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Running
James McCool
Refactor summary statistics calculations in app.py: streamline the computation of minimum, average, maximum, and standard deviation for salary, project, and own metrics by correcting column indices, enhancing data accuracy and clarity in the displayed results.
02d2bcd
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import pymongo | |
| st.set_page_config(layout="wide") | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["NBA_DFS"] | |
| wnba_db = client["WNBA_DFS"] | |
| return db, wnba_db | |
| db, wnba_db = init_conn() | |
| dk_nba_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| dk_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_nba_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| dk_wnba_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| dk_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_wnba_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| fd_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] | |
| roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'} | |
| st.markdown(""" | |
| <style> | |
| /* Tab styling */ | |
| .stTabs [data-baseweb="tab-list"] { | |
| gap: 8px; | |
| padding: 4px; | |
| } | |
| .stTabs [data-baseweb="tab"] { | |
| height: 50px; | |
| white-space: pre-wrap; | |
| background-color: #DAA520; | |
| color: white; | |
| border-radius: 10px; | |
| gap: 1px; | |
| padding: 10px 20px; | |
| font-weight: bold; | |
| transition: all 0.3s ease; | |
| } | |
| .stTabs [aria-selected="true"] { | |
| background-color: #DAA520; | |
| border: 3px solid #FFD700; | |
| color: white; | |
| } | |
| .stTabs [data-baseweb="tab"]:hover { | |
| background-color: #FFD700; | |
| cursor: pointer; | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| def load_overall_stats(league: str): | |
| if league == 'NBA': | |
| collection = db["DK_Player_Stats"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["DK_Player_Stats"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| if league == 'NBA': | |
| raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT', | |
| 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']] | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}) | |
| elif league == 'WNBA': | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"}) | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| dk_raw = raw_display.sort_values(by='Median', ascending=False) | |
| if league == 'NBA': | |
| collection = db["FD_Player_Stats"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["FD_Player_Stats"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| if league == 'NBA': | |
| raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT', | |
| 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']] | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}) | |
| elif league == 'WNBA': | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"}) | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| fd_raw = raw_display.sort_values(by='Median', ascending=False) | |
| if league == 'NBA': | |
| collection = db["Secondary_DK_Player_Stats"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["Secondary_DK_Player_Stats"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| if league == 'NBA': | |
| raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT', | |
| 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']] | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}) | |
| elif league == 'WNBA': | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"}) | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| dk_raw_sec = raw_display.sort_values(by='Median', ascending=False) | |
| if league == 'NBA': | |
| collection = db["Secondary_FD_Player_Stats"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["Secondary_FD_Player_Stats"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| if league == 'NBA': | |
| raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT', | |
| 'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']] | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"}) | |
| elif league == 'WNBA': | |
| raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"}) | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| fd_raw_sec = raw_display.sort_values(by='Median', ascending=False) | |
| if league == 'NBA': | |
| collection = db["Player_SD_Range_Of_Outcomes"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["Player_SD_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']] | |
| raw_display['Median'] = raw_display['Median'].replace('', 0).astype(float) | |
| raw_display = raw_display.rename(columns={"player_id": "player_ID"}) | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| sd_raw = raw_display.sort_values(by='Median', ascending=False) | |
| dk_sd_raw = sd_raw[sd_raw['site'] == 'Draftkings'] | |
| fd_sd_raw = sd_raw[sd_raw['site'] == 'Fanduel'] | |
| fd_sd_raw['player_ID'] = fd_sd_raw['player_ID'].astype(str) | |
