inkhorn

recipes = readLines('recipes combined.tsv')

# Once I read it into R, I have to get rid of the /t
# characters so that it's more acceptable to the tm package

recipes.new = apply(as.matrix(recipes), 1, function (x) gsub('\t',' ', x))

recipes.corpus = Corpus(VectorSource(recipes.new))
recipes.dtm = DocumentTermMatrix(recipes.corpus)

stucchio

from matplotlib import use

from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os

# Input data

miki725

#!/bin/bash
#
# DESCRIPTION:
#
#   Set the bash prompt according to:
#    * the active virtualenv
#    * the branch of the current git/mercurial repository
#    * the return value of the previous command
#    * the fact you just came from Windows and are used to having newlines in
#      your prompts.

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)

abresler

library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
	read.table() %>% data.frame %>% tbl_df -> data
names(data) <- c("month", "day", "year", "temp")

data %>%
	group_by(year, month) %>%

johnynek

/**
 * To get started:
 * git clone https://github.com/twitter/algebird
 * cd algebird
 * ./sbt algebird-core/console
 */

/**
 * Let's get some data. Here is Alice in Wonderland, line by line
 */

hadley

If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:

• Statistical knowledge
• Programming/hacking skills
• Domain expertise

Statistical knowledge

steadyfish

# using dplyr finctions in non-interactive mode
# examples

library(plyr)
library(dplyr)

d1 = data_frame(x = seq(1,20),y = rep(1:10,2),z = rep(1:5,4))
head(d1)

#### single table verbs ####

inkhorn

library(jsonlite)

cp = fromJSON(txt = "Cell Phone Data.txt", simplifyDataFrame = TRUE)

num.atts = c(4,9,11,12,13,14,15,16,18,22)

cp[,num.atts] = sapply(cp[,num.atts], function (x) as.numeric(x))
cp$aspect.ratio = cp$att_pixels_y / cp$att_pixels_x
cp$isSmartPhone = ifelse(grepl("smart|iphone|blackberry", cp$name, ignore.case=TRUE) == TRUE | cp$att_screen_size >= 4, "Yes", "No")

mortehu

#!/usr/bin/env python

"""Sample Google Cloud Storage API client.

Based on <https://cloud.google.com/storage/docs/json_api/v1/json-api-python-samples>,
but removed parts that are not relevant to the Cloud Storage API.

Assumes the use of a service account, whose secrets are stored in
$HOME/google-api-secrets.json"""