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| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| for name in dir(): | |
| if not name.startswith('_'): | |
| del globals()[name] | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import gc | |
| 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": "0e0bc2fdef04e771172fe5807392b9d6639d945e", | |
| "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" | |
| } | |
| gc_con = gspread.service_account_from_dict(credentials, scope) | |
| return gc_con | |
| gcservice_account = init_conn() | |
| NBA_Data = 'https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=1808117109' | |
| percentages_format = {'PG': '{:.2%}', 'SG': '{:.2%}', 'SF': '{:.2%}', 'PF': '{:.2%}', 'C': '{:.2%}'} | |
| def init_baselines(): | |
| sh = gcservice_account.open_by_url(NBA_Data) | |
| worksheet = sh.worksheet('Gamelog') | |
| raw_display = pd.DataFrame(worksheet.get_values()) | |
| raw_display.columns = raw_display.iloc[0] | |
| raw_display = raw_display[1:] | |
| raw_display = raw_display.reset_index(drop=True) | |
| gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""] | |
| gamelog_table = gamelog_table[['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A', | |
| 'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB', | |
| 'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy']] | |
| gamelog_table['assists'].replace("", 0, inplace=True) | |
| gamelog_table['reboundChancesTotal'].replace("", 0, inplace=True) | |
| gamelog_table['passes'].replace("", 0, inplace=True) | |
| gamelog_table['touches'].replace("", 0, inplace=True) | |
| gamelog_table['MIN'].replace("", 0, inplace=True) | |
| gamelog_table['Fantasy'].replace("", 0, inplace=True) | |
| gamelog_table['FD_Fantasy'].replace("", 0, inplace=True) | |
| gamelog_table['REB'] = gamelog_table['REB'].astype(int) | |
| gamelog_table['assists'] = gamelog_table['assists'].astype(int) | |
| gamelog_table['reboundChancesTotal'] = gamelog_table['reboundChancesTotal'].astype(int) | |
| gamelog_table['passes'] = gamelog_table['passes'].astype(int) | |
| gamelog_table['touches'] = gamelog_table['touches'].astype(int) | |
| gamelog_table['MIN'] = gamelog_table['MIN'].astype(int) | |
| gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float) | |
| gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float) | |
| gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal'] | |
| gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes'] | |
| gamelog_table['Touch_per_min'] = gamelog_table['touches'] / gamelog_table['MIN'] | |
| gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches'] | |
| gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches'] | |
| data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP']) | |
| gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce') | |
| gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date | |
| gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', | |
| 'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1) | |
| worksheet = sh.worksheet('Rotations') | |
| raw_display = pd.DataFrame(worksheet.get_values()) | |
| raw_display.columns = raw_display.iloc[0] | |
| raw_display = raw_display[1:] | |
| raw_display = raw_display.reset_index(drop=True) | |
| rot_table = raw_display[raw_display['Player'] != ""] | |
| rot_table = rot_table[['Player', 'Team', 'PG', 'SG', 'SF', 'PF', 'C', 'Given Pos']] | |
| data_cols = ['PG', 'SG', 'SF', 'PF', 'C'] | |
| rot_table[data_cols] = rot_table[data_cols].apply(pd.to_numeric, errors='coerce') | |
| return gamelog_table, rot_table | |
| def seasonlong_build(data_sample): | |
| season_long_table = data_sample[['Player', 'Pos', 'Team']] | |
| season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float) | |
| season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float) | |
| season_long_table['Touch/Min'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('sum').astype(int)) | |
| season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float) | |
| season_long_table['FGM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('mean').astype(float) | |
| season_long_table['FGA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('mean').astype(float) | |
| season_long_table['FG%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('sum').astype(int)) | |
| season_long_table['FG3M'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('mean').astype(float) | |
| season_long_table['FG3A'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('mean').astype(float) | |
| season_long_table['FG3%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('sum').astype(int)) | |
| season_long_table['FTM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('mean').astype(float) | |
| season_long_table['FTA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('mean').astype(float) | |
| season_long_table['FT%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('sum').astype(int)) | |
