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batting_update.py

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+import pandas as pd
+import numpy as np
+import joblib
+import math
+import pickle
+
+loaded_model = joblib.load('joblib_model/barrel_model.joblib')
+in_zone_model = joblib.load('joblib_model/in_zone_model_knn_20240410.joblib')
+attack_zone_model = joblib.load('joblib_model/model_attack_zone.joblib')
+xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
+px_model = joblib.load('joblib_model/linear_reg_model_x.joblib')
+pz_model = joblib.load('joblib_model/linear_reg_model_z.joblib')
+barrel_model = joblib.load('joblib_model/barrel_model.joblib')
+
+
+def percentile(n):
+    def percentile_(x):
+        return np.nanpercentile(x, n)
+    percentile_.__name__ = 'percentile_%s' % n
+    return percentile_
+
+
+def df_update(df=pd.DataFrame()):
+    df.loc[df['sz_top']==0,'sz_top'] = np.nan
+    df.loc[df['sz_bot']==0,'sz_bot'] = np.nan
+
+
+    df['in_zone'] = [x < 10 if x > 0 else np.nan for x in df['zone']]
+    if len(df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px']) > 0:
+        df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px'] = px_model.predict(df.loc[(~df['x'].isnull())&(df['px'].isnull())][['x']])
+        df.loc[(~df['y'].isnull())&(df['pz'].isnull()),'pz'] = px_model.predict(df.loc[(~df['y'].isnull())&(df['pz'].isnull())][['y']]) + 3.2
+
+    
+        # df['in_zone'] =  [x < 10 if x > 0 else np.nan for x in df['zone']]
+        # df_a['in_zone'] =  [x < 10 if x > 0 else np.nan for x in df_a['zone']]
+    if len(df.loc[(~df['px'].isna())&
+                  (df['in_zone'].isna())&
+                  (~df['sz_top'].isna())]) > 0:
+            print('We found missing data')
+            df.loc[(~df['px'].isna())&
+                            (df['in_zone'].isna())&
+                            (~df['sz_top'].isna())&
+                            (~df['pz'].isna())&
+                            (~df['sz_bot'].isna())
+                            ,'in_zone'] = in_zone_model.predict(df.loc[(~df['px'].isna())&
+                                                                (df['in_zone'].isna())&
+                                                                (~df['sz_top'].isna())&
+                                                                (~df['pz'].isna())&
+                                                                (~df['sz_bot'].isna())][['px','pz','sz_top','sz_bot']].values)
+    hit_codes = ['single',
+        'double','home_run', 'triple']
+
+    ab_codes = ['single', 'strikeout', 'field_out',
+        'grounded_into_double_play', 'fielders_choice', 'force_out',
+        'double', 'field_error', 'home_run', 'triple',
+        'double_play',
+        'fielders_choice_out', 'strikeout_double_play',
+        'other_out','triple_play']
+
+
+    obp_true_codes = ['single', 'walk',
+        'double','home_run', 'triple',
+        'hit_by_pitch', 'intent_walk']
+
+    obp_codes = ['single', 'strikeout', 'walk', 'field_out',
+        'grounded_into_double_play', 'fielders_choice', 'force_out',
+        'double', 'sac_fly', 'field_error', 'home_run', 'triple',
+        'hit_by_pitch', 'double_play', 'intent_walk',
+        'fielders_choice_out', 'strikeout_double_play',
+        'sac_fly_double_play',
+        'other_out','triple_play']
+
+
+    contact_codes = ['In play, no out',
+            'Foul', 'In play, out(s)',
+        'In play, run(s)',
+        'Foul Bunt']
+
+
+
+    conditions_hit = [df.event_type.isin(hit_codes)]
+    choices_hit = [True]
+    df['hits'] = np.select(conditions_hit, choices_hit, default=False)
+
+    conditions_ab = [df.event_type.isin(ab_codes)]
+    choices_ab = [True]
+    df['ab'] = np.select(conditions_ab, choices_ab, default=False)
+
+    conditions_obp_true = [df.event_type.isin(obp_true_codes)]
+    choices_obp_true = [True]
+    df['on_base'] = np.select(conditions_obp_true, choices_obp_true, default=False)
+
+    conditions_obp = [df.event_type.isin(obp_codes)]
+    choices_obp = [True]
+    df['obp'] = np.select(conditions_obp, choices_obp, default=False)
+
+    bip_codes = ['In play, no out', 'In play, run(s)','In play, out(s)']
+    
+    conditions_bip = [df.play_description.isin(bip_codes)]
+    choices_bip = [True]
+    df['bip'] = np.select(conditions_bip, choices_bip, default=False)
+
+    # conditions = [
+    #     (df['launch_speed'].isna()),
+    #     (df['launch_speed']*1.5 - df['launch_angle'] >= 117 ) & (df['launch_speed'] + df['launch_angle'] >= 124) & (df['launch_speed'] > 98) & (df['launch_angle'] >= 8) & (df['launch_angle'] <= 50)
+    # ]
+    df['bip_div'] = ~df.launch_speed.isna()
+    # choices = [False,True]
+    # df['barrel'] = np.select(conditions, choices, default=np.nan)
+    # df['barrel'] = loaded_model.predict(df[['launch_speed','launch_angle']].fillna(0).values)
+    df['barrel'] = np.nan
+    if len(df.loc[(~df['launch_speed'].isnull())]) > 0:
+        df.loc[(~df['launch_speed'].isnull())&(~df['launch_angle'].isnull()),'barrel'] = barrel_model.predict(df.loc[(~df['launch_speed'].isnull())&(~df['launch_angle'].isnull())][['launch_speed','launch_angle']])
+
+
+    conditions_ss = [
+        (df['launch_angle'].isna()),
+        (df['launch_angle'] >= 8 ) * (df['launch_angle'] <= 32 )
+    ]
+
+    choices_ss = [False,True]
+    df['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
+
+    conditions_hh = [
+        (df['launch_speed'].isna()),
+        (df['launch_speed'] >= 94.5 )
+    ]
+
+    choices_hh = [False,True]
+    df['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)
+
+
+    conditions_tb = [
+        (df['event_type']=='single'),
+        (df['event_type']=='double'),
+        (df['event_type']=='triple'),
+        (df['event_type']=='home_run'),
+    ]
+
+    choices_tb = [1,2,3,4]
+
+    df['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)
+
+    conditions_woba = [
+        (df['event_type'].isin(['strikeout', 'field_out', 'sac_fly', 'force_out',
+                                'grounded_into_double_play', 'fielders_choice', 'field_error',
+                                'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play',
+                                 'sac_fly_double_play', 'other_out'])),
+        (df['event_type']=='walk'),
+        (df['event_type']=='hit_by_pitch'),
+        (df['event_type']=='single'),
+        (df['event_type']=='double'),
+        (df['event_type']=='triple'),
+        (df['event_type']=='home_run'),
+    ]
+
+    choices_woba =    [0,
+                    0.696,
+                    0.726,
+                    0.883,
+                    1.244,
+                    1.569,
+                    2.004]
+
+    df['woba'] = np.select(conditions_woba, choices_woba, default=np.nan)
+
+
+    woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
+        'double', 'sac_fly', 'force_out', 'home_run',
+        'grounded_into_double_play', 'fielders_choice', 'field_error',
+        'triple', 'sac_bunt', 'double_play',
+        'fielders_choice_out', 'strikeout_double_play',
+        'sac_fly_double_play', 'other_out']
+
+
+    
+    
+    
+
+    conditions_woba_code = [
+        (df['event_type'].isin(woba_codes))
+    ]
+
+    choices_woba_code = [1]
+
+    df['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)
+
+
+    df['woba_contact'] = [df['woba'].values[x] if df['bip'].values[x] == 1 else np.nan for x in range(len(df['woba_codes']))]
+
+    #df['in_zone'] =  [x < 10 if type(x) == int else np.nan for x in df['zone']]
+
+    # df['in_zone_2'] = in_zone_model.predict(df[['x','y','sz_bot','sz_top']].fillna(0).values)
+    # df['in_zone_3'] = df['in_zone_2'] < 10
+    # df.loc[df['in_zone'].isna(),'in_zone'] = df.loc[df['in_zone'].isna(),'in_zone_3'].fillna(0)
+
+
+    df['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in df.play_code]
+    df['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in df.play_code]
+    df['swings'] = [1 if x == True else 0 for x in df.is_swing]
+
+
+    df['out_zone'] = df.in_zone == False
+    df['zone_swing'] = (df.in_zone == True)&(df.swings == 1)
+    df['zone_contact'] = (df.in_zone == True)&(df.swings == 1)&(df.whiffs == 0)
+    df['ozone_swing'] = (df.in_zone==False)&(df.swings == 1)
+    df['ozone_contact'] = (df.in_zone==False)&(df.swings == 1)&(df.whiffs == 0)
+
+    df['k'] = df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in df.event_type.dropna().unique()])))
+    df['bb'] = df.event_type.isin(['walk','intent_walk'])
+
+    df['k_minus_bb'] = df['k'].astype(np.float32)-df['bb'].astype(np.float32)
+    df['bb_minus_k'] = df['bb'].astype(np.float32)-df['k'].astype(np.float32)
+
+    df['pa']  = [1 if isinstance(x, str) else 0 for x in df.event_type]
+    df['pitches']  = [1 if x else 0 for x in df.is_pitch]
+
+
+    df.loc[df['launch_speed'].isna(),'barrel'] = np.nan
+
+
+    pitch_cat = {'FA':'Fastball',
+    'FF':'Fastball',
+    'FT':'Fastball',
+    'FC':'Fastball',
+    'FS':'Off-Speed',
+    'FO':'Off-Speed',
+    'SI':'Fastball',
+    'ST':'Breaking',
+    'SL':'Breaking',
+    'CU':'Breaking',
+    'KC':'Breaking',
+    'SC':'Off-Speed',
+    'GY':'Off-Speed',
+    'SV':'Breaking',
+    'CS':'Breaking',
+    'CH':'Off-Speed',
+    'KN':'Off-Speed',
+    'EP':'Breaking',
+    'UN':np.nan,
+    'IN':np.nan,
+    'PO':np.nan,
+    'AB':np.nan,
+    'AS':np.nan,
+    'NP':np.nan}
+    df['pitch_category'] = df['pitch_type'].map(pitch_cat).fillna('Unknown')
+    df['average'] = 'average'
+
+    df.loc[df['trajectory'] == 'bunt_popup','trajectory'] = 'popup'
+    df.loc[df['trajectory'] == 'bunt_grounder','trajectory'] = 'ground_ball'
+    df.loc[df['trajectory'] == '','trajectory'] = np.nan
+    df.loc[df['trajectory'] == 'bunt_line_drive','trajectory'] = 'line_drive'
+    df[['trajectory_fly_ball','trajectory_ground_ball','trajectory_line_drive','trajectory_popup']] = pd.get_dummies(df['trajectory'], prefix='trajectory')
+
+    df['attack_zone'] = np.nan
+
+
+
+    df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0,'attack_zone'] = attack_zone_model.predict(df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0][['px','pz','sz_top','sz_bot']])
+
+
+
+    df['heart'] = df['attack_zone'] == 0
+    df['shadow'] = df['attack_zone'] == 1
+    df['chase'] = df['attack_zone'] == 2
+    df['waste'] = df['attack_zone'] == 3
+
+    df['heart_swing'] = (df['attack_zone'] == 0)&(df['swings']==1)
+    df['shadow_swing'] = (df['attack_zone'] == 1)&(df['swings']==1)
+    df['chase_swing'] = (df['attack_zone'] == 2)&(df['swings']==1)
+    df['waste_swing'] = (df['attack_zone'] == 3)&(df['swings']==1)
+    
+    df['xwoba'] = np.nan
+    df['xwoba_contact'] = np.nan
+
+    if len(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'xwoba']) > 0:
+        
+
+        df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'xwoba']  = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
+
+        ## Assign a value of 0.696 to every walk in the dataset
+        df.loc[df['event_type'].isin(['walk']),'xwoba'] = 0.696
+
+        ## Assign a value of 0.726 to every hit by pitch in the dataset
+        df.loc[df['event_type'].isin(['hit_by_pitch']),'xwoba'] = 0.726
+
+        ## Assign a value of 0 to every Strikeout in the dataset 
+        df.loc[df['event_type'].isin(['strikeout','strikeout_double_play']),'xwoba'] = 0
+
+        
+        df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'xwoba_contact']  = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
+    
+    df['xwoba_codes'] = np.nan
+    df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'xwoba_codes']  = 1
+    ## Assign a value of 0.696 to every walk in the dataset
+    df.loc[df['event_type'].isin(['walk']),'xwoba_codes'] = 1
+
+    ## Assign a value of 0.726 to every hit by pitch in the dataset
+    df.loc[df['event_type'].isin(['hit_by_pitch']),'xwoba_codes'] = 1
+
+    ## Assign a value of 0 to every Strikeout in the dataset 
+    df.loc[df['event_type'].isin(['strikeout','strikeout_double_play']),'xwoba_codes'] = 1
+    return df
+
+def df_update_summ(df=pd.DataFrame()):
+    df_summ = df.groupby(['batter_id','batter_name']).agg(
+        pa = ('pa','sum'),
+        ab = ('ab','sum'),
+        obp_pa = ('obp','sum'),
+        hits = ('hits','sum'),
+        on_base = ('on_base','sum'),
+        k = ('k','sum'),
+        bb = ('bb','sum'),
+        bb_minus_k = ('bb_minus_k','sum'),
+        csw = ('csw','sum'),
+        bip = ('bip','sum'),
+        bip_div = ('bip_div','sum'),
+        tb = ('tb','sum'),
+        woba = ('woba','sum'),
+        woba_contact = ('woba_contact','sum'),
+        xwoba = ('xwoba','sum'),
+        xwoba_contact = ('xwoba_contact','sum'),       
+        woba_codes = ('woba_codes','sum'),
+        xwoba_codes = ('xwoba_codes','sum'),
+        hard_hit = ('hard_hit','sum'),
+        barrel = ('barrel','sum'),
+        sweet_spot = ('sweet_spot','sum'),
+        max_launch_speed = ('launch_speed','max'),
+        launch_speed_90 =  ('launch_speed',percentile(90)),
+        launch_speed = ('launch_speed','mean'),
+        launch_angle = ('launch_angle','mean'),
+        pitches = ('is_pitch','sum'),
