In [1]:

#Much of the scraping code is taken from this repository: https://github.com/chmartin/FBref_EPL
#I've made the the necessary changes for the recently added data and for combining it

In [2]:

#standard(stats)
stats = ["player","nationality","position","squad","age","birth_year","games","games_starts","minutes","goals","assists","pens_made","pens_att","cards_yellow","cards_red","goals_per90","assists_per90","goals_assists_per90","goals_pens_per90","goals_assists_pens_per90","xg","npxg","xa","xg_per90","xa_per90","xg_xa_per90","npxg_per90","npxg_xa_per90"]
stats3 = ["players_used","possession","games","games_starts","minutes","goals","assists","pens_made","pens_att","cards_yellow","cards_red","goals_per90","assists_per90","goals_assists_per90","goals_pens_per90","goals_assists_pens_per90","xg","npxg","xa","xg_per90","xa_per90","xg_xa_per90","npxg_per90","npxg_xa_per90"] 
#goalkeeping(keepers)
keepers = ["player","nationality","position","squad","age","birth_year","games_gk","games_starts_gk","minutes_gk","goals_against_gk","goals_against_per90_gk","shots_on_target_against","saves","save_pct","wins_gk","draws_gk","losses_gk","clean_sheets","clean_sheets_pct","pens_att_gk","pens_allowed","pens_saved","pens_missed_gk"]
keepers3 = ["players_used","games_gk","games_starts_gk","minutes_gk","goals_against_gk","goals_against_per90_gk","shots_on_target_against","saves","save_pct","wins_gk","draws_gk","losses_gk","clean_sheets","clean_sheets_pct","pens_att_gk","pens_allowed","pens_saved","pens_missed_gk"]
#advance goalkeeping(keepersadv)
keepersadv = ["player","nationality","position","squad","age","birth_year","minutes_90s_gk","goals_against_gk","pens_allowed","free_kick_goals_against_gk","corner_kick_goals_against_gk","own_goals_against_gk","psxg_gk","psnpxg_per_shot_on_target_against","psxg_net_gk","psxg_net_per90_gk","passes_completed_launched_gk","passes_launched_gk","passes_pct_launched_gk","passes_gk","passes_throws_gk","pct_passes_launched_gk","passes_length_avg_gk","goal_kicks","pct_goal_kicks_launched","goal_kick_length_avg","crosses_gk","crosses_stopped_gk","crosses_stopped_pct_gk","def_actions_outside_pen_area_gk","def_actions_outside_pen_area_per90_gk","avg_distance_def_actions_gk"]
keepersadv2 = ["minutes_90s_gk","goals_against_gk","pens_allowed","free_kick_goals_against_gk","corner_kick_goals_against_gk","own_goals_against_gk","psxg_gk","psnpxg_per_shot_on_target_against","psxg_net_gk","psxg_net_per90_gk","passes_completed_launched_gk","passes_launched_gk","passes_pct_launched_gk","passes_gk","passes_throws_gk","pct_passes_launched_gk","passes_length_avg_gk","goal_kicks","pct_goal_kicks_launched","goal_kick_length_avg","crosses_gk","crosses_stopped_gk","crosses_stopped_pct_gk","def_actions_outside_pen_area_gk","def_actions_outside_pen_area_per90_gk","avg_distance_def_actions_gk"]
#shooting(shooting)
shooting = ["player","nationality","position","squad","age","birth_year","minutes_90s","goals","pens_made","pens_att","shots_total","shots_on_target","shots_free_kicks","shots_on_target_pct","shots_total_per90","shots_on_target_per90","goals_per_shot","goals_per_shot_on_target","xg","npxg","npxg_per_shot","xg_net","npxg_net"]
shooting2 = ["minutes_90s","goals","pens_made","pens_att","shots_total","shots_on_target","shots_free_kicks","shots_on_target_pct","shots_total_per90","shots_on_target_per90","goals_per_shot","goals_per_shot_on_target","xg","npxg","npxg_per_shot","xg_net","npxg_net"]
shooting3 = ["goals","pens_made","pens_att","shots_total","shots_on_target","shots_free_kicks","shots_on_target_pct","shots_total_per90","shots_on_target_per90","goals_per_shot","goals_per_shot_on_target","xg","npxg","npxg_per_shot","xg_net","npxg_net"]
#passing(passing)
passing = ["player","nationality","position","squad","age","birth_year","minutes_90s","passes_completed","passes","passes_pct","passes_total_distance","passes_progressive_distance","passes_completed_short","passes_short","passes_pct_short","passes_completed_medium","passes_medium","passes_pct_medium","passes_completed_long","passes_long","passes_pct_long","assists","xa","xa_net","assisted_shots","passes_into_final_third","passes_into_penalty_area","crosses_into_penalty_area","progressive_passes"]
passing2 = ["passes_completed","passes","passes_pct","passes_total_distance","passes_progressive_distance","passes_completed_short","passes_short","passes_pct_short","passes_completed_medium","passes_medium","passes_pct_medium","passes_completed_long","passes_long","passes_pct_long","assists","xa","xa_net","assisted_shots","passes_into_final_third","passes_into_penalty_area","crosses_into_penalty_area","progressive_passes"]
