docstrings

SearchResults.py

@@ -568,6 +568,9 @@ class Article:
 
 
     def related_enough(self):
+        '''
+
+        '''
         links = self.all_peripheral_links(self.__soup, self.url)
         articles, categories = self.extract_titles(links)
         actual_related_articles = {}
@@ -590,6 +593,10 @@ class Article:
     
 
     def pre_processing(self, wh_page, caps):
+        '''
+        Takes a single string representing all of the text on a WikiHow page
+        and returns it as a list of individual words.
+        '''
         tokenizer = nltk.RegexpTokenizer(r'\w+')
         list_of_words = tokenizer.tokenize(wh_page)
         list_of_words = [word for word in list_of_words if word not in stopwords.words('english')]
@@ -597,6 +604,9 @@ class Article:
 
 
     def n_gram(self, wh_page, caps, n):
+        '''
+        Takes a list of individual words and creates ngrams of size n
+        '''
         n_gram_lst = []
         list_of_words = self.pre_processing(wh_page,caps)
         start_value = 0
@@ -609,6 +619,9 @@ class Article:
     
     
     def find_common_words(self, string):
+        '''
+        Takes a list of ngrams and finds the most common entries.
+        '''
         words_lst = Counter()
         string_lst = self.n_gram(string, False, 1)
         words_lst.update(w for w in string_lst)