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brew install icu4c
CFLAGS=-I/usr/local/opt/icu4c/include LDFLAGS=-L/usr/local/opt/icu4c/lib pip install pyicu

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import json, codecs


topics = ["CoreOS", 
	"Google", 
	"Microsoft", 
	"Joyent", 
	"ECS", 
	"Rancher", 
	"Hashicorp",

spara

import urllib, json, sys, codecs
import unicodecsv as csv

CLIENT_ID = ''
base_url = "https://api.meetup.com/"

dockergroups = [
"Docker-VA",
"Docker-Atlanta",
"docker-austin",

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#! /bin/bash

# Convert Landsat 8 GeoTIFF images into RGB pan-sharpened JPEGs.
#
# Requirements:
#              * gdal http://www.mapbox.com/tilemill/docs/guides/gdal/
#              * convert (image-magick)
#
# Reference info:
#                 http://www.mapbox.com/blog/putting-landsat-8-bands-to-work/

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select [Month], Title, id, [link], ViewCount, Score from
(
  select CAST(
      cast(DATEPART(YYYY, CreationDate) as varchar) + '-' + cast(DATEPART(MM, CreationDate) as varchar) + '-01'
    as datetime) [Month],
    Title,
    p.Id, 
    'https://stackoverflow.com/questions/' + CAST(p.Id AS nvarchar) [link],
    ViewCount,
    Score

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The ridiculousness of the number of guns found at airports by the TSA compared to the number of air passengers at the airport. This is the top 10 airports for gun catches by the TSA.

Airport	Guns Found	Number of Passengers (2014)	Percentage
Dallas/Fort Worth International Airport (DFW)	153	63523489	0.00024085578%
Hartsfield-Jackson Atlanta International Airport (ATL)	144	96178899	0.00014972099%
George Bush Intercontinental Airport - Houston (IAH)	100	41194558	0.00024275051%
Denver International Airport (DEN)	90	53472514	0.00016831076%
Phoenix Sky Harbor International Airport (PHX)	73	42125212	0.00017329289%

spara

select [Month], Title, id, ViewCount, Score from
(
  select CAST(
      cast(DATEPART(YYYY, CreationDate) as varchar) + '-' + cast(DATEPART(MM, CreationDate) as varchar) + '-01'
    as datetime) [Month],
    Title,p.Id, ViewCount,Score
  from Posts p 
  join PostTags pt on pt.PostId = p.Id
  join Tags t on t.Id = pt.TagId 
  where TagName LIKE '%docker%' 

spara

Characterizing Conferences Based on Past Presentations

Process: Characterize conferences based on past presentations. The majority of conferences no longer require a paper and have relatively few artifacts with the exception of slides and videos. However, many conference sites maintain previous conference schedule that contain the session titles. Many sites provide the schedules in machine readable formats such as RSS, iCal, and HTML (to a lesser degree). In addition, Lanyrd may also provide the schedule in an iCal format if not available on the the conference site.

Parsing presentation titles

Example:

Parsing titles from iCal files

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#
# Food access data from USDA: http://www.ers.usda.gov/datafiles/Food_Access_Research_Atlas/Download_the_Data/Current_Version/DataDownload.xlsx
# data is in Microsoft Excel format, convert to CSV in your favorite spreadsheet software (note: too large for Google Sheets)
#

#
# create database in postgres
#

createdb -h localhost -p 5432 -U postgres food_access

spara

require 'rubygems'
require 'cgi'
require 'net/http'
require 'open-uri'
require 'json'



file = File.open('bev01-22.tsv').read
data = Array.new