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Update app.py
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app.py
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@@ -69,50 +69,54 @@ def init_baselines():
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data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'TEAM_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
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gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
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season_long_table
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season_long_table = season_long_table.drop_duplicates(subset='PLAYER_NAME')
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return
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@st.cache_data(show_spinner=False)
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def split_frame(input_df, rows):
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@@ -122,18 +126,19 @@ def split_frame(input_df, rows):
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def convert_df_to_csv(df):
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return df.to_csv().encode('utf-8')
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gamelog_table
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col1, col2 = st.columns([1, 9])
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with col1:
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if st.button("Reset Data", key='reset1'):
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st.cache_data.clear()
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gamelog_table
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split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')
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with col2:
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if split_var1 == 'Season Logs':
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display = st.container()
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display.dataframe(season_long_table.style.format(precision=2), use_container_width = True)
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elif split_var1 == 'Gamelogs':
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data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'TEAM_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP'])
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gamelog_table[data_cols] = gamelog_table[data_cols].apply(pd.to_numeric, errors='coerce')
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return gamelog_table
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@st.cache_data(show_spinner=False)
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def seasonlong_build(data_sample):
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season_long_table = data_sample[['PLAYER_NAME', 'TEAM_NAME']]
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season_long_table['MIN'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['MIN'].transform('mean').astype(float)
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season_long_table['touches'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('mean').astype(float)
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season_long_table['FGM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('mean').astype(float)
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season_long_table['FGA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('mean').astype(float)
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season_long_table['FG_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGM'].transform('sum').astype(int) /
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data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FGA'].transform('sum').astype(int))
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season_long_table['FG3M'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('mean').astype(float)
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season_long_table['FG3A'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('mean').astype(float)
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season_long_table['FG3_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3M'].transform('sum').astype(int) /
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data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FG3A'].transform('sum').astype(int))
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season_long_table['FTM'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('mean').astype(float)
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season_long_table['FTA'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('mean').astype(float)
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season_long_table['FT_PCT'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTM'].transform('sum').astype(int) /
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data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FTA'].transform('sum').astype(int))
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season_long_table['reboundChancesOffensive'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesOffensive'].transform('mean').astype(float)
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season_long_table['OREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['OREB'].transform('mean').astype(float)
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season_long_table['reboundChancesDefensive'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesDefensive'].transform('mean').astype(float)
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season_long_table['DREB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DREB'].transform('mean').astype(float)
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season_long_table['reboundChancesTotal'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesTotal'].transform('mean').astype(float)
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season_long_table['REB'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('mean').astype(float)
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season_long_table['passes'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['passes'].transform('mean').astype(float)
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season_long_table['secondaryAssists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['secondaryAssists'].transform('mean').astype(float)
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season_long_table['freeThrowAssists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['freeThrowAssists'].transform('mean').astype(float)
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season_long_table['assists'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('mean').astype(float)
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season_long_table['STL'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['STL'].transform('mean').astype(float)
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season_long_table['BLK'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['BLK'].transform('mean').astype(float)
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season_long_table['TOV'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['TOV'].transform('mean').astype(float)
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season_long_table['PF'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['PF'].transform('mean').astype(float)
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season_long_table['DD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['DD'].transform('mean').astype(float)
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season_long_table['TD'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['TD'].transform('mean').astype(float)
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season_long_table['Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('mean').astype(float)
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season_long_table['FD_Fantasy'] = data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('mean').astype(float)
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season_long_table['rebound%'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['REB'].transform('sum').astype(int) /
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data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['reboundChancesTotal'].transform('sum').astype(int))
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season_long_table['assists_per_pass'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['assists'].transform('sum').astype(int) /
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data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['passes'].transform('sum').astype(int))
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season_long_table['Fantasy_per_touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['Fantasy'].transform('sum').astype(int) /
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data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
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season_long_table['FD_Fantasy_per_touch'] = (data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['FD_Fantasy'].transform('sum').astype(int) /
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data_sample.groupby(['PLAYER_NAME', 'SEASON_ID'], sort=False)['touches'].transform('sum').astype(int))
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season_long_table = season_long_table.drop_duplicates(subset='PLAYER_NAME')
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return season_long_table
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@st.cache_data(show_spinner=False)
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def split_frame(input_df, rows):
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def convert_df_to_csv(df):
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return df.to_csv().encode('utf-8')
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gamelog_table = init_baselines()
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col1, col2 = st.columns([1, 9])
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with col1:
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if st.button("Reset Data", key='reset1'):
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st.cache_data.clear()
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gamelog_table = init_baselines()
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split_var1 = st.radio("What table would you like to view?", ('Season Logs', 'Gamelogs'), key='split_var1')
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with col2:
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if split_var1 == 'Season Logs':
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display = st.container()
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season_long_table = seasonlong_build(gamelog_table)
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display.dataframe(season_long_table.style.format(precision=2), use_container_width = True)
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elif split_var1 == 'Gamelogs':
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