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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: | |
| 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) |