Update app.py

app.py

@@ -50,6 +50,17 @@ def init_baselines():
                                    'Shots', 'SH%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
                                    'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
                                    'Faceoffs Lost', 'Faceoffs %']]
+    data_cols = gamelog_table.columns.drop(['Player', 'Team', 'Position', 'Date'])
+    gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
+    gamelog_table['Date'] = pd.to_datetime(gamelog_table['Date']).dt.date
+    # gamelog_table['Shots'].replace("", 0, inplace=True)
+    # gamelog_table['Shots Blocked'].replace("", 0, inplace=True)
+    # gamelog_table['Goals'].replace("", 0, inplace=True)
+    # gamelog_table['Total Points'].replace("", 0, inplace=True)
+    # gamelog_table['Shots'] = gamelog_table['Shots'].astype(int)
+    # gamelog_table['Shots Blocked'] = gamelog_table['Shots Blocked'].astype(int)
+    # gamelog_table['Goals'] = gamelog_table['Goals'].astype(int)
+    # gamelog_table['Total Points'] = gamelog_table['Total Points'].astype(int)
     gamelog_table['dk_shots_bonus'] = np.where((gamelog_table['Shots'] >= 5), 1, 0)
     gamelog_table['dk_blocks_bonus'] = np.where((gamelog_table['Shots Blocked'] >= 3), 1, 0)
     gamelog_table['dk_goals_bonus'] = np.where((gamelog_table['Goals'] >= 3), 1, 0)
@@ -59,20 +70,20 @@ def init_baselines():
                                        (gamelog_table['dk_goals_bonus'] * 3), (gamelog_table['dk_points_bonus'] * 3)]).astype(float).round(2)
     gamelog_table['fd_fantasy'] = sum([(gamelog_table['Goals'] * 12), (gamelog_table['Total Assists'] * 8), (gamelog_table['Shots'] * 1.6),
                                        (gamelog_table['Shots Blocked'] * 1.6)]).astype(float).round(2)
-    data_cols = gamelog_table.columns.drop(['Player', 'Team', 'Position', 'Date'])
-    gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
-    gamelog_table['Date'] = pd.to_datetime(gamelog_table['Date']).dt.date
+    
+    
     
     gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points', 'IPP',
                                             'Shots', 'Shots%', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties', 'Minor',
                                             'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
-                                            'Faceoffs Lost', 'Faceoffs%'], axis=1)
+                                            'Faceoffs Lost', 'Faceoffs%', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+                                            'dk_fantasy', 'fd_fantasy'], axis=1)
     
     return gamelog_table
 
 @st.cache_data(show_spinner=False)
 def seasonlong_build(data_sample):
-    season_long_table = data_sample[['Player', 'Team']]
+    season_long_table = data_sample[['Player', 'Team', 'Position']]
     season_long_table['TOI'] = data_sample.groupby(['Player', 'Team'], sort=False)['TOI'].transform('mean').astype(float)
     season_long_table['Goals'] = data_sample.groupby(['Player', 'Team'], sort=False)['Goals'].transform('mean').astype(float)
     season_long_table['Total Assists'] = data_sample.groupby(['Player', 'Team'], sort=False)['Total Assists'].transform('mean').astype(float)
@@ -100,12 +111,20 @@ def seasonlong_build(data_sample):
     season_long_table['Shots Blocked'] = data_sample.groupby(['Player', 'Team'], sort=False)['Shots Blocked'].transform('mean').astype(float)
     season_long_table['Faceoffs Won'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Won'].transform('mean').astype(float)
     season_long_table['Faceoffs Lost'] = data_sample.groupby(['Player', 'Team'], sort=False)['Faceoffs Lost'].transform('mean').astype(float)
+    season_long_table['dk_shots_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_shots_bonus'].transform('mean').astype(float)
+    season_long_table['dk_blocks_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_blocks_bonus'].transform('mean').astype(float)
+    season_long_table['dk_goals_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_goals_bonus'].transform('mean').astype(float)
+    season_long_table['dk_points_bonus'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_points_bonus'].transform('mean').astype(float)
+    season_long_table['dk_fantasy'] = data_sample.groupby(['Player', 'Team'], sort=False)['dk_fantasy'].transform('mean').astype(float)
+    season_long_table['fd_fantasy'] = data_sample.groupby(['Player', 'Team'], sort=False)['fd_fantasy'].transform('mean').astype(float)
     season_long_table = season_long_table.drop_duplicates(subset='Player')
     
-    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Position', 'Date', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
+    
+    season_long_table = season_long_table.set_axis(['Player', 'Team', 'Position', 'TOI', 'Goals', 'Total Assists', 'First Assists', 'Second Assists', 'Total Points',
                                                     'IPP', 'Shots', 'ixG', 'iCF', 'iFF', 'iSCF', 'iHDCF', 'Rush Attempts', 'Rebounds Created', 'PIM', 'Total Penalties',
                                                     'Minor', 'Major', 'Penalties Drawn', 'Giveaways', 'Takeaways', 'Hits', 'Hits Taken', 'Shots Blocked', 'Faceoffs Won',
-                                                    'Faceoffs Lost'], axis=1)
+                                                    'Faceoffs Lost', 'dk_shots_bonus', 'dk_blocks_bonus', 'dk_goals_bonus', 'dk_points_bonus',
+                                                    'dk_fantasy', 'fd_fantasy'], axis=1)
 
     return season_long_table