In [1]:

    

def cube(x): return x*x*x

map(cube,range(1,11))


    

    
Out[1]:





[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

In [2]:

    

seq = range(8)
def add(x,y): return x+y
map(add, seq,seq)


    

    
Out[2]:





[0, 2, 4, 6, 8, 10, 12, 14]

In [3]:

    

result = map(add, seq,seq)
reduce(add, result)  # adding each element of the result together


    

    
Out[3]:





56

In [4]:

    

import re
import pandas as pd
import numpy as np
aliceFile = open('data/canterbury/alice29.txt','r')
map1=[]

WORD_RE = re.compile(r"[\w']+")

# Create the map of words with prelminary counts
for line in aliceFile:
    for w in WORD_RE.findall(line):
        map1.append([w,1])

#sort the map            
map2 = sorted(map1)

#Separate the map into groups by the key values
df = pd.DataFrame(map2)

uniquewords = df[0].unique()
DataFrameDict = {elem : pd.DataFrame for elem in uniquewords}

for key in DataFrameDict.keys():
    DataFrameDict[key] = df[:][df[0] == key]


    

In [5]:

    

def wordcount(x,y):
    x[1] = x[1] + y[1]
    return x

#Add up the counts using reduce

for uw in uniquewords:
    uarray = np.array(DataFrameDict[uw])
    print reduce(wordcount,uarray)


    

    




