Python: How to compute the top X most frequently used words in an NLTK corpus? [duplicate]

Question

I'm unsure if I've understood correctly how the FreqDist functions works on Python. As I am following a tutorial, I am led to believe that the following code constructs a frequency distribution for a given list of words and calculates the top x frequently used words. (In the example below let corpus be an NLTK corpus and file to be a filename of a file in that corpus)

words = corpus.words('file.txt')
fd_words = nltk.FreqDist(word.lower() for word in words)
fd_words.items()[:x]

However, when I go through the following commands on Python, it seems to suggest otherwise:

>>> from nltk import *
>>> fdist = FreqDist(['hi','my','name','is','my','name'])
>>> fdist
FreqDist({'my': 2, 'name':2, 'is':1, 'hi':1}
>>> fdist.items()
[('is',1),('hi',1),('my',2),('name',2)]
>>> fdist.items[:2]
[('is',1),('hi',1)]

The fdist.items()[:x] method is in fact returning the x least common words?

Can someone tell me if I have done something wrong or if the mistake lies in the tutorial I am following?

Answer 1

By default a FreqDist is not sorted. I think you are looking for most_common method:

from nltk import FreqDist
fdist = FreqDist(['hi','my','name','is','my','name'])
fdist.most_common(2)

Returns:

[('my', 2), ('name', 2)]