From 144b07d3ec2740becf6b79cfaa246d4e358df62e Mon Sep 17 00:00:00 2001 From: Susan Li Date: Thu, 3 May 2018 00:55:25 -0400 Subject: [PATCH] Add file --- safaribooks_online.ipynb | 18726 +++++++++++++++++++++++++++++++++++++ 1 file changed, 18726 insertions(+) create mode 100644 safaribooks_online.ipynb diff --git a/safaribooks_online.ipynb b/safaribooks_online.ipynb new file mode 100644 index 0000000..5735310 --- /dev/null +++ b/safaribooks_online.ipynb @@ -0,0 +1,18726 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "import collections\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('RatingsHistogram')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "lines = sc.textFile('u.data')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['196\\t242\\t3\\t881250949', '186\\t302\\t3\\t891717742', '22\\t377\\t1\\t878887116']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "ratings = lines.map(lambda x: x.split()[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['3', '3', '1', '2', '1', '4']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ratings.take(6)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "results = ratings.countByValue()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(int, {'1': 6110, '2': 11370, '3': 27145, '4': 34174, '5': 21201})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 6110\n", + "2 11370\n", + "3 27145\n", + "4 34174\n", + "5 21201\n" + ] + } + ], + "source": [ + "sortedResults = collections.OrderedDict(sorted(results.items()))\n", + "for key, value in sortedResults.items():\n", + " print('%s %i' % (key, value))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('FriendsByAge')\n", + "sc = SparkContext(conf = conf)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def parseLine(line):\n", + " fields = line.split(',')\n", + " age = int(fields[2])\n", + " numFriends = int(fields[3])\n", + " \n", + " return (age, numFriends)\n", + "\n", + "lines = sc.textFile('fakefriends.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['0,Will,33,385', '1,Jean-Luc,26,2', '2,Hugh,55,221']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(33, 385), (26, 2), (55, 221)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rdd = lines.map(parseLine)\n", + "rdd.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(33, (3904, 12)), (26, (4115, 17)), (55, (3842, 13))]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "totalsByAge = rdd.mapValues(lambda x: (x, 1)).reduceByKey(lambda x, y: (x[0] + y[0], x[1] + y[1]))\n", + "totalsByAge.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(33, 325.3333333333333), (26, 242.05882352941177), (55, 295.53846153846155)]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "averagesByAge = totalsByAge.mapValues(lambda x: (x[0] / x[1]))\n", + "averagesByAge.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(33, 325.3333333333333)\n", + "(26, 242.05882352941177)\n", + "(55, 295.53846153846155)\n", + "(40, 250.8235294117647)\n", + "(68, 269.6)\n", + "(59, 220.0)\n", + "(37, 249.33333333333334)\n", + "(54, 278.0769230769231)\n", + "(38, 193.53333333333333)\n", + "(27, 228.125)\n", + "(53, 222.85714285714286)\n", + "(57, 258.8333333333333)\n", + "(56, 306.6666666666667)\n", + "(43, 230.57142857142858)\n", + "(36, 246.6)\n", + "(22, 206.42857142857142)\n", + "(35, 211.625)\n", + "(45, 309.53846153846155)\n", + "(60, 202.71428571428572)\n", + "(67, 214.625)\n", + "(19, 213.27272727272728)\n", + "(30, 235.8181818181818)\n", + "(51, 302.14285714285717)\n", + "(25, 197.45454545454547)\n", + "(21, 350.875)\n", + "(42, 303.5)\n", + "(49, 184.66666666666666)\n", + "(48, 281.4)\n", + "(50, 254.6)\n", + "(39, 169.28571428571428)\n", + "(32, 207.9090909090909)\n", + "(58, 116.54545454545455)\n", + "(64, 281.3333333333333)\n", + "(31, 267.25)\n", + "(52, 340.6363636363636)\n", + "(24, 233.8)\n", + "(20, 165.0)\n", + "(62, 220.76923076923077)\n", + "(41, 268.55555555555554)\n", + "(44, 282.1666666666667)\n", + "(69, 235.2)\n", + "(65, 298.2)\n", + "(61, 256.22222222222223)\n", + "(28, 209.1)\n", + "(66, 276.44444444444446)\n", + "(46, 223.69230769230768)\n", + "(29, 215.91666666666666)\n", + "(18, 343.375)\n", + "(47, 233.22222222222223)\n", + "(34, 245.5)\n", + "(63, 384.0)\n", + "(23, 246.3)\n" + ] + } + ], + "source": [ + "results = averagesByAge.collect()\n", + "for result in results:\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ITE00100554,18000101,TMAX,-75,,,E,',\n", + " 'ITE00100554,18000101,TMIN,-148,,,E,',\n", + " 'GM000010962,18000101,PRCP,0,,,E,']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('minTemperatures')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "def parseLine(line):\n", + " fields = line.split(',')\n", + " stationID = fields[0]\n", + " entryType = fields[2]\n", + " temperature = float(fields[3]) * 0.1 * (9.0/5.0) + 32.0\n", + " return (stationID, entryType, temperature)\n", + "\n", + "lines = sc.textFile('1800.csv')\n", + "lines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 'TMAX', 18.5),\n", + " ('ITE00100554', 'TMIN', 5.359999999999999),\n", + " ('GM000010962', 'PRCP', 32.0)]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parsedLines = lines.map(parseLine)\n", + "parsedLines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 'TMIN', 5.359999999999999),\n", + " ('EZE00100082', 'TMIN', 7.699999999999999),\n", + " ('ITE00100554', 'TMIN', 9.5)]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "minTemps = parsedLines.filter(lambda x: 'TMIN' in x[1])\n", + "minTemps.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 5.359999999999999),\n", + " ('EZE00100082', 7.699999999999999),\n", + " ('ITE00100554', 9.5)]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stationTemps = minTemps.map(lambda x: (x[0], x[2]))\n", + "stationTemps.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "730" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stationTemps.count()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 5.359999999999999), ('EZE00100082', 7.699999999999999)]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "minTemps = stationTemps.reduceByKey(lambda x, y: min(x, y))\n", + "minTemps.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ITE00100554\t5.36F\n", + "EZE00100082\t7.70F\n" + ] + } + ], + "source": [ + "results = minTemps.collect()\n", + "for result in results: \n", + " print(result[0] + '\\t{:.2f}F'.format(result[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('maxTemperatures')\n", + "sc = SparkContext(conf = conf)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ITE00100554,18000101,TMAX,-75,,,E,',\n", + " 'ITE00100554,18000101,TMIN,-148,,,E,',\n", + " 'GM000010962,18000101,PRCP,0,,,E,']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def parseLine(line):\n", + " fields = line.split(',')\n", + " stationID = fields[0]\n", + " entryType = fields[2]\n", + " temperature = float(fields[3]) * 0.1 * (9.0/5.0) + 32\n", + " \n", + " return (stationID, entryType, temperature)\n", + "lines = sc.textFile('1800.csv')\n", + "lines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 'TMAX', 18.5),\n", + " ('ITE00100554', 'TMIN', 5.359999999999999),\n", + " ('GM000010962', 'PRCP', 32.0)]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parsedLines = lines.map(parseLine)\n", + "parsedLines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 'TMAX', 18.5),\n", + " ('EZE00100082', 'TMAX', 16.52),\n", + " ('ITE00100554', 'TMAX', 21.2)]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "maxTemps = parsedLines.filter(lambda x: 'TMAX' in x[1])\n", + "maxTemps.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 18.5), ('EZE00100082', 16.52), ('ITE00100554', 21.2)]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stationTemps = maxTemps.map(lambda x: (x[0], x[2]))\n", + "stationTemps.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ITE00100554', 90.14000000000001), ('EZE00100082', 90.14000000000001)]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "maxTemps = stationTemps.reduceByKey(lambda x, y: max(x, y))\n", + "maxTemps.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ITE00100554\t90.14F\n", + "EZE00100082\t90.14F\n" + ] + } + ], + "source": [ + "results = maxTemps.collect()\n", + "for result in results:\n", + " print(result[0] + '\\t{:.2f}F'.format(result[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['The quick red fox jumped over the lazy brown dogs']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "conf = SparkConf().setMaster('local').setAppName('RedFox')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "lines = sc.textFile('redfox.txt')\n", + "lines.