Massive renaming!

10-the-one/tf-10.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+import sys, re, operator, string
+
+#
+# The One class for this example
+#
+class TFTheOne:
+    def __init__(self, v):
+        self._value = v
+
+    def bind(self, func):
+        self._value = func(self._value)
+        return self
+
+    def printme(self):
+        print(self._value)
+
+#
+# The functions
+#
+def read_file(path_to_file):
+    with open(path_to_file) as f:
+        data = f.read()
+    return data
+
+def filter_chars(str_data):
+    pattern = re.compile('[\W_]+')
+    return pattern.sub(' ', str_data)
+
+def normalize(str_data):
+    return str_data.lower()
+
+def scan(str_data):
+    return str_data.split()
+
+def remove_stop_words(word_list):
+    with open('../stop_words.txt') as f:
+        stop_words = f.read().split(',')
+    # 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):
+    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):
+    return sorted(word_freq.items(), key=operator.itemgetter(1), reverse=True)
+
+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
+#
+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)\
+.printme()
+