+        swings =  ('swings','sum'),
+        in_zone = ('in_zone','sum'),
+        out_zone = ('out_zone','sum'),
+        whiffs = ('whiffs','sum'),
+        zone_swing = ('zone_swing','sum'),
+        zone_contact = ('zone_contact','sum'),
+        ozone_swing = ('ozone_swing','sum'),
+        ozone_contact = ('ozone_contact','sum'),
+        ground_ball = ('trajectory_ground_ball','sum'),
+        line_drive = ('trajectory_line_drive','sum'),
+        fly_ball =('trajectory_fly_ball','sum'),
+        pop_up = ('trajectory_popup','sum'),
+        attack_zone = ('attack_zone','count'),
+        heart = ('heart','sum'),
+        shadow = ('shadow','sum'),
+        chase = ('chase','sum'),
+        waste = ('waste','sum'),
+        heart_swing = ('heart_swing','sum'),
+        shadow_swing = ('shadow_swing','sum'),
+        chase_swing = ('chase_swing','sum'),
+        waste_swing = ('waste_swing','sum'),
+        ).reset_index()
+    return df_summ
+
+def df_update_summ_avg(df=pd.DataFrame()):
+    df_summ_avg = df.groupby(['average']).agg(
+        pa = ('pa','sum'),
+        ab = ('ab','sum'),
+        obp_pa = ('obp','sum'),
+        hits = ('hits','sum'),
+        on_base = ('on_base','sum'),
+        k = ('k','sum'),
+        bb = ('bb','sum'),
+        bb_minus_k = ('bb_minus_k','sum'),
+        csw = ('csw','sum'),
+        bip = ('bip','sum'),
+        bip_div = ('bip_div','sum'),
+        tb = ('tb','sum'),
+        woba = ('woba','sum'),
+        woba_contact = ('woba_contact','sum'),
+        xwoba = ('xwoba','sum'),
+        xwoba_contact = ('xwoba_contact','sum'),       
+        woba_codes = ('woba_codes','sum'),
+        xwoba_codes = ('xwoba_codes','sum'),
+        hard_hit = ('hard_hit','sum'),
+        barrel = ('barrel','sum'),
+        sweet_spot = ('sweet_spot','sum'),
+        max_launch_speed = ('launch_speed','max'),
+        launch_speed_90 =  ('launch_speed',percentile(90)),
+        launch_speed = ('launch_speed','mean'),
+        launch_angle = ('launch_angle','mean'),
+        pitches = ('is_pitch','sum'),
+        swings =  ('swings','sum'),
+        in_zone = ('in_zone','sum'),
+        out_zone = ('out_zone','sum'),
+        whiffs = ('whiffs','sum'),
+        zone_swing = ('zone_swing','sum'),
+        zone_contact = ('zone_contact','sum'),
+        ozone_swing = ('ozone_swing','sum'),
+        ozone_contact = ('ozone_contact','sum'),
+        ground_ball = ('trajectory_ground_ball','sum'),
+        line_drive = ('trajectory_line_drive','sum'),
+        fly_ball =('trajectory_fly_ball','sum'),
+        pop_up = ('trajectory_popup','sum'),
+        attack_zone = ('attack_zone','count'),
+        heart = ('heart','sum'),
+        shadow = ('shadow','sum'),
+        chase = ('chase','sum'),
+        waste = ('waste','sum'),
+        heart_swing = ('heart_swing','sum'),
+        shadow_swing = ('shadow_swing','sum'),
+        chase_swing = ('chase_swing','sum'),
+        waste_swing = ('waste_swing','sum'),       
+
+
+
+
+        ).reset_index()
+    return df_summ_avg
+
+def df_summ_changes(df_summ=pd.DataFrame()):
+    df_summ['avg'] = [df_summ.hits[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['obp'] = [df_summ.on_base[x]/df_summ.obp_pa[x] if df_summ.obp_pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['slg'] =  [df_summ.tb[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['ops'] = df_summ['obp']+df_summ['slg']
+
+    df_summ['k_percent'] = [df_summ.k[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['bb_percent'] =[df_summ.bb[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['bb_minus_k_percent'] =[(df_summ.bb_minus_k[x])/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['bb_over_k_percent'] =[df_summ.bb[x]/df_summ.k[x] if df_summ.k[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+
+
+    df_summ['csw_percent'] =[df_summ.csw[x]/df_summ.pitches[x] if df_summ.pitches[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+    df_summ['sweet_spot_percent'] = [df_summ.sweet_spot[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['woba_percent'] = [df_summ.woba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['woba_percent_contact'] = [df_summ.woba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+    #df_summ['hard_hit_percent'] = [df_summ.sweet_spot[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['hard_hit_percent'] = [df_summ.hard_hit[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+    df_summ['barrel_percent'] = [df_summ.barrel[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_contact_percent'] = [df_summ.zone_contact[x]/df_summ.zone_swing[x] if df_summ.zone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_swing_percent'] = [df_summ.zone_swing[x]/df_summ.in_zone[x] if df_summ.in_zone[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_percent'] = [df_summ.in_zone[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['chase_percent'] = [df_summ.ozone_swing[x]/(df_summ.pitches[x] - df_summ.in_zone[x]) if (df_summ.pitches[x]- df_summ.in_zone[x]) != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['chase_contact'] = [df_summ.ozone_contact[x]/df_summ.ozone_swing[x] if df_summ.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['swing_percent'] = [df_summ.swings[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['whiff_rate'] = [df_summ.whiffs[x]/df_summ.swings[x] if df_summ.swings[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['swstr_rate'] = [df_summ.whiffs[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['ground_ball_percent'] = [df_summ.ground_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['line_drive_percent'] = [df_summ.line_drive[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['fly_ball_percent'] = [df_summ.fly_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['pop_up_percent'] = [df_summ.pop_up[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]  
+
+
+    
+    df_summ['heart_zone_percent'] = [df_summ.heart[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['shadow_zone_percent'] = [df_summ.shadow[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['chase_zone_percent'] = [df_summ.chase[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['waste_zone_percent'] = [df_summ.waste[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+    
+    
+    df_summ['heart_zone_swing_percent'] = [df_summ.heart_swing[x]/df_summ.heart[x] if df_summ.heart[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['shadow_zone_swing_percent'] = [df_summ.shadow_swing[x]/df_summ.shadow[x] if df_summ.shadow[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['chase_zone_swing_percent'] = [df_summ.chase_swing[x]/df_summ.chase[x] if df_summ.chase[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['waste_zone_swing_percent'] = [df_summ.waste_swing[x]/df_summ.waste[x] if df_summ.waste[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+
