Python nltk counting word and phrase frequency

Question

I am using NLTK and trying to get the word phrase count up to a certain length for a particular document as well as the frequency of each phrase. I tokenize the string to get the data list.

from nltk.util import ngrams
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.collocations import *


data = ["this", "is", "not", "a", "test", "this", "is", "real", "not", "a", "test", "this", "is", "this", "is", "real", "not", "a", "test"]

bigrams = ngrams(data, 2)

bigrams_c = {}
for b in bigrams:
    if b not in bigrams_c:
        bigrams_c[b] = 1
    else:
        bigrams_c[b] += 1

the above code gives and output like this:

(('is', 'this'), 1)
(('test', 'this'), 2)
(('a', 'test'), 3)
(('this', 'is'), 4)
(('is', 'not'), 1)
(('real', 'not'), 2)
(('is', 'real'), 2)
(('not', 'a'), 3)

which is partially what I am looking for.

My question is, is there a more convenient way to do this for say up to phrases that are 4 or 5 in length without duplicating this code only to change the count variable?

Answer 1

Since you tagged this nltk, here's how to do it using the nltk's methods, which have some more features than the ones in the standard python collection.

from nltk import ngrams, FreqDist
all_counts = dict()
for size in 2, 3, 4, 5:
    all_counts[size] = FreqDist(ngrams(data, size))

Each element of the dictionary all_counts is a dictionary of ngram frequencies. For example, you can get the five most common trigrams like this:

all_counts[3].most_common(5)

Answer 2

Yeah don't run this loop, use collections.Counter(bigrams) or pandas.Series(bigrams).value_counts() to compute the counts in a one-liner.