Use python requests to download CSV

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).

To simplify these answers, and increase performance when downloading a large file, the below may work a bit more efficiently.

import requests
from contextlib import closing
import csv
from codecs import iterdecode

url = "http://download-and-process-csv-efficiently/python.csv"

with closing(requests.get(url, stream=True)) as r:
    reader = iterdecode(csv.reader(r.iter_lines(), 'utf-8'), 
                        delimiter=',', 
                        quotechar='"')
    for row in reader:
        print(row)

By setting stream=True in the GET request, when we pass r.iter_lines() to csv.reader(), we are passing a generator to csv.reader(). By doing so, we enable csv.reader() to lazily iterate over each line in the response with for row in reader.

This avoids loading the entire file into memory before we start processing it, drastically reducing memory overhead for large files.