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    <p>Another thing that may slow up performance - deleting from dictionary. Re-building dictionary may be much more efficient:</p> <pre><code>word_dict = {key: count for key, count in takewhile(lambda key_count: itemgetter(1) &gt;= 5, main_dict.most_common()) </code></pre> <p>On the whole, I am a bit lazy to get into all the details, but I using a little bit references may be more efficient. As far as I see, you don't need *doc_count* variable - it's redundant and inefficient, and re-evaluating it reduces your performance too. *main_dict.keys()* does the same - give you list of all words once.</p> <p>This is a sketch of what I have in mind - I cannot prove that it's more efficient, but it certainly looks more pythonic</p> <pre><code>with open(inp,'r') as plot_data: word_dict = Counter() file1, file2 = itertools.tee(plot_data, 2) line_one = itertools.islice(file1, 0, None, 4) line_two = itetools.islice(file2, 2, None, 4) all_stop_words = stopwords.words('english') movie_dict = defaultdict(Counter) stemmer_func = PorterStemmer().stem_word for movie_name, movie_plot in itertools.izip(line_one, line_two): movie_plot = movie_plot.lower() words = &lt;see above - I am updating original post&gt; all_words = [stemmer_func(word) for word in words if not word in all_stop_words] current_word_counter = Counter(all_words) movie_dict[movie_name].update(current_word_counter) word_dict.update(current_word_counter) </code></pre> <p>The last - <em>dictionary</em> is not a good variable name, it does not tell you what it contains</p>
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