| fd_sd_raw['player_ID'] = fd_sd_raw['player_ID'].str.rsplit('-', n=1).str[0].astype(str) | |
| print(sd_raw.head(10)) | |
| if league == 'NBA': | |
| collection = db["Player_Range_Of_Outcomes"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| raw_display = pd.DataFrame(list(cursor)) | |
| try: | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']] | |
| except: | |
| raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']] | |
| raw_display = raw_display.rename(columns={"player_id": "player_ID"}) | |
| raw_display['Median'] = raw_display['Median'].replace('', 0).astype(float) | |
| raw_display = raw_display.loc[raw_display['Median'] > 0] | |
| raw_display = raw_display.apply(pd.to_numeric, errors='ignore') | |
| roo_raw = raw_display.sort_values(by='Median', ascending=False) | |
| timestamp = raw_display['timestamp'].values[0] | |
| return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp | |
| def init_DK_lineups(slate_desig: str, league: str): | |
| if slate_desig == 'Main Slate': | |
| if league == 'NBA': | |
| collection = db['DK_NBA_name_map'] | |
| elif league == 'WNBA': | |
| collection = wnba_db['DK_WNBA_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| if league == 'NBA': | |
| collection = db["DK_NBA_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["DK_WNBA_seed_frame"] | |
| cursor = collection.find().limit(10000) | |
| elif slate_desig == 'Secondary': | |
| if league == 'NBA': | |
| collection = db['DK_NBA_Secondary_name_map'] | |
| elif league == 'WNBA': | |
| collection = wnba_db['DK_WNBA_Secondary_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| if league == 'NBA': | |
| collection = db["DK_NBA_Secondary_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["DK_WNBA_Secondary_seed_frame"] | |
| cursor = collection.find().limit(10000) | |
| elif slate_desig == 'Auxiliary': | |
| collection = db['DK_NBA_Auxiliary_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| collection = db["DK_NBA_Auxiliary_seed_frame"] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| if league == 'NBA': | |
| raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX'] | |
| elif league == 'WNBA': | |
| raw_display = raw_display[['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL'] | |
| for col in dict_columns: | |
| raw_display[col] = raw_display[col].map(names_dict) | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_DK_SD_lineups(slate_desig: str, league: str): | |
| if slate_desig == 'Main Slate': | |
| if league == 'NBA': | |
| collection = db["DK_NBA_SD_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["DK_WNBA_SD_seed_frame"] | |
| elif slate_desig == 'Secondary': | |
| if league == 'NBA': | |
| collection = db["DK_NBA_Secondary_SD_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["DK_WNBA_Secondary_SD_seed_frame"] | |
| elif slate_desig == 'Auxiliary': | |
| collection = db["DK_NBA_Auxiliary_SD_seed_frame"] | |
| cursor = collection.find({"Team_count": {"$lt": 6}}).limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_FD_lineups(slate_desig: str, league: str): | |
| if slate_desig == 'Main Slate': | |
| if league == 'NBA': | |
| collection = db['FD_NBA_name_map'] | |
| elif league == 'WNBA': | |
| collection = wnba_db['FD_WNBA_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| if league == 'NBA': | |
| collection = db["FD_NBA_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["FD_WNBA_seed_frame"] | |
| cursor = collection.find().limit(10000) | |
| elif slate_desig == 'Secondary': | |
| if league == 'NBA': | |
| collection = db['FD_NBA_Secondary_name_map'] | |
| elif league == 'WNBA': | |
| collection = wnba_db['FD_WNBA_Secondary_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| if league == 'NBA': | |
| collection = db["FD_NBA_Secondary_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["FD_WNBA_Secondary_seed_frame"] | |
| cursor = collection.find().limit(10000) | |
| elif slate_desig == 'Auxiliary': | |
| collection = db['FD_NBA_Auxiliary_name_map'] | |
| cursor = collection.find() | |
| raw_data = pd.DataFrame(list(cursor)) | |
| names_dict = dict(zip(raw_data['key'], raw_data['value'])) | |
| collection = db["FD_NBA_Auxiliary_seed_frame"] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| if league == 'NBA': | |
| raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1'] | |
| elif league == 'WNBA': | |
| raw_display = raw_display[['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| dict_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4'] | |
| for col in dict_columns: | |
| raw_display[col] = raw_display[col].map(names_dict) | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def init_FD_SD_lineups(slate_desig: str, league: str): | |
| if slate_desig == 'Main Slate': | |
| if league == 'NBA': | |
| collection = db["FD_NBA_SD_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["FD_WNBA_SD_seed_frame"] | |
