diff --git a/frequency.py b/frequency.py
index acaa449..56f53a4 100644
--- a/frequency.py
+++ b/frequency.py
@@ -1,17 +1,40 @@
 """ Analyzes the word frequencies in a book downloaded from
 	Project Gutenberg """
 
+import pickle
+import collections
+from collections import deque
 import string
+from pattern.web import *
+
+""" Pickling Gutenberg Text"""
+
+# text_FULL = URL('http://www.gutenberg.org/cache/epub/1567/pg1567.txt').download()
+
+# # Save data to a file (will be part of your data fetching script)
+# f = open('text_full.pickle','w')
+# pickle.dump(text_FULL,f)
+# f.close()
+
+# """ Importing Pickled Text"""
+input_file = open('text_full.pickle','r')
+reloaded_copy_of_text = pickle.load(input_file)
 
 def get_word_list(file_name):
+
 	""" Reads the specified project Gutenberg book.  Header comments,
 		punctuation, and whitespace are stripped away.  The function
 		returns a list of the words used in the book as a list.
 		All words are converted to lower case.
 	"""
-	pass
+	text_nopunct = reloaded_copy_of_text.translate(string.maketrans("",""), string.punctuation) # eliminates all punctuation
+	text_lower = text_nopunct.lower()								# converting all words to lowercase
+	words_in_text = text_lower.split()
+	return words_in_text
+
+text_words_list = get_word_list(reloaded_copy_of_text)
 
-def get_top_n_words(word_list, n):
+def get_top_n_words(words, n):
 	""" Takes a list of words as input and returns a list of the n most frequently
 		occurring words ordered from most to least frequently occurring.
 
@@ -21,4 +44,11 @@ def get_top_n_words(word_list, n):
 		returns: a list of n most frequently occurring words ordered from most
 				 frequently to least frequentlyoccurring
 	"""
-	pass
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+	t = dict()																	# initialize dictionary
+	for word in words:															# check for each word in text
+		t[word] = 1 + t.get(word, 0)											# count the number of times the word appears in the dic
+	ordered_t = collections.OrderedDict(sorted(t.items(), key=lambda t: t[1]))	# creates a list of the frequencies of words in order (greatest --> least)
+	l = ordered_t.keys()														# list of frequencies
+	return l[-n:-1]
+
+print get_top_n_words(text_words_list, 10)
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