James McCool commited on
Commit
f7fac57
·
1 Parent(s): 7e42d3a

fixing NHL formatting

Browse files
Files changed (1) hide show
  1. global_func/load_contest_file.py +6 -0
global_func/load_contest_file.py CHANGED
@@ -113,6 +113,8 @@ def load_contest_file(upload, type, helper = None, sport = None, portfolio = Non
113
  if type == 'Classic':
114
  if sport == 'NFL':
115
  cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' QB ', 'QB ', ' RB ', 'RB ', ' WR ', 'WR ', ' TE ', 'TE ', ' DST ', 'DST ', ' FLEX ', 'FLEX '], value=',', regex=True)
 
 
116
  elif sport == 'MLB':
117
  cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF '], value=',', regex=True)
118
  elif sport == 'MMA':
@@ -129,6 +131,8 @@ def load_contest_file(upload, type, helper = None, sport = None, portfolio = Non
129
  check_lineups = cleaned_df.copy()
130
  if sport == 'NFL':
131
  cleaned_df[['Remove', 'DST', 'FLEX', 'QB', 'RB1', 'RB2', 'TE', 'WR1', 'WR2', 'WR3']] = cleaned_df['Lineup'].str.split(',', expand=True)
 
 
132
  elif sport == 'MLB':
133
  cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
134
  elif sport == 'MMA':
@@ -146,6 +150,8 @@ def load_contest_file(upload, type, helper = None, sport = None, portfolio = Non
146
  cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
147
  if sport == 'NFL':
148
  cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
 
 
149
  elif sport == 'MLB':
150
  cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
151
  elif sport == 'MMA':
 
113
  if type == 'Classic':
114
  if sport == 'NFL':
115
  cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' QB ', 'QB ', ' RB ', 'RB ', ' WR ', 'WR ', ' TE ', 'TE ', ' DST ', 'DST ', ' FLEX ', 'FLEX '], value=',', regex=True)
116
+ elif sport == 'NHL':
117
+ cleaned_df['Lineup'] = cleaned_df['Lineup'].replace(['C ', ' C ', 'G ', ' G ', 'W ', ' W ', ' UTIL ', 'UTIL ', 'D ', ' D ',], value=',', regex=True)
118
  elif sport == 'MLB':
119
  cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF '], value=',', regex=True)
120
  elif sport == 'MMA':
 
131
  check_lineups = cleaned_df.copy()
132
  if sport == 'NFL':
133
  cleaned_df[['Remove', 'DST', 'FLEX', 'QB', 'RB1', 'RB2', 'TE', 'WR1', 'WR2', 'WR3']] = cleaned_df['Lineup'].str.split(',', expand=True)
134
+ elif sport == 'NHL':
135
+ cleaned_df[['Remove', 'C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']] = cleaned_df['Lineup'].str.split(',', expand=True)
136
  elif sport == 'MLB':
137
  cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
138
  elif sport == 'MMA':
 
150
  cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
151
  if sport == 'NFL':
152
  cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
153
+ elif sport == 'NHL':
154
+ cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']]
155
  elif sport == 'MLB':
156
  cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
157
  elif sport == 'MMA':