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| 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) | |