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request_get.py
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request_get.py
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import requests
from bs4 import BeautifulSoup as soup
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import matplotlib.pyplot as plt
import seaborn as sns
import time
import re
def clean_fbref(df):
df.columns = df.columns.droplevel(0)
df.drop(df[df['Pos.'] == 'Pos.'].index, inplace = True)
df= df[["Giocatore","Ruolo","Nazione","Età","Nato","Squadra","PG","Tit","Min","Reti","Assist","Amm.","Esp."]]
df.columns=["Giocatori","Ruolo","Nazione","Eta","Nato","Squadra","Presenze","Tit","Minuti giocati","Reti","Reti_per_90","Assist","Assist_per_90","Amm.","Esp."]
df = df.drop(columns=["Reti_per_90","Assist_per_90"])
df.dropna(subset=['Eta'], inplace=True)
#df["Eta"]=df["Eta"].astype(int)
df.fillna(0,inplace=True)
df[["Minuti giocati","Eta","Nato","Tit","Reti","Assist","Presenze","Amm.","Esp."]]=df[["Minuti giocati","Eta","Nato","Tit","Reti","Assist","Presenze","Amm.","Esp."]].astype(int)
#df["Reti_per_90"]=df["Reti_per_90"].str.replace(",",".").astype(float)
#df["Assist_per_90"]=df["Assist_per_90"].str.replace(",",".").astype(float)
df=df[df["Eta"]<21].sort_values(by=['Minuti giocati'],ascending=False)
df.reset_index(inplace=True,drop=True)
df["Nazione"]=df["Nazione"].str.split(" ").str[1]
#convert presenze in int
return df
def get_fbref():
"""Function to get data from FBref"""
base_url = 'https://fbref.com/it/comp/11/'
current_year = 2022
# Define the number of years you want to subtract
years_to_subtract = 34
for i in range(years_to_subtract):
# Calculate the year to use in the URL
year_url = f"{current_year - i}-{current_year + 1 - i}"
full_url = f'{base_url}{year_url}/stats/Statistiche-di-Serie-A-{year_url}'
# Fetch data from the URL here
data_players=pd.read_html(requests.get(full_url).text.replace('<!--','').replace('-->','')
,attrs={'id':'stats_standard'},thousands='.'
)[0]
data_players=clean_fbref(data_players)
data_players.to_excel(f"SerieA{year_url}-Under21.xlsx",index=False)
def get_2_squadre(season,headers,url_name):
url = f"https://www.transfermarkt.it{url_name}"
r= requests.get(url, headers=headers)
r.status_code
soups = soup(r.text, 'html.parser') # r.content 대신 r.text도 가능
player_info= soups.find_all('table', class_=['items'])
player_info_odd_even= soups.find_all('tr', class_=['odd', 'even'])
#player_info
squadrta=[]
minuti=[]
for info in player_info_odd_even:
player = info.find_all("td")
#print(int(player[8].text.replace('-','0').replace("'","").replace('.','')))
if (player[0].text)==season[2:4]+"/"+season[7:]:
squadrta.append(player[3].img['alt'])
#squadrta.append(re.search(r'\/(.*?)\/', player[3].a['href']).group(1))
minuti.append(int(player[8].text.replace('-','0').replace("'","").replace('.','')))
#stampa i due array creati
if minuti[0]>minuti[1]:
return(squadrta[0])
else:
return(squadrta[1])
def get_data_transfermarkt(url_base,headers,last_page,season):
name=[]
position=[]
age=[]
nation=[]
team=[]
presenze=[]
cambio=[]
mins=[]
reti=[]
for i in range(1, last_page+1):
url = f"{url_base}&page={i}"
r= requests.get(url, headers=headers)
r.status_code
soups = soup(r.text, 'html.parser') # r.content 대신 r.text도 가능
player_info= soups.find_all('tr', class_=['odd', 'even'])
