如何使用多处理加速此 python 脚本
How to speed up this python script with multiprocessing
我有一个从数据帧获取数据的脚本,使用这些数据向网站发出请求,使用 fuzzywuzzy 模块找到确切的 href,然后运行一个函数来收集赔率。我会用多处理模块加速这个脚本,这可能吗?
Date HomeTeam AwayTeam
0 Monday 6 December 2021 20:00 Everton Arsenal
1 Monday 6 December 2021 17:30 Empoli Udinese
2 Monday 6 December 2021 19:45 Cagliari Torino
3 Monday 6 December 2021 20:00 Getafe Athletic Bilbao
4 Monday 6 December 2021 15:00 Real Zaragoza Eibar
5 Monday 6 December 2021 17:15 Cartagena Tenerife
6 Monday 6 December 2021 20:00 Girona Leganes
7 Monday 6 December 2021 19:45 Niort Toulouse
8 Monday 6 December 2021 19:00 Jong Ajax FC Emmen
9 Monday 6 December 2021 19:00 Jong AZ Excelsior
脚本
df = pd.read_excel(path)
dates = df.Date
hometeams = df.HomeTeam
awayteams = df.AwayTeam
matches_odds = list()
for i,(a,b,c) in enumerate(zip(dates, hometeams, awayteams)):
try:
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
except requests.exceptions.ConnectionError:
sleep(10)
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
soup = BeautifulSoup(r.text, 'html.parser')
f = soup.find_all('td', class_="table-main__tt")
for tag in f:
match = fuzz.ratio(f'{b} - {c}', tag.find('a').text)
hour = a.split(" ")[4]
if hour.split(':')[0] == '23':
act_hour = '00' + ':' + hour.split(':')[1]
else:
act_hour = str(int(hour.split(':')[0]) + 1) + ':' + hour.split(':')[1]
if match > 70 and act_hour == tag.find('span').text:
href_id = tag.find('a')['href']
table = get_odds(href_id)
matches_odds.append(table)
print(i, ' of ', len(dates))
PS: monthToNum
函数只是将月份名称替换为他的数字
首先,您使用输入 i、a、b 和 c 创建循环体的函数。然后,您创建一个 multiprocessing.Pool
并使用适当的参数(i、a、b、c)将此函数提交到池中。
import multiprocessing
df = pd.read_excel(path)
dates = df.Date
hometeams = df.HomeTeam
awayteams = df.AwayTeam
matches_odds = list()
def fetch(data):
i, (a, b, c) = data
try:
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
except requests.exceptions.ConnectionError:
sleep(10)
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
soup = BeautifulSoup(r.text, 'html.parser')
f = soup.find_all('td', class_="table-main__tt")
for tag in f:
match = fuzz.ratio(f'{b} - {c}', tag.find('a').text)
hour = a.split(" ")[4]
if hour.split(':')[0] == '23':
act_hour = '00' + ':' + hour.split(':')[1]
else:
act_hour = str(int(hour.split(':')[0]) + 1) + ':' + hour.split(':')[1]
if match > 70 and act_hour == tag.find('span').text:
href_id = tag.find('a')['href']
table = get_odds(href_id)
matches_odds.append(table)
print(i, ' of ', len(dates))
if __name__ == '__main__':
num_processes = 20
with multiprocessing.Pool(num_processes) as pool:
pool.map(fetch, enumerate(zip(dates, hometeams, awayteams)))
此外,multiprocessing
并不是提高速度的唯一方法。异步编程也可以使用,并且可能更适合这种情况,尽管 multiprocessing
也可以完成这项工作 - 只是想提一下。
如果仔细阅读Python multiprocessing documentation,就会一目了然。
我有一个从数据帧获取数据的脚本,使用这些数据向网站发出请求,使用 fuzzywuzzy 模块找到确切的 href,然后运行一个函数来收集赔率。我会用多处理模块加速这个脚本,这可能吗?
Date HomeTeam AwayTeam
0 Monday 6 December 2021 20:00 Everton Arsenal
1 Monday 6 December 2021 17:30 Empoli Udinese
2 Monday 6 December 2021 19:45 Cagliari Torino
3 Monday 6 December 2021 20:00 Getafe Athletic Bilbao
4 Monday 6 December 2021 15:00 Real Zaragoza Eibar
5 Monday 6 December 2021 17:15 Cartagena Tenerife
6 Monday 6 December 2021 20:00 Girona Leganes
7 Monday 6 December 2021 19:45 Niort Toulouse
8 Monday 6 December 2021 19:00 Jong Ajax FC Emmen
9 Monday 6 December 2021 19:00 Jong AZ Excelsior
脚本
df = pd.read_excel(path)
dates = df.Date
hometeams = df.HomeTeam
awayteams = df.AwayTeam
matches_odds = list()
for i,(a,b,c) in enumerate(zip(dates, hometeams, awayteams)):
try:
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
except requests.exceptions.ConnectionError:
sleep(10)
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
soup = BeautifulSoup(r.text, 'html.parser')
f = soup.find_all('td', class_="table-main__tt")
for tag in f:
match = fuzz.ratio(f'{b} - {c}', tag.find('a').text)
hour = a.split(" ")[4]
if hour.split(':')[0] == '23':
act_hour = '00' + ':' + hour.split(':')[1]
else:
act_hour = str(int(hour.split(':')[0]) + 1) + ':' + hour.split(':')[1]
if match > 70 and act_hour == tag.find('span').text:
href_id = tag.find('a')['href']
table = get_odds(href_id)
matches_odds.append(table)
print(i, ' of ', len(dates))
PS: monthToNum
函数只是将月份名称替换为他的数字
首先,您使用输入 i、a、b 和 c 创建循环体的函数。然后,您创建一个 multiprocessing.Pool
并使用适当的参数(i、a、b、c)将此函数提交到池中。
import multiprocessing
df = pd.read_excel(path)
dates = df.Date
hometeams = df.HomeTeam
awayteams = df.AwayTeam
matches_odds = list()
def fetch(data):
i, (a, b, c) = data
try:
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
except requests.exceptions.ConnectionError:
sleep(10)
r = requests.get(f'https://www.betexplorer.com/results/soccer/?year={a.split(" ")[3]}&month={monthToNum(a.split(" ")[2])}&day={a.split(" ")[1]}')
soup = BeautifulSoup(r.text, 'html.parser')
f = soup.find_all('td', class_="table-main__tt")
for tag in f:
match = fuzz.ratio(f'{b} - {c}', tag.find('a').text)
hour = a.split(" ")[4]
if hour.split(':')[0] == '23':
act_hour = '00' + ':' + hour.split(':')[1]
else:
act_hour = str(int(hour.split(':')[0]) + 1) + ':' + hour.split(':')[1]
if match > 70 and act_hour == tag.find('span').text:
href_id = tag.find('a')['href']
table = get_odds(href_id)
matches_odds.append(table)
print(i, ' of ', len(dates))
if __name__ == '__main__':
num_processes = 20
with multiprocessing.Pool(num_processes) as pool:
pool.map(fetch, enumerate(zip(dates, hometeams, awayteams)))
此外,multiprocessing
并不是提高速度的唯一方法。异步编程也可以使用,并且可能更适合这种情况,尽管 multiprocessing
也可以完成这项工作 - 只是想提一下。
如果仔细阅读Python multiprocessing documentation,就会一目了然。