Lots of work to set up transfer from gspread to mongo, added some sidebar action to stacks page for testing

app.py

@@ -10,30 +10,51 @@ import numpy as np
 import pandas as pd
 import streamlit as st
 import gspread
+import pymongo
 from itertools import combinations
 
 @st.cache_resource
 def init_conn():
-          scope = ['https://www.googleapis.com/auth/spreadsheets',
-                    "https://www.googleapis.com/auth/drive"]
-          
-          credentials = {
-            "type": "service_account",
-            "project_id": "sheets-api-connect-378620",
-            "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9",
-            "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
-            "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
-            "client_id": "106625872877651920064",
-            "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-            "token_uri": "https://oauth2.googleapis.com/token",
-            "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-            "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
-          }
-
-          gc = gspread.service_account_from_dict(credentials)
-          return gc
-
-gc = init_conn()
+        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
+
+        credentials = {
+          "type": "service_account",
+          "project_id": "model-sheets-connect",
+          "private_key_id": st.secrets['model_sheets_connect_pk'],
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
+          "client_email": "[email protected]",
+          "client_id": "100369174533302798535",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
+        }
+        
+        credentials2 = {
+          "type": "service_account",
+          "project_id": "sheets-api-connect-378620",
+          "private_key_id": st.secrets['sheets_api_connect_pk'],
+          "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
+          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
+          "client_id": "106625872877651920064",
+          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+          "token_uri": "https://oauth2.googleapis.com/token",
+          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
+        }
+
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["NFL_Database"]
+     
+        NFL_Data = st.secrets['NFL_Data']
+
+        gc = gspread.service_account_from_dict(credentials)
+        gc2 = gspread.service_account_from_dict(credentials2)
+
+        return gc, gc2, db, NFL_Data
+    
+gcservice_account, gcservice_account2, db, NFL_Data = init_conn()
 
 game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
               'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
@@ -45,52 +66,64 @@ all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tft
 
 @st.cache_resource(ttl=600)
 def player_stat_table():
-    sh = gc.open_by_url(all_dk_player_projections)
-    worksheet = sh.worksheet('Site_Info')
-    site_slates = pd.DataFrame(worksheet.get_all_records())
-
-    worksheet = sh.worksheet('Player_Projections')
-    player_stats = pd.DataFrame(worksheet.get_all_records())
-    player_stats = player_stats[player_stats['Position'] != 'K']
-
-    worksheet = sh.worksheet('DK_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display = load_display[load_display['Position'] != 'K']
+
+    collection = db["Player_Stats"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['name', 'Team', 'Opp', 'Position', 'Salary', 'team_plays', 'team_pass', 'team_rush', 'team_tds', 'team_pass_tds', 'team_rush_tds', 'dropbacks', 'pass_yards', 'pass_tds',
+                               'rush_att', 'rush_yards', 'rush_tds', 'targets', 'rec', 'rec_yards', 'rec_tds', 'PPR', 'Half_PPR', 'Own']]
+    player_stats = raw_display[raw_display['Position'] != 'K']
+
+    collection = db["DK_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']]
+    load_display = raw_display[raw_display['Position'] != 'K']
     dk_roo_raw = load_display.dropna(subset=['Median'])
 
-    worksheet = sh.worksheet('FD_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display = load_display[load_display['Position'] != 'K']
+    collection = db["FD_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']]
+    load_display = raw_display[raw_display['Position'] != 'K']
     fd_roo_raw = load_display.dropna(subset=['Median'])
 
-    worksheet = sh.worksheet('DK_Stacks_Totals')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    dk_stacks_raw = load_display
+    collection = db["DK_DFS_Stacks"] 
+    cursor = collection.find()
 
-    worksheet = sh.worksheet('FD_Stacks_Totals')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    fd_stacks_raw = load_display
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    dk_stacks_raw = raw_display.copy()
 
-    return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates
+    collection = db["FD_DFS_Stacks"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate', 'version']]
+    fd_stacks_raw = raw_display.copy()
+
+    return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw
 
 @st.cache_data
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
 t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
 
 tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Team Stacks Range of Outcomes", "Overall Range of Outcomes", "QB Range of Outcomes", "RB Range of Outcomes", "WR Range of Outcomes", "TE Range of Outcomes"])
 
 with tab1:
-    col1, col2 = st.columns([1, 5])
-    with col1:
+    with st.sidebar:
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset1'):
             st.cache_data.clear()
-            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
             t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
         slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var1')
         site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
@@ -125,185 +158,185 @@ with tab1:
                 team_var1 = raw_baselines.Team.values.tolist()
     
