James McCool commited on
Commit
94323a4
·
1 Parent(s): f290cdf

Refactor app.py to ensure unique player entries across multiple DataFrames. Removed duplicate player entries in nfl_dk_sd_raw, nfl_fd_sd_raw, display_Proj, and display_baselines DataFrames by implementing drop_duplicates method for 'Player'. This enhancement improves data integrity and consistency in player projections.

Browse files
Files changed (1) hide show
  1. app.py +4 -4
app.py CHANGED
@@ -60,7 +60,6 @@ def init_baselines():
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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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  nfl_dk_sd_raw = raw_display.sort_values(by='Median', ascending=False)
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- nfl_dk_sd_raw = nfl_dk_sd_raw.drop_duplicates(subset=['Player'])
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  collection = nfl_db["FD_SD_NFL_ROO"]
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  cursor = collection.find()
@@ -71,7 +70,6 @@ def init_baselines():
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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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  nfl_fd_sd_raw = raw_display.sort_values(by='Median', ascending=False)
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- nfl_fd_sd_raw = nfl_fd_sd_raw.drop_duplicates(subset=['Player'])
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  try:
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  nba_timestamp = nba_dk_sd_raw['timestamp'].values[0]
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  except:
@@ -145,7 +143,8 @@ with tab1:
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  with hold_container:
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  hold_container = st.empty()
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- display_Proj = display_Proj
 
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  if sport_var2 == 'NBA':
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  st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nba_player_roo_format, precision=2), height=1000, use_container_width = True)
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  elif sport_var2 == 'NFL':
@@ -245,7 +244,8 @@ with tab2:
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  display_baselines = display_baselines.sort_values(by='Median', ascending=False)
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  display_baselines['cpt_lock'] = np.where(display_baselines['Player'].isin(lock_var1), 1, 0)
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  display_baselines['lock'] = np.where(display_baselines['Player'].isin(lock_var2), 1, 0)
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-
 
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  st.session_state.display_baselines = display_baselines.copy()
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  st.session_state.export_baselines = export_baselines.copy()
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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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  nfl_dk_sd_raw = raw_display.sort_values(by='Median', ascending=False)
 
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  collection = nfl_db["FD_SD_NFL_ROO"]
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  cursor = collection.find()
 
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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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  nfl_fd_sd_raw = raw_display.sort_values(by='Median', ascending=False)
 
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  try:
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  nba_timestamp = nba_dk_sd_raw['timestamp'].values[0]
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  except:
 
143
 
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  with hold_container:
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  hold_container = st.empty()
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+ display_Proj = display_Proj.drop_duplicates(subset=['Player'])
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+
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  if sport_var2 == 'NBA':
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  st.dataframe(display_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(nba_player_roo_format, precision=2), height=1000, use_container_width = True)
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  elif sport_var2 == 'NFL':
 
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  display_baselines = display_baselines.sort_values(by='Median', ascending=False)
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  display_baselines['cpt_lock'] = np.where(display_baselines['Player'].isin(lock_var1), 1, 0)
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  display_baselines['lock'] = np.where(display_baselines['Player'].isin(lock_var2), 1, 0)
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+ display_baselines = display_baselines.drop_duplicates(subset=['Player'])
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+
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  st.session_state.display_baselines = display_baselines.copy()
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  st.session_state.export_baselines = export_baselines.copy()
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