| elif slate_desig == 'Secondary': | |
| if league == 'NBA': | |
| collection = db["FD_NBA_Secondary_SD_seed_frame"] | |
| elif league == 'WNBA': | |
| collection = wnba_db["FD_WNBA_Secondary_SD_seed_frame"] | |
| elif slate_desig == 'Auxiliary': | |
| collection = db["FD_NBA_Auxiliary_SD_seed_frame"] | |
| cursor = collection.find({"Team_count": {"$lt": 6}}).limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA') | |
| salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary)) | |
| id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID)) | |
| salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary)) | |
| dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID)) | |
| fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID)) | |
| dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns) | |
| dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns) | |
| fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns) | |
| fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns) | |
| dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns) | |
| dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns) | |
| fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns) | |
| fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns) | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals']) | |
| with tab1: | |
| with st.expander("Info and Filters"): | |
| with st.container(): | |
| st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile") | |
| with st.container(): | |
| # First row - timestamp and reset button | |
| col1, col2 = st.columns([3, 1]) | |
| with col1: | |
| st.info(t_stamp) | |
| with col2: | |
| if st.button("Load/Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA') | |
| salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary)) | |
| id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID)) | |
| salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary)) | |
| dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID)) | |
| fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID)) | |
| dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns) | |
| dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns) | |
| fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns) | |
| fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns) | |
| dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns) | |
| dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns) | |
| fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns) | |
| fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns) | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| col1, col2, col3, col4, col5, col6 = st.columns(6) | |
| with col1: | |
| view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2') | |
| with col2: | |
| league_var = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var') | |
| dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var) | |
| with col3: | |
| slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2') | |
| with col4: | |
| site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2') | |
| # Process site selection | |
| if site_var2 == 'Draftkings': | |
| if slate_type_var2 == 'Regular': | |
| site_baselines = roo_raw[roo_raw['site'] == 'Draftkings'] | |
| elif slate_type_var2 == 'Showdown': | |
| site_baselines = sd_raw[sd_raw['site'] == 'Draftkings'] | |
| elif site_var2 == 'Fanduel': | |
| if slate_type_var2 == 'Regular': | |
| site_baselines = roo_raw[roo_raw['site'] == 'Fanduel'] | |
| elif slate_type_var2 == 'Showdown': | |
| site_baselines = sd_raw[sd_raw['site'] == 'Fanduel'] | |
| with col5: | |
| slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split') | |
| if slate_split == 'Main Slate': | |
| if slate_type_var2 == 'Regular': | |
| raw_baselines = site_baselines[site_baselines['slate'] == 'Main Slate'] | |
| elif slate_type_var2 == 'Showdown': | |
| raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #1'] | |
| elif slate_split == 'Secondary': | |
| if slate_type_var2 == 'Regular': | |
| raw_baselines = site_baselines[site_baselines['slate'] == 'Secondary Slate'] | |
| elif slate_type_var2 == 'Showdown': | |
| raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2'] | |
| with col6: | |
| split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2') | |
| if split_var2 == 'Specific Games': | |
| team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2') | |
| else: | |
| team_var2 = raw_baselines.Team.values.tolist() | |
| pos_var2 = st.selectbox('Position Filter', options=['All', 'PG', 'SG', 'SF', 'PF', 'C'], key='pos_var2') | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| low_salary = st.number_input('Enter Lowest Salary', min_value=300, max_value=15000, value=300, step=100, key='low_salary') | |
| with col2: | |
| high_salary = st.number_input('Enter Highest Salary', min_value=300, max_value=25000, value=25000, step=100, key='high_salary') | |
| display_container_1 = st.empty() | |