| season_long_table['OREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB Chance'].transform('mean').astype(float) | |
| season_long_table['OREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB'].transform('mean').astype(float) | |
| season_long_table['DREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB Chance'].transform('mean').astype(float) | |
| season_long_table['DREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB'].transform('mean').astype(float) | |
| season_long_table['REB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('mean').astype(float) | |
| season_long_table['REB'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('mean').astype(float) | |
| season_long_table['Passes'] = data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('mean').astype(float) | |
| season_long_table['Alt Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Alt Assists'].transform('mean').astype(float) | |
| season_long_table['FT Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['FT Assists'].transform('mean').astype(float) | |
| season_long_table['Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('mean').astype(float) | |
| season_long_table['Stl'] = data_sample.groupby(['Player', 'Season'], sort=False)['Stl'].transform('mean').astype(float) | |
| season_long_table['Blk'] = data_sample.groupby(['Player', 'Season'], sort=False)['Blk'].transform('mean').astype(float) | |
| season_long_table['Tov'] = data_sample.groupby(['Player', 'Season'], sort=False)['Tov'].transform('mean').astype(float) | |
| season_long_table['PF'] = data_sample.groupby(['Player', 'Season'], sort=False)['PF'].transform('mean').astype(float) | |
| season_long_table['DD'] = data_sample.groupby(['Player', 'Season'], sort=False)['DD'].transform('mean').astype(float) | |
| season_long_table['TD'] = data_sample.groupby(['Player', 'Season'], sort=False)['TD'].transform('mean').astype(float) | |
| season_long_table['Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('mean').astype(float) | |
| season_long_table['FD_Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('mean').astype(float) | |
| season_long_table['Rebound%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('sum').astype(int)) | |
| season_long_table['Assists/Pass'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('sum').astype(int)) | |
| season_long_table['Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int)) | |
| season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('sum').astype(int) / | |
| data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int)) | |
| season_long_table = season_long_table.drop_duplicates(subset='Player') | |
| season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False) | |
| season_long_table = season_long_table.set_axis(['Player', 'Pos', 'Team', 'Min', 'Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A', | |
| 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1) | |
| return season_long_table | |
| def run_fantasy_corr(data_sample): | |
| cor_testing = data_sample | |
| cor_testing = cor_testing[cor_testing['Season'] == '22023'] | |
| date_list = cor_testing['Date'].unique().tolist() | |
| player_list = cor_testing['Player'].unique().tolist() | |
| corr_frame = pd.DataFrame() | |
| corr_frame['DATE'] = date_list | |
| for player in player_list: | |
| player_testing = cor_testing[cor_testing['Player'] == player] | |
| fantasy_map = dict(zip(player_testing['Date'], player_testing['Fantasy'])) | |
| corr_frame[player] = corr_frame['DATE'].map(fantasy_map) | |
| players_fantasy = corr_frame.drop('DATE', axis=1) | |
| corrM = players_fantasy.corr() | |
| return corrM | |
| def run_min_corr(data_sample): | |
| cor_testing = data_sample | |
| cor_testing = cor_testing[cor_testing['Season'] == '22023'] | |
| date_list = cor_testing['Date'].unique().tolist() | |
| player_list = cor_testing['Player'].unique().tolist() | |
| corr_frame = pd.DataFrame() | |
| corr_frame['DATE'] = date_list | |
| for player in player_list: | |
| player_testing = cor_testing[cor_testing['Player'] == player] | |
| fantasy_map = dict(zip(player_testing['Date'], player_testing['Min'])) | |
| corr_frame[player] = corr_frame['DATE'].map(fantasy_map) | |
| players_fantasy = corr_frame.drop('DATE', axis=1) | |
| corrM = players_fantasy.corr() | |
| return corrM | |
| def split_frame(input_df, rows): | |
| df = [input_df.loc[i : i + rows - 1, :] for i in range(0, len(input_df), rows)] | |
| return df | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| gamelog_table, rot_table = init_baselines() | |
| basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min'] | |
| basic_season_cols = ['Pos', 'Team', 'Min'] | |
| data_cols = ['Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', | |
| 'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A', | |
| 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| indv_teams = gamelog_table.drop_duplicates(subset='Team') | |
