Dana Bauer danabauer

Visualizing Github

A treasure trove of data is captured daily by Github; it has become our shared consciousness of thoughts made code. What stories can that data tell us about how we think, work, and interact? How would one go about finding and telling those stories? This talk is a soup-to-nuts tour of practical data visualization with Python and web technologies, covering both the extraction and display of data in illumination of a familiar dataset.

Detailed Description

In the time that we have been crafting software, our collective efforts have never been cataloged neatly in one centralized location. Some projects have long developed in the open, and some have even exposed their development history in some form or another—but the connections between multiple projects remained hidden.

These connections between multiple developers and multiple projects are the glue that binds us together into larger developer communities—they are our mirror, and for the first time we can take a look at ourselves with

##Intro to Python

Curious about programming? Want to learn one language that you can use to build websites, program robots, visualize data, run servers, and make art? Sounds like you're ready to get started with Python.

Python powers some of the popular apps we all know and love (or hate), like Pinterest and Instagram. It's used by tech companies all over the world, including many companies right here in Philadelphia. Monetate, Azavea, SunGard, SIG, and Love Park Robotics are all Python shops. Employers love Python programmers.

Join us on June 15 for GDI Philly's first Introduction to Python workshop, designed for women who have no or very limited programming experience. During this interactive workshop, we'll explore the basic components of a programming language: data types, functions, conditional statements, and loops. We'l

	iMac (2 yrs old): primary work machine; OS X 10.6 / Windows 7 dual boot
	* I keep Windows around because I run mapping software that only works on that operating system
	* I'm working hard to transition to 100% open source mapping/GIS software so I can ditch Windows
	* I spin up Ubuntu VMs when I need them
	Macbook (3.75 yrs old): kid/music/entertainment machine, traveling work machine; OS X 10.6
	* Lately I've been using this for work-related tasks during the day (when I'm coworking); family stuff at night
	* I spin up Ubuntu VMs when I need them, but they feel painfully slow

	I also have an old Dell around that is semi-functional.

	#!/usr/bin/env python
	#import suds
	from suds.client import Client

	#WSDLURL = 'http://lr01.internal.xxxxx.com/HUDSON_ULRSWebServices_581/GISService.asmx?WSDL'
	WSDLURL = 'http://lr05.internal.xxxxx.com/ULRS.WebServices2/GISService.asmx?WSDL'
	_ulrsws = Client(WSDLURL, cache=None)

	def geocode(address):
	return _ulrsws.service.Geocode(clientName='test', input=address)

	import simplejson
	import urllib

	GEOCODE_BASE_URL = 'http://maps.googleapis.com/maps/api/geocode/json'

	def geocode(address,sensor, **geo_args):
	geo_args.update({
	'address': address,
	'sensor': sensor
	})

	# ---------------------------------------------------------------------------
	# VectDasy.py
	# By Torrin Hultgren and Jeremy Mennis
	# For use with ArcGIS 9.2 - 2007
	# Usage: VectDasy <PopFeatureClass> <PopFieldName> <AreaFieldName>
	# <PopKeyName> <AncFeatureClass> <AncCatName> <PresetTable> <PresetField>
	# <OutFeatureClass> <SelectMethod> <Percent> <SampleMin>
	# ---------------------------------------------------------------------------

	import string, arcgisscripting

	# ---------------------------------------------------------------------------
	# walkshed_friction_nyc.py
	# ---------------------------------------------------------------------------

	# Import system modules
	import ConfigParser, walkshed, sys, string, os, datetime, arcgisscripting

	temp_path = "C:\projects\dbauer_walkshed\NYC\data\temp"
	shape_path = "C:\projects\dbauer_walkshed\NYC\data\shape"
	raster_path = "C:\projects\dbauer_walkshed\NYC\data\raster"

	repo = Repo('path/to/repo') commits =
	repo.revision_history(repo.head()) for c in commits:
	print c.message()

	Virtual machine is a city

	Virtual environment is a company within a city

	"""
	---------------------------------------------------------------------------
	dbf_to_csv.py
	Dana Bauer
	Feb 1, 2012
	---------------------------------------------------------------------------
	"""

	import csv
	from dbfpy import dbf