Which is best in Python: urllib2, PycURL or mechanize?

Ok so I need to download some web pages using Python and did a quick investigation of my options.

Included with Python:

urllib - seems to me that I should use urllib2 instead. urllib has no cookie support, HTTP/FTP/local files only (no SSL)

urllib2 - complete HTTP/FTP client, supports most needed things like cookies, does not support all HTTP verbs (only GET and POST, no TRACE, etc.)

Full featured:

mechanize - can use/save Firefox/IE cookies, take actions like follow second link, actively maintained (0.2.5 released in March 2011)

PycURL - supports everything curl does (FTP, FTPS, HTTP, HTTPS, GOPHER, TELNET, DICT, FILE and LDAP), bad news: not updated since Sep 9, 2008 (7.19.0)

New possibilities:

urllib3 - supports connection re-using/pooling and file posting

Deprecated (a.k.a. use urllib/urllib2 instead):

httplib - HTTP/HTTPS only (no FTP)

httplib2 - HTTP/HTTPS only (no FTP)

The first thing that strikes me is that urllib/urllib2/PycURL/mechanize are all pretty mature solutions that work well. mechanize and PycURL ship with a number of Linux distributions (e.g. Fedora 13) and BSDs so installation is a non issue typically (so that's good).

urllib2 looks good but I'm wondering why PycURL and mechanize both seem very popular, is there something I am missing (i.e. if I use urllib2 will I paint myself in to a corner at some point?). I'd really like some feedback on the pros/cons of these things so I can make the best choice for myself.

Edit: added note on verb support in urllib2

Solution 1:

I think this talk (at pycon 2009), has the answers for what you're looking for (Asheesh Laroia has lots of experience on the matter). And he points out the good and the bad from most of your listing

• Scrape the Web: Strategies for programming websites that don't expect it (Part 1 of 3)
• Scrape the Web: Strategies for programming websites that don't expect it (Part 2 of 3)
• Scrape the Web: Strategies for programming websites that don't expect it (Part 3 of 3)

From the PYCON 2009 schedule:

Do you find yourself faced with websites that have data you need to extract? Would your life be simpler if you could programmatically input data into web applications, even those tuned to resist interaction by bots?

We'll discuss the basics of web scraping, and then dive into the details of different methods and where they are most applicable.

You'll leave with an understanding of when to apply different tools, and learn about a "heavy hammer" for screen scraping that I picked up at a project for the Electronic Frontier Foundation.

Atendees should bring a laptop, if possible, to try the examples we discuss and optionally take notes.

Update: Asheesh Laroia has updated his presentation for pycon 2010

• PyCon 2010: Scrape the Web: Strategies for programming websites that don't expected it

  * My motto: "The website is the API."
  * Choosing a parser: BeautifulSoup, lxml, HTMLParse, and html5lib.
  * Extracting information, even in the face of bad HTML: Regular expressions, BeautifulSoup, SAX, and XPath.
  * Automatic template reverse-engineering tools.
  * Submitting to forms.
  * Playing with XML-RPC
  * DO NOT BECOME AN EVIL COMMENT SPAMMER.
  * Countermeasures, and circumventing them:
        o IP address limits
        o Hidden form fields
        o User-agent detection
        o JavaScript
        o CAPTCHAs 
  * Plenty of full source code to working examples:
        o Submitting to forms for text-to-speech.
        o Downloading music from web stores.
        o Automating Firefox with Selenium RC to navigate a pure-JavaScript service. 
  * Q&A; and workshopping
  * Use your power for good, not evil. 
  

Update 2:

PyCon US 2012 - Web scraping: Reliably and efficiently pull data from pages that don't expect it

Exciting information is trapped in web pages and behind HTML forms. In this tutorial, >you'll learn how to parse those pages and when to apply advanced techniques that make >scraping faster and more stable. We'll cover parallel downloading with Twisted, gevent, >and others; analyzing sites behind SSL; driving JavaScript-y sites with Selenium; and >evading common anti-scraping techniques.

Solution 2:

Python requests is also a good candidate for HTTP stuff. It has a nicer api IMHO, an example http request from their offcial documentation:

>>> r = requests.get('https://api.github.com', auth=('user', 'pass'))
>>> r.status_code
204
>>> r.headers['content-type']
'application/json'
>>> r.content
...

Solution 3:

• urllib2 is found in every Python install everywhere, so is a good base upon which to start.
• PycURL is useful for people already used to using libcurl, exposes more of the low-level details of HTTP, plus it gains any fixes or improvements applied to libcurl.
• mechanize is used to persistently drive a connection much like a browser would.

It's not a matter of one being better than the other, it's a matter of choosing the appropriate tool for the job.

Solution 4:

To "get some webpages", use requests!

From http://docs.python-requests.org/en/latest/ :

Python’s standard urllib2 module provides most of the HTTP capabilities you need, but the API is thoroughly broken. It was built for a different time — and a different web. It requires an enormous amount of work (even method overrides) to perform the simplest of tasks.

Things shouldn’t be this way. Not in Python.

>>> r = requests.get('https://api.github.com/user', auth=('user', 'pass'))
>>> r.status_code
200
>>> r.headers['content-type']
'application/json; charset=utf8'
>>> r.encoding
'utf-8'
>>> r.text
u'{"type":"User"...'
>>> r.json()
{u'private_gists': 419, u'total_private_repos': 77, ...}