all done!

frequency.py

@@ -1,24 +1,98 @@
 """ Analyzes the word frequencies in a book downloaded from
 	Project Gutenberg """
 
+from pattern.web import *
 import string
+import urllib2
+import pickle
 
-def get_word_list(file_name):
+def get_text(URL_string, name):
+	"""This function grabs the text from a text file on the web and pickles it for future use.
+Arguments: URL of text file to be saved as a string, name of the file for use in naming data variables and pickle files (also a string).
+ Returns: Pickled data!"""
+
+	text = URL(URL_string).download()
+	save_file = open(name + '.pickle', 'w')
+	pickle.dump(text, save_file)
+	save_file.close()
+
+
+
+def open_text(name):
+	"""Imports previously-pickled book data (in string format from disk) and returns a list of the strings.
+	Arguements: name of the pickle file of previously pickled data (as a string, without the .pickle ending)!
+	Returns: a pickle-imported string"""
+	# Load data for each from from a file (will be part of your data processing script)
+	input_file = open(name+ '.pickle','r')
+	text = pickle.load(input_file)
+	return text
+
+
+def is_punct_char(char):
+	"""From python.org (link in get_word_listdescription), all this does is check if a character
+	is puncutation or not! the ultimate helper funcion!
+	Arguments: character
+	Returns: True/False if the character it is given is a puncuation mark - 1 is punctuation, 0 is not """
+	return char in string.punctuation #1 is punctuation, 0 is not punctuation
+
+def get_word_list(text_string):
 	""" 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
+		All words are converted to lower case. NOTE: https://mail.python.org/pipermail/tutor/2001-October/009454.html explains punctuation removal method that I used
+"""
+	text_no_punc = ''
+	text = text_string[600:] #kill the header
+	for char in text: #killing punctuation
+		if not is_punct_char(char):
+			text_no_punc = text_no_punc+char #so extend the string everytime we run into a letter
+	text_no_punc_lower = string.lower(text_no_punc)
+	list_of_words = []
+	list_of_words = text_no_punc_lower.split( ) #splitting the string into the list
+	return list_of_words
+
 
 def get_top_n_words(word_list, 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.
-
+	occurring words ordered from most to least frequently occurring.
 		word_list: a list of words (assumed to all be in lower case with no
 					punctuation
 		n: the number of words to return
 		returns: a list of n most frequently occurring words ordered from most
-				 frequently to least frequentlyoccurring
-	"""
-	pass
+				 frequently to least frequently occurring" """
+	freq_dict = make_freq_dict (word_list) # get a dictionary
+	ordered_by_frequency = sorted(freq_dict, key=freq_dict.get, reverse=True) # sort
+	print ordered_by_frequency[0:n] # print
+	return ordered_by_frequency[0:n]
+
+
+
+def make_freq_dict(word_list):
+	"""This is a helper function for
+	get_top_n_words. All this does is make a dictionary; takes a list of words in and makes a frequency dictionary;.
+	Arguement: list of words.
+	Returns: dictionary with words as keys and frequnecies as values.
+		"""
+
+	freq_dict = {}
+
+	for word in word_list: #need to slice each tale into a list of words for this to work
+		if word in freq_dict:
+			current_val = freq_dict.get(word)
+			val = current_val + 1
+			freq_dict[word] = val #made a dictionary of the string (word, frequnecy)
+		else: #if it isn't in the dictionary
+			freq_dict[word] = 1
+	return freq_dict
+
+
+
+
+
+URL_string = 'http://www.gutenberg.org/cache/epub/2591/pg2591.txt'
+name = 'grimm'
+n = 100 #just doing some definitions to make things run automatically
+get_text(URL_string, name) #dump the text into a pickle file
+text_string = open_text(name) #open that file and return the string
+word_list= get_word_list(text_string) # make a list of words, formatted happily
+get_top_n_words(word_list, n) #get a list of the top 100 words!