Clean Data using NLTK

[disruptfwd8]

Need help creating a function that cleans data and puts frequency in dictionary.

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import string

#create a function and dictionary
def clean_data(tokenizeFreq)
token_frequency_dic = {}

# load data
article = open('sample_data.txt','r')
text = article.read()
file.close()

# split into words
tokens = word_tokenize(text)

# convert to lower case
tokens = [w.lower() for w in tokens]

# remove punctuation from each word
table = str.maketrans('', '', string.punctuation)
stripped = [w.translate(table) for w in tokens]

# remove remaining tokens that are not alphabetic
words = [word for word in stripped if word.isalpha()]

# filter out stop words and sort
stop_words = set(stopwords.words('english'))
words = [w for w in words if not w in stop_words]
words.sort()

# print frequency distribution
req = nltk.FreqDist(words)
for k,v in req.items():
    print(str(k) + ': ' + str(v))

can this be condense into a for loop...