Update app.py
Browse files
app.py
CHANGED
@@ -61,6 +61,7 @@ def init_baselines():
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raw_display.replace('', np.nan, inplace=True)
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raw_display = raw_display[['NBAID', 'PID', 'Player', 'TC', 'MP (Today)', 'Next Game', 'H/R', 'Injury Notes', 'Player Impact per 48', 'Player Impact',
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'Team PM', 'Last Updated']]
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public_minutes = raw_display[raw_display['NBAID'] != ""]
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sh = gcservice_account.open_by_url(NBABettingModel)
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@@ -73,6 +74,7 @@ def init_baselines():
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raw_display.replace('', np.nan, inplace=True)
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raw_display = raw_display[['PID', 'Player', 'Team', 'Avg Minutes last 30 days for team', 'Minutes Projection', 'Rotation Impact (versus last 30 days)',
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'Injury Notes', 'Minute Change', 'Baseline Team PM', 'Net Rotation PM +/- for Team', 'Projected PM for Game', 'Offset', 'Rank']]
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player_impact = raw_display[raw_display['PID'] != ""]
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return public_minutes, player_impact
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@@ -98,9 +100,9 @@ with tab1:
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player_min_disp = player_min_disp.sort_values(by=['TC', 'MP (Today)'], ascending=[False, True])
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st.dataframe(player_min_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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st.download_button(
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label="Export
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data=convert_df_to_csv(public_minutes),
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file_name='
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mime='text/csv',
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)
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@@ -118,8 +120,8 @@ with tab2:
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player_impact_disp = player_impact_disp.sort_values(by=['Team', 'Rotation Impact (versus last 30 days)'], ascending=[False, True])
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st.dataframe(player_impact_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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st.download_button(
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label="Export
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data=convert_df_to_csv(player_impact),
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file_name='
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mime='text/csv',
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)
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raw_display.replace('', np.nan, inplace=True)
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raw_display = raw_display[['NBAID', 'PID', 'Player', 'TC', 'MP (Today)', 'Next Game', 'H/R', 'Injury Notes', 'Player Impact per 48', 'Player Impact',
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'Team PM', 'Last Updated']]
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+
raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
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public_minutes = raw_display[raw_display['NBAID'] != ""]
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sh = gcservice_account.open_by_url(NBABettingModel)
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raw_display.replace('', np.nan, inplace=True)
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raw_display = raw_display[['PID', 'Player', 'Team', 'Avg Minutes last 30 days for team', 'Minutes Projection', 'Rotation Impact (versus last 30 days)',
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'Injury Notes', 'Minute Change', 'Baseline Team PM', 'Net Rotation PM +/- for Team', 'Projected PM for Game', 'Offset', 'Rank']]
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raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
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player_impact = raw_display[raw_display['PID'] != ""]
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return public_minutes, player_impact
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player_min_disp = player_min_disp.sort_values(by=['TC', 'MP (Today)'], ascending=[False, True])
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st.dataframe(player_min_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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st.download_button(
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label="Export Minutes Baselines",
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data=convert_df_to_csv(public_minutes),
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file_name='AmericanNumbers_Min_Baseline_export.csv',
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mime='text/csv',
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)
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player_impact_disp = player_impact_disp.sort_values(by=['Team', 'Rotation Impact (versus last 30 days)'], ascending=[False, True])
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st.dataframe(player_impact_disp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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st.download_button(
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label="Export Player Impacts",
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data=convert_df_to_csv(player_impact),
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file_name='AmericanNumbers_Impact_export.csv',
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mime='text/csv',
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)
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