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R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

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#!/usr/bin/env python2

"""
In this challenge we are given a binary that checks an input given from stdin.
If it is correct, it will call get_flag in a separate library and print(it.)
However, we don't have the library so need to find the correct input and input
it over netcat. If it is incorrect, only 'Goodbye' is printed.
Reversing shows that the program verifies the input character by character.]
Because of the program's linear nature and reliance on verbose constraints,
angr is perfect for solving this challenge quickly. On a virtual machine

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import scala.math.{BigInt, BigDecimal}

object RecursiveStreams
{
  // natural numbers
  lazy val N: Stream[BigInt] = Stream.cons(BigInt(1), N.map(_ + 1))

  // fibonacci series
  lazy val fib: Stream[BigInt] = Stream.cons(BigInt(0), Stream.cons(BigInt(1), fib.zip(fib.tail).map(a => a._1 + a._2)))

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NCHR(8364)

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package com.tumblr.fibr.service.filter

import com.tumblr.fibr.config.FilterConfig
import com.tumblr.fibr.tsdb.{TsdbRequest, TsdbResponse}
import com.google.common.cache.{Cache, CacheBuilder}
import com.twitter.finagle.Service
import com.twitter.util.Future
import java.util.concurrent.{Callable, TimeUnit}

/**

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Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

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Twitter公式クライアントのコンシューマキー

Twitter for iPhone

Consumer key: IQKbtAYlXLripLGPWd0HUA
Consumer secret: GgDYlkSvaPxGxC4X8liwpUoqKwwr3lCADbz8A7ADU

Twitter for Android

Consumer key: 3nVuSoBZnx6U4vzUxf5w
Consumer secret: Bcs59EFbbsdF6Sl9Ng71smgStWEGwXXKSjYvPVt7qys

Twitter for iPad

Consumer key: CjulERsDeqhhjSme66ECg

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import scala.annotation.tailrec

object paresmayores extends App {
  
  def mayoresPares(numPares: Int, lte: Int) = {
  	val startNum = if (lte % 2 == 0) lte else lte - 1
  	
  	@tailrec
  	def mp(numPares: Int, start: Int, listaPares: List[Int]): List[Int] = {
  		if (numPares == 0)

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object factorion extends App {

	lazy val N: Stream[Int] = Stream.cons(1, N.map(_ + 1))
	lazy val fact: Stream[Int] = Stream.cons(1, fact.zip(N).map(a => a._1 * a._2))

	val factmap =  0 to 9 map { a => a.toString -> fact(a) } toMap

	def esFactorion(n: Int) = (n.toString.toList map (c => factmap.get(c.toString).get) sum) == n

	val start = System.nanoTime

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#include <stdio.h>

void expand(const char c, char b[4]) {
	b[0] = c & 3;
	b[1] = (c >> 2) & 3;
	b[2] = (c >> 4) & 3;
	b[3] = (c >> 6) & 3;
}

int main(int argc,char* argv[]) {