+    df_summ['xwoba_percent'] = [df_summ.xwoba[x]/df_summ.xwoba_codes[x] if df_summ.xwoba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['xwoba_percent_contact'] = [df_summ.xwoba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ = df_summ.dropna(subset=['bip'])
+    return df_summ
+
+def df_summ_filter_out(df_summ=pd.DataFrame(),batter_select = 0,date_min=0):
+    import datetime
+
+    def weeks_after(day):
+        today = datetime.date.today()
+        time_difference = today - day
+        weeks = time_difference.days // 7
+        return weeks
+
+    df_summ_filter = df_summ[df_summ['pa'] >= min(math.floor(df_summ.xs(batter_select,level=0)['pa']/10)*10,500,weeks_after(date_min)*20)]
+    df_summ_filter_pct = df_summ_filter.rank(pct=True,ascending=True)
+    df_summ_player = df_summ.xs(batter_select,level=0)
+    df_summ_player_pct = df_summ_filter_pct.xs(batter_select,level=0)
+    return df_summ_filter,df_summ_filter_pct,df_summ_player,df_summ_player_pct
+
+
+def df_summ_batter_pitch_up(df=pd.DataFrame()):
+    df_summ_batter_pitch = df.dropna(subset=['pitch_category']).groupby(['batter_id','batter_name','pitch_category']).agg(
+        pa = ('pa','sum'),
+        ab = ('ab','sum'),
+        obp_pa = ('obp','sum'),
+        hits = ('hits','sum'),
+        on_base = ('on_base','sum'),
+        k = ('k','sum'),
+        bb = ('bb','sum'),
+        bb_minus_k = ('bb_minus_k','sum'),
+        csw = ('csw','sum'),
+        bip = ('bip','sum'),
+        bip_div = ('bip_div','sum'),
+        tb = ('tb','sum'),
+        woba = ('woba','sum'),
+        woba_contact = ('xwoba_contact','sum'),
+        xwoba = ('xwoba','sum'),
+        xwoba_contact = ('xwoba','sum'),       
+        woba_codes = ('woba_codes','sum'),
+        xwoba_codes = ('xwoba_codes','sum'),
+        hard_hit = ('hard_hit','sum'),
+        barrel = ('barrel','sum'),
+        sweet_spot = ('sweet_spot','sum'),
+        max_launch_speed = ('launch_speed','max'),
+        launch_speed_90 =  ('launch_speed',percentile(90)),
+        launch_speed = ('launch_speed','mean'),
+        launch_angle = ('launch_angle','mean'),
+        pitches = ('is_pitch','sum'),
+        swings =  ('swings','sum'),
+        in_zone = ('in_zone','sum'),
+        out_zone = ('out_zone','sum'),
+        whiffs = ('whiffs','sum'),
+        zone_swing = ('zone_swing','sum'),
+        zone_contact = ('zone_contact','sum'),
+        ozone_swing = ('ozone_swing','sum'),
+        ozone_contact = ('ozone_contact','sum'),
+        ground_ball = ('trajectory_ground_ball','sum'),
+        line_drive = ('trajectory_line_drive','sum'),
+        fly_ball =('trajectory_fly_ball','sum'),
+        pop_up = ('trajectory_popup','sum'),
+        attack_zone = ('attack_zone','count'),
+        heart = ('heart','sum'),
+        shadow = ('shadow','sum'),
+        chase = ('chase','sum'),
+        waste = ('waste','sum'),
+        heart_swing = ('heart_swing','sum'),
+        shadow_swing = ('shadow_swing','sum'),
+        chase_swing = ('chase_swing','sum'),
+        waste_swing = ('waste_swing','sum'),
+        ).reset_index()
+
+    #return df_summ_batter_pitch
+    df_summ_batter_pitch['avg'] = [df_summ_batter_pitch.hits[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['obp'] = [df_summ_batter_pitch.on_base[x]/df_summ_batter_pitch.obp_pa[x] if df_summ_batter_pitch.obp_pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['slg'] =  [df_summ_batter_pitch.tb[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['ops'] = df_summ_batter_pitch['obp']+df_summ_batter_pitch['slg']
+
+    df_summ_batter_pitch['k_percent'] = [df_summ_batter_pitch.k[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['bb_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['bb_minus_k_percent'] =[(df_summ_batter_pitch.bb_minus_k[x])/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['bb_over_k_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.k[x] if df_summ_batter_pitch.k[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+
+
+    df_summ_batter_pitch['csw_percent'] =[df_summ_batter_pitch.csw[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+    df_summ_batter_pitch['sweet_spot_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['woba_percent'] = [df_summ_batter_pitch.woba[x]/df_summ_batter_pitch.woba_codes[x] if df_summ_batter_pitch.woba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['woba_percent_contact'] = [df_summ_batter_pitch.woba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    #df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.hard_hit[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+    df_summ_batter_pitch['barrel_percent'] = [df_summ_batter_pitch.barrel[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_contact_percent'] = [df_summ_batter_pitch.zone_contact[x]/df_summ_batter_pitch.zone_swing[x] if df_summ_batter_pitch.zone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_swing_percent'] = [df_summ_batter_pitch.zone_swing[x]/df_summ_batter_pitch.in_zone[x] if df_summ_batter_pitch.in_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_percent'] = [df_summ_batter_pitch.in_zone[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['chase_percent'] = [df_summ_batter_pitch.ozone_swing[x]/(df_summ_batter_pitch.pitches[x] - df_summ_batter_pitch.in_zone[x]) if (df_summ_batter_pitch.pitches[x]- df_summ_batter_pitch.in_zone[x]) != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['chase_contact'] = [df_summ_batter_pitch.ozone_contact[x]/df_summ_batter_pitch.ozone_swing[x] if df_summ_batter_pitch.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['swing_percent'] = [df_summ_batter_pitch.swings[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['whiff_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.swings[x] if df_summ_batter_pitch.swings[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['swstr_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['heart_zone_percent'] = [df_summ_batter_pitch.heart[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['shadow_zone_percent'] = [df_summ_batter_pitch.shadow[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['chase_zone_percent'] = [df_summ_batter_pitch.chase[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['waste_zone_percent'] = [df_summ_batter_pitch.waste[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+    
+    
+    df_summ_batter_pitch['heart_zone_swing_percent'] = [df_summ_batter_pitch.heart_swing[x]/df_summ_batter_pitch.heart[x] if df_summ_batter_pitch.heart[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['shadow_zone_swing_percent'] = [df_summ_batter_pitch.shadow_swing[x]/df_summ_batter_pitch.shadow[x] if df_summ_batter_pitch.shadow[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['chase_zone_swing_percent'] = [df_summ_batter_pitch.chase_swing[x]/df_summ_batter_pitch.chase[x] if df_summ_batter_pitch.chase[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['waste_zone_swing_percent'] = [df_summ_batter_pitch.waste_swing[x]/df_summ_batter_pitch.waste[x] if df_summ_batter_pitch.waste[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+    
+
+
+
+    df_summ_batter_pitch['xwoba_percent'] = [df_summ_batter_pitch.xwoba[x]/df_summ_batter_pitch.xwoba_codes[x] if df_summ_batter_pitch.xwoba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['xwoba_percent_contact'] = [df_summ_batter_pitch.xwoba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+
+
+    df_summ_batter_pitch['bip'] = df_summ_batter_pitch['bip'].fillna(0)
+
+    return df_summ_batter_pitch

pitcher_update.py

@@ -0,0 +1,573 @@
+import pandas as pd
+import numpy as np
+import joblib
+import math
+import pickle
+
+loaded_model = joblib.load('joblib_model/barrel_model.joblib')
+in_zone_model = joblib.load('joblib_model/in_zone_model_knn_20240410.joblib')
+attack_zone_model = joblib.load('joblib_model/model_attack_zone.joblib')
+xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
+px_model = joblib.load('joblib_model/linear_reg_model_x.joblib')
+pz_model = joblib.load('joblib_model/linear_reg_model_z.joblib')
+
+
+def percentile(n):
+    def percentile_(x):
+        return np.nanpercentile(x, n)
+    percentile_.__name__ = 'percentile_%s' % n
+    return percentile_
+
+
+def df_update(df=pd.DataFrame()):
+    df.loc[df['sz_top']==0,'sz_top'] = np.nan
+    df.loc[df['sz_bot']==0,'sz_bot'] = np.nan
+
+
+    df['in_zone'] = [x < 10 if x > 0 else np.nan for x in df['zone']]
+    if len(df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px']) > 0:
+        df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px'] = px_model.predict(df.loc[(~df['x'].isnull())&(df['px'].isnull())][['x']])
+        df.loc[(~df['y'].isnull())&(df['pz'].isnull()),'pz'] = px_model.predict(df.loc[(~df['y'].isnull())&(df['pz'].isnull())][['y']]) + 3.2
+
+    
+        # df['in_zone'] =  [x < 10 if x > 0 else np.nan for x in df['zone']]
+    if len(df.loc[(~df['px'].isna())&
+                  (df['in_zone'].isna())&
+                  (~df['sz_top'].isna())]) > 0:
+            print('We found missing data')
+            df.loc[(~df['px'].isna())&
+                            (df['in_zone'].isna())&
+                            (~df['sz_top'].isna())&
+                            (~df['pz'].isna())&
+                            (~df['sz_bot'].isna())
+                            ,'in_zone'] = in_zone_model.predict(df.loc[(~df['px'].isna())&
+                                                                (df['in_zone'].isna())&
+                                                                (~df['sz_top'].isna())&
+                                                                (~df['pz'].isna())&
+                                                                (~df['sz_bot'].isna())][['px','pz','sz_top','sz_bot']].values)
+    hit_codes = ['single',
+        'double','home_run', 'triple']
+
+    ab_codes = ['single', 'strikeout', 'field_out',
+        'grounded_into_double_play', 'fielders_choice', 'force_out',
+        'double', 'field_error', 'home_run', 'triple',
+        'double_play',
+        'fielders_choice_out', 'strikeout_double_play',
+        'other_out','triple_play']
+
+
+    obp_true_codes = ['single', 'walk',
+        'double','home_run', 'triple',
+        'hit_by_pitch', 'intent_walk']
+
+    obp_codes = ['single', 'strikeout', 'walk', 'field_out',
+        'grounded_into_double_play', 'fielders_choice', 'force_out',
+        'double', 'sac_fly', 'field_error', 'home_run', 'triple',
+        'hit_by_pitch', 'double_play', 'intent_walk',
+        'fielders_choice_out', 'strikeout_double_play',
+        'sac_fly_double_play',
+        'other_out','triple_play']
+
+
+    contact_codes = ['In play, no out',
+            'Foul', 'In play, out(s)',
+        'In play, run(s)',
+        'Foul Bunt']
+
+
+
+    conditions_hit = [df.event_type.isin(hit_codes)]
+    choices_hit = [True]
+    df['hits'] = np.select(conditions_hit, choices_hit, default=False)
+
+    conditions_ab = [df.event_type.isin(ab_codes)]
+    choices_ab = [True]
+    df['ab'] = np.select(conditions_ab, choices_ab, default=False)
+
+    conditions_obp_true = [df.event_type.isin(obp_true_codes)]
+    choices_obp_true = [True]
+    df['on_base'] = np.select(conditions_obp_true, choices_obp_true, default=False)
+
+    conditions_obp = [df.event_type.isin(obp_codes)]
+    choices_obp = [True]
+    df['obp'] = np.select(conditions_obp, choices_obp, default=False)
+
+    bip_codes = ['In play, no out', 'In play, run(s)','In play, out(s)']
+    
+    conditions_bip = [df.play_description.isin(bip_codes)]
+    choices_bip = [True]
+    df['bip'] = np.select(conditions_bip, choices_bip, default=False)
+
+    conditions = [
+        (df['launch_speed'].isna()),
+        (df['launch_speed']*1.5 - df['launch_angle'] >= 117 ) & (df['launch_speed'] + df['launch_angle'] >= 124) & (df['launch_speed'] > 98) & (df['launch_angle'] >= 8) & (df['launch_angle'] <= 50)
+    ]
+    df['bip_div'] = ~df.launch_speed.isna()
+    choices = [False,True]
+    df['barrel'] = np.select(conditions, choices, default=np.nan)
+    df['barrel'] = loaded_model.predict(df[['launch_speed','launch_angle']].fillna(0).values)
+    conditions_ss = [
+        (df['launch_angle'].isna()),
+        (df['launch_angle'] >= 8 ) * (df['launch_angle'] <= 32 )
+    ]
+
+    choices_ss = [False,True]
+    df['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
+
+    conditions_hh = [
+        (df['launch_speed'].isna()),
+        (df['launch_speed'] >= 94.5 )
+    ]
+
+    choices_hh = [False,True]
+    df['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)
+
+
+    conditions_tb = [
+        (df['event_type']=='single'),
+        (df['event_type']=='double'),
+        (df['event_type']=='triple'),
+        (df['event_type']=='home_run'),
+    ]
+
+    choices_tb = [1,2,3,4]
+
+    df['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)
+
+    conditions_woba = [
