Update app.py

app.py

@@ -5,15 +5,11 @@ for name in dir():
     if not name.startswith('_'):
         del globals()[name]
 
-import pulp
 import numpy as np
 import pandas as pd
 import streamlit as st
 import gspread
-import time
 import random
-import scipy.stats
-import os
 
 @st.cache_resource
 def init_conn():
@@ -38,30 +34,8 @@ def init_conn():
 
 gc = 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%}'}
-
-player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
-                   '4x%': '{:.2%}','GPP%': '{:.2%}'}
-
 freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
 
-@st.cache_resource(ttl = 300)
-def set_slate_teams():
-    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
-    worksheet = sh.worksheet('Site_Info')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-
-    return raw_display
-
-@st.cache_resource(ttl = 300)
-def player_stat_table():
-    sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
-    worksheet = sh.worksheet('Player_Projections')
-    raw_display = pd.DataFrame(worksheet.get_all_records())
-
-    return raw_display
-
 @st.cache_resource(ttl = 300)
 def load_dk_player_projections():
     sh = gc.open_by_url('https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348')
@@ -251,6 +225,8 @@ def create_random_portfolio(Total_Sample_Size, raw_baselines):
             RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
             RandomPortfolio['User/Field'] = 0
             
+            del total_elements
+            del all_choices
             del O_merge
           
             return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
@@ -263,28 +239,6 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size):
     stack_num = random.randint(1, 3)
     stacking_dict = create_stack_options(raw_baselines, stack_num)
     
-    # # Create a dictionary for mapping positions to their corresponding dictionaries
-    # dict_map = {
-    #     'QB': qb_dict,
-    #     'RB1': full_pos_player_dict['pos_dicts'][0],
-    #     'RB2': full_pos_player_dict['pos_dicts'][0],
-    #     'WR1': full_pos_player_dict['pos_dicts'][1],
-    #     'WR2': full_pos_player_dict['pos_dicts'][1],
-    #     'WR3': full_pos_player_dict['pos_dicts'][1],
-    #     'TE': full_pos_player_dict['pos_dicts'][2],
-    #     'FLEX': full_pos_player_dict['pos_dicts'][3],
-    #     'DST': def_dict
-    # }
-    
-    # # Apply mapping for each position
-    # for pos, mapping in dict_map.items():
-    #     RandomPortfolio[pos] = RandomPortfolio[pos].map(mapping).astype("string[pyarrow]")
-    
-    # # This part appears to be for filtering. Consider if it can be optimized depending on the data characteristics
-    # RandomPortfolio['plyr_list'] = RandomPortfolio.values.tolist()
-    # RandomPortfolio['plyr_count'] = RandomPortfolio['plyr_list'].apply(lambda x: len(set(x)))
-    # RandomPortfolio = RandomPortfolio[RandomPortfolio['plyr_count'] == 10].drop(columns=['plyr_list','plyr_count']).reset_index(drop=True)
-    
     RandomPortfolio['QB'] = pd.Series(list(RandomPortfolio['QB'].map(qb_dict)), dtype="string[pyarrow]")
     RandomPortfolio['RB1'] = pd.Series(list(RandomPortfolio['RB1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
     RandomPortfolio['RB2'] = pd.Series(list(RandomPortfolio['RB2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
@@ -305,8 +259,6 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size):
     del stack_num
     del stacking_dict    
     
-    
-    
     RandomPortfolio['QBs'] = RandomPortfolio['QB'].map(maps_dict['Salary_map']).astype(np.int32)
     RandomPortfolio['RB1s'] = RandomPortfolio['RB1'].map(maps_dict['Salary_map']).astype(np.int32)
     RandomPortfolio['RB2s'] = RandomPortfolio['RB2'].map(maps_dict['Salary_map']).astype(np.int32)
@@ -838,26 +790,6 @@ with tab1:
 with tab2:
     col1, col2 = st.columns([1, 7])
     with col1:
-        if 'Sim_Winner_Display' not in st.session_state:
-            st.session_state.Sim_Winner_Display = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-        if 'Sim_Winner_Frame' not in st.session_state:
-            st.session_state.Sim_Winner_Frame = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-        if 'Sim_Winner_Export' not in st.session_state:
-            st.session_state.Sim_Winner_Export = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-        if 'player_freq' not in st.session_state: 
-            st.session_state.player_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'qb_freq' not in st.session_state: 
-            st.session_state.qb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'rb_freq' not in st.session_state: 
-            st.session_state.rb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'wr_freq' not in st.session_state: 
-            st.session_state.wr_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'te_freq' not in st.session_state: 
-            st.session_state.te_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'flex_freq' not in st.session_state: 
-            st.session_state.flex_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'dst_freq' not in st.session_state: 
-            st.session_state.dst_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset1'):
               st.cache_data.clear()
@@ -914,26 +846,6 @@ with tab2:
 