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['THE QUICK RED FOX JUMPED OVER THE LAZY BROWN DOGS']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rageCaps = lines.map(lambda x: x.upper())\n", + "rageCaps.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['The',\n", + " 'quick',\n", + " 'red',\n", + " 'fox',\n", + " 'jumped',\n", + " 'over',\n", + " 'the',\n", + " 'lazy',\n", + " 'brown',\n", + " 'dogs']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "words = lines.flatMap(lambda x: x.split())\n", + "words.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "conf = SparkConf().setMaster('local').setAppName('WordCount')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "input = sc.textFile('book.txt')\n", + "words = input.flatMap(lambda x: x.split())\n", + "wordCounts = words.countByValue()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Self-Employment: 1\n", + "Building 5\n", + "an 172\n", + "Internet 13\n", + "Business 19\n", + "of 941\n", + "One 12\n", + "Achieving 1\n", + "Financial 3\n", + "and 901\n", + "Personal 3\n", + "Freedom 7\n", + "through 55\n", + "a 1148\n", + "Lifestyle 5\n", + "Technology 2\n", + "By 9\n", + "Frank 10\n", + "Kane 7\n", + "Copyright 1\n", + "2015 3\n", + "Kane. 1\n", + "All 13\n", + "rights 3\n", + "reserved 2\n", + "worldwide. 2\n", + "CONTENTS 1\n", + "Disclaimer 1\n", + "Preface 1\n", + "Part 2\n", + "I: 2\n", + "Making 5\n", + "the 1176\n", + "Big 1\n", + "Decision 1\n", + "Overcoming 1\n", + "Inertia 1\n", + "Fear 1\n", + "Failure 1\n", + "Career 1\n", + "Indoctrination 2\n", + "The 88\n", + "Carrot 1\n", + "on 399\n", + "Stick 2\n", + "Ego 1\n", + "Protection 1\n", + "Your 62\n", + "Employer 2\n", + "as 297\n", + "Security 2\n", + "Blanket 1\n", + "Why 3\n", + "it�s 28\n", + "Worth 1\n", + "it 311\n", + "Unlimited 2\n", + "Growth 4\n", + "Potential 1\n", + "Investing 3\n", + "in 552\n", + "Yourself, 1\n", + "Not 7\n", + "Someone 2\n", + "Else 1\n", + "No 14\n", + "Dependencies 1\n", + "Commute 1\n", + "to 1789\n", + "Live 3\n", + "Where 2\n", + "You 144\n", + "Want 5\n", + "Work 4\n", + "When 31\n", + "How 29\n", + "Is 17\n", + "Self-Employment 1\n", + "for 500\n", + "You? 1\n", + "Flowchart: 1\n", + "Should 3\n", + "I 322\n", + "Even 35\n", + "Consider 5\n", + "Self-Employment? 2\n", + "Having 2\n", + "Safety 2\n", + "Net 2\n", + "Planning 3\n", + "Health 2\n", + "Care 2\n", + "Self-Assessment 1\n", + "Quiz 1\n", + "PART 5\n", + "II: 2\n", + "Happen 1\n", + "Designing 1\n", + "Fallacy 1\n", + "Introducing 1\n", + "Ideal 1\n", + "Case 1\n", + "Study: 1\n", + "Sundog 20\n", + "Software 12\n", + "Other 4\n", + "Ideas 2\n", + "Key 1\n", + "Points 1\n", + "Evaluating 1\n", + "Idea 1\n", + "Writing 4\n", + "Plan 4\n", + "Description 2\n", + "Vision 2\n", + "Definition 1\n", + "Market 2\n", + "Product 2\n", + "Organization 1\n", + "Management 2\n", + "Marketing 5\n", + "Sales 2\n", + "Strategy 1\n", + "your 1339\n", + "Reality 1\n", + "Check 4\n", + "Employment 1\n", + "Agreement 1\n", + "Developing 1\n", + "Side 1\n", + "Naming 1\n", + "Obtaining 1\n", + "Licenses 1\n", + "Tax 2\n", + "Launching 2\n", + "Product, 1\n", + "Measuring 1\n", + "Results 1\n", + "Risk 1\n", + "Mitigation 1\n", + "Don't 22\n", + "Have 9\n", + "Go 7\n", + "It 42\n", + "Alone 1\n", + "Pulling 1\n", + "Trigger 1\n", + "Final 1\n", + "Checklist 1\n", + "Telling 1\n", + "Boss 1\n", + "III: 2\n", + "Last 1\n", + "Fueling 1\n", + "Fire 1\n", + "Website 2\n", + "Basics 5\n", + "Public 1\n", + "Relations 1\n", + "Online 8\n", + "Advertising 3\n", + "Offline 1\n", + "Search 6\n", + "Engine 2\n", + "Optimization 3\n", + "Landing 4\n", + "Page 2\n", + "Importance 1\n", + "Email 5\n", + "Campaigns 1\n", + "Using 5\n", + "Social 2\n", + "Networks 1\n", + "Measure 1\n", + "Act 2\n", + "Interpreting 1\n", + "Metrics 2\n", + "Tracking 1\n", + "Closing 1\n", + "Avoiding 2\n", + "Pitfalls 1\n", + "Beware 5\n", + "Leeches 1\n", + "Well-Meaning 1\n", + "Advice 1\n", + "Hiring 3\n", + "Salespeople 1\n", + "Pressure 1\n", + "Hire 1\n", + "Misleading 1\n", + "Statistics 4\n", + "Finding 4\n", + "Time 2\n", + "People 4\n", + "Compounding 1\n", + "Effect 1\n", + "Adapting 1\n", + "New 6\n", + "Life 2\n", + "Survivor's 1\n", + "Guilt 1\n", + "Letting 1\n", + "Fixed 1\n", + "Schedules 1\n", + "Little 1\n", + "Recommended 1\n", + "Reading 1\n", + "Acknowledgments 1\n", + "About 4\n", + "Author 1\n", + "DISCLAIMER 1\n", + "What 38\n", + "you 1267\n", + "do 156\n", + "with 292\n", + "career 18\n", + "is 531\n", + "own 114\n", + "decision. 1\n", + "This 57\n", + "book 31\n", + "tells 4\n", + "how 127\n", + "improved 2\n", + "my 199\n", + "life 21\n", + "by 109\n", + "quitting 8\n", + "job, 24\n", + "creating 14\n", + "sustainable 3\n", + "growing 9\n", + "income 17\n", + "self-employment. 10\n", + "learned 10\n", + "along 10\n", + "way 42\n", + "could 44\n", + "help 42\n", + "same. 1\n", + "But 38\n", + "no 60\n", + "matter 10\n", + "much 78\n", + "advice 34\n", + "or 267\n", + "many 63\n", + "stories 3\n", + "give 31\n", + "you, 31\n", + "end 39\n", + "success 12\n", + "relies 5\n", + "preparation, 1\n", + "planning, 2\n", + "skillset, 1\n", + "talents, 2\n", + "determination, 1\n", + "little 27\n", + "bit 8\n", + "luck. 1\n", + "can't 22\n", + "all 109\n", + "that 641\n", + "you. 53\n", + "If 237\n", + "this 208\n", + "gives 5\n", + "confidence 1\n", + "strike 1\n", + "out 138\n", + "that�s 6\n", + "great. 2\n", + "responsibly, 2\n", + "realize 10\n", + "failure 1\n", + "very 55\n", + "real 27\n", + "possibility 3\n", + "must 28\n", + "plan 43\n", + "for. 9\n", + "blame 1\n", + "me 26\n", + "decisions 5\n", + "make; 1\n", + "they 192\n", + "are 391\n", + "ultimately 7\n", + "own. 14\n", + "Now, 4\n", + "adventure. 1\n", + "PREFACE 1\n", + "Three 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"flowchart 2\n", + "crazy 1\n", + "this. 2\n", + "reading 6\n", + "device 1\n", + "display 17\n", + "main 6\n", + "points: 1\n", + "HAVING 1\n", + "FINANCIAL 2\n", + "SAFETY 1\n", + "NET 1\n", + "Assuming 1\n", + "initial 27\n", + "legwork 1\n", + "figure 11\n", + "month 11\n", + "instead. 6\n", + "decrease 3\n", + "estimate, 1\n", + "particular 2\n", + "experience. 2\n", + "burning 1\n", + "last 7\n", + "months� 1\n", + "reserves, 2\n", + "monthly 10\n", + "expenses 27\n", + "again 5\n", + "later. 3\n", + "bust. 1\n", + "doesn�t 6\n", + "prove 2\n", + "viable, 1\n", + "testing 3\n", + "factor 5\n", + "side. 2\n", + "lucky, 3\n", + "supportive 2\n", + "spouse 4\n", + "during 11\n", + "talking 10\n", + "withdraw 1\n", + "needed, 1\n", + "penalties. 1\n", + "about? 2\n", + "mistake 3\n", + "adding 2\n", + "basic 6\n", + "rent, 1\n", + "cell 1\n", + "phone 8\n", + "bill, 1\n", + "etc. 4\n", + "tighten 1\n", + "belt 1\n", + "while, 1\n", + "think. 1\n", + "Instead, 4\n", + "6 2\n", + "bank 7\n", + "statements, 1\n", + "month. 5\n", + "Multiply 1\n", + "4, 1\n", + "account. 4\n", + "looming 1\n", + "ahead 5\n", + "tax 23\n", + "payments, 1\n", + "account 16\n", + "today, 1\n", + "future. 6\n", + "bonus 2\n", + "up? 1\n", + "Stock 1\n", + "vesting? 1\n", + "refund? 1\n", + "Something 2\n", + "longer? 1\n", + "Those 5\n", + "fund 5\n", + "endeavor. 1\n", + "contract 5\n", + "spins 1\n", + "idea, 5\n", + "wouldn't 2\n", + "contracts 3\n", + "bad, 1\n", + "trouble 3\n", + "collecting 4\n", + "invoices 1\n", + "timely 3\n", + "manner. 3\n", + "revenue, 2\n", + "billable 2\n", + "endeavor 3\n", + "saddled 1\n", + "depleted 1\n", + "savings. 1\n", + "Uncle 2\n", + "Bob, 1\n", + "loan, 2\n", + "bet 6\n", + "college 6\n", + "fund. 1\n", + "God's 1\n", + "sake, 1\n", + "max 1\n", + "cards. 1\n", + "don't. 1\n", + "Remember 7\n", + "succeeds, 1\n", + "interest 10\n", + "avoided 1\n", + "severance 1\n", + "package 6\n", + "mean 6\n", + "PLANNING 3\n", + "HEALTH 1\n", + "CARE 1\n", + "coverage, 2\n", + "For 19\n", + "our 5\n", + "readers 4\n", + "deciding 1\n", + "laws 2\n", + "surrounding 3\n", + "changing 2\n", + "research 9\n", + "writing 9\n", + "2015, 1\n", + "successfully 5\n", + "high. 1\n", + "political 2\n", + "statement, 1\n", + "premiums 4\n", + "passage 1\n", + "Affordable 1\n", + "(�Obamacare�,) 1\n", + "worse 4\n", + "coverage 8\n", + "subsidies 2\n", + "owners 10\n", + "exist, 2\n", + "checking 6\n", + "system 7\n", + "friendlier 1\n", + "employer-provided 1\n", + "offered 4\n", + "parents. 