+        (df['event_type'].isin(['strikeout', 'field_out', 'sac_fly', 'force_out',
+                                'grounded_into_double_play', 'fielders_choice', 'field_error',
+                                'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play',
+                                 'sac_fly_double_play', 'other_out'])),
+        (df['event_type']=='walk'),
+        (df['event_type']=='hit_by_pitch'),
+        (df['event_type']=='single'),
+        (df['event_type']=='double'),
+        (df['event_type']=='triple'),
+        (df['event_type']=='home_run'),
+    ]
+
+    choices_woba =    [0,
+                    0.696,
+                    0.726,
+                    0.883,
+                    1.244,
+                    1.569,
+                    2.004]
+
+    df['woba'] = np.select(conditions_woba, choices_woba, default=np.nan)
+
+
+    woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
+        'double', 'sac_fly', 'force_out', 'home_run',
+        'grounded_into_double_play', 'fielders_choice', 'field_error',
+        'triple', 'sac_bunt', 'double_play',
+        'fielders_choice_out', 'strikeout_double_play',
+        'sac_fly_double_play', 'other_out']
+
+
+    
+    
+    
+
+    conditions_woba_code = [
+        (df['event_type'].isin(woba_codes))
+    ]
+
+    choices_woba_code = [1]
+
+    df['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)
+
+
+    df['woba_contact'] = [df['woba'].values[x] if df['bip'].values[x] == 1 else np.nan for x in range(len(df['woba_codes']))]
+
+    #df['in_zone'] =  [x < 10 if type(x) == int else np.nan for x in df['zone']]
+
+    # df['in_zone_2'] = in_zone_model.predict(df[['x','y','sz_bot','sz_top']].fillna(0).values)
+    # df['in_zone_3'] = df['in_zone_2'] < 10
+    # df.loc[df['in_zone'].isna(),'in_zone'] = df.loc[df['in_zone'].isna(),'in_zone_3'].fillna(0)
+
+
+    df['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in df.play_code]
+    df['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in df.play_code]
+    df['swings'] = [1 if x == True else 0 for x in df.is_swing]
+
+
+    df['out_zone'] = df.in_zone == False
+    df['zone_swing'] = (df.in_zone == True)&(df.swings == 1)
+    df['zone_contact'] = (df.in_zone == True)&(df.swings == 1)&(df.whiffs == 0)
+    df['ozone_swing'] = (df.in_zone==False)&(df.swings == 1)
+    df['ozone_contact'] = (df.in_zone==False)&(df.swings == 1)&(df.whiffs == 0)
+
+    df['k'] = df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in df.event_type.dropna().unique()])))
+    df['bb'] = df.event_type.isin(['walk','intent_walk'])
+
+    df['k_minus_bb'] = df['k'].astype(np.float32)-df['bb'].astype(np.float32)
+    df['bb_minus_k'] = df['bb'].astype(np.float32)-df['k'].astype(np.float32)
+
+    df['pa']  = [1 if isinstance(x, str) else 0 for x in df.event_type]
+    df['pitches']  = [1 if x else 0 for x in df.is_pitch]
+
+
+    df.loc[df['launch_speed'].isna(),'barrel'] = np.nan
+
+
+    pitch_cat = {'FA':'Fastball',
+    'FF':'Fastball',
+    'FT':'Fastball',
+    'FC':'Fastball',
+    'FS':'Off-Speed',
+    'FO':'Off-Speed',
+    'SI':'Fastball',
+    'ST':'Breaking',
+    'SL':'Breaking',
+    'CU':'Breaking',
+    'KC':'Breaking',
+    'SC':'Off-Speed',
+    'GY':'Off-Speed',
+    'SV':'Breaking',
+    'CS':'Breaking',
+    'CH':'Off-Speed',
+    'KN':'Off-Speed',
+    'EP':'Breaking',
+    'UN':np.nan,
+    'IN':np.nan,
+    'PO':np.nan,
+    'AB':np.nan,
+    'AS':np.nan,
+    'NP':np.nan}
+    #df['pitch_type'] = df['pitch_type'].map(pitch_cat).fillna('Unknown')
+    df['average'] = 'average'
+
+    df.loc[df['trajectory'] == 'bunt_popup','trajectory'] = 'popup'
+    df.loc[df['trajectory'] == 'bunt_grounder','trajectory'] = 'ground_ball'
+    df.loc[df['trajectory'] == '','trajectory'] = np.nan
+    df.loc[df['trajectory'] == 'bunt_line_drive','trajectory'] = 'line_drive'
+    df[['trajectory_fly_ball','trajectory_ground_ball','trajectory_line_drive','trajectory_popup']] = pd.get_dummies(df['trajectory'], prefix='trajectory')
+
+    df['attack_zone'] = np.nan
+
+
+
+    df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0,'attack_zone'] = attack_zone_model.predict(df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0][['px','pz','sz_top','sz_bot']])
+
+
+
+    df['heart'] = df['attack_zone'] == 0
+    df['shadow'] = df['attack_zone'] == 1
+    df['chase'] = df['attack_zone'] == 2
+    df['waste'] = df['attack_zone'] == 3
+
+    df['heart_swing'] = (df['attack_zone'] == 0)&(df['swings']==1)
+    df['shadow_swing'] = (df['attack_zone'] == 1)&(df['swings']==1)
+    df['chase_swing'] = (df['attack_zone'] == 2)&(df['swings']==1)
+    df['waste_swing'] = (df['attack_zone'] == 3)&(df['swings']==1)
+
+    df['heart_whiff'] = (df['attack_zone'] == 0)&(df['whiffs']==1)
+    df['shadow_whiff'] = (df['attack_zone'] == 1)&(df['whiffs']==1)
+    df['chase_whiff'] = (df['attack_zone'] == 2)&(df['whiffs']==1)
+    df['waste_whiff'] = (df['attack_zone'] == 3)&(df['whiffs']==1)  
+
+    df['woba_pred'] = np.nan
+    df['woba_pred_contact'] = np.nan
+
+    if len(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred']) > 0:
+        
+
+        df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred']  = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
+
+        ## Assign a value of 0.696 to every walk in the dataset
+        df.loc[df['event_type'].isin(['walk']),'woba_pred'] = 0.696
+
+        ## Assign a value of 0.726 to every hit by pitch in the dataset
+        df.loc[df['event_type'].isin(['hit_by_pitch']),'woba_pred'] = 0.726
+
+        ## Assign a value of 0 to every Strikeout in the dataset 
+        df.loc[df['event_type'].isin(['strikeout','strikeout_double_play']),'woba_pred'] = 0
+
+        
+        df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred_contact']  = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
+    
+    df['xwoba_codes'] = np.nan
+    df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'xwoba_codes']  = 1
+    ## Assign a value of 0.696 to every walk in the dataset
+    df.loc[df['event_type'].isin(['walk']),'xwoba_codes'] = 1
+
+    ## Assign a value of 0.726 to every hit by pitch in the dataset
+    df.loc[df['event_type'].isin(['hit_by_pitch']),'xwoba_codes'] = 1
+
+    ## Assign a value of 0 to every Strikeout in the dataset 
+    df.loc[df['event_type'].isin(['strikeout','strikeout_double_play']),'xwoba_codes'] = 1
+    return df
+
+def df_update_summ(df=pd.DataFrame()):
+    df_summ = df.groupby(['pitcher_id','pitcher_name']).agg(
+        pa = ('pa','sum'),
+        ab = ('ab','sum'),
+        obp_pa = ('obp','sum'),
