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SI13/LIQUID/MANIFESTO/nltk-frequency-distribution...

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NLTK - Frequency Distribution

In [1]:
import nltk
import random
In [2]:
lines = open('manifesto.txt').readlines()
sentence = random.choice(lines)
print(sentence)

LIQUID BODIES ARE POLITICAL AGENTS. THEY RE-DEFINE THE BOUNDARIES AND CONDITIONS FOR EXISTENCE IN THE CONTEXT OF DYNAMIC, UNRULY ENVIRONMENTS. THEY PROPOSE ALTERNATIVE MODES OF LIVING THAT ARE RADICALLY TRANSFORMED, MONSTROUS, COHERENT, RAW – AND SELECTIVELY PERMEATED BY THEIR NURTURING MEDIA.

Tokens

In [3]:
tokens = nltk.word_tokenize(sentence)
print(tokens)

['LIQUID', 'BODIES', 'ARE', 'POLITICAL', 'AGENTS', '.', 'THEY', 'RE-DEFINE', 'THE', 'BOUNDARIES', 'AND', 'CONDITIONS', 'FOR', 'EXISTENCE', 'IN', 'THE', 'CONTEXT', 'OF', 'DYNAMIC', ',', 'UNRULY', 'ENVIRONMENTS', '.', 'THEY', 'PROPOSE', 'ALTERNATIVE', 'MODES', 'OF', 'LIVING', 'THAT', 'ARE', 'RADICALLY', 'TRANSFORMED', ',', 'MONSTROUS', ',', 'COHERENT', ',', 'RAW', '–', 'AND', 'SELECTIVELY', 'PERMEATED', 'BY', 'THEIR', 'NURTURING', 'MEDIA', '.']
In [ ]:

Frequency Distribution

In [4]:
# frequency of characters
fd = nltk.FreqDist(sentence)
print(fd)

<FreqDist with 29 samples and 295 outcomes>
In [5]:
print(fd.most_common(50))

[(' ', 40), ('E', 33), ('T', 22), ('N', 22), ('I', 21), ('R', 19), ('O', 17), ('A', 17), ('D', 13), ('S', 12), ('L', 10), ('C', 8), ('U', 7), ('H', 7), ('Y', 7), ('M', 7), ('F', 5), ('P', 4), (',', 4), ('V', 4), ('B', 3), ('G', 3), ('.', 3), ('X', 2), ('Q', 1), ('-', 1), ('W', 1), ('–', 1), ('\n', 1)]
In [ ]:
In [6]:
# frequency of words
fd = nltk.FreqDist(tokens)
print(fd)

<FreqDist with 38 samples and 48 outcomes>
In [7]:
print(fd.most_common(50))

[(',', 4), ('.', 3), ('ARE', 2), ('THEY', 2), ('THE', 2), ('AND', 2), ('OF', 2), ('LIQUID', 1), ('BODIES', 1), ('POLITICAL', 1), ('AGENTS', 1), ('RE-DEFINE', 1), ('BOUNDARIES', 1), ('CONDITIONS', 1), ('FOR', 1), ('EXISTENCE', 1), ('IN', 1), ('CONTEXT', 1), ('DYNAMIC', 1), ('UNRULY', 1), ('ENVIRONMENTS', 1), ('PROPOSE', 1), ('ALTERNATIVE', 1), ('MODES', 1), ('LIVING', 1), ('THAT', 1), ('RADICALLY', 1), ('TRANSFORMED', 1), ('MONSTROUS', 1), ('COHERENT', 1), ('RAW', 1), ('–', 1), ('SELECTIVELY', 1), ('PERMEATED', 1), ('BY', 1), ('THEIR', 1), ('NURTURING', 1), ('MEDIA', 1)]
In [ ]:
In [8]:
# frequency of a text
txt = open('manifesto.txt').read()
tokens = nltk.word_tokenize(txt)
fd = nltk.FreqDist(tokens)
print(fd)

<FreqDist with 660 samples and 1463 outcomes>
In [9]:
print(fd.most_common(50))

[(',', 100), ('.', 69), ('OF', 68), ('THE', 57), ('AND', 50), ('LIQUID', 25), ('A', 25), ('ARE', 19), ('THAT', 19), ('TO', 18), ('ITS', 17), ('IN', 15), ('THEY', 15), ('IS', 14), ('THEIR', 14), ('LIFE', 11), ('’', 11), ('BODIES', 11), ('IT', 10), ('AS', 9), ('WHICH', 9), ('THESE', 9), ('‘', 8), ('–', 8), ('OUR', 7), ('THROUGH', 7), ('MATTER', 7), ('NOT', 6), ('CAN', 6), ('INTO', 6), ('FROM', 6), ('WITH', 6), ('BEING', 6), ('LIKE', 5), ('ON', 5), ('AN', 4), ('OR', 4), ('WE', 4), ('LIVING', 4), ('BE', 4), ('METABOLIC', 4), ('CHEMICAL', 4), ('FOR', 4), ('OWN', 4), ('US', 4), ('ACTS', 4), ('REALM', 3), ('YET', 3), ('THEM', 3), ('RECOGNISE', 3)]
In [ ]:
In [10]:
# Requesting the frequency of a specific word
print(fd['language'])

47
In [ ]: