Refactor data retrieval in app.py to include specific columns for player statistics, lines, and power play data from MongoDB collections, improving data structure and usability.

app.py

@@ -31,14 +31,18 @@ def player_stat_table():
     collection = db["Player_Level_ROO"] 
     cursor = collection.find()
     player_frame = pd.DataFrame(cursor)
+    player_frame = player_frame[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own',
+                                 'Small Field Own%', 'Large Field Own%', 'Cash Own%', 'CPT_Own', 'Site', 'Type', 'Slate', 'player_id', 'timestamp']]
 
     collection = db["Player_Lines_ROO"] 
     cursor = collection.find()
     line_frame = pd.DataFrame(cursor)
+    line_frame = line_frame[['Player', 'SK1', 'SK2', 'SK3', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '50+%', '2x%', '3x%', '4x%', 'Own', 'Site', 'Type', 'Slate']]
 
     collection = db["Player_PowerPlay_ROO"] 
     cursor = collection.find()
     pp_frame = pd.DataFrame(cursor)
+    pp_frame = pp_frame[['Player', 'SK1', 'SK2', 'SK3', 'SK4', 'SK5', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '75+%', '2x%', '3x%', '4x%', 'Own', 'Site', 'Type', 'Slate']]
 
     timestamp = player_frame['timestamp'].values[0]