Jason Thorpe jdthorpe

These are a few of my favorite courses on tech topics

General Coding:

Python Concurrency
Nginx: Quikly build high performance servers and proxies

Aside: when writing python packages (not just scripts), consider haveing the phrase "If its name has no dots, you cannot use relative imports" tattooed someplace readily accessible.

Aside 2: In python multiprocessing, you cannot use interactive telatype to interact with subprocesses, so if your subprocess that reaches a a call to pdb.set_trace(), the sub-process will simply hang waiting for a reponse, which can be a real pain to debug

When imputing using the sklearn.impute.SimpleImputer with the option strategy="most_frequent" calling the .fit() method takes an obserdly long time. This happens scipy.stats.mode is rediculously inefficient for string variables. For example, imputing a single feature with half a million values takes ~15 minutes without the shim and with this shim it takes less than one milliseond.

Usage:

import SimpleImputerShim  # thats it!

Having to add typings to each require is almost as useless as not having typings at all. For example,

import DB from "nedb"
const myCollection = new nedb(DB)
// insert one kind of document
myCollection.insert<{name:string}>({name:"nedb"})
// expect to find a different kind of document
myCollection.findOne<{name:number}>({},(err,doc) => console.log(typeof doc.name))

Extending `python-docx`

Background

I was recently tasked with scraping data from a few thousand of word documents each of which contained nested docx documents which were referenced by w:altChunk elements in the body of the main ./word/document.xml file, like so:

	-----------------------------------------
	-- simple database
	-----------------------------------------

	SELECT CONVERT(VARCHAR(140),s.name) AS [Schema Name],
	CONVERT(VARCHAR(140),o.name) AS [Object Name],
	CONVERT(VARCHAR(140),c.name) AS [Field Name],
	CONVERT(VARCHAR,t.name) AS [Field Type] ,
	CONVERT( VARCHAR, CASE
	WHEN o.type = 'U' THEN 'Table'

	[
	{
	"key": "ctrl+h",
	"command": "workbench.action.focusPreviousGroup"
	},
	{
	"key": "ctrl+l",
	"command": "workbench.action.focusNextGroup"
	},
	{


	function kernelDensityEstimator(kernel:{(x:number):number}, X:number[]) {
	return function(V:number[]) {
	return X.map(function(x) {
	return [x, d3.d3.mean(V, function(v:number) { return kernel(x - v); })];
	});
	};
	}

	function kernelEpanechnikov(k:number): {(x:number):number}{

	/******/ (function(modules) { // webpackBootstrap
	/******/ // The module cache
	/******/ var installedModules = {};
	/******/
	/******/ // The require function
	/******/ function __webpack_require__(moduleId) {
	/******/
	/******/ // Check if module is in cache
	/******/ if(installedModules[moduleId]) {
	/******/ return installedModules[moduleId].exports;

	<svg height="210" width="400">
	<path d="M150 0 L75 200 L225 200 Z" />
	Sorry, your browser does not support inline SVG.
	</svg>

Jason Thorpe jdthorpe

General Coding:

Extending python-docx

Background

Extending `python-docx`