     
-    with col2:
-        if custom_var1 == 'No':
-            final_stacks = raw_baselines[raw_baselines['Team'].isin(team_var1)]
-            if view_var1 == 'Simple':
-                final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
-            elif view_var1 == 'Advanced':
-                final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
-                                                             'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
-            st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
-            st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(final_stacks),
-                    file_name='NFL_stacks_export.csv',
-                    mime='text/csv',
-            )
-        elif custom_var1 == 'Yes':
-            hold_container = st.empty()
-            if st.button('Create Range of Outcomes for Slate'):
-                with hold_container:
-                    if site_var1 == 'Draftkings':
-                            working_roo = player_stats
-                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
-                            working_roo.replace('', 0, inplace=True)
-                    if site_var1 == 'Fanduel':
-                            working_roo = player_stats
-                            working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
-                            working_roo.replace('', 0, inplace=True)
-                    working_roo = working_roo[working_roo['Team'].isin(team_var1)]
-                    
-                    total_sims = 1000
-                    
-                    salary_dict = dict(zip(working_roo.name, working_roo.Salary))
-                    own_dict = dict(zip(working_roo.name, working_roo.Own))
-                    fantasy_dict = dict(zip(working_roo.name, working_roo.Fantasy))
-
-                    QB_group = working_roo.loc[working_roo['Position'] == 'QB']
-                    stacks_df = pd.DataFrame(columns=['Team','QB', 'WR1', 'WR2_TE'])
-
-                    for stack in range(0,len(QB_group)):
-                        team_var = QB_group.iat[stack,1]
-                        WR_group_1 = working_roo.loc[working_roo['Position'] == 'WR']
-                        WR_group_2 = WR_group_1.loc[working_roo['Team'] == team_var]
-                        TE_group_1 = working_roo.loc[working_roo['Position'] == 'TE']
-                        TE_group_2 = TE_group_1.loc[working_roo['Team'] == team_var]
-                        cur_list = []
-                        qb_piece = QB_group.iat[stack,0]
-                        wr_piece = WR_group_2.iat[0,0]
-                        te_piece = TE_group_2.iat[0,0]
-                        cur_list.append(team_var)
-                        cur_list.append(qb_piece)
-                        cur_list.append(wr_piece)
-                        cur_list.append(te_piece)
-                        stacks_df.loc[len(stacks_df)] = cur_list
-                        cur_list = []
-                        qb_piece = QB_group.iat[stack,0]
-                        wr_piece = WR_group_2.iat[1,0]
-                        te_piece = TE_group_2.iat[0,0]
-                        cur_list.append(team_var)
-                        cur_list.append(qb_piece)
-                        cur_list.append(wr_piece)
-                        cur_list.append(te_piece)
-                        stacks_df.loc[len(stacks_df)] = cur_list
-                        cur_list = []
-                        qb_piece = QB_group.iat[stack,0]
-                        wr_piece = WR_group_2.iat[0,0]
-                        te_piece = WR_group_2.iat[1,0]
-                        cur_list.append(team_var)
-                        cur_list.append(qb_piece)
-                        cur_list.append(wr_piece)
-                        cur_list.append(te_piece)
-                        stacks_df.loc[len(stacks_df)] = cur_list
-                    
-                    stacks_df['Salary'] = sum([stacks_df['QB'].map(salary_dict),
-                            stacks_df['WR1'].map(salary_dict),
-                            stacks_df['WR2_TE'].map(salary_dict)])
-
-                    stacks_df['Fantasy'] = sum([stacks_df['QB'].map(fantasy_dict),
-                            stacks_df['WR1'].map(fantasy_dict),
-                            stacks_df['WR2_TE'].map(fantasy_dict)])
-
-                    stacks_df['Own'] = sum([stacks_df['QB'].map(own_dict),
-                            stacks_df['WR1'].map(own_dict),
-                            stacks_df['WR2_TE'].map(own_dict)])
-
-                    stacks_df['team_combo'] = stacks_df['Team'] + " " + stacks_df['QB'] + " " + stacks_df['WR1'] + " " + stacks_df['WR2_TE']
-
-                    own_dict = dict(zip(stacks_df.team_combo, stacks_df.Own))
-                    qb_dict = dict(zip(stacks_df.team_combo, stacks_df.QB))
-                    wr1_dict = dict(zip(stacks_df.team_combo, stacks_df.WR1))
-                    wr2_dict = dict(zip(stacks_df.team_combo, stacks_df.WR2_TE))
-                    team_dict = dict(zip(stacks_df.team_combo, stacks_df.Team))
-
-                    flex_file = stacks_df[['team_combo', 'Salary', 'Fantasy']]
-                    flex_file.rename(columns={"Fantasy": "Median"}, inplace = True)
-                    flex_file['Floor'] = flex_file['Median']*.25
-                    flex_file['Ceiling'] = flex_file['Median'] + flex_file['Floor']
-                    flex_file['STD'] = flex_file['Median']/4
-                    flex_file = flex_file[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                    hold_file = flex_file
-                    overall_file = flex_file
-                    salary_file = flex_file
-
-                    overall_players = overall_file[['team_combo']]
-
-                    for x in range(0,total_sims):    
-                        salary_file[x] = salary_file['Salary']
-
-                    salary_file=salary_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                    salary_file.astype('int').dtypes
-
-                    salary_file = salary_file.div(1000)
-
-                    for x in range(0,total_sims):    
-                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-
-                    overall_file=overall_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                    overall_file.astype('int').dtypes
 
-                    players_only = hold_file[['team_combo']]
-                    raw_lineups_file = players_only
-
-                    for x in range(0,total_sims):
-                        maps_dict = {'proj_map':dict(zip(hold_file.team_combo,hold_file[x]))}
-                        raw_lineups_file[x] = sum([raw_lineups_file['team_combo'].map(maps_dict['proj_map'])])
-                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
-
-                    players_only=players_only.drop(['team_combo'], axis=1)
-                    players_only.astype('int').dtypes
-
-                    salary_2x_check = (overall_file - (salary_file*2))
-                    salary_3x_check = (overall_file - (salary_file*3))
-                    salary_4x_check = (overall_file - (salary_file*4))
-
-                    players_only['Average_Rank'] = players_only.mean(axis=1)
-                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                    players_only['60+%'] = overall_file[overall_file >= 60].count(axis=1)/float(total_sims)
-                    players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                    players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-
-                    players_only['team_combo'] = hold_file[['team_combo']]
-
-                    final_outcomes = players_only[['team_combo', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]
-
-                    final_stacks = pd.merge(hold_file, final_outcomes, on="team_combo")
-                    final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]
-                    final_stacks['Own'] = final_stacks['team_combo'].map(own_dict)
-                    final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own']]
-                    final_stacks['Projection Rank'] = final_stacks.Median.rank(pct = True)
-                    final_stacks['Own Rank'] = final_stacks.Own.rank(pct = True)
-                    final_stacks['LevX'] = final_stacks['Projection Rank'] - final_stacks['Own Rank']
-                    final_stacks['Team'] = final_stacks['team_combo'].map(team_dict)
-                    final_stacks['QB'] = final_stacks['team_combo'].map(qb_dict)
-                    final_stacks['WR1_TE'] = final_stacks['team_combo'].map(wr1_dict)
-                    final_stacks['WR2_TE'] = final_stacks['team_combo'].map(wr2_dict)
-
-                    final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
-                                                        'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
-
-                    final_stacks = final_stacks.sort_values(by='Median', ascending=False)
-
-                with hold_container:
-                    hold_container = st.empty()
-                    final_stacks = final_stacks
-                    if view_var1 == 'Simple':
-                        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
-                    elif view_var1 == 'Advanced':
-                        final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
-                                                                    'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
-                    st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+    if custom_var1 == 'No':
+        final_stacks = raw_baselines[raw_baselines['Team'].isin(team_var1)]
+        if view_var1 == 'Simple':
+            final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
+        elif view_var1 == 'Advanced':
+            final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
+                                                            'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+        st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
+        st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_stacks),
+                file_name='NFL_stacks_export.csv',
+                mime='text/csv',
+        )
+    elif custom_var1 == 'Yes':
+        hold_container = st.empty()
+        if st.button('Create Range of Outcomes for Slate'):
+            with hold_container:
+                if site_var1 == 'Draftkings':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                if site_var1 == 'Fanduel':
+                        working_roo = player_stats
+                        working_roo.rename(columns={"name": "Player", "rush_yards": "Rush Yards", "rec": "Receptions", "Half_PPR": "Fantasy"}, inplace = True)
+                        working_roo.replace('', 0, inplace=True)
+                working_roo = working_roo[working_roo['Team'].isin(team_var1)]
+                
+                total_sims = 1000
+                
+                salary_dict = dict(zip(working_roo.name, working_roo.Salary))
+                own_dict = dict(zip(working_roo.name, working_roo.Own))
+                fantasy_dict = dict(zip(working_roo.name, working_roo.Fantasy))
+
+                QB_group = working_roo.loc[working_roo['Position'] == 'QB']
+                stacks_df = pd.DataFrame(columns=['Team','QB', 'WR1', 'WR2_TE'])
+
+                for stack in range(0,len(QB_group)):
+                    team_var = QB_group.iat[stack,1]
+                    WR_group_1 = working_roo.loc[working_roo['Position'] == 'WR']
+                    WR_group_2 = WR_group_1.loc[working_roo['Team'] == team_var]
+                    TE_group_1 = working_roo.loc[working_roo['Position'] == 'TE']
+                    TE_group_2 = TE_group_1.loc[working_roo['Team'] == team_var]
+                    cur_list = []
+                    qb_piece = QB_group.iat[stack,0]
+                    wr_piece = WR_group_2.iat[0,0]
+                    te_piece = TE_group_2.iat[0,0]
+                    cur_list.append(team_var)
+                    cur_list.append(qb_piece)
+                    cur_list.append(wr_piece)
+                    cur_list.append(te_piece)
+                    stacks_df.loc[len(stacks_df)] = cur_list
+                    cur_list = []
+                    qb_piece = QB_group.iat[stack,0]
+                    wr_piece = WR_group_2.iat[1,0]
+                    te_piece = TE_group_2.iat[0,0]
+                    cur_list.append(team_var)
+                    cur_list.append(qb_piece)
