n-grams in python, four, five, six grams?

I'm surprised that this hasn't shown up yet:

In [34]: sentence = "I really like python, it's pretty awesome.".split()

In [35]: N = 4

In [36]: grams = [sentence[i:i+N] for i in xrange(len(sentence)-N+1)]

In [37]: for gram in grams: print gram
['I', 'really', 'like', 'python,']
['really', 'like', 'python,', "it's"]
['like', 'python,', "it's", 'pretty']
['python,', "it's", 'pretty', 'awesome.']

People have already answered pretty nicely for the scenario where you need bigrams or trigrams but if you need everygram for the sentence in that case you can use nltk.util.everygrams

>>> from nltk.util import everygrams

>>> message = "who let the dogs out"

>>> msg_split = message.split()

>>> list(everygrams(msg_split))
[('who',), ('let',), ('the',), ('dogs',), ('out',), ('who', 'let'), ('let', 'the'), ('the', 'dogs'), ('dogs', 'out'), ('who', 'let', 'the'), ('let', 'the', 'dogs'), ('the', 'dogs', 'out'), ('who', 'let', 'the', 'dogs'), ('let', 'the', 'dogs', 'out'), ('who', 'let', 'the', 'dogs', 'out')]

Incase you have a limit like in case of trigrams where the max length should be 3 then you can use max_len param to specify it.

>>> list(everygrams(msg_split, max_len=2))
[('who',), ('let',), ('the',), ('dogs',), ('out',), ('who', 'let'), ('let', 'the'), ('the', 'dogs'), ('dogs', 'out')]

You can just modify the max_len param to achieve whatever gram i.e four gram, five gram, six or even hundred gram.

The previous mentioned solutions can be modified to implement the above mentioned solution but this solution is much straight forward than that.

For further reading click here

And when you just need a specific gram like bigram or trigram etc you can use the nltk.util.ngrams as mentioned in M.A.Hassan's answer.

Great native python based answers given by other users. But here's the nltk approach (just in case, the OP gets penalized for reinventing what's already existing in the nltk library).

There is an ngram module that people seldom use in nltk. It's not because it's hard to read ngrams, but training a model base on ngrams where n > 3 will result in much data sparsity.

from nltk import ngrams

sentence = 'this is a foo bar sentences and i want to ngramize it'

n = 6
sixgrams = ngrams(sentence.split(), n)

for grams in sixgrams:
  print grams