removed use of gspread and transferred to mongo

app.py

@@ -58,7 +58,7 @@ fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team
 @st.cache_data(ttl = 599)
 def init_DK_seed_frames(sport): 
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
         
@@ -75,7 +75,7 @@ def init_DK_seed_frames(sport):
 def init_DK_secondary_seed_frames(sport):  
         
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
     
@@ -92,7 +92,7 @@ def init_DK_secondary_seed_frames(sport):
 def init_FD_seed_frames(sport):  
         
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
     
@@ -109,7 +109,7 @@ def init_FD_seed_frames(sport):
 def init_FD_secondary_seed_frames(sport):  
         
         if sport == 'NFL':
-            db = client["testing_db"]
+            db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
     
@@ -125,51 +125,50 @@ def init_FD_secondary_seed_frames(sport):
 @st.cache_data(ttl = 599)
 def init_baselines(sport):
     if sport == 'NFL':
-        try:
-            sh = gcservice_account.open_by_url(NFL_Data)
-        except:
-            sh = gcservice_account2.open_by_url(NFL_Data)
-        
-        worksheet = sh.worksheet('DK_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        db = client["NFL_Database"] 
+        collection = db['DK_SD_NFL_ROO'] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['STDev'] = raw_display['Median'] / 4
         
-        dk_raw = load_display.dropna(subset=['Median'])
+        dk_raw = raw_display.dropna(subset=['Median'])
         
-        worksheet = sh.worksheet('FD_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        collection = db['FD_SD_NFL_ROO'] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['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_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['STDev'] = raw_display['Median'] / 4
         
-        fd_raw = load_display.dropna(subset=['Median'])
+        fd_raw = raw_display.dropna(subset=['Median'])
     
     elif sport == 'NBA':
-         
-        try:
-            sh = gcservice_account.open_by_url(NBA_Data)
-        except:
-            sh = gcservice_account2.open_by_url(NBA_Data)
-        
-        worksheet = sh.worksheet('Player_Level_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display[load_display['site'] == 'Draftkings']
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        db = client["NBA_DFS"] 
+        collection = db['Player_SD_Range_Of_Outcomes'] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
+        raw_display = raw_display[raw_display['site'] == 'Draftkings']
+        raw_display['STDev'] = raw_display['Median'] / 4
         
-        dk_raw = load_display.dropna(subset=['Median'])
+        dk_raw = raw_display.dropna(subset=['Median'])
         
-        worksheet = sh.worksheet('Player_Level_SD_ROO')
-        load_display = pd.DataFrame(worksheet.get_all_records())
-        load_display.replace('', np.nan, inplace=True)
-        load_display['STDev'] = load_display['Median'] / 4
-        load_display = load_display[load_display['site'] == 'Fanduel']
-        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        collection = db['Player_SD_Range_Of_Outcomes'] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                                   'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp']]
+        raw_display = raw_display[raw_display['site'] == 'Fanduel']
+        raw_display['STDev'] = raw_display['Median'] / 4
         
-        fd_raw = load_display.dropna(subset=['Median'])
+        fd_raw = raw_display.dropna(subset=['Median'])
 
     return dk_raw, fd_raw