malero

# Sort a list of dictionary objects by a key - case sensitive
from operator import itemgetter
mylist = sorted(mylist, key=itemgetter('name'))

# Sort a list of dictionary objects by a key - case insensitive 
mylist = sorted(mylist, key=lambda k: k['name'].lower())

mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

rednaxelafx

package fx.jvm.hotspot.tools;

import java.util.List;

import sun.jvm.hotspot.tools.Tool;

public class PrintThreadIds extends Tool {
    public static void main(String[] args) {
        PrintThreadIds tool = new PrintThreadIds();
        tool.start(args);

ftao

#!/bin/sh
mkdir ~/down/
cd ~/down/
sudo apt-get install build-essential
wget http://www.python.org/ftp/python/2.7.2/Python-2.7.2.tgz
tar -xzf Python-2.7.2.tgz
cd Python-2.7.2
sudo apt-get install libsqlite3-dev zlib1g-dev libncurses5-dev
sudo apt-get install libgdbm-dev libbz2-dev libreadline5-dev
sudo apt-get install libssl-dev libdb-dev

lukeorland

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Luke Orland
# orluke@gmail.com
# Fri Apr 27 14:51:01 EDT 2012

# EXAMPLE USAGE:
# $ zcat gigaword/data/*/*gz | python gigaword-separate-sentences.py > text

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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kidpixo

Warning This is SEVERELY outdated, the current jupyter version is > 6.X, please refer to your current jupyter notebook installation!

Disclaimer : I just copied those shortcuts from Jupyter Menú > Help > Keyboard Shortcuts, I didn't wrote them myself.

Check your current shortcuts in your Help, shortcuts coule have been modified by extensions or your past self.

Toc

Keyboard shortcuts

mrocklin