1\n", + "subsidizes 1\n", + "certainly 6\n", + "cheaper 2\n", + "purchasing 9\n", + "complicated. 2\n", + "Ask 4\n", + "planned 3\n", + "on. 12\n", + "insurance, 3\n", + "reading. 1\n", + "insurance? 1\n", + "else's 2\n", + "U.S., 1\n", + "visit 8\n", + "www.healthcare.gov. 1\n", + "sorts 6\n", + "cost. 5\n", + "vary 5\n", + "conservative 1\n", + "assume 5\n", + "government 7\n", + "offset 3\n", + "premium 2\n", + "costs. 2\n", + "Done? 1\n", + "sticker 1\n", + "shock 1\n", + "Depending 3\n", + "size 9\n", + "looking 9\n", + "$1,000 2\n", + "$10,000 8\n", + "out-of-pocket 1\n", + "bother 1\n", + "angry 1\n", + "law 1\n", + "coverage. 2\n", + "pesky 1\n", + "deductible. 2\n", + "health, 1\n", + "incur 1\n", + "months. 2\n", + "prudent 1\n", + "horribly 1\n", + "wrong, 2\n", + "somehow 4\n", + "medical 2\n", + "bill 3\n", + "trip 1\n", + "hospital 1\n", + "takes. 1\n", + "amount 15\n", + "deductible 3\n", + "somewhere, 2\n", + "addition 3\n", + "Otherwise, 2\n", + "numbers 3\n", + "savings� 1\n", + "budget 9\n", + "care. 1\n", + "cover 11\n", + "months, 3\n", + "factored 2\n", + "in? 1\n", + "great�we 1\n", + "more, 5\n", + "revisit 2\n", + "necessary 5\n", + "reserves. 1\n", + "SELF-ASSESSMENT 1\n", + "QUIZ 1\n", + "gotten 4\n", + "should. 2\n", + "scientific 1\n", + "quiz; 1\n", + "merely 1\n", + "collection 1\n", + "qualities 5\n", + "found 22\n", + "essential 1\n", + "have, 4\n", + "better. 4\n", + "few, 1\n", + "Be 11\n", + "honest, 2\n", + "now! 1\n", + "Quiz: 1\n", + "Qualities 1\n", + "count 3\n", + "positive 5\n", + "responses 2\n", + "questions. 2\n", + "___ 28\n", + "chosen 2\n", + "consistently 2\n", + "productive 4\n", + "alone, 1\n", + "examples 2\n", + "skill 2\n", + "short 6\n", + "leave 7\n", + "half-finished; 1\n", + "finish 2\n", + "unfinished 1\n", + "zero 2\n", + "value! 1\n", + "am 5\n", + "responsive 1\n", + "contact 11\n", + "ignore 2\n", + "voice-mails. 1\n", + "manage 2\n", + "overwhelmed 1\n", + "task 3\n", + "hand 3\n", + "tuning 1\n", + "distractions. 1\n", + "communication 5\n", + "100 1\n", + "networks. 3\n", + "boss 4\n", + "monitoring 1\n", + "What's 4\n", + "score? 1\n", + "8-10: 1\n", + "possess 2\n", + "pull 1\n", + "Look 4\n", + "two 15\n", + "items 6\n", + "didn't 13\n", + "check 9\n", + "important, 1\n", + "augment 3\n", + "6-8: 1\n", + "necessity 1\n", + "putting 3\n", + "motivate 1\n", + "missing 3\n", + "end. 3\n", + "before, 1\n", + "address 8\n", + "services 12\n", + "0-5: 1\n", + "different 16\n", + "is, 4\n", + "by, 1\n", + "$40,000 2\n", + "independent 2\n", + "child-care 1\n", + "(Census 1\n", + "data, 5\n", + "aggregated 1\n", + "QuickBooks 1\n", + "BLS 1\n", + "data 15\n", + "agree 1\n", + "number). 1\n", + "runaway 1\n", + "success, 1\n", + "partnering 1\n", + "possesses 1\n", + "lack, 1\n", + "whom 1\n", + "implicitly 1\n", + "partner. 1\n", + "Catch-22 1\n", + "there. 14\n", + "Keep 7\n", + "mind 10\n", + "guarantee 2\n", + "matter. 2\n", + "point, 13\n", + "determined 1\n", + "whether 21\n", + "rough 1\n", + "outlook 1\n", + "decision? 1\n", + "Try 6\n", + "career? 1\n", + "answer; 1\n", + "everyone. 1\n", + "embark 2\n", + "adventure 2\n", + "forget. 1\n", + "happen. 3\n", + "HAPPEN 1\n", + "DESIGNING 1\n", + "BUSINESS 10\n", + "LIFESTYLE 4\n", + "obvious 3\n", + "section 6\n", + "term, 2\n", + "maximize 9\n", + "benefits, 2\n", + "evolve 3\n", + "marketplace 4\n", + "Ultimately 1\n", + "offer 20\n", + "variety 1\n", + "diversify 1\n", + "offerings 2\n", + "reliable. 1\n", + "paycheck. 1\n", + "go. 1\n", + "Get 6\n", + "Moleskine 1\n", + "notebooks, 1\n", + "notes 1\n", + "information, 6\n", + "advice, 7\n", + "ideas, 1\n", + "learnings 1\n", + "acquire 2\n", + "Revisiting 1\n", + "continually 1\n", + "refine 2\n", + "something, 2\n", + "period, 2\n", + "does 23\n", + "offer. 7\n", + "freelancer, 2\n", + "clients 5\n", + "specifications. 3\n", + "on-site. 1\n", + "cases, 3\n", + "benefits 17\n", + "employing 1\n", + "period 5\n", + "mentioned 3\n", + "earlier, 3\n", + "gestating 1\n", + "expectation 3\n", + "tasks. 1\n", + "pursue 1\n", + "objectives 1\n", + "We're 2\n", + "explore 2\n", + "objective 3\n", + "FALLACY 1\n", + "entrepreneurs 3\n", + "press 40\n", + "tends 1\n", + "\"growth 3\n", + "businesses.\" 2\n", + "These 16\n", + "startups 4\n", + "TV 4\n", + "\"Shark 2\n", + "Tank.\" 1\n", + "seek 5\n", + "amounts 3\n", + "stage, 5\n", + "debt, 1\n", + "attempt 1\n", + "offsets 1\n", + "investment. 1\n", + "Their 4\n", + "ultimate 2\n", + "process 3\n", + "stake 5\n", + "investors 6\n", + "is: 1\n", + "works 6\n", + "93% 1\n", + "companies 27\n", + "started 14\n", + "much-touted 1\n", + "\"Y 1\n", + "Combinator\" 1\n", + "accelerator 1\n", + "Valley 3\n", + "2013 1\n", + "article 2\n", + "Insider11. 1\n", + "consistent 1\n", + "wisdom 2\n", + "succeeds. 1\n", + "Venture 2\n", + "capitalists 2\n", + "rare 1\n", + "succeeds 1\n", + "losses 1\n", + "sold 4\n", + "\"acqui-hires.\" 1\n", + "startup 4\n", + "failed 3\n", + "stream, 1\n", + "acquired 4\n", + "talented 1\n", + "once. 6\n", + "Usually 4\n", + "founder 4\n", + "Apple, 1\n", + "shoestring 2\n", + "exchange 8\n", + "riches, 1\n", + "AND 8\n", + "awesome 1\n", + "attract 2\n", + "capital, 1\n", + "Oculus 5\n", + "WhatsApp 1\n", + "gets 7\n", + "outrageous 1\n", + "sum 1\n", + "ridiculously 2\n", + "slim 1\n", + "happening, 1\n", + "York, 1\n", + "20-something 1\n", + "male, 1\n", + "further 6\n", + "(See 1\n", + "Brutal 1\n", + "Ageism 1\n", + "Tech\", 1\n", + "Republic, 1\n", + "\"Why 1\n", + "Women 1\n", + "Hell 1\n", + "Out 1\n", + "Ageist, 1\n", + "Sexist 1\n", + "Companies\", 1\n", + "Inc., 1\n", + "212,000 1\n", + "searching 10\n", + "\"ageism 1\n", + "sexism 2\n", + "Valley\".) 1\n", + "young, 1\n", + "energetic 1\n", + "founders 2\n", + "happily 1\n", + "remind 5\n", + "themselves 8\n", + "fair, 1\n", + "hopeful 1\n", + "subside 1\n", + "drawn 1\n", + "ageism 1\n", + "persist. 1\n", + "tech-related 1\n", + "begin 1\n", + "with, 2\n", + "all. 4\n", + "funds 2\n", + "tech, 1\n", + "women 1\n", + "minority-owned 1\n", + "exception. 1\n", + "freedom. 5\n", + "billionaire, 1\n", + "INTRODUCING 1\n", + "astute 1\n", + "say, 1\n", + "\"Only 1\n", + "survive? 1\n", + "Didn't 1\n", + "earlier 2\n", + "more?\" 1\n", + "did. 2\n", + "Both 2\n", + "statements 2\n", + "true 1\n", + "businesses. 5\n", + "designed 6\n", + "fast, 1\n", + "above 2\n", + "else. 6\n", + "contrast, 4\n", + "exists 1\n", + "sole 6\n", + "purpose 3\n", + "family-owned 1\n", + "restaurants, 1\n", + "barber 1\n", + "shops, 1\n", + "cleaners, 1\n", + "landscapers, 1\n", + "roofing 1\n", + "companies, 2\n", + "freelancers, 2\n", + "selling 31\n", + "crafts 1\n", + "imported 1\n", + "goods 8\n", + "employs 1\n", + "Google 53\n", + "billions 1\n", + "love 4\n", + "terms, 5\n", + "be, 7\n", + "surrounded 1\n", + "landscaper 1\n", + "restaurateur 1\n", + "it; 4\n", + "options 4\n", + "involve 4\n", + "responsibility 1\n", + "hiring 13\n", + "managing 2\n", + "others, 7\n", + "physical 17\n", + "location. 1\n", + "IDEAL 1\n", + "discussed 5\n", + "earlier. 2\n", + "start. 4\n", + "review 4\n", + "fullest. 1\n", + "minimal 3\n", + "limited, 1\n", + "scale. 2\n", + "freelancer's 2\n", + "can. 6\n", + "ideal 1\n", + "digital 10\n", + "front, 2\n", + "indefinitely 2\n", + "distribution, 3\n", + "completely 7\n", + "automatically. 3\n", + "largely 6\n", + "automated 9\n", + "targeted 14\n", + "ads 46\n", + "optimized 1\n", + "conversions. 1\n", + "Physical 1\n", + "outsource 5\n", + "production 1\n", + "distribution 2\n", + "reasonable 6\n", + "bootstrap 2\n", + "using 19\n", + "employed. 1\n", + "Investors 1\n", + "generally 5\n", + "interested 11\n", + "funding 1\n", + "anyhow 3\n", + "payoff 2\n", + "Bank 1\n", + "loans 2\n", + "difficult 7\n", + "(unless 2\n", + "backed 1\n", + "collateral), 1\n", + "leaves 2\n", + "bootstrapping 2\n", + "crowd-funding 1\n", + "anyhow. 4\n", + "best, 2\n", + "Self-funding 1\n", + "forward. 3\n", + "news 10\n", + "modern 1\n", + "outlay 2\n", + "often, 1\n", + "website, 13\n", + "computer, 2\n", + "connection 1\n", + "plus 3\n", + "talked 6\n", + "dependencies. 1\n", + "Of 3\n", + "course, 1\n", + "base 4\n", + "way? 2\n", + "example 3\n", + "contracts. 1\n", + "Will 10\n", + "Congress 1\n", + "temporary 2\n", + "shutdown? 1\n", + "arcane 1\n", + "paperwork 2\n", + "regulations 1\n", + "manner? 2\n", + "penny 2\n", + "control, 1\n", + "absolutely 1\n", + "relying 2\n", + "suppliers 2\n", + "able 22\n", + "provide, 1\n", + "requires 6\n", + "small. 2\n", + "tying 1\n", + "specific 32\n", + "location, 2\n", + "Instead 3\n", + "open 4\n", + "beauty 3\n", + "salon 1\n", + "individuals, 1\n", + "Then, 2\n", + "becomes 4\n", + "successful, 4\n", + "Tahiti 1\n", + "losing 2\n", + "location 4\n", + "oversight. 1\n", + "grows, 2\n", + "remotely 2\n", + "out? 1\n", + "Can 21\n", + "face-to-face 2\n", + "grow, 1\n", + "physically 1\n", + "present 4\n", + "case. 2\n", + "commute, 1\n", + "reclaiming 1\n", + "life. 4\n", + "inside 3\n", + "city, 2\n", + "suburbs, 1\n", + "presence 5\n", + "counts. 1\n", + "First 3\n", + "violates 1\n", + "tenet 1\n", + "\"unlimited 1\n", + "potential\" 1\n", + "physically. 1\n", + "client's 1\n", + "available. 2\n", + "Yet 2\n", + "theme 2\n", + "supply 1\n", + "services, 3\n", + "respond 5\n", + "messages 6\n", + "within 17\n", + "happy. 1\n", + "batch 1\n", + "throughout 3\n", + "window 1\n", + "dedicate 7\n", + "remains 1\n", + "employees, 2\n", + "supervision, 1\n", + "fit. 1\n", + "9-5 1\n", + "experienced 6\n", + "independently, 1\n", + "9-5. 2\n", + "aren't, 1\n", + "wasting 2\n", + "figuring 2\n", + "incorrectly, 1\n", + "sticking 1\n", + "considering 2\n", + "boss. 2\n", + "certain 6\n", + "kinds 3\n", + "afford 4\n", + "freelancing 3\n", + "stage 1\n", + "freelancer 2\n", + "trading 1\n", + "ones: 1\n", + "produced 2\n", + "exact 3\n", + "client 2\n", + "employee, 1\n", + "meetings, 2\n", + "hopefully 6\n", + "compensate 1\n", + "benefits. 1\n", + "caution 2\n", + "investors, 2\n", + "Bob 1\n", + "Mom 1\n", + "& 2\n", + "Dad. 1\n", + "soon 7\n", + "endeavor, 1\n", + "shots. 1\n", + "well-meaning 1\n", + "line, 2\n", + "act 7\n", + "Free 1\n", + "strings, 1\n", + "advise 1\n", + "Choosing 1\n", + "admittedly 1\n", + "self-centered. 1\n", + "involving 4\n", + "ties 3\n", + "community. 3\n", + "community 8\n", + "revolve 1\n", + "fine. 2\n", + "deeper 2\n", + "relationships 1\n", + "members 1\n", + "motivates 2\n", + "off, 3\n", + "warm 1\n", + "whenever 4\n", + "to, 7\n", + "stream. 1\n", + "beachside 1\n", + "caf� 1\n", + "Hawaii 1\n", + "story 5\n", + "ends, 1\n", + "craft 4\n", + "To 6\n", + "freedom, 2\n", + "portable, 1\n", + "global, 3\n", + "replaced 1\n", + "remotely. 1\n", + "CASE 1\n", + "STUDY: 1\n", + "SUNDOG 1\n", + "SOFTWARE 1\n", + "empty 2\n", + "reflects 1\n", + "myself. 1\n", + "LLC, 3\n", + "advantages 1\n", + "\"designed\" 1\n", + "word 3\n", + "choices. 2\n", + "specializes 4\n", + "real-time 2\n", + "graphics 1\n", + "environmental 1\n", + "effects. 2\n", + "skies, 2\n", + "clouds, 1\n", + "oceans 1\n", + "video 7\n", + "games, 1\n", + "simulators, 2\n", + "niche, 3\n", + "license 4\n", + "defense 6\n", + "contractors, 3\n", + "aerospace 2\n", + "flight 6\n", + "simulator 3\n", + "enthusiasts, 1\n", + "developers. 4\n", + "Today 1\n", + "$300,000 2\n", + "vast 2\n", + "majority 4\n", + "profit. 4\n", + "potential, 2\n", + "software, 3\n", + "additional 6\n", + "fix 2\n", + "bugs 1\n", + "modern, 1\n", + "linear 1\n", + "product, 22\n", + "manufacturing 3\n", + "shipping 1\n", + "with. 11\n", + "Customers 1\n", + "licenses 8\n", + "card, 1\n", + "automatically 10\n", + "download 3\n", + "advertising 25\n", + "campaigns 10\n", + "Prospective 1\n", + "obtain 5\n", + "demos, 3\n", + "trial 3\n", + "versions 2\n", + "interacting 1\n", + "literally 2\n", + "sleep. 2\n", + "hobby, 1\n", + "Since 5\n", + "realized 1\n", + "commercial 2\n", + "value, 1\n", + "offering 9\n", + "sale 4\n", + "price 9\n", + "test 11\n", + "market. 8\n", + "used 15\n", + "designer 4\n", + "WordPress-based 1\n", + "storefront 2\n", + "earned 1\n", + "Note 2\n", + "remarkably 1\n", + "generous 1\n", + "regards 1\n", + "endeavors 1\n", + "Still, 3\n", + "equipment. 4\n", + "aren't 17\n", + "laying 1\n", + "claim 1\n", + "upset 1\n", + "\"moonlighting\" 2\n", + "jeopardy. 1\n", + "though 2\n", + "playing 1\n", + "(and 2\n", + "helping 2\n", + "technical 11\n", + "job), 1\n", + 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"aspects. 1\n", + "DON'T 1\n", + "HAVE 1\n", + "GO 2\n", + "ALONE 1\n", + "Transitioning 1\n", + "easy. 1\n", + "shake 2\n", + "habits. 1\n", + "imperative 1\n", + "coached 1\n", + "beer 1\n", + "learning 3\n", + "personally, 1\n", + "everyone 2\n", + "replicate 1\n", + "book. 3\n", + "ask. 1\n", + "reached 2\n", + "(ones 1\n", + "embarking 1\n", + "Every 4\n", + "frankly 1\n", + "immersed 1\n", + "entrepreneurial 1\n", + "detailed 3\n", + "notes, 2\n", + "revisited 1\n", + "government-funded 2\n", + "incubation 2\n", + "program, 2\n", + "commission. 1\n", + "Local 1\n", + "governments 1\n", + "fostering 1\n", + "creation 2\n", + "cities, 2\n", + "programs 1\n", + "University 3\n", + "Central 3\n", + "program. 1\n", + "cost, 2\n", + "consultations 1\n", + "designers, 1\n", + "accountants, 1\n", + "lawyers, 1\n", + "officials, 1\n", + "entrepreneurship 2\n", + "program 1\n", + "plunge. 1\n", + "locale 1\n", + "angel 1\n", + "investors. 1\n", + "proceeding 1\n", + "accepting 1\n", + "investments 1\n", + "sort, 2\n", + "meetup.com 1\n", + "regularly 1\n", + "choice; 1\n", + "Express�s 1\n", + "OPEN 1\n", + "forum 1\n", + "I�ve 1\n", + "seen. 1\n", + "http://www.openforum.com. 1\n", + "for, 2\n", + "advice. 1\n", + "contradicts 1\n", + "another. 1\n", + "Everyone's 1\n", + "techniques, 1\n", + "distraction. 1\n", + "provided 2\n", + "listening 1\n", + "gospel 1\n", + "weigh 1\n", + "similarity 1\n", + "distance 1\n", + "horror 2\n", + "handing 1\n", + "ownership 7\n", + "advisors, 1\n", + "promptly 1\n", + "disappears 2\n", + "signed 3\n", + "shortage 1\n", + "free. 3\n", + "really, 1\n", + "bizarre 1\n", + "agreement 3\n", + "drafted 1\n", + "explicit 2\n", + "retain 4\n", + "stake. 1\n", + "uncommon 1\n", + "CEO, 2\n", + "leaving 2\n", + "really. 1\n", + "PULLING 1\n", + "TRIGGER 1\n", + "checklist 1\n", + "\"I 1\n", + "tender 1\n", + "resignation.\" 1\n", + "FINAL 1\n", + "CHECKLIST 1\n", + "gathered. 1\n", + "expanding 1\n", + "development. 1\n", + "attention. 3\n", + "patents, 2\n", + "trademarks, 2\n", + "copyrights 2\n", + "filed 1\n", + "need. 3\n", + "individual, 2\n", + "moment 2\n", + "burn 3\n", + "obtaining 1\n", + "deductibles. 1\n", + "lose, 1\n", + "vision 1\n", + "without. 1\n", + "levels. 1\n", + "opened 1\n", + "selected 1\n", + "explored 1\n", + "them; 1\n", + "date 1\n", + "checkups, 1\n", + "exams, 2\n", + "glasses, 1\n", + "refilled 1\n", + "purchases 1\n", + "finalized. 1\n", + "touch 2\n", + "via 1\n", + "collected 1\n", + "belongings 3\n", + "brought 1\n", + "nature 2\n", + "delete 1\n", + "ducks 1\n", + "row, 1\n", + "lined 1\n", + "TELLING 1\n", + "BOSS 1\n", + "bridges. 1\n", + "scenario 1\n", + "former 3\n", + "welcomes 1\n", + "arms 2\n", + "grown 2\n", + "gone 1\n", + "adventure, 1\n", + "appreciates 1\n", + "restate 1\n", + "importance 3\n", + "maintaining 5\n", + "separation 1\n", + "suffer 1\n", + "experimenting 2\n", + "burned 1\n", + "bridge 1\n", + "startup. 1\n", + "managers 1\n", + "appeal 2\n", + "feelings 2\n", + "goes, 1\n", + "private 1\n", + "means, 1\n", + "say. 1\n", + "Tell 1\n", + "so. 2\n", + "moment. 1\n", + "Remain 1\n", + "upbeat 1\n", + "upon. 1\n", + "self-motivated 1\n", + "too. 2\n", + "dangle 1\n", + "incentives 1\n", + "bear 1\n", + "inequities 1\n", + "retention 1\n", + "bonuses 2\n", + "raises 2\n", + "wondering 1\n", + "dreaming 1\n", + "leaving. 1\n", + "who's 1\n", + "scare 1\n", + "leaving, 2\n", + "throwing 2\n", + "statistics 3\n", + "fail. 1\n", + "genuinely 3\n", + "listen. 1\n", + "agenda 1\n", + "resignation, 1\n", + "shocked 1\n", + "loyal 1\n", + "unidirectional 1\n", + "was. 1\n", + "escort 1\n", + "desk 2\n", + "box 2\n", + "gather 2\n", + "unceremoniously 1\n", + "walk 3\n", + "door 1\n", + "accompanied 3\n", + "guard. 1\n", + "stern 1\n", + "lecture 1\n", + "upholding 1\n", + "confidentiality 1\n", + "signed. 1\n", + "spared 1\n", + "indignities, 1\n", + "applied 1\n", + "Leaving 2\n", + "trigger 1\n", + "whirlwind, 1\n", + "chaos 2\n", + "wrap 2\n", + "lock 1\n", + "office: 1\n", + "moving, 1\n", + "forms, 1\n", + "certificate 1\n", + "prior 2\n", + "file 1\n", + "included 3\n", + "H.S.A. 1\n", + "(health 1\n", + "account,) 1\n", + "automatically; 1\n", + "bleed 1\n", + "dry 1\n", + "gone. 1\n", + "Accept 2\n", + "COBRA 2\n", + "healthcare.gov 1\n", + "Compare 1\n", + "lasts 1\n", + "exchange. 2\n", + "exit 2\n", + "interview, 1\n", + "vent 1\n", + "frustrations 1\n", + "Leave 1\n", + "friendly 1\n", + "forgivable 1\n", + "competitor. 1\n", + "left, 1\n", + "employee! 1\n", + "LAST 1\n", + "cardboard 1\n", + "belongings, 1\n", + "feels 2\n", + "weird. 1\n", + "remember, 1\n", + "unemployed 1\n", + "Soon, 1\n", + "unproductive 1\n", + "artificially-set 1\n", + "deadlines 1\n", + "appeasing 1\n", + "executives, 1\n", + "fearing 1\n", + "reputation 2\n", + "advancement 1\n", + "politics. 1\n", + "paycheck! 1\n", + "hard, 1\n", + "survival 1\n", + "instinct 1\n", + "effort. 3\n", + "final 2\n", + "tips 2\n", + "fuel 1\n", + "growth. 2\n", + "bits 1\n", + "permanent 1\n", + "FUELING 1\n", + "FIRE 1\n", + "now's 1\n", + "fully 1\n", + "secret 1\n", + "launch. 2\n", + "offering! 1\n", + "grabbing 1\n", + "bare 1\n", + "exposure 2\n", + "shying 1\n", + "help, 2\n", + "effectiveness. 2\n", + "BUILDING 2\n", + "WEBSITE 1\n", + "Unless 3\n", + "designer, 2\n", + "cornerstone 1\n", + "efforts, 3\n", + "attracts 1\n", + "repelling 1\n", + "complexity 1\n", + "situations, 1\n", + "storefronts 1\n", + "Etsy, 1\n", + "cares 1\n", + "referrals 3\n", + "profiles 1\n", + "avoid. 1\n", + "channels, 2\n", + "accordingly. 3\n", + "modern. 1\n", + "\"modern\" 1\n", + "immortalize 1\n", + "heavily 1\n", + "adapt 2\n", + "seamlessly 1\n", + "mobile 8\n", + "tablet 3\n", + "devices. 2\n", + "Conventional 1\n", + "anymore, 1\n", + "seconds 2\n", + "capture 2\n", + "emulate, 1\n", + "credits 1\n", + "built 4\n", + "looking. 1\n", + "budget, 1\n", + "prices. 1\n", + "maintainable 1\n", + "something? 1\n", + "blog 8\n", + "(SEO) 1\n", + "basis. 4\n", + "SEO? 1\n", + "answers. 1\n", + "(LPO?) 1\n", + "perform 3\n", + "action 16\n", + "conversion 4\n", + "others? 1\n", + "LPO, 1\n", + "sign. 1\n", + "mobile? 1\n", + "\"responsive\"? 1\n", + "integrated 2\n", + "platform? 1\n", + "securing 1\n", + "site? 3\n", + "WordPress, 2\n", + "system. 2\n", + "modify 2\n", + "library 2\n", + "plugins 2\n", + "WordPress 1\n", + "website's 2\n", + "capabilities 1\n", + "Provide 1\n", + "sketches 1\n", + "navigation 1\n", + "mind, 1\n", + "pages 12\n", + "like, 1\n", + "wireframes 1\n", + "approved 1\n", + "starts, 1\n", + "approval 1\n", + "lightly 1\n", + "literally. 1\n", + "Listen 1\n", + "recommendations 1\n", + "adjust 2\n", + "designs 1\n", + "appropriate, 1\n", + "clear 4\n", + "Poor 1\n", + "misunderstandings. 1\n", + "image 4\n", + "\"call 1\n", + "action\" 1\n", + "visitors 10\n", + "scroll 2\n", + "trend 1\n", + "lately, 1\n", + "tradeoff 1\n", + "challenging 3\n", + "sites, 2\n", + "static 1\n", + "content. 2\n", + "launched, 1\n", + "public 2\n", + "relations 2\n", + "BASICS 5\n", + "PUBLIC 1\n", + "RELATIONS 1\n", + "interesting. 2\n", + "Focus 1\n", + "Press 2\n", + "format: 1\n", + "Attention-Grabbing 1\n", + "Headline 1\n", + "Geared 1\n", + "Toward 1\n", + "Customer 2\n", + "CITY, 1\n", + "State 1\n", + "(Month, 1\n", + "Day 1\n", + "Year) 1\n", + "-- 2\n", + "Cover 1\n", + "who, 1\n", + "where, 1\n", + "why, 1\n", + "paragraph. 1\n", + "catchy 1\n", + "explain 1\n", + "solves 2\n", + "customer. 2\n", + "Insert 1\n", + "quote 1\n", + "and/or 4\n", + "news, 1\n", + "item, 1\n", + "*** 1\n", + "contact: 2\n", + "(Your 2\n", + "title, 1\n", + "address) 1\n", + "Company) 1\n", + "one-paragraph 1\n", + "blurb 1\n", + "history 1\n", + "releases. 3\n", + "sake 2\n", + "here's 1\n", + "affiliated 1\n", + "copywriting 1\n", + "complimentary 1\n", + "journalism 1\n", + "degree, 1\n", + "refines 1\n", + "Military 1\n", + "Rift 4\n", + "Gaming 1\n", + "Headset 1\n", + "Realistic 1\n", + "3-D 2\n", + 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1\n", + "dashes 1\n", + "see; 1\n", + "published. 1\n", + "obscured 1\n", + "telephone 1\n", + "number. 1\n", + "preferred 1\n", + "body 2\n", + "prefer; 1\n", + "phone, 1\n", + "listing 1\n", + "appropriate. 2\n", + "steers 1\n", + "toward. 1\n", + "release. 3\n", + "understood 1\n", + "cursory 1\n", + "engineers 1\n", + "reach. 3\n", + "audiences. 1\n", + "crafted 1\n", + "intended 1\n", + "mass 5\n", + "jargon 1\n", + "maritime 1\n", + "announcement. 1\n", + "Customizing 1\n", + "submitting 2\n", + "Avoid 2\n", + "hyperbole 1\n", + "release; 1\n", + "journalists 1\n", + "objective, 1\n", + "concrete 1\n", + "substantiation 1\n", + "OK. 1\n", + "toot 2\n", + "horn 1\n", + "announcement 2\n", + "release, 5\n", + "stick 2\n", + "facts. 1\n", + "proofread 1\n", + "double 1\n", + "errors. 1\n", + "twofold: 2\n", + "placement 4\n", + "links 10\n", + "submission 1\n", + "goals. 1\n", + "broad 1\n", + "category 1\n", + "geographical 2\n", + "region. 1\n", + "newsworthy 1\n", + "headline 2\n", + "catches 1\n", + "editor, 1\n", + "ignored. 1\n", + "pick 4\n", + "placements. 1\n", + "handful 1\n", + "newspaper 1\n", + "highlight 1\n", + "Industry-specific 1\n", + "bet, 1\n", + "publication. 1\n", + "Contact 2\n", + "editorial 1\n", + "staff 1\n", + "publication 3\n", + "300 2\n", + "DPI 1\n", + "(dots 1\n", + "inch), 1\n", + "non-compressed 1\n", + "PNG, 1\n", + "span 1\n", + "printed 1\n", + "DPI. 1\n", + "industry-specific 1\n", + "sites. 4\n", + "news. 1\n", + "tailor 2\n", + "message 6\n", + "covers, 1\n", + "headline. 2\n", + "formal 1\n", + "informal 1\n", + "summary 1\n", + "link 10\n", + "(hosted 1\n", + "website) 1\n", + "Generally 2\n", + "speaking, 2\n", + "equivalent 1\n", + "introductory 1\n", + "text 8\n", + "accompanies 1\n", + "Editors 1\n", + "things; 1\n", + "stand 1\n", + "message, 1\n", + "�PR:� 1\n", + "followed 4\n", + "print-quality 1\n", + "attach 1\n", + "free! 1\n", + "push 3\n", + "salespeople 2\n", + "publications. 1\n", + "pitch 1\n", + "\"Good 1\n", + "news! 1\n", + "issue. 1\n", + "Wouldn't 1\n", + "accompany 1\n", + "ad? 1\n", + "first-time 1\n", + "discount...\" 1\n", + "marketing; 1\n", + "worthwhile 1\n", + "admit 1\n", + "publicly, 1\n", + "establish 2\n", + "expert 1\n", + "interviewed 1\n", + "article� 1\n", + "editor 1\n", + "submission, 1\n", + "reply 1\n", + "readership, 1\n", + "articles. 1\n", + "connecting 1\n", + "reporters 1\n", + "experts, 1\n", + "helpareporter.com, 1\n", + "speak 4\n", + "Far 1\n", + "quoted 1\n", + "representatives 2\n", + "billion-dollar 1\n", + "awareness 1\n", + "generate! 1\n", + "install 3\n", + "configure 2\n", + "plugin 3\n", + "W3 1\n", + "Total 1\n", + "Cache 1\n", + "(W3TC) 1\n", + "cache 1\n", + "server 1\n", + "page, 5\n", + "(LPO) 2\n", + "use� 1\n", + "below 2\n", + "convert 3\n", + "follow 4\n", + "Keeping 1\n", + "archive 1\n", + "hurt, 1\n", + "Additional 1\n", + "reinforce 1\n", + "wonder 1\n", + "viable! 1\n", + "releases, 1\n", + "convention 2\n", + "show? 1\n", + "distribute 1\n", + "white 2\n", + "paper 1\n", + "industry? 2\n", + "guest-blog 2\n", + "name� 3\n", + "ONLINE 1\n", + "ADVERTISING 1\n", + "appear 7\n", + "searches 2\n", + "does; 2\n", + "banner 8\n", + "shown 4\n", + "profiles. 1\n", + "Especially 1\n", + "suggestions 1\n", + "optimizing 5\n", + "channels. 1\n", + "impressive 1\n", + "toolset 1\n", + "seeing 1\n", + "web. 2\n", + "familiarizing 1\n", + "First, 2\n", + "terminology: 1\n", + "\"search 2\n", + "ads\" 3\n", + "Google. 2\n", + "Usually, 1\n", + "placements, 1\n", + "bid 2\n", + "play 1\n", + "\"Display 1\n", + "ad 49\n", + "Banner 2\n", + "ads, 10\n", + "\"skyscraper 1\n", + "square-shaped 1\n", + "ads� 2\n", + "sizes. 1\n", + "AdSense, 1\n", + "placements 3\n", + "participate 2\n", + "target 10\n", + "websites, 2\n", + "various 5\n", + "lessons 1\n", + "spending: 1\n", + "stunning 1\n", + "graphs 1\n", + "campaigns� 2\n", + "matters: 1\n", + "(CPA). 1\n", + "average, 1\n", + "CPA, 2\n", + "indicates 1\n", + "customer, 1\n", + "third-party 1\n", + "tie 2\n", + "insert 1\n", + "notifies 1\n", + "occurs. 1\n", + "CPA 4\n", + "referred 3\n", + "\"cost 1\n", + "converted 1\n", + "click\" 1\n", + "column 1\n", + "views 4\n", + "\"action\" 2\n", + "data. 3\n", + "high-dollar 1\n", + "proxy 2\n", + "downloading 3\n", + "sufficient 1\n", + "retail 1\n", + "store. 1\n", + "restrict 1\n", + "geographically 2\n", + "storefront. 1\n", + "restricted, 1\n", + "campaign 17\n", + "inexpensive, 2\n", + "directly, 2\n", + "$10 1\n", + "$5 1\n", + "enough� 1\n", + "measuring, 1\n", + "tuning. 