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: 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: 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) portfolio_dataframe.columns=["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST"] split_portfolio = portfolio_dataframe split_portfolio[['QB', 'QB_ID']] = split_portfolio.QB.str.split("(", n=1, expand = True) split_portfolio[['RB1', 'RB1_ID']] = split_portfolio.RB1.str.split("(", n=1, expand = True) split_portfolio[['RB2', 'RB2_ID']] = split_portfolio.RB2.str.split("(", n=1, expand = True) split_portfolio[['WR1', 'WR1_ID']] = split_portfolio.WR1.str.split("(", n=1, expand = True) split_portfolio[['WR2', 'WR2_ID']] = split_portfolio.WR2.str.split("(", n=1, expand = True) split_portfolio[['WR3', 'WR3_ID']] = split_portfolio.WR3.str.split("(", n=1, expand = True) split_portfolio[['TE', 'TE_ID']] = split_portfolio.TE.str.split("(", n=1, expand = True) split_portfolio[['FLEX', 'FLEX_ID']] = split_portfolio.FLEX.str.split("(", n=1, expand = True) split_portfolio[['DST', 'DST_ID']] = split_portfolio.DST.str.split("(", n=1, expand = True) split_portfolio['QB'] = split_portfolio['QB'].str.strip() split_portfolio['RB1'] = split_portfolio['RB1'].str.strip() split_portfolio['RB2'] = split_portfolio['RB2'].str.strip() split_portfolio['WR1'] = split_portfolio['WR1'].str.strip() split_portfolio['WR2'] = split_portfolio['WR2'].str.strip() split_portfolio['WR3'] = split_portfolio['WR3'].str.strip() split_portfolio['TE'] = split_portfolio['TE'].str.strip() split_portfolio['FLEX'] = split_portfolio['FLEX'].str.strip() split_portfolio['DST'] = split_portfolio['DST'].str.strip() st.table(split_portfolio.head(10)) split_portfolio['Salary'] = sum([split_portfolio['QB'].map(player_salary_dict), split_portfolio['RB1'].map(player_salary_dict), split_portfolio['RB2'].map(player_salary_dict), split_portfolio['WR1'].map(player_salary_dict), split_portfolio['WR2'].map(player_salary_dict), split_portfolio['WR3'].map(player_salary_dict), split_portfolio['TE'].map(player_salary_dict), split_portfolio['FLEX'].map(player_salary_dict), split_portfolio['DST'].map(player_salary_dict)]) split_portfolio['Projection'] = sum([split_portfolio['QB'].map(player_proj_dict), split_portfolio['RB1'].map(player_proj_dict), split_portfolio['RB2'].map(player_proj_dict), split_portfolio['WR1'].map(player_proj_dict), split_portfolio['WR2'].map(player_proj_dict), split_portfolio['WR3'].map(player_proj_dict), split_portfolio['TE'].map(player_proj_dict), split_portfolio['FLEX'].map(player_proj_dict), split_portfolio['DST'].map(player_proj_dict)]) split_portfolio['Ownership'] = sum([split_portfolio['QB'].map(player_own_dict), split_portfolio['RB1'].map(player_own_dict), split_portfolio['RB2'].map(player_own_dict), split_portfolio['WR1'].map(player_own_dict), split_portfolio['WR2'].map(player_own_dict), split_portfolio['WR3'].map(player_own_dict), split_portfolio['TE'].map(player_own_dict), split_portfolio['FLEX'].map(player_own_dict), split_portfolio['DST'].map(player_own_dict)]) display_portfolio = split_portfolio[["QB", "RB1", "RB2", "WR1", "WR2", "WR3", "TE", "FLEX", "DST", '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[:,0:9].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 own_dict = dict(zip(working_roo.Player, working_roo.Own)) 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] player_var = player_var.reset_index() working_roo = working_roo[working_roo['Position'].isin(pos_var_list)] working_roo = working_roo[working_roo['Team'].isin(team_var1)] 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 = 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*2)) salary_3x_check = (overall_file - (salary_file*3)) salary_4x_check = (overall_file - (salary_file*4)) 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['Player'] = hold_file[['Player']] final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%']] 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['Own'] = final_Proj['Player'].map(own_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 = final_Proj.sort_values(by='Top_finish', 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)