Created
October 12, 2019 17:43
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| player_df = df | |
| player_df['mins_played'] = player_df['mins_played'].astype('int') | |
| player_df['mins_played_2'] = player_df['mins_played'].astype('int') | |
| player_df['ontarget_scoring_att'] = player_df['ontarget_scoring_att'].astype( | |
| 'int') | |
| player_df['total_att_assist'] = player_df['total_att_assist'].astype('int') | |
| player_df['aerial_won'] = player_df['aerial_won'].astype('int') | |
| player_df['outfielder_block'] = player_df['outfielder_block'].astype('int') | |
| player_df['ball_recovery'] = player_df['ball_recovery'].astype('int') | |
| player_df['att_ibox_blocked'] = player_df['att_ibox_blocked'].astype('int') | |
| player_df['att_ibox_goal'] = player_df['att_ibox_goal'].astype('int') | |
| player_df['att_ibox_miss'] = player_df['att_ibox_miss'].astype('int') | |
| player_df['att_ibox_post'] = player_df['att_ibox_post'].astype('int') | |
| player_df['att_ibox_target'] = player_df['att_ibox_target'].astype('int') | |
| player_df['attempts_ibox'] = player_df['attempts_ibox'].astype('int') | |
| player_df['big_chance_created'] = player_df['big_chance_created'].astype('int') | |
| player_df['won_contest'] = player_df['won_contest'].astype('int') | |
| player_df['accurate_pass'] = player_df['accurate_pass'].astype('int') | |
| player_df['interception'] = player_df['interception'].astype('int') | |
| player_df['won_tackle'] = player_df['won_tackle'].astype('int') | |
| player_df['accurate_fwd_zone_pass'] = player_df['accurate_fwd_zone_pass'].astype( | |
| 'int') | |
| player_df['accurate_cross'] = player_df['accurate_cross'].astype('int') | |
| player_df['final_third_entries'] = player_df['final_third_entries'].astype( | |
| 'int') | |
| player_df['pen_area_entries'] = player_df['pen_area_entries'].astype('int') | |
| player_df['penalty_save'] = player_df['penalty_save'].astype('int') | |
| player_df['saved_ibox'] = player_df['saved_ibox'].astype('int') | |
| player_df['saved_obox'] = player_df['saved_obox'].astype('int') | |
| player_df['saves'] = player_df['saves'].astype('int') | |
| player_df['attempts_conceded_ibox'] = player_df['attempts_conceded_ibox'].astype( | |
| 'int') | |
| player_df['accurate_fwd_zone_pass'] = player_df['accurate_fwd_zone_pass'].astype( | |
| 'int') | |
| player_df['successful_final_third_passes'] = player_df['successful_final_third_passes'].astype( | |
| 'int') | |
| player_df['clean_sheet'] = player_df['clean_sheet'].astype('int') | |
| player_df['final_third_entries'] = player_df['final_third_entries'].astype( | |
| 'int') | |
| player_df['long_pass_own_to_opp_success'] = player_df['long_pass_own_to_opp_success'].astype( | |
| 'int') | |
| player_df['accurate_keeper_sweeper'] = player_df['accurate_keeper_sweeper'].astype( | |
| 'int') | |
| player_df['goals_conceded'] = player_df['goals_conceded'].astype('int') | |
| player_df['accurate_long_balls'] = player_df['accurate_long_balls'].astype( | |
| 'int') | |
| player_df['accurate_goal_kicks'] = player_df['accurate_goal_kicks'].astype( | |
| 'int') | |
| player_df['effective_clearance'] = player_df['effective_clearance'].astype( | |
| 'int') | |
| player_df['goals'] = player_df['goals'].astype('int') | |
| player_df['goal_assist'] = player_df['goal_assist'].astype('int') | |
| full_df = player_df.groupby('player_ref').agg({'first_name': 'first', | |
| 'price': 'first', | |
| 'last_name': 'first', | |
| 'pos': 'first', | |
| 'attempts_ibox': 'sum', | |
| 'mins_played_2': 'sum', | |
| 'ontarget_scoring_att': 'sum', | |
| 'total_att_assist': 'sum', | |
| 'aerial_won': 'sum', | |
| 'outfielder_block': 'sum', | |
| 'ball_recovery': 'sum', | |
| 'att_ibox_blocked': 'sum', | |
| 'att_ibox_goal': 'sum', | |
| 'att_ibox_miss': 'sum', | |
| 'att_ibox_post': 'sum', | |
| 'att_ibox_target': 'sum', | |
| 'big_chance_created': 'sum', | |
| 'won_contest': 'sum', | |
| 'accurate_pass': 'sum', | |
| 'interception': 'sum', | |
| 'won_tackle': 'sum', | |
| 'successful_final_third_passes': 'sum', | |
| 'goals': 'sum', | |
| 'goal_assist': 'sum', | |
| 'accurate_fwd_zone_pass': 'sum', | |
| 'accurate_cross': 'sum', | |
| 'final_third_entries': 'sum', | |
| 'pen_area_entries': 'sum', | |
| 'attempts_conceded_ibox': 'sum', | |
| 'effective_clearance': 'sum', | |
| 'penalty_save': 'sum', | |
| 'saved_ibox': 'sum', | |
| 'saved_obox': 'sum', | |
| 'saves': 'sum', | |
| 'accurate_long_balls': 'sum', | |
| 'accurate_goal_kicks': 'sum', | |
| 'clean_sheet': 'sum', | |
| 'long_pass_own_to_opp_success': 'sum', | |
| 'accurate_keeper_sweeper': 'sum', | |
| 'goals_conceded': 'sum' | |
| }) | |
| is_90 = full_df['mins_played_2'] >= 90 | |
| full_df = full_df[is_90] | |