#passtypes(passing_types)
passing_types = ["player","nationality","position","squad","age","birth_year","minutes_90s","passes","passes_live","passes_dead","passes_free_kicks","through_balls","passes_pressure","passes_switches","crosses","corner_kicks","corner_kicks_in","corner_kicks_out","corner_kicks_straight","passes_ground","passes_low","passes_high","passes_left_foot","passes_right_foot","passes_head","throw_ins","passes_other_body","passes_completed","passes_offsides","passes_oob","passes_intercepted","passes_blocked"]
passing_types2 = ["passes","passes_live","passes_dead","passes_free_kicks","through_balls","passes_pressure","passes_switches","crosses","corner_kicks","corner_kicks_in","corner_kicks_out","corner_kicks_straight","passes_ground","passes_low","passes_high","passes_left_foot","passes_right_foot","passes_head","throw_ins","passes_other_body","passes_completed","passes_offsides","passes_oob","passes_intercepted","passes_blocked"]
#goal and shot creation(gca)
gca = ["player","nationality","position","squad","age","birth_year","minutes_90s","sca","sca_per90","sca_passes_live","sca_passes_dead","sca_dribbles","sca_shots","sca_fouled","gca","gca_per90","gca_passes_live","gca_passes_dead","gca_dribbles","gca_shots","gca_fouled","gca_og_for"]
gca2 = ["sca","sca_per90","sca_passes_live","sca_passes_dead","sca_dribbles","sca_shots","sca_fouled","gca","gca_per90","gca_passes_live","gca_passes_dead","gca_dribbles","gca_shots","gca_fouled","gca_og_for"]
#defensive actions(defense)
defense = ["player","nationality","position","squad","age","birth_year","minutes_90s","tackles","tackles_won","tackles_def_3rd","tackles_mid_3rd","tackles_att_3rd","dribble_tackles","dribbles_vs","dribble_tackles_pct","dribbled_past","pressures","pressure_regains","pressure_regain_pct","pressures_def_3rd","pressures_mid_3rd","pressures_att_3rd","blocks","blocked_shots","blocked_shots_saves","blocked_passes","interceptions","clearances","errors"]
defense2 = ["tackles","tackles_won","tackles_def_3rd","tackles_mid_3rd","tackles_att_3rd","dribble_tackles","dribbles_vs","dribble_tackles_pct","dribbled_past","pressures","pressure_regains","pressure_regain_pct","pressures_def_3rd","pressures_mid_3rd","pressures_att_3rd","blocks","blocked_shots","blocked_shots_saves","blocked_passes","interceptions","clearances","errors"]
#possession(possession)
possession = ["player","nationality","position","squad","age","birth_year","minutes_90s","touches","touches_def_pen_area","touches_def_3rd","touches_mid_3rd","touches_att_3rd","touches_att_pen_area","touches_live_ball","dribbles_completed","dribbles","dribbles_completed_pct","players_dribbled_past","nutmegs","carries","carry_distance","carry_progressive_distance","pass_targets","passes_received","passes_received_pct","miscontrols","dispossessed"]
possession2 = ["touches","touches_def_pen_area","touches_def_3rd","touches_mid_3rd","touches_att_3rd","touches_att_pen_area","touches_live_ball","dribbles_completed","dribbles","dribbles_completed_pct","players_dribbled_past","nutmegs","carries","carry_distance","carry_progressive_distance","pass_targets","passes_received","passes_received_pct","miscontrols","dispossessed"]
#playingtime(playingtime)
playingtime = ["player","nationality","position","squad","age","birth_year","minutes_90s","games","minutes","minutes_per_game","minutes_pct","games_starts","minutes_per_start","games_subs","minutes_per_sub","unused_subs","points_per_match","on_goals_for","on_goals_against","plus_minus","plus_minus_per90","plus_minus_wowy","on_xg_for","on_xg_against","xg_plus_minus","xg_plus_minus_per90","xg_plus_minus_wowy"]
playingtime2 = ["games","minutes","minutes_per_game","minutes_pct","games_starts","minutes_per_start","games_subs","minutes_per_sub","unused_subs","points_per_match","on_goals_for","on_goals_against","plus_minus","plus_minus_per90","plus_minus_wowy","on_xg_for","on_xg_against","xg_plus_minus","xg_plus_minus_per90","xg_plus_minus_wowy"]
#miscallaneous(misc)
misc = ["player","nationality","position","squad","age","birth_year","minutes_90s","cards_yellow","cards_red","cards_yellow_red","fouls","fouled","offsides","crosses","interceptions","tackles_won","pens_won","pens_conceded","own_goals","ball_recoveries","aerials_won","aerials_lost","aerials_won_pct"]
misc2 = ["cards_yellow","cards_red","cards_yellow_red","fouls","fouled","offsides","crosses","interceptions","tackles_won","pens_won","pens_conceded","own_goals","ball_recoveries","aerials_won","aerials_lost","aerials_won_pct"]

