There were 986 roadway fatalities in Illinois in 2013. Where's the data?
Seen on garish LED roadway signs all around Chicago on New Year's Eve, 2013: 986 TRAFFIC DEATHS IN 2013. It leads to many questions: On what roads? When did the accidents happen? What do we do now? I'm scared to drive. I will talk about purging my fears by finding the data to answer some of those questions. http://tothebeat.github.io/fatal-car-crashes/ This talk will involve PythonAnywhere, IPython, a module that's not even on PyPi (dbfpy), searching for and finding open government data, CartoDB, Google Fusion Tables, csv, and maybe Pandas. Rest assured, there will be no graphic photos.
Garbage Collection w/ Ref. Cycles
Reference counting is very useful but it has an odd problem. We employ a technique from graphs to approach it. The solution works but it's a bit slow.
Lexical Graphs with Natural Language Processing using NLTK
Brian will talk about his experiences using Python and NLTK http://nltk.org/ to run language comparisons to generate lexical difference graphs like the one mentioned in the "Lexical Distance Among the Languages of Europe" article. http://bit.ly/1cS46Ba
The focus will be on the NLTK and how its internals work to process a language. This talk will be his best one ever.
80 Python enthusiasts attended this meeting.
Storm (with python (and a side of clojure))
A walking tour of Storm, what it is, what you can do, and how you can use it with python.
The Chicago Process: How Braintree Develops Software
Braintree needs to be highly available and secure, while still maintaining a rapid development pace and strict backwards compatibility. In order to achieve that, we use what has become known as the "Chicago Process". This involves pairing, strict TDD, a team structure, and weekly iterations, all to empower the devs to make decisions and get work of a high quality done while avoiding siloing.
A Visual Guide To Pandas
Pandas is the data-munging Swiss Army knife of the Python world. Often you know how your data should look but it's not so obvious how to get there, so I'll present a visual approach to learning the library and data manipulation.
59 Python enthusiasts attended this meeting.
PyData Recap Lightning Talk
Recap of last weeks PyData conference in NYC.
What happened at #aaronswhack?
Many python programmers showed up to participate in the Chicago #aaronswhack. Here's a list of what they worked on, and here are pointers to local projects as well as worldwide projects.
Python heart Open Source Hardware
Open Source Hardware is going to change the world.
But the hardware is still going to need software to control it.
Can Python take the lead and become the de facto language of open source hardware control?
Paul Ebreo talks about the three keys to Python's success in open hardware.
Monoids in Python
Monoids are largely badly explained, but actually quite beautiful. I would like to take a brief tour of what a monoid is and how they can help out with mundane every day tasks in python.
measure_it provides timing and counting for iterators (and other code segments).
CivicLab and Between the Bars
In this talk, I will present on a slice of the maker movement called "civic making" and a new space that has opened up in Chicago to encourage this type creation, CivicLab. As an example of "civic making" I will discuss Between the Bars, a paper based blogging platform for those who are incarcerated, built in Django. I will also discuss our choice in framework and the pros/cons of our approach.
112 Python enthusiasts attended this meeting.
Finite State Machine: fysom
5-Minutes Of Pandas [Lightning Talk]
A lighting talk introducing Pandas, a library for data manipulating and overall munging goodness.
If you do stuff with data and you don't use Pandas, you're doing it all wrong.
Rendering Data with D3
How to begin rendering data with D3, by way of a simple python web server.
87 Python enthusiasts attended this meeting.
What's Love Got to do with It? / Love: for techies
What you think Love is - is (probably) wrong.
The correct metaphor / definition for live will make much more sense to the software person. In fact, it will help with team building and design too. Yup.
Grab a beer. I'll tell you a story about how this evolved (turing machine example), how and where evolution selected it, and why it works - and how it works for approaching problems (design) too.
Then I'll lay out the "api" (functional description).
Don't take it too seriously.
You couldn't have known.
Now you will.
Set it, and forget it! Auto Scale on Rackspace
Rackspace is rolling out a new service to allow your cloud to scale on its own, called Auto Scale. Built on Monitoring, Auto Scale allows you to grow or shrink your fleet of resources as demand changes.
pyrax, a Python package for working with OpenStack-based clouds like Rackspace's, just released Auto Scale and Monitoring support with version 1.5.0.
I'll show how you can use pyrax to deploy servers and automatically add or remove them based on their usage.
Post djangocon: An overview of edX
edx is a major django application serving huge numbers of students for MIT, Harvard, Stanford, Berkely, and more.
- A brief history of Computer-Based Instruction (python has a role);
- incomplete survey of current open-source CBI;
- edX: how's it different / what's it's rough structure, what (besides django/python) is involved;
- edX: hacking the platform (django development);
- edX: hacking courses; a deployment-level VM, and how to get started there;
- finally: future topics: deployment; what this can't do (maybe) and why;
- wrapup: call for interest & edx project night(s);
I'll try to have some USBs for anyone who want to try one of the edX VMs during the talk
129 Python enthusiasts attended this meeting.