| display_dl_container_1 = st.empty() | |
| display_proj = raw_baselines[raw_baselines['Team'].isin(team_var2)] | |
| display_proj = display_proj[display_proj['Salary'].between(low_salary, high_salary)] | |
| if view_var2 == 'Advanced': | |
| display_proj = display_proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', | |
| 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']] | |
| elif view_var2 == 'Simple': | |
| display_proj = display_proj[['Player', 'Position', 'Salary', 'Median', 'GPP%', 'Own']] | |
| export_data = display_proj.copy() | |
| # display_proj = display_proj.set_index('Player') | |
| st.session_state.display_proj = display_proj.set_index('Player', drop=True) | |
| with display_container_1: | |
| display_container = st.empty() | |
| if 'display_proj' in st.session_state: | |
| if pos_var2 == 'All': | |
| st.session_state.display_proj = st.session_state.display_proj | |
| elif pos_var2 != 'All': | |
| st.session_state.display_proj = st.session_state.display_proj[st.session_state.display_proj['Position'].str.contains(pos_var2)] | |
| st.dataframe(st.session_state.display_proj.style.set_properties(**{'font-size': '6pt'}).background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2), | |
| height=1000, use_container_width = True) | |
| with display_dl_container_1: | |
| display_dl_container = st.empty() | |
| if 'display_proj' in st.session_state: | |
| st.download_button( | |
| label="Export Tables", | |
| data=convert_df_to_csv(export_data), | |
| file_name='NBA_ROO_export.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| with st.expander("Info and Filters"): | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats('NBA') | |
| salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary)) | |
| id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID)) | |
| salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary)) | |
| dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID)) | |
| fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID)) | |
| dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns) | |
| dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns) | |
| fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns) | |
| fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns) | |
| dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns) | |
| dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns) | |
| fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns) | |
| fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns) | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| col1, col2, col3, col4, col5, col6 = st.columns(6) | |
| with col1: | |
| league_var2 = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var2') | |
| with col2: | |
| slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary')) | |
| with col3: | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) | |
| if 'working_seed' in st.session_state: | |
| del st.session_state['working_seed'] | |
| with col4: | |
| slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown')) | |
| with col5: | |
| lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1) | |
| with col6: | |
| if site_var1 == 'Draftkings': | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| column_names = dk_nba_columns | |
| elif slate_type_var1 == 'Showdown': | |
| column_names = dk_nba_sd_columns | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| column_names = dk_wnba_columns | |
| elif slate_type_var1 == 'Showdown': | |
| column_names = dk_wnba_sd_columns | |
| player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') | |
| if player_var1 == 'Specific Players': | |
| player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = dk_raw.Player.values.tolist() | |
| elif site_var1 == 'Fanduel': | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| column_names = fd_nba_columns | |
| elif slate_type_var1 == 'Showdown': | |
| column_names = fd_nba_sd_columns | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| column_names = fd_wnba_columns | |
| elif slate_type_var1 == 'Showdown': | |
| column_names = fd_wnba_sd_columns | |
| player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') | |
| if player_var1 == 'Specific Players': | |
| player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = fd_raw.Player.values.tolist() | |
| if st.button("Prepare data export", key='data_export'): | |
| if site_var1 == 'Draftkings': | |
| if slate_type_var1 == 'Regular': | |
| data_export = init_DK_lineups(slate_var1, league_var2) | |
| data_export_names = data_export.copy() | |
| for col_idx in range(8): | |
| data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| elif slate_type_var1 == 'Showdown': | |
| data_export = init_DK_SD_lineups(slate_var1, league_var2) | |
| data_export_names = data_export.copy() | |
| for col_idx in range(6): | |
| data_export[:, col_idx] = np.array([dk_id_dict_sd.get(player, player) for player in data_export[:, col_idx]]) | |
