Unigrams, Bigrams and Ngrams in Python

#tokenization and n-gramms
# nltk.download('punkt')

text = """Fear leads to anger; anger leads to hatred;
hatred leads to conflict, conflict leads to destroy, destroy is the last conflict leads to suffering. 
suffering is again fear"""

# normalizung the data
text = text.lower()
text

# character wise frequency distribution
myFD = nltk.FreqDist(text)
myFD

# generating tokens before creating ngrams
tokens = nltk.word_tokenize(text)
tokens


# unigrams
unigrams = nltk.ngrams(tokens, 1)
unigramFD = nltk.FreqDist(unigrams)

for token in list(unigramFD.items()):
    print(token[0], token[1])

# bigrams
bigrams = nltk.ngrams(tokens, 2)
bigramsFD = nltk.FreqDist(bigrams)

for bigram in list(bigramsFD.items()):
    print(bigram[0], bigram[1])

# trigrams
trigrams = nltk.ngrams(tokens, 3)
trigramsFD = nltk.FreqDist(trigrams)

for trigram in list(trigramsFD.items()):
    print(trigram[0], trigram[1])