1\n", + "Tread 2\n", + "default, 2\n", + "desktop 1\n", + "PC's, 1\n", + "tablets, 2\n", + "desired 1\n", + "PC, 1\n", + "devices 3\n", + "screen, 1\n", + "tap 1\n", + "charge 3\n", + "click, 1\n", + "actions� 1\n", + "tapping 1\n", + "reason. 1\n", + "embedded 1\n", + "apps. 1\n", + "App 1\n", + "design 4\n", + "apps 1\n", + "disable 1\n", + "technique, 1\n", + "writing, 1\n", + "exclude 1\n", + "app 2\n", + 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2\n", + "27 3\n", + "05 1\n", + "usa 1\n", + "iduskbn0e721m20140527 1\n", + "crash 2\n", + "forbes 2\n", + "moneybuilder 1\n", + "07 1\n", + "national 1\n", + "brookings 1\n", + "institution 1\n", + "tgeonetta 1\n", + "vs 1\n", + "47 1\n", + "washington 1\n", + "september 1\n", + "apa 1\n", + "org 1\n", + "helpcenter 1\n", + "aspx 1\n", + "dhhs 1\n", + "niosh 2\n", + "99 2\n", + "101 2\n", + "cdc 1\n", + "docs 1\n", + "businessinsider 1\n", + "12 1\n", + "treated 1\n", + "13 1\n", + "tricking 1\n" + ] + } + ], + "source": [ + "import re\n", + "from pyspark import SparkConf, SparkContext\n", + "\n", + "def normalizeWords(text):\n", + " return re.compile(r'\\W+', re.UNICODE).split(text.lower())\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('WordCount')\n", + "sc = SparkContext(conf = conf)\n", + "input = sc.textFile('book.txt')\n", + "words = input.flatMap(normalizeWords)\n", + "wordCounts = words.countByValue()\n", + "for word, count in wordCounts.items():\n", + " cleanWord = word.encode('ascii', 'ignore')\n", + " if cleanWord:\n", + " print(word, count) " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "import re\n", + "\n", + "def normalizeWords(text):\n", + " return re.compile(r'\\W+', re.UNICODE).split(text.lower())\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('WordCount')\n", + "sc = SparkContext(conf = conf)\n", + "input = sc.textFile('book.txt')\n", + "words = input.flatMap(normalizeWords)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['self',\n", + " 'employment',\n", + " 'building',\n", + " 'an',\n", + " 'internet',\n", + " 'business',\n", + " 'of',\n", + " 'one',\n", + " 'achieving',\n", + " 'financial']" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "words.take(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('self', 111),\n", + " ('employment', 75),\n", + " ('building', 33),\n", + " ('an', 178),\n", + " ('internet', 26),\n", + " ('business', 383),\n", + " ('of', 970),\n", + " ('one', 100),\n", + " ('achieving', 1),\n", + " ('financial', 17)]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wordCounts = words.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x+y)\n", + "wordCounts.take(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 'achieving'),\n", + " (1, 'contents'),\n", + " (1, 'preparation'),\n", + " (1, 'skillset'),\n", + " (1, 'determination'),\n", + " (1, 'confidence'),\n", + " (1, 'strike'),\n", + " (1, 'blame'),\n", + " (1, 'devoted'),\n", + " (1, 'commuted')]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wordCountsSorted = wordCounts.map(lambda x: (x[1], x[0])).sortByKey()\n", + "wordCountsSorted.take(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "achieving:\t\t1\n", + "contents:\t\t1\n", + "preparation:\t\t1\n", + "skillset:\t\t1\n", + "determination:\t\t1\n", + "confidence:\t\t1\n", + "strike:\t\t1\n", + "blame:\t\t1\n", + "devoted:\t\t1\n", + "commuted:\t\t1\n", + "complaint:\t\t1\n", + "rewarded:\t\t1\n", + "role:\t\t1\n", + "marriage:\t\t1\n", + "combat:\t\t1\n", + "secondary:\t\t1\n", + "ultimatum:\t\t1\n", + "weeks:\t\t1\n", + "walked:\t\t1\n", + "matches:\t\t1\n", + "nor:\t\t1\n", + "requirement:\t\t1\n", + "43:\t\t1\n", 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"investment:\t\t19\n", + "cash:\t\t19\n", + "based:\t\t19\n", + "again:\t\t19\n", + "accountant:\t\t19\n", + "person:\t\t19\n", + "doesn:\t\t19\n", + "show:\t\t19\n", + "physical:\t\t19\n", + "com:\t\t19\n", + "hire:\t\t20\n", + "attention:\t\t20\n", + "dollars:\t\t20\n", + "ones:\t\t20\n", + "terms:\t\t20\n", + "around:\t\t20\n", + "usually:\t\t20\n", + "aren:\t\t20\n", + "ask:\t\t20\n", + "term:\t\t20\n", + "sort:\t\t20\n", + "contractors:\t\t20\n", + "services:\t\t20\n", + "benefits:\t\t20\n", + "example:\t\t20\n", + "niche:\t\t20\n", + "facebook:\t\t20\n", + "developing:\t\t21\n", + "quit:\t\t21\n", + "costs:\t\t21\n", + "spent:\t\t21\n", + "place:\t\t21\n", + "available:\t\t21\n", + "grow:\t\t21\n", + "down:\t\t21\n", + "different:\t\t21\n", + "whether:\t\t21\n", + "action:\t\t21\n", + "click:\t\t21\n", + "campaign:\t\t21\n", + "later:\t\t22\n", + "focus:\t\t22\n", + "learn:\t\t22\n", + "paying:\t\t22\n", + "extra:\t\t22\n", + "problem:\t\t22\n", + "found:\t\t22\n", + 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"perhaps:\t\t26\n", + "likely:\t\t26\n", + "ideas:\t\t27\n", + "measure:\t\t27\n", + "successful:\t\t27\n", + "home:\t\t27\n", + "never:\t\t27\n", + "living:\t\t27\n", + "run:\t\t27\n", + "isn:\t\t27\n", + "full:\t\t27\n", + "initial:\t\t27\n", + "similar:\t\t27\n", + "relevant:\t\t27\n", + "because:\t\t28\n", + "too:\t\t28\n", + "done:\t\t28\n", + "look:\t\t28\n", + "___:\t\t28\n", + "tax:\t\t29\n", + "little:\t\t29\n", + "same:\t\t29\n", + "must:\t\t29\n", + "had:\t\t29\n", + "thing:\t\t29\n", + "office:\t\t29\n", + "here:\t\t29\n", + "offer:\t\t29\n", + "having:\t\t30\n", + "side:\t\t30\n", + "m:\t\t30\n", + "means:\t\t30\n", + "possible:\t\t30\n", + "come:\t\t30\n", + "does:\t\t30\n", + "large:\t\t30\n", + "account:\t\t30\n", + "adwords:\t\t30\n", + "career:\t\t31\n", + "life:\t\t31\n", + "give:\t\t31\n", + "real:\t\t31\n", + "getting:\t\t31\n", + "least:\t\t31\n", + "sell:\t\t31\n", + "companies:\t\t31\n", + "websites:\t\t31\n", + "every:\t\t32\n", + "let:\t\t32\n", + "order:\t\t32\n", + "actually:\t\t32\n", + "easy:\t\t32\n", + "talk:\t\t32\n", + "being:\t\t32\n", + "release:\t\t32\n", + "building:\t\t33\n", + "part:\t\t33\n", + "else:\t\t33\n", + "email:\t\t33\n", + "after:\t\t33\n", + "traffic:\t\t33\n", + "selling:\t\t33\n", + "me:\t\t34\n", + "back:\t\t34\n", + "per:\t\t34\n", + "health:\t\t35\n", + "hours:\t\t35\n", + "understand:\t\t35\n", + "web:\t\t35\n", + "free:\t\t35\n", + "specific:\t\t35\n", + "social:\t\t36\n", + "however:\t\t36\n", + "would:\t\t36\n", + "its:\t\t36\n", + "which:\t\t37\n", + "point:\t\t37\n", + "instead:\t\t37\n", + "others:\t\t37\n", + "potential:\t\t38\n", + "years:\t\t38\n", + "employed:\t\t38\n", + "things:\t\t38\n", + "expenses:\t\t38\n", + "site:\t\t38\n", + "worth:\t\t39\n", + "growth:\t\t39\n", + "book:\t\t39\n", + "were:\t\t39\n", + "won:\t\t39\n", + "local:\t\t39\n", + "without:\t\t40\n", + "few:\t\t40\n", + "has:\t\t40\n", + "freedom:\t\t41\n", + "advertising:\t\t41\n", + "number:\t\t41\n", + "customer:\t\t41\n", + "big:\t\t42\n", + "marketing:\t\t42\n", + "page:\t\t42\n", + "keep:\t\t42\n", + "really:\t\t42\n", + "we:\t\t43\n", + "while:\t\t43\n", + "spend:\t\t43\n", + "such:\t\t43\n", + "press:\t\t43\n", + "lifestyle:\t\t44\n", + "employer:\t\t44\n", + "advice:\t\t44\n", + "revenue:\t\t44\n", + "start:\t\t44\n", + "best:\t\t44\n", + "go:\t\t45\n", + "pay:\t\t45\n", + "create:\t\t45\n", + "could:\t\t46\n", + "hard:\t\t47\n", + "try:\t\t47\n", + "small:\t\t47\n", + "businesses:\t\t47\n", + "long:\t\t47\n", + "personal:\t\t48\n", + "now:\t\t48\n", + "see:\t\t48\n", + "end:\t\t49\n", + "better:\t\t49\n", + "year:\t\t49\n", + "online:\t\t50\n", + "help:\t\t50\n", + "doing:\t\t51\n", + "first:\t\t51\n", + "know:\t\t52\n", + "where:\t\t53\n", + "cost:\t\t53\n", + "way:\t\t55\n", + "take:\t\t55\n", + "going:\t\t55\n", + "lot:\t\t55\n", + "search:\t\t56\n", + "something:\t\t56\n", + "through:\t\t57\n", + "very:\t\t58\n", + "working:\t\t58\n", + "think:\t\t58\n", + "important:\t\t58\n", + "use:\t\t58\n", + "enough:\t\t59\n", + "once:\t\t59\n", + "then:\t\t59\n", + "software:\t\t60\n", + "idea:\t\t60\n", + "someone:\t\t62\n", + "over:\t\t62\n", + "any:\t\t62\n", + "google:\t\t63\n", + "plan:\t\t64\n", + "sure:\t\t65\n", + "still:\t\t65\n", + "market:\t\t66\n", + "well:\t\t66\n", + "products:\t\t67\n", + "those:\t\t68\n", + "should:\t\t69\n", + "before:\t\t70\n", + "most:\t\t70\n", + "might:\t\t70\n", + "ad:\t\t70\n", + "many:\t\t71\n", + "good:\t\t72\n", + "day:\t\t73\n", + "employment:\t\t75\n", + "ads:\t\t75\n", + "no:\t\t76\n", + "probably:\t\t76\n", + "yourself:\t\t78\n", + "other:\t\t78\n", + "like:\t\t78\n", + "only:\t\t79\n", + "into:\t\t79\n", + "sales:\t\t80\n", + "find:\t\t81\n", + "these