+        hits = ('hits','sum'),
+        on_base = ('on_base','sum'),
+        k = ('k','sum'),
+        bb = ('bb','sum'),
+        bb_minus_k = ('bb_minus_k','sum'),
+        csw = ('csw','sum'),
+        bip = ('bip','sum'),
+        bip_div = ('bip_div','sum'),
+        tb = ('tb','sum'),
+        woba = ('woba','sum'),
+        woba_contact = ('woba_contact','sum'),
+        xwoba = ('woba_pred','sum'),
+        xwoba_contact = ('woba_pred_contact','sum'),       
+        woba_codes = ('woba_codes','sum'),
+        xwoba_codes = ('xwoba_codes','sum'),
+        hard_hit = ('hard_hit','sum'),
+        barrel = ('barrel','sum'),
+        sweet_spot = ('sweet_spot','sum'),
+        max_launch_speed = ('launch_speed','max'),
+        launch_speed_90 =  ('launch_speed',percentile(90)),
+        launch_speed = ('launch_speed','mean'),
+        launch_angle = ('launch_angle','mean'),
+        pitches = ('is_pitch','sum'),
+        swings =  ('swings','sum'),
+        in_zone = ('in_zone','sum'),
+        out_zone = ('out_zone','sum'),
+        whiffs = ('whiffs','sum'),
+        zone_swing = ('zone_swing','sum'),
+        zone_contact = ('zone_contact','sum'),
+        ozone_swing = ('ozone_swing','sum'),
+        ozone_contact = ('ozone_contact','sum'),
+        ground_ball = ('trajectory_ground_ball','sum'),
+        line_drive = ('trajectory_line_drive','sum'),
+        fly_ball =('trajectory_fly_ball','sum'),
+        pop_up = ('trajectory_popup','sum'),
+        attack_zone = ('attack_zone','count'),
+        heart = ('heart','sum'),
+        shadow = ('shadow','sum'),
+        chase = ('chase','sum'),
+        waste = ('waste','sum'),
+        heart_swing = ('heart_swing','sum'),
+        shadow_swing = ('shadow_swing','sum'),
+        chase_swing = ('chase_swing','sum'),
+        waste_swing = ('waste_swing','sum'),
+        ).reset_index()
+    return df_summ
+
+def df_update_summ_avg(df=pd.DataFrame()):
+    df_summ_avg = df.groupby(['average']).agg(
+       
+        ).reset_index()
+    return df_summ_avg
+
+def df_summ_changes(df_summ=pd.DataFrame()):
+    df_summ['avg'] = [df_summ.hits[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['obp'] = [df_summ.on_base[x]/df_summ.obp_pa[x] if df_summ.obp_pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['slg'] =  [df_summ.tb[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['ops'] = df_summ['obp']+df_summ['slg']
+
+    df_summ['k_percent'] = [df_summ.k[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['bb_percent'] =[df_summ.bb[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['bb_minus_k_percent'] =[(df_summ.bb_minus_k[x])/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['bb_over_k_percent'] =[df_summ.bb[x]/df_summ.k[x] if df_summ.k[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+
+
+    df_summ['csw_percent'] =[df_summ.csw[x]/df_summ.pitches[x] if df_summ.pitches[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+    df_summ['sweet_spot_percent'] = [df_summ.sweet_spot[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['woba_percent'] = [df_summ.woba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['woba_percent_contact'] = [df_summ.woba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+    #df_summ['hard_hit_percent'] = [df_summ.sweet_spot[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['hard_hit_percent'] = [df_summ.hard_hit[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+    df_summ['barrel_percent'] = [df_summ.barrel[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_contact_percent'] = [df_summ.zone_contact[x]/df_summ.zone_swing[x] if df_summ.zone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_swing_percent'] = [df_summ.zone_swing[x]/df_summ.in_zone[x] if df_summ.in_zone[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_percent'] = [df_summ.in_zone[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['chase_percent'] = [df_summ.ozone_swing[x]/(df_summ.pitches[x] - df_summ.in_zone[x]) if (df_summ.pitches[x]- df_summ.in_zone[x]) != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['chase_contact'] = [df_summ.ozone_contact[x]/df_summ.ozone_swing[x] if df_summ.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['swing_percent'] = [df_summ.swings[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['whiff_rate'] = [df_summ.whiffs[x]/df_summ.swings[x] if df_summ.swings[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['swstr_rate'] = [df_summ.whiffs[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['ground_ball_percent'] = [df_summ.ground_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['line_drive_percent'] = [df_summ.line_drive[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['fly_ball_percent'] = [df_summ.fly_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['pop_up_percent'] = [df_summ.pop_up[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]  
+
+
+    
+    df_summ['heart_zone_percent'] = [df_summ.heart[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['shadow_zone_percent'] = [df_summ.shadow[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['chase_zone_percent'] = [df_summ.chase[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['waste_zone_percent'] = [df_summ.waste[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+    
+    
+    df_summ['heart_zone_swing_percent'] = [df_summ.heart_swing[x]/df_summ.heart[x] if df_summ.heart[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['shadow_zone_swing_percent'] = [df_summ.shadow_swing[x]/df_summ.shadow[x] if df_summ.shadow[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['chase_zone_swing_percent'] = [df_summ.chase_swing[x]/df_summ.chase[x] if df_summ.chase[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['waste_zone_swing_percent'] = [df_summ.waste_swing[x]/df_summ.waste[x] if df_summ.waste[x] != 0 else np.nan for x in range(len(df_summ))]    
+    
+
+
+
+    df_summ['xwoba_percent'] = [df_summ.xwoba[x]/df_summ.xwoba_codes[x] if df_summ.xwoba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['xwoba_percent_contact'] = [df_summ.xwoba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ = df_summ.dropna(subset=['bip'])
+    return df_summ
+
+def df_summ_filter_out(df_summ=pd.DataFrame(),batter_select = 0):
+    df_summ_filter = df_summ[df_summ['pa'] >= min(math.floor(df_summ.xs(batter_select,level=0)['pa']/10)*10,500)]
+    df_summ_filter_pct = df_summ_filter.rank(pct=True,ascending=True)
+    df_summ_player = df_summ.xs(batter_select,level=0)
+    df_summ_player_pct = df_summ_filter_pct.xs(batter_select,level=0)
