diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,3 +1,4 @@
+Learned more complicated python
 # Calculate word frequency
 
 ## Description
diff --git a/__pycache__/word_frequency.cpython-34.pyc b/__pycache__/word_frequency.cpython-34.pyc
new file mode 100644
index 0000000..613e306
Binary files /dev/null and b/__pycache__/word_frequency.cpython-34.pyc differ
diff --git a/word_frequency.py b/word_frequency.py
new file mode 100644
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--- /dev/null
+++ b/word_frequency.py
@@ -0,0 +1,51 @@
+import re
+import collections
+from collections import OrderedDict
+
+
+final_order = []
+
+ignored_words = """the,a,able,about,across,after,all,almost,also,am,among,an,and,any,are,as,at,be,
+because,been,but,by,can,cannot,could,dear,did,do,does,either,else,ever,every,
+for,from,get,got,had,has,have,he,her,hers,him,his,how,however,i,if,in,into,is,
+it,its,just,least,let,like,likely,may,me,might,most,must,my,neither,no,nor,
+not,of,off,often,on,only,or,other,our,own,rather,said,say,says,she,should,
+since,so,some,than,that,the,their,them,then,there,these,they,this,tis,to,too,
+twas,us,wants,was,we,were,what,when,where,which,while,who,whom,why,will,with,
+would,yet,you,your"""
+
+
+def word_frequency(text):
+    clean_text = re.sub(r'[^A-Za-z\s]', "", text).lower().split()
+    clean_ignored_text = ignored_words.lower().split(',')
+    for word in clean_text:
+        for ignore_word in ignored_words:
+            if word == ignore_word:
+                print(word)
+                print(ignore_word)
+                try:
+                    clean_text.remove(word)
+                except:
+                    pass
+            else:
+                pass
+    print(clean_ignored_text)
+    word_count = {}
+    i = 1
+    for word in clean_text:
+        if word in word_count:
+            word_count[word] = word_count[word] + 1
+        else:
+            word_count[word] = 1
+    new_word_count = OrderedDict(
+            reversed(sorted(word_count.items(), key=lambda t: t[1])))
+    return new_word_count
+
+with open('sample.txt') as file_text:
+    text = file_text.read()
+
+
+for key, value in iter(word_frequency(text).items()):
+    list_value = [key, value]
+    final_order.append(list_value)
+print(final_order[:20])