["'" 1091L]
["'TIS" 1L]
["'Tis" 2L]
["'em" 3L]
["'tis" 2L]
['2' 1L]
['9' 1L]
['A' 17L]
['ADVENTURES' 1L]
["ALICE'S" 3L]
['ALL' 4L]
['AND' 3L]
['ARE' 6L]
['AT' 1L]
['Ada' 1L]
['Adventures' 2L]
['Advice' 1L]
['After' 6L]
['Ah' 5L]
['Ahem' 1L]
['Alas' 1L]
['Alice' 386L]
["Alice's" 9L]
['All' 5L]
['Allow' 1L]
['Always' 1L]
['Ambition' 1L]
['An' 5L]
['And' 67L]
['Ann' 4L]
['Antipathies' 1L]
['Anything' 1L]
['Are' 4L]
['Arithmetic' 1L]
['As' 17L]
['At' 9L]
['Atheling' 1L]
['Australia' 1L]
['BE' 1L]
['BEE' 1L]
['BEFORE' 1L]
['BEG' 1L]
['BEST' 2L]
['BOOTS' 1L]
['BUSY' 1L]
['Back' 1L]
['Be' 2L]
['Beau' 4L]
['Beautiful' 5L]
['Because' 1L]
['Before' 1L]
['Begin' 1L]
['Behead' 1L]
['Besides' 1L]
['Between' 1L]
['Bill' 12L]
["Bill's" 4L]
['Birds' 1L]
['Boots' 1L]
['Brandy' 1L]
['Bring' 1L]
['But' 37L]
['By' 4L]
['C' 1L]
['CAN' 4L]
['CHAPTER' 12L]
['CHORUS' 2L]
['COULD' 4L]
['COURT' 1L]
['CURTSEYING' 1L]
['Call' 4L]
['Can' 1L]
["Can't" 1L]
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['Canterbury' 1L]
['Carroll' 1L]
['Cat' 24L]
["Cat's" 2L]
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['Caterpillar' 26L]
["Caterpillar's" 1L]
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['Certainly' 1L]
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['Collar' 1L]
['Come' 21L]
['Coming' 1L]
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['D' 1L]
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['DOTH' 1L]
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['Dear' 1L]
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['Did' 3L]
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["Dinah'll" 2L]
["Dinah's" 1L]
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['Distraction' 1L]
['Do' 13L]
['Dodo' 13L]
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["Don't" 9L]
['Dormouse' 39L]
["Dormouse's" 1L]
['Down' 3L]
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['Drink' 1L]
['Drive' 1L]
['Duchess' 38L]
["Duchess's" 3L]
['Duck' 3L]
['Dutchess' 1L]
['EAT' 1L]
['EDITION' 1L]
['END' 1L]
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['EVEN' 1L]
['EVER' 1L]
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['Each' 1L]
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['English' 6L]
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['Everybody' 2L]
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['FOOT' 1L]
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['Fifteenth' 1L]
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['Fish' 2L]
['Five' 7L]
['Footman' 10L]
["Footman's" 1L]
['For' 13L]
['Forty' 1L]
['Found' 2L]
['Fourteenth' 1L]
['France' 1L]
['French' 4L]
['Frog' 1L]
['From' 1L]
['Fury' 2L]
['GAVE' 2L]
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['Geography' 1L]
['Get' 2L]
['Give' 3L]
['Go' 3L]
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['Gryphon' 54L]
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['HER' 2L]
['HERE' 1L]
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['HIM' 3L]
['HIS' 1L]
['HOW' 1L]
["Hadn't" 1L]
['Half' 1L]
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['Hatter' 54L]
["Hatter's" 1L]
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['Keep' 2L]
['King' 59L]
["King's" 2L]
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['Knave' 9L]
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["Lizard's" 1L]
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['Longitude' 2L]
['Look' 1L]
['Lory' 7L]
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['M' 3L]
["MARMALADE'" 1L]
['ME' 5L]
["ME'" 2L]
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['MINE' 2L]
['MORE' 2L]
['MUST' 3L]
['MYSELF' 1L]
['Ma' 1L]
["Ma'am" 1L]
['Mabel' 4L]
['Mad' 1L]
['Magpie' 1L]
['Majesty' 12L]
['March' 34L]
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['Nothing' 4L]
['Now' 12L]
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['O' 3L]
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['ONE' 4L]
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['Owl' 3L]
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['Paris' 2L]
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['Pat' 3L]
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['Perhaps' 3L]
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['Pigeon' 11L]
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['Queen' 67L]
["Queen's" 7L]
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["RABBIT'" 1L]
['RED' 1L]
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['VERY' 13L]
['VI' 1L]
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['Very' 5L]
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['W' 1L]
['WAISTCOAT' 1L]
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['WASHING' 1L]
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['WHAT' 6L]
['WHATEVER' 1L]
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['WILLIAM' 2L]
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['be' 145L]
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['till' 21L]
['time' 68L]
['times' 6L]
['timid' 3L]
['timidly' 9L]
['tinkling' 1L]
['tiny' 4L]
['tipped' 1L]
['tiptoe' 2L]
['tired' 7L]
['tittered' 1L]
['to' 725L]
['toast' 1L]
['today' 1L]
['toes' 3L]
['toffee' 1L]
['together' 9L]
['told' 6L]
['tomorrow' 1L]
['tone' 40L]
['tones' 2L]
['tongue' 4L]
['too' 25L]
['took' 23L]
['top' 7L]
['tops' 1L]
['toss' 1L]
['tossing' 3L]
['touch' 1L]
['tougher' 1L]
['towards' 1L]
['toys' 1L]
['trampled' 1L]
['traps' 1L]
['tray' 1L]
['treacle' 5L]
['treading' 2L]
['treat' 1L]
['treated' 1L]
['tree' 8L]
['trees' 7L]
['tremble' 1L]
['trembled' 2L]
['trembling' 6L]
['tremulous' 1L]
['trial' 7L]
["trial's" 3L]
['trials' 1L]
['trickling' 1L]
['tricks' 1L]
['tried' 19L]
['triumphantly' 2L]
['trot' 1L]
['trotting' 2L]
['trouble' 6L]
['true' 3L]
['trumpet' 3L]
['trusts' 1L]
['truth' 1L]
['truthful' 1L]
['try' 12L]
['trying' 14L]
['tucked' 3L]
['tulip' 1L]
['tumbled' 1L]
['tumbling' 2L]
['tunnel' 1L]
['tureen' 1L]
['turkey' 1L]
['turn' 8L]
['turned' 16L]
['turning' 12L]
['turns' 3L]
['turtles' 2L]
['tut' 1L]
['twelfth' 1L]
['twelve' 4L]
['twentieth' 1L]
['twenty' 2L]
['twice' 5L]
['twinkle' 5L]
['twinkled' 1L]
['twinkling' 4L]
['twist' 2L]
['two' 32L]
['uglify' 1L]
['uglifying' 1L]
['ugly' 2L]
['unable' 1L]
['uncivil' 1L]
['uncomfortable' 4L]
['uncomfortably' 1L]
['uncommon' 1L]
['uncommonly' 1L]
['uncorked' 1L]
['under' 16L]
['underneath' 1L]
['understand' 6L]
['understood' 1L]
['undertone' 2L]
['undo' 1L]
['undoing' 1L]
['uneasily' 2L]
['uneasy' 1L]
['unfolded' 2L]
['unfortunate' 3L]
['unhappy' 2L]
['unimportant' 3L]
['unjust' 1L]
['unless' 2L]
['unlocking' 1L]
['unpleasant' 2L]
['unrolled' 2L]
['until' 5L]
['untwist' 1L]
['unusually' 1L]
['unwillingly' 1L]
['up' 98L]
['upon' 26L]
['upright' 1L]
['upset' 3L]
['upsetting' 1L]
['upstairs' 1L]
['us' 14L]
['use' 18L]
['used' 13L]
['useful' 2L]
['using' 2L]
['usual' 5L]
['usually' 2L]
['usurpation' 1L]
['vague' 1L]
['vanished' 4L]
['vanishing' 1L]
['variations' 1L]
['various' 1L]
['vegetable' 1L]
['velvet' 1L]
['venture' 3L]
['ventured' 4L]
['verdict' 4L]
['verse' 4L]
['verses' 4L]
['very' 126L]
['vinegar' 1L]
['violence' 1L]
['violent' 2L]
['violently' 4L]
['voice' 47L]
['voices' 2L]
['vote' 1L]
['vulgar' 1L]
['wag' 1L]
['wags' 1L]
['waist' 1L]
['waistcoat' 1L]
['wait' 1L]
['waited' 11L]
['waiting' 8L]
['walk' 5L]
['walked' 10L]
['walking' 5L]
['walrus' 1L]
['wander' 1L]
['wandered' 2L]
['wandering' 2L]
['want' 9L]
['wanted' 4L]
['wants' 2L]
['warning' 1L]
['was' 352L]
['wash' 2L]
['washing' 2L]
["wasn't" 11L]
['waste' 1L]
['wasting' 2L]
['watch' 7L]
['watched' 2L]
['watching' 3L]
['water' 5L]
['waters' 1L]
['waving' 5L]
['way' 56L]
['ways' 1L]
['we' 19L]
["we're" 2L]
["we've" 2L]
['weak' 2L]
['wearily' 1L]
['week' 3L]
['weeks' 1L]
['welcome' 1L]
['well' 40L]
['went' 83L]
['wept' 1L]
['were' 84L]
["were'" 1L]
["weren't" 1L]
['wet' 2L]
['what' 90L]
["what's" 3L]
['whatever' 2L]
['when' 69L]
['whenever' 1L]
['where' 9L]
["where's" 1L]
['whereupon' 1L]
['wherever' 2L]
['whether' 11L]
['which' 41L]
['while' 20L]
['whiles' 1L]
['whiskers' 3L]
['whisper' 3L]
['whispered' 5L]
['whispers' 1L]
['whistle' 1L]
['whistling' 1L]
['white' 8L]
['whiting' 8L]
['who' 48L]
['whole' 13L]
['whom' 1L]
['whose' 2L]
['why' 13L]
['wide' 2L]
['wider' 1L]
['wife' 1L]
['wig' 2L]
['wild' 2L]
['wildly' 2L]
['will' 24L]
['win' 1L]
['wind' 2L]
['window' 8L]
['wine' 2L]
['wings' 1L]
['wink' 2L]
['winter' 1L]
['wise' 2L]
['wish' 21L]
['with' 175L]
['within' 2L]
['without' 26L]
['witness' 10L]
['wits' 1L]
['woke' 1L]
['woman' 2L]
['won' 2L]
["won't" 23L]
["won't'" 1L]
['wonder' 18L]
['wondered' 1L]
['wonderful' 2L]
['wondering' 7L]
['wood' 8L]
['wooden' 1L]
['word' 10L]
['words' 21L]
['wore' 1L]
['work' 8L]
['works' 1L]
['world' 7L]
['worm' 1L]
['worried' 1L]
['worry' 1L]
['worse' 3L]
['worth' 4L]
['would' 70L]
["wouldn't" 12L]
['wow' 4L]
['wrapping' 1L]
['wretched' 2L]
['wriggling' 1L]
['write' 5L]
['writing' 6L]
['written' 6L]
['wrong' 5L]
['wrote' 3L]
['yard' 1L]
['yards' 1L]
['yawned' 2L]
['yawning' 2L]
['ye' 1L]
['year' 2L]
['years' 1L]
['yelp' 1L]
['yer' 4L]
['yes' 1L]
['yesterday' 3L]
['yet' 21L]
['you' 312L]
["you'd" 8L]
["you'll" 4L]
["you're" 15L]
["you've" 6L]
['young' 5L]
['your' 53L]
['yours' 2L]
['yourself' 10L]
['youth' 6L]
['zigzag' 1L]

