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    <p>For your smaller example, with a limited diversity of elements, you can use a set and a dict comprehension:</p> <pre><code>&gt;&gt;&gt; mylist = [1,1,1,1,1,1,2,3,2,2,2,2,3,3,4,5,5,5,5] &gt;&gt;&gt; {k:mylist.count(k) for k in set(mylist)} {1: 6, 2: 5, 3: 3, 4: 1, 5: 4} </code></pre> <p>To break it down, <code>set(mylist)</code> uniquifies the list and makes it more compact:</p> <pre><code>&gt;&gt;&gt; set(mylist) set([1, 2, 3, 4, 5]) </code></pre> <p>Then the dictionary comprehension steps through the unique values and sets the count from the list. </p> <p>This also is <em>significantly</em> faster than using Counter and faster than using setdefault:</p> <pre><code>from __future__ import print_function from collections import Counter from collections import defaultdict import random mylist=[1,1,1,1,1,1,2,3,2,2,2,2,3,3,4,5,5,5,5]*10 def s1(mylist): return {k:mylist.count(k) for k in set(mylist)} def s2(mlist): return Counter(mylist) def s3(mylist): mydict=dict() for index in mylist: mydict[index] = mydict.setdefault(index, 0) + 1 return mydict def s4(mylist): mydict={}.fromkeys(mylist,0) for k in mydict: mydict[k]=mylist.count(k) return mydict def s5(mylist): mydict={} for k in mylist: mydict[k]=mydict.get(k,0)+1 return mydict def s6(mylist): mydict=defaultdict(int) for i in mylist: mydict[i] += 1 return mydict def s7(mylist): mydict={}.fromkeys(mylist,0) for e in mylist: mydict[e]+=1 return mydict if __name__ == '__main__': import timeit n=1000000 print(timeit.timeit("s1(mylist)", setup="from __main__ import s1, mylist",number=n)) print(timeit.timeit("s2(mylist)", setup="from __main__ import s2, mylist, Counter",number=n)) print(timeit.timeit("s3(mylist)", setup="from __main__ import s3, mylist",number=n)) print(timeit.timeit("s4(mylist)", setup="from __main__ import s4, mylist",number=n)) print(timeit.timeit("s5(mylist)", setup="from __main__ import s5, mylist",number=n)) print(timeit.timeit("s6(mylist)", setup="from __main__ import s6, mylist, defaultdict",number=n)) print(timeit.timeit("s7(mylist)", setup="from __main__ import s7, mylist",number=n)) </code></pre> <p>On my machine that prints (Python 3):</p> <pre><code>18.123854104997008 # set and dict comprehension 78.54796334600542 # Counter 33.98185228800867 # setdefault 19.0563529439969 # fromkeys / count 34.54294775899325 # dict.get 21.134678319009254 # defaultdict 22.760544238000875 # fromkeys / loop </code></pre> <p>For Larger lists, like 10 million integers, with more diverse elements (1,500 random ints), use defaultdict or fromkeys in a loop:</p> <pre><code>from __future__ import print_function from collections import Counter from collections import defaultdict import random mylist = [random.randint(0,1500) for _ in range(10000000)] def s1(mylist): return {k:mylist.count(k) for k in set(mylist)} def s2(mlist): return Counter(mylist) def s3(mylist): mydict=dict() for index in mylist: mydict[index] = mydict.setdefault(index, 0) + 1 return mydict def s4(mylist): mydict={}.fromkeys(mylist,0) for k in mydict: mydict[k]=mylist.count(k) return mydict def s5(mylist): mydict={} for k in mylist: mydict[k]=mydict.get(k,0)+1 return mydict def s6(mylist): mydict=defaultdict(int) for i in mylist: mydict[i] += 1 return mydict def s7(mylist): mydict={}.fromkeys(mylist,0) for e in mylist: mydict[e]+=1 return mydict if __name__ == '__main__': import timeit n=1 print(timeit.timeit("s1(mylist)", setup="from __main__ import s1, mylist",number=n)) print(timeit.timeit("s2(mylist)", setup="from __main__ import s2, mylist, Counter",number=n)) print(timeit.timeit("s3(mylist)", setup="from __main__ import s3, mylist",number=n)) print(timeit.timeit("s4(mylist)", setup="from __main__ import s4, mylist",number=n)) print(timeit.timeit("s5(mylist)", setup="from __main__ import s5, mylist",number=n)) print(timeit.timeit("s6(mylist)", setup="from __main__ import s6, mylist, defaultdict",number=n)) print(timeit.timeit("s7(mylist)", setup="from __main__ import s7, mylist",number=n)) </code></pre> <p>Prints:</p> <pre><code>2825.2697427899984 # set and dict comprehension 42.607481333994656 # Counter 22.77713537499949 # setdefault 2853.11187016801 # fromkeys / count 23.241977066005347 # dict.get 15.023175164998975 # defaultdict 18.28165417900891 # fromkeys / loop </code></pre> <p>You can see that solutions that relay on <code>count</code> with a moderate number of times through the large list will suffer badly/catastrophically in comparison to other solutions. </p>
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