olee

// create a bookmark and use this code as the URL, you can now toggle the css on/off
// thanks+credit: https://dev.to/gajus/my-favorite-css-hack-32g3
javascript: (function() {
	var styleEl = document.getElementById('css-layout-hack');
	if (styleEl) {
		styleEl.remove();
		return;
	}
	styleEl = document.createElement('style');
	styleEl.id = 'css-layout-hack';

h4

from __future__ import with_statement
from alembic import context
from sqlalchemy import engine_from_config, pool
from logging.config import fileConfig

from models import Base

config = context.config

fileConfig(config.config_file_name)

vedit

#!/bin/bash

DYNAMODB_USER=vagrant

sudo apt-get install openjdk-7-jre-headless -y

cd /home/${DYNAMODB_USER}/
mkdir -p dynamodb
cd dynamodb

jie-qin

# First update apt-get no matter what
$ sudo apt-get update

$ sudo apt-get install build-essential libssl-dev libffi-dev python-dev python-pip

# Install caravel
$ sudo pip install caravel

# Create an admin user
$ sudo fabmanager create-admin --app caravel

jlafon

An introduction to DynamoDB

DynamoDB is a powerful, fully managed, low latency, NoSQL database service provided by Amazon. DynamoDB allows you to pay for dedicated throughput, with predictable performance for "any level of request traffic". Scalability is handled for you, and data is replicated across multiple availability zones automatically. Amazon handles all of the pain points associated with managing a distributed datastore for you, including replication, load balancing, provisioning, and backups. All that is left is for you to take your data, and its access patterns, and make it work in the denormalized world of NoSQL.

Modeling your data

The single most important part of using DynamoDB begins before you ever put data into it: designing the table(s) and keys. Keys (Amazon calls them primary keys) can be composed of one attribute, called a hash key, or a compound key called the hash and range key. The key is used to uniquely identify an item in a table. The choice of the primary key is particularl

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

mikhailov

events {
  worker_connections  1024;
}

http {
  default_type       text/html;
  access_log         /dev/stdout;
  sendfile           on;
  keepalive_timeout  65;

addyosmani

Update May 2017

Eric Bidelman has documented some of the common workflows possible with headless Chrome over in https://developers.google.com/web/updates/2017/04/headless-chrome.

Update

If you're looking at this in 2016 and beyond, I strongly recommend investigating real headless Chrome: https://chromium.googlesource.com/chromium/src/+/lkgr/headless/README.md

Windows and Mac users might find using Justin Ribeiro's Docker setup useful here while full support for these platforms is being worked out.

mburst

from bitarray import bitarray
import mmh3

class BloomFilter:
    
    def __init__(self, size, hash_count):
        self.size = size
        self.hash_count = hash_count
        self.bit_array = bitarray(size)
        self.bit_array.setall(0)

chrisguitarguy

from __future__ import with_statement # we'll use this later, has to be here
from argparse import ArgumentParser

import requests
from BeautifulSoup import BeautifulStoneSoup as Soup

def parse_sitemap(url):
	resp = requests.get(url)
	
	# we didn't get a valid response, bail