Kevin Nguyen kvn219

Pony Considerations

If you're thinking of checking out the Pony programming language, here's a list of things that I think are important to know. This list is based on a Tweet that I wrote.

Editor/IDE support

There are Pony packages for several popular editors.

All Your Data Does Not Belong In a Database

Businesses are machines producing mountains of data about sales, usage, customer, costs, etc... Traditionally data processing is highly centralised with teams of staff and computer running hot a whirling ready to process. We can do better than moving the mountain of data into the corporate data machine - so long as that machinary is light enough to be moved to the data.

Don't move the mountain - Bring the processing to the data

We've had this problem; a huge directory of files in CSV format, conataining vital information for our business. But it's in CSV, requires analysis, and don't you don't feel like learning sed/grep/awk today - besides it's 2017 and no-one thinks those tools are easy to use.

Set the default size for new Docker for Mac disk images

UPDATE: The instructions here are no longer necessary! Resizing the disk image is now possible right from the UI since Docker for Mac Version 17.12.0-ce-mac49 (21995).

If you are getting the error: No space left on device

Configuring the qcow2 size cap is possible in the current versions:

# my disk is currently 64GiB

Install MySQL on macOS Sierra

This procedure explains how to install MySQL using Homebrew on macOS Sierra 10.12

Install Homebrew

Installing Homebrew is effortless, open Terminal and enter :
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Note: Homebrew will download and install Command Line Tools for Xcode 8.0 as part of the installation process.

Install MySQL

At this time of writing, Homebrew has MySQL version 5.7.15 as default formulae in its main repository :

R to python data wrangling snippets

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:

Open API Specification (fka Swagger Specification) tutorial

All these Gist files are explained on my Open API Specification (fka Swagger Specification) tutorial on API Handyman blog.

The tutorial

This tutorial is composed of several posts, here are the posts links and files used for each one:

Part 1: Introduction
- A JSON specification
- A YAML specification
Part 2: The basics

Postgres Cheatsheet

This is a collection of the most common commands I run while administering Postgres databases. The variables shown between the open and closed tags, "<" and ">", should be replaced with a name you choose. Postgres has multiple shortcut functions, starting with a forward slash, "". Any SQL command that is not a shortcut, must end with a semicolon, ";". You can use the keyboard UP and DOWN keys to scroll the history of previous commands you've run.

Setup

installation, Ubuntu

http://www.postgresql.org/download/linux/ubuntu/ https://help.ubuntu.com/community/PostgreSQL

Moved

Now located at https://github.com/JeffPaine/beautiful_idiomatic_python.

Why it was moved

Github gists don't support Pull Requests or any notifications, which made it impossible for me to maintain this (surprisingly popular) gist with fixes, respond to comments and so on. In the interest of maintaining the quality of this resource for others, I've moved it to a proper repo. Cheers!

	<annotation>
	<folder>GeneratedData_Train</folder>
	<filename>000001.png</filename>
	<path>/my/path/GeneratedData_Train/000001.png</path>
	<source>
	<database>Unknown</database>
	</source>
	<size>
	<width>224</width>
	<height>224</height>

	package main

	import (
	"encoding/json"
	"log"
	"net/http"
	"reflect"
	"time"

	"github.com/gorilla/context"