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") @st.cache_resource def init_conn(): uri = st.secrets['mongo_uri'] client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) db = client["NBA_DFS"] return db db = init_conn() dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'] fd_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.cache_data(ttl=60) def load_overall_stats(): collection = db["DK_Player_Stats"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) 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"}) 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) collection = db["FD_Player_Stats"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) 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"}) 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) collection = db["Secondary_DK_Player_Stats"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) 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"}) 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) collection = db["Secondary_FD_Player_Stats"] cursor = collection.find() raw_display = pd.DataFrame(list(cursor)) 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"}) 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) collection = 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 = 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) print(sd_raw.head(10)) collection = db["Player_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 = 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, timestamp @st.cache_data(ttl = 60) def init_DK_lineups(slate_desig: str): if slate_desig == 'Main Slate': collection = db['DK_NBA_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_seed_frame"] cursor = collection.find().limit(10000) elif slate_desig == 'Secondary': collection = db['DK_NBA_Secondary_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_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)) 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'] for col in dict_columns: raw_display[col] = raw_display[col].map(names_dict) DK_seed = raw_display.to_numpy() return DK_seed @st.cache_data(ttl = 60) def init_DK_SD_lineups(slate_desig: str): if slate_desig == 'Main Slate': collection = db["DK_NBA_SD_seed_frame"] elif slate_desig == 'Secondary': collection = db["DK_NBA_Secondary_SD_seed_frame"] elif slate_desig == 'Auxiliary': collection = db["DK_NBA_Auxiliary_SD_seed_frame"] cursor = collection.find().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 @st.cache_data(ttl = 60) def init_FD_lineups(slate_desig: str): if slate_desig == 'Main Slate': collection = db['FD_NBA_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_seed_frame"] cursor = collection.find().limit(10000) elif slate_desig == 'Secondary': collection = db['FD_NBA_Secondary_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_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)) 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'] for col in dict_columns: raw_display[col] = raw_display[col].map(names_dict) FD_seed = raw_display.to_numpy() return FD_seed @st.cache_data(ttl = 60) def init_FD_SD_lineups(slate_desig: str): if slate_desig == 'Main Slate': collection = db["FD_NBA_SD_seed_frame"] elif slate_desig == 'Secondary': collection = db["FD_NBA_Secondary_SD_seed_frame"] elif slate_desig == 'Auxiliary': collection = db["FD_NBA_Auxiliary_SD_seed_frame"] cursor = collection.find().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') @st.cache_data 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, timestamp = load_overall_stats() 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)) id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID)) dk_lineups = pd.DataFrame(columns=dk_columns) dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns) fd_lineups = pd.DataFrame(columns=fd_columns) fd_sd_lineups = pd.DataFrame(columns=fd_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, timestamp = load_overall_stats() 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)) id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID)) dk_lineups = pd.DataFrame(columns=dk_columns) dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns) fd_lineups = pd.DataFrame(columns=fd_columns) fd_sd_lineups = pd.DataFrame(columns=fd_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 = st.columns(5) with col1: view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2') with col2: slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2') with col3: 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 col4: 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 col5: 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, timestamp = load_overall_stats() 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)) id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID)) dk_lineups = pd.DataFrame(columns=dk_columns) dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns) fd_lineups = pd.DataFrame(columns=fd_columns) fd_sd_lineups = pd.DataFrame(columns=fd_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 = st.columns(5) with col1: slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary')) with col2: 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 col3: slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown')) with col4: 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 col5: if site_var1 == 'Draftkings': if slate_type_var1 == 'Regular': column_names = dk_columns elif slate_type_var1 == 'Showdown': column_names = dk_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 slate_type_var1 == 'Regular': column_names = fd_columns elif slate_type_var1 == 'Showdown': column_names = fd_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'): data_export = st.session_state.working_seed.copy() if site_var1 == 'Draftkings': 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 site_var1 == 'Fanduel': for col_idx in range(9): data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) st.download_button( label="Export optimals set", 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) elif slate_type_var1 == 'Showdown': st.session_state.working_seed = init_DK_SD_lineups(slate_var1) 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) elif slate_type_var1 == 'Showdown': st.session_state.working_seed = init_DK_SD_lineups(slate_var1) 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) elif slate_type_var1 == 'Showdown': st.session_state.working_seed = init_FD_SD_lineups(slate_var1) 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) elif slate_type_var1 == 'Showdown': st.session_state.working_seed = init_FD_SD_lineups(slate_var1) 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(5): export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(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(5): export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(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 slate_type_var1 == 'Regular': st.session_state.working_seed = dk_lineups.copy() elif slate_type_var1 == 'Showdown': st.session_state.working_seed = dk_sd_lineups.copy() elif site_var1 == 'Fanduel': if slate_type_var1 == 'Regular': st.session_state.working_seed = fd_lineups.copy() elif slate_type_var1 == 'Showdown': st.session_state.working_seed = fd_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 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 site_var1 == 'Fanduel': 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]) ] }) # 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 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] # 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 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] # 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', )