Multichem commited on
Commit
fd88138
·
1 Parent(s): b5d82d2

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +4 -4
app.py CHANGED
@@ -53,7 +53,7 @@ def init_baselines():
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  gamelog_table['reboundChancesTotal'].replace("", 0, inplace=True)
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  gamelog_table['passes'].replace("", 0, inplace=True)
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  gamelog_table['touches'].replace("", 0, inplace=True)
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- gamelog_table['MIN'].replace("", 0, inplace=True)
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  gamelog_table['Fantasy'].replace("", 0, inplace=True)
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  gamelog_table['FD_Fantasy'].replace("", 0, inplace=True)
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  gamelog_table['REB'] = gamelog_table['REB'].astype(int)
@@ -61,7 +61,7 @@ def init_baselines():
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  gamelog_table['reboundChancesTotal'] = gamelog_table['reboundChancesTotal'].astype(int)
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  gamelog_table['passes'] = gamelog_table['passes'].astype(int)
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  gamelog_table['touches'] = gamelog_table['touches'].astype(int)
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- gamelog_table['MIN'] = gamelog_table['MIN'].astype(float)
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  gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float)
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  gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
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  gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
@@ -85,6 +85,8 @@ def seasonlong_build(data_sample):
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  season_long_table = data_sample[['Player', 'Pos', 'Team']]
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  season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
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  season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
 
 
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  season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
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  season_long_table['FGM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('mean').astype(float)
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  season_long_table['FGA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('mean').astype(float)
@@ -120,8 +122,6 @@ def seasonlong_build(data_sample):
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  data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('sum').astype(int))
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  season_long_table['Assists/Pass'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('sum').astype(int) /
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  data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('sum').astype(int))
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- season_long_table['Touch/Min'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int) /
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- data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('sum').astype(int))
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  season_long_table['Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('sum').astype(int) /
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  data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int))
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  season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
 
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  gamelog_table['reboundChancesTotal'].replace("", 0, inplace=True)
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  gamelog_table['passes'].replace("", 0, inplace=True)
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  gamelog_table['touches'].replace("", 0, inplace=True)
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+ # gamelog_table['MIN'].replace("", 0, inplace=True)
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  gamelog_table['Fantasy'].replace("", 0, inplace=True)
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  gamelog_table['FD_Fantasy'].replace("", 0, inplace=True)
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  gamelog_table['REB'] = gamelog_table['REB'].astype(int)
 
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  gamelog_table['reboundChancesTotal'] = gamelog_table['reboundChancesTotal'].astype(int)
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  gamelog_table['passes'] = gamelog_table['passes'].astype(int)
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  gamelog_table['touches'] = gamelog_table['touches'].astype(int)
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+ gamelog_table['MIN'] = gamelog_table['MIN'].astype(int)
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  gamelog_table['Fantasy'] = gamelog_table['Fantasy'].astype(float)
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  gamelog_table['FD_Fantasy'] = gamelog_table['FD_Fantasy'].astype(float)
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  gamelog_table['rebound%'] = gamelog_table['REB'] / gamelog_table['reboundChancesTotal']
 
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  season_long_table = data_sample[['Player', 'Pos', 'Team']]
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  season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
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  season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
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+ season_long_table['Touch/Min'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int) /
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+ data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('sum').astype(int))
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  season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
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  season_long_table['FGM'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGM'].transform('mean').astype(float)
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  season_long_table['FGA'] = data_sample.groupby(['Player', 'Season'], sort=False)['FGA'].transform('mean').astype(float)
 
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  data_sample.groupby(['Player', 'Season'], sort=False)['REB Chance'].transform('sum').astype(int))
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  season_long_table['Assists/Pass'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Assists'].transform('sum').astype(int) /
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  data_sample.groupby(['Player', 'Season'], sort=False)['Passes'].transform('sum').astype(int))
 
 
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  season_long_table['Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['Fantasy'].transform('sum').astype(int) /
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  data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('sum').astype(int))
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  season_long_table['FD Fantasy/Touch'] = (data_sample.groupby(['Player', 'Season'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /