To check the performance of Normal Operation vs MulitThreaded Operations

syncMulitThreadVsNormal.py

'''
Excercise for Async Multithreaded vs Normal
Problem : 
    1. To call 2 api 
    2. Get the response 
    3. Convert the response data into pandas Dataframe


RUN 1 :
Time take by Async multithreaded = 0.4613368511199951
Time take by normal = 0.37778782844543457
RUN 2:
Time take by Async multithreaded = 0.1995089054107666
Time take by normal = 0.38010501861572266
RUN 3:
Time take by Async multithreaded = 0.19882512092590332
Time take by normal = 0.38787388801574707

'''



import httpx,requests
import pandas as pd
import json
from datetime import datetime
import asyncio

def nse():
    headers={
        "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7"
    ,"accept-language": "en-US,en;q=0.9"
    ,"user-agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36"
    }
    path="https://www.nseindia.com/api/marketStatus"
    resp=httpx.get(path,headers=headers)
    df=pd.DataFrame(resp.json()["indicativenifty50"],index=[0])

def bse():
    path= "https://api.bseindia.com/BseIndiaAPI/api/SensexGraphData/w?index=98&flag=0&sector=&seriesid=R&frd=null&tod=null"
    headers={"Referer":"https://www.bseindia.com"}
    resp=httpx.get(url=path,headers=headers)
    res=resp.json().split("#@#")[1]
    df=pd.DataFrame(json.loads(res))

async def main():
    t1=datetime.now().timestamp()
    res1=asyncio.gather(asyncio.to_thread(nse),asyncio.to_thread(bse))
    await res1
    print(f"Time take by Async multithreaded = {datetime.now().timestamp() - t1}") 

def normal():
    t1=datetime.now().timestamp()
    nse()
    bse()
    print(f"Time take by normal = {datetime.now().timestamp() - t1}") 

asyncio.run(main())
normal()