In [ ]:

    

# %load code/MRWordFrequencyCount.py
from mrjob.job import MRJob

class MRWordFrequencyCount(MRJob):
    
    def mapper(self, _ , line):
        yield "chars", len(line)
        yield "words", len(line.split())
        yield "lines", 1
        
    def reducer(self, key, values):
        yield key, sum(values)
        
if __name__ == '__main__':
    MRWordFrequencyCount.run()


    

In [1]:

    

%run code/MRWordFrequencyCount.py  ./.mrjob.conf data/canterbury/alice29.txt


    

    




no configs found; falling back on auto-configuration
INFO:mrjob.conf:no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
INFO:mrjob.conf:no configs found; falling back on auto-configuration
creating tmp directory c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000
INFO:mrjob.runner:creating tmp directory c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000

WARNING:mrjob.runner:
PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols
WARNING:mrjob.runner:PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols

WARNING:mrjob.runner:
writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00000
INFO:mrjob.sim:writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00000
writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00001
INFO:mrjob.sim:writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00001
Counters from step 1:
INFO:mrjob.runner:Counters from step 1:
  (no counters found)
INFO:mrjob.runner:  (no counters found)
writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper-sorted
INFO:mrjob.runner:writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper-sorted
> sort 'c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00000' 'c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00001'
INFO:mrjob.runner:> sort 'c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00000' 'c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-mapper_part-00001'
Piping files into sort for Windows compatibility
INFO:mrjob.runner:Piping files into sort for Windows compatibility
> sort
INFO:mrjob.runner:> sort
writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-reducer_part-00000
INFO:mrjob.sim:writing to c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-reducer_part-00000
Counters from step 1:
INFO:mrjob.runner:Counters from step 1:
  (no counters found)
INFO:mrjob.runner:  (no counters found)
Moving c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-reducer_part-00000 -> c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\output\part-00000
INFO:mrjob.sim:Moving c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\step-0-reducer_part-00000 -> c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\output\part-00000
Streaming final output from c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\output
INFO:mrjob.runner:Streaming final output from c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000\output






    




"chars"	148685
"lines"	12
"words"	26474






    




removing tmp directory c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000
INFO:mrjob.runner:removing tmp directory c:\users\ps\appdata\local\temp\MRWordFrequencyCount.PS.20150922.022135.283000

In [ ]:

    

# MRJOB Word count function


    

In [ ]:

    

# %load code\MRWordFreqCount.py
from mrjob.job import MRJob
import re

WORD_RE = re.compile(r"[\w']+")


class MRWordFreqCount(MRJob):

    def mapper(self, _, line):
        for word in WORD_RE.findall(line):
            yield word.lower(), 1

    def combiner(self, word, counts):
        yield word, sum(counts)

    def reducer(self, word, counts):
        yield word, sum(counts)


if __name__ == '__main__':
    MRWordFreqCount.run()


    

In [ ]:

    




    

In [14]:

    

%run code/MRWordFreqCount.py data/canterbury/alice29.txt


    

    




no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
INFO:mrjob.conf:no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
INFO:mrjob.conf:no configs found; falling back on auto-configuration
creating tmp directory c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000
creating tmp directory c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000
creating tmp directory c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000
INFO:mrjob.runner:creating tmp directory c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000