| total_teams = indv_teams.Team.values.tolist() | |
| indv_players = gamelog_table.drop_duplicates(subset='Player') | |
| total_players = indv_players.Player.values.tolist() | |
| total_dates = gamelog_table.Date.values.tolist() | |
| tab1, tab2, tab3, tab4 = st.tabs(['Gamelogs', 'Correlation Matrix', 'Position vs. Opp', 'Rotations']) | |
| with tab1: | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| if st.button("Reset Data", key='reset1'): | |
| st.cache_data.clear() | |
| gamelog_table, rot_table = init_baselines() | |
| basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min'] | |
| basic_season_cols = ['Pos', 'Team', 'Min'] | |
| data_cols = ['Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', | |
| 'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A', | |
| 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| indv_teams = gamelog_table.drop_duplicates(subset='Team') | |
| total_teams = indv_teams.Team.values.tolist() | |
| indv_players = gamelog_table.drop_duplicates(subset='Player') | |
| total_players = indv_players.Player.values.tolist() | |
| total_dates = gamelog_table.Date.values.tolist() | |
| split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1') | |
| split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2') | |
| if split_var2 == 'Specific Teams': | |
| team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = total_teams, key='team_var1') | |
| elif split_var2 == 'All': | |
| team_var1 = total_teams | |
| split_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3') | |
| if split_var3 == 'Specific Dates': | |
| low_date = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date') | |
| if low_date is not None: | |
| low_date = pd.to_datetime(low_date).date() | |
| high_date = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date') | |
| if high_date is not None: | |
| high_date = pd.to_datetime(high_date).date() | |
| elif split_var3 == 'All': | |
| low_date = gamelog_table['Date'].min() | |
| high_date = gamelog_table['Date'].max() | |
| split_var4 = st.radio("Would you like to view all players or specific ones?", ('All', 'Specific Players'), key='split_var4') | |
| if split_var4 == 'Specific Players': | |
| player_var1 = st.multiselect('Which players would you like to include in the tables?', options = total_players, key='player_var1') | |
| elif split_var4 == 'All': | |
| player_var1 = total_players | |
| min_var1 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var1') | |
| with col2: | |
| working_data = gamelog_table | |
| if split_var1 == 'Season Logs': | |
| choose_cols = st.container() | |
| with choose_cols: | |
| choose_disp = st.multiselect('Which stats would you like to view?', options = season_data_cols, default = season_data_cols, key='col_display') | |
| disp_stats = basic_season_cols + choose_disp | |
| display = st.container() | |
| working_data = working_data[working_data['Date'] >= low_date] | |
| working_data = working_data[working_data['Date'] <= high_date] | |
| working_data = working_data[working_data['Min'] >= min_var1[0]] | |
| working_data = working_data[working_data['Min'] <= min_var1[1]] | |
| working_data = working_data[working_data['Team'].isin(team_var1)] | |
| working_data = working_data[working_data['Player'].isin(player_var1)] | |
| season_long_table = seasonlong_build(working_data) | |
| season_long_table = season_long_table.set_index('Player') | |
| season_long_table_disp = season_long_table.reindex(disp_stats,axis="columns") | |
| display.dataframe(season_long_table_disp.style.format(precision=2), height=750, use_container_width = True) | |
| st.download_button( | |
| label="Export seasonlogs Model", | |
| data=convert_df_to_csv(season_long_table), | |
| file_name='Seasonlogs_NBA_View.csv', | |
| mime='text/csv', | |
| ) | |
| elif split_var1 == 'Gamelogs': | |
| choose_cols = st.container() | |
| with choose_cols: | |
| choose_disp_gamelog = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='choose_disp_gamelog') | |
| gamelog_disp_stats = basic_cols + choose_disp_gamelog | |
| working_data = working_data[working_data['Date'] >= low_date] | |
| working_data = working_data[working_data['Date'] <= high_date] | |
| working_data = working_data[working_data['Min'] >= min_var1[0]] | |
| working_data = working_data[working_data['Min'] <= min_var1[1]] | |
| working_data = working_data[working_data['Team'].isin(team_var1)] | |
| working_data = working_data[working_data['Player'].isin(player_var1)] | |
| working_data = working_data.reset_index(drop=True) | |
| gamelog_data = working_data.reindex(gamelog_disp_stats,axis="columns") | |
| display = st.container() | |
| bottom_menu = st.columns((4, 1, 1)) | |
| with bottom_menu[2]: | |
| batch_size = st.selectbox("Page Size", options=[25, 50, 100]) | |
| with bottom_menu[1]: | |
| total_pages = ( | |
| int(len(gamelog_data) / batch_size) if int(len(gamelog_data) / batch_size) > 0 else 1 | |
