James McCool
commited on
Commit
·
f7fac57
1
Parent(s):
7e42d3a
fixing NHL formatting
Browse files
global_func/load_contest_file.py
CHANGED
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@@ -113,6 +113,8 @@ def load_contest_file(upload, type, helper = None, sport = None, portfolio = Non
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| 113 |
if type == 'Classic':
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| 114 |
if sport == 'NFL':
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| 115 |
cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' QB ', 'QB ', ' RB ', 'RB ', ' WR ', 'WR ', ' TE ', 'TE ', ' DST ', 'DST ', ' FLEX ', 'FLEX '], value=',', regex=True)
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| 116 |
elif sport == 'MLB':
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| 117 |
cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF '], value=',', regex=True)
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| 118 |
elif sport == 'MMA':
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@@ -129,6 +131,8 @@ def load_contest_file(upload, type, helper = None, sport = None, portfolio = Non
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| 129 |
check_lineups = cleaned_df.copy()
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| 130 |
if sport == 'NFL':
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| 131 |
cleaned_df[['Remove', 'DST', 'FLEX', 'QB', 'RB1', 'RB2', 'TE', 'WR1', 'WR2', 'WR3']] = cleaned_df['Lineup'].str.split(',', expand=True)
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| 132 |
elif sport == 'MLB':
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| 133 |
cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
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| 134 |
elif sport == 'MMA':
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@@ -146,6 +150,8 @@ def load_contest_file(upload, type, helper = None, sport = None, portfolio = Non
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| 146 |
cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
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| 147 |
if sport == 'NFL':
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cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
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| 149 |
elif sport == 'MLB':
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cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
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| 151 |
elif sport == 'MMA':
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| 113 |
if type == 'Classic':
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| 114 |
if sport == 'NFL':
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| 115 |
cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' QB ', 'QB ', ' RB ', 'RB ', ' WR ', 'WR ', ' TE ', 'TE ', ' DST ', 'DST ', ' FLEX ', 'FLEX '], value=',', regex=True)
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| 116 |
+
elif sport == 'NHL':
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| 117 |
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cleaned_df['Lineup'] = cleaned_df['Lineup'].replace(['C ', ' C ', 'G ', ' G ', 'W ', ' W ', ' UTIL ', 'UTIL ', 'D ', ' D ',], value=',', regex=True)
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| 118 |
elif sport == 'MLB':
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| 119 |
cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF '], value=',', regex=True)
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| 120 |
elif sport == 'MMA':
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| 131 |
check_lineups = cleaned_df.copy()
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| 132 |
if sport == 'NFL':
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| 133 |
cleaned_df[['Remove', 'DST', 'FLEX', 'QB', 'RB1', 'RB2', 'TE', 'WR1', 'WR2', 'WR3']] = cleaned_df['Lineup'].str.split(',', expand=True)
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| 134 |
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elif sport == 'NHL':
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| 135 |
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cleaned_df[['Remove', 'C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']] = cleaned_df['Lineup'].str.split(',', expand=True)
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| 136 |
elif sport == 'MLB':
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| 137 |
cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
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| 138 |
elif sport == 'MMA':
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cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
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if sport == 'NFL':
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cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']]
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| 153 |
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elif sport == 'NHL':
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cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'UTIL']]
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| 155 |
elif sport == 'MLB':
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cleaned_df = cleaned_df[['BaseName', 'EntryCount', 'P1', 'P2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']]
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| 157 |
elif sport == 'MMA':
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