| qgrid.show_grid(full_df, show_toolbar=True, grid_options={ | |
| 'forceFitColumns': False, 'defaultColumnWidth': 150}) | |
| full_df['Successful Dribbles p90'] = full_df.apply( | |
| lambda row: row['won_contest'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Accurate Pass p90'] = full_df.apply( | |
| lambda row: row['accurate_pass'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Big Chance Created p90'] = full_df.apply( | |
| lambda row: row['big_chance_created'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Inbox Attempts p90'] = full_df.apply( | |
| lambda row: row['attempts_ibox'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Key Passes p90'] = full_df.apply( | |
| lambda row: row['total_att_assist'] / (row['mins_played_2']/90), axis=1) | |
| full_df['SOT p90'] = full_df.apply( | |
| lambda row: row['ontarget_scoring_att'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Aerial Won p90'] = full_df.apply( | |
| lambda row: row['aerial_won'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Blocks p90'] = full_df.apply( | |
| lambda row: row['outfielder_block'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Recoveries p90'] = full_df.apply( | |
| lambda row: row['ball_recovery'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Interceptions p90'] = full_df.apply( | |
| lambda row: row['interception'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Tackles p90'] = full_df.apply( | |
| lambda row: row['won_tackle'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Acc Final 3rd Passes p90'] = full_df.apply( | |
| lambda row: row['successful_final_third_passes'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Goals p90'] = full_df.apply( | |
| lambda row: row['goals'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Assists p90'] = full_df.apply( | |
| lambda row: row['goal_assist'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Acc Fwd Pass p90'] = full_df.apply(lambda row: ( | |
| row['accurate_fwd_zone_pass']-row['accurate_cross']) / (row['mins_played_2']/90), axis=1) | |
| full_df['Final 3rd Entries p90'] = full_df.apply( | |
| lambda row: row['final_third_entries'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Pen Area Entries p90'] = full_df.apply( | |
| lambda row: row['pen_area_entries'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Att Conceded Ibox p90'] = full_df.apply( | |
| lambda row: row['attempts_conceded_ibox'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Clearance p90'] = full_df.apply( | |
| lambda row: row['effective_clearance'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Penalty saves p90'] = full_df.apply( | |
| lambda row: row['penalty_save'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Saves Ibox p90'] = full_df.apply( | |
| lambda row: row['saved_ibox'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Saves Obox p90'] = full_df.apply( | |
| lambda row: row['saved_obox'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Saves p90'] = full_df.apply( | |
| lambda row: row['saves'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Acc Long Balls p90'] = full_df.apply( | |
| lambda row: row['accurate_long_balls'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Acc GK p90'] = full_df.apply( | |
| lambda row: row['accurate_goal_kicks'] / (row['mins_played_2']/90), axis=1) | |
| full_df['CS p90'] = full_df.apply( | |
| lambda row: row['clean_sheet'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Long Pass to Opp p90'] = full_df.apply( | |
| lambda row: row['long_pass_own_to_opp_success'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Acc Keeper Sweeper p90'] = full_df.apply( | |
| lambda row: row['accurate_keeper_sweeper'] / (row['mins_played_2']/90), axis=1) | |
| full_df['Goals Conceded p90'] = full_df.apply( | |
| lambda row: row['goals_conceded'] / (row['mins_played_2']/90), axis=1) | |
| full_df = full_df[['first_name', 'price', 'pos', 'mins_played_2', 'Aerial Won p90', 'Successful Dribbles p90', 'Accurate Pass p90', | |
| 'Big Chance Created p90', 'Inbox Attempts p90', 'Key Passes p90', 'SOT p90', | |
| 'Blocks p90', 'Recoveries p90', 'Interceptions p90', 'Tackles p90', 'Acc Final 3rd Passes p90', | |
| 'Goals p90', 'Assists p90', 'Acc Fwd Pass p90', 'Final 3rd Entries p90', 'Pen Area Entries p90', | |
| 'Att Conceded Ibox p90', 'Clearance p90', 'Penalty saves p90', 'Saves Ibox p90', 'Saves Obox p90', 'Saves p90', | |
| 'Acc Long Balls p90', 'Acc GK p90', 'CS p90', 'Long Pass to Opp p90', 'Acc Keeper Sweeper p90', 'Goals Conceded p90']] | |
| qgrid.show_grid(full_df, show_toolbar=True, grid_options={ | |
| 'forceFitColumns': False, 'defaultColumnWidth': 180}) | |
| full_df.to_csv(f'overall_stats_{today_str}.csv') |
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