False)['BK'].transform('sum').astype(int)
+    season_long_table['SO'] = data_sample.groupby(['Team'], sort=False)['SO'].transform('sum').astype(int)
+    season_long_table['Avg K/9'] = data_sample.groupby(['Team'], sort=False)['K/9'].transform('mean').astype(float)
+    season_long_table['Avg BB/9'] = data_sample.groupby(['Team'], sort=False)['BB/9'].transform('mean').astype(float)
+    season_long_table['Avg WHIP'] = data_sample.groupby(['Team'], sort=False)['WHIP'].transform('mean').astype(float)
+    season_long_table['Avg BABIP'] = data_sample.groupby(['Team'], sort=False)['BABIP'].transform('mean').astype(float)
+    season_long_table['Avg LOB%'] = data_sample.groupby(['Team'], sort=False)['LOB%'].transform('mean').astype(int)
+    season_long_table['Avg FIP'] = data_sample.groupby(['Team'], sort=False)['FIP'].transform('mean').astype(float)
+    season_long_table['Avg xFIP'] = data_sample.groupby(['Team'], sort=False)['xFIP'].transform('mean').astype(float)
+    season_long_table['Avg K%'] = data_sample.groupby(['Team'], sort=False)['K%'].transform('mean').astype(float)
+    season_long_table['Avg BB%'] = data_sample.groupby(['Team'], sort=False)['BB%'].transform('mean').astype(float)
+    season_long_table['Avg SIERA'] = data_sample.groupby(['Team'], sort=False)['SIERA'].transform('mean').astype(float)
+    season_long_table['Avg LD%'] = data_sample.groupby(['Team'], sort=False)['LD%'].transform('mean').astype(float)
+    season_long_table['Avg GB%'] = data_sample.groupby(['Team'], sort=False)['GB%'].transform('mean').astype(float)
+    season_long_table['Avg FB%'] = data_sample.groupby(['Team'], sort=False)['FB%'].transform('mean').astype(float)
+    season_long_table['Avg HR/FB'] = data_sample.groupby(['Team'], sort=False)['HR/FB'].transform('mean').astype(float)
+    season_long_table['Avg Hard%'] = data_sample.groupby(['Team'], sort=False)['Hard%'].transform('mean').astype(float)
+    season_long_table['Barrels'] = data_sample.groupby(['Team'], sort=False)['Barrels'].transform('sum').astype(int)
+    season_long_table['Avg Barrel%'] = data_sample.groupby(['Team'], sort=False)['Barrel%'].transform('mean').astype(float)
+    season_long_table['Avg xERA'] = data_sample.groupby(['Team'], sort=False)['xERA'].transform('mean').astype(float)
+    season_long_table['Avg vFA'] = data_sample.groupby(['Team'], sort=False)['vFA'].transform('mean').astype(float)
+    season_long_table['Avg vFT'] = data_sample.groupby(['Team'], sort=False)['vFT'].transform('mean').astype(float)
+    season_long_table['Avg vFC'] = data_sample.groupby(['Team'], sort=False)['vFC'].transform('mean').astype(float)
+    season_long_table['Avg vFS'] = data_sample.groupby(['Team'], sort=False)['vFS'].transform('mean').astype(float)
+    season_long_table['Avg vFO'] = data_sample.groupby(['Team'], sort=False)['vFO'].transform('mean').astype(float)
+    season_long_table['Avg vSI'] = data_sample.groupby(['Team'], sort=False)['vSI'].transform('mean').astype(float)
+    season_long_table['Avg vSL'] = data_sample.groupby(['Team'], sort=False)['vSL'].transform('mean').astype(float)
+    season_long_table['Avg vCU'] = data_sample.groupby(['Team'], sort=False)['vCU'].transform('mean').astype(float)
+    season_long_table['Avg vKC'] = data_sample.groupby(['Team'], sort=False)['vKC'].transform('mean').astype(float)
+    season_long_table['Avg vEP'] = data_sample.groupby(['Team'], sort=False)['vEP'].transform('mean').astype(float)
+    season_long_table['Avg vCH'] = data_sample.groupby(['Team'], sort=False)['vCH'].transform('mean').astype(float)
+    season_long_table['Avg vSC'] = data_sample.groupby(['Team'], sort=False)['vSC'].transform('mean').astype(float)
+    season_long_table['Avg vKN'] = data_sample.groupby(['Team'], sort=False)['vKN'].transform('mean').astype(float)
+    season_long_table = season_long_table.drop_duplicates(subset='Team')
+
+    season_long_table = season_long_table.sort_values(by='SO', ascending=False)
+    
+    season_long_table = season_long_table.set_axis(['Team', 'G', 'GS', 'CG', 'W', 'L', 'Avg ERA', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF', 'H', 'R', 'ER', 'HR',
+                                                    'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'Avg K/9', 'Avg BB/9', 'Avg WHIP', 'Avg BABIP', 'Avg LOB%', 'Avg FIP', 'Avg xFIP', 'Avg K%',
+                                                    'Avg BB%', 'Avg SIERA', 'Avg LD%', 'Avg GB%', 'Avg FB%', 'Avg HR/FB', 'Avg Hard%', 'Barrels', 'Avg Barrel%', 'Avg xERA', 'Avg vFA',
+                                                    'Avg vFT', 'Avg vFC', 'Avg vFS', 'Avg vFO', 'Avg vSI', 'Avg vSL', 'Avg vCU', 'Avg vKC', 'Avg vEP', 'Avg vCH', 'Avg vSC', 'Avg vKN'], axis=1)
+
+    return season_long_table
+
 @st.cache_data(show_spinner=False)
 def split_frame(input_df, rows):
     df = [input_df.loc[i : i + rows - 1, :] for i in range(0, len(input_df), rows)]
@@ -275,7 +382,7 @@ with tab1:
                   total_pitchers = indv_pitchers.Player.values.tolist()
                   total_dates = hitter_gamelog_table.Date.values.tolist()
         
-        split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')
+        split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Game logs', 'Team Logs'), key='split_var1')
         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':
@@ -328,7 +435,29 @@ with tab1:
                     mime='text/csv',
             )
             
-        elif split_var1 == 'Gamelogs':
+        elif split_var1 == 'Team Logs':
+            choose_cols = st.container()
+            with choose_cols:
+                choose_disp = st.multiselect('Which stats would you like to view?', options = season_hitter_data_cols, default = season_hitter_data_cols, key='col_display')
+            disp_stats = basic_season_cols + choose_disp
+            display = st.container()
+            working_data = working_data[working_data['Date'] >= low_date]
+            working_data = working_data[working_data['Date'] <= high_date]
+            working_data = working_data[working_data['Team'].isin(team_var1)]
+            team_table = hitter_team_build(working_data)
+            team_table = team_table.set_index('Team')
+            
+            team_table_disp = team_table.reindex(disp_stats,axis="columns")
+            team_table_disp = team_table_disp.drop(['Team', 'Date'], axis=1)
+            display.dataframe(team_table_disp.style.format(precision=2), height=750, use_container_width = True)  
+            st.download_button(
+                    label="Export hitter team logs Model",
+                    data=convert_df_to_csv(season_long_table),
+                    file_name='Seasonlogs_Hitter_View.csv',
+                    mime='text/csv',
+            )
+            
+        elif split_var1 == 'Game logs':
             choose_cols = st.container()
             with choose_cols:
                 choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = hitter_data_cols, default = hitter_data_cols, key='choose_disp_gamelog')