Sharepoint Scraping with Python, Mechanize, and Beautiful Soup

Over the Christmas break, I decided to write a SharePoint scraper in Python.

At work we have a SharePoint based wiki that is universally disliked and thus is poorly used despite containing a lot of useful information. I've been trialling Atlassian Confluence as a replacement and it seems a much better solution. However we don't want to run multiple wikis so I'll need to copy the existing data across.

Gaining permission to use any APIs that SharePoint might expose over the Christmas break was obviously not going to happen, so I decided to use Python and Mechanize to scrape the wiki.

Problem 1 - Authentication

After some quick research, I found our SharePoint instance doesn't accept basic authentication, but rather only uses the proprietary NTLM protocol.

I found a Python module, python-ntlm, which provided an authentication handler for NTLM.

Following some discussion on Stack Overflow here, I was able to hook the NTLM handler up to the Mechanize module.

import mechanize
from ntlm import HTTPNtlmAuthHandler

passman = mechanize.HTTPPasswordMgrWithDefaultRealm()
passman.add_password(None, url, user, password)
# create the NTLM authentication handler
auth_NTLM = HTTPNtlmAuthHandler.HTTPNtlmAuthHandler(passman)

# create and install the opener
browser = mechanize.Browser()
browser.add_handler(auth_NTLM)
browser.open(url)

I ran into a couple of hurdles with this approach.

Firstly, Mechanize correctly follows the robots.txt. However, since this is a once off scrape off our own site, I was happy to bypass the robots.txt.

browser.set_handle_robots(False)

Secondly, Mechanize seems to have a bug when using NTLM authentication. With a patch described here, I was able to get Mechanize up and running. A pull request is open on GitHub, however it hasn't been merged.

Problem 2 - Traversing and filtering the tree

Since SharePoint HTML is, well ... not pretty, not to mention there's a bunch of site chrome we don't want to scrape, I needed to do some cleansing.

I used the module Beautiful Soup to filter the tree and clean up the code.

Beautiful Soup's tagline reads as follows:

You didn't write that awful page. You're just trying to get some data out of it. Beautiful Soup is here to help.

And that certainly fit my use case.

I filtered to within a particular div (the obviously named ctl00_PlaceHolderMain_WikiField for those of you playing at home) that excluded all the extraneous SharePoint navigation stuff.

Beautiful Soup can use a number of different parsers, depending on your usage.

I started off using the default Python html.parser, however the SharePoint code was too dirty for it, and it fell down on a number of pages. So I changed to the much more permissive html5lib and found all of my pages scraped correctly.

Once I'd filtered and scraped my pages, I had more or less well formed HTML of the page content only, which led to my final challenge.

I then recursively iterated through each of the links parsing local SharePoint links.

Problem 3 - Converting to Markdown

I decided early on that converting to Markdown would probably be a good idea. This would allow me to strip it back to basics, getting rid of the nasty font tags, inline styles, and other junk that had polluted the SharePoint pages.

This proved to be a pretty easy task. I used the html2markdown module.

I ran my newly well formed HTML through it and voila, Markdown!

Get the source

Fork or clone the sharepoint-ripper on GitHub. Issues and pull requests welcome!


Stay tuned for my next post where I import the exported Markdown into Confluence.

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