| elif site_var1 == 'Fanduel': | |
| if slate_type_var1 == 'Regular': | |
| data_export = init_FD_lineups(slate_var1, league_var2) | |
| data_export_names = data_export.copy() | |
| for col_idx in range(9): | |
| data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| elif slate_type_var1 == 'Showdown': | |
| data_export = init_FD_SD_lineups(slate_var1, league_var2) | |
| data_export_names = data_export.copy() | |
| for col_idx in range(6): | |
| data_export[:, col_idx] = np.array([fd_id_dict_sd.get(player, player) for player in data_export[:, col_idx]]) | |
| st.download_button( | |
| label="Export optimals (Names)", | |
| data=convert_df(data_export_names), | |
| file_name='NBA_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| st.download_button( | |
| label="Export optimals (IDs)", | |
| data=convert_df(data_export), | |
| file_name='NBA_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| if site_var1 == 'Draftkings': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = st.session_state.working_seed | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = init_DK_lineups(slate_var1, league_var2) | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var2) | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = init_DK_lineups(slate_var1, league_var2) | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var2) | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif site_var1 == 'Fanduel': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = st.session_state.working_seed | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = init_FD_lineups(slate_var1, league_var2) | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var2) | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = init_FD_lineups(slate_var1, league_var2) | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var2) | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| export_file = st.session_state.data_export_display.copy() | |
| if site_var1 == 'Draftkings': | |
| if slate_type_var1 == 'Regular': | |
| for col_idx in range(8): | |
| export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| elif slate_type_var1 == 'Showdown': | |
| for col_idx in range(6): | |
| export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(dk_id_dict_sd) | |
| elif site_var1 == 'Fanduel': | |
| if slate_type_var1 == 'Regular': | |
| for col_idx in range(9): | |
| export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| elif slate_type_var1 == 'Showdown': | |
| for col_idx in range(6): | |
| export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(fd_id_dict_sd) | |
| with st.container(): | |
| if st.button("Reset Optimals", key='reset3'): | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| if site_var1 == 'Draftkings': | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = dk_nba_lineups.copy() | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = dk_nba_sd_lineups.copy() | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = dk_wnba_lineups.copy() | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = dk_wnba_sd_lineups.copy() | |
| elif site_var1 == 'Fanduel': | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = fd_nba_lineups.copy() | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = fd_nba_sd_lineups.copy() | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| st.session_state.working_seed = fd_wnba_lineups.copy() | |
| elif slate_type_var1 == 'Showdown': | |
| st.session_state.working_seed = fd_wnba_sd_lineups.copy() | |
| if 'data_export_display' in st.session_state: | |
| st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True) | |
| st.download_button( | |
| label="Export display optimals", | |
| data=convert_df(export_file), | |
| file_name='NBA_display_optimals.csv', | |
| mime='text/csv', | |
| ) | |
| with st.container(): | |
| if 'working_seed' in st.session_state: | |
| # Create a new dataframe with summary statistics | |
| if site_var1 == 'Draftkings': | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,9]), | |
| np.mean(st.session_state.working_seed[:,9]), | |
| np.max(st.session_state.working_seed[:,9]), | |
| np.std(st.session_state.working_seed[:,9]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,14]), | |
| np.mean(st.session_state.working_seed[:,14]), | |
| np.max(st.session_state.working_seed[:,14]), | |
| np.std(st.session_state.working_seed[:,14]) | |
| ] | |
| }) | |
| elif slate_type_var1 == 'Showdown': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,12]), | |
| np.mean(st.session_state.working_seed[:,12]), | |
| np.max(st.session_state.working_seed[:,12]), | |
| np.std(st.session_state.working_seed[:,12]) | |
| ] | |
| }) | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,12]), | |
| np.mean(st.session_state.working_seed[:,12]), | |
| np.max(st.session_state.working_seed[:,12]), | |
| np.std(st.session_state.working_seed[:,12]) | |
| ] | |
| }) | |
| elif slate_type_var1 == 'Showdown': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,12]), | |
| np.mean(st.session_state.working_seed[:,12]), | |
| np.max(st.session_state.working_seed[:,12]), | |
| np.std(st.session_state.working_seed[:,12]) | |
| ] | |
| }) | |
| elif site_var1 == 'Fanduel': | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,9]), | |
| np.mean(st.session_state.working_seed[:,9]), | |
| np.max(st.session_state.working_seed[:,9]), | |
| np.std(st.session_state.working_seed[:,9]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,10]), | |
| np.mean(st.session_state.working_seed[:,10]), | |
| np.max(st.session_state.working_seed[:,10]), | |
| np.std(st.session_state.working_seed[:,10]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,15]), | |
| np.mean(st.session_state.working_seed[:,15]), | |
| np.max(st.session_state.working_seed[:,15]), | |
| np.std(st.session_state.working_seed[:,15]) | |
| ] | |
| }) | |
| elif slate_type_var1 == 'Showdown': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,12]), | |
| np.mean(st.session_state.working_seed[:,12]), | |
| np.max(st.session_state.working_seed[:,12]), | |
| np.std(st.session_state.working_seed[:,12]) | |
| ] | |
| }) | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,13]), | |
| np.mean(st.session_state.working_seed[:,13]), | |
| np.max(st.session_state.working_seed[:,13]), | |
| np.std(st.session_state.working_seed[:,13]) | |
| ] | |
| }) | |
| elif slate_type_var1 == 'Showdown': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,12]), | |
| np.mean(st.session_state.working_seed[:,12]), | |
| np.max(st.session_state.working_seed[:,12]), | |
| np.std(st.session_state.working_seed[:,12]) | |
| ] | |
| }) | |
| # Set the index of the summary dataframe as the "Metric" column | |
| summary_df = summary_df.set_index('Metric') | |
| # Display the summary dataframe | |
| st.subheader("Optimal Statistics") | |
| st.dataframe(summary_df.style.format({ | |
| 'Salary': '{:.2f}', | |
| 'Proj': '{:.2f}', | |
| 'Own': '{:.2f}' | |
| }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True) | |
| with st.container(): | |
| tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"]) | |
| with tab1: | |
| if 'data_export_display' in st.session_state: | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.data_export_display.iloc[:, :8] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.data_export_display.iloc[:, :9] | |
| elif slate_type_var1 == 'Showdown': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.data_export_display.iloc[:, :5] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.data_export_display.iloc[:, :5] | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.data_export_display.iloc[:, :7] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.data_export_display.iloc[:, :8] | |
| elif slate_type_var1 == 'Showdown': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.data_export_display.iloc[:, :5] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.data_export_display.iloc[:, :5] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.values.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / lineup_num_var * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Salary': [salary_dict.get(player, player) for player in value_counts.index], | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| # Display the table | |
| st.write("Player Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export player frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='NBA_player_frequency.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| if 'working_seed' in st.session_state: | |
| if league_var2 == 'NBA': | |
| if slate_type_var1 == 'Regular': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.working_seed[:, :8] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.working_seed[:, :9] | |
| elif slate_type_var1 == 'Showdown': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.working_seed[:, :5] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.working_seed[:, :5] | |
| elif league_var2 == 'WNBA': | |
| if slate_type_var1 == 'Regular': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.working_seed[:, :7] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.working_seed[:, :8] | |
| elif slate_type_var1 == 'Showdown': | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.working_seed[:, :5] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.working_seed[:, :5] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Salary': [salary_dict.get(player, player) for player in value_counts.index], | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| # Display the table | |
| st.write("Seed Frame Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}, precision=2), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export seed frame frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='NBA_seed_frame_frequency.csv', | |
| mime='text/csv', | |
| ) | |