NDJSON is a convenient format for storing or streaming structured data that may be processed one record at a time.
- Each line is a valid JSON value
- Line separator is ‘\n’
cat test.json | jq -c '.[]' > testNDJSON.json
NDJSON is a convenient format for storing or streaming structured data that may be processed one record at a time.
cat test.json | jq -c '.[]' > testNDJSON.json
In 2002, the Help America Vote Act required (among other things) that states must maintain a "computerized statewide voter registration list". These lists (henceforth "voterfiles") contain information about every registered voter and their voting history.
When people who have not worked with voterfile data before hear about voterfiles, their first response is almost always "But in my 8th grade civics class, I learned that a critical component of American democracy is the secret ballot! How can states have a list of how you vote?" Voterfiles do NOT include information about how an individual voted. They report whether an individual voted in a specific election.
The exact format and contents of a publicly available voterfile differ from state to state. At a minimum, a file will contain:
var multiCrowbar = (function() { | |
/* | |
* SVG Export | |
* converts html labels to svg text nodes | |
* will produce incorrect results when used with multi-line html texts | |
* | |
* Author: Gregor Aisch | |
* based on https://github.com/NYTimes/svg-crowbar/blob/gh-pages/svg-crowbar-2.js | |
*/ |
Disclaimer: The majority of this list was created pre-COVID. Many other organizations are likely hiring remote now.
The dplyr
package in R makes data wrangling significantly easier.
The beauty of dplyr
is that, by design, the options available are limited.
Specifically, a set of key verbs form the core of the package.
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas
package).
dplyr is organised around six key verbs:
const transitionToPromise = (el, property, value) => | |
new Promise(resolve => { | |
el.style[property] = value; | |
const transitionEnded = e => { | |
if (e.propertyName !== property) return; | |
el.removeEventListener('transitionend', transitionEnded); | |
resolve(); | |
} | |
el.addEventListener('transitionend', transitionEnded); | |
}); |
People
:bowtie: |
😄 :smile: |
😆 :laughing: |
---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
// http://paulirish.com/2011/requestanimationframe-for-smart-animating/ | |
// http://my.opera.com/emoller/blog/2011/12/20/requestanimationframe-for-smart-er-animating | |
// requestAnimationFrame polyfill by Erik Möller. fixes from Paul Irish and Tino Zijdel | |
// MIT license | |
(function() { | |
var lastTime = 0; | |
var vendors = ['ms', 'moz', 'webkit', 'o']; |