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279ff8e
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1 Parent(s): 1b37964

Update app.py

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Files changed (1) hide show
  1. app.py +44 -42
app.py CHANGED
@@ -78,48 +78,48 @@ def init_baselines():
78
 
79
  @st.cache_data(show_spinner=False)
80
  def seasonlong_build(data_sample):
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- season_long_table = data_sample[['PLAYER_NAME', 'TEAM_NAME']]
82
- season_long_table['Min'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Min'].transform('mean').astype(float)
83
- season_long_table['Touches'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Touches'].transform('mean').astype(float)
84
- season_long_table['FGM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('mean').astype(float)
85
- season_long_table['FGA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('mean').astype(float)
86
- season_long_table['FG%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('sum').astype(int) /
87
- data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('sum').astype(int))
88
- season_long_table['FG3M'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('mean').astype(float)
89
- season_long_table['FG3A'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('mean').astype(float)
90
- season_long_table['FG3%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('sum').astype(int) /
91
- data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('sum').astype(int))
92
- season_long_table['FTM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('mean').astype(float)
93
- season_long_table['FTA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('mean').astype(float)
94
- season_long_table['FT%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('sum').astype(int) /
95
- data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('sum').astype(int))
96
- season_long_table['OREB Chance'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['OREB Chance'].transform('mean').astype(float)
97
- season_long_table['OREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['OREB'].transform('mean').astype(float)
98
- season_long_table['DREB Chance'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DREB Chance'].transform('mean').astype(float)
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- season_long_table['DREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DREB'].transform('mean').astype(float)
100
- season_long_table['REB Chance'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB Chance'].transform('mean').astype(float)
101
- season_long_table['REB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('mean').astype(float)
102
- season_long_table['Passes'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Passes'].transform('mean').astype(float)
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- season_long_table['Alt Assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Alt Assists'].transform('mean').astype(float)
104
- season_long_table['FT Assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FT Assists'].transform('mean').astype(float)
105
- season_long_table['Assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Assists'].transform('mean').astype(float)
106
- season_long_table['Stl'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Stl'].transform('mean').astype(float)
107
- season_long_table['Blk'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Blk'].transform('mean').astype(float)
108
- season_long_table['Tov'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Tov'].transform('mean').astype(float)
109
- season_long_table['PF'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['PF'].transform('mean').astype(float)
110
- season_long_table['DD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DD'].transform('mean').astype(float)
111
- season_long_table['TD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['TD'].transform('mean').astype(float)
112
- season_long_table['Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('mean').astype(float)
113
- season_long_table['FD_Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('mean').astype(float)
114
- season_long_table['Rebound%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('sum').astype(int) /
115
- data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesTotal'].transform('sum').astype(int))
116
- season_long_table['Assists/Pass'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('sum').astype(int) /
117
- data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['passes'].transform('sum').astype(int))
118
- season_long_table['Fantasy/Touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('sum').astype(int) /
119
- data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
120
- season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
121
- data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
122
- season_long_table = season_long_table.drop_duplicates(subset='PLAYER_NAME')
123
 
124
  season_long_table = season_long_table.set_axis(['Player', 'Team', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
125
  'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
@@ -158,6 +158,7 @@ with col2:
158
  display = st.container()
159
  gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
160
  season_long_table = seasonlong_build(gamelog_table)
 
161
  display.dataframe(season_long_table.style.format(precision=2), use_container_width = True)
162
 
163
  elif split_var1 == 'Gamelogs':
@@ -176,5 +177,6 @@ with col2:
176
  with bottom_menu[0]:
177
  st.markdown(f"Page **{current_page}** of **{total_pages}** ")
178
  gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
 
179
  pages = split_frame(gamelog_table, batch_size)
180
  display.dataframe(data=pages[current_page - 1].style.format(precision=2), use_container_width=True)
 
