Consumer key: IQKbtAYlXLripLGPWd0HUA
Consumer secret: GgDYlkSvaPxGxC4X8liwpUoqKwwr3lCADbz8A7ADU
Consumer key: 3nVuSoBZnx6U4vzUxf5w
Consumer secret: Bcs59EFbbsdF6Sl9Ng71smgStWEGwXXKSjYvPVt7qys
Consumer key: CjulERsDeqhhjSme66ECg
/* | |
* Copyright © 2009, Componentix. All rights reserved. | |
*/ | |
package com.componentix.hibernate.dialect; | |
import java.sql.Types; | |
/** | |
* A proper dialect for Microsoft SQL Server 2000 and 2005. |
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))) |
Consumer key: IQKbtAYlXLripLGPWd0HUA
Consumer secret: GgDYlkSvaPxGxC4X8liwpUoqKwwr3lCADbz8A7ADU
Consumer key: 3nVuSoBZnx6U4vzUxf5w
Consumer secret: Bcs59EFbbsdF6Sl9Ng71smgStWEGwXXKSjYvPVt7qys
Consumer key: CjulERsDeqhhjSme66ECg
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
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} | |
/** |
package whoop.whoop | |
import java.util.concurrent.TimeUnit | |
import org.openjdk.jmh.annotations._ | |
import scala.util.control.NoStackTrace | |
@State(Scope.Benchmark) | |
@BenchmarkMode(Array(Mode.AverageTime)) |
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: