System:
- Debian, running on Amazong
/root
is backed by NFS (Amazon's EFS)
Launch jupyter console --kernel python3
in one terminal, then pstree
in another.
DROP TABLE IF EXISTS baby_names; | |
CREATE TEMPORARY TABLE baby_names( name TEXT, rank INTEGER ); | |
-- From most popular baby names of 2020 according to the Social Security Administration | |
-- https://www.ssa.gov/oact/babynames/ | |
INSERT INTO baby_names ('name', 'rank') VALUES ('Liam', 1); | |
INSERT INTO baby_names ('name', 'rank') VALUES ('Olivia', 1); | |
INSERT INTO baby_names ('name', 'rank') VALUES ('Noah', 2); | |
INSERT INTO baby_names ('name', 'rank') VALUES ('Emma', 2); | |
INSERT INTO baby_names ('name', 'rank') VALUES ('Oliver', 3); |
(function(console){ | |
console.save = function(data, filename){ | |
if(!data) { | |
data = Jupyter.notebook.toJSON(); | |
} | |
filename = Jupyter.notebook.notebook_name; | |
if(!filename) { |
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: