Multichem commited on
Commit
6b1ef5e
·
1 Parent(s): 0edd0c1

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +48 -6
app.py CHANGED
@@ -1,3 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  import streamlit as st
2
  st.set_page_config(layout="wide")
3
 
@@ -46,7 +88,7 @@ def init_baselines():
46
  raw_display = raw_display[1:]
47
  raw_display = raw_display.reset_index(drop=True)
48
  gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
49
- gamelog_table = gamelog_table[['PLAYER_NAME', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A',
50
  'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
51
  'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy']]
52
  gamelog_table['assists'].replace("", 0, inplace=True)
@@ -66,11 +108,11 @@ def init_baselines():
66
  gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
67
  gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
68
  gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
69
- data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
70
  gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
71
  gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
72
 
73
- gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
74
  'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
75
  'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
76
  'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
@@ -79,7 +121,7 @@ def init_baselines():
79
 
80
  @st.cache_data(show_spinner=False)
81
  def seasonlong_build(data_sample):
82
- season_long_table = data_sample[['Player', 'Team']]
83
  season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
84
  season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
85
  season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
@@ -125,7 +167,7 @@ def seasonlong_build(data_sample):
125
 
126
  season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False)
127
 
128
- season_long_table = season_long_table.set_axis(['Player', 'Team', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
129
  'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
130
  'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
131
  'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
@@ -329,4 +371,4 @@ with tab2:
329
  corr_display = run_fantasy_corr(gamelog_table)
330
  elif corr_var == 'Minutes':
331
  corr_display = run_min_corr(gamelog_table)
332
- display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
 
1
+ Hugging Face's logo
2
+ Hugging Face
3
+ Search models, datasets, users...
4
+ Models
5
+ Datasets
6
+ Spaces
7
+ Docs
8
+ Solutions
9
+ Pricing
10
+
11
+
12
+
13
+ Spaces:
14
+
15
+ Multichem
16
+ /
17
+ NBA_Gamelogs
18
+
19
+
20
+ like
21
+ 0
22
+
23
+ Logs
24
+ App
25
+ Files
26
+ Community
27
+ Settings
28
+ NBA_Gamelogs
29
+ /
30
+ app.py
31
+ Multichem's picture
32
+ Multichem
33
+ Update app.py
34
+ 0edd0c1
35
+ 17 minutes ago
36
+ raw
37
+ history
38
+ blame
39
+ edit
40
+ delete
41
+ No virus
42
+ 23.1 kB
43
  import streamlit as st
44
  st.set_page_config(layout="wide")
45
 
 
88
  raw_display = raw_display[1:]
89
  raw_display = raw_display.reset_index(drop=True)
90
  gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
91
+ 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',
92
  'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
93
  'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy']]
94
  gamelog_table['assists'].replace("", 0, inplace=True)
 
108
  gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
109
  gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
110
  gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
111
+ data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
112
  gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
113
  gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
114
 
115
+ gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
116
  'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
117
  'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
118
  'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
 
121
 
122
  @st.cache_data(show_spinner=False)
123
  def seasonlong_build(data_sample):
124
+ season_long_table = data_sample[['Player', 'Pos', 'Team']]
125
  season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
126
  season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
127
  season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
 
167
 
168
  season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False)
169
 
170
+ season_long_table = season_long_table.set_axis(['Player', 'Pos', 'Team', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
171
  'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
172
  'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
173
  'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
 
371
  corr_display = run_fantasy_corr(gamelog_table)
372
  elif corr_var == 'Minutes':
373
  corr_display = run_min_corr(gamelog_table)
374
+ display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)