Create app.py

app.py

@@ -0,0 +1,339 @@
+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
+import random
+import gc
+
+tab1, tab2 = st.tabs(['Uploads', 'Manage Portfolio'])
+
+with tab1:
+    with st.container():          
+          col1, col2 = st.columns([3, 3])
+          
+          with col1:
+                    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'. Upload your projections first to avoid an error message.")
+                    proj_file = st.file_uploader("Upload Projections File", key = 'proj_uploader')
+
+                    if proj_file is not None:
+                              try:
+                                        proj_dataframe = pd.read_csv(proj_file)
+                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
+                                        proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
+                                        try:
+                                            proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
+                                        except:
+                                            pass
+                                        
+                              except:
+                                        proj_dataframe = pd.read_excel(proj_file)
+                                        proj_dataframe = proj_dataframe.dropna(subset='Median')
+                                        proj_dataframe['Player'] = proj_dataframe['Player'].str.strip()
+                                        try:
+                                            proj_dataframe['Own'] = proj_dataframe['Own'].str.strip('%').astype(float)
+                                        except:
+                                            pass
+                              st.table(proj_dataframe.head(10))
+                              player_salary_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Salary))
+                              player_proj_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Median))
+                              player_own_dict = dict(zip(proj_dataframe.Player, proj_dataframe.Own))
+                              
+          with col2:
+                    st.info("The Portfolio file must contain only columns in order and explicitly named: 'PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', and 'UTIL'. Upload your projections first to avoid an error message.")
+                    portfolio_file = st.file_uploader("Upload Portfolio File", key = 'portfolio_uploader')
+
+                    if portfolio_file is not None:
+                            try:
+                                      portfolio_dataframe = pd.read_csv(portfolio_file)
+                                      
+                            except:
+                                      portfolio_dataframe = pd.read_excel(portfolio_file)
+                              
+                            try:
+                                try:
+                                    portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
+                                    split_portfolio = portfolio_dataframe
+                                    split_portfolio[['PG', 'PG_ID']] = split_portfolio.PG.str.split("(", n=1, expand = True)
+                                    split_portfolio[['SG', 'SG_ID']] = split_portfolio.SG.str.split("(", n=1, expand = True)
+                                    split_portfolio[['SF', 'SF_ID']] = split_portfolio.SF.str.split("(", n=1, expand = True)
+                                    split_portfolio[['PF', 'PF_ID']] = split_portfolio.PF.str.split("(", n=1, expand = True)
+                                    split_portfolio[['C', 'C_ID']] = split_portfolio.C.str.split("(", n=1, expand = True)
+                                    split_portfolio[['G', 'G_ID']] = split_portfolio.G.str.split("(", n=1, expand = True)
+                                    split_portfolio[['F', 'F_ID']] = split_portfolio.F.str.split("(", n=1, expand = True)
+                                    split_portfolio[['UTIL', 'UTIL_ID']] = split_portfolio.UTIL.str.split("(", n=1, expand = True)
+      
+                                    split_portfolio['PG'] = split_portfolio['PG'].str.strip()
+                                    split_portfolio['SG'] = split_portfolio['SG'].str.strip()
+                                    split_portfolio['SF'] = split_portfolio['SF'].str.strip()
+                                    split_portfolio['PF'] = split_portfolio['PF'].str.strip()
+                                    split_portfolio['C'] = split_portfolio['C'].str.strip()
+                                    split_portfolio['G'] = split_portfolio['G'].str.strip()
+                                    split_portfolio['F'] = split_portfolio['F'].str.strip()
+                                    split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
+                                    
+                                    split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
+                                              split_portfolio['SG'].map(player_salary_dict),
+                                              split_portfolio['SF'].map(player_salary_dict),
+                                              split_portfolio['PF'].map(player_salary_dict),
+                                              split_portfolio['C'].map(player_salary_dict),
+                                              split_portfolio['G'].map(player_salary_dict),
+                                              split_portfolio['F'].map(player_salary_dict),
+                                              split_portfolio['UTIL'].map(player_salary_dict)])
+                                    
+                                    split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
+                                              split_portfolio['SG'].map(player_proj_dict),
+                                              split_portfolio['SF'].map(player_proj_dict),
+                                              split_portfolio['PF'].map(player_proj_dict),
+                                              split_portfolio['C'].map(player_proj_dict),
+                                              split_portfolio['G'].map(player_proj_dict),
+                                              split_portfolio['F'].map(player_proj_dict),
+                                              split_portfolio['UTIL'].map(player_proj_dict)])
+                                    
+                                    split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
+                                              split_portfolio['SG'].map(player_own_dict),
+                                              split_portfolio['SF'].map(player_own_dict),