| ) | |
| current_page = st.number_input( | |
| "Page", min_value=1, max_value=total_pages, step=1 | |
| ) | |
| with bottom_menu[0]: | |
| st.markdown(f"Page **{current_page}** of **{total_pages}** ") | |
| pages = split_frame(gamelog_data, batch_size) | |
| # pages = pages.set_index('Player') | |
| display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export gamelogs Model", | |
| data=convert_df_to_csv(gamelog_data), | |
| file_name='Gamelogs_NBA_View.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| if st.button("Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| gamelog_table, rot_table = init_baselines() | |
| basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min'] | |
| basic_season_cols = ['Pos', 'Team', 'Min'] | |
| data_cols = ['Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', | |
| 'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A', | |
| 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| indv_teams = gamelog_table.drop_duplicates(subset='Team') | |
| total_teams = indv_teams.Team.values.tolist() | |
| indv_players = gamelog_table.drop_duplicates(subset='Player') | |
| total_players = indv_players.Player.values.tolist() | |
| total_dates = gamelog_table.Date.values.tolist() | |
| corr_var = st.radio("Are you correlating fantasy or minutes?", ('Fantasy', 'Minutes'), key='corr_var') | |
| split_var1_t2 = st.radio("Would you like to view specific teams or specific players?", ('Specific Teams', 'Specific Players'), key='split_var1_t2') | |
| if split_var1_t2 == 'Specific Teams': | |
| corr_var1_t2 = st.multiselect('Which teams would you like to include in the correlation?', options = total_teams, key='corr_var1_t2') | |
| elif split_var1_t2 == 'Specific Players': | |
| corr_var1_t2 = st.multiselect('Which players would you like to include in the correlation?', options = total_players, key='corr_var1_t2') | |
| split_var2_t2 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='split_var3_t2') | |
| if split_var2_t2 == 'Specific Dates': | |
| low_date_t2 = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date_t2') | |
| if low_date_t2 is not None: | |
| low_date_t2 = pd.to_datetime(low_date_t2).date() | |
| high_date_t2 = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date_t2') | |
| if high_date_t2 is not None: | |
| high_date_t2 = pd.to_datetime(high_date_t2).date() | |
| elif split_var2_t2 == 'All': | |
| low_date_t2 = gamelog_table['Date'].min() | |
| high_date_t2 = gamelog_table['Date'].max() | |
| min_var1_t2 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var1_t2') | |
| with col2: | |
| working_data = gamelog_table | |
| if split_var1_t2 == 'Specific Teams': | |
| display = st.container() | |
| working_data = working_data.sort_values(by='Fantasy', ascending=False) | |
| working_data = working_data[working_data['Date'] >= low_date_t2] | |
| working_data = working_data[working_data['Date'] <= high_date_t2] | |
| working_data = working_data[working_data['Min'] >= min_var1_t2[0]] | |
| working_data = working_data[working_data['Min'] <= min_var1_t2[1]] | |
| working_data = working_data[working_data['Team'].isin(corr_var1_t2)] | |
| if corr_var == 'Fantasy': | |
| corr_display = run_fantasy_corr(working_data) | |
| elif corr_var == 'Minutes': | |
| corr_display = run_min_corr(working_data) | |
| display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True) | |
| elif split_var1_t2 == 'Specific Players': | |
| display = st.container() | |
| working_data = working_data.sort_values(by='Fantasy', ascending=False) | |
| working_data = working_data[working_data['Date'] >= low_date_t2] | |
| working_data = working_data[working_data['Date'] <= high_date_t2] | |
| working_data = working_data[working_data['Min'] >= min_var1_t2[0]] | |
| working_data = working_data[working_data['Min'] <= min_var1_t2[1]] | |
| working_data = working_data[working_data['Player'].isin(corr_var1_t2)] | |
| if corr_var == 'Fantasy': | |
| corr_display = run_fantasy_corr(working_data) | |
| elif corr_var == 'Minutes': | |
| corr_display = run_min_corr(working_data) | |
| display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True) | |
| st.download_button( | |
| label="Export Correlations Model", | |
| data=convert_df_to_csv(corr_display), | |
| file_name='Correlations_NBA_View.csv', | |
| mime='text/csv', | |
| ) | |
| with tab3: | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| if st.button("Reset Data", key='reset3'): | |
| st.cache_data.clear() | |
| gamelog_table, rot_table = init_baselines() | |
| basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min'] | |
| basic_season_cols = ['Pos', 'Team', 'Min'] | |
| data_cols = ['Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', | |
| 'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A', | |
| 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| indv_teams = gamelog_table.drop_duplicates(subset='Team') | |
