tkb

Early/simplifed version of You Draw It: What Got Better or Worse During Obama’s Presidency

d3.drag is pretty great!

tkb

Automate WDI data importing /scraping & transforming from external sources 
Automation of data collection for high value datasets 
Subnational code mapping to GAUL and validation routines
Build out data site analytics dashboard from Omniture API  
Tableau Connectors for World Bank Data API(external and internal)
WDI archives database - indicator code/name changes; metadata extraction 
System for archiving dataset versions in catalogs 
Dimensioning of data-structures 
Format the WDI input excel to DCS format to speed up WDI production
Metadata extraction from deposited data (micro and macro) in DDH. 

tkb

Country_Name	Sex	Ages	Year	Population
United Kingdom	Male	0-4	2010	2003247.19
United Kingdom	Female	0-4	2010	1908290.832
United Kingdom	Male	10-15	2010	1824959.855
United Kingdom	Female	10-15	2010	1743050.339
United Kingdom	Male	15-19	2010	1988796.685
United Kingdom	Female	15-19	2010	1880327.955
United Kingdom	Male	20-24	2010	2146206.428
United Kingdom	Female	20-24	2010	2050609.997
United Kingdom	Male	25-29	2010	2144750.718

tkb

##Here's an embed of a visualization

<iframe frameborder="0" height="700px" src="http://tkb.github.io/lifeexpectancy-post/index.html" style="margin-top:-90px" width="650px"></iframe>

##Here's an embed of a youtube video

<iframe width="560" height="315" src="//www.youtube.com/embed/sPlHUXHVOHY" frameborder="0" allowfullscreen></iframe>

Above me you should see the contents of:

tkb

This interactive chart shows "survival curves" for females in different countries.

Calcualted from life tables, these survival curves show the expected number of survivors from birth to 85 at 5-year intervals from a hypothetical cohort of 100,000.

tkb

This interactive chart shows "survival curves" for females in different countries.

Calcualted from life tables, these survival curves show the expected number of survivors from birth to 85 at 5-year intervals from a hypothetical cohort of 100,000.

tkb

{
 "metadata": {
  "name": "unpivoting-csv-pandas"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

tkb

import pandas as pd

#read the normalized CSV file
df = pandas.read_csv('lifeexpectancy.csv')

#melt the normalized file, hold the country name and code variables, rename the melted columns
le = pd.melt(df, id_vars=['Country Name','Country Code'], var_name="year", value_name="life_expectancy")

#sort by country name for convenience
le2 =  le.sort(['Country Name'])

tkb

tkb