# player_info에서 'td'태그만 모두 찾기
for info in player_info:
player = info.find_all("td")
name.append(player[3].text.replace("\n",""))
position.append(player[4].text)
age.append(player[6].text)
if player[7].text == "2 Squadre":
#team.append(player[7].text)
team.append(get_2_squadre(season,headers,player[7].find("a").get("href")))
#player[7].find("a").get("href")
else:
team.append(player[7].img['alt'])
#team.append(re.search(r'\/(.*?)\/', player[7].a['href']).group(1))
img_tags = player[5].find_all("img")
if len(img_tags) >= 2:
nation.append(f"{img_tags[0]['alt']}/{img_tags[1]['alt']}")
else:
nation.append(player[5].img['alt'])
presenze.append(player[8].text)
cambio.append(player[9].text)
mins.append(int(player[11].text.replace(".","")))
reti.append(player[12].text)
time.sleep(1)
# pd.DataFrame()으로 저장하기
giocatori = pd.DataFrame(
{
"Giocatori":name,
"Ruolo":position,
"age":age,
"Nazione":nation,
"Squadra":team,
"Presenze":presenze,
"Cambio":cambio,
"Minuti giocati":mins,
"Reti":reti
}
)
#split the column Nasc./Età in two columns
giocatori["Nato"]=giocatori["age"].str.split(" ").str[0]
giocatori["Eta"]=giocatori["age"].str.split(" ").str[1]
#chech if there is a † in the column Eta
giocatori["Eta"]=giocatori["Eta"].str.replace("†","")
giocatori["Eta"]=giocatori["Eta"].str.replace("(","").str.replace(")","").astype(int)
giocatori["Reti"]=giocatori["Reti"].replace("-","0").astype(int)
giocatori["Cambio"]=giocatori["Cambio"].replace("-","0").astype(int)
giocatori["Presenze"]=giocatori["Presenze"].replace("-","0").astype(int)
#giocatori["Minuti giocati"]=giocatori["Minuti giocati"].replace(".",",").astype(int)
giocatori.drop(columns=["age"],inplace=True)
return giocatori
def get_transfermarkt(current_year,years_to_subtract):
"""Function to get data of under 21 player of Serie a from Transfermarkt"""
#current_year = 2022
# Define the number of years you want to subtract
#years_to_subtract = 54
headers = {'User-Agent':
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}
for i in range(years_to_subtract):
# Calculate the year to use in the URL
year_url = f"{current_year - i}"
season=f"{current_year - i}-{current_year + 1 - i}"
url = f"https://www.transfermarkt.it/serie-a/dauerbrenner/wettbewerb/IT1/plus/1/galerie/0?saison_id={year_url}&pos=&detailpos=&altersklasse=u20"
#url=f"https://www.transfermarkt.it/premier-league/dauerbrenner/wettbewerb/GB1/plus/1/galerie/0?saison_id={year_url}&pos=&detailpos=&altersklasse=u20"
#url=f"https://www.transfermarkt.it/serie-b/dauerbrenner/wettbewerb/IT2/plus/1/galerie/0?saison_id={year_url}&pos=&detailpos=&altersklasse=u20"
r= requests.get(url, headers=headers)
parse = soup(r.text, 'html.parser')
pages=parse.find_all("li",class_="tm-pagination__list-item tm-pagination__list-item--icon-last-page")
last_page=int(pages[0].find("a",class_="tm-pagination__link").get("href")[-1])
data_players=get_data_transfermarkt(url,headers,last_page,season)
data_players.to_excel(fr"C:\Users\franc\Documents\Progetto_Calciatori_Under21\transfermarkt\SerieA{season}-Under21.xlsx",index=False)
#data_players.to_excel(fr"C:\Users\franc\Documents\Progetto_Calciatori_Under21\SerieB\B{season}-Under21.xlsx",index=False)
def get_uefa_ranking():
base_url="https://kassiesa.net/uefa/data/"
mylist=["method1/crank1998.html","method2/crank2003.html","method3/crank2008.html","method4/crank2013.html","method5/crank2018.html","method5/crank2023.html"]
