如何使用多线程简化以下 python 代码

Question

我有以下 python 代码，该代码从 yahoo Finance 获取实时股票数据并将其更新到 excel 表中...有没有人通过使用多线程来简化它以提高性能（这样它会同时更新 ZBF57C906FA7D2BB66D07372E415 单元格）...这是代码

import bs4
import requests
import xlwings as xw
from multiprocessing import Process

from bs4 import BeautifulSoup

def bajparsePrice():
    baj_r=requests.get('https://in.finance.yahoo.com/quote/BAJFINANCE.NS/')
    baj_soup=bs4.BeautifulSoup(baj_r.text,"lxml")
    baj_price=baj_soup.find_all('div', {'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}) 
    [0].find('span').text
    return baj_price

def infyparsePrice():
    infy_r=requests.get('https://in.finance.yahoo.com/quote/INFY.NS/')
    soup=bs4.BeautifulSoup(infy_r.text,"lxml")
    infy_price=soup.find_all('div', {'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}) 
    [0].find('span').text
    return infy_price

def relparsePrice():
    rel_r=requests.get('https://in.finance.yahoo.com/quote/RELIANCE.NS/')
    rel_soup=bs4.BeautifulSoup(rel_r.text,"lxml")
    rel_price=rel_soup.find_all('div', {'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}) 
    [0].find('span').text
    return rel_price

def sbinparsePrice():
    sbin_r=requests.get('https://in.finance.yahoo.com/quote/SBIN.NS/')
    sbin_soup=bs4.BeautifulSoup(sbin_r.text,"lxml")
    sbin_price=sbin_soup.find_all('div', {'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}) 
    [0].find('span').text
    return sbin_price

def canbkparsePrice():
    canbk_r=requests.get('https://in.finance.yahoo.com/quote/CANBK.NS/')
    canbk_soup=bs4.BeautifulSoup(canbk_r.text,"lxml")
    canbk_price=canbk_soup.find_all('div', {'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}) 
    [0].find('span').text
    return canbk_price

while True:

    wb = xw.Book('fetchlivedata.xlsx')
    sht1 = wb.sheets['Sheet1']
    sht1.range('B2').value = str(bajparsePrice())
    sht1.range('B3').value = str(infyparsePrice())
    sht1.range('B4').value = str(relparsePrice())
    sht1.range('B5').value = str(sbinparsePrice())
    sht1.range('B6').value = str(canbkparsePrice())

Answer 1

Joblib 是一个很好的了解，非常容易用于并行 for 循环。

示例（来自他们的网站）：

>>> from joblib import Parallel, delayed
>>> from math import sqrt
>>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]

链接： https://joblib.readthedocs.io/en/latest/