added trending FPPM

app.py

@@ -47,9 +47,9 @@ def init_baselines():
     raw_display = raw_display.reset_index(drop=True)
     trend_table = raw_display[raw_display['PLAYER_NAME'] != ""]
     trend_table.replace('', np.nan, inplace=True)
-    trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'FD_Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling', 'L10 FD_Fantasy',
-                               'L10 FD_Ceiling', 'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 Fantasy',
-                               'L3 Ceiling', 'L3 FD_Fantasy', 'L3 FD_Ceiling', 'Trend Min', 'Trend Median', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
+    trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'FD_Position', 'L10 MIN', 'L10 Fantasy', 'L10 FPPM', 'L10 Ceiling', 'L10 FD_Fantasy',
+                               'L10 FD_Ceiling', 'L5 MIN', 'L5 Fantasy', 'L5 FPPM', 'L5 Ceiling', 'L5 FD_Fantasy', 'L5 FD_Ceiling', 'L3 MIN', 'L3 Fantasy',
+                               'L3 FPPM', 'L3 Ceiling', 'L3 FD_Fantasy', 'L3 FD_Ceiling', 'Trend Min', 'Trend Median', 'DK_Proj', 'Adj Median', 'Adj Ceiling',
                                'Trend FD_Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling', 'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value',
                                'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
     trend_table['DK_Salary'] = trend_table['DK_Salary'].str.replace(',', '').astype(float)
@@ -64,24 +64,28 @@ def init_baselines():
     dk_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 Fantasy', 'L5 Fantasy', 'L3 Fantasy', 'Trend Median']]
     
     fd_medians_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FD_Fantasy', 'L5 FD_Fantasy', 'L3 FD_Fantasy', 'Trend FD_Median']]
+
+    dk_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
+    
+    fd_fppm_table = trend_table[['PLAYER_NAME', 'Team', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM']]
     
     dk_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'DK_Salary', 'DK_Proj', 'Adj Median', 'DK_Avg_Val', 'Adj Ceiling', 'DK_Ceiling_Value']]
     
     fd_proj_medians_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'FD_Salary', 'FD_Proj', 'Adj FD_Median', 'FD_Avg_Val', 'Adj FD_Ceiling', 'FD_Ceiling_Value']]
 
-    return trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table
+    return trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_fppm_table, fd_fppm_table, dk_proj_medians_table, fd_proj_medians_table
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
+trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_fppm_table, fd_fppm_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
 
 col1, col2 = st.columns([1, 9])
 with col1:
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
               trend_table, dk_minutes_table, fd_minutes_table, dk_medians_table, fd_medians_table, dk_proj_medians_table, fd_proj_medians_table = init_baselines()
-    split_var1 = st.radio("What table would you like to view?", ('Minutes Trends', 'Fantasy Trends', 'Slate specific', 'Overall'), key='split_var1')
+    split_var1 = st.radio("What table would you like to view?", ('Minutes Trends', 'Fantasy Trends', 'FPPM Trends', 'Slate specific', 'Overall'), key='split_var1')
     site_var1 = st.radio("What site would you like to view?", ('Draftkings', 'Fanduel'), key='site_var1')
     if site_var1 == 'Draftkings':
         trend_table = trend_table[['PLAYER_NAME', 'Team', 'Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling',
@@ -90,6 +94,7 @@ with col1:
                                    'DK_Salary', 'DK_Avg_Val', 'DK_Ceiling_Value']]
         minutes_table = dk_minutes_table
         medians_table = dk_medians_table
+        fppm_table = dk_fppm_table
         proj_medians_table = dk_proj_medians_table
     elif site_var1 == 'Fanduel':
         trend_table = trend_table[['PLAYER_NAME', 'Team', 'FD_Position', 'L10 MIN', 'L10 FD_Fantasy',
@@ -98,13 +103,15 @@ with col1:
                                    'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value']]
         minutes_table = fd_minutes_table
         medians_table = fd_medians_table
+        fppm_table = fd_fppm_table
         proj_medians_table = fd_proj_medians_table
-    trend_table = trend_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'L10 MIN', 'L10 Fantasy', 'L10 Ceiling',
-                                        'L5 MIN', 'L5 Fantasy', 'L5 Ceiling', 'L3 MIN', 'L3 Fantasy',
-                                        'L3 Ceiling', 'Trend Min', 'Trend Median', 'Proj', 'Adj Median', 'Adj Ceiling',
-                                        'Salary', 'Avg_Val', 'Ceiling_Value'], axis=1)
+    trend_table = trend_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'L10 MIN', 'L10 Fantasy', 'L10 FPPM', 'L10 Ceiling',
+                                        'L5 MIN', 'L5 Fantasy', 'L5 FPPM', 'L5 Ceiling', 'L3 MIN', 'L3 Fantasy',
+                                        'L3 FPPM', 'L3 Ceiling', 'Trend Min', 'Trend Median', 'FD_Proj', 'Adj FD_Median', 'Adj FD_Ceiling',
+                                        'FD_Salary', 'FD_Avg_Val', 'FD_Ceiling_Value'], axis=1)
     minutes_table = minutes_table.set_axis(['PLAYER_NAME', 'Team', 'L10 MIN', 'L5 MIN', 'L3 MIN', 'Trend Min'], axis=1)
     medians_table = medians_table.set_axis(['PLAYER_NAME', 'Team', 'L10 Fantasy','L5 Fantasy', 'L3 Fantasy', 'Trend Median'], axis=1)
+    fppm_table = fppm_table.set_axis(['PLAYER_NAME', 'Team', 'L10 FPPM', 'L5 FPPM', 'L3 FPPM', 'Trend FPPM'], axis=1)
     proj_medians_table = proj_medians_table.set_axis(['PLAYER_NAME', 'Team', 'Position', 'Salary', 'Proj',
                                                       'Adj Median', 'Avg_Val', 'Adj Ceiling', 'Ceiling_Value'], axis=1)
     if split_var1 == 'Overall':
@@ -141,7 +148,16 @@ with col1:
             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
+            
+    elif split_var1 == 'FPPM Trends':
+        view_var1 = trend_table.Team.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
+            
     elif split_var1 == 'Slate specific':
         view_var1 = trend_table.Team.values.tolist()
         split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
@@ -197,6 +213,17 @@ with col2:
             mime='text/csv',
         )
         
+    elif split_var1 == 'FPPM Trends':
+        table_display = fppm_table[fppm_table['Team'].isin(team_var1)]
+        table_display = table_display.set_index('PLAYER_NAME')
+        st.dataframe(table_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        st.download_button(
+            label="Export Trending Numbers",
+            data=convert_df_to_csv(table_display),
+            file_name='Trending_export.csv',
+            mime='text/csv',
+        )
+        
     elif split_var1 == 'Slate specific':
         table_display = proj_medians_table[proj_medians_table['Proj'] >= proj_var1[0]]
         table_display = table_display[table_display['Proj'] <= proj_var1[1]]