Update app.py

app.py

@@ -128,23 +128,23 @@ 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']]
+        # 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)