| total_teams = indv_teams.Team.values.tolist() | |
| indv_players = gamelog_table.drop_duplicates(subset='Player') | |
| total_players = indv_players.Player.values.tolist() | |
| total_dates = gamelog_table.Date.values.tolist() | |
| team_var3 = st.selectbox('Which opponent would you like to view?', options = total_teams, key='team_var3') | |
| pos_var3 = st.selectbox('Which position would you like to view?', options = ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_var3') | |
| disp_var3 = st.radio('Which view would you like to see?', options = ['Fantasy', 'Stats'], key='disp_var3') | |
| date_var3 = st.radio("Would you like to view all dates or specific ones?", ('All', 'Specific Dates'), key='date_var3') | |
| if date_var3 == 'Specific Dates': | |
| low_date3 = st.date_input('Min Date:', value=None, format="YYYY-MM-DD", key='low_date3') | |
| if low_date3 is not None: | |
| low_date3 = pd.to_datetime(low_date3).date() | |
| high_date3 = st.date_input('Max Date:', value=None, format="YYYY-MM-DD", key='high_date3') | |
| if high_date3 is not None: | |
| high_date3 = pd.to_datetime(high_date3).date() | |
| elif date_var3 == 'All': | |
| low_date3 = gamelog_table['Date'].min() | |
| high_date3 = gamelog_table['Date'].max() | |
| min_var3 = st.slider("Is there a certain minutes range you want to view?", 0, 60, (0, 60), key='min_var3') | |
| with col2: | |
| if disp_var3 == 'Stats': | |
| choose_cols = st.container() | |
| with choose_cols: | |
| choose_disp_matchup = st.multiselect('Which stats would you like to view?', options = data_cols, default = data_cols, key='choose_disp_matchup') | |
| matchup_disp_stats = basic_cols + choose_disp_matchup | |
| working_data = gamelog_table | |
| working_data = working_data[gamelog_table['Date'] >= low_date3] | |
| working_data = working_data[gamelog_table['Date'] <= high_date3] | |
| season_long_table = seasonlong_build(working_data) | |
| fantasy_dict = dict(zip(season_long_table['Player'], season_long_table['Fantasy'])) | |
| fd_fantasy_dict = dict(zip(season_long_table['Player'], season_long_table['FD_Fantasy'])) | |
| working_data = working_data[working_data['Pos'] == pos_var3] | |
| working_data = working_data[working_data['Min'] >= min_var3[0]] | |
| working_data = working_data[working_data['Min'] <= min_var3[1]] | |
| working_data = working_data[working_data['Opp'] == team_var3] | |
| working_data = working_data.reset_index(drop=True) | |
| if disp_var3 == 'Fantasy': | |
| gamelog_display = working_data[['Player', 'Pos', 'Team', 'Opp', 'Date', 'Min', 'Fantasy', 'FD_Fantasy']] | |
| elif disp_var3 == 'Stats': | |
| gamelog_data = working_data.reindex(matchup_disp_stats,axis="columns") | |
| gamelog_display = gamelog_data | |
| gamelog_display['Avg_Fantasy'] = gamelog_display['Player'].map(fantasy_dict) | |
| gamelog_display['Avg_FD_Fantasy'] = gamelog_display['Player'].map(fd_fantasy_dict) | |
| display = st.container() | |
| # pages = pages.set_index('Player') | |
| display.dataframe(gamelog_display.style.format(precision=2), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export Matchups Model", | |
| data=convert_df_to_csv(gamelog_display), | |
| file_name='Matchups_NBA_View.csv', | |
| mime='text/csv', | |
| ) | |
| with tab4: | |
| col1, col2 = st.columns([1, 9]) | |
| with col1: | |
| if st.button("Reset Data", key='reset3'): | |
| st.cache_data.clear() | |
| gamelog_table, rot_table = init_baselines() | |
| basic_cols = ['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min'] | |
| basic_season_cols = ['Pos', 'Team', 'Min'] | |
| data_cols = ['Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', | |
| 'FG3A', 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Touch_per_min', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| season_data_cols = ['Touches', 'Touch/Min', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A', | |
| 'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB', | |
| 'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy', | |
| 'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'] | |
| indv_teams = gamelog_table.drop_duplicates(subset='Team') | |
| total_teams = indv_teams.Team.values.tolist() | |
| indv_players = gamelog_table.drop_duplicates(subset='Player') | |
| total_players = indv_players.Player.values.tolist() | |
| total_dates = gamelog_table.Date.values.tolist() | |
| team_var4 = st.selectbox('Which team would you like to view?', options = total_teams, key='team_var4') | |
| with col2: | |
| working_data = rot_table | |
| rot_display = working_data[working_data['Team'] == team_var4] | |
| display = st.container() | |
| rot_display = rot_display.set_index('Player') | |
| display.dataframe(rot_display.style.format(percentages_format, precision=2), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export Rotations Model", | |
| data=convert_df_to_csv(rot_display), | |
| file_name='Rotations_NBA_View.csv', | |
| mime='text/csv', | |
| ) |