+                    cur_list.append(wr_piece)
+                    cur_list.append(te_piece)
+                    stacks_df.loc[len(stacks_df)] = cur_list
+                    cur_list = []
+                    qb_piece = QB_group.iat[stack,0]
+                    wr_piece = WR_group_2.iat[0,0]
+                    te_piece = WR_group_2.iat[1,0]
+                    cur_list.append(team_var)
+                    cur_list.append(qb_piece)
+                    cur_list.append(wr_piece)
+                    cur_list.append(te_piece)
+                    stacks_df.loc[len(stacks_df)] = cur_list
+                
+                stacks_df['Salary'] = sum([stacks_df['QB'].map(salary_dict),
+                        stacks_df['WR1'].map(salary_dict),
+                        stacks_df['WR2_TE'].map(salary_dict)])
+
+                stacks_df['Fantasy'] = sum([stacks_df['QB'].map(fantasy_dict),
+                        stacks_df['WR1'].map(fantasy_dict),
+                        stacks_df['WR2_TE'].map(fantasy_dict)])
+
+                stacks_df['Own'] = sum([stacks_df['QB'].map(own_dict),
+                        stacks_df['WR1'].map(own_dict),
+                        stacks_df['WR2_TE'].map(own_dict)])
+
+                stacks_df['team_combo'] = stacks_df['Team'] + " " + stacks_df['QB'] + " " + stacks_df['WR1'] + " " + stacks_df['WR2_TE']
+
+                own_dict = dict(zip(stacks_df.team_combo, stacks_df.Own))
+                qb_dict = dict(zip(stacks_df.team_combo, stacks_df.QB))
+                wr1_dict = dict(zip(stacks_df.team_combo, stacks_df.WR1))
+                wr2_dict = dict(zip(stacks_df.team_combo, stacks_df.WR2_TE))
+                team_dict = dict(zip(stacks_df.team_combo, stacks_df.Team))
+
+                flex_file = stacks_df[['team_combo', 'Salary', 'Fantasy']]
+                flex_file.rename(columns={"Fantasy": "Median"}, inplace = True)
+                flex_file['Floor'] = flex_file['Median']*.25
+                flex_file['Ceiling'] = flex_file['Median'] + flex_file['Floor']
+                flex_file['STD'] = flex_file['Median']/4
+                flex_file = flex_file[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                hold_file = flex_file
+                overall_file = flex_file
+                salary_file = flex_file
+
+                overall_players = overall_file[['team_combo']]
+
+                for x in range(0,total_sims):    
+                    salary_file[x] = salary_file['Salary']
+
+                salary_file=salary_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                salary_file.astype('int').dtypes
+
+                salary_file = salary_file.div(1000)
+
+                for x in range(0,total_sims):    
+                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+
+                overall_file=overall_file.drop(['team_combo', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                overall_file.astype('int').dtypes
+
+                players_only = hold_file[['team_combo']]
+                raw_lineups_file = players_only
+
+                for x in range(0,total_sims):
+                    maps_dict = {'proj_map':dict(zip(hold_file.team_combo,hold_file[x]))}
+                    raw_lineups_file[x] = sum([raw_lineups_file['team_combo'].map(maps_dict['proj_map'])])
+                    players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+                players_only=players_only.drop(['team_combo'], axis=1)
+                players_only.astype('int').dtypes
+
+                salary_2x_check = (overall_file - (salary_file*2))
+                salary_3x_check = (overall_file - (salary_file*3))
+                salary_4x_check = (overall_file - (salary_file*4))
+
+                players_only['Average_Rank'] = players_only.mean(axis=1)
+                players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                players_only['60+%'] = overall_file[overall_file >= 60].count(axis=1)/float(total_sims)
+                players_only['2x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['3x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+
+                players_only['team_combo'] = hold_file[['team_combo']]
+
+                final_outcomes = players_only[['team_combo', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]
+
+                final_stacks = pd.merge(hold_file, final_outcomes, on="team_combo")
+                final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%']]
+                final_stacks['Own'] = final_stacks['team_combo'].map(own_dict)
+                final_stacks = final_stacks[['team_combo', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own']]
+                final_stacks['Projection Rank'] = final_stacks.Median.rank(pct = True)
+                final_stacks['Own Rank'] = final_stacks.Own.rank(pct = True)
+                final_stacks['LevX'] = final_stacks['Projection Rank'] - final_stacks['Own Rank']
+                final_stacks['Team'] = final_stacks['team_combo'].map(team_dict)
+                final_stacks['QB'] = final_stacks['team_combo'].map(qb_dict)
+                final_stacks['WR1_TE'] = final_stacks['team_combo'].map(wr1_dict)
+                final_stacks['WR2_TE'] = final_stacks['team_combo'].map(wr2_dict)
+
+                final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
+                                                    'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+
+                final_stacks = final_stacks.sort_values(by='Median', ascending=False)
+
+            with hold_container:
+                hold_container = st.empty()
+                final_stacks = final_stacks
+                if view_var1 == 'Simple':
+                    final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Salary', 'Median', '60+%', '4x%']]
+                elif view_var1 == 'Advanced':
+                    final_stacks = final_stacks[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish',
+                                                                'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                st.dataframe(final_stacks.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True)
 
