如何使用多線程簡化以下 python 代碼

Question

我有以下 python 代碼，該代碼從 yahoo Finance 獲取實時股票數據並將其更新到 excel 表中...有沒有人通過使用多線程來簡化它以提高性能（這樣它會同時更新 ZBF57C906FA7D2BB66D07372E415 單元格）...這是代碼

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 是一個很好的了解，非常容易用於並行 for 循環。

示例（來自他們的網站）：

>>> 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]

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