James McCool commited on
Commit
9fa0745
·
1 Parent(s): 9f6bb94

Convert 'Median' column to float type in app.py: ensure consistent data handling and prevent type-related issues during overall stats retrieval.

Browse files
Files changed (1) hide show
  1. app.py +2 -0
app.py CHANGED
@@ -137,6 +137,7 @@ def load_overall_stats(league: str):
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  raw_display = pd.DataFrame(list(cursor))
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  raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
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  'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
 
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  raw_display = raw_display.rename(columns={"player_id": "player_ID"})
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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')
@@ -162,6 +163,7 @@ def load_overall_stats(league: str):
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  raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
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  'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
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  raw_display = raw_display.rename(columns={"player_id": "player_ID"})
 
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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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  roo_raw = raw_display.sort_values(by='Median', ascending=False)
 
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  raw_display = pd.DataFrame(list(cursor))
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  raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
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  'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
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+ raw_display['Median'] = raw_display['Median'].astype(float)
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  raw_display = raw_display.rename(columns={"player_id": "player_ID"})
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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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  raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
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  'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_id']]
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  raw_display = raw_display.rename(columns={"player_id": "player_ID"})
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+ raw_display['Median'] = raw_display['Median'].astype(float)
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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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  roo_raw = raw_display.sort_values(by='Median', ascending=False)