counting n-gram frequency in python nltk

The finder.ngram_fd.viewitems() function works

I tried all the above and found a simpler solution. NLTK comes with a simple Most Common freq Ngrams.

filtered_sentence is my word tokens

import nltk
from nltk.util import ngrams
from nltk.collocations import BigramCollocationFinder
from nltk.metrics import BigramAssocMeasures

word_fd = nltk.FreqDist(filtered_sentence)
bigram_fd = nltk.FreqDist(nltk.bigrams(filtered_sentence))

bigram_fd.most_common()

This should give the output as:

[(('working', 'hours'), 31),
 (('9', 'hours'), 14),
 (('place', 'work'), 13),
 (('reduce', 'working'), 11),
 (('improve', 'experience'), 9)]

from nltk import FreqDist
from nltk.util import ngrams    
def compute_freq():
   textfile = open('corpus.txt','r')

   bigramfdist = FreqDist()
   threeramfdist = FreqDist()

   for line in textfile:
        if len(line) > 1:
        tokens = line.strip().split(' ')

        bigrams = ngrams(tokens, 2)
        bigramfdist.update(bigrams)
compute_freq()

NLTK comes with its own bigrams generator, as well as a convenient FreqDist() function.

f = open('a_text_file')
raw = f.read()

tokens = nltk.word_tokenize(raw)

#Create your bigrams
bgs = nltk.bigrams(tokens)

#compute frequency distribution for all the bigrams in the text
fdist = nltk.FreqDist(bgs)
for k,v in fdist.items():
    print k,v

Once you have access to the BiGrams and the frequency distributions, you can filter according to your needs.

Hope that helps.