gabrielgrant

import Ember from 'ember';

export default Ember.Controller.extend({
  appName:'Checkbox focus test',
  
  actions: {
  	focusInAction() {
      let args = Array.from(arguments)
      let alerts = this.get('alerts');
    	this.set('alerts', alerts + '\nFocused: ' + args.join(', '));

gabrielgrant

import Ember from 'ember';

export default Ember.Controller.extend({
  appName: 'Ember Twiddle',
  dragCoordinator: Ember.inject.service(),
  actions: {
    hide() {
      this.set('hidden', true);
    }
  }

gabrielgrant

import Ember from 'ember';

export default Ember.Component.extend({
});

gabrielgrant

import Ember from 'ember';
import Parent from './parent-component';

export default Parent.extend({
  foo: 'bar'
});

gabrielgrant

import Ember from 'ember';

export default Ember.Controller.extend({
  appName: 'Ember Twiddle'
});

gabrielgrant

import FirebaseAdapter from 'emberfire/adapters/firebase';

export default FirebaseAdapter.extend({
});

gabrielgrant

Estimated Calorie Needs per Day by Age, Gender, and Physical Activity Level

Based on USDA guidelines: http://www.cnpp.usda.gov/sites/default/files/usda_food_patterns/EstimatedCalorieNeedsPerDayTable.pdf

Estimated amounts of caloriesa needed to maintain calorie balance for various gender and age groups at three different levels of physical activity. The estimates are rounded to the nearest 200 calories for assignment to a USDA Food Pattern. An individual’s calorie needs may be higher or lower than these average estimates.

gabrielgrant

import requests

zips = "59221, 73949, 86515, 42223, 86044, 84536, 97635, 02861, 71749, 81137, 63673, 88063, 89439".split(', ')

url = 'https://congress.api.sunlightfoundation.com/districts/locate?apikey=4356d7e2b68340a69fb9b5e156e9cd49&zip='

for z in zips:
  r = requests.get(url + z).json()['results']
  print z, r

gabrielgrant

It seems the Census has two different two-digit codings for states. Both are numbers assigned based on the state's alphabetical ordering, but one seems to apply the numbers directly from 1-51 (50 states + DC), while the other skips some numbers

I was unable to find a CSV version of the un-skipped version, so that is included here.

gabrielgrant

""" Loads IMDB's Ratings data into Pandas

    Assumes you've already downloaded the raw data by running:
    wget -O - ftp://ftp.funet.fi/pub/mirrors/ftp.imdb.com/pub/ratings.list.gz | gunzip > ratings.list

    See: http://www.imdb.com/interfaces
"""

import pandas as pd