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Python YQL is a client library for making queries with Yahoo Query Language.

The Yahoo! Query Language is an expressive SQL-like language that lets you query, filter, and join data across Web services. With YQL, apps run faster with fewer lines of code and a smaller network footprint.

geo-autocomplete is an extension to Jörn Zaefferer's excellent Autocomplete plugin

geo-autocomplete will autocomplete any user-input location field, using Geocoder results from the Google Maps v3 API Geocoder.

The Google Static Maps v2 API is also used to display location thumbnails of each autocomplete option, to help users choose the right one.

You can use all the standard Autocomplete options to customize the look and behaviour of your geo-autocomplete field, plus these additional option

# If a spatial query is vulnerable to faultlines, it is split into multiple sub-queries that do not cross the faultline.
# Sub-query limits are approximately weighted according to their relative size.
# Sub-queries are executed in parallel, taking advantage of BigTable's distributed goodness, and the results combined, so all this happens very fast.
# Even though the sub-queries are executed in parallel without any significant impact on user experience, App Engine CPU costs can increase to around 2x the CPU cost of a single less accurate query. Sub-query results are memcached to reduce this CPU overhead.

PiCloud is a fresh take on cloud computing. Our platform allows you to easily run any Python code on an auto-scaling, high-performance cluster. No server management required.

AppScale is a platform that allows users to deploy and host their own Google App Engine applications. It executes automatically over Amazon EC2 and Eucalyptus as well as Xen and KVM. It has been developed and is maintained by the RACELab at UC Santa Barbara.

CommonCrawl Foundation was organized in December 2007 as a non-profit California corporation, and we are currently awaiting approval as a 501(c)3. We are just getting off the ground, and look forward to communicating more of our plans in the near future. We aim to be as transparent as possible in our operations, and welcome various levels of involvement in the project.

At work we have to manage a ton of Django based sites. Just for our World Company sites, we have over 50 different settings files, and this doesn't take into account the sites that we host for other clients. At this size it becomes basically impossible to test each site in a browser when you push things to production. To solve this problem I have written a very basic server description tool. This allows you to describe sites (settings file, python path, url, etc.) and servers.

You can see a basic version running for my personal site. It is super barebones, but it should give you an idea of what exactly is possible.

The source is available on Github. A 0.1 release will be uploaded to Pypi soon, after a few of the blemishes have been worked out. I would like to thank Nathan Borror for the design parts that are pretty :)

Python has a debugger, which is available as a module called pdb (for “Python DeBugger”, naturally!). Unfortunately, most discussions of pdb are not very useful to a Python newbie — most are very terse and simply rehash the description of pdb in the Python library reference manual. The discussion that I have found most accessible is in the first four pages of Chapter 27 of the Python 2.1 Bible.

So here is my own personal gentle introduction to using pdb. It assumes that you are not using any IDE — that you’re coding Python with a text editor and running your Python programs from the command line.