jdthorpe

Load and Dump JSON and YAML to / From NamesSpaces

Why

Typed Namespaces ensure object typing is correct in the IDE!

Usage

import yaml

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

jdthorpe

{"lastUpload":"2022-03-09T18:48:13.706Z","extensionVersion":"v3.4.3"}

jdthorpe

-----------------------------------------
-- 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' 

jdthorpe

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

jdthorpe

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!

jdthorpe

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))

jdthorpe

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}{

jdthorpe

/******/ (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;

jdthorpe

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