WARNING:mrjob.runner:
PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols
PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols
PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols
WARNING:mrjob.runner:PLEASE NOTE: Starting in mrjob v0.5.0, protocols will be strict by default. It's recommended you run your job with --strict-protocols or set up mrjob.conf as described at https://pythonhosted.org/mrjob/whats-new.html#ready-for-strict-protocols



WARNING:mrjob.runner:
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000
INFO:mrjob.sim:writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000
Counters from step 1:
Counters from step 1:
Counters from step 1:
INFO:mrjob.runner:Counters from step 1:
  (no counters found)
  (no counters found)
  (no counters found)
INFO:mrjob.runner:  (no counters found)
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper-sorted
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper-sorted
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper-sorted
INFO:mrjob.runner:writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper-sorted
> sort 'c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000'
> sort 'c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000'
> sort 'c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000'
INFO:mrjob.runner:> sort 'c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-mapper_part-00000'
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000
writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000
INFO:mrjob.sim:writing to c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000
Counters from step 1:
Counters from step 1:
Counters from step 1:
INFO:mrjob.runner:Counters from step 1:
  (no counters found)
  (no counters found)
  (no counters found)
INFO:mrjob.runner:  (no counters found)
Moving c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000 -> c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output\part-00000
Moving c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000 -> c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output\part-00000
Moving c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000 -> c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output\part-00000
INFO:mrjob.sim:Moving c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\step-0-reducer_part-00000 -> c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output\part-00000
Streaming final output from c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output
Streaming final output from c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output
Streaming final output from c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output
INFO:mrjob.runner:Streaming final output from c:\users\ps\appdata\local\temp\MRWordFreqCount.PS.20150922.023122.602000\output






    