:\t\t82\n", + "was:\t\t85\n", + "money:\t\t86\n", + "who:\t\t88\n", + "job:\t\t90\n", + "much:\t\t90\n", + "also:\t\t91\n", + "than:\t\t92\n", + "ve:\t\t95\n", + "one:\t\t100\n", + "when:\t\t102\n", + "even:\t\t104\n", + "may:\t\t107\n", + "make:\t\t108\n", + "website:\t\t109\n", + "self:\t\t111\n", + "ll:\t\t114\n", + "some:\t\t121\n", + "by:\t\t122\n", + "want:\t\t122\n", + "company:\t\t122\n", + "their:\t\t122\n", + "get:\t\t123\n", + "customers:\t\t123\n", + "don:\t\t133\n", + "all:\t\t137\n", + "own:\t\t140\n", + "just:\t\t142\n", + "so:\t\t143\n", + "work:\t\t144\n", + "people:\t\t145\n", + "new:\t\t153\n", + "out:\t\t161\n", + "there:\t\t162\n", + "how:\t\t163\n", + "them:\t\t166\n", + "from:\t\t166\n", + "need:\t\t174\n", + "up:\t\t177\n", + "an:\t\t178\n", + "product:\t\t182\n", + "more:\t\t200\n", + "about:\t\t202\n", + "not:\t\t203\n", + "do:\t\t207\n", + "re:\t\t214\n", + "my:\t\t215\n", + "at:\t\t220\n", + "what:\t\t229\n", + "will:\t\t231\n", + "they:\t\t234\n", + "but:\t\t242\n", + "time:\t\t255\n", + "or:\t\t278\n", + "this:\t\t280\n", + "t:\t\t301\n", + "with:\t\t315\n", + "have:\t\t321\n", + "as:\t\t343\n", + "be:\t\t369\n", + "can:\t\t376\n", + "business:\t\t383\n", + "i:\t\t387\n", + "s:\t\t391\n", + "if:\t\t411\n", + "are:\t\t424\n", + "on:\t\t428\n", + "for:\t\t537\n", + "is:\t\t560\n", + "in:\t\t616\n", + "it:\t\t649\n", + "that:\t\t747\n", + "and:\t\t934\n", + "of:\t\t970\n", + "a:\t\t1191\n", + "the:\t\t1292\n", + "your:\t\t1420\n", + "to:\t\t1828\n", + "you:\t\t1878\n" + ] + } + ], + "source": [ + "results = wordCountsSorted.collect()\n", + "for result in results:\n", + " count = str(result[0])\n", + " word = result[1].encode('ascii', 'ignore')\n", + " if (word):\n", + " print(word.decode() + ':\\t\\t' + count)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('SpendByCustomer')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "def parseLine(line):\n", + " fields = line.split(',')\n", + " return (int(fields[0]), float(fields[2]))\n", + "\n", + "lines = sc.textFile('customer-orders.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['44,8602,37.19', '35,5368,65.89', '2,3391,40.64']" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(44, 37.19), (35, 65.89), (2, 40.64)]" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rdd = lines.map(parseLine)\n", + "rdd.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(44, 4756.8899999999985), (35, 5155.419999999999), (2, 5994.59)]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "totalByCustomer = rdd.reduceByKey(lambda x, y: x+y)\n", + "totalByCustomer.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(44, 4756.8899999999985)\n", + "(35, 5155.419999999999)\n", + "(2, 5994.59)\n", + "(47, 4316.299999999999)\n", + "(29, 5032.529999999999)\n", + "(91, 4642.259999999999)\n", + "(70, 5368.249999999999)\n", + "(85, 5503.43)\n", + "(53, 4945.299999999999)\n", + "(14, 4735.030000000001)\n", + "(51, 4975.22)\n", + "(42, 5696.840000000003)\n", + "(79, 3790.570000000001)\n", + "(50, 4517.27)\n", + "(20, 4836.859999999999)\n", + "(15, 5413.510000000001)\n", + "(5, 4561.069999999999)\n", + "(48, 4384.33)\n", + "(31, 4765.05)\n", + "(4, 4815.050000000002)\n", + "(36, 4278.049999999997)\n", + "(57, 4628.4)\n", + "(12, 4664.589999999998)\n", + "(22, 5019.449999999999)\n", + "(54, 6065.389999999999)\n", + "(0, 5524.949999999998)\n", + "(88, 4830.549999999999)\n", + "(86, 4908.81)\n", + "(13, 4367.62)\n", + "(40, 5186.429999999999)\n", + "(98, 4297.260000000001)\n", + "(55, 5298.090000000002)\n", + "(95, 4876.840000000002)\n", + "(61, 5497.479999999998)\n", + "(27, 4915.889999999999)\n", + "(78, 4524.509999999999)\n", + "(83, 4635.799999999997)\n", + "(6, 5397.879999999998)\n", + "(26, 5250.4)\n", + "(75, 4178.500000000001)\n", + "(25, 5057.610000000001)\n", + "(71, 5995.660000000003)\n", + "(39, 6193.109999999999)\n", + "(60, 5040.709999999999)\n", + "(97, 5977.189999999995)\n", + "(7, 4755.070000000001)\n", + "(21, 4707.41)\n", + "(69, 5123.010000000001)\n", + "(37, 4735.200000000002)\n", + "(1, 4958.600000000001)\n", + "(64, 5288.689999999996)\n", + "(82, 4812.489999999998)\n", + "(72, 5337.44)\n", + "(99, 4172.289999999998)\n", + "(34, 5330.8)\n", + "(73, 6206.199999999999)\n", + "(49, 4394.599999999999)\n", + "(8, 5517.240000000001)\n", + "(46, 5963.109999999999)\n", + "(23, 4042.6499999999987)\n", + "(19, 5059.4299999999985)\n", + "(65, 5140.3499999999985)\n", + "(80, 4727.860000000001)\n", + "(16, 4979.06)\n", + "(9, 5322.649999999999)\n", + "(18, 4921.27)\n", + "(59, 5642.89)\n", + "(74, 4647.129999999999)\n", + "(30, 4990.72)\n", + "(56, 4701.019999999999)\n", + "(90, 5290.409999999998)\n", + "(68, 6375.449999999997)\n", + "(11, 5152.290000000002)\n", + "(10, 4819.700000000001)\n", + "(41, 5637.62)\n", + "(58, 5437.7300000000005)\n", + "(87, 5206.4)\n", + "(17, 5032.679999999999)\n", + "(33, 5254.659999999998)\n", + "(62, 5253.3200000000015)\n", + "(92, 5379.280000000002)\n", + "(76, 4904.209999999999)\n", + "(66, 4681.919999999999)\n", + "(43, 5368.83)\n", + "(52, 5245.059999999999)\n", + "(77, 4327.729999999999)\n", + "(81, 5112.709999999999)\n", + "(84, 4652.939999999999)\n", + "(3, 4659.63)\n", + "(93, 5265.750000000001)\n", + "(89, 4851.479999999999)\n", + "(45, 3309.38)\n", + "(24, 5259.920000000003)\n", + "(96, 3924.230000000001)\n", + "(67, 4505.79)\n", + "(63, 5415.150000000001)\n", + "(94, 4475.569999999999)\n", + "(32, 5496.050000000004)\n", + "(38, 4898.460000000002)\n", + "(28, 5000.709999999998)\n" + ] + } + ], + "source": [ + "results = totalByCustomer.collect()\n", + "for result in results:\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('SpendByCustomerSorted')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "def parseLine(line):\n", + " fields = line.split(',')\n", + " return (int(fields[0]), float(fields[2]))\n", + "\n", + "lines = sc.textFile('customer-orders.csv')\n", + "rdd = lines.map(parseLine)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(44, 37.19), (35, 65.89), (2, 40.64)]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rdd.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(44, 4756.8899999999985), (35, 5155.419999999999), (2, 5994.59)]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "totalByCustomer = rdd.reduceByKey(lambda x, y: x + y)\n", + "totalByCustomer.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(4756.8899999999985, 44), (5155.419999999999, 35), (5994.59, 2)]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flipped = totalByCustomer.map(lambda x: (x[1], x[0]))\n", + "flipped.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(3309.38, 45), (3790.570000000001, 79), (3924.230000000001, 96)]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "totalByCustomerSorted = flipped.sortByKey()\n", + "totalByCustomerSorted.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3309.38, 45)\n", + "(3790.570000000001, 79)\n", + "(3924.230000000001, 96)\n", + "(4042.6499999999987, 23)\n", + "(4172.289999999998, 99)\n", + "(4178.500000000001, 75)\n", + "(4278.049999999997, 36)\n", + "(4297.260000000001, 98)\n", + "(4316.299999999999, 47)\n", + "(4327.729999999999, 77)\n", + "(4367.62, 13)\n", + "(4384.33, 48)\n", + "(4394.599999999999, 49)\n", + "(4475.569999999999, 94)\n", + "(4505.79, 67)\n", + "(4517.27, 50)\n", + "(4524.509999999999, 78)\n", + "(4561.069999999999, 5)\n", + "(4628.4, 57)\n", + "(4635.799999999997, 83)\n", + "(4642.259999999999, 91)\n", + "(4647.129999999999, 74)\n", + "(4652.939999999999, 84)\n", + "(4659.63, 3)\n", + "(4664.589999999998, 12)\n", + "(4681.919999999999, 66)\n", + "(4701.019999999999, 56)\n", + "(4707.41, 21)\n", + "(4727.860000000001, 80)\n", + "(4735.030000000001, 14)\n", + "(4735.200000000002, 37)\n", + "(4755.070000000001, 7)\n", + "(4756.8899999999985, 44)\n", + "(4765.05, 31)\n", + "(4812.489999999998, 82)\n", + "(4815.050000000002, 4)\n", + "(4819.700000000001, 10)\n", + "(4830.549999999999, 88)\n", + "(4836.859999999999, 20)\n", + "(4851.479999999999, 89)\n", + "(4876.840000000002, 95)\n", + "(4898.460000000002, 38)\n", + "(4904.209999999999, 76)\n", + "(4908.81, 86)\n", + "(4915.889999999999, 27)\n", + "(4921.27, 18)\n", + "(4945.299999999999, 53)\n", + "(4958.600000000001, 1)\n", + "(4975.22, 51)\n", + "(4979.06, 16)\n", + "(4990.72, 30)\n", + "(5000.709999999998, 28)\n", + "(5019.449999999999, 22)\n", + "(5032.529999999999, 29)\n", + "(5032.679999999999, 17)\n", + "(5040.709999999999, 