rgbkrk

nteract doesn't support requirejs because we have the builtin require at our fingertips. Jupyter notebook however has long operated under the assumption that you can use the builtin requirejs for loading modules asynchronously:

require(['d3'], function(d3) {...

I started exploring the idea of providing some of these modules in a "quirks" sort of mode where we provide limited access to "requirejs" while still sandboxed. To find out what modules were commonly required, I turned to Google BigQuery, the GitHub dataset, and a User Defined Function (UDF) written in JavaScript.

I'll flesh out this gist or a blog post later. For now, I'll just provide my query code:

rgbkrk

How many times shouldn't it happen...

-- https://news.ycombinator.com/item?id=11396045

SELECT count(*)
FROM (SELECT id, repo_name, path
        FROM [bigquery-public-data:github_repos.sample_files]
 ) AS F

rgbkrk

import IPython.display
import requests

def random_image_url(tags=None):
    query = ""
    if tags is not None:
        query = "&tag=" + "+".join(tags)

    api_url = "http://api.giphy.com/v1/gifs/random?api_key=dc6zaTOxFJmzC" + query
    resp = requests.get(api_url)

rgbkrk

rgbkrk

rgbkrk

/**
 * The cicada principle in animations
 * Remember the cicada principle that used prime numbers to make multiple overlaid repeated backgrounds seem more random?
 * There’s no reason it can’t be applied to repeating linear animations too (using primes for the durations, divided by 10)
 */
 
@keyframes spin { to { transform: rotate(1turn); } }
@keyframes radius { 50% { border-radius: 50%; } }
@keyframes color { 33% { color: rebeccapurple; } 66% { color: deeppink } }
@keyframes width { 50% { border-width: .3em; } }

rgbkrk

/**
 * Pseudorandom stripes
 */

background: #D5D5D5;
background-image: 
	linear-gradient(89deg, #EEE 23px, transparent 0),
	linear-gradient(97deg, #DDD 17px, transparent 0),
	linear-gradient(101deg, #D0D0D0 23px, transparent 0);
background-size: 83px 100%, 61px 100%, 41px 100%;

rgbkrk

> flights_tbl
Source:   query [3.368e+05 x 19]
Database: spark connection master=yarn-client app=sparklyr local=FALSE

    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1   2013     1     1      517            515         2      830            819
2   2013     1     1      533            529         4      850            830
3   2013     1     1      542            540         2      923            850
4   2013     1     1      544            545        -1     1004           1022

rgbkrk

{
 "display_name": "Python 2 (PySpark 1.6.1)",
 "argv": [
  "/usr/bin/python2",
  "-m",
  "ipykernel",
  "-f",
  "{connection_file}",
  "--IPKernelApp.file_to_run=/path/to/spark_home/python/pyspark/shell.py"
 ],

rgbkrk

>>> import pandas as pd
>>> pd.read_csv("./gboeing/urban-data-science/07-Intro-to-Pandas/data/countries.csv").head()
  country google_country_code country_group   name_en   name_fr     name_de  \
0      at                  AT            eu   Austria  Autriche  Österreich
1      be                  BE            eu   Belgium  Belgique     Belgien
2      bg                  BG            eu  Bulgaria  Bulgarie   Bulgarien
3      hr                  HR        non-eu   Croatia   Croatie    Kroatien
4      cy                  CY            eu    Cyprus    Chypre      Zypern

    latitude  longitude