Update app.py

app.py

@@ -115,73 +115,17 @@ def convert_df_to_csv(df):
 rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
 pos_list = ['RB', 'WR', 'TE']
 
-tab1, tab2 = st.tabs(["Season Long Research", "Slate Specific"])
+tab1, tab2 = st.tabs(["Slate Specific", "Season Long Research"])
 with tab1:
     col1, col2 = st.columns([1, 8])
     
-    with col1:
-        if st.button("Load/Reset Data", key='reset1'):
-              st.cache_data.clear()
-              rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
-        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':
-            pos_var1 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE'])
-        elif pos_split1 == 'All Positions':
-            pos_var1 = pos_list 
-        if split_var1 == 'Specific Teams':
-            team_var1 = st.multiselect('Which teams would you like to include in the Table?', options = rb_search['Team-Season'].unique(), key='team_var1')
-        elif split_var1 == 'All Teams':
-            team_var1 = rb_search['Team-Season'].unique().tolist()
-        if stat_type_var1 == 'Macro Table':
-            table_instance = macro_data
-            table_instance = table_instance.set_index('Team')  
-        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 (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 (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 (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='NFL_Research_export.csv',
-                        mime='text/csv',
-        )
-with tab2:
-    col1, col2 = st.columns([1, 8])
-    
     with col1:
         if st.button("Load/Reset Data", key='reset2'):
               st.cache_data.clear()
               rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
-        stat_type_var2 = st.radio("What table are you loading?", ('WR/TE Coverage Matchups', 'Ownership Trends', 'Nothing idk lol'))
+        stat_type_var2 = st.radio("What table are you loading?", ('Macro Stats', 'WR/TE Coverage Matchups', 'Ownership Trends', 'Nothing idk lol'))
         if stat_type_var2 == 'WR/TE Coverage Matchups':
-            routes_var2 = st.slider("Is there a certain price range of routes you want to include?", 0, 50, (10, 50), key='sal_var2')
+            routes_var2 = st.slider("Is there a certain range of routes you want to include?", 0, 50, (10, 50), key='sal_var2')
         split_var2 = st.radio("Are you running the the whole league or certain teams?", ('All Teams', 'Specific Teams'))
         pos_split2 = st.radio("Are you viewing all positions or specific positions?", ('All Positions', 'Specific Positions'))      
         if pos_split2 == 'Specific Positions':
@@ -195,7 +139,10 @@ with tab2:
             team_var2 = st.multiselect('Which teams would you like to include in the Table?', options = wr_matchups['Team'].unique())
         elif split_var2 == 'All Teams':
             team_var2 = wr_matchups['Team'].unique().tolist()
-        if stat_type_var2 == 'WR/TE Coverage Matchups':
+        if stat_type_var2 == 'Macro Table':
+            slate_table_instance = macro_data
+            slate_table_instance = slate_table_instance.set_index('Team')  
+        elif stat_type_var2 == 'WR/TE Coverage Matchups':
             slate_table_instance = wr_matchups
             slate_table_instance = slate_table_instance[slate_table_instance['Team'].isin(team_var2)]  
             slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)]
@@ -206,11 +153,13 @@ with tab2:
             slate_table_instance = trending_data
             slate_table_instance = slate_table_instance[slate_table_instance['Team'].isin(team_var2)]  
             slate_table_instance = slate_table_instance[slate_table_instance['Position'].isin(pos_var2)]
-        elif stat_type_var1 == 'Nothing idk lol':
+        elif stat_type_var2 == 'Nothing idk lol':
             slate_table_instance = wr_matchups
     
     with col2:
-        if stat_type_var2 == 'WR/TE Coverage Matchups':
+        if stat_type_var2 == 'Macro Table':
+            st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(game_format, precision=2), height=1000, use_container_width = True)
+        elif stat_type_var2 == 'WR/TE Coverage Matchups':
             st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(wr_matchups_form, precision=2), use_container_width = True)
         elif stat_type_var2 == 'Ownership Trends':
             st.dataframe(slate_table_instance.style.background_gradient(axis=0).background_gradient(cmap = 'RdYlGn').format(trending_form, precision=2), use_container_width = True)
@@ -222,4 +171,56 @@ with tab2:
                             data=convert_df_to_csv(slate_table_instance),
                             file_name='NFL_Slate_Research_export.csv',
                             mime='text/csv',
-            )
+            )
+
+with tab2:
+    col1, col2 = st.columns([1, 8])
+    
+    with col1:
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              rb_search, wr_search, rb_season, wr_season, wr_matchups, macro_data, trending_data = pull_baselines()
+        stat_type_var1 = st.radio("What table are you loading?", ('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':
+            pos_var1 = st.multiselect('What Positions would you like to view?', options = ['RB', 'WR', 'TE'])
+        elif pos_split1 == 'All Positions':
+            pos_var1 = pos_list 
+        if split_var1 == 'Specific Teams':
+            team_var1 = st.multiselect('Which teams would you like to include in the Table?', options = rb_search['Team-Season'].unique(), key='team_var1')
+        elif split_var1 == 'All Teams':
+            team_var1 = rb_search['Team-Season'].unique().tolist()
+        if 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 (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 == '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), height=1000, 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), height=1000, 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), height=1000, use_container_width = True)
+        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), height=1000, use_container_width = True)
+    
+        st.download_button(
+                        label="Export Tables",
+                        data=convert_df_to_csv(table_instance),
+                        file_name='NFL_Research_export.csv',
+                        mime='text/csv',
+        )