You should just be able to use Pandas as follows:

import pandas as pd

with open('PeshVsQuetta.json', encoding='utf-8-sig') as f_input:
    df = pd.read_json(f_input)

df.to_csv('PeshVsQuetta.csv', encoding='utf-8', index=False)

This assumes that your JSON file contains a BOM at the start. For the data you have given above, this produces the following CSV file:

date_time,tweet_content,tweet_id,user_id
2018-03-20 18:38:35,RT @PTISPOfficial: پاکستان تحریک انصاف کے وائس چیئرمین شاہ محمود قریشی  بغیر کسی پروٹوکول کے پاکستان سپر لیگ کا میچ دیکھنے کے لئے اسٹیڈیم م…,976166125502427136,938118866135343104
2018-03-20 18:38:35,"At last, Pakistan Have Witnessed The Most Thrilling Match Of Cricket In Pakistan, The Home. 

#PZvQG 
#ABC",976166125535973378,959235642
2018-03-20 18:38:35,"RT @thePSLt20: SIX! 19.4 Liam Dawson to Anwar Ali
Watch ball by ball highlights at (link removed)

#PZvQG #HBLPSL #PSL2018 @_crici…",976166126202839040,395163528
2018-03-20 18:38:35,RT @JeremyMcLellan: Rumor has it Amir Liaquat isn’t allowed to play in #PSL2018 because he keeps switching teams every week.,976166126483902466,3117825702
2018-03-20 18:38:35,RT @daniel86cricket: Peshawar beat Quetta by 1 run in one of the best T20 thrillers. PSL played in front of full house in Lahore Pakistan i…,976166126559354880,3310967346
2018-03-20 18:38:35,"I wanted a super over😭
#PZvQG",976166126836178944,701494826194354179
2018-03-20 18:38:35,RT @hinaparvezbutt: Congratulations Peshawar Zalmi over great win but Quetta Gladiators won our hearts ♥️  #PZvQG,976166126685171713,347132028
2018-03-20 18:38:35,"RT @walterMiitty: It's harder than I thought to tell the truth
It's gonna leave you in pieces
All alone with your demons
And I know that we…",976166126924201986,3461853618

Note: some of your fields contain newlines, so the output might look a bit odd. An application reading this though will handle it correctly (as long as you tell it the encoding is UTF-8 when importing it)

There are multiple ways to get CSV data from URLs. From your example, namely Yahoo Finance, you can copy the Historical data link and call it in Pandas

...
HISTORICAL_URL = "https://query1.finance.yahoo.com/v7/finance/download/GOOG?period1=1582781719&period2=1614404119&interval=1d&events=history&includeAdjustedClose=true"

df = pd.read_csv(HISTORICAL_URL)

A general pattern could involve tools like requests or httpx to make a GET|POST request and then get the contents to io.

import pandas as pd
import requests
import io

url = 'https://query1.finance.yahoo.com/v7/finance/download/GOOG'
params ={'period1':1538761929,
         'period2':1541443929,
         'interval':'1d',
         'events':'history',
         'crumb':'v4z6ZpmoP98',
        }

r = requests.post(url,data=params)
if r.ok:
    data = r.content.decode('utf8')
    df = pd.read_csv(io.StringIO(data))

To get the params, I just followed the liked and copied everything after ‘?’. Check that they match ;)

Results:

Update:

If you can see the raw csv contents directly in url, just pass the url in pd.read_csv Example data directly from url:

data_url ='https://raw.githubusercontent.com/pandas-dev/pandas/master/pandas/tests/data/iris.csv'

df = pd.read_csv(data_url)

I tried comparing your conversion process with pandas and used this code:

import io
import pandas as pd
import requests
import json
import csv
import time

r_bytes = requests.get("https://www.stats.govt.nz/assets/Uploads/Annual-enterprise-survey/Annual-enterprise-survey-2020-financial-year-provisional/Download-data/annual-enterprise-survey-2020-financial-year-provisional-csv.csv").content
print("finished download")
r = r_bytes.decode('utf8')
print("finished decode")

start_df_timestamp = time.time()
df = pd.read_csv(io.StringIO(r), sep=";")
result_df = json.dumps(df.to_dict('records'))
end_df_timestamp = time.time()
print("The df method took {d_t}s".format(d_t=end_df_timestamp-start_df_timestamp))


start_csv_reader_timestamp = time.time()
reader = csv.DictReader(io.StringIO(r))
result_csv_reader = json.dumps(list(reader))
end_csv_reader_timestamp = time.time()
print("The csv-reader method took {d_t}s".format(d_t=end_csv_reader_timestamp-start_csv_reader_timestamp))

and the result was:

finished download
finished decode
The df method took 0.200181245803833s
The csv-reader method took 0.3164360523223877s

this was using a random 37k row CSV file and i noticed that downloading it was by far the most time-intensive thing to do. Even if the the pandas.df functions were faster for me, you should probably try to profile your code, to see whether the conversion really is significantly adding to your runtime. :-)

PS: If you need to constantly monitor the CSV and processing updates turns out to be time-intensive, you could use hashes to only process alterations if the CSV has changed since your last download.

Try this for converting CSV to JSON

    import pandas as pd
    df = pd.read_csv (r'Fayzan-Bhatti\test.csv')
    df.to_json (r'Fayzan-Bhatti\new_test.json')

import requests
import pandas as pd

headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:74.0) Gecko/20100101 Firefox/74.0'
}


def main(url):
    r = requests.get(url, headers=headers).json()
    x = []
    for item in r['data']:
        df = pd.DataFrame.from_dict([item])
        x.append(df)
    new = pd.concat(x, ignore_index=True)
    print(new)
    new.to_csv("Data.csv")


main("https://www1.nseindia.com/live_market/dynaContent/live_watch/stock_watch/foSecStockWatch.json")

Output: view online

Try this:

import pandas as pd

def parse_nested_json(json_d):
    result = {}
    for key in json_d.keys():
        if not isinstance(json_d[key], dict):
            result[key] = json_d[key]
        else:
            result.update(parse_nested_json(json_d[key]))
    return result

json_data = pd.read_json("my_json_file.json")
json_list = [j[1][0] for j in json_data.iterrows()]
parsed_list = [parse_nested_json(j) for j in json_list]
result = pd.DataFrame(parsed_list)
result.to_csv("my_csv_file.csv", index=False)

Update(12/3/2018):

I read the docs, there is a convenient way:

from pandas.io.json import json_normalize
df = json_normalize(data["profile_set"])
df.to_csv(...)

Update(11/7/2021):

pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead

from pandas import json_normalize
df = json_normalize(data["profile_set"])
df.to_csv(...)