35

如何从字典中检索前 3 个列表?

>>> d
{'a': 2, 'and': 23, 'this': 14, 'only.': 21, 'is': 2, 'work': 2, 'will': 2, 'as': 2, 'test': 4}

预期结果:

and: 23
only: 21
this: 14
4

4 回答 4

52

使用collections.Counter

>>> d = Counter({'a': 2, 'and': 23, 'this': 14, 'only.': 21, 'is': 2, 'work': 2, 'will': 2, 'as': 2, 'test': 4})
>>> d.most_common()
[('and', 23), ('only.', 21), ('this', 14), ('test', 4), ('a', 2), ('is', 2), ('work', 2), ('will', 2), ('as', 2)]
>>> for k, v in d.most_common(3):
...     print '%s: %i' % (k, v)
... 
and: 23
only.: 21
this: 14

计数器对象提供了各种其他优点,例如首先收集计数几乎是微不足道的。

于 2012-08-10T13:27:59.623 回答
27
>>> d = {'a': 2, 'and': 23, 'this': 14, 'only.': 21, 'is': 2, 'work': 2, 'will': 2, 'as': 2, 'test': 4}
>>> t = sorted(d.iteritems(), key=lambda x:-x[1])[:3]

>>> for x in t:
...     print "{0}: {1}".format(*x)
... 
and: 23
only.: 21
this: 14
于 2012-08-10T13:28:57.700 回答
4

您已经得到的答复是正确的,但是我会创建自己的键函数以在调用 sorted() 时使用。

d = {'a': 2, 'and': 23, 'this': 14, 'only.': 21, 'is': 2, 'work': 2, 'will': 2, 'as': 2, 'test': 4}

# create a function which returns the value of a dictionary
def keyfunction(k):
    return d[k]

# sort by dictionary by the values and print top 3 {key, value} pairs
for key in sorted(d, key=keyfunction, reverse=True)[:3]:
    print "%s: %i" % (key, d[key])
于 2015-05-03T13:42:37.873 回答
4

鉴于上述解决方案:

def most_popular(L):
  # using lambda
  start = datetime.datetime.now()
  res=dict(sorted([(k,v) for k, v in L.items()], key=lambda x: x[1])[-2:])
  delta=datetime.datetime.now()-start
  print "Microtime (lambda:%d):" % len(L), str( delta.microseconds )

  # using collections
  start=datetime.datetime.now()
  res=dict(collections.Counter(L).most_common()[:2])
  delta=datetime.datetime.now()-start
  print "Microtime (collections:%d):" % len(L), str( delta.microseconds )

# list of 10
most_popular({el:0 for el in list(range(10))})

# list of 100
most_popular({el:0 for el in list(range(100))})

# list of 1000
most_popular({el:0 for el in list(range(1000))})

# list of 10000
most_popular({el:0 for el in list(range(10000))})

# list of 100000
most_popular({el:0 for el in list(range(100000))})

# list of 1000000
most_popular({el:0 for el in list(range(1000000))})

处理大小从 10^1 到 10^6 对象的数据集字典,例如

print {el:0 for el in list(range(10))}
{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}

我们有以下基准

Python 2.7.10 (default, Jul 14 2015, 19:46:27)
[GCC 4.8.2] on linux

Microtime (lambda:10): 24
Microtime (collections:10): 106
Microtime (lambda:100): 49
Microtime (collections:100): 50
Microtime (lambda:1000): 397
Microtime (collections:1000): 178
Microtime (lambda:10000): 4347
Microtime (collections:10000): 2782
Microtime (lambda:100000): 55738
Microtime (collections:100000): 26546
Microtime (lambda:1000000): 798612
Microtime (collections:1000000): 361970
=> None

所以我们可以说,对于小列表使用lambda,但对于大列表,collections具有更好的性能。

请参阅此处运行的基准测试。

于 2016-12-03T22:19:48.450 回答