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Running
Running
James McCool
commited on
Commit
·
6e328e4
1
Parent(s):
82b17d9
Enhance overall stats retrieval in app.py: implement conditional logic for NBA and WNBA leagues to customize data frame structure and column renaming, improving data handling and clarity for player statistics.
Browse files
app.py
CHANGED
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@@ -67,9 +67,12 @@ def load_overall_stats(league: str):
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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dk_raw = raw_display.sort_values(by='Median', ascending=False)
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@@ -81,9 +84,12 @@ def load_overall_stats(league: str):
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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fd_raw = raw_display.sort_values(by='Median', ascending=False)
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@@ -95,9 +101,12 @@ def load_overall_stats(league: str):
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
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@@ -109,9 +118,12 @@ def load_overall_stats(league: str):
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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if league == 'NBA':
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raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
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'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
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elif league == 'WNBA':
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"})
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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dk_raw = raw_display.sort_values(by='Median', ascending=False)
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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if league == 'NBA':
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raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
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'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
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elif league == 'WNBA':
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"})
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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fd_raw = raw_display.sort_values(by='Median', ascending=False)
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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if league == 'NBA':
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raw_display = raw_display[['Name', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
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'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
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elif league == 'WNBA':
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "DK_Proj": "Median", "DK_ID": "ID", "DK_Pos": "Position", "DK_Salary": "Salary", "DK_Own": "Own"})
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
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cursor = collection.find()
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raw_display = pd.DataFrame(list(cursor))
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if league == 'NBA':
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raw_display = raw_display[['Nickname', 'Salary', 'Position', 'Team', 'Opp', 'Minutes', 'FGM', 'FGA', 'FG2M', 'FG2A', 'Threes', 'FG3A', 'FTM', 'FTA', 'TRB', 'AST', 'STL', 'BLK', 'TOV', '2P', '3P', 'FT',
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'Points', 'Rebounds', 'Assists', 'PRA', 'PR', 'PA', 'RA', 'Steals', 'Blocks', 'Turnovers', 'Fantasy', 'Raw', 'Own']]
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "Fantasy": "Median"})
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elif league == 'WNBA':
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raw_display = raw_display.rename(columns={"Name": "Player", "Nickname": "Player", "FD_Proj": "Median", "FD_ID": "ID", "FD_Pos": "Position", "FD_Salary": "Salary", "FD_Own": "Own"})
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raw_display = raw_display.loc[raw_display['Median'] > 0]
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raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
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fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
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