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Update app.py
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app.py
CHANGED
@@ -1,3 +1,45 @@
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import streamlit as st
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st.set_page_config(layout="wide")
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@@ -46,7 +88,7 @@ def init_baselines():
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raw_display = raw_display[1:]
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raw_display = raw_display.reset_index(drop=True)
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gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
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gamelog_table = gamelog_table[['PLAYER_NAME', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A',
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'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
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'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy']]
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gamelog_table['assists'].replace("", 0, inplace=True)
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@@ -66,11 +108,11 @@ def init_baselines():
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gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
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gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
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gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
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data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'TEAM_NAME', 'OPP_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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gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
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gamelog_table = gamelog_table.set_axis(['Player', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
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'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
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'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
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'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
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@@ -79,7 +121,7 @@ def init_baselines():
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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', 'Team']]
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season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
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season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
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season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
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@@ -125,7 +167,7 @@ def seasonlong_build(data_sample):
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season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False)
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season_long_table = season_long_table.set_axis(['Player', 'Team', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
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'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
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'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
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'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
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@@ -329,4 +371,4 @@ with tab2:
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corr_display = run_fantasy_corr(gamelog_table)
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elif corr_var == 'Minutes':
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corr_display = run_min_corr(gamelog_table)
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display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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app.py
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Multichem's picture
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Multichem
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Update app.py
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0edd0c1
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17 minutes ago
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No virus
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23.1 kB
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import streamlit as st
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st.set_page_config(layout="wide")
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raw_display = raw_display[1:]
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raw_display = raw_display.reset_index(drop=True)
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gamelog_table = raw_display[raw_display['PLAYER_NAME'] != ""]
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gamelog_table = gamelog_table[['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_NAME', 'SEASON_ID', 'GAME_DATE', 'MATCHUP', 'MIN', 'touches', 'PTS', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A',
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'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'reboundChancesOffensive', 'OREB', 'reboundChancesDefensive', 'DREB', 'reboundChancesTotal', 'REB',
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'passes', 'secondaryAssists', 'freeThrowAssists', 'assists', 'STL', 'BLK', 'TOV', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy']]
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gamelog_table['assists'].replace("", 0, inplace=True)
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gamelog_table['assists_per_pass'] = gamelog_table['assists'] / gamelog_table['passes']
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gamelog_table['Fantasy_per_touch'] = gamelog_table['Fantasy'] / gamelog_table['touches']
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gamelog_table['FD_Fantasy_per_touch'] = gamelog_table['FD_Fantasy'] / gamelog_table['touches']
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data_cols = gamelog_table.columns.drop(['PLAYER_NAME', 'POS', 'TEAM_NAME', 'OPP_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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gamelog_table['GAME_DATE'] = pd.to_datetime(gamelog_table['GAME_DATE']).dt.date
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gamelog_table = gamelog_table.set_axis(['Player', 'Pos', 'Team', 'Opp', 'Season', 'Date', 'Matchup', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
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'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
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'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
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'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
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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', 'Pos', 'Team']]
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season_long_table['Min'] = data_sample.groupby(['Player', 'Season'], sort=False)['Min'].transform('mean').astype(float)
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season_long_table['Touches'] = data_sample.groupby(['Player', 'Season'], sort=False)['Touches'].transform('mean').astype(float)
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season_long_table['Pts'] = data_sample.groupby(['Player', 'Season'], sort=False)['Pts'].transform('mean').astype(float)
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season_long_table = season_long_table.sort_values(by='Fantasy', ascending=False)
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season_long_table = season_long_table.set_axis(['Player', 'Pos', 'Team', 'Min', 'Touches', 'Pts', 'FGM', 'FGA', 'FG%', 'FG3M', 'FG3A',
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'FG3%', 'FTM', 'FTA', 'FT%', 'OREB Chance', 'OREB', 'DREB Chance', 'DREB', 'REB Chance', 'REB',
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'Passes', 'Alt Assists', 'FT Assists', 'Assists', 'Stl', 'Blk', 'Tov', 'PF', 'DD', 'TD', 'Fantasy', 'FD_Fantasy',
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'Rebound%', 'Assists/Pass', 'Fantasy/Touch', 'FD Fantasy/Touch'], axis=1)
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corr_display = run_fantasy_corr(gamelog_table)
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elif corr_var == 'Minutes':
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corr_display = run_min_corr(gamelog_table)
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display.dataframe(corr_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
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