This should help:

import csv
import requests

CSV_URL = 'http://samplecsvs.s3.amazonaws.com/Sacramentorealestatetransactions.csv'


with requests.Session() as s:
    download = s.get(CSV_URL)

    decoded_content = download.content.decode('utf-8')

    cr = csv.reader(decoded_content.splitlines(), delimiter=',')
    my_list = list(cr)
    for row in my_list:
        print(row)

Ouput sample:

['street', 'city', 'zip', 'state', 'beds', 'baths', 'sq__ft', 'type', 'sale_date', 'price', 'latitude', 'longitude']
['3526 HIGH ST', 'SACRAMENTO', '95838', 'CA', '2', '1', '836', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '59222', '38.631913', '-121.434879']
['51 OMAHA CT', 'SACRAMENTO', '95823', 'CA', '3', '1', '1167', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '68212', '38.478902', '-121.431028']
['2796 BRANCH ST', 'SACRAMENTO', '95815', 'CA', '2', '1', '796', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '68880', '38.618305', '-121.443839']
['2805 JANETTE WAY', 'SACRAMENTO', '95815', 'CA', '2', '1', '852', 'Residential', 'Wed May 21 00:00:00 EDT 2008', '69307', '38.616835', '-121.439146']
[...]

Related question with answer: https://stackoverflow.com/a/33079644/295246

Edit: Other answers are useful if you need to download large files (i.e. stream=True).

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.

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')

You can use the requests module to download the file, and the csv module to read csv files. Here is a very simple example:

import csv
import requests

filename = "file.csv"


with open(filename, 'r') as file:
  # Get list from csv file
  csvFile = csv.reader(file)

  # data of download
  dat = None
  for item in csvFile:
      # get the length of the list
      args = len(item)

      pairs = args // 2
      for i in range(pairs):
          
        try:
          dat = requests.get(item[i], timeout=0.5)
        except:
          continue
        dat = dat.content
        with open(item[i+1], "wb") as download:
          download.write(dat)

It is recommended to download data as stream, and flush it into the target or intermediate local file.

import requests


def download_file(url, output_file, compressed=True):
    """
    compressed: enable response compression support
    """
    # NOTE the stream=True parameter. It enable a more optimized and buffer support for data loading.
    headers = {}
    if compressed:
        headers["Accept-Encoding"] = "gzip"

    r = requests.get(url, headers=headers, stream=True)

    with open(output_file, 'wb') as f: #open as block write.
        for chunk in r.iter_content(chunk_size=4096): 
            if chunk: # filter out keep-alive new chunks
                f.write(chunk)
        f.flush() #Afterall, force data flush into output file (optional)

    return output_file

Considering original post:

remote_csv = "https://data.baltimorecity.gov/api/views/dz54-2aru/rows.csv?accessType=DOWNLOAD"
local_output_file = "test.csv"

download_file(remote_csv, local_output_file)

#Check file content, just for test purposes:
print(open(local_output_file).read())

Base code was extracted from this post: https://stackoverflow.com/a/16696317/176765

Here, you can have more detailed information about body stream usage with requests lib:

http://docs.python-requests.org/en/latest/user/advanced/#body-content-workflow

If that is really the exact code that you ran, then the file is named filename (with no extension) and is in the folder that you were in when you ran the script (probably the same folder the script file itself is stored in). The file will not be called myfile, that's just an identifier in the script.

Probably what you wanted to do was

 with open('your_data_in_here.csv', 'wb') as myfile:

which would create a file called your_data_in_here.csv in the current working folder.