+    return df_summ_filter,df_summ_filter_pct,df_summ_player,df_summ_player_pct
+
+def df_summ_batter_pitch_up(df=pd.DataFrame()):
+    df_summ_batter_pitch = df.dropna(subset=['pitch_type']).groupby(['pitcher_id','pitcher_name','pitch_type']).agg(
+        pa = ('pa','sum'),
+        ab = ('ab','sum'),
+        obp_pa = ('obp','sum'),
+        hits = ('hits','sum'),
+        on_base = ('on_base','sum'),
+        k = ('k','sum'),
+        bb = ('bb','sum'),
+        bb_minus_k = ('bb_minus_k','sum'),
+        csw = ('csw','sum'),
+        bip = ('bip','sum'),
+        bip_div = ('bip_div','sum'),
+        tb = ('tb','sum'),
+        woba = ('woba','sum'),
+        woba_contact = ('woba_pred_contact','sum'),
+        xwoba = ('woba_pred','sum'),
+        xwoba_contact = ('woba_pred','sum'),       
+        woba_codes = ('woba_codes','sum'),
+        xwoba_codes = ('xwoba_codes','sum'),
+        hard_hit = ('hard_hit','sum'),
+        barrel = ('barrel','sum'),
+        sweet_spot = ('sweet_spot','sum'),
+        max_launch_speed = ('launch_speed','max'),
+        launch_speed_90 =  ('launch_speed',percentile(90)),
+        launch_speed = ('launch_speed','mean'),
+        launch_angle = ('launch_angle','mean'),
+        pitches = ('is_pitch','sum'),
+        swings =  ('swings','sum'),
+        in_zone = ('in_zone','sum'),
+        out_zone = ('out_zone','sum'),
+        whiffs = ('whiffs','sum'),
+        zone_swing = ('zone_swing','sum'),
+        zone_contact = ('zone_contact','sum'),
+        ozone_swing = ('ozone_swing','sum'),
+        ozone_contact = ('ozone_contact','sum'),
+        ground_ball = ('trajectory_ground_ball','sum'),
+        line_drive = ('trajectory_line_drive','sum'),
+        fly_ball =('trajectory_fly_ball','sum'),
+        pop_up = ('trajectory_popup','sum'),
+        attack_zone = ('attack_zone','count'),
+        heart = ('heart','sum'),
+        shadow = ('shadow','sum'),
+        chase = ('chase','sum'),
+        waste = ('waste','sum'),
+        heart_swing = ('heart_swing','sum'),
+        shadow_swing = ('shadow_swing','sum'),
+        chase_swing = ('chase_swing','sum'),
+        waste_swing = ('waste_swing','sum'),
+        ).reset_index()
+
+    #return df_summ_batter_pitch
+    df_summ_batter_pitch['avg'] = [df_summ_batter_pitch.hits[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['obp'] = [df_summ_batter_pitch.on_base[x]/df_summ_batter_pitch.obp_pa[x] if df_summ_batter_pitch.obp_pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['slg'] =  [df_summ_batter_pitch.tb[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['ops'] = df_summ_batter_pitch['obp']+df_summ_batter_pitch['slg']
+
+    df_summ_batter_pitch['k_percent'] = [df_summ_batter_pitch.k[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['bb_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['bb_minus_k_percent'] =[(df_summ_batter_pitch.bb_minus_k[x])/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['bb_over_k_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.k[x] if df_summ_batter_pitch.k[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+
+
+    df_summ_batter_pitch['csw_percent'] =[df_summ_batter_pitch.csw[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+    df_summ_batter_pitch['sweet_spot_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['woba_percent'] = [df_summ_batter_pitch.woba[x]/df_summ_batter_pitch.woba_codes[x] if df_summ_batter_pitch.woba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['woba_percent_contact'] = [df_summ_batter_pitch.woba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    #df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.hard_hit[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+    df_summ_batter_pitch['barrel_percent'] = [df_summ_batter_pitch.barrel[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_contact_percent'] = [df_summ_batter_pitch.zone_contact[x]/df_summ_batter_pitch.zone_swing[x] if df_summ_batter_pitch.zone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_swing_percent'] = [df_summ_batter_pitch.zone_swing[x]/df_summ_batter_pitch.in_zone[x] if df_summ_batter_pitch.in_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_percent'] = [df_summ_batter_pitch.in_zone[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['chase_percent'] = [df_summ_batter_pitch.ozone_swing[x]/(df_summ_batter_pitch.pitches[x] - df_summ_batter_pitch.in_zone[x]) if (df_summ_batter_pitch.pitches[x]- df_summ_batter_pitch.in_zone[x]) != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['chase_contact'] = [df_summ_batter_pitch.ozone_contact[x]/df_summ_batter_pitch.ozone_swing[x] if df_summ_batter_pitch.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['swing_percent'] = [df_summ_batter_pitch.swings[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['whiff_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.swings[x] if df_summ_batter_pitch.swings[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['swstr_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['heart_zone_percent'] = [df_summ_batter_pitch.heart[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['shadow_zone_percent'] = [df_summ_batter_pitch.shadow[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['chase_zone_percent'] = [df_summ_batter_pitch.chase[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['waste_zone_percent'] = [df_summ_batter_pitch.waste[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+    
+    
+    df_summ_batter_pitch['heart_zone_swing_percent'] = [df_summ_batter_pitch.heart_swing[x]/df_summ_batter_pitch.heart[x] if df_summ_batter_pitch.heart[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['shadow_zone_swing_percent'] = [df_summ_batter_pitch.shadow_swing[x]/df_summ_batter_pitch.shadow[x] if df_summ_batter_pitch.shadow[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['chase_zone_swing_percent'] = [df_summ_batter_pitch.chase_swing[x]/df_summ_batter_pitch.chase[x] if df_summ_batter_pitch.chase[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['waste_zone_swing_percent'] = [df_summ_batter_pitch.waste_swing[x]/df_summ_batter_pitch.waste[x] if df_summ_batter_pitch.waste[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+    
+
+
+
+    df_summ_batter_pitch['xwoba_percent'] = [df_summ_batter_pitch.xwoba[x]/df_summ_batter_pitch.xwoba_codes[x] if df_summ_batter_pitch.xwoba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['xwoba_percent_contact'] = [df_summ_batter_pitch.xwoba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+
+
+    df_summ_batter_pitch['bip'] = df_summ_batter_pitch['bip'].fillna(0)
+
+    return df_summ_batter_pitch