Create app.py

app.py

@@ -0,0 +1,330 @@
+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
+
+@st.cache_resource
+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": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
+          "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'
+
+@st.cache_resource(ttl = 600)
+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', 'TEAM_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['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['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['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', 'TEAM_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', 'Team', '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', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
+    
+    return gamelog_table
+
+@st.cache_data(show_spinner=False)
+def seasonlong_build(data_sample):
+    season_long_table = data_sample[['Player', '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['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.set_axis(['Player', 'Team', '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', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
+
+    return season_long_table
+
+@st.cache_data(show_spinner=False)
+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
+
+@st.cache_data(show_spinner=False)
+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
+
+@st.cache_data(show_spinner=False)
+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 = init_baselines()
+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 = st.tabs(['Gamelogs', 'Correlation Matrix'])
+
+with tab1:
+    col1, col2 = st.columns([1, 9])
+    with col1:
+        if st.button("Reset Data", key='reset1'):
+                  st.cache_data.clear()
+                  gamelog_table = init_baselines()
+                  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:
+        if split_var1 == 'Season Logs':
+            display = st.container()
+            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date]
+            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1[1]]
+            gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
+            gamelog_table = gamelog_table[gamelog_table['Player'].isin(player_var1)]
+            season_long_table = seasonlong_build(gamelog_table)
+            season_long_table = season_long_table.set_index('Player')
+            display.dataframe(season_long_table.style.format(precision=2), height=750, use_container_width = True)  
+            
+        elif split_var1 == 'Gamelogs':
+            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date]
+            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1[1]]
+            gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
+            gamelog_table = gamelog_table[gamelog_table['Player'].isin(player_var1)]
+            gamelog_table = gamelog_table.reset_index(drop=True)
+            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_table) / batch_size) if int(len(gamelog_table) / 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_table, batch_size)
+            # pages = pages.set_index('Player')
+            display.dataframe(data=pages[current_page - 1].style.format(precision=2), height=500, use_container_width=True)
+            
+with tab2:
+    col1, col2 = st.columns([1, 9])
+    with col1:
+        if st.button("Reset Data", key='reset2'):
+                  st.cache_data.clear()
+                  gamelog_table = init_baselines()
+                  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:
+        if split_var1_t2 == 'Specific Teams':
+            display = st.container()
+            gamelog_table = gamelog_table.sort_values(by='Fantasy', ascending=False)
+            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1_t2[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1_t2[1]]
+            gamelog_table = gamelog_table[gamelog_table['Team'].isin(corr_var1_t2)]
+            if corr_var == 'Fantasy':
+                corr_display = run_fantasy_corr(gamelog_table)
+            elif corr_var == 'Minutes':
+                corr_display = run_min_corr(gamelog_table)
+            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()
+            gamelog_table = gamelog_table.sort_values(by='Fantasy', ascending=False)
+            gamelog_table = gamelog_table[gamelog_table['Date'] >= low_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['Date'] <= high_date_t2]
+            gamelog_table = gamelog_table[gamelog_table['Min'] >= min_var1_t2[0]]
+            gamelog_table = gamelog_table[gamelog_table['Min'] <= min_var1_t2[1]]
+            gamelog_table = gamelog_table[gamelog_table['Player'].isin(corr_var1_t2)]
+            if corr_var == 'Fantasy':
+                corr_display = run_fantasy_corr(gamelog_table)
+            elif corr_var == 'Minutes':
+                corr_display = run_min_corr(gamelog_table)
+            display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)