In [3]:

import requests
from bs4 import BeautifulSoup
import pandas as pd
import re
import sys, getopt
import csv

In [4]:

#Functions to get the data in a dataframe using BeautifulSoup

def get_tables(url):
    res = requests.get(url)
    ## The next two lines get around the issue with comments breaking the parsing.
    comm = re.compile("<!--|-->")
    soup = BeautifulSoup(comm.sub("",res.text),'lxml')
    all_tables = soup.findAll("tbody")
    team_table = all_tables[0]
    player_table = all_tables[1]
    return player_table, team_table

def get_frame(features, player_table):
    pre_df_player = dict()
    features_wanted_player = features
    rows_player = player_table.find_all('tr')
    for row in rows_player:
        if(row.find('th',{"scope":"row"}) != None):
    
            for f in features_wanted_player:
                cell = row.find("td",{"data-stat": f})
                a = cell.text.strip().encode()
                text=a.decode("utf-8")
                if(text == ''):
                    text = '0'
                if((f!='player')&(f!='nationality')&(f!='position')&(f!='squad')&(f!='age')&(f!='birth_year')):
                    text = float(text.replace(',',''))
                if f in pre_df_player:
                    pre_df_player[f].append(text)
                else:
                    pre_df_player[f] = [text]
    df_player = pd.DataFrame.from_dict(pre_df_player)
    return df_player

def get_frame_team(features, team_table):
    pre_df_squad = dict()
    #Note: features does not contain squad name, it requires special treatment
    features_wanted_squad = features
    rows_squad = team_table.find_all('tr')
    for row in rows_squad:
        if(row.find('th',{"scope":"row"}) != None):
            name = row.find('th',{"data-stat":"squad"}).text.strip().encode().decode("utf-8")
            if 'squad' in pre_df_squad:
                pre_df_squad['squad'].append(name)
            else:
                pre_df_squad['squad'] = [name]
            for f in features_wanted_squad:
                cell = row.find("td",{"data-stat": f})
                a = cell.text.strip().encode()
                text=a.decode("utf-8")
                if(text == ''):
                    text = '0'
                if((f!='player')&(f!='nationality')&(f!='position')&(f!='squad')&(f!='age')&(f!='birth_year')):
                    text = float(text.replace(',',''))
                if f in pre_df_squad:
                    pre_df_squad[f].append(text)
                else:
                    pre_df_squad[f] = [text]
    df_squad = pd.DataFrame.from_dict(pre_df_squad)
    return df_squad

def frame_for_category(category,top,end,features):
    url = (top + category + end)
    player_table, team_table = get_tables(url)
    df_player = get_frame(features, player_table)
    return df_player

def frame_for_category_team(category,top,end,features):
    url = (top + category + end)
    player_table, team_table = get_tables(url)
    df_team = get_frame_team(features, team_table)
    return df_team

