World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITian's, only for AI Learners.
Download our e-book of Introduction To Python
Related Blog
Top Discussion
How to leave/exit/deactivate a Python virtualenvironment Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) How to Partitioning a dataset in training and test sets using Scikit-learn? What is Ensemble Learning? ValueError: Found input variables with inconsistent numbers of samples: [143, 426] How do you identify important variables while working on a data set in machine learning? Why not getting result of a as [4, 7,8,3,2] Which are different modes to open a file ? Join Discussion
Top Courses
Machine Learning with Python & Statistics
4 (4,001 Ratings)
218 Learners
Webinars
Live Masterclass on "Python for Artificial Intelligence"
Dec 4th (7:00 PM) 195 Registered
Python - Frequency Distribution
Neha Kumawat
a year ago
Frequency Distrisbution | Insideaiml
Counting the frequency of occurrence of a word in a body of text is often needed during text processing. This can be achieved by applying the word_tokenize() function and appending the result to a list to keep count of the words as shown in the below program.
from nltk.tokenize import word_tokenize
from nltk.corpus import gutenberg
sample = gutenberg.raw("blake-poems.txt")
token = word_tokenize(sample)
wlist = []
for i in range(50):
wlist.append(token[i])
wordfreq = [wlist.count(w) for w in wlist]
print("Pairs\n" + str(zip(token, wordfreq)))
When we run the above program, we get the following output −
[([', 1), (Poems', 1), (by', 1), (William', 1), (Blake', 1), (1789', 1), (]', 1), (SONGS', 2), (OF', 3), (INNOCENCE', 2), (AND', 1), (OF', 3), (EXPERIENCE', 1), (and', 1), (THE', 1), (BOOK', 1), (of', 2), (THEL', 1), (SONGS', 2), (OF', 3), (INNOCENCE', 2), (INTRODUCTION', 1), (Piping', 2), (down', 1), (the', 1), (valleys', 1), (wild', 1), (,', 3), (Piping', 2), (songs', 1), (of', 2), (pleasant', 1), (glee', 1), (,', 3), (On', 1), (a', 2), (cloud', 1), (I', 1), (saw', 1), (a', 2), (child', 1), (,', 3), (And', 1), (he', 1), (laughing', 1), (said', 1), (to', 1), (me', 1), (:', 1), (``', 1)]
Conditional Frequency Distribution
Conditional Frequency Distribution is used when we want to count words meeting specific crteria satisfying a set of text.
import nltk
#from nltk.tokenize import word_tokenize
from nltk.corpus import brown
cfd = nltk.ConditionalFreqDist(
(genre, word)
for genre in brown.categories()
for word in brown.words(categories=genre))
categories = ['hobbies', 'romance','humor']
searchwords = [ 'may', 'might', 'must', 'will']
cfd.tabulate(conditions=categories, samples=searchwords)
When we run the above program, we get the following output −
may might must will
hobbies 131 22 83 264
romance 11 51 45 43
humor 8 8 9 13
For
more related articles and courses visit InsideAIML.
I hope you enjoyed reading this article and finally, you came
to know about Python - Frequency Distribution.
For more such blogs/courses on data science, machine
learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.
Thanks for reading…
Happy Learning…