"'"	1091
"'em"	3
"'tis"	5
"_i_"	2
"2"	1
"9"	1
"a"	632
"abide"	1
"able"	1
"about"	94
"above"	3
"absence"	1
"absurd"	2
"acceptance"	1
"accident"	2
"accidentally"	1
"account"	1
"accounting"	1
"accounts"	1
"accusation"	1
"accustomed"	1
"ache"	1
"across"	5
"act"	1
"actually"	1
"ada"	1
"added"	23
"adding"	1
"addressed"	2
"addressing"	1
"adjourn"	1
"adoption"	1
"advance"	3
"advantage"	3
"adventures"	7
"advice"	2
"advisable"	2
"advise"	1
"affair"	1
"affectionately"	1
"afford"	1
"afore"	1
"afraid"	12
"after"	43
"afterwards"	2
"again"	82
"against"	9
"age"	4
"ago"	2
"agony"	1
"agree"	2
"ah"	5
"ahem"	1
"air"	15
"airs"	1
"alarm"	2
"alarmed"	1
"alas"	4
"alice's"	12
"alice"	386
"alive"	3
"all"	182
"allow"	3
"almost"	6
"alone"	4
"along"	6
"aloud"	5
"already"	2
"also"	2
"altered"	1
"alternately"	1
"altogether"	5
"always"	13
"am"	15
"ambition"	1
"among"	12
"an"	57
"ancient"	1
"and"	872
"anger"	2
"angrily"	9
"angry"	5
"animal's"	1
"animal"	1
"animals"	4
"ann"	4
"annoy"	1
"annoyed"	1
"another"	22
"answer"	9
"answered"	4
"answers"	1
"antipathies"	1
"anxious"	3
"anxiously"	14
"any"	39
"anything"	20
"anywhere"	1
"appealed"	1
"appear"	2
"appearance"	1
"appeared"	8
"appearing"	1
"applause"	1
"apple"	1
"apples"	2
"arch"	1
"archbishop"	2
"arches"	4
"are"	54
"argue"	1
"argued"	1
"argument"	4
"arguments"	1
"arithmetic"	1
"arm"	15
"arms"	6
"around"	3
"arranged"	1
"arrived"	1
"arrow"	1
"arrum"	1
"as"	263
"ashamed"	2
"ask"	11
"askance"	1
"asked"	17
"asking"	5
"asleep"	8
"assembled"	2
"at"	211
"ate"	1
"atheling"	1
"atom"	2
"attempt"	1
"attempted"	1
"attempts"	1
"attended"	1
"attending"	3
"attends"	1
"audibly"	1
"australia"	1
"authority"	2
"avoid"	1
"away"	25
"awfully"	1
"axes"	1
"axis"	1
"baby"	14
"back"	39
"backs"	1
"bad"	2
"bag"	1
"baked"	1
"balanced"	1
"balls"	1
"bank"	3
"banks"	1
"banquet"	1
"bark"	2
"barking"	1
"barley"	1
"barrowful"	2
"bat"	3
"bathing"	1
"bats"	4
"bawled"	1
"be"	148
"beak"	1
"bear"	2
"beast"	1
"beasts"	2
"beat"	4
"beating"	2
"beau"	4
"beauti"	1
"beautiful"	13
"beautifully"	2
"beautify"	1
"became"	2
"because"	15
"become"	5
"becoming"	1
"bed"	1
"beds"	2
"bee"	1
"been"	38
"before"	38
"beg"	8
"began"	58
"begged"	1
"begin"	13
"beginning"	14
"begins"	4
"begun"	7
"behead"	1
"beheaded"	3
"beheading"	1
"behind"	13
"being"	19
"believe"	9
"believed"	1
"bells"	1
"belong"	1
"belongs"	2
"beloved"	1
"below"	3
"belt"	1
"bend"	2
"bent"	1
"besides"	4
"best"	12
"better'"	1
"better"	13
"between"	6
"bill's"	4
"bill"	13
"bird"	2
"birds"	10
"birthday"	1
"bit"	16
"bite"	2
"bitter"	1
"blacking"	1
"blades"	1
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"time"	71
"times"	6
"timid"	3
"timidly"	9
"tinkling"	1
"tiny"	4
"tipped"	1
"tiptoe"	2
"tired"	7
"tittered"	1
"to"	729
"toast"	1
"today"	1
"toes"	3
"toffee"	1
"together"	9
"told"	6
"tomorrow"	1
"tone"	40
"tones"	2
"tongue"	4
"too"	26
"took"	24
"top"	7
"tops"	1
"tortoise"	3
"toss"	1
"tossing"	3
"touch"	1
"tougher"	1
"towards"	1
"toys"	1
"trampled"	1
"traps"	1
"tray"	1
"treacle"	7
"treading"	2
"treat"	1
"treated"	1
"tree"	8
"trees"	7
"tremble"	1
"trembled"	2
"trembling"	6
"tremulous"	1
"trial's"	3
"trial"	7
"trials"	1
"trickling"	1
"tricks"	1
"tried"	19
"trims"	1
"triumphantly"	2
"trot"	1
"trotting"	2
"trouble"	6
"true"	4
"trumpet"	3
"trusts"	1
"truth"	1
"truthful"	1
"try"	12
"trying"	14
"tucked"	3
"tulip"	1
"tumbled"	1
"tumbling"	2
"tunnel"	1
"tureen"	1
"turkey"	1
"turn"	11
"turned"	16
"turning"	12
"turns"	3
"turtle's"	2
"turtle"	57
"turtles"	2
"tut"	2