In [5]:

#Function to get the player data for outfield player, includes all categories - standard stats, shooting
#passing, passing types, goal and shot creation, defensive actions, possession, and miscallaneous
def get_outfield_data(top, end):
    df1 = frame_for_category('stats',top,end,stats)
    df2 = frame_for_category('shooting',top,end,shooting2)
    df3 = frame_for_category('passing',top,end,passing2)
    df4 = frame_for_category('passing_types',top,end,passing_types2)
    df5 = frame_for_category('gca',top,end,gca2)
    df6 = frame_for_category('defense',top,end,defense2)
    df7 = frame_for_category('possession',top,end,possession2)
    df8 = frame_for_category('misc',top,end,misc2)
    df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8], axis=1)
    df = df.loc[:,~df.columns.duplicated()]
    return df

In [6]:

#Function to get keeping and advance goalkeeping data
def get_keeper_data(top,end):
    df1 = frame_for_category('keepers',top,end,keepers)
    df2 = frame_for_category('keepersadv',top,end,keepersadv2)
    df = pd.concat([df1, df2], axis=1)
    df = df.loc[:,~df.columns.duplicated()]
    return df

In [7]:

#Function to get team-wise data accross all categories as mentioned above
def get_team_data(top,end):
    df1 = frame_for_category_team('stats',top,end,stats3)
    df2 = frame_for_category_team('keepers',top,end,keepers3)
    df3 = frame_for_category_team('keepersadv',top,end,keepersadv2)
    df4 = frame_for_category_team('shooting',top,end,shooting3)
    df5 = frame_for_category_team('passing',top,end,passing2)
    df6 = frame_for_category_team('passing_types',top,end,passing_types2)
    df7 = frame_for_category_team('gca',top,end,gca2)
    df8 = frame_for_category_team('defense',top,end,defense2)
    df9 = frame_for_category_team('possession',top,end,possession2)
    df10 = frame_for_category_team('misc',top,end,misc2)
    df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8, df9, df10], axis=1)
    df = df.loc[:,~df.columns.duplicated()]
    return df
    

In [8]:

#Go to the 'Standard stats' page of the league
#For Premier League 2019/20, the link is this: https://fbref.com/en/comps/9/stats/Premier-League-Stats
#Remove the 'stats', and pass the first and third part of the link as parameters like below
df_outfield = get_outfield_data('https://fbref.com/en/comps/9/','/Premier-League-Stats')
df_outfield

Out[8]:

	player	nationality	position	squad	age	birth_year	games	games_starts	minutes	goals	...	fouls	fouled	offsides	pens_won	pens_conceded	own_goals	ball_recoveries	aerials_won	aerials_lost	aerials_won_pct
0	Patrick van Aanholt	nl NED	DF	Crystal Palace	28	1990	25.0	25.0	2162.0	3.0	...	6.0	6.0	2.0	0.0	0.0	1.0	203.0	10.0	14.0	41.7
1	Max Aarons	eng ENG	DF	Norwich City	19	2000	30.0	30.0	2700.0	0.0	...	26.0	40.0	2.0	1.0	0.0	0.0	236.0	18.0	36.0	33.3
2	Tammy Abraham	eng ENG	FW	Chelsea	21	1997	28.0	24.0	2034.0	13.0	...	16.0	21.0	23.0	1.0	0.0	1.0	86.0	58.0	97.0	37.4
3	Che Adams	eng ENG	FW	Southampton	23	1996	24.0	8.0	756.0	0.0	...	14.0	8.0	7.0	1.0	0.0	0.0	54.0	18.0	58.0	23.7
4	Adrián	es ESP	GK	Liverpool	32	1987	11.0	9.0	875.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	47.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
503	Wilfried Zaha	ci CIV	MF,FW	Crystal Palace	26	1992	32.0	31.0	2740.0	3.0	...	42.0	99.0	15.0	1.0	0.0	0.0	208.0	11.0	25.0	30.6
504	Christoph Zimmermann	de GER	DF	Norwich City	26	1993	15.0	15.0	1295.0	0.0	...	8.0	8.0	0.0	0.0	2.0	0.0	139.0	36.0	10.0	78.3
505	Oleksandr Zinchenko	ua UKR	DF	Manchester City	22	1996	14.0	11.0	1028.0	0.0	...	11.0	2.0	0.0	0.0	0.0	0.0	119.0	23.0	19.0	54.8
506	Richairo Živković	nl NED	FW	Sheffield Utd	22	1996	2.0	0.0	44.0	0.0	...	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	4.0	20.0
507	Kurt Zouma	fr FRA	DF	Chelsea	24	1994	22.0	19.0	1804.0	0.0	...	26.0	12.0	1.0	0.0	1.0	1.0	240.0	76.0	22.0	77.6

508 rows × 151 columns

In [13]:

#Go to the 'Standard stats' page of the league
#For Premier League 2019/20, the link is this: https://fbref.com/en/comps/9/stats/Premier-League-Stats
#Remove the 'stats', and pass the first and third part of the link as parameters like below
df_team = get_team_data('https://fbref.com/en/comps/9/','/Premier-League-Stats')
df_team

Out[13]:

	squad	players_used	possession	games	games_starts	minutes	goals	assists	pens_made	pens_att	...	fouls	fouled	offsides	pens_won	pens_conceded	own_goals	ball_recoveries	aerials_won	aerials_lost	aerials_won_pct
0	Arsenal	29.0	54.8	32.0	352.0	2880.0	47.0	29.0	2.0	2.0	...	346.0	367.0	63.0	1.0	5.0	0.0	2593.0	403.0	471.0	46.1
1	Aston Villa	28.0	45.2	32.0	352.0	2880.0	35.0	28.0	1.0	3.0	...	376.0	425.0	51.0	2.0	5.0	1.0	2860.0	581.0	644.0	47.4
2	Bournemouth	27.0	45.5	32.0	352.0	2880.0	29.0	19.0	1.0	1.0	...	304.0	331.0	54.0	1.0	3.0	0.0	3031.0	627.0	663.0	48.6
3	Brighton	25.0	54.0	32.0	352.0	2880.0	30.0	20.0	1.0	2.0	...	324.0	269.0	55.0	2.0	2.0	2.0	3010.0	549.0	468.0	54.0
4	Burnley	22.0	40.3	32.0	352.0	2880.0	35.0	25.0	2.0	2.0	...	356.0	320.0	73.0	1.0	5.0	1.0	3022.0	786.0	746.0	51.3
5	Chelsea	27.0	60.0	32.0	352.0	2880.0	57.0	37.0	6.0	6.0	...	330.0	319.0	56.0	5.0	2.0	3.0	3129.0	552.0	539.0	50.6
6	Crystal Palace	24.0	44.2	32.0	352.0	2880.0	26.0	18.0	3.0	3.0	...	349.0	393.0	48.0	2.0	1.0	2.0	2938.0	604.0	622.0	49.3
7	Everton	23.0	48.9	32.0	352.0	2880.0	38.0	28.0	1.0	1.0	...	387.0	324.0	51.0	0.0	1.0	2.0	3067.0	708.0	720.0	49.6
8	Leicester City	21.0	57.7	32.0	352.0	2880.0	58.0	42.0	5.0	7.0	...	340.0	380.0	57.0	6.0	8.0	0.0	2942.0	565.0	496.0	53.3
9	Liverpool	24.0	63.1	32.0	352.0	2880.0	68.0	56.0	5.0	5.0	...	268.0	244.0	45.0	4.0	1.0	1.0	3309.0	539.0	473.0	53.3
10	Manchester City	23.0	65.6	32.0	352.0	2880.0	80.0	58.0	6.0	10.0	...	311.0	249.0	56.0	9.0	2.0	1.0	2874.0	423.0	390.0	52.0
11	Manchester Utd	28.0	55.6	32.0	352.0	2880.0	50.0	34.0	7.0	11.0	...	357.0	365.0	45.0	10.0	1.0	1.0	2979.0	499.0	425.0	54.0
12	Newcastle Utd	27.0	37.9	32.0	352.0	2880.0	33.0	25.0	0.0	1.0	...	310.0	347.0	67.0	0.0	0.0	1.0	2816.0	625.0	643.0	49.3
13	Norwich City	28.0	50.2	32.0	352.0	2880.0	24.0	17.0	2.0	2.0	...	306.0	371.0	41.0	1.0	7.0	2.0	2868.0	443.0	552.0	44.5
14	Sheffield Utd	25.0	43.5	32.0	352.0	2880.0	30.0	21.0	1.0	1.0	...	346.0	249.0	66.0	1.0	2.0	0.0	3093.0	736.0	725.0	50.4
15	Southampton	24.0	47.9	32.0	352.0	2880.0	41.0	24.0	1.0	2.0	...	389.0	319.0	49.0	2.0	4.0	1.0	3341.0	683.0	758.0	47.4
16	Tottenham	29.0	53.1	32.0	352.0	2880.0	48.0	33.0	3.0	4.0	...	338.0	359.0	57.0	4.0	7.0	2.0	3081.0	529.0	539.0	49.5
17	Watford	26.0	42.6	32.0	352.0	2880.0	28.0	19.0	4.0	5.0	...	398.0	272.0	54.0	4.0	6.0	3.0	3176.0	664.0	666.0	49.9
18	West Ham	28.0	43.2	32.0	352.0	2880.0	38.0	29.0	3.0	3.0	...	324.0	289.0	63.0	3.0	6.0	3.0	2973.0	659.0	669.0	49.6
19	Wolves	20.0	47.7	32.0	352.0	2880.0	43.0	31.0	3.0	3.0	...	350.0	304.0	45.0	3.0	2.0	1.0	2992.0	617.0	583.0	51.4