     with col2:
         with st.container():
-            # if 'Sim_Winner_Display' not in st.session_state:
-            #     st.session_state.Sim_Winner_Display = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-            # if 'Sim_Winner_Frame' not in st.session_state:
-            #     st.session_state.Sim_Winner_Frame = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-            # if 'Sim_Winner_Export' not in st.session_state:
-            #     st.session_state.Sim_Winner_Export = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-            # if 'player_freq' not in st.session_state: 
-            #     st.session_state.player_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-            # if 'qb_freq' not in st.session_state: 
-            #     st.session_state.qb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-            # if 'rb_freq' not in st.session_state: 
-            #     st.session_state.rb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-            # if 'wr_freq' not in st.session_state: 
-            #     st.session_state.wr_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-            # if 'te_freq' not in st.session_state: 
-            #     st.session_state.te_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-            # if 'flex_freq' not in st.session_state: 
-            #     st.session_state.flex_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-            # if 'dst_freq' not in st.session_state: 
-            #     st.session_state.dst_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
             if st.button("Simulate Contest"):
                 try:
                     del dst_freq
@@ -991,10 +903,11 @@ with tab2:
                             OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
                         Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
+                        del proj_dataframe
                         del OwnFrame
                         
                     elif slate_var1 != 'User':
-                        initial_proj = raw_baselines
+                        initial_proj = raw_baselines.copy()
                         drop_frame = initial_proj.drop_duplicates(subset = 'Player',keep = 'first')
                         OwnFrame = drop_frame[['Player', 'Team', 'Position', 'Median', 'Own', 'Floor', 'Ceiling', 'Salary']]
                         if contest_var1 == 'Small':
@@ -1014,6 +927,7 @@ with tab2:
                             OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
                         Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
+                        del raw_baselines
                         del initial_proj
                         del drop_frame
                         del OwnFrame
@@ -1254,12 +1168,15 @@ with tab2:
                     del vec_stdev_map
                     del sample_arrays
                     del final_array
-                    # del best_lineup
+                    del fp_array
+                    del fp_random
                     st.write('Contest simulation complete')
                     # Initial setup
                     Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
                     Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
                     
+                    del FinalPortfolio
+                    
                     # Type Casting
                     type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float16}
                     Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
@@ -1270,6 +1187,8 @@ with tab2:
                     # Data Copying
                     st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
                     
+                    del Sim_Winner_Frame
+                    
                     # Conditional Replacement
                     columns_to_replace = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
                     
@@ -1285,7 +1204,7 @@ with tab2:
                         st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True)
     
                     
-                    player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)),
+                    player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     player_freq['Freq'] = player_freq['Freq'].astype(int)
                     player_freq['Position'] = player_freq['Player'].map(maps_dict['Pos_map'])
@@ -1298,8 +1217,9 @@ with tab2:
                                         player_freq['Team'] = player_freq['Team'].replace(item_list, team_list)
     
                     st.session_state.player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    del player_freq
                     
-                    qb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
+                    qb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     qb_freq['Freq'] = qb_freq['Freq'].astype(int)
                     qb_freq['Position'] = qb_freq['Player'].map(maps_dict['Pos_map'])
@@ -1312,8 +1232,9 @@ with tab2:
                                         qb_freq['Team'] = qb_freq['Team'].replace(item_list, team_list)
     
                     st.session_state.qb_freq = qb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    del qb_freq
     
-                    rb_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2]].values, return_counts=True)),
+                    rb_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[1, 2]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     rb_freq['Freq'] = rb_freq['Freq'].astype(int)
                     rb_freq['Position'] = rb_freq['Player'].map(maps_dict['Pos_map'])
@@ -1326,8 +1247,9 @@ with tab2:
                                         rb_freq['Team'] = rb_freq['Team'].replace(item_list, team_list)
     
                     st.session_state.rb_freq = rb_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    del rb_freq
                     
-                    wr_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[3, 4, 5]].values, return_counts=True)),
+                    wr_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[3, 4, 5]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     wr_freq['Freq'] = wr_freq['Freq'].astype(int)
                     wr_freq['Position'] = wr_freq['Player'].map(maps_dict['Pos_map'])
@@ -1340,8 +1262,9 @@ with tab2:
                                         wr_freq['Team'] = wr_freq['Team'].replace(item_list, team_list)
     
                     st.session_state.wr_freq = wr_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    del wr_freq
                     
-                    te_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[6]].values, return_counts=True)),
+                    te_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[6]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     te_freq['Freq'] = te_freq['Freq'].astype(int)
                     te_freq['Position'] = te_freq['Player'].map(maps_dict['Pos_map'])
@@ -1354,8 +1277,9 @@ with tab2:
                                         te_freq['Team'] = te_freq['Team'].replace(item_list, team_list)
     
                     st.session_state.te_freq = te_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    del te_freq
                     
-                    flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[7]].values, return_counts=True)),
+                    flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[7]].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     flex_freq['Freq'] = flex_freq['Freq'].astype(int)
                     flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
@@ -1368,8 +1292,9 @@ with tab2:
                                         flex_freq['Team'] = flex_freq['Team'].replace(item_list, team_list)
     
                     st.session_state.flex_freq = flex_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    del flex_freq
                     
-                    dst_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,8:9].values, return_counts=True)),
+                    dst_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,8:9].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     dst_freq['Freq'] = dst_freq['Freq'].astype(int)
                     dst_freq['Position'] = dst_freq['Player'].map(maps_dict['Pos_map'])
@@ -1382,6 +1307,8 @@ with tab2:
                                         dst_freq['Team'] = dst_freq['Team'].replace(item_list, team_list)
     
                     st.session_state.dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
+                    del dst_freq
+                    del maps_dict
                 
         with st.container():
             simulate_container = st.empty()