FreqDist in NLTK does not sort output

Question

I am new to Python and I am trying to teach myself how to handle a language. NLTK in python has a FreqDist function that gives the frequency of words in the text, but for some reason it does not work properly.

Here is what I have a lesson:

fdist1 = FreqDist(text1)
vocabulary1 = fdist1.keys()
vocabulary1[:50]

So basically he should give me a list of the 50 most frequently occurring words in the text. However, when I run the code, the result is the 50 least frequently occurring words in the order of the least frequent, and not vice versa. The result that I get is as follows:

[u'succour', u'four', u'woods', u'hanging', u'woody', u'conjure', u'looking', u'eligible', u'scold', u'unsuitableness', u'meadows', u'stipulate', u'leisurely', u'bringing', u'disturb', u'internally', u'hostess', u'mohrs', u'persisted', u'Does', u'succession', u'tired', u'cordially', u'pulse', u'elegant', u'second', u'sooth', u'shrugging', u'abundantly', u'errors', u'forgetting', u'contributed', u'fingers', u'increasing', u'exclamations', u'hero', u'leaning', u'Truth', u'here', u'china', u'hers', u'natured', u'substance', u'unwillingness...]

I copy the tutorial for sure, but I have to do something wrong.

Here is the link to the tutorial:

http://www.nltk.org/book/ch01.html#sec-computing-with-language-texts-and-words

" 1.3: , ( )"

- , ?

Answer 1

, :

1. , nltk :

>>> import nltk
>>> print nltk.__version__
2.0.4  # preferably 2.0 or higher

nltk FreqDist.keys.

2. , text1 vocabulary1:

:

>>> from nltk.book import *
*** Introductory Examples for the NLTK Book ***
Loading text1, ..., text9 and sent1, ..., sent9
Type the name of the text or sentence to view it.
Type: 'texts()' or 'sents()' to list the materials.
text1: Moby Dick by Herman Melville 1851
text2: Sense and Sensibility by Jane Austen 1811
text3: The Book of Genesis
text4: Inaugural Address Corpus
text5: Chat Corpus
text6: Monty Python and the Holy Grail
text7: Wall Street Journal
text8: Personals Corpus
text9: The Man Who Was Thursday by G . K . Chesterton 1908
>>> from nltk import FreqDist
>>> fdist1 = FreqDist(text1)
>>> vocabulary1 = fdist1.keys()
>>> vocabulary1[:50]
[',', 'the', '.', 'of', 'and', 'a', 'to', ';', 'in', 'that', "'", '-', 'his', 'it', 'I', 's', 'is', 'he', 'with', 'was', 'as', '"', 'all', 'for', 'this', '!', 'at', 'by', 'but', 'not', '--', 'him', 'from', 'be', 'on', 'so', 'whale', 'one', 'you', 'had', 'have', 'there', 'But', 'or', 'were', 'now', 'which', '?', 'me', 'like']

, 1 u'succour' ( ):

>>> vocabulary1.count(u'succour')  # vocabulary1 does **not** contain the string u'succour'
0

3. , , , , :

>>> import inspect
>>> print inspect.getsource(FreqDist.keys)  # make sure your source code matches the source code below
    def keys(self):
        """
        Return the samples sorted in decreasing order of frequency.

        :rtype: list(any)
        """
        self._sort_keys_by_value()
        return map(itemgetter(0), self._item_cache)

>>> print inspect.getsource(FreqDist._sort_keys_by_value)  # and matches this source code
    def _sort_keys_by_value(self):
        if not self._item_cache:
            self._item_cache = sorted(dict.items(self), key=lambda x:(-x[1], x[0]))  # <= check this line especially

>>> text1[:40]  # does the first part of your text list match this one?
['[', 'Moby', 'Dick', 'by', 'Herman', 'Melville', '1851', ']', 'ETYMOLOGY', '.', '(', 'Supplied', 'by', 'a', 'Late', 'Consumptive', 'Usher', 'to', 'a', 'Grammar', 'School', ')', 'The', 'pale', 'Usher', '--', 'threadbare', 'in', 'coat', ',', 'heart', ',', 'body', ',', 'and', 'brain', ';', 'I', 'see', 'him']

>>> text1[-40:]  # and what about the end of your text list?
['second', 'day', ',', 'a', 'sail', 'drew', 'near', ',', 'nearer', ',', 'and', 'picked', 'me', 'up', 'at', 'last', '.', 'It', 'was', 'the', 'devious', '-', 'cruising', 'Rachel', ',', 'that', 'in', 'her', 'retracing', 'search', 'after', 'her', 'missing', 'children', ',', 'only', 'found', 'another', 'orphan', '.']

, nltk .

Answer 2

NLTK GitHub:

FreqDist NLTK3 . Counter; most_common() . FreqDist.keys() ; . , , stdlib.

docs googlecode , 2011 . - http://nltk.org.

, NLKT 3 fdist1.keys()[:50] fdist1.most_common(50).

:

fdist1 = FreqDist(text1)
>>> print(fdist1)
<FreqDist with 19317 samples and 260819 outcomes>
>>> fdist1.most_common(50)
[(',', 18713), ('the', 13721), ('.', 6862), ('of', 6536), ('and', 6024),
('a', 4569), ('to', 4542), (';', 4072), ('in', 3916), ('that', 2982),
("'", 2684), ('-', 2552), ('his', 2459), ('it', 2209), ('I', 2124),
('s', 1739), ('is', 1695), ('he', 1661), ('with', 1659), ('was', 1632),
('as', 1620), ('"', 1478), ('all', 1462), ('for', 1414), ('this', 1280),
('!', 1269), ('at', 1231), ('by', 1137), ('but', 1113), ('not', 1103),
('--', 1070), ('him', 1058), ('from', 1052), ('be', 1030), ('on', 1005),
('so', 918), ('whale', 906), ('one', 889), ('you', 841), ('had', 767),
('have', 760), ('there', 715), ('But', 705), ('or', 697), ('were', 680),
('now', 646), ('which', 640), ('?', 637), ('me', 627), ('like', 624)]
>>> fdist1['whale']
906

Answer 3

As an alternative to using FreqDist, you can simply use Counterfrom collections, see also https://stackoverflow.com/questions/22952069/how-to-get-the-rank-of-a-word-from-a-dictionary-with -word-frequencies-python / 22953416 # 22953416 :

>>> from collections import Counter
>>> text = """foo foo bar bar foo bar hello bar hello world  hello world hello world hello world  hello world hello hello hello"""
>>> dictionary = Counter(text.split())
>>> dictionary
{"foo":3, "bar":4, "hello":9, "world":5}
>>> dictionary.most_common()
[('hello', 9), ('world', 5), ('bar', 4), ('foo', 3)]
>>> [i[0] for i in dictionary.most_common()]
['hello', 'world', 'bar', 'foo']