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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 pulp | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import time | |
| from itertools import combinations | |
| def init_conn(): | |
| scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] | |
| credentials = { | |
| "type": "service_account", | |
| "project_id": "model-sheets-connect", | |
| "private_key_id": st.secrets['model_sheets_connect_pk'], | |
| "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n", | |
| "client_email": "[email protected]", | |
| "client_id": "100369174533302798535", | |
| "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%40model-sheets-connect.iam.gserviceaccount.com" | |
| } | |
| credentials2 = { | |
| "type": "service_account", | |
| "project_id": "sheets-api-connect-378620", | |
| "private_key_id": st.secrets['sheets_api_connect_pk'], | |
| "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" | |
| } | |
| NHL_Data = st.secrets['NHL_Data'] | |
| gc = gspread.service_account_from_dict(credentials) | |
| gc2 = gspread.service_account_from_dict(credentials2) | |
| return gc, gc2, NHL_Data | |
| gcservice_account, gcservice_account2, NHL_Data = init_conn() | |
| expose_format = {'Proj Own': '{:.2%}','Exposure': '{:.2%}'} | |
| def grab_baseline_stuff(): | |
| try: | |
| sh = gcservice_account.open_by_url(NHL_Data) | |
| except: | |
| sh = gcservice_account2.open_by_url(NHL_Data) | |
| worksheet = sh.worksheet('Player_Data_Master') | |
| raw_display = pd.DataFrame(worksheet.get_all_records()) | |
| raw_display.replace(' - ', 0, inplace=True) | |
| raw_display.replace('', np.nan, inplace=True) | |
| raw_display = raw_display.dropna(subset=' Clean Name ') | |
| dk_raw_proj = raw_display[[' Clean Name ', ' Team ', ' Opp ', ' Line ', ' PP Unit ', ' Position ', ' DK Salary ', ' Final DK Projection ', ' DK uploadID ', 'DK_Own', ' MainSlateDK ']] | |
| dk_raw_proj = dk_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own', 'MainSlateDK'], axis=1) | |
| dk_raw_proj = dk_raw_proj.dropna(subset='Salary') | |
| fd_raw_proj = raw_display[[' Clean Name ', ' Team ', ' Opp ', ' Line ', ' PP Unit ', ' FD Position ', ' FD Salary ', ' Final FD Projection ', ' FD uploadID ', 'FD_Own', ' MainSlateFD ']] | |
| fd_raw_proj = fd_raw_proj.set_axis(['Player', 'Team', 'Opp', 'Line', 'PP Unit', 'Position', 'Salary', 'Median', 'player_id', 'Own', 'MainSlateFD'], axis=1) | |
| dk_raw_proj['Own'] = dk_raw_proj['Own'].astype(float) | |
| fd_raw_proj['Own'] = fd_raw_proj['Own'].astype(float) | |
| dk_raw_proj['player_id'] = dk_raw_proj['player_id'].astype(str) | |
| fd_raw_proj['player_id'] = fd_raw_proj['player_id'].astype(str) | |
| dk_raw_proj['Name_ID'] = dk_raw_proj['Player'] + ' (' + dk_raw_proj['player_id'].str[:-2] + ')' | |
| fd_raw_proj['Name_ID'] = fd_raw_proj['player_id'].str[:-2] + ':' + fd_raw_proj['Player'] | |
| dk_raw_proj = dk_raw_proj.sort_values(by='Median', ascending=False) | |
| fd_raw_proj = fd_raw_proj.sort_values(by='Median', ascending=False) | |
| dk_raw_proj['Player'] = dk_raw_proj['Player'].str.strip() | |
| fd_raw_proj['Player'] = fd_raw_proj['Player'].str.strip() | |
| dk_ids = dict(zip(dk_raw_proj['Player'], dk_raw_proj['Name_ID'])) | |
| fd_ids = dict(zip(fd_raw_proj['Player'], fd_raw_proj['Name_ID'])) | |
| worksheet = sh.worksheet('Timestamp') | |
| timestamp = worksheet.acell('A1').value | |
| worksheet = sh.worksheet('Player_Lines_ROO') | |
| line_frame = pd.DataFrame(worksheet.get_all_records()) | |
| return dk_raw_proj, fd_raw_proj, dk_ids, fd_ids, timestamp, line_frame | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| dk_raw_proj, fd_raw_proj, dkid_dict, fdid_dict, timestamp, line_frame = grab_baseline_stuff() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| opp_dict = dict(zip(dk_raw_proj.Team, dk_raw_proj.Opp)) | |
| tab1, tab2 = st.tabs(['Optimizer', 'Uploads and Info']) | |
| with tab1: | |
| col1, col2 = st.columns([1, 5]) | |
| with col1: | |
| st.info(t_stamp) | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| dk_raw_proj, fd_raw_proj, dk_ids, fd_ids, timestamp, line_frame = grab_baseline_stuff() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| slate_var1 = st.radio("Which data are you loading?", ('Paydirt', 'User'), key='slate_var1') | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1') | |
| if slate_var1 != 'User': | |
| mainvar1 = st.radio("Main slate or Secondary?", ('Main Slate', 'Secondary'), key='mainvar1') | |
| if site_var1 == 'Draftkings': | |
| if slate_var1 == 'User': | |
| init_baselines = proj_dataframe | |
| elif slate_var1 != 'User': | |
| init_baselines = dk_raw_proj | |
| if mainvar1 == 'Main Slate': | |
| init_baselines = init_baselines.loc[init_baselines['MainSlateDK'] == ' Main '] | |
| if mainvar1 != 'Main Slate': | |
| init_baselines = init_baselines.loc[init_baselines['MainSlateDK'] != ' Main '] | |
| elif site_var1 == 'Fanduel': | |
| if slate_var1 == 'User': | |
| init_baselines = proj_dataframe | |
| elif slate_var1 != 'User': | |
| init_baselines = fd_raw_proj | |
| if mainvar1 == 'Main Slate': | |
| init_baselines = init_baselines.loc[init_baselines['MainSlateFD'] == ' Main '] | |
| if mainvar1 != 'Main Slate': | |
| init_baselines = init_baselines.loc[init_baselines['MainSlateFD'] != ' Main '] | |
| contest_var1 = st.selectbox("What contest type are you optimizing for?", ('Cash', 'GPP'), key='contest_var1') | |
| split_var1 = st.radio("Are you running the full slate or certain games?", ('Full Slate Run', 'Specific Games'), key='split_var1') | |
| if split_var1 == 'Specific Games': | |
| team_var1 = st.multiselect('Which teams would you like to include in the optimization?', options = init_baselines['Team'].unique(), key='team_var1') | |
| elif split_var1 == 'Full Slate Run': | |
| team_var1 = init_baselines.Team.values.tolist() | |
| lock_var1 = st.multiselect("Are there any players you want to use in all lineups (Lock Button)?", options = init_baselines['Player'].unique(), key='lock_var1') | |
| avoid_var1 = st.multiselect("Are there any players you want to remove from the pool (Drop Button)?", options = init_baselines['Player'].unique(), key='avoid_var1') | |
| linenum_var1 = st.number_input("How many lineups would you like to produce?", min_value = 1, max_value = 300, value = 1, step = 1, key='linenum_var1') | |
| if site_var1 == 'Draftkings': | |
| min_sal1 = st.number_input('Min Salary', min_value = 35000, max_value = 49900, value = 49000, step = 100, key='min_sal1') | |
| max_sal1 = st.number_input('Max Salary', min_value = 35000, max_value = 50000, value = 50000, step = 100, key='max_sal1') | |
| elif site_var1 == 'Fanduel': | |
| min_sal1 = st.number_input('Min Salary', min_value = 45000, max_value = 54900, value = 54000, step = 100, key='min_sal1') | |
| max_sal1 = st.number_input('Max Salary', min_value = 45000, max_value = 55000, value = 55000, step = 100, key='max_sal1') | |
| with col2: | |
| init_baselines = init_baselines[init_baselines['Team'].isin(team_var1)] | |
| init_baselines = init_baselines[~init_baselines['Player'].isin(avoid_var1)] | |
| ownframe = init_baselines.copy() | |
| raw_baselines = ownframe[['Player', 'Salary', 'Position', 'Team', 'Opp', 'Line', 'PP Unit', 'Median', 'Own']] | |
| raw_baselines = raw_baselines.sort_values(by='Median', ascending=False) | |
| raw_baselines['lock'] = np.where(raw_baselines['Player'].isin(lock_var1), 1, 0) | |
| st.session_state.export_baselines = raw_baselines.copy() | |
| st.session_state.display_baselines = raw_baselines.copy() | |
| st.session_state.display_lines = line_frame[line_frame['Slate'] == mainvar1] | |
| display_container = st.empty() | |
| display_dl_container = st.empty() | |
| optimize_container = st.empty() | |
| download_container = st.empty() | |
| freq_container = st.empty() | |
| if st.button('Optimize'): | |
| max_proj = 1000 | |
| max_own = 1000 | |
| total_proj = 0 | |
| total_own = 0 | |
| lineup_display = [] | |
| check_list = [] | |
| lineups = [] | |
| portfolio = pd.DataFrame() | |
| x = 1 | |
| with st.spinner('Wait for it...'): | |
| with optimize_container: | |
| while x <= linenum_var1: | |
| 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_var1), 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'])) | |
| player_line = dict(zip(flex_file['Player'], flex_file['Line'])) | |
| player_ppunit = dict(zip(flex_file['Player'], flex_file['PP Unit'])) | |
| 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_sal1 | |