+                                              split_portfolio['PF'].map(player_own_dict),
+                                              split_portfolio['C'].map(player_own_dict),
+                                              split_portfolio['G'].map(player_own_dict),
+                                              split_portfolio['F'].map(player_own_dict),
+                                              split_portfolio['UTIL'].map(player_own_dict)])
+                                    
+                                    st.table(split_portfolio.head(10))
+                                
+                                
+                                except:
+                                    portfolio_dataframe.columns=['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL']
+                                    
+                                    split_portfolio = portfolio_dataframe
+                                    split_portfolio[['PG_ID', 'PG']] = split_portfolio.PG.str.split(":", n=1, expand = True)
+                                    split_portfolio[['SG_ID', 'SG']] = split_portfolio.SG.str.split(":", n=1, expand = True)
+                                    split_portfolio[['SF_ID', 'SF']] = split_portfolio.SF.str.split(":", n=1, expand = True)
+                                    split_portfolio[['PF_ID', 'PF']] = split_portfolio.PF.str.split(":", n=1, expand = True)
+                                    split_portfolio[['C_ID', 'C']] = split_portfolio.C.str.split(":", n=1, expand = True)
+                                    split_portfolio[['G_ID', 'G']] = split_portfolio.G.str.split(":", n=1, expand = True)
+                                    split_portfolio[['F_ID', 'F']] = split_portfolio.F.str.split(":", n=1, expand = True)
+                                    split_portfolio[['UTIL_ID', 'UTIL']] = split_portfolio.UTIL.str.split(":", n=1, expand = True)
+                                    
+                                    split_portfolio['PG'] = split_portfolio['PG'].str.strip()
+                                    split_portfolio['SG'] = split_portfolio['SG'].str.strip()
+                                    split_portfolio['SF'] = split_portfolio['SF'].str.strip()
+                                    split_portfolio['PF'] = split_portfolio['PF'].str.strip()
+                                    split_portfolio['C'] = split_portfolio['C'].str.strip()
+                                    split_portfolio['G'] = split_portfolio['G'].str.strip()
+                                    split_portfolio['F'] = split_portfolio['F'].str.strip()
+                                    split_portfolio['UTIL'] = split_portfolio['UTIL'].str.strip()
+      
+                                    split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
+                                              split_portfolio['SG'].map(player_salary_dict),
+                                              split_portfolio['SF'].map(player_salary_dict),
+                                              split_portfolio['PF'].map(player_salary_dict),
+                                              split_portfolio['C'].map(player_salary_dict),
+                                              split_portfolio['G'].map(player_salary_dict),
+                                              split_portfolio['F'].map(player_salary_dict),
+                                              split_portfolio['UTIL'].map(player_salary_dict)])
+                                    
+                                    split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
+                                              split_portfolio['SG'].map(player_proj_dict),
+                                              split_portfolio['SF'].map(player_proj_dict),
+                                              split_portfolio['PF'].map(player_proj_dict),
+                                              split_portfolio['C'].map(player_proj_dict),
+                                              split_portfolio['G'].map(player_proj_dict),
+                                              split_portfolio['F'].map(player_proj_dict),
+                                              split_portfolio['UTIL'].map(player_proj_dict)])
+                                                                    
+                                    
+                                    split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
+                                              split_portfolio['SG'].map(player_own_dict),
+                                              split_portfolio['SF'].map(player_own_dict),
+                                              split_portfolio['PF'].map(player_own_dict),
+                                              split_portfolio['C'].map(player_own_dict),
+                                              split_portfolio['G'].map(player_own_dict),
+                                              split_portfolio['F'].map(player_own_dict),
+                                              split_portfolio['UTIL'].map(player_own_dict)])
+                                    
+                                    st.table(split_portfolio.head(10))
+                                  
+                            except:
+                                 split_portfolio = portfolio_dataframe
+                                
+                                 split_portfolio['Salary'] = sum([split_portfolio['PG'].map(player_salary_dict),
+                                           split_portfolio['SG'].map(player_salary_dict),
+                                           split_portfolio['SF'].map(player_salary_dict),
+                                           split_portfolio['PF'].map(player_salary_dict),
+                                           split_portfolio['C'].map(player_salary_dict),
+                                           split_portfolio['G'].map(player_salary_dict),
+                                           split_portfolio['F'].map(player_salary_dict),
+                                           split_portfolio['UTIL'].map(player_salary_dict)])
+                                 
+                                 split_portfolio['Projection'] = sum([split_portfolio['PG'].map(player_proj_dict),
+                                           split_portfolio['SG'].map(player_proj_dict),
+                                           split_portfolio['SF'].map(player_proj_dict),
+                                           split_portfolio['PF'].map(player_proj_dict),
+                                           split_portfolio['C'].map(player_proj_dict),
+                                           split_portfolio['G'].map(player_proj_dict),
+                                           split_portfolio['F'].map(player_proj_dict),
+                                           split_portfolio['UTIL'].map(player_proj_dict)])
+                                                                 
+                                 
+                                 split_portfolio['Ownership'] = sum([split_portfolio['PG'].map(player_own_dict),
+                                           split_portfolio['SG'].map(player_own_dict),
+                                           split_portfolio['SF'].map(player_own_dict),
+                                           split_portfolio['PF'].map(player_own_dict),
+                                           split_portfolio['C'].map(player_own_dict),
+                                           split_portfolio['G'].map(player_own_dict),
+                                           split_portfolio['F'].map(player_own_dict),
+                                           split_portfolio['UTIL'].map(player_own_dict)])
+                                 
+                            display_portfolio = split_portfolio[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'UTIL', 'Salary', 'Projection', 'Ownership']]
+                            st.session_state.display_portfolio = display_portfolio
+                            hold_portfolio = display_portfolio.sort_values(by='Projection', ascending=False)
+                            
+                            st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:8].values, return_counts=True)),
+                                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                            st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
+                            st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
+                            
+                            gc.collect() 
+                            
+with tab2:
+    with st.container():
+        hold_container = st.empty()
+        col1, col2, col3 = st.columns([3, 3, 3])
+        with col1:
+            if st.button("Load/Reset Data", key='reset1'):
+                for key in st.session_state.keys():
+                      del st.session_state[key]
+                display_portfolio = hold_portfolio
+                st.session_state.display_portfolio = display_portfolio
+                st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
+                st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
+        with col2:
+            if st.button("Trim Lineups", key='trim1'):
+                max_proj = 10000
+                max_own = display_portfolio['Ownership'].iloc[0]
+                x = 0
+                for index, row in display_portfolio.iterrows():
+                    if row['Ownership'] > max_own:
+                        display_portfolio.drop(index, inplace=True)
+                    elif row['Ownership'] <= max_own:
+                        max_own = row['Ownership']
+                st.session_state.display_portfolio = display_portfolio
+                st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:8].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'] / len(st.session_state.display_portfolio)
+                st.session_state.player_freq = st.session_state.player_freq.set_index('Player')
+        with col3:
+            player_check = st.selectbox('Select player to create comps', options = proj_dataframe['Player'].unique(), key='dk_player')
+            if st.button('Simulate appropriate pivots'):
+                with hold_container:
+                    
+                    working_roo = proj_dataframe
+                    working_roo.rename(columns={"Minutes Proj": "Minutes_Proj"}, inplace = True)
+                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                    min_dict = dict(zip(working_roo.Player, working_roo.Minutes_Proj))
+                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                    total_sims = 1000
+                    
+                    player_var = working_roo.loc[working_roo['Player'] == player_check]
+                    player_var = player_var.reset_index()
+                    
+                    working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - 300) & (working_roo['Salary'] <= player_var['Salary'][0] + 300)]
+                    working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - 3) & (working_roo['Median'] <= player_var['Median'][0] + 3)]
+                    
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes_Proj']]
+                    flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes_Proj'] * .25)
+                    flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes_Proj'] * .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
+                    salary_file = flex_file
+      
+                    overall_players = overall_file[['Player']]
+      
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+      
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', '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(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+      
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+      
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+      
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+    
+                    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['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+%', '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+%', '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['Own'] = final_Proj['Own'].astype('float')
+                    final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
+                    final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+                    final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
+                    final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
+    
+                    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.sort_values(by='Median', ascending=False)
+                    final_Proj['Player_swap'] = player_check
+                    st.session_state.final_Proj = final_Proj
+                    
+                    hold_container = st.empty()
+    with st.container():
+        col1, col2 = st.columns([7, 2])         
+        with col1:
+                 if 'display_portfolio' in st.session_state:
+                     st.dataframe(st.session_state.display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            
+            # with display_container:
+            #      display_container = st.empty()
+            #      if 'final_Proj' in st.session_state:
+            #          st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        with col2:
+                 if 'player_freq' in st.session_state:
+                     st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)