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functions.py
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functions.py
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### functions
# OddsPortal scraper functions
import os
import urllib
from selenium import webdriver
import time
from selenium.webdriver.common.keys import Keys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import sys
from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
import signal
from create_clean_table import *
global DRIVER_LOCATION
DRIVER_LOCATION = "C:\\Users\\Utilisateur\\Desktop\\chromedriver.exe"
global TYPE_ODDS
TYPE_ODDS = 'CLOSING' # you can change to 'OPENING' if you want to collect opening odds, any other value will make the program collect CLOSING odds
def get_opening_odd(xpath):
# I. Get the raw data by hovering and collecting
data = driver.find_element("xpath", xpath)
hov = ActionChains(driver).move_to_element(data)
hov.perform()
data_in_the_bubble = driver.find_element("xpath", "//*[@id='tooltiptext']")
hover_data = data_in_the_bubble.get_attribute("innerHTML")
# II. Extract opening odds
b = re.split('<br>', hover_data)
c = [re.split('</strong>',y)[0] for y in b][-2]
opening_odd = re.split('<strong>', c)[1]
#print(opening_odd)
return(opening_odd)
def fi(a):
try:
driver.find_element("xpath", a).text
except:
return False
def ffi(a):
if fi(a) != False :
return driver.find_element("xpath", a).text
def fffi(a):
if TYPE_ODDS == 'OPENING':
try:
return get_opening_odd(a)
except:
return ffi(a)
else:
return(ffi(a))
def fi2(a):
try:
driver.find_element("xpath", a).click()
except:
return False
def ffi2(a):
if fi2(a) != False :
fi2(a)
return(True)
else:
return(None)
def get_data_typeA(i, link):
driver.get(link)
reject_ads()
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a'.format(i)
a = ffi2(target)
if a == True:
print('We wait 4 seconds')
L = []
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, final_score, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date, final_score)]
return(L)
return(None)
def get_data_next_games_typeA(i, link):
L = None
driver.get(link)
reject_ads()
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a[2]'.format(i)
a = ffi2(target)
L = []
if a == True:
print('We wait 4 seconds')
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed, CHANGE 01/05/2020 -> div[1] BECOMES div + WE STOP AT td[...] for odds
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/a'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/a'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a'.format(i)
a = ffi2(target)
if a == True:
print('We wait 4 seconds')
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed, CHANGE 01/05/2020 -> div[1] BECOMES div + WE STOP AT td[...] for odds
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/a'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/a'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
return(L)
def scrape_page_typeA(page, sport, country, tournament, SEASON):
link = 'https://www.oddsportal.com/{}/{}/{}-{}/results/page/1/#/page/{}'.format(sport, country, tournament, SEASON, page)
DATA = []
for i in range(1,100):
content = get_data_typeA(i, link)
if content != None:
DATA = DATA + content
print(DATA)
return(DATA)
def scrape_page_next_games_typeA(country, sport, tournament, nmax = 20):
link = 'https://www.oddsportal.com/{}/{}/{}/'.format(sport, country,tournament)
DATA = []
for i in range(1,nmax):
print(i)
content = get_data_next_games_typeA(i, link)
if content != None:
DATA = DATA + content
print(DATA)
return(DATA)
def scrape_page_current_season_typeA(page,sport, country, tournament):
link = 'https://www.oddsportal.com/{}/{}/{}/results/page/1/#/page/{}'.format(sport,country,tournament,page)
DATA = []
for i in range(1,100):
content = get_data_typeA(i, link)
if content != None:
DATA = DATA + content
print(DATA)
return(DATA)
def scrape_current_tournament_typeA(sport, tournament, country, SEASON, max_page = 25):
global driver
############### NOW WE SEEK TO SCRAPE THE ODDS AND MATCH INFO################################
DATA_ALL = []
for page in range(1, max_page):
print('We start to scrape the page n°{}'.format(page))
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
data = scrape_page_typeA(page,sport, country, tournament, SEASON)
DATA_ALL = DATA_ALL + [y for y in data if y != None]
driver.close()
data_df = pd.DataFrame(DATA_ALL)
try:
data_df.columns = ['TeamsRaw', 'Bookmaker', 'OddHome', 'OddAway', 'DateRaw' ,'ScoreRaw']
except:
print('Function crashed, probable reason : no games scraped (empty season)')
return(1)
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
'''Now we simply need to split team names, transform date, split score'''
# (0) Filter out None rows
data_df = data_df[~data_df['Bookmaker'].isnull()].dropna().reset_index()
data_df["TO_KEEP"] = 1
for i in range(len(data_df["TO_KEEP"])):
if len(re.split(':',data_df["ScoreRaw"][i]))<2 :
data_df["TO_KEEP"].iloc[i] = 0
data_df = data_df[data_df["TO_KEEP"] == 1]
# (a) Split team names
data_df["Home_id"] = [re.split(' - ',y)[0] for y in data_df["TeamsRaw"]]
data_df["Away_id"] = [re.split(' - ',y)[1] for y in data_df["TeamsRaw"]]
# (b) Transform date
data_df["Date"] = [re.split(', ',y)[1] for y in data_df["DateRaw"]]
# (c) Split score
data_df["Score_home"] = [re.split(':',y)[0][-3:] for y in data_df["ScoreRaw"]]
data_df["Score_away"] = [re.split(':',y)[1][:3] for y in data_df["ScoreRaw"]]
for j in range(len(data_df["Score_home"])):
str_home = data_df["Score_home"].iloc[j]
str_away = data_df["Score_away"].iloc[j]
if str_home[0] == 't':
data_df["Score_home"].iloc[j] = str_home[1:]
if str_away[-1] == '(':
data_df["Score_away"].iloc[j] = str_away[:-1]
# (e) Set season column
data_df["Season"] = SEASON
# Finally we save results
if not os.path.exists('./{}_FULL'.format(tournament)):
os.makedirs('./{}_FULL'.format(tournament))
if not os.path.exists('./{}'.format(tournament)):
os.makedirs('./{}'.format(tournament))
data_df.to_csv('./{}_FULL/{}_{}_FULL.csv'.format(tournament,tournament, SEASON), sep=';', encoding='utf-8', index=False)
data_df[['Home_id', 'Away_id', 'Bookmaker', 'OddHome', 'OddAway', 'Date', 'Score_home', 'Score_away','Season']].to_csv('./{}/{}_{}.csv'.\
format(tournament,tournament, SEASON), sep=';', encoding='utf-8', index=False)
return(data_df)
def scrape_current_season_typeA(tournament, sport, country, SEASON, max_page = 25):
global driver
############### NOW WE SEEK TO SCRAPE THE ODDS AND MATCH INFO################################
DATA_ALL = []
for page in range(1, max_page):
print('We start to scrape the page n°{}'.format(page))
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
data = scrape_page_current_season_typeA(page, sport, country, tournament)
DATA_ALL = DATA_ALL + [y for y in data if y != None]
driver.close()
data_df = pd.DataFrame(DATA_ALL)
try:
data_df.columns = ['TeamsRaw', 'Bookmaker', 'OddHome', 'OddAway', 'DateRaw' ,'ScoreRaw']
except:
print('Function crashed, probable reason : no games scraped (empty season)')
return(1)
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
'''Now we simply need to split team names, transform date, split score'''
# (0) Filter out None rows
data_df = data_df[~data_df['Bookmaker'].isnull()].dropna().reset_index()
data_df["TO_KEEP"] = 1
for i in range(len(data_df["TO_KEEP"])):
if len(re.split(':',data_df["ScoreRaw"][i]))<2 :
data_df["TO_KEEP"].iloc[i] = 0
data_df = data_df[data_df["TO_KEEP"] == 1]
# (a) Split team names
data_df["Home_id"] = [re.split(' - ',y)[0] for y in data_df["TeamsRaw"]]
data_df["Away_id"] = [re.split(' - ',y)[1] for y in data_df["TeamsRaw"]]
# (b) Transform date
data_df["Date"] = [re.split(', ',y)[1] for y in data_df["DateRaw"]]
# (c) Split score
data_df["Score_home"] = [re.split(':',y)[0][-3:] for y in data_df["ScoreRaw"]]
data_df["Score_away"] = [re.split(':',y)[1][:3] for y in data_df["ScoreRaw"]]
for j in range(len(data_df["Score_home"])):
str_home = data_df["Score_home"].iloc[j]
str_away = data_df["Score_away"].iloc[j]
if str_home[0] == 't':
data_df["Score_home"].iloc[j] = str_home[1:]
if str_away[-1] == '(':
data_df["Score_away"].iloc[j] = str_away[:-1]
# (e) Set season column
data_df["Season"] = SEASON
# Finally we save results
if not os.path.exists('./{}_FULL'.format(tournament)):
os.makedirs('./{}_FULL'.format(tournament))
if not os.path.exists('./{}'.format(tournament)):
os.makedirs('./{}'.format(tournament))
data_df.to_csv('./{}_FULL/{}_CurrentSeason_FULL.csv'.format(tournament,tournament), sep=';', encoding='utf-8', index=False)
data_df[['Home_id', 'Away_id', 'Bookmaker', 'OddHome', 'OddAway', 'Date', 'Score_home', 'Score_away','Season']].\
to_csv('./{}/{}_CurrentSeason.csv'.format(tournament,tournament), sep=';', encoding='utf-8', index=False)
return(data_df)
def scrape_league_typeA(Season, sport, country1, tournament1, nseason, current_season = 'yes', max_page = 25):
long_season = (len(Season) > 6) # indicates whether Season is in format '2010-2011' or '2011' depends on the league)
Season = int(Season[0:4])
for i in range(nseason):
SEASON1 = '{}'.format(Season)
if long_season:
SEASON1 = '{}-{}'.format(Season, Season+1)
print('We start to collect season {}'.format(SEASON1))
scrape_current_tournament_typeA(sport = sport, tournament = tournament1, country = country1, SEASON = SEASON1, max_page = max_page)
print('We finished to collect season {} !'.format(SEASON1))
Season+=1
if current_season == 'yes' :
SEASON1 = '{}'.format(Season)
if long_season:
SEASON1 = '{}-{}'.format(Season, Season+1)
print('We start to collect current season')
scrape_current_season_typeA(tournament = tournament1, sport = sport, country = country1, SEASON = 'CurrentSeason', max_page = max_page)
print('We finished to collect current season !')
# Finally we merge all files
file1 = pd.read_csv('./{}/'.format(tournament1) + os.listdir('./{}/'.format(tournament1))[0], sep=';')
print(os.listdir('./{}/'.format(tournament1))[0])
for filename in os.listdir('./{}/'.format(tournament1))[1:]:
file = pd.read_csv('./{}/'.format(tournament1) + filename, sep=';')
print(filename)
file1 = file1.append(file)
file1 = file1.reset_index()
#Correct falsly collected data for away (in case of 1X2 instead of H/A odds)
for i in range(file1.shape[0]):
if (1/file1["OddHome"].iloc[i] + 1/file1["OddAway"].iloc[i]) < 1 :
file1["OddAway"].iloc[i] = 1 / ((1 - 1/file1["OddHome"].iloc[i])*1.07) # 1/1.07 = 0.934 => 6.5 % margin (estimation)
print(file1["OddHome"].iloc[i], file1["OddAway"].iloc[i], i)
file1.to_csv("./{}/All_data_{}.csv".format(tournament1, tournament1))
print('All good! ')
return(file1)
def scrape_next_games_typeA(tournament, sport, country, SEASON, nmax = 30):
global driver
############### NOW WE SEEK TO SCRAPE THE ODDS AND MATCH INFO################################
DATA_ALL = []
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
data = scrape_page_next_games_typeA(country, sport, tournament, nmax)
DATA_ALL = DATA_ALL + [y for y in data if y != None]
driver.close()
data_df = pd.DataFrame(DATA_ALL)
try:
data_df.columns = ['TeamsRaw', 'Bookmaker', 'OddHome', 'OddAway', 'DateRaw']
except:
print('Function crashed, probable reason : no games scraped (empty season)')
return(1)
data_df["ScoreRaw"] = '0:0'
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
'''Now we simply need to split team names, transform date, split score'''
# (0) Filter out None rows
data_df = data_df[~data_df['Bookmaker'].isnull()].dropna().reset_index()
data_df["TO_KEEP"] = 1
for i in range(len(data_df["TO_KEEP"])):
if len(re.split(':',data_df["ScoreRaw"][i]))<2 :
data_df["TO_KEEP"].iloc[i] = 0
data_df = data_df[data_df["TO_KEEP"] == 1]
# (a) Split team names
data_df["Home_id"] = [re.split(' - ',y)[0] for y in data_df["TeamsRaw"]]
data_df["Away_id"] = [re.split(' - ',y)[1] for y in data_df["TeamsRaw"]]
# (b) Transform date
data_df["Date"] = [re.split(', ',y)[1] for y in data_df["DateRaw"]]
# (c) Split score
data_df["Score_home"] = 0
data_df["Score_away"] = 0
# (d) Set season column
data_df["Season"] = SEASON
# Finally we save results
if not os.path.exists('./{}'.format(tournament)):
os.makedirs('./{}'.format(tournament))
data_df[['Home_id', 'Away_id', 'Bookmaker', 'OddHome', 'OddAway', 'Date', 'Score_home', 'Score_away','Season']].to_csv('./{}/NextGames_{}_{}_08042020.csv'.format(tournament,tournament, SEASON), sep=';', encoding='utf-8', index=False)
return(data_df)
def get_data_typeB(i, link):
driver.get(link)
reject_ads()
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a'.format(i)
a = ffi2(target)
if a == True:
print('We wait 4 seconds')
L = []
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, final_score, i, '/ 500 ')
if final_score[-1].isalpha(): # if last character is a letter
print('We detect one player retired!')
return(None)
L = L + [(match, Book, Odd_1, Odd_2, date, final_score)]
return(L)
return(None)
def get_data_next_games_typeB(i, link):
L = None
driver.get(link)
reject_ads()
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a[2]'.format(i)
a = ffi2(target)
L = []
if a == True:
print('We wait 4 seconds')
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed, CHANGE 01/05/2020 -> div[1] BECOMES div + WE STOP AT td[...] for odds
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/a'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/a'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a'.format(i)
a = ffi2(target)
if a == True:
print('We wait 4 seconds')
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed, CHANGE 01/05/2020 -> div[1] BECOMES div + WE STOP AT td[...] for odds
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/a'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/a'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
#final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_2, date, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_2, date)]
return(L)
def scrape_page_typeB(page, country, tournament, SEASON, alpha_game = ['We_scrape_full_season','We_scrape_full_season','We_scrape_full_season']):
link = 'https://www.oddsportal.com/tennis/{}/{}/results/#/page/{}/'.format(country,tournament,page)
DATA = []
REACH_END = 0
print("alpha_game = ", alpha_game)
for i in range(1,100):
if REACH_END == 0 :
content = get_data_typeB(i, link)
if content != None:
#print(content)
p1 = re.split(' - ', content[0][0])[0]
p2 = re.split(' - ', content[0][0])[1]
date = re.split(', ', content[0][4])[1]
if (p1 == alpha_game[0]) & (p2 == alpha_game[1]) & (date == alpha_game[2]):
REACH_END = 1
print("REACH_END = 1, the scraping should stop!", "\n")
if (p1 != alpha_game[0]) | (p2 != alpha_game[1]) | (date != alpha_game[2]):
DATA = DATA + list(content)
#print(DATA)
print("REACH_END = ", REACH_END)
return(DATA, REACH_END)
def scrape_page_next_games_typeB(country, tournament, nmax = 20):
link = 'https://www.oddsportal.com/tennis/{}/{}/'.format(country,tournament)
DATA = []
for i in range(1,nmax):
print(i)
content = get_data_next_games_typeB(i, link)
if content != None:
DATA = DATA + content
print(DATA)
return(DATA)
def scrape_current_tournament_typeB(Surface, bestof = 3, tournament = 'wta-lyon', country = 'france', name_to_write = 'WTA Lyon', SEASON = '2020', max_page = 20):
global driver
REACH_END = 0
if not os.path.exists('./{}/{}_{}.csv'.format(tournament,tournament, SEASON)):
alpha_game = ['We_scrape_full_season','We_scrape_full_season','We_scrape_full_season']
elif os.path.exists('./{}/{}_{}.csv'.format(tournament,tournament, SEASON)):
file = pd.read_csv('./{}/{}_{}.csv'.format(tournament,tournament, SEASON), sep=';', encoding='utf-8')
alpha_game = [file["P1"].iloc[0],file["P2"].iloc[0],file["Date"].iloc[0]]
print("We start to scrape the following tournament :", tournament)
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
#SEASON = '''2020'''
DATA_ALL = []
for page in range(1, max_page + 1):
if REACH_END == 0 :
print('We start to scrape the page n°{}'.format(page))
data, REACH_END = scrape_page_typeB(page, country, tournament, SEASON, alpha_game)
DATA_ALL = DATA_ALL + [y for y in data if y != None]
print('We finished to scrape the page n°{}'.format(page))
driver.close()
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
DATA_ALL_CLEANED = DATA_ALL
#for i in range(len(DATA_ALL)):
#DATA_ALL_CLEANED+= DATA_ALL[i]
#print(DATA_ALL_CLEANED)
#print(len(DATA_ALL_CLEANED))
scores = getScore(DATA_ALL_CLEANED)
teams = [re.split(' - ',y) for y in [y[0] for y in DATA_ALL_CLEANED]]
set_scores = [re.split(':',y) for y in [y[5][13:16] for y in DATA_ALL_CLEANED]]
DATE = [re.split(', ',y) for y in [y[4] for y in DATA_ALL_CLEANED]]
games_score = [re.split(',',y) for y in [y[5][18:] for y in DATA_ALL_CLEANED]]
set1 = [re.split(',',y) for y in [y[0] for y in games_score]]
set2 = [re.split(',',y) for y in [y[1] for y in games_score]]
#set3 = [re.split(',',y) for y in [y[2] for y in games_score]]
set1games = [re.split(':',y) for y in [y[0] for y in set1]]
set2games = [re.split(':',y) for y in [y[0] for y in set2]]
FINAL_DATASET = pd.DataFrame({'Date' : [y[1] for y in DATE], #'Time': [y[2] for y in DATE],
'Bookmaker' : [y[1] for y in DATA_ALL_CLEANED],
'Home_id' : [y[0] for y in teams],
'Away_id' : [y[1] for y in teams],
'OddHome' : [y[2] for y in DATA_ALL_CLEANED],
'OddAway' : [y[3] for y in DATA_ALL_CLEANED],
'Score_home': [scores[y[0] + ' - ' + y[1]]['setP1'] for y in teams], #[y[0][len(y[0])-1:len(y[0])] for y in set_scores],
'Score_away': [scores[y[0] + ' - ' + y[1]]['setP2'] for y in teams], #[y[1][0] for y in set_scores],
'Set1score1' : [scores[y[0] + ' - ' + y[1]]['jeux']['set1P1'] for y in teams], #[y[0] for y in set1games],
'Set1score2' : [scores[y[0] + ' - ' + y[1]]['jeux']['set1P2'] for y in teams], #[y[1] for y in set1games],
'Set2score1' : [scores[y[0] + ' - ' + y[1]]['jeux']['set2P1'] for y in teams], #[y[0] for y in set2games],
'Set2score2' : [scores[y[0] + ' - ' + y[1]]['jeux']['set2P2'] for y in teams], #[y[1] for y in set2games]
'Set3score1' : [scores[y[0] + ' - ' + y[1]]['jeux']['set3P1'] for y in teams],
'Set3score2' : [scores[y[0] + ' - ' + y[1]]['jeux']['set3P2'] for y in teams],
'Set4score1' : [scores[y[0] + ' - ' + y[1]]['jeux']['set4P1'] for y in teams],
'Set4score2' : [scores[y[0] + ' - ' + y[1]]['jeux']['set4P2'] for y in teams],
'Set5score1' : [scores[y[0] + ' - ' + y[1]]['jeux']['set5P1'] for y in teams],
'Set5score2' : [scores[y[0] + ' - ' + y[1]]['jeux']['set5P2'] for y in teams]
})
#print(FINAL_DATASET.head())
FINAL_DATASET["Tournament"] = name_to_write
FINAL_DATASET["Season"] = SEASON
FINAL_DATASET["Surface"] = Surface
FINAL_DATASET["Best.of"] = bestof
# (0) Filter out None rows
FINAL_DATASET = FINAL_DATASET[~FINAL_DATASET['OddHome'].isnull()].dropna().reset_index()
FINAL_DATASET = FINAL_DATASET[~FINAL_DATASET['OddAway'].isnull()].dropna().reset_index()
if not os.path.exists('./{}'.format(tournament)):
os.makedirs('./{}'.format(tournament))
FINAL_DATASET.to_csv('./{}/{}_{}.csv'.format(tournament, tournament, SEASON), sep=';', encoding='utf-8', index=False)
#print(os.listdir())
#print(os.getcwd())
return(FINAL_DATASET)
def scrape_next_games_typeB(Surface, bestof, tournament , country , name_to_write, SEASON = '2020'):
global driver
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
#SEASON = '''2020'''
DATA_ALL = []
for page in range(1):
print('We start to scrape the page n°{}'.format(page+1))
data = scrape_page_next_games_typeB(country, tournament)
DATA_ALL.append([y for y in data if y != None])
driver.close()
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
DATA_ALL_CLEANED = []
for i in range(len(DATA_ALL)):
DATA_ALL_CLEANED+= DATA_ALL[i]
#print(DATA_ALL_CLEANED)
#print(len(DATA_ALL_CLEANED))
#scores = getScore(DATA_ALL_CLEANED)
teams = [re.split(' - ',y) for y in [y[0] for y in DATA_ALL_CLEANED]]
#set_scores = [re.split(':',y) for y in [y[5][13:16] for y in DATA_ALL_CLEANED]]
DATE = [re.split(', ',y) for y in [y[4] for y in DATA_ALL_CLEANED]]
#games_score = [re.split(',',y) for y in [y[5][18:] for y in DATA_ALL_CLEANED]]
#set1 = [re.split(',',y) for y in [y[0] for y in games_score]]
#set2 = [re.split(',',y) for y in [y[1] for y in games_score]]
#set3 = [re.split(',',y) for y in [y[2] for y in games_score]]
#set1games = [re.split(':',y) for y in [y[0] for y in set1]]
#set2games = [re.split(':',y) for y in [y[0] for y in set2]]
FINAL_DATASET = pd.DataFrame({'Date' : [y[1] for y in DATE], #'Time': [y[2] for y in DATE],
'Bookmaker' : [y[1] for y in DATA_ALL_CLEANED],
'Home_id' : [y[0] for y in teams],
'Away_id' : [y[1] for y in teams],
'OddHome' : [y[2] for y in DATA_ALL_CLEANED],
'OddAway' : [y[3] for y in DATA_ALL_CLEANED],
'Score_home': 0, #[y[0][len(y[0])-1:len(y[0])] for y in set_scores],
'Score_away': 0, #[y[1][0] for y in set_scores],
'Set1score1' : 0, #[y[0] for y in set1games],
'Set1score2' : 0, #[y[1] for y in set1games],
'Set2score1' : 0, #[y[0] for y in set2games],
'Set2score2' : 0, #[y[1] for y in set2games]
'Set3score1' : 0,
'Set3score2' : 0,
'Set4score1' : 0,
'Set4score2' : 0,
'Set5score1' : 0,
'Set5score2' : 0
})
#print(FINAL_DATASET.head())
FINAL_DATASET["Tournament"] = name_to_write
FINAL_DATASET["Season"] = SEASON
FINAL_DATASET["Surface"] = Surface
FINAL_DATASET["Best.of"] = bestof
# (0) Filter out None rows
FINAL_DATASET = FINAL_DATASET[~FINAL_DATASET['OddHome'].isnull()].dropna().reset_index()
FINAL_DATASET = FINAL_DATASET[~FINAL_DATASET['OddAway'].isnull()].dropna().reset_index()
if not os.path.exists('./{}'.format(tournament)):
os.makedirs('./{}'.format(tournament))
FINAL_DATASET.to_csv('./{}/NextGames_{}_{}.csv'.format(tournament, tournament, SEASON), sep=';', encoding='utf-8', index=False)
#print(os.listdir())
#print(os.getcwd())
return(FINAL_DATASET)
def get_data_typeC(i, link):
driver.get(link)
reject_ads()
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a'.format(i)
a = ffi2(target)
if a == True:
print('We wait 4 seconds')
L = []
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_X = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # draw odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[4]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_X, Odd_2, date, final_score, i, '/ 500 ')
L = L + [(match, Book, Odd_1, Odd_X, Odd_2, date, final_score)]
return(L)
return(None)
def get_data_next_games_typeC(i, link):
driver.get(link)
reject_ads()
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a[2]'.format(i)
a = ffi2(target)
L = []
if a == True:
print('We wait 4 seconds')
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/a'.format(j)) # first home odd
Odd_X = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/a'.format(j)) # draw odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[4]/a'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_X, Odd_2, date, final_score, i, '/ 30 ')
L = L + [(match, Book, Odd_1, Odd_X, Odd_2, date, final_score)]
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_X = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # draw odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[4]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_X, Odd_2, date, final_score, i, '/ 30 ')
L = L + [(match, Book, Odd_1, Odd_X, Odd_2, date, final_score)]
target = '//*[@id="tournamentTable"]/tbody/tr[{}]/td[2]/a'.format(i)
a = ffi2(target)
if a == True:
print('We wait 4 seconds')
time.sleep(4)
# Now we collect all bookmaker
for j in range(1,30): # only first 10 bookmakers displayed
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/a'.format(j)) # first home odd
Odd_X = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/a'.format(j)) # draw odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[4]/a'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_X, Odd_2, date, final_score, i, '/ 30 ')
L = L + [(match, Book, Odd_1, Odd_X, Odd_2, date, final_score)]
Book = ffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[1]/div/a[2]'.format(j)) # first bookmaker name
Odd_1 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[2]/div'.format(j)) # first home odd
Odd_X = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[3]/div'.format(j)) # draw odd
Odd_2 = fffi('//*[@id="odds-data-table"]/div[1]/table/tbody/tr[{}]/td[4]/div'.format(j)) # first away odd
match = ffi('//*[@id="col-content"]/h1') # match teams
final_score = ffi('//*[@id="event-status"]')
date = ffi('//*[@id="col-content"]/p[1]') # Date and time
print(match, Book, Odd_1, Odd_X, Odd_2, date, final_score, i, '/ 30 ')
L = L + [(match, Book, Odd_1, Odd_X, Odd_2, date, final_score)]
return(L)
def scrape_page_typeC(page, sport, country, tournament, SEASON):
link = 'https://www.oddsportal.com/{}/{}/{}-{}/results/page/1/#/page/{}'.format(sport,country,tournament,SEASON,page)
DATA = []
for i in range(1,100):
content = get_data_typeC(i, link)
if content != None:
DATA = DATA + content
print(DATA)
return(DATA)
def scrape_page_next_games_typeC(country,sport, tournament, nmax = 20):
link = 'https://www.oddsportal.com/{}/{}/{}/'.format(sport, country,tournament)
DATA = []
for i in range(1,nmax):
print(i)
content = get_data_next_games_typeC(i, link)
if content != None:
DATA = DATA + content
print(DATA)
return(DATA)
def scrape_page_current_season_typeC(page,sport, country, tournament):
link = 'https://www.oddsportal.com/{}/{}/{}/results/page/1/#/page/{}'.format(sport,country,tournament,page)
DATA = []
for i in range(1,100):
content = get_data_typeC(i, link)
if content != None:
DATA = DATA + content
print(DATA)
return(DATA)
def scrape_current_tournament_typeC(sport, tournament, country, SEASON, max_page = 25):
global driver
############### NOW WE SEEK TO SCRAPE THE ODDS AND MATCH INFO################################
DATA_ALL = []
for page in range(1, max_page + 1):
print('We start to scrape the page n°{}'.format(page))
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
data = scrape_page_typeC(page, sport, country, tournament, SEASON)
DATA_ALL = DATA_ALL + [y for y in data if y != None]
driver.close()
data_df = pd.DataFrame(DATA_ALL)
try:
data_df.columns = ['TeamsRaw', 'Bookmaker', 'OddHome','OddDraw', 'OddAway', 'DateRaw' ,'ScoreRaw']
except:
print('Function crashed, probable reason : no games scraped (empty season)')
return(1)
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
'''Now we simply need to split team names, transform date, split score'''
# (0) Filter out None rows
data_df = data_df[~data_df['Bookmaker'].isnull()].dropna().reset_index()
data_df["TO_KEEP"] = 1
for i in range(len(data_df["TO_KEEP"])):
if len(re.split(':',data_df["ScoreRaw"][i]))<2 :
data_df["TO_KEEP"].iloc[i] = 0
data_df = data_df[data_df["TO_KEEP"] == 1]
# (a) Split team names
data_df["Home_id"] = [re.split(' - ',y)[0] for y in data_df["TeamsRaw"]]
data_df["Away_id"] = [re.split(' - ',y)[1] for y in data_df["TeamsRaw"]]
# (b) Transform date
data_df["Date"] = [re.split(', ',y)[1] for y in data_df["DateRaw"]]
# (c) Split score
data_df["Score_home"] = [re.split(':',y)[0][-2:] for y in data_df["ScoreRaw"]]
data_df["Score_away"] = [re.split(':',y)[1][:2] for y in data_df["ScoreRaw"]]
# (e) Set season column
data_df["Season"] = SEASON
# Finally we save results
if not os.path.exists('./{}_FULL'.format(tournament)):
os.makedirs('./{}_FULL'.format(tournament))
if not os.path.exists('./{}'.format(tournament)):
os.makedirs('./{}'.format(tournament))
data_df.to_csv('./{}_FULL/{}_{}_FULL.csv'.format(tournament,tournament, SEASON), sep=';', encoding='utf-8', index=False)
data_df[['Home_id', 'Away_id', 'Bookmaker', 'OddHome','OddDraw', 'OddAway', 'Date', 'Score_home', 'Score_away','Season']].to_csv('./{}/{}_{}.csv'.\
format(tournament,tournament, SEASON), sep=';', encoding='utf-8', index=False)
return(data_df)
def scrape_current_season_typeC(tournament, sport, country, SEASON, max_page = 25):
global driver
############### NOW WE SEEK TO SCRAPE THE ODDS AND MATCH INFO################################
DATA_ALL = []
for page in range(1, max_page + 1):
print('We start to scrape the page n°{}'.format(page))
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
data = scrape_page_current_season_typeC(page, sport, country, tournament)
DATA_ALL = DATA_ALL + [y for y in data if y != None]
driver.close()
data_df = pd.DataFrame(DATA_ALL)
try:
data_df.columns = ['TeamsRaw', 'Bookmaker', 'OddHome','OddDraw', 'OddAway', 'DateRaw' ,'ScoreRaw']
except:
print('Function crashed, probable reason : no games scraped (empty season)')
return(1)
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
'''Now we simply need to split team names, transform date, split score'''
# (0) Filter out None rows
data_df = data_df[~data_df['Bookmaker'].isnull()].dropna().reset_index()
data_df["TO_KEEP"] = 1
for i in range(len(data_df["TO_KEEP"])):
if len(re.split(':',data_df["ScoreRaw"][i]))<2 :
data_df["TO_KEEP"].iloc[i] = 0
data_df = data_df[data_df["TO_KEEP"] == 1]
# (a) Split team names
data_df["Home_id"] = [re.split(' - ',y)[0] for y in data_df["TeamsRaw"]]
data_df["Away_id"] = [re.split(' - ',y)[1] for y in data_df["TeamsRaw"]]
# (b) Transform date
data_df["Date"] = [re.split(', ',y)[1] for y in data_df["DateRaw"]]
# (c) Split score
data_df["Score_home"] = [re.split(':',y)[0][-2:] for y in data_df["ScoreRaw"]]
data_df["Score_away"] = [re.split(':',y)[1][:2] for y in data_df["ScoreRaw"]]
# (e) Set season column
data_df["Season"] = SEASON
# Finally we save results
if not os.path.exists('./{}_FULL'.format(tournament)):
os.makedirs('./{}_FULL'.format(tournament))
if not os.path.exists('./{}'.format(tournament)):
os.makedirs('./{}'.format(tournament))
data_df.to_csv('./{}_FULL/{}_CurrentSeason_FULL.csv'.format(tournament,tournament), sep=';', encoding='utf-8', index=False)
data_df[['Home_id', 'Away_id', 'Bookmaker', 'OddHome','OddDraw', 'OddAway', 'Date', 'Score_home', 'Score_away','Season']].\
to_csv('./{}/{}_CurrentSeason.csv'.format(tournament,tournament), sep=';', encoding='utf-8', index=False)
return(data_df)
def scrape_league_typeC(Season, sport, country1, tournament1, nseason, current_season = 'yes', max_page = 25):
long_season = (len(Season) > 6) # indicates whether Season is in format '2010-2011' or '2011' depends on the league)
Season = int(Season[0:4])
for i in range(nseason):
SEASON1 = '{}'.format(Season)
if long_season:
SEASON1 = '{}-{}'.format(Season, Season+1)
print('We start to collect season {}'.format(SEASON1))
scrape_current_tournament_typeC(sport = sport, tournament = tournament1, country = country1, SEASON = SEASON1, max_page = max_page)
print('We finished to collect season {} !'.format(SEASON1))
Season+=1
if current_season == 'yes' :
SEASON1 = '{}'.format(Season)
if long_season:
SEASON1 = '{}-{}'.format(Season, Season+1)
print('We start to collect current season')
scrape_current_season_typeC(tournament = tournament1, sport = sport, country = country1, SEASON = 'CurrentSeason', max_page = max_page)
print('We finished to collect current season !')
# Finally we merge all files
file1 = pd.read_csv('./{}/'.format(tournament1) + os.listdir('./{}/'.format(tournament1))[0], sep=';')
print(os.listdir('./{}/'.format(tournament1))[0])
for filename in os.listdir('./{}/'.format(tournament1))[1:]:
file = pd.read_csv('./{}/'.format(tournament1) + filename, sep=';')
print(filename)
file1 = file1.append(file)
file1 = file1.reset_index()
#Correct falsly collected data for away (in case of 1X2 instead of H/A odds)
return(file1)
def scrape_next_games_typeC(tournament, sport, country, SEASON, nmax = 30):
global driver
############### NOW WE SEEK TO SCRAPE THE ODDS AND MATCH INFO################################
DATA_ALL = []
try:
driver.quit() # close all widows
except:
pass
driver = webdriver.Chrome(executable_path = DRIVER_LOCATION)
data = scrape_page_next_games_typeC(country, sport, tournament, nmax)
DATA_ALL = DATA_ALL + [y for y in data if y != None]
driver.close()
data_df = pd.DataFrame(DATA_ALL)
try:
data_df.columns = ['TeamsRaw', 'Bookmaker', 'OddHome','OddDraw', 'OddAway', 'DateRaw', 'ScoreRaw']
except:
print('Function crashed, probable reason : no games scraped (empty season)')
return(1)
data_df["ScoreRaw"] = '0:0'
##################### FINALLY WE CLEAN THE DATA AND SAVE IT ##########################
'''Now we simply need to split team names, transform date, split score'''
# (0) Filter out None rows
data_df = data_df[~data_df['Bookmaker'].isnull()].dropna().reset_index()
data_df["TO_KEEP"] = 1
for i in range(len(data_df["TO_KEEP"])):
if len(re.split(':',data_df["ScoreRaw"][i]))<2 :
data_df["TO_KEEP"].iloc[i] = 0
data_df = data_df[data_df["TO_KEEP"] == 1]
# (a) Split team names
data_df["Home_id"] = [re.split(' - ',y)[0] for y in data_df["TeamsRaw"]]