AlenkaF

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
     7    147.8 MiB    147.8 MiB           1   @profile
     8                                         def my_func():
     9                                             # Load Vaex example
    10    173.0 MiB     25.3 MiB           1       df = vaex.example()
    11                                             # Create a virtual column
    12    173.0 MiB      0.0 MiB           1       df.add_virtual_column("r", "sqrt(x**2 + y**2 + z**2)")
    13                                         
    14                                             # Create a __dataframe__ instance

illepic

#!/usr/bin/env bash

# Authorize to GitHub to get the latest release tar.gz
# Requires: oauth token, https://help.github.com/articles/creating-an-access-token-for-command-line-use/
# Requires: jq package to parse json

# Your oauth token goes here, see link above
OAUTH_TOKEN="34k234lk234lk2j3lk4j2l3k4j2kj3lk"
# Repo owner (user id)
OWNER="your-user-name"

piccolbo

Dplyr is a well known R package to work on structured data, either in memory or in DB and, more recently, in cluster. The in memory implementations have in general capabilities that are not found in the others, so the notion of backend is used with a bit of a poetic license. Even the different DB and cluster backends differ in subtle ways. But it sure is better than writing SQL directly! Here I provide a list of backends with links to the packages that implement them when necessary. I've done my best to provide links to active projects, but I am not endorsing any of them. Do your own testing. Enjoy and please contribute any corrections or additions, in the comments.

Backend	Package
data.frame	builtin
data.table	builtin
arrays	builtin
SQLite	builtin
PostgreSQL/Redshift	builtin

hadley

library(httr)
library(digest)
library(XML)

s3_request <- function(verb, bucket, path = "/", query = NULL,
                       content = NULL, date = NULL) {
  list(
    verb = verb,
    bucket = bucket,
    path = path,

chitchcock

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

sandro

// TODO: remove spaces and newlines
javascript:(function(){
  s=document.createElement('script');
  s.type='text/javascript';
  s.src='http://example.com/bookmarklet.js';
  document.body.appendChild(s);
})();

// TODO: bookmarklet code needs to be in an anchor tag
// <a href="javascript:(function(){s=document.createElement('script');s.type='text/javascript';s.src='http://example.com/bookmarklet.js';document.body.appendChild(s);})();">My Bookmarklet</a>