简式:

[s.pop(k) for k in list(s.keys()) if k not in keep]

正如大多数答案所建议的那样，为了保持简洁，我们必须创建一个重复的对象，无论是list还是dict。这个函数创建了一个丢弃列表，但删除了原始字典中的键。

我们可以这样简单地处理函数:

>>> dict_filter = lambda x, y: dict([ (i,x[i]) for i in x if i in set(y) ])
>>> large_dict = {"a":1,"b":2,"c":3,"d":4}
>>> new_dict_keys = ("c","d")
>>> small_dict=dict_filter(large_dict, new_dict_keys)
>>> print(small_dict)
{'c': 3, 'd': 4}
>>> 

在我看来，这是最简单的方法:

d1 = {'a':1, 'b':2, 'c':3}
d2 = {k:v for k,v in d1.items() if k in ['a','c']}

我也喜欢这样做来揭示价值观:

a, c = {k:v for k,v in d1.items() if k in ['a','c']}.values()

下面是python 2.6中的一个例子:

>>> a = {1:1, 2:2, 3:3}
>>> dict((key,value) for key, value in a.iteritems() if key == 1)
{1: 1}

过滤部分是if语句。

如果你只想选择很多键中的几个，这个方法比delnan的答案要慢。

根据问题的标题，人们会期望在适当的地方过滤字典-几个答案建议了这样做的方法-仍然不明显的一个明显的方法是什么-我添加了一些时间:

import random
import timeit
import collections

repeat = 3
numbers = 10000

setup = ''
def timer(statement, msg='', _setup=None):
    print(msg, min(
        timeit.Timer(statement, setup=_setup or setup).repeat(
            repeat, numbers)))

timer('pass', 'Empty statement')

dsize = 1000
d = dict.fromkeys(range(dsize))
keep_keys = set(random.sample(range(dsize), 500))
drop_keys = set(random.sample(range(dsize), 500))

def _time_filter_dict():
    """filter a dict"""
    global setup
    setup = r"""from __main__ import dsize, collections, drop_keys, \
keep_keys, random"""
    timer('d = dict.fromkeys(range(dsize));'
          'collections.deque((d.pop(k) for k in drop_keys), maxlen=0)',
          "pop inplace - exhaust iterator")
    timer('d = dict.fromkeys(range(dsize));'
          'drop_keys = [k for k in d if k not in keep_keys];'
          'collections.deque('
              '(d.pop(k) for k in list(d) if k not in keep_keys), maxlen=0)',
          "pop inplace - exhaust iterator (drop_keys)")
    timer('d = dict.fromkeys(range(dsize));'
          'list(d.pop(k) for k in drop_keys)',
          "pop inplace - create list")
    timer('d = dict.fromkeys(range(dsize));'
          'drop_keys = [k for k in d if k not in keep_keys];'
          'list(d.