Mark Simithraaratchy marksimi

distinct column -> For each row returned, return only the unique members of a set. Think of it as for each row in a projection, concatenate all the column values and return only the strings that are unique.

test_db=# SELECT DISTINCT parent_id, child_id, id FROM test.foo_table ORDER BY parent_id, child_id, id LIMIT 10;
parent_id | child_id | id
-----------+------------+-----------------------------
1000040 | 103 | 1000040|2645405726|0001|103

	### MATPLOTLIBRC FORMAT

	# This is a sample matplotlib configuration file - you can find a copy
	# of it on your system in
	# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it
	# there, please note that it will be overridden in your next install.
	# If you want to keep a permanent local copy that will not be
	# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux
	# like systems) and C:\Documents and Settings\yourname\.matplotlib
	# (win32 systems).

	#!/usr/bin/env sh

	# Download lists, unpack and filter, write to stdout
	curl -s https://www.iblocklist.com/lists.php \
	\| sed -n "s/.value='\(http:.=bt_.\)'./\1/p" \
	\| xargs wget -O - \
	\| gunzip \
	\| egrep -v '^#'

	# =============
	# Introduction
	# =============
	# I've been doing some data mining lately and specially looking into `Gradient
	# Boosting Trees <http://en.wikipedia.org/wiki/Gradient_boosting>`_ since it is
	# claimed that this is one of the techniques with best performance out of the
	# box. In order to have a better understanding of the technique I've reproduced
	# the example of section 10.14.1 California Housing in the book `The Elements of Statistical Learning <http://www-stat.stanford.edu/~tibs/ElemStatLearn/>`_.
	# Each point of this dataset represents the house value of a property with some
	# attributes of that house. You can get the data and the description of those

	<?php
	require 'tmhOAuth.php'; // Get it from: https://github.com/themattharris/tmhOAuth

	// Use the data from http://dev.twitter.com/apps to fill out this info
	// notice the slight name difference in the last two items)

	$connection = new tmhOAuth(array(
	'consumer_key' => '',
	'consumer_secret' => '',
	'user_token' => '', //access token

	{
	"metadata": {
	"name": "unpivoting-csv-pandas"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{

	"""
	Implementation of the beta-geometric/NBD (BG/NBD) model from '"Counting Your Customers" the Easy Way: An Alternative to
	the Pareto/NBD Model' (Fader, Hardie and Lee 2005) http://brucehardie.com/papers/018/fader_et_al_mksc_05.pdf and
	accompanying technical note http://www.brucehardie.com/notes/004/

	Apache 2 License
	"""
	from math import log, exp

	import numpy as np

	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

	{
	"lines.linewidth": 2.0,
	"examples.download": true,
	"patch.linewidth": 0.5,
	"legend.fancybox": true,
	"axes.color_cycle": [
	"#30a2da",
	"#fc4f30",
	"#e5ae38",
	"#6d904f",

	import pandas as pd
	# http://blog.yhathq.com/static/misc/data/WineKMC.xlsx
	df_offers = pd.read_excel("./WineKMC.xlsx", sheetname=0)
	df_offers.columns = ["offer_id", "campaign", "varietal", "min_qty", "discount", "origin", "past_peak"]
	df_offers.head()

	df_transactions = pd.read_excel("./WineKMC.xlsx", sheetname=1)
	df_transactions.columns = ["customer_name", "offer_id"]
	df_transactions['n'] = 1
	df_transactions.head()