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| import pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| from itertools import combinations | |
| st.set_page_config(layout="wide") | |
| def init_conn(): | |
| scope = ['https://www.googleapis.com/auth/spreadsheets', | |
| "https://www.googleapis.com/auth/drive"] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": "1005124050c80d085e2c5b344345715978dd9cc9", | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n", | |
| "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com", | |
| "client_id": "106625872877651920064", | |
| "auth_uri": "https://accounts.google.com/o/oauth2/auth", | |
| "token_uri": "https://oauth2.googleapis.com/token", | |
| "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", | |
| "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com" | |
| } | |
| gc = gspread.service_account_from_dict(credentials) | |
| return gc | |
| gc = init_conn() | |
| wrong_acro = ['WSH', 'AZ'] | |
| right_acro = ['WAS', 'ARI'] | |
| game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}', | |
| 'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'} | |
| team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}', | |
| '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'} | |
| dk_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852' | |
| fd_player_projections = 'https://docs.google.com/spreadsheets/d/1MdzPFqIT0MFid2IhegWf39VNR8IXUyo_Fb5dolOSt3o/edit#gid=340831852' | |
| secondary_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852' | |
| secondary_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1lP4t8N7UhjR94MEwPn6powRyLl_cQBDUMSCs6cbL9ms/edit#gid=340831852' | |
| all_dk_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479' | |
| all_fd_player_projections = 'https://docs.google.com/spreadsheets/d/1f42Ergav8K1VsOLOK9MUn7DM_MLMvv4GR2Fy7EfnZTc/edit#gid=500994479' | |
| final_Proj = 0 | |
| def load_time(): | |
| sh = gc.open_by_url(dk_player_projections) | |
| worksheet = sh.worksheet('Timestamp') | |
| raw_stamp = worksheet.acell('a1').value | |
| t_stamp = f"Last update was at {raw_stamp}" | |
| return t_stamp | |
| def load_dk_player_projections(URL): | |
| sh = gc.open_by_url(URL) | |
| worksheet = sh.worksheet('DK_Projections') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| load_display = load_display.drop_duplicates(subset='Player') | |
| raw_display = load_display.dropna(subset=['Median']) | |
| return raw_display | |
| def load_fd_player_projections(URL): | |
| sh = gc.open_by_url(URL) | |
| worksheet = sh.worksheet('FD_Projections') | |
| load_display = pd.DataFrame(worksheet.get_all_records()) | |
| load_display.replace('', np.nan, inplace=True) | |
| load_display = load_display.drop_duplicates(subset='Player') | |
| raw_display = load_display.dropna(subset=['Median']) | |
| return raw_display | |
| def set_slate_teams(): | |
| sh = gc.open_by_url(all_dk_player_projections) | |
| worksheet = sh.worksheet('Site_Info') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['FD Main'] = raw_display['FD Main'].replace(wrong_acro, right_acro) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['FD Secondary'] = raw_display['FD Secondary'].replace(wrong_acro, right_acro) | |
| for checkVar in range(len(wrong_acro)): | |
| raw_display['FD Overall'] = raw_display['FD Overall'].replace(wrong_acro, right_acro) | |
| return raw_display | |
| def load_scoring_percentages(URL): | |
| sh = gc.open_by_url(URL) | |
| worksheet = sh.worksheet('Scoring_Percentages') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display['8+ runs'] = raw_display['8+ runs'].str.replace('%', '').astype(float)/100 | |
| raw_display['Win Percentage'] = raw_display['Win Percentage'].str.replace('%', '').astype(float)/100 | |
| raw_display['DK LevX'] = raw_display['DK LevX'].str.replace('%', '').astype(float)/100 | |
| raw_display['FD LevX'] = raw_display['FD LevX'].str.replace('%', '').astype(float)/100 | |
| return raw_display | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| t_stamp = load_time() | |
| site_slates = set_slate_teams() | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset5'): | |
| st.cache_data.clear() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| team_baselines = load_scoring_percentages(all_dk_player_projections) | |
| slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'All Games'), key='slate_var1') | |
| site_var5 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var5') | |
| if slate_var1 == 'Main Slate': | |
| if site_var5 == 'Draftkings': | |
| slate_teams = site_slates['DK Main'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| team_baselines = load_scoring_percentages(all_dk_player_projections) | |
| team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] | |
| Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) | |
| SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) | |
| team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] | |
| Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) | |
| raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] | |
| elif site_var5 == 'Fanduel': | |
| slate_teams = site_slates['FD Main'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| team_baselines = load_scoring_percentages(all_fd_player_projections) | |
| team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] | |
| Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) | |
| SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) | |
| team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] | |
| Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) | |
| raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] | |
| elif slate_var1 == 'Secondary Slate': | |
| if site_var5 == 'Draftkings': | |
| slate_teams = site_slates['DK Secondary'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| team_baselines = load_scoring_percentages(all_dk_player_projections) | |
| team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] | |
| Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) | |
| SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) | |
| team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] | |
| Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) | |
| raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] | |
| elif site_var5 == 'Fanduel': | |
| slate_teams = site_slates['FD Secondary'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| team_baselines = load_scoring_percentages(all_fd_player_projections) | |
| team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] | |
| Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) | |
| SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) | |
| team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] | |
| Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) | |
| raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] | |
| elif slate_var1 == 'All Games': | |
| if site_var5 == 'Draftkings': | |
| slate_teams = site_slates['DK Overall'].values.tolist() | |
| raw_baselines = load_dk_player_projections(all_dk_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| team_baselines = load_scoring_percentages(all_dk_player_projections) | |
| team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] | |
| Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) | |
| SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) | |
| team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] | |
| Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) | |
| raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] | |
| elif site_var5 == 'Fanduel': | |
| slate_teams = site_slates['FD Overall'].values.tolist() | |
| raw_baselines = load_fd_player_projections(all_fd_player_projections) | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(slate_teams)] | |
| team_baselines = load_scoring_percentages(all_fd_player_projections) | |
| team_baselines = team_baselines[team_baselines['Names'].isin(slate_teams)] | |
| Max_Rank = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Names']) | |
| SP_remove = list(team_baselines[team_baselines['Own Rank'] == team_baselines['Own Rank'].max()]['Opp SP']) | |
| team_baselines = team_baselines[~team_baselines['Names'].isin(Max_Rank)] | |
| Max_Upside = list(team_baselines[team_baselines['8+ Rank'] == team_baselines['8+ Rank'].min()]['Names']) | |
| raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own']] | |
| contest_var5 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'Small Field GPP', 'Large Field GPP'), key='contest_var5') | |
| if contest_var5 == 'Small Field GPP': | |
| opto_var5 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var5') | |
| if opto_var5 == "Manual": | |
| stack_var5 = st.selectbox('Which teams are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var5') | |
| elif opto_var5 == "Pivot Optimal": | |
| stack_var5 = Max_Rank[0] | |
| elif contest_var5 == 'Large Field GPP': | |
| opto_var5 = st.selectbox("Pivot optimal or Manual?", ('Pivot Optimal', 'Manual'), key='opto_var5') | |
| if opto_var5 == "Manual": | |
| stack_var5 = st.selectbox('Which team are you stacking?', options = raw_baselines['Team'].unique(), key='stack_var5') | |
| ministack_var5 = st.selectbox('Which team is your secondary stack?', options = raw_baselines['Team'].unique(), key='ministack_var5') | |
| elif opto_var5 == "Pivot Optimal": | |
| stack_var5 = Max_Upside[0] | |
| ministack_var5 = Max_Rank[0] | |
| split_var5 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var5') | |
| if split_var5 == 'Specific Games': | |
| team_var5 = st.multiselect('Which teams would you like to include in the ROO?', options = raw_baselines['Team'].unique(), key='team_var5') | |
| elif split_var5 == 'Full Slate Run': | |
| team_var5 = raw_baselines.Team.values.tolist() | |
| lock_var5 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = raw_baselines['Player'].unique(), key='lock_var5') | |
| avoid_var5 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = raw_baselines['Player'].unique(), key='avoid_var5') | |
| linenum_var5 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 20, step = 1, key='linenum_var5') | |
| if site_var5 == 'Draftkings': | |
| min_sal5 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal5') | |
| max_sal5 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal5') | |
| elif site_var5 == 'Fanduel': | |
| min_sal5 = st.number_input('Min Salary', min_value = 25000, max_value = 34900, value = 34000, step = 100, key='min_sal5') | |
| max_sal5 = st.number_input('Max Salary', min_value = 25000, max_value = 35000, value = 35000, step = 100, key='max_sal5') | |
| with col2: | |
| raw_baselines = raw_baselines[raw_baselines['Team'].isin(team_var5)] | |
| raw_baselines = raw_baselines[~raw_baselines['Player'].isin(avoid_var5)] | |
| if contest_var5 == 'Small Field GPP': | |
| if site_var5 == 'Draftkings': | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own']) | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (10 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%']) | |
| raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) | |
| elif site_var5 == 'Fanduel': | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own']) | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (10 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%']) | |
| raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) | |
| elif contest_var5 == 'Large Field GPP': | |
| if site_var5 == 'Draftkings': | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (2.5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own']) | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%']) | |
| raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) | |
| elif site_var5 == 'Fanduel': | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (2.5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own']) | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (5 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%']) | |
| raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) | |
| elif contest_var5 == 'Cash': | |
| if site_var5 == 'Draftkings': | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (6 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] == 'SP', 'Own'].mean(), raw_baselines['Own']) | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'SP') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean() >= 0), raw_baselines['Own'] * (11 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean())/100) + raw_baselines.loc[raw_baselines['Position'] != 'SP', 'Own'].mean(), raw_baselines['Own%']) | |
| raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) | |
| elif site_var5 == 'Fanduel': | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] == 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (6 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean())/50) + raw_baselines.loc[raw_baselines['Position'] == 'P', 'Own'].mean(), raw_baselines['Own']) | |
| raw_baselines['Own%'] = np.where((raw_baselines['Position'] != 'P') & (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean() >= 0), raw_baselines['Own'] * (11 * (raw_baselines['Own'] - raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean())/150) + raw_baselines.loc[raw_baselines['Position'] != 'P', 'Own'].mean(), raw_baselines['Own%']) | |
| raw_baselines['Own%'] = np.where(raw_baselines['Own%'] > 75, 75, raw_baselines['Own%']) | |
| raw_baselines = raw_baselines[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Own%']] | |
| raw_baselines.rename(columns={"Own%": "Own"}, inplace = True) | |
| raw_baselines = raw_baselines.sort_values(by='Median', ascending=False) | |
| raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var5), 1, 0) | |
| st.dataframe(raw_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Projections", | |
| data=convert_df_to_csv(raw_baselines), | |
| file_name='MLB_proj_export.csv', | |
| mime='text/csv', | |
| ) | |
| if st.button('Optimize'): | |
| max_proj = 1000 | |
| max_own = 1000 | |
| total_proj = 0 | |
| total_own = 0 | |
| optimize_container = st.empty() | |
| lineup_display = [] | |
| check_list = [] | |
| lineups = [] | |
| portfolio = pd.DataFrame() | |
| x = 1 | |
| with st.spinner('Wait for it...'): | |
| with optimize_container: | |
| while x <= linenum_var5: | |
| sorted_lineup = [] | |
| p_used = [] | |
| cvar = 0 | |
| firvar = 0 | |
| secvar = 0 | |
| thirvar = 0 | |
| raw_proj_file = raw_baselines | |
| raw_flex_file = raw_proj_file.dropna(how='all') | |
| raw_flex_file = raw_flex_file.loc[raw_flex_file['Median'] > 0] | |
| flex_file = raw_flex_file | |
| flex_file.rename(columns={"Own": "Proj DK Own%"}, inplace = True) | |
| flex_file['name_var'] = flex_file['Player'] | |
| flex_file['lock'] = np.where(flex_file['Player'].isin(lock_var5), 1, 0) | |
| player_ids = flex_file.index | |
| overall_players = flex_file[['Player']] | |
| overall_players['player_var_add'] = flex_file.index | |
| overall_players['player_var'] = 'player_vars_' + overall_players['player_var_add'].astype(str) | |
| player_vars = pulp.LpVariable.dicts("player_vars", flex_file.index, 0, 1, pulp.LpInteger) | |
| total_score = pulp.LpProblem("Fantasy_Points_Problem", pulp.LpMaximize) | |
| player_match = dict(zip(overall_players['player_var'], overall_players['Player'])) | |
| player_index_match = dict(zip(overall_players['player_var'], overall_players['player_var_add'])) | |
| player_own = dict(zip(flex_file['Player'], flex_file['Proj DK Own%'])) | |
| player_team = dict(zip(flex_file['Player'], flex_file['Team'])) | |
| player_pos = dict(zip(flex_file['Player'], flex_file['Position'])) | |
| player_sal = dict(zip(flex_file['Player'], flex_file['Salary'])) | |
| player_proj = dict(zip(flex_file['Player'], flex_file['Median'])) | |
| # obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} | |
| # total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) | |
| # obj_points_max = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} | |
| # obj_own_max = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} | |
| obj_salary = {idx: (flex_file['Salary'][idx]) for idx in flex_file.index} | |
| total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) <= max_sal5 | |
| total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal5 | |
| if site_var5 == 'Draftkings': | |
| if contest_var5 == 'Cash': | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'SP')].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 | |
| elif contest_var5 == 'Small Field GPP': | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'SP')].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5 | |
| elif contest_var5 == 'Large Field GPP': | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'SP')].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 5 | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == ministack_var5) & (flex_file['Position'] != 'SP')].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 3 | |
| for flex in flex_file['lock'].unique(): | |
| sub_idx = flex_file[flex_file['lock'] == 1].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var5) | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] != "Var"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 10 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("SP")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "C"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "1B"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "2B"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "3B"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "SS"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "OF"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("C")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("1B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "3B/SS")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B")| (flex_file['Position'] == "2B/SS")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "2B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "2B/3B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "3B")| (flex_file['Position'] == "1B/3B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "1B") | (flex_file['Position'] == "C")| (flex_file['Position'] == "1B/C")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "OF")| (flex_file['Position'] == "SS/OF")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 | |
| elif site_var5 == 'Fanduel': | |
| if contest_var5 == 'Cash': | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == flex) & (flex_file['Position'] != 'P')].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 | |
| elif contest_var5 == 'Small Field GPP': | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == stack_var5) & (flex_file['Position'] != 'P')].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4 | |
| elif contest_var5 == 'Large Field GPP': | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == stack_var5)].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4 | |
| for flex in flex_file['Team'].unique(): | |
| sub_idx = flex_file[(flex_file['Team'] == ministack_var5)].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 4 | |
| for flex in flex_file['lock'].unique(): | |
| sub_idx = flex_file[flex_file['lock'] == 1].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == len(lock_var5) | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] != "Var"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 9 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("P")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "1B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "1B") | (flex_file['Position'] == "OF")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "2B"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "3B"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "SS"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] == "OF"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "OF") | (flex_file['Position'] == "C")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "OF") | (flex_file['Position'] == "1B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'].str.contains("C")) | (flex_file['Position'].str.contains("1B"))].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'].str.contains("2B")) | (flex_file['Position'].str.contains("SS"))].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 2 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "C") | (flex_file['Position'] == "SS")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 3 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[(flex_file['Position'] == "SS") | (flex_file['Position'] == "2B") | (flex_file['Position'] == "OF") | (flex_file['Position'] == "2B/SS/OF")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 5 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("2B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("3B")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("SS")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 1 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) >= 3 | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'].str.contains("OF")].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) <= 4 | |
| player_count = [] | |
| player_trim = [] | |
| lineup_list = [] | |
| if contest_var5 == 'Cash': | |
| obj_points = {idx: (flex_file['Proj DK Own%'][idx]) for idx in flex_file.index} | |
| total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) | |
| total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_own - .001 | |
| elif contest_var5 != 'Cash': | |
| obj_points = {idx: (flex_file['Median'][idx]) for idx in flex_file.index} | |
| total_score += sum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) | |
| total_score += pulp.lpSum([player_vars[idx]*obj_points[idx] for idx in flex_file.index]) <= max_proj - .01 | |
| total_score.solve() | |
| for v in total_score.variables(): | |
| if v.varValue > 0: | |
| lineup_list.append(v.name) | |
| df = pd.DataFrame(lineup_list) | |
| df['Names'] = df[0].map(player_match) | |
| df['Cost'] = df['Names'].map(player_sal) | |
| df['Proj'] = df['Names'].map(player_proj) | |
| df['Own'] = df['Names'].map(player_own) | |
| total_cost = sum(df['Cost']) | |
| total_own = sum(df['Own']) | |
| total_proj = sum(df['Proj']) | |
| lineup_raw = pd.DataFrame(lineup_list) | |
| lineup_raw['Names'] = lineup_raw[0].map(player_match) | |
| lineup_raw['value'] = lineup_raw[0].map(player_index_match) | |
| lineup_final = lineup_raw.sort_values(by=['value']) | |
| del lineup_final[lineup_final.columns[0]] | |
| del lineup_final[lineup_final.columns[1]] | |
| lineup_final = lineup_final.reset_index(drop=True) | |
| if site_var5 == 'Draftkings': | |
| line_hold = lineup_final[['Names']] | |
| line_hold['pos'] = line_hold['Names'].map(player_pos) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'SP': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'C': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| cvar = 1 | |
| p_used.extend(sorted_lineup) | |
| if cvar != 1: | |
| for pname in range(0,len(line_hold)): | |
| if 'C' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == '1B': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| firvar = 1 | |
| p_used.extend(sorted_lineup) | |
| if firvar != 1: | |
| for pname in range(0,len(line_hold)): | |
| if '1B' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == '2B': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| secvar = 1 | |
| p_used.extend(sorted_lineup) | |
| if secvar != 1: | |
| for pname in range(0,len(line_hold)): | |
| if '2B' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == '3B': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| thirvar = 1 | |
| p_used.extend(sorted_lineup) | |
| if thirvar != 1: | |
| for pname in range(0,len(line_hold)): | |
| if '3B' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'SS': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if 'SS' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'OF': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if 'OF' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| lineup_final['sorted'] = sorted_lineup | |
| lineup_final = lineup_final.drop(columns=['Names']) | |
| lineup_final.rename(columns={"sorted": "Names"}, inplace = True) | |
| elif site_var5 == 'Fanduel': | |
| line_hold = lineup_final[['Names']] | |
| line_hold['pos'] = line_hold['Names'].map(player_pos) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'P': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'C' or line_hold.iat[pname,1] == '1B': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| cvar = 1 | |
| p_used.extend(sorted_lineup) | |
| if cvar != 1: | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] in ['C', '1B']: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == '2B': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| secvar = 1 | |
| p_used.extend(sorted_lineup) | |
| if secvar != 1: | |
| for pname in range(0,len(line_hold)): | |
| if '2B' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == '3B': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| thirvar = 1 | |
| p_used.extend(sorted_lineup) | |
| if thirvar != 1: | |
| for pname in range(0,len(line_hold)): | |
| if '3B' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'SS': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if 'SS' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,1] == 'OF': | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if 'OF' in line_hold.iat[pname,1]: | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| for pname in range(0,len(line_hold)): | |
| if line_hold.iat[pname,0] not in p_used: | |
| sorted_lineup.append(line_hold.iat[pname,0]) | |
| p_used.extend(sorted_lineup) | |
| lineup_final['sorted'] = sorted_lineup | |
| lineup_final = lineup_final.drop(columns=['Names']) | |
| lineup_final.rename(columns={"sorted": "Names"}, inplace = True) | |
| lineup_test = lineup_final | |
| lineup_final = lineup_final.T | |
| lineup_final['Cost'] = total_cost | |
| lineup_final['Proj'] = total_proj | |
| lineup_final['Own'] = total_own | |
| lineup_test['Team'] = lineup_test['Names'].map(player_team) | |
| lineup_test['Position'] = lineup_test['Names'].map(player_pos) | |
| lineup_test['Salary'] = lineup_test['Names'].map(player_sal) | |
| lineup_test['Proj'] = lineup_test['Names'].map(player_proj) | |
| lineup_test['Own'] = lineup_test['Names'].map(player_own) | |
| lineup_test = lineup_test.set_index('Names') | |
| lineup_test.loc['Column_Total'] = lineup_test.sum(numeric_only=True, axis=0) | |
| lineup_display.append(lineup_test) | |
| with col2: | |
| with st.container(): | |
| st.table(lineup_test) | |
| max_proj = total_proj | |
| max_own = total_own | |
| check_list.append(total_proj) | |
| portfolio = pd.concat([portfolio, lineup_final], ignore_index = True) | |
| x += 1 | |
| if site_var5 == 'Draftkings': | |
| portfolio.rename(columns={0: "SP1", 1: "SP2", 2: "C", 3: "1B", 4: "2B", 5: "3B", 6: "SS", 7: "OF1", 8: "OF2", 9: "OF3"}, inplace = True) | |
| elif site_var5 == 'Fanduel': | |
| portfolio.rename(columns={0: "SP1", 1: "C/1B", 2: "2B", 3: "3B", 4: "SS", 5: "OF1", 6: "OF2", 7: "OF3", 8: "UTIL"}, inplace = True) | |
| portfolio = portfolio.dropna() | |
| portfolio = portfolio.reset_index() | |
| portfolio['Lineup_num'] = portfolio['index'] + 1 | |
| portfolio.rename(columns={'Lineup_num': "Lineup"}, inplace = True) | |
| portfolio = portfolio.set_index('Lineup') | |
| portfolio = portfolio.drop(columns=['index']) | |
| final_outcomes = portfolio | |
| with optimize_container: | |
| optimize_container = st.empty() | |
| st.dataframe(portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(final_outcomes), | |
| file_name='MLB_optimals_export.csv', | |
| mime='text/csv', | |
| ) |