| total_score += pulp.lpSum([player_vars[idx]*obj_salary[idx] for idx in flex_file.index]) >= min_sal1 | |
| if site_var1 == 'Draftkings': | |
| 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_var1) | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] != "RIP"].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'] == "G"].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"].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'] == "W"].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'] == "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'] == "W"].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'] == "D"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2 | |
| elif site_var1 == 'Fanduel': | |
| 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_var1) | |
| for flex in flex_file['Position'].unique(): | |
| sub_idx = flex_file[flex_file['Position'] != "RIP"].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'] == "G"].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"].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'] == "W"].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'] == "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'] == "W"].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'] == "D"].index | |
| total_score += pulp.lpSum([player_vars[idx] for idx in sub_idx]) == 2 | |
| player_count = [] | |
| player_trim = [] | |
| lineup_list = [] | |
| if contest_var1 == '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]) | |
| elif contest_var1 != '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.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) | |
| df['Line'] = df['Names'].map(player_line) | |
| 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) | |
| 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['Line'] = lineup_test['Names'].map(player_line) | |
| 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_var1 == 'Draftkings': | |
| portfolio.rename(columns={0: "C1", 1: "C2", 2: "W1", 3: "W2", 4: "W3", 5: "D1", 6: "D2", 7: "G", 8: "UTIL"}, inplace = True) | |
| elif site_var1 == 'Fanduel': | |
| portfolio.rename(columns={0: "C1", 1: "C2", 2: "W1", 3: "W2", 4: "D1", 5: "D2", 6: "UTIL1", 7: "UTIL2", 8: "G"}, 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']) | |
| st.session_state.portfolio = portfolio.drop_duplicates() | |
| st.session_state.final_outcomes = portfolio | |
| if site_var1 == 'Draftkings': | |
| final_outcomes = portfolio[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL', 'Cost', 'Proj', 'Own']] | |
| final_outcomes_export = pd.DataFrame() | |
| final_outcomes_export['C1'] = final_outcomes['C1'] | |
| final_outcomes_export['C2'] = final_outcomes['C2'] | |
| final_outcomes_export['W1'] = final_outcomes['W1'] | |
| final_outcomes_export['W2'] = final_outcomes['W2'] | |
| final_outcomes_export['W3'] = final_outcomes['W3'] | |
| final_outcomes_export['D1'] = final_outcomes['D1'] | |
| final_outcomes_export['D2'] = final_outcomes['D2'] | |
| final_outcomes_export['G'] = final_outcomes['G'] | |
| final_outcomes_export['UTIL'] = final_outcomes['UTIL'] | |
| final_outcomes_export['Salary'] = final_outcomes['Cost'] | |
| final_outcomes_export['Own'] = final_outcomes['Own'] | |
| final_outcomes_export['Proj'] = final_outcomes['Proj'] | |
| final_outcomes_export['C1'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['C2'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['W1'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['W2'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['W3'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['D1'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['D2'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['G'].replace(dkid_dict, inplace=True) | |
| final_outcomes_export['UTIL'].replace(dkid_dict, inplace=True) | |
| st.session_state.final_outcomes_export = final_outcomes_export.copy() | |
| elif site_var1 == 'Fanduel': | |
| final_outcomes = portfolio[['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'UTIL1', 'UTIL2', 'G', 'Cost', 'Proj', 'Own']] | |
| final_outcomes_export = pd.DataFrame() | |
| final_outcomes_export['C1'] = final_outcomes['C1'] | |
| final_outcomes_export['C2'] = final_outcomes['C2'] | |
| final_outcomes_export['W1'] = final_outcomes['W1'] | |
