coordt

from __future__ import with_statement
from fabric.api import env, run, settings, hide
from fabric.decorators import hosts, runs_once

venv = "/home/websites/.virtualenvs/twtv3/"

env.user = 'webdev'
env.hosts = [
    '192.168.1.10', 
    '192.168.1.11', 

ptone

Setting up Webfaction for modern Django deployment

last updated: 4/5/2011

note that this stuff is always a moving target, much of this has been cribbed and combined from various blog posts. Much of the information was out of date from those, and if it is more than a couple months after the last updated date above, consider some of this likely to now be out of date.

mihow

-----BEGIN WEBFACTION INSTALL SCRIPT-----
#!/bin/env python2.6

VERSION = "1.0.0"

"""
Nginx Installer

"autostart": not applicable
"extra info": Enter domain name for the nginx app

mihow

Setting up Webfaction for modern Django deployment

last updated: 4/5/2011

note that this stuff is always a moving target, much of this has been cribbed and combined from various blog posts. Much of the information was out of date from those, and if it is more than a couple months after the last updated date above, consider some of this likely to now be out of date.

ryanb

First install the required gems:

gem install octokit awesomeprint rainbow

Then run it to extract all of your open GitHub issues into files (with comments).

ruby my-gh-issues.rb

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@d3/d3-horizon-chart

daveaugustine

initialize: ->
  @bind 'all', @_trackPageview

_trackPageview: ->
  url = Backbone.history.getFragment()
  _gaq.push(['_trackPageview', "/#{url}"])

miratcan

from MySQLdb import connect

conn = connect(user="[USER]", passwd= "[PASSWORD]")
cur = conn.cursor()

cur.execute("show databases;")
dbs_to_update = filter(
  lambda db: db not in ('information_schema', 'mysql', 'performance_schema'),
	[dbname[0] for dbname in cur.fetchall()])

yong27

import multiprocessing
import pandas as pd
import numpy as np

def _apply_df(args):
    df, func, kwargs = args
    return df.apply(func, **kwargs)

def apply_by_multiprocessing(df, func, **kwargs):
    workers = kwargs.pop('workers')

madjar

import asyncio
import aiohttp
import bs4
import tqdm


@asyncio.coroutine
def get(*args, **kwargs):
    response = yield from aiohttp.request('GET', *args, **kwargs)
    return (yield from response.read_and_close(decode=True))