Update app.py

app.py

@@ -1,10 +1,15 @@
+import streamlit as st
+st.set_page_config(layout="wide")
+
+for name in dir():
+    if not name.startswith('_'):
+        del globals()[name]
+
 import numpy as np
 import pandas as pd
 import streamlit as st
 import gspread
 
-st.set_page_config(layout="wide")
-
 @st.cache_resource
 def init_conn():
           scope = ['https://www.googleapis.com/auth/spreadsheets',
@@ -28,65 +33,43 @@ def init_conn():
 
 gcservice_account = init_conn()
 
+all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260'
 
-
-wrong_acro = ['WSH', 'AZ']
-right_acro = ['WAS', 'ARI']
-
-game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
-              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}
-
-team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
-                   '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.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%}'}
-
-all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1I_1Ve3F4tftgfLQQoRKOJ351XfEG48s36OxXUKxmgS8/edit#gid=1391856348'
-
-@st.cache_resource(ttl = 600)
-def player_stat_table():
+@st.cache_resource(ttl = 300)
+def init_stat_load():
     sh = gcservice_account.open_by_url(all_dk_player_projections)
-    worksheet = sh.worksheet('Player_Projections')
-    player_stats = pd.DataFrame(worksheet.get_all_records())
-    
-    worksheet = sh.worksheet('DK_Stacks')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display = load_display
-    dk_stacks_raw = raw_display.sort_values(by='Own', ascending=False)
+    worksheet = sh.worksheet('DK_Build_Up')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    dk_raw = raw_display.sort_values(by='Median', ascending=False)
     
-    worksheet = sh.worksheet('FD_Stacks')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    raw_display = load_display
-    fd_stacks_raw = raw_display.sort_values(by='Own', ascending=False)
-    
-    worksheet = sh.worksheet('DK_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    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)
-    fd_roo_raw = load_display.dropna(subset=['Median'])
-    
-    worksheet = sh.worksheet('Site_Info')
-    site_slates = pd.DataFrame(worksheet.get_all_records())
+    worksheet = sh.worksheet('FD_Build_Up')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}, inplace = True)
+    raw_display.replace("", 'Welp', inplace=True)
+    raw_display = raw_display.loc[raw_display['Player'] != 'Welp']
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    fd_raw = raw_display.sort_values(by='Median', ascending=False)
 
-    return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, site_slates
+    return dk_raw, fd_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()
-opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
-t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+dk_raw, fd_raw = init_stat_load()
+opp_dict = dict(zip(dk_raw.Team, dk_raw.Opp))
+t_stamp = "Fix this later"
 
 tab1, tab2 = st.tabs(['Uploads and Info', 'Pivot Finder'])
 
 with tab1:
-    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', and 'Own'.")
+    st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'Median', 'Own'.")
     col1, col2 = st.columns([1, 5])
 
     with col1:
@@ -102,36 +85,36 @@ with tab1:
                   st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
         
 with tab2:
-    col1, col2 = st.columns([1, 5])
+    col1, col2 = st.columns([1, 9])
     with col1:
         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()
-              opp_dict = dict(zip(dk_roo_raw.Team, dk_roo_raw.Opp))
-              t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+              dk_raw, fd_raw = init_stat_load()
+              opp_dict = dict(zip(dk_raw.Team, dk_raw.Opp))
+              t_stamp = "Fix this later"
+              for key in st.session_state.keys():
+                  del st.session_state[key]
         data_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='data_var1')
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
               if data_var1 == 'User':
                   raw_baselines = proj_dataframe
               elif data_var1 != 'User':
-                  raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == 'Main Slate']
-                  raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
+                  raw_baselines = dk_raw
         elif site_var1 == 'Fanduel':
               if data_var1 == 'User':
                   raw_baselines = proj_dataframe
               elif data_var1 != 'User':
-                  raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate']
-                  raw_baselines = raw_baselines[raw_baselines['version'] == 'overall']
-        player_check = st.selectbox('Select player to create comps', options = dk_roo_raw['Player'].unique(), key='dk_player')
+                  raw_baselines = fd_raw
+        player_check = st.selectbox('Select player to create comps', options = dk_raw['Player'].unique(), key='dk_player')
         Salary_var = st.number_input('Acceptable +/- Salary range', min_value = 0, max_value = 1000, value = 300, step = 100)
         Median_var = st.number_input('Acceptable +/- Median range', min_value = 0, max_value = 10, value = 3, step = 1)
         pos_var1 = st.radio("Compare to all positions or specific positions?", ('All Positions', 'Specific Positions'), key='pos_var1')
         if pos_var1 == 'Specific Positions':
-            pos_var_list = st.multiselect('Which positions would you like to include?', options = raw_baselines['Position'].unique(), key='pos_var_list')
+            pos_var_list = st.multiselect('Which positions would you like to include?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var_list')
         elif pos_var1 == 'All Positions':
-            pos_var_list = raw_baselines.Position.values.tolist()
+            pos_var_list = ['PG', 'SG', 'SF', 'PF', 'C']
         split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
         if split_var1 == 'Specific Games':
             team_var1 = st.multiselect('Which teams would you like to include?', options = raw_baselines['Team'].unique(), key='team_var1')
@@ -139,20 +122,21 @@ with tab2:
             team_var1 = raw_baselines.Team.values.tolist()
         
     with col2:
+        display_container = st.empty()
+        display_dl_container = st.empty()
         hold_container = st.empty()
         if st.button('Simulate appropriate pivots'):
             with hold_container:
                 if site_var1 == 'Draftkings':
-                          working_roo = raw_baselines
-                          working_roo.replace('', 0, inplace=True)
-                if site_var1 == 'Fanduel':
-                          working_roo = raw_baselines
-                          working_roo.replace('', 0, inplace=True)
-                          
-                          
+                    raw_baselines = dk_raw
+                elif site_var1 == 'Fanduel':
+                    raw_baselines = fd_raw
+                    
+                working_roo = raw_baselines
+                working_roo = working_roo[working_roo['Team'].isin(team_var1)]
                 own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                min_dict = dict(zip(working_roo.Player, working_roo.Minutes))
                 team_dict = dict(zip(working_roo.Player, working_roo.Team))
-                opp_dict = dict(zip(working_roo.Player, working_roo.Opp))
                 total_sims = 1000
                 
                 player_var = working_roo.loc[working_roo['Player'] == player_check]
@@ -163,14 +147,10 @@ with tab2:
                 working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - Salary_var) & (working_roo['Salary'] <= player_var['Salary'][0] + Salary_var)]
                 working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - Median_var) & (working_roo['Median'] <= player_var['Median'][0] + Median_var)]
   
-                flex_file = working_roo[['Player', 'Position', 'Salary', 'Median']]
-                flex_file['Floor_raw'] = flex_file['Median'] * .20
-                flex_file['Ceiling_raw'] = flex_file['Median'] * 1.9
-                flex_file['Floor'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * .33), flex_file['Floor_raw'])
-                flex_file['Floor'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * .15), flex_file['Floor_raw'])
-                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'QB', (flex_file['Median'] * 1.75), flex_file['Ceiling_raw'])
-                flex_file['Ceiling'] = np.where(flex_file['Position'] == 'RB', (flex_file['Median'] * 1.85), flex_file['Ceiling_raw'])
-                flex_file['STD'] = flex_file['Median'] / 4
+                flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
+                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+                flex_file['STD'] = (flex_file['Median']/4)
                 flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
                 hold_file = flex_file
                 overall_file = flex_file
@@ -202,51 +182,59 @@ with tab2:
   
                 players_only=players_only.drop(['Player'], 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))
-  
+
+                salary_2x_check = (overall_file - (salary_file*4))
+                salary_3x_check = (overall_file - (salary_file*5))
+                salary_4x_check = (overall_file - (salary_file*6))
+                gpp_check = (overall_file - ((salary_file*5)+10))
+
                 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['20+%'] = overall_file[overall_file >= 20].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['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+                players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+
                 players_only['Player'] = hold_file[['Player']]
-  
-                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+
+                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
   
                 final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']]
+                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
+
                 final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
                 final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
-                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own']]
-                final_Proj['Projection Rank'] = final_Proj.Median.rank(pct = True)
-                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
-                final_Proj['LevX'] = 0
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'QB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'TE', final_Proj[['Projection Rank', '2x%']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'RB', final_Proj[['Projection Rank', 'Top_5_finish']].mean(axis=1) + final_Proj['20+%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['LevX'] = np.where(final_Proj['Position'] == 'WR', final_Proj[['Projection Rank', 'Top_10_finish']].mean(axis=1) + final_Proj['4x%'] - final_Proj['Own Rank'], final_Proj['LevX'])
-                final_Proj['CPT_Own'] = final_Proj['Own'] / 4
-  
-                final_Proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own', 'LevX']]
+                final_Proj['Own'] = final_Proj['Own'].astype('float')
+                final_Proj['LevX'] = ((final_Proj[['Top_finish', '4x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
+                final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+
+                final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
                 final_Proj = final_Proj.set_index('Player')
-                final_Proj = final_Proj.sort_values(by='Top_finish', ascending=False)
-  
-            with hold_container:
+                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+                
+                st.session_state.final_Proj = final_Proj
+                
                 hold_container = st.empty()
-                final_Proj = final_Proj
-                st.dataframe(final_Proj.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_Proj),
-                    file_name='NFL_pivot_export.csv',
-                    mime='text/csv',
-            )
+        with display_container:
+             display_container = st.empty()
+             if 'final_Proj' in st.session_state:
+                 if pos_var1 == 'All':
+                     st.session_state.final_Proj = st.session_state.final_Proj
+                 elif pos_var1 != 'All':
+                     st.session_state.final_Proj = st.session_state.final_Proj[st.session_state.final_Proj['Position'].str.contains(pos_var1)]
+                 st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        
+        with display_dl_container:
+                 display_dl_container = st.empty()
+                 if 'final_Proj' in st.session_state:
+                     st.download_button(
+                        label="Export Tables",
+                        data=convert_df_to_csv(st.session_state.final_Proj),
+                        file_name='Custom_NBA_export.csv',
+                        mime='text/csv',
+                     )