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)

The code you attempt is downloading the content from the url and pasting it in the data frame named 'df'. You need to save the output csv by using the following line. You will find the output file in the same directory where the python script is saved.

import pandas as pd

url = "https://raw.githubusercontent.com/UofGAnalyticsData/"\
          "DPIP/main/assesment_datasets/assessment3/starwars.csv"

df = pd.read_csv(url)
df.to_csv('output.csv')

If you're trying to write this data to a CSV file, you can first download it using requests.get, then save each line to a CSV file.

import csv
import requests

url = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=MSFT&apikey=demo&datatype=csv'
response = requests.get(url)        

with open('out.csv', 'w') as f:
    writer = csv.writer(f)
    for line in response.iter_lines():
        writer.writerow(line.decode('utf-8').split(','))

Alternatively, if you have pandas installed (pip install --user pandas), you can load data by passing a URL directly.

import pandas as pd

df = pd.read_csv(url)   
df.head()

    timestamp    open    high     low   close  adjusted_close    volume  dividend_amount  split_coefficient
0  2019-06-19  135.00  135.93  133.81  135.69          135.69  17946556              0.0                1.0
1  2019-06-18  134.19  135.24  133.57  135.16          135.16  25908534              0.0                1.0
2  2019-06-17  132.63  133.73  132.53  132.85          132.85  14517785              0.0                1.0
3  2019-06-14  132.26  133.79  131.64  132.45          132.45  17821703              0.0                1.0
4  2019-06-13  131.98  132.67  131.56  132.32          132.32  17200848              0.0                1.0

df.to_csv('out.csv')

Try:

import requests

url = "https://qubeshub.org/publications/1220/serve/1/3861?el=1&download=1"

r = requests.get(url)
filename = r.headers["Content-Disposition"].split('"')[1]

with open(filename, "wb") as f_out:
    print(f"Downloading {filename}")
    f_out.write(r.content)

Prints:

Downloading Elephant Morphometrics and Tusk Size-originaldata-3861.csv

and saves the file.