60)\n", + "(5057.610000000001, 25)\n", + "(5059.4299999999985, 19)\n", + "(5112.709999999999, 81)\n", + "(5123.010000000001, 69)\n", + "(5140.3499999999985, 65)\n", + "(5152.290000000002, 11)\n", + "(5155.419999999999, 35)\n", + "(5186.429999999999, 40)\n", + "(5206.4, 87)\n", + "(5245.059999999999, 52)\n", + "(5250.4, 26)\n", + "(5253.3200000000015, 62)\n", + "(5254.659999999998, 33)\n", + "(5259.920000000003, 24)\n", + "(5265.750000000001, 93)\n", + "(5288.689999999996, 64)\n", + "(5290.409999999998, 90)\n", + "(5298.090000000002, 55)\n", + "(5322.649999999999, 9)\n", + "(5330.8, 34)\n", + "(5337.44, 72)\n", + "(5368.249999999999, 70)\n", + "(5368.83, 43)\n", + "(5379.280000000002, 92)\n", + "(5397.879999999998, 6)\n", + "(5413.510000000001, 15)\n", + "(5415.150000000001, 63)\n", + "(5437.7300000000005, 58)\n", + "(5496.050000000004, 32)\n", + "(5497.479999999998, 61)\n", + "(5503.43, 85)\n", + "(5517.240000000001, 8)\n", + "(5524.949999999998, 0)\n", + "(5637.62, 41)\n", + "(5642.89, 59)\n", + "(5696.840000000003, 42)\n", + "(5963.109999999999, 46)\n", + "(5977.189999999995, 97)\n", + "(5994.59, 2)\n", + "(5995.660000000003, 71)\n", + "(6065.389999999999, 54)\n", + "(6193.109999999999, 39)\n", + "(6206.199999999999, 73)\n", + "(6375.449999999997, 68)\n" + ] + } + ], + "source": [ + "results = totalByCustomerSorted.collect()\n", + "for result in results:\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['196\\t242\\t3\\t881250949', '186\\t302\\t3\\t891717742', '22\\t377\\t1\\t878887116']" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('RatingsHistogram')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "lines = sc.textFile('u.data')\n", + "lines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['3', '3', '1']" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ratings = lines.map(lambda x: x.split()[2])\n", + "ratings.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(int, {'1': 6110, '2': 11370, '3': 27145, '4': 34174, '5': 21201})" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = ratings.countByValue()\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 6110\n", + "2 11370\n", + "3 27145\n", + "4 34174\n", + "5 21201\n" + ] + } + ], + "source": [ + "import collections\n", + "sortedResults = collections.OrderedDict(sorted(result.items()))\n", + "for key, value in sortedResults.items():\n", + " print('%s %i' % (key, value))" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['196\\t242\\t3\\t881250949', '186\\t302\\t3\\t891717742', '22\\t377\\t1\\t878887116']" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "conf = SparkConf().setMaster('local').setAppName('PopularMovies')\n", + "sc = SparkContext(conf = conf)\n", + "\n", + "lines = sc.textFile('u.data')\n", + "lines.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(242, 1), (302, 1), (377, 1)]" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movies = lines.map(lambda x: (int(x.split()[1]), 1))\n", + "movies.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(242, 117), (302, 297), (377, 13)]" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movieCounts = movies.reduceByKey(lambda x, y: x + y)\n", + "movieCounts.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(117, 242), (297, 302), (13, 377)]" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flipped = movieCounts.map(lambda x: (x[1], x[0]))\n", + "flipped.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 1348), (1, 1320), (1, 1492)]" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sortedMovies = flipped.sortByKey()\n", + "sortedMovies.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 1348)\n", + "(1, 1320)\n", + "(1, 1492)\n", + "(1, 1364)\n", + "(1, 1493)\n", + "(1, 830)\n", + "(1, 1498)\n", + "(1, 814)\n", + "(1, 1520)\n", + "(1, 711)\n", + "(1, 1373)\n", + "(1, 1309)\n", + "(1, 857)\n", + "(1, 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"(171, 651)\n", + "(172, 255)\n", + "(172, 99)\n", + "(173, 511)\n", + "(174, 432)\n", + "(174, 154)\n", + "(174, 24)\n", + "(175, 327)\n", + "(175, 58)\n", + "(175, 326)\n", + "(176, 845)\n", + "(176, 83)\n", + "(177, 283)\n", + "(178, 419)\n", + "(178, 591)\n", + "(179, 480)\n", + "(179, 479)\n", + "(179, 566)\n", + "(180, 514)\n", + "(180, 751)\n", + "(180, 732)\n", + "(182, 23)\n", + "(182, 203)\n", + "(183, 14)\n", + "(184, 13)\n", + "(185, 147)\n", + "(187, 124)\n", + "(188, 307)\n", + "(189, 340)\n", + "(190, 274)\n", + "(191, 209)\n", + "(192, 393)\n", + "(193, 284)\n", + "(194, 474)\n", + "(194, 298)\n", + "(195, 527)\n", + "(197, 250)\n", + "(198, 272)\n", + "(198, 134)\n", + "(199, 230)\n", + "(200, 208)\n", + "(201, 515)\n", + "(201, 403)\n", + "(202, 588)\n", + "(206, 200)\n", + "(206, 597)\n", + "(206, 211)\n", + "(208, 385)\n", + "(208, 175)\n", + "(209, 603)\n", + "(209, 4)\n", + "(209, 187)\n", + "(211, 271)\n", + "(212, 215)\n", + "(213, 88)\n", + "(215, 508)\n", + "(216, 435)\n", + "(217, 235)\n", + "(218, 322)\n", + "(219, 95)\n", + "(219, 427)\n", + "(219, 678)\n", + "(219, 8)\n", + "(220, 71)\n", + "(220, 161)\n", + "(221, 471)\n", + "(221, 179)\n", + "(221, 180)\n", + "(222, 143)\n", + "(223, 273)\n", + "(226, 182)\n", + "(227, 265)\n", + "(227, 655)\n", + "(230, 568)\n", + "(230, 301)\n", + "(231, 496)\n", + "(232, 282)\n", + "(236, 11)\n", + "(239, 197)\n", + "(239, 185)\n", + "(240, 245)\n", + "(240, 323)\n", + "(241, 194)\n", + "(243, 144)\n", + "(243, 483)\n", + "(244, 228)\n", + "(244, 125)\n", + "(246, 132)\n", + "(247, 153)\n", + "(250, 475)\n", + "(251, 196)\n", + "(251, 333)\n", + "(251, 186)\n", + "(251, 70)\n", + "(254, 546)\n", + "(255, 268)\n", + "(256, 97)\n", + "(256, 238)\n", + "(259, 135)\n", + "(259, 289)\n", + "(261, 82)\n", + "(264, 357)\n", + "(267, 12)\n", + "(267, 742)\n", + "(268, 275)\n", + "(272, 111)\n", + "(275, 89)\n", + "(276, 191)\n", + "(276, 28)\n", + "(280, 234)\n", + "(280, 202)\n", + "(283, 64)\n", + "(284, 176)\n", + "(290, 216)\n", + "(291, 183)\n", + "(293, 118)\n", + "(293, 15)\n", + "(293, 25)\n", + "(295, 328)\n", + "(295, 96)\n", + "(297, 302)\n", + "(297, 22)\n", + "(298, 318)\n", + "(298, 276)\n", + "(299, 9)\n", + "(300, 423)\n", + "(301, 195)\n", + "(303, 257)\n", + "(315, 269)\n", + "(316, 748)\n", + "(316, 168)\n", + "(321, 69)\n", + "(324, 173)\n", + "(326, 151)\n", + "(331, 210)\n", + "(336, 79)\n", + "(344, 405)\n", + "(350, 204)\n", + "(350, 313)\n", + "(365, 222)\n", + "(367, 172)\n", + "(378, 117)\n", + "(384, 237)\n", + "(390, 98)\n", + "(392, 7)\n", + "(394, 56)\n", + "(413, 127)\n", + "(420, 174)\n", + "(429, 121)\n", + "(431, 300)\n", + "(452, 1)\n", + "(478, 288)\n", + "(481, 286)\n", + "(485, 294)\n", + "(507, 181)\n", + "(508, 100)\n", + "(509, 258)\n", + "(583, 50)\n" + ] + } + ], + "source": [ + "results = sortedMovies.collect()\n", + "for result in results:\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [], + "source": [ + "sc.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark import SparkConf, SparkContext\n", + "\n", + "def loadMovieNames():\n", + " movieNames = {}\n", + " with open('u.item', encoding = \"ISO-8859-1\") as f:\n", + " for line in f:\n", + " fields = line.split('|')\n", + " movieNames[int(fields[0])] = fields[1]\n", + " return movieNames\n", + "\n", + "conf = SparkConf().setMaster('local').setAppName('PopularMovies')\n", + "sc = SparkContext(conf = conf)\n", + "nameDict = sc.broadcast(loadMovieNames())" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(242, 1), (302, 1), (377, 1)]" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lines = sc.textFile('u.data')\n", + "movies = lines.map(lambda x: (int(x.split()[1]), 1))\n", + "movies.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(242, 117), (302, 297), (377, 13)]" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "movieCounts = movies.reduceByKey(lambda x, y:x + y )\n", + "movieCounts.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(117, 242), (297, 302), (13, 377)]" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flipped = movieCounts.map(lambda x: (x[1], x[0]))\n", + "flipped.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 1348), (1, 1320), (1, 1492)]" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sortedMovies = flipped.sortByKey()\n", + "sortedMovies.take(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "conda_python3", + "language": "python", + "name": "conda_python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}