diff --git a/06-the-one/README.md b/06-the-one/README.md
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+Style #6
+==============================
+
+Constraints:
+
+- Existence of an abstraction to which all others can be converted. This
+  main abstraction provides an interface to chain abstractions
+  independent of the types of values that are consumed and produced by
+  those abstractions
+
+- Larger problem is solved as a pipeline of functions bound together
+  by the single interface that chains them
+
+Possible names:
+
+- Monadic
+- The wrapper of all things
+- Imperative functional style
\ No newline at end of file
diff --git a/06-the-one/tf-06.py b/06-the-one/tf-06.py
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+import sys, re, operator, string
+
+#
+# The monadic class for this example
+#
+class TFTheOne:
+    def __init__(self, v):
+        self._value = v
+
+    def bind(self, func):
+        result = func(self._value)
+        return TFTheOne(result)
+
+def printm(monad):
+    print monad._value
+
+#
+# The functions
+#
+def read_file(path_to_file):
+    """
+    Takes a path to a file and returns the entire
+    contents of the file as a string
+    """
+    f = open(path_to_file)
+    data = f.read()
+    f.close()
+    return data
+
+def filter_chars(str_data):
+    """
+    Takes a string and returns a copy with all nonalphanumeric chars
+    replaced by white space
+    """
+    pattern = re.compile('[\W_]+')
+    return pattern.sub(' ', str_data)
+
+def normalize(str_data):
+    """
+    Takes a string and returns a copy with all characters in lower case """
+    return str_data.lower()
+
+def scan(str_data):
+    """
+    Takes a string and scans for words, returning
+    a list of words.
+    """
+    return str_data.split()
+
+def remove_stop_words(word_list):
+    """ Takes a list of words and returns a copy with all stop words removed """
+    f = open('../stop_words.txt')
+    stop_words = f.read().split(',')
+    f.close()
+    # add single-letter words
+    stop_words.extend(list(string.ascii_lowercase))
+    return [w for w in word_list if not w in stop_words]
+
+def frequencies(word_list):
+    """
+    Takes a list of words and returns a dictionary associating
+    words with frequencies of occurrence
+    """
+    word_freqs = {}
+    for w in word_list:
+        if w in word_freqs:
+            word_freqs[w] += 1
+        else:
+            word_freqs[w] = 1
+    return word_freqs
+
+def sort(word_freq):
+    """
+    Takes a dictionary of words and their frequencies and returns a list 
+    of pairs where the entries are sorted by frequency 
+    """
+    return sorted(word_freq.iteritems(), key=operator.itemgetter(1), reverse=True)
+
+def print_freqs(word_freqs):
+    for tf in word_freqs[0:25]:
+        print tf[0], ' - ', tf[1]
+
+def top25_freqs(word_freqs):
+    top25 = ""
+    for tf in word_freqs[0:25]:
+        top25 += str(tf[0]) + ' - ' + str(tf[1]) + '\n'
+    return top25
+
+#
+# The main function
+#
+printm(TFTheOne(sys.argv[1]).bind(read_file).bind(filter_chars).bind(normalize).bind(scan).bind(remove_stop_words).bind(frequencies).bind(sort).bind(top25_freqs))
+