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'].astype(int) 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)