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Model and Underlying Data

Data about Hospitals

Total number of beds by state draws upon aggregates from this report from ESRI based on Definitive Healthcare.

• It includes a listing of total actual beds, ICU beds, and licensed beds. The licensed bed number can offer a clue as to how much capacity headroom a hospital has to grow in emergency.
• It also includes bed utilization rates.
• The AHA also has data about this, see KFF.org. That's beds per 1000 people, and it's not as granular. The source report is available for purchase.
• [cms.gov has a report](https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provi

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import './style.scss'
import React, { PropTypes } from 'react'

export default class {{ componentName }} extends React.Component {
  static propTypes = {
  };

  static defaultProps = {
  };

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export function addClip (id) {
  return (dispatch, getState) => {
    // prevent adding duplicate clips
    const state = getState()
    if (state.Clips.has(id)) {
      return
    }

    // create the entry with a asyncState of PENDING
    dispatch({

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import axios from 'axios'
import uuid from 'uuid'
import {datastoreConstants} from 'constants'
import * as backgroundTaskActions from './backgroundTaskActions'

export function fetchAllDatastores () {
  return (dispatch, getState) => {
    const taskId = uuid.v4()
    dispatch(backgroundTaskActions.addTask(taskId, 'Fetching datastores…'))
    axios.get('/graphql', {

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function foo ({required, optional='default!'} = {}) {
  console.debug(`${required}, ${optional}`)
}

foo({required: 'lala', optional: 'lolo'})
// -> "lala, lolo"

foo({required: 'lala', optional: null})
// -> "lala, null"

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Libraries

• 960 Grid System - An effort to streamline web development workflow by providing commonly used dimensions, based on a width of 960 pixels. There are two variants: 12 and 16 columns, which can be used separately or in tandem.
• Compass - Open source CSS Authoring Framework.
• Bootstrap - Sleek, intuitive, and powerful mobile first front-end framework for faster and easier web development.
• Font Awesome - The iconic font designed for Bootstrap.
• Zurb Foundation - Framework for writing responsive web sites.
• SASS - CSS extension language which allows variables, mixins and rules nesting.
• Skeleton - Boilerplate for responsive, mobile-friendly development.

Guides

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Packages

• lxml - Pythonic binding for the C libraries libxml2 and libxslt.
• boto - Python interface to Amazon Web Services
• Django - Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
• Fabric - Library and command-line tool for streamlining the use of SSH for application deployment or systems administration task.
• PyMongo - Tools for working with MongoDB, and is the recommended way to work with MongoDB from Python.
• Celery - Task queue to distribute work across threads or machines.
• pytz - pytz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.4 or higher.

Guides

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Libraries

• jQuery - The de-facto library for the modern age. It makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler with an easy-to-use API that works across a multitude of browsers.
• Backbone - Backbone.js gives structure to web applications by providing models with key-value binding and custom events, collections with a rich API of enumerable functions, views with declarative event handling, and connects it all to your existing API over a RESTful JSON interface.
• AngularJS - Conventions based MVC framework for HTML5 apps.
• Underscore - Underscore is a utility-belt library for JavaScript that provides a lot of the functional programming support that you would expect in Prototype.js (or Ruby), but without extending any of the built-in JavaScript objects.
• lawnchair - Key/value store adapter for indexdb, localStorage

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

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def explode_hstore(df, column):
    """Explode a column of a dataframe containing PostgreSQL hstore k-v pairs in the format of:
    
    "foo"=>"bar", "baz"=>"quux", ...
    
    Every key becomes a column. If a given row's pairs doesn't have a key, then the resulting column's value
    will contain NaN.
    """
    # split the tags column out as a new series, and break up each k=>v pair
    s = df[column].str.split(', ').apply(pd.Series, 1).stack()