Update app.py

app.py

@@ -43,7 +43,7 @@ wr_te_util = {'Routes%': '{:.2%}','Targets%': '{:.2%}', 'Air Yards%': '{:.2%}',
 all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=179416653'
 
 @st.cache_resource(ttl = 300)
-def rb_util_season():
+def rb_util_weekly():
     sh = gc.open_by_url(all_dk_player_projections)
     worksheet = sh.worksheet('RB_Util')
     raw_display = pd.DataFrame(worksheet.get_all_records())
@@ -56,7 +56,7 @@ def rb_util_season():
     return raw_display
 
 @st.cache_resource(ttl = 300)
-def wr_te_util_season():
+def wr_te_util_weekly():
     sh = gc.open_by_url(all_dk_player_projections)
     worksheet = sh.worksheet('WR_TE_Util')
     raw_display = pd.DataFrame(worksheet.get_all_records())
@@ -68,6 +68,32 @@ def wr_te_util_season():
     raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
     return raw_display
 
+@st.cache_resource(ttl = 300)
+def rb_util_season():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('RB_Util_Season')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = raw_display.replace('', np.nan)      
+    raw_display = raw_display[['player_name', 'position', 'team_season', 'player_snaps_per', 'rush_attempts_per', 'routes_per', 'targets_per',
+                               'tprr', 'player_SDD_snaps_per', 'inside_five_rush_per', 'player_LDD_snaps_per', 'two_min_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
+    raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Player Snaps%', 'Rush Att%', 'Routes%', 'Targets%',
+                               'TPRR', 'SDD Snaps%', 'i5 Rush%', 'LDD Snaps%', '2-min%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
+    raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
+    return raw_display
+
+@st.cache_resource(ttl = 300)
+def wr_te_util_season():
+    sh = gc.open_by_url(all_dk_player_projections)
+    worksheet = sh.worksheet('WR_TE_Util_Season')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = raw_display.replace('', np.nan)      
+    raw_display = raw_display[['player_name', 'position', 'team_season', 'routes_per', 'targets_per', 'tprr' , 'adot', 'air_yards_per',
+                               'ayprr', 'endzone_targets_per', 'third_fourth_per', 'third_fourth_target_per', 'play_action_targets_per', 'exPPR', 'ppr_fantasy', 'UR_Rank']]
+    raw_display = raw_display.set_axis(['Player', 'Position', 'Week', 'Team-Season', 'Routes%', 'Targets%', 'TPRR' , 'ADOT', 'Air Yards%',
+                               'AYPRR', 'Endzone Targets%', 'Third/Fourth%', 'Third/Fourth Targets%', 'Play Action Targets%', 'Expected PPR', 'PPR', 'Utilization Rank'], axis='columns')
+    raw_display = raw_display.sort_values(by='Utilization Rank', ascending=True)
+    return raw_display
+
 @st.cache_resource(ttl = 300)
 def macro_pull():
     sh = gc.open_by_url(all_dk_player_projections)
@@ -80,9 +106,10 @@ def macro_pull():
 @st.cache_data
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
-
-rb_search = rb_util_season()
-wr_search = wr_te_util_season()
+rb_search = rb_util_weekly()
+wr_search = wr_te_util_weekly()
+rb_season = rb_util_season()
+wr_season = wr_te_util_season()
 macro_data = macro_pull()
 pos_list = ['RB', 'WR', 'TE']
 
@@ -94,7 +121,7 @@ with col1:
           rb_search = rb_util_season()
           wr_search = wr_te_util_season()
           macro_data = macro_pull()
-    stat_type_var1 = st.radio("What table are you loading?", ('Macro Table', 'RB Usage', 'WR/TE Usage'), key='stat_type_var1')
+    stat_type_var1 = st.radio("What table are you loading?", ('Macro Table', 'RB Usage (Weekly)', 'WR/TE Usage (Weekly)', 'RB Usage (Season)', 'WR/TE Usage (Season)'), key='stat_type_var1')
     split_var1 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'), key='split_var1')
     pos_split1 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')      
     if pos_split1 == 'Specific Positions':
@@ -108,26 +135,38 @@ with col1:
     if stat_type_var1 == 'Macro Table':
         table_instance = macro_data
         table_instance = table_instance.set_index('team')  
-    elif stat_type_var1 == 'RB Usage':
+    elif stat_type_var1 == 'RB Usage (Weekly)':
         table_instance = rb_search
         table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
         table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
-    elif stat_type_var1 == 'WR/TE Usage':
+    elif stat_type_var1 == 'WR/TE Usage (Weekly)':
         table_instance = wr_search    
         table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
         table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
+    elif stat_type_var1 == 'RB Usage (Season)':
+        table_instance = rb_season
+        table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
+        table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
+    elif stat_type_var1 == 'WR/TE Usage (Season)':
+        table_instance = wr_season
+        table_instance = table_instance[table_instance['Team-Season'].isin(team_var1)]  
+        table_instance = table_instance[table_instance['Position'].isin(pos_var1)]
 
 with col2:
     if stat_type_var1 == 'Macro Table':
         st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(game_format, precision=2), use_container_width = True)
-    elif stat_type_var1 == 'RB Usage':
+    elif stat_type_var1 == 'RB Usage (Weekly)':
+        st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(rb_util, precision=2), use_container_width = True)
+    elif stat_type_var1 == 'WR/TE Usage (Weekly)':
+        st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(wr_te_util, precision=2), use_container_width = True)
+    elif stat_type_var1 == 'RB Usage (Season)':
         st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(rb_util, precision=2), use_container_width = True)
-    elif stat_type_var1 == 'WR/TE Usage':
+    elif stat_type_var1 == 'WR/TE Usage (Season)':
         st.dataframe(table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').background_gradient(cmap='RdYlGn_r', subset = 'Utilization Rank').format(wr_te_util, precision=2), use_container_width = True)
 
     st.download_button(
                     label="Export Tables",
                     data=convert_df_to_csv(table_instance),
-                    file_name='MLB_Research_export.csv',
+                    file_name='NFL_Research_export.csv',
                     mime='text/csv',
     )