| final_outcomes_export['W2'] = final_outcomes['W2'] | |
| final_outcomes_export['D1'] = final_outcomes['D1'] | |
| final_outcomes_export['D2'] = final_outcomes['D2'] | |
| final_outcomes_export['UTIL1'] = final_outcomes['UTIL1'] | |
| final_outcomes_export['UTIL2'] = final_outcomes['UTIL2'] | |
| final_outcomes_export['G'] = final_outcomes['G'] | |
| final_outcomes_export['Salary'] = final_outcomes['Cost'] | |
| final_outcomes_export['Own'] = final_outcomes['Own'] | |
| final_outcomes_export['Proj'] = final_outcomes['Proj'] | |
| final_outcomes_export['C1'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['C2'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['W1'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['W2'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['D1'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['D2'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['UTIL1'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['UTIL2'].replace(fdid_dict, inplace=True) | |
| final_outcomes_export['G'].replace(fdid_dict, inplace=True) | |
| st.session_state.FD_final_outcomes_export = final_outcomes_export.copy() | |
| st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.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) | |
| st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(player_pos) | |
| st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(player_sal) | |
| st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(player_own) / 100 | |
| st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(linenum_var1) | |
| st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(player_team) | |
| st.session_state.player_freq = st.session_state.player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure']] | |
| st.session_state.player_freq = st.session_state.player_freq.set_index('Player') | |
| with display_container: | |
| display_container = st.empty() | |
| if 'display_baselines' in st.session_state: | |
| tab1, tab2 = st.tabs(['Line Combo ROO', 'Player Projections']) | |
| with tab1: | |
| st.dataframe(st.session_state.display_lines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| with tab2: | |
| st.dataframe(st.session_state.display_baselines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| with display_dl_container: | |
| display_dl_container = st.empty() | |
| if 'export_baselines' in st.session_state: | |
| st.download_button( | |
| label="Export Projections", | |
| data=convert_df_to_csv(st.session_state.export_baselines), | |
| file_name='NHL_proj_export.csv', | |
| mime='text/csv', | |
| ) | |
| with optimize_container: | |
| optimize_container = st.empty() | |
| if 'final_outcomes' in st.session_state: | |
| st.dataframe(st.session_state.final_outcomes.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| with download_container: | |
| download_container = st.empty() | |
| if site_var1 == 'Draftkings': | |
| if 'final_outcomes_export' in st.session_state: | |
| st.download_button( | |
| label="Export Optimals", | |
| data=convert_df_to_csv(st.session_state.final_outcomes_export), | |
| file_name='NHL_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| elif site_var1 == 'Fanduel': | |
| if 'FD_final_outcomes_export' in st.session_state: | |
| st.download_button( | |
| label="Export Optimals", | |
| data=convert_df_to_csv(st.session_state.FD_final_outcomes_export), | |
| file_name='FD_NHL_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| with freq_container: | |
| freq_container = st.empty() | |
| if 'player_freq' in st.session_state: | |
| st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(expose_format, precision=2), use_container_width = True) | |
| with tab2: | |
| st.info("The Projections file can have any columns in any order, but must contain columns explicitly named: 'Player', 'Salary', 'Position', 'Team', 'Opp', 'Median', 'Line', 'PP Unit', 'Own', and 'player_id'. The player_id is the draftkings or fanduel ID associated with the player for upload.") | |
| col1, col2 = st.columns([1, 5]) | |
| 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) | |
| except: | |
| proj_dataframe = pd.read_excel(proj_file) | |
| with col2: | |
| if proj_file is not None: | |
| st.dataframe(proj_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) |