-                st.download_button(
-                    label="Export Tables",
-                    data=convert_df_to_csv(final_stacks),
-                    file_name='Custom_NFL_stacks_export.csv',
-                    mime='text/csv',
-                )
+            st.download_button(
+                label="Export Tables",
+                data=convert_df_to_csv(final_stacks),
+                file_name='Custom_NFL_stacks_export.csv',
+                mime='text/csv',
+            )
 
 with tab2:
     col1, col2 = st.columns([1, 5])
@@ -311,7 +344,7 @@ with tab2:
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset2'):
               st.cache_data.clear()
-              player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+              player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
               t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
         slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var2')
         site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
@@ -498,7 +531,7 @@ with tab3:
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset3'):
             st.cache_data.clear()
-            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
             t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
         slate_var3 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var3')
         site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3')
@@ -689,7 +722,7 @@ with tab4:
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset4'):
             st.cache_data.clear()
-            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
             t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
         slate_var4 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var4')
         site_var4 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var4')
@@ -879,7 +912,7 @@ with tab5:
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset5'):
             st.cache_data.clear()
-            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
             t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
         slate_var5 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var5')
         site_var5 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var5')
@@ -1069,7 +1102,7 @@ with tab6:
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset6'):
             st.cache_data.clear()
-            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates = player_stat_table()
+            player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw = player_stat_table()
             t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
         slate_var6 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var6')
         site_var6 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var6')