"twelfth"	1
"twelve"	4
"twentieth"	1
"twenty"	3
"twice"	5
"twinkle"	8
"twinkled"	1
"twinkling"	4
"twist"	2
"two"	40
"ugh"	2
"uglification"	2
"uglify"	1
"uglifying"	1
"ugly"	2
"unable"	1
"uncivil"	1
"uncomfortable"	4
"uncomfortably"	1
"uncommon"	1
"uncommonly"	1
"uncorked"	1
"under"	16
"underneath"	1
"understand"	6
"understood"	1
"undertone"	2
"undo"	1
"undoing"	1
"uneasily"	2
"uneasy"	1
"unfolded"	2
"unfortunate"	3
"unhappy"	2
"unimportant"	5
"unjust"	1
"unless"	2
"unlocking"	1
"unpleasant"	2
"unrolled"	2
"until"	5
"untwist"	1
"unusually"	1
"unwillingly"	1
"up"	100
"upon"	26
"upright"	1
"upset"	3
"upsetting"	1
"upstairs"	1
"us"	14
"use"	18
"used"	13
"useful"	2
"using"	2
"usual"	5
"usually"	2
"usurpation"	1
"v"	1
"vague"	1
"vanished"	4
"vanishing"	1
"variations"	1
"various"	1
"vegetable"	1
"velvet"	1
"venture"	3
"ventured"	4
"verdict"	4
"verse"	4
"verses"	4
"very"	144
"vi"	1
"vii"	1
"viii"	1
"vinegar"	1
"violence"	1
"violent"	2
"violently"	4
"visit"	1
"voice"	48
"voices"	2
"vote"	1
"vulgar"	1
"w"	1
"wag"	1
"wags"	1
"waist"	1
"waistcoat"	2
"wait"	1
"waited"	11
"waiting"	9
"wake"	2
"walk"	5
"walked"	10
"walking"	5
"walrus"	1
"wander"	1
"wandered"	2
"wandering"	2
"want"	9
"wanted"	4
"wants"	2
"warning"	1
"was"	357
"wash"	2
"washing"	3
"wasn't"	11
"waste"	1
"wasting"	2
"watch"	8
"watched"	2
"watching"	3
"water"	5
"waters"	1
"waving"	5
"way"	56
"ways"	1
"we're"	2
"we've"	2
"we"	30
"weak"	2
"wearily"	1
"week"	3
"weeks"	1
"welcome"	1
"well"	63
"went"	83
"wept"	1
"were'"	1
"were"	84
"weren't"	1
"wet"	2
"what's"	5
"what"	136
"whatever"	3
"when"	79
"whenever"	1
"where's"	2
"where"	13
"whereupon"	1
"wherever"	2
"whether"	11
"which"	49
"while"	25
"whiles"	1
"whiskers"	3
"whisper"	3
"whispered"	5
"whispers"	1
"whistle"	1
"whistling"	1
"white"	30
"whiting"	8
"who's"	2
"who"	61
"whoever"	1
"whole"	13
"whom"	1
"whose"	2
"why"	40
"wide"	2
"wider"	1
"wife"	1
"wig"	2
"wild"	2
"wildly"	2
"will"	33
"william's"	1
"william"	7
"win"	1
"wind"	2
"window"	8
"wine"	2
"wings"	1
"wink"	2
"winter"	1
"wise"	2
"wish"	21
"with"	180
"within"	2
"without"	26
"witness"	10
"wits"	1
"woke"	1
"woman"	2
"won't'"	1
"won't"	23
"won"	2
"wonder"	18
"wondered"	1
"wonderful"	2
"wondering"	7
"wonderland"	3
"wood"	8
"wooden"	1
"word"	10
"words"	21
"wore"	1
"work"	8
"works"	1
"world"	7
"worm"	1
"worried"	1
"worry"	1
"worse"	3
"worth"	4
"would"	83
"wouldn't"	13
"wow"	6
"wrapping"	1
"wretched"	2
"wriggling"	1
"write"	6
"writhing"	1
"writing"	6
"written"	6
"wrong"	5
"wrote"	3
"x"	1
"xi"	1
"xii"	1
"yard"	1
"yards"	1
"yawned"	2
"yawning"	2
"ye"	1
"year"	2
"years"	1
"yelp"	1
"yer"	4
"yes"	13
"yesterday"	3
"yet"	25
"you'd"	10
"you'll"	6
"you're"	23
"you've"	7
"you"	365
"young"	5
"your"	62
"yours"	3
"yourself"	10
"youth"	6
"zealand"	1
"zigzag"	1






    




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In [ ]:

    

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from __future__ import print_function

import sys
from operator import add

from pyspark import SparkContext


if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: wordcount <file>", file=sys.stderr)
        exit(-1)
    sc = SparkContext(appName="PythonWordCount")
    lines = sc.textFile(sys.argv[1], 1)
    counts = lines.flatMap(lambda x: x.split(' ')) \
                  .map(lambda x: (x, 1)) \
                  .reduceByKey(add)
    output = counts.collect()
    for (word, count) in output:
        print("%s: %i" % (word, count))

    sc.stop()