78
 
79
  @st.cache_data(show_spinner=False)
80
  def seasonlong_build(data_sample):
81
+ season_long_table = data_sample[['Player', 'Team']]
82
+ season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
83
+ season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
84
+ season_long_table['FGM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('mean').astype(float)
85
+ season_long_table['FGA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('mean').astype(float)
86
+ season_long_table['FG%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('sum').astype(int) /
87
+ data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('sum').astype(int))
88
+ season_long_table['FG3M'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('mean').astype(float)
89
+ season_long_table['FG3A'] = data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('mean').astype(float)
90
+ season_long_table['FG3%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FG3M'].transform('sum').astype(int) /
91
+ data_sample.groupby(['Player', 'Season'], sort=False)['FG3A'].transform('sum').astype(int))
92
+ season_long_table['FTM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('mean').astype(float)
93
+ season_long_table['FTA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('mean').astype(float)
94
+ season_long_table['FT%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FTM'].transform('sum').astype(int) /
95
+ data_sample.groupby(['Player', 'Season'], sort=False)['FTA'].transform('sum').astype(int))
96
+ season_long_table['OREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB Chance'].transform('mean').astype(float)
97
+ season_long_table['OREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['OREB'].transform('mean').astype(float)
98
+ season_long_table['DREB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB Chance'].transform('mean').astype(float)
99
+ season_long_table['DREB'] = data_sample.groupby(['Player', 'Season'], sort=False)['DREB'].transform('mean').astype(float)
100
+ season_long_table['REB Chance'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('mean').astype(float)
101
+ season_long_table['REB'] = data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('mean').astype(float)
102
+ season_long_table['Passes'] = data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('mean').astype(float)
103
+ season_long_table['Alt Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Alt Assists'].transform('mean').astype(float)
104
+ season_long_table['FT Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['FT Assists'].transform('mean').astype(float)
105
+ season_long_table['Assists'] = data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('mean').astype(float)
106
+ season_long_table['Stl'] = data_sample.groupby(['Player', 'Season'], sort=False)['Stl'].transform('mean').astype(float)
107
+ season_long_table['Blk'] = data_sample.groupby(['Player', 'Season'], sort=False)['Blk'].transform('mean').astype(float)
108
+ season_long_table['Tov'] = data_sample.groupby(['Player', 'Season'], sort=False)['Tov'].transform('mean').astype(float)
109
+ season_long_table['PF'] = data_sample.groupby(['Player', 'Season'], sort=False)['PF'].transform('mean').astype(float)
110
+ season_long_table['DD'] = data_sample.groupby(['Player', 'Season'], sort=False)['DD'].transform('mean').astype(float)
111
+ season_long_table['TD'] = data_sample.groupby(['Player', 'Season'], sort=False)['TD'].transform('mean').astype(float)
112
+ season_long_table['Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('mean').astype(float)
113
+ season_long_table['FD_Fantasy'] = data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('mean').astype(float)
114
+ season_long_table['Rebound%'] = (data_sample.groupby(['Player', 'Season'], sort=False)['REB'].transform('sum').astype(int) /
115
+ data_sample.groupby(['Player', 'Season'], sort=False)['reboundChancesTotal'].transform('sum').astype(int))
116
+ season_long_table['Assists/Pass'] = (data_sample.groupby(['Player', 'Season'], sort=False)['assists'].transform('sum').astype(int) /
117
+ data_sample.groupby(['Player', 'Season'], sort=False)['passes'].transform('sum').astype(int))
118
+ season_long_table['Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('sum').astype(int) /
119
+ data_sample.groupby(['Player', 'Season'], sort=False)['touches'].transform('sum').astype(int))
120
+ season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
121
+ data_sample.groupby(['Player', 'Season'], sort=False)['touches'].transform('sum').astype(int))
122
+ season_long_table = season_long_table.drop_duplicates(subset='Player')
123
 
124
  season_long_table = season_long_table.set_axis(['Player', 'Team', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
125
  'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
 
158
  display = st.container()
159
  gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
160
  season_long_table = seasonlong_build(gamelog_table)
161
+ season_long_table = season_long_table.set_index('Player')
162
  display.dataframe(season_long_table.style.format(precision=2), use_container_width = True)
163
 
164
  elif split_var1 == 'Gamelogs':
 
177
  with bottom_menu[0]:
178
  st.markdown(f"Page **{current_page}** of **{total_pages}** ")
179
  gamelog_table = gamelog_table[gamelog_table['Team'].isin(team_var1)]
180
+ gamelog_table = gamelog_table.set_index('Player')
181
  pages = split_frame(gamelog_table, batch_size)
182
  display.dataframe(data=pages[current_page - 1].style.format(precision=2), use_container_width=True)