Hugging Face's logo

Spaces:
Multichem
/
NBA_Gamelogs
Runtime error

App Files Community
NBA_Gamelogs
File size: 12,928 Bytes 
eb17727
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c276129
eb17727
 
 
 
c276129
 
 
 
67e2e04
 
 
 
f81438a
 
bf83885
 
 
 
 
588d083
 
c276129
 
f81438a
 
cc95c78
 
eb17727
d9ad3e8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c276129
 
d9ad3e8
eb17727
c276129
 
 
 
eb17727
 
 
 
d9ad3e8
eb17727
 
 
 
 
d9ad3e8
6919d54
eb17727
 
c276129
f7d4c5a
d9ad3e8
6919d54
 
c276129
f7d4c5a
c276129
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6919d54
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
 
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": "[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'

@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')
    
    return gamelog_table

@st.cache_data(show_spinner=False)
def seasonlong_build(data_sample):
    season_long_table = data_sample[['PLAYER_NAME', 'TEAM_NAME']]
    season_long_table['MIN'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['MIN'].transform('mean').astype(float)
    season_long_table['touches'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('mean').astype(float)
    season_long_table['FGM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('mean').astype(float)
    season_long_table['FGA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('mean').astype(float)
    season_long_table['FG_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('sum').astype(int) /
                                   data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('sum').astype(int))
    season_long_table['FG3M'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('mean').astype(float)
    season_long_table['FG3A'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('mean').astype(float)
    season_long_table['FG3_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('sum').astype(int) /
                                   data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('sum').astype(int))
    season_long_table['FTM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('mean').astype(float)
    season_long_table['FTA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('mean').astype(float)
    season_long_table['FT_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('sum').astype(int) /
                                   data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('sum').astype(int))
    season_long_table['reboundChancesOffensive'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesOffensive'].transform('mean').astype(float)
    season_long_table['OREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['OREB'].transform('mean').astype(float)
    season_long_table['reboundChancesDefensive'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesDefensive'].transform('mean').astype(float)
    season_long_table['DREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DREB'].transform('mean').astype(float)
    season_long_table['reboundChancesTotal'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesTotal'].transform('mean').astype(float)
    season_long_table['REB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('mean').astype(float)
    season_long_table['passes'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['passes'].transform('mean').astype(float)
    season_long_table['secondaryAssists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['secondaryAssists'].transform('mean').astype(float)
    season_long_table['freeThrowAssists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['freeThrowAssists'].transform('mean').astype(float)
    season_long_table['assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('mean').astype(float)
    season_long_table['STL'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['STL'].transform('mean').astype(float)
    season_long_table['BLK'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['BLK'].transform('mean').astype(float)
    season_long_table['TOV'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['TOV'].transform('mean').astype(float)
    season_long_table['PF'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['PF'].transform('mean').astype(float)
    season_long_table['DD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DD'].transform('mean').astype(float)
    season_long_table['TD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['TD'].transform('mean').astype(float)
    season_long_table['Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('mean').astype(float)
    season_long_table['FD_Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('mean').astype(float)
    season_long_table['rebound%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('sum').astype(int) /
                                     data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesTotal'].transform('sum').astype(int))
    season_long_table['assists_per_pass'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('sum').astype(int) /
                                             data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['passes'].transform('sum').astype(int))
    season_long_table['Fantasy_per_touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('sum').astype(int) /
                                              data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
    season_long_table['FD_Fantasy_per_touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
                                                 data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
    season_long_table = season_long_table.drop_duplicates(subset='PLAYER_NAME')

    return season_long_table

@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()

col1, col2 = st.columns([1, 9])
with col1:
    if st.button("Reset Data", key='reset1'):
              st.cache_data.clear()
              gamelog_table = init_baselines()
    split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')

with col2:
    if split_var1 == 'Season Logs':
        display = st.container()
        season_long_table = seasonlong_build(gamelog_table)
        display.dataframe(season_long_table.style.format(precision=2), use_container_width = True)  
        
    elif split_var1 == 'Gamelogs':
        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)
        display.dataframe(data=pages[current_page - 1].style.format(precision=2), use_container_width=True)