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14664499
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2013-02-02T17:30:21.870
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2013-10-11T10:19:25.063
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2013-10-11T10:19:25.063
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For this specific case, use either a <a href="http://docs.python.org/2/library/collections.html#collections.Counter" rel="nofollow"><code>collections.Counter()</code></a> or a <a href="http://docs.python.org/2/library/collections.html#collections.defaultdict" rel="nofollow"><code>collections.defaultdict()</code></a> object instead: <pre><code>import collections dct = collections.defaultdict(int) for c in string: dict[c] += 1 </code></pre> or <pre><code>dct = collections.Counter(string) </code></pre> Both are subclasses of the standard <code>dict</code> type. The <code>Counter</code> type adds some more helpful functionality like summing two counters or listing the most common entities that have been counted. The <code>defaultdict</code> class can also be given other default types; use <code>defaultdict(list)</code> for example to collect things into lists per key. When you want to compare performance of two different approaches, you want to use the <a href="http://docs.python.org/2/library/timeit.html" rel="nofollow"><code>timeit</code> module</a>: <pre><code>>>> import timeit >>> def intest(dct, values): ... for c in values: ... if c in dct: ... dct[c]+=1 ... else: ... dct[c]=1 ... >>> def get(dct, values): ... for c in values: ... dct[c] = dct.get(c, 0) + 1 ... >>> values = range(10) * 10 >>> timeit.timeit('test(dct, values)', 'from __main__ import values, intest as test; dct={}') 22.210275888442993 >>> timeit.timeit('test(dct, values)', 'from __main__ import values, get as test; dct={}') 27.442166090011597 </code></pre> This shows that using <code>in</code> is a little faster. There is, however, a third option to consider; catching the <code>KeyError</code> exception: <pre><code>>>> def tryexcept(dct, values): ... for c in values: ... try: ... dct[c] += 1 ... except KeyError: ... dct[c] = 1 ... >>> timeit.timeit('test(dct, values)', 'from __main__ import values, tryexcept as test; dct={}') 18.023509979248047 </code></pre> which happens to be the fastest, because only 1 in 10 cases are for a new key. Last but not least, the two alternatives I proposed: <pre><code>>>> def default(dct, values): ... for c in values: ... dct[c] += 1 ... >>> timeit.timeit('test(dct, values)', 'from __main__ import values, default as test; from collections import defaultdict; dct=defaultdict(int)') 15.277361154556274 >>> timeit.timeit('Counter(values)', 'from __main__ import values; from collections import Counter') 38.657804012298584 </code></pre> So the <code>Counter()</code> type is slowest, but <code>defaultdict</code> is very fast indeed. <code>Counter()</code>s do a lot more work though, and the extra functionality can bring ease of development and execution speed benefits elsewhere.
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