df=pd.DataFrame(columns=['stagione','valore'])
i=0
for objec in mylist:
full_url = f'{base_url}{objec}'
data_players=pd.read_html(requests.get(full_url).text.replace('<!--','').replace('-->','')
,attrs={'class':'t1'},thousands=',')[0]
#data_players=data_players[data_players['country']=='Italy']
data_players=data_players[data_players['country']=='Italy'].drop(["#","Unnamed: 1","country","ranking","teams"],axis=1)/data_players[0:20].drop(["#","Unnamed: 1","country","ranking","teams"],axis=1).sum()*100
#drop the first and second column
#data_players=data_players.drop(["#","Unnamed: 1","country","ranking","teams"],axis=1)
new_df = data_players.T.reset_index()
new_df.columns = ['stagione', 'valore']
df=pd.concat([df,new_df]).reset_index(drop=True)
current_year = 1968
years_to_subtract = 1998
dataframe=pd.DataFrame(columns=['stagione','valore'])
for i in range(current_year,years_to_subtract,5):
full_url = f'{base_url}method1/crank{i}.html'
data_players=pd.read_html(requests.get(full_url).text.replace('<!--','').replace('-->','')
,attrs={'class':'t1'},thousands=',')[0]
#data_players=data_players[data_players['country']=='Italy']
#data_players=data_players.drop(["#","Unnamed: 1","country","ranking","teams"],axis=1)
data_players=data_players[data_players['country']=='Italy'].drop(["#","Unnamed: 1","country","ranking","teams"],axis=1)/data_players[0:20].drop(["#","Unnamed: 1","country","ranking","teams"],axis=1).sum()*100
new_df = data_players.T.reset_index()
new_df.columns = ['stagione', 'valore']
dataframe=pd.concat([dataframe,new_df]).reset_index(drop=True)
uefa_ranking=pd.concat([dataframe,df]).reset_index(drop=True)
uefa_ranking['stagione'][0:37]=uefa_ranking['stagione'][0:37].astype(str).str[:2].astype(int)+1900
uefa_ranking['stagione'][37:]=uefa_ranking['stagione'][37:].astype(str).str[:2].astype(int)+2000
#drop the first row
uefa_ranking=uefa_ranking.iloc[1:]
uefa_ranking.set_index('stagione',inplace=True)
uefa_ranking.index.name = None
#rename valore column in Uefa_Ranking and convert it in int
uefa_ranking.rename(columns={'valore':'Uefa_Ranking'},inplace=True)
uefa_ranking['Uefa_Ranking']=uefa_ranking['Uefa_Ranking'].astype(float)
return uefa_ranking
def get_table_result():
base_url = f"https://www.transfermarkt.it/serie-a/tabelle/wettbewerb/IT1?saison_id="
current_year = 2022
# Define the number of years you want to subtract
years_to_subtract = 56
for i in range(years_to_subtract):
# Calculate the year to use in the URL
full_url = f'{base_url}{current_year-i}'
headers = {'User-Agent':
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36'}
r= requests.get(full_url, headers=headers)
r.status_code
soups = soup(r.text, 'html.parser') # r.content 대신 r.text도 가능
player_info_odd_even= soups.find(id="yw1").find("tbody").find_all("tr")
squadra=[]
posizione=[]
for info in player_info_odd_even:
player = info.find_all("td")
#print(player)
squadra.append(player[1].img['alt'])
#squadra.append(re.search(r'\/(.*?)\/', player[1].a['href']).group(1))
posizione.append(int(player[0].text))
giocatori = pd.DataFrame(
{
"Posizione":posizione,
"Squadra":squadra,
}
)
#giocatori.head(20)
giocatori.to_excel(fr"C:\Users\franc\Documents\Progetto_Calciatori_Under21\classifica\Classifica_{current_year-i}-{current_year + 1 - i}.xlsx",index=False)