Webscraping with Python
Mechanize – Writing Bots in Python Made Simple by Guy Rutenberg. I’ve been using python to write various bots and crawler for a long time.
Few days ago I needed to write some simple bot to remove some 400+ spam pages in Sikumuna, I took an old script of mine (from 2006) in order to modify it. The script used ClientForm, a python module that allows you to easily parse and fill html forms using python. I quickly found that ClientForm is now deprecated in favor of mechanize. In the beginning I was partly set back by the change, as ClientForm was pretty easy to use, and mechanize‘s documentation could use some improvement.
However, I quickly changed my mind about mechanize. Mechanize — Documentation. Full API documentation is in the docstrings and the documentation of urllib2.
The documentation in these web pages is in need of reorganisation at the moment, after the merge of ClientCookie and ClientForm into mechanize. Tests and examples Examples The front page has some introductory examples. Mechanize. Stateful programmatic web browsing in Python, after Andy Lester’s Perl module WWW::Mechanize.
The examples below are written for a website that does not exist (example.com), so cannot be run. There are also some working examples that you can run. import reimport mechanize. Julian_Todd / Python mechanize cheat sheet. Scrape.py. Scrape.py is a Python module for scraping content from webpages.
Using it, you can easily fetch pages, follow links, and submit forms. Cookies, redirections, and SSL are handled automatically. (For SSL, you either need a version of Python with the socket.ssl function, or the curl command-line utility.) scrape.py does not parse the page into a complete parse tree, so it can handle pages with sloppy syntax. You are free to locate content in the page according to nearby text, tags, or even comments. Setting up Aptana Studio 3 for Django. I am describing how to set up Aptana Studio 3 with Django.
Our first few lessons focused on how you can use Python to goof with a bunch of local files. This time we're going to try something different: using Python to go online and screw around with the Web. Whenever I caucus with aspiring NICARians and other data hungry reporters, it's not long before the topic of web scraping comes up. Python 2.7 Pt 1 [Getting started] Python 2.7 Pt 2 [Tuple/List] Python 2.7 Pt 3 [Dictionary / String Manip] Python 2.7 Pt 4 [Conditional Expressions] Python 2.7 Pt 5 [Looping] BeginnersGuide/NonProgrammers. Scrapy | An open source web scraping framework for Python.
Tutorial — Scrapy 0.15.1 documentation. In this tutorial, we’ll assume that Scrapy is already installed on your system.
If that’s not the case, see Installation guide. We are going to use Open directory project (dmoz) as our example domain to scrape. This tutorial will walk you through these tasks: Beautiful Soup Documentation — Beautiful Soup v4.0.0 documentation. Beautiful Soup is a Python library for pulling data out of HTML and XML files.
It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work. These instructions illustrate all major features of Beautiful Soup 4, with examples. I show you what the library is good for, how it works, how to use it, how to make it do what you want, and what to do when it violates your expectations. The examples in this documentation should work the same way in Python 2.7 and Python 3.2. Setting up Python in Windows 7 | Anthony DeBarros. An all-wise journalist once told me that “everything is easier in Linux,” and after working with it for a few years I’d have to agree — especially when it comes to software setup for data journalism.
But … Many newsroom types spend the day in Windows without the option of Ubuntu or another Linux OS. I’ve been planning some training around Python soon, so I compiled this quick setup guide as a reference. I hope you find it helpful. Set up Python on Windows 7. Scraping CDC flu data with Python | Anthony DeBarros. Getting my flu shot this week reminded me about weekly surveillance data the Centers for Disease Control and Prevention provides on flu prevalence across the nation.
I’d been planning to do some Python training for my team at work, so it seemed like a natural to write a quick Python scraper that grabs the main table on the site and turns it into a delimited text file. So I did, and I’m sharing.