Billy billyfung

This guide assumes:

You have a VPS (something like DigitalOcean, Linode, etc.)
You have a Flask application and a basic understanding of command line instructions
You've got nginx installed and know where your configuration files are

Instructions

First, you should install gunicorn on your box or virtualenv with pip:

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:

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

Clarify and agree on the scope of the system

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

Give me back my sanity

One of the many things I do for my group at work is to take care of automating as many things as possible. It usually brings me a lot of satisfaction, mostly because I get a kick out of making people's lives easier.

But sometimes, maybe too often, I end up in drawn-out struggles with machines and programs. And sometimes, these struggles bring me to the edge of despair, so much so that I regularly consider living on a computer-less island growing vegetables for a living.

This is the story of how I had to install Pandoc in a CentOS 6 Docker container. But more generally, this is the story of how I think computing is inherently broken, how programmers (myself included) tend to think that their way is the way, how we're ultimately replicating what most of us think is wrong with society, building upon layers and layers of (best-case scenario) obscure and/or weak foundations.

*I would like to extend my gratitude to Google, StackOverflow, GitHub issues but mostly, the people who make the

	# One of the fastest ways to insert bulk data into Postgres (at least, aside from COPY) is using the psycopg2 extras function execute_values.
	# However, this doesn't return an accurate row count value - instead, it just returns the row count for the last page inserted.
	# This wraps the execute_values function with its own pagination to return an accurate count of rows inserted.
	# Performance is approximately equivalent to underlying execute_values function - within 5-10% or so in my brief tests.

	import psycopg2
	import psycopg2.extras
	import math

	db_connection_string = "dbname=EDITME host=EDITME"

	INSTANT RANCHER-SERVER ON AUTOMATED SSL in 10 Lines of Bash Code

	# make a rancher-server:443 for ca-validated A+ SSL using Ubuntu-Trusty/jWilder-Nginx-Reverse-Proxy/LetsEncrypt+proxy-companion on AWS EC2 (or basically any cloud host using Ubuntu Trusty)
	# by Dr. C.Hogan


	# install docker fresh on ubuntu-trusty & set things up proper
	sudo apt-get install apt-transport-https ca-certificates
	sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D
	sudo echo "deb https://apt.dockerproject.org/repo ubuntu-trusty main" >> /etc/apt/sources.list.d/docker.list; sudo apt-get-update

	from flask import Flask, jsonify, Blueprint, current_app
	import json

	## REST api ###################################################################

	api = Blueprint('api', __name__)

	@api.route("/users")
	def users():
	return jsonify(users=[