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    <p>Let me start off by saying that I suspect that you are optimizing when you don't need to. Python is a high-level language that supports thinking about computation in a high-level manner. A solution that is readable, elegant, and reusable is often going to be better than one that is blazingly fast, but hard to understand.</p> <p>When, and <strong>only</strong> when, you determine that speed is an issue, then you should proceed with the optimizations. Perhaps even write a C extension for the computationally intense parts.</p> <p>That being said, here's a comparison of a few techniques:</p> <pre><code>def unique_chars_set(s): return len(s) == len(set(s)) def unique_chars_frozenset(s): return len(s) == len(frozenset(s)) def unique_chars_counter(s): return Counter(s).most_common(1)[0][1] &gt; 1 def unique_chars_sort(s): ss = ''.join(sorted(s)) prev = '' for c in ss: if c == prev: return False prev = c return True def unique_chars_bucket(s): buckets = 255 * [False] for c in s: o = ord(c) if buckets[o]: return False buckets[o] = True return True </code></pre> <p>And here is the performance comparisons (in IPython):</p> <pre><code>In [0]: %timeit -r10 [unique_chars_set(s) for s in candidate_strings] 100000 loops, best of 10: 6.63 us per loop In [1]: %timeit -r10 [unique_chars_frozenset(s) for s in candidate_strings] 100000 loops, best of 10: 6.81 us per loop In [2]: %timeit -r10 [unique_chars_counter(s) for s in candidate_strings] 10000 loops, best of 10: 83.1 us per loop In [3]: %timeit -r10 [unique_chars_sort(s) for s in candidate_strings] 100000 loops, best of 10: 13.1 us per loop In [4]: %timeit -r10 [unique_chars_bucket(s) for s in candidate_strings] 100000 loops, best of 10: 15 us per loop </code></pre> <p>Conclusion: <code>set</code> is elegant and faster than many other obvious methods. But the differences are so small, it doesn't matter anyway.</p> <p>For more benchmarks, see <a href="https://stackoverflow.com/a/15751669/716118">@FrancisAvila</a>'s answer.</p>
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