tdsmith

On a narrow build:

>>> ghost = u'👻'
>>> repr(ghost)
"u'\\U0001f47b'"
>>> print u'\U0001f47b'
👻
>>> g = 0x0001f47b
>>> unichr(g)

Traceback (most recent call last):

tdsmith

> a = data.frame(foo=c(1,2,3), bar=c(-1,2,-3))
> a
  foo bar
1   1  -1
2   2   2
3   3  -3
> ifelse(a$bar < 0, -a$bar, a$bar)
[1] 1 2 3
> 

tdsmith

>>> s = pd.Series([0,1,1])
>>> t = pd.Series(['foo', 'bar', 'baz'])
>>> map = pd.Series({0: 'foo', 1: 'bar', 2: 'baz'})
>>> map[s]
0    foo
1    bar
1    bar
dtype: object
>>> map[s].value_counts() - t.value_counts()
bar     1

tdsmith

>>> s = pd.Series(["foo", "bar", "bar"])
>>> t = pd.Series(["foo", "bar", "baz"])
>>> s.value_counts() - t.value_counts()
bar     1
baz   NaN
foo     0

tdsmith

import cairo
import numpy as np
from PIL import Image

# imdata is a 2D numpy array of dtype np.uint8 containing grayscale pixel intensities on [0, 255]
# repeat for each of R, G, B, and add a deck of 255s for alpha
cairo_imdata = np.dstack([imdata, imdata, imdata, np.ones_like(imdata)*255])
surface = cairo.ImageSurface.create_for_data(cairo_imdata, cairo.FORMAT_ARGB32, *(reversed(imdata.shape)))

# create a context and do some doodling

tdsmith

library(plyr)
library(dplyr)
donors = read.csv("donors.csv", header=TRUE, stringsAsFactors=FALSE,
                  na.strings=c("", " "))
names(donors) = tolower(names(donors))
library(lubridate)
donors$donation_time = parse_date_time(donors$donation_time,
                                       orders=c("ymdhms", "mdyImsp"),
                                       tz="America/Chicago")
donors[which.min(donors$donation_time), "donation_time"] = as.POSIXct("2014/05/01 00:00", tz="America/Chicago")

tdsmith

df = data.frame(id=rep(c("A","B","C","D"), each=5),n=1:20)
flags = numeric(20)
flags[c(4,5,7)] = 1
df$flag = flags

reduce_me = function(df, flagname)
{
  to_copy = integer(NROW(df)*2) * NA
  ptr = 1
  for (i in seq_along(df[,1])) {

tdsmith

df = data.frame(id=1:20)
flags = numeric(20)
flags[c(5,7)] = 1
df$flag = flags

to_copy = numeric(20) * NA
ptr = 1
for (i in seq_along(df$id)) {
  if(!df[i, "flag"]) next
  candidates = c((i-5):(i-1), (i+1):(i+5))

tdsmith

require "formula"

class UniversalPython < Requirement
  satisfy(:build_env => false) { archs_for_command("python").universal? }

  def message; <<-EOS.undent
    A universal build was requested, but Python is not a universal build

    Boost compiles against the Python it finds in the path; if this Python
    is not a universal build then linking will likely fail.

tdsmith

require "formula"

class UniversalPython < Requirement
  satisfy(:build_env => false) { archs_for_command("python").universal? }

  def message; <<-EOS.undent
    A universal build was requested, but Python is not a universal build

    Boost compiles against the Python it finds in the path; if this Python
    is not a universal build then linking will likely fail.