Download google docs public spreadsheet to csv with python

Just use requests, it is way better than using urllib:

import requests
response = requests.get('https://docs.google.com/spreadsheet/ccc?key=0ArM5yzzCw9IZdEdLWlpHT1FCcUpYQ2RjWmZYWmNwbXc&output=csv')
assert response.status_code == 200, 'Wrong status code'
print(response.content)

You can install it with

pip install requests

You're not storing cookies.

First let me say that I completely endorse the recommendation to use the most-excellent requests library.

However, if you need to do this in vanilla Python 2, the problem lies in the fact that Google is bouncing you around via HTTP 302 redirects, and it expects you to remember the cookies it's setting with each response. When it detects that you aren't storing cookies, it redirects you to the login page.

By default, urllib2.urlopen (or the opener returned from build_opener) will follow 302 redirects, but it won't store HTTP cookies. You have to teach your opener how to do that. Like so:

>>> from cookielib import CookieJar
>>> from urllib2 import build_opener, HTTPCookieProcessor
>>> opener = build_opener(HTTPCookieProcessor(CookieJar()))
>>> resp = opener.open('https://docs.google.com/spreadsheet/ccc?key=0ArM5yzzCw9IZdEdLWlpHT1FCcUpYQ2RjWmZYWmNwbXc&output=csv')
>>> data = resp.read()

Again, use requests if at all possible, but if it's not possible, the standard library can get the job done.

The requests library is great and the gold standard for HTTP requests from Python, however this style of download is, while not deprecated yet, not likely to last, specifically referring to the download-link style. In fact, the downloadUrl field in the Google Drive API v2 is already deprecated. The currently accepted way of exporting Google Sheets as CSV is by using the (current) Google Drive API.

So why the Drive API? Isn't this supposed to be something for the Sheets API instead? Well, the Sheets API is for spreadsheet-oriented functionality, i.e., data formatting, column resize, creating charts, cell validation, etc., while the Drive API is for file-oriented functionality, i.e., import/export.

Below is a complete cmd-line solution. (If you don't do Python, you can use it as pseudocode and pick any language supported by the Google APIs Client Libraries.) For the code snippet, assume the most current Sheet named inventory (older files with that name are ignored) and DRIVE is the API service endpoint:

FILENAME = 'inventory'
SRC_MIMETYPE = 'application/vnd.google-apps.spreadsheet'
DST_MIMETYPE = 'text/csv'

# query for latest file named FILENAME
files = DRIVE.files().list(
    q='name="%s" and mimeType="%s"' % (FILENAME, SRC_MIMETYPE),
    orderBy='modifiedTime desc,name').execute().get('files', [])

# if found, export 1st matching Sheets file as CSV
if files:
    fn = '%s.csv' % os.path.splitext(files[0]['name'].replace(' ', '_'))[0]
    print('Exporting "%s" as "%s"... ' % (files[0]['name'], fn), end='')
    data = DRIVE.files().export(fileId=files[0]['id'], mimeType=DST_MIMETYPE).execute()

    # if non-empty file
    if data:
        with open(fn, 'wb') as f:
            f.write(data)
        print('DONE')

If your Sheet is large, you may have to export it in chunks -- see this page on how to do that. If you're generally new to Google APIs, I have a (somewhat dated but) user-friendly intro video for you. (There are 2 videos after that maybe useful too.)

Doesn't get any simpler than using Pandas:

def build_sheet_url(doc_id, sheet_id):
    return f'https://docs.google.com/spreadsheets/d/{doc_id}/export?format=csv&gid={sheet_id}'

def write_df_to_local(df, file_path):
    df.to_csv(file_path)

doc_id = 'DOC_ID'
sheet_id = 'SHEET_ID'
sheet_url = build_sheet_url(doc_id, sheet_id)
df = pd.read_csv(sheet_url)
file_path = 'FILE_PATH'
write_df_to_local(df, file_path)