Update app.py

app.py

@@ -43,6 +43,7 @@ def grab_baseline_stuff():
     sh = gcservice_account.open_by_url(all_dk_player_projections)
     worksheet = sh.worksheet('Player_Data_Master')
     raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display = raw_display[raw_display['Final DK Projection'] != '  -   ']
     dk_raw_proj = raw_display[[' Clean Name ', 'Team', 'Opp', 'Line', 'PP Unit', ' Position ', ' DK Salary ', 'Final DK Projection', 'DK uploadID', 'DK_Own']]
     dk_raw_proj = dk_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own'], axis=1)
     fd_raw_proj = raw_display[[' Clean Name ', 'Team', 'Opp', 'Line', 'PP Unit', ' FD Position ', 'FD Salary', 'Final FD Projection', 'FD uploadID', 'FD_Own']]
@@ -128,24 +129,24 @@ with tab2:
     with col2:
         raw_baselines = raw_baselines[raw_baselines['Team'].isin(team_var1)]
         raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var1)]
-        # ownframe = raw_baselines.copy()
-        # if contest_var1 == 'Cash':
-        #         ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (10 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
-        #         ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
-        #         ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-        #         ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum())
-        # if contest_var1 == 'Small Field GPP':
-        #         ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
-        #         ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
-        #         ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-        #         ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum())
-        # if contest_var1 == 'Large Field GPP':
-        #         ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
-        #         ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (1.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
-        #         ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
-        #         ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum())
-        # raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Line', 'PP Unit', 'Median', 'Own']]
-        # raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
+        ownframe = raw_baselines.copy()
+        if contest_var1 == 'Cash':
+                ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (10 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
+                ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
+                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
+                ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum())
+        if contest_var1 == 'Small Field GPP':
+                ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (6 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
+                ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
+                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
+                ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum())
+        if contest_var1 == 'Large Field GPP':
+                ownframe['Own%'] = np.where((ownframe['Position'] == 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean() >= 0), ownframe['Own'] * (3 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] == 'G', 'Own'].mean(), ownframe['Own'])
+                ownframe['Own%'] = np.where((ownframe['Position'] != 'G') & (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean() >= 0), ownframe['Own'] * (1.5 * (ownframe['Own'] - ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean())/100) + ownframe.loc[ownframe['Position'] != 'G', 'Own'].mean(), ownframe['Own%'])
+                ownframe['Own%'] = np.where(ownframe['Own%'] > 75, 75, ownframe['Own%'])
+                ownframe['Own'] = ownframe['Own%'] * (800 / ownframe['Own%'].sum())
+        raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Line', 'PP Unit', 'Median', 'Own']]
+        raw_baselines = raw_baselines.sort_values(by='Median', ascending=False)
         raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var1), 1, 0)
         st.dataframe(raw_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         st.download_button(