20 rows × 188 columns

In [15]:

#Save csv file to Desktop
df_team.to_csv('PL1920_Team.csv')

In [10]:

#Similar for keepers like above
df_keeper = get_keeper_data('https://fbref.com/en/comps/Big5/','/players/Big-5-European-Leagues-Stats')
df_keeper

Out[10]:

	player	nationality	position	squad	age	birth_year	games_gk	games_starts_gk	minutes_gk	goals_against_gk	...	passes_length_avg_gk	goal_kicks	pct_goal_kicks_launched	goal_kick_length_avg	crosses_gk	crosses_stopped_gk	crosses_stopped_pct_gk	def_actions_outside_pen_area_gk	def_actions_outside_pen_area_per90_gk	avg_distance_def_actions_gk
0	Antonio Adán	es ESP	GK	Atlético Madrid	32	1987	1.0	0.0	25.0	0.0	...	44.0	1.0	100.0	67.0	2.0	0.0	0.0	1.0	3.60	14.7
1	Adrián	es ESP	GK	Liverpool	32	1987	11.0	9.0	875.0	10.0	...	38.3	68.0	48.5	40.4	91.0	8.0	8.8	7.0	0.72	13.3
2	Rúnar Alex Rúnarsson	is ISL	GK	Dijon	24	1995	11.0	9.0	900.0	17.0	...	35.9	81.0	64.2	49.2	89.0	8.0	9.0	5.0	0.50	14.1
3	Enrico Alfonso	it ITA	GK	Brescia	31	1988	4.0	4.0	279.0	4.0	...	37.0	24.0	37.5	32.8	38.0	2.0	5.3	4.0	1.29	16.3
4	Alisson	br BRA	GK	Liverpool	26	1992	23.0	23.0	2003.0	15.0	...	31.9	141.0	39.7	39.1	151.0	10.0	6.6	25.0	1.12	17.1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
186	Mauro Vigorito	it ITA	GK	Lecce	29	1990	5.0	4.0	423.0	9.0	...	36.3	49.0	38.8	32.2	61.0	4.0	6.6	1.0	0.21	11.6
187	Felix Wiedwald	de GER	GK	Eint Frankfurt	29	1990	3.0	3.0	270.0	8.0	...	38.0	24.0	54.2	41.8	28.0	2.0	7.1	3.0	1.00	16.1
188	Yoel	es ESP	GK	Eibar	30	1988	2.0	2.0	90.0	0.0	...	61.2	8.0	87.5	66.5	16.0	1.0	6.3	2.0	2.00	17.0
189	Robin Zentner	de GER	GK	Mainz 05	24	1994	22.0	22.0	1935.0	40.0	...	40.8	196.0	62.8	51.6	226.0	23.0	10.2	20.0	0.93	16.2
190	Leopold Zingerle	de GER	GK	Paderborn 07	25	1994	28.0	28.0	2520.0	57.0	...	32.3	235.0	31.9	32.5	260.0	10.0	3.8	17.0	0.61	14.0

191 rows × 47 columns

In [11]:

#Save csv file to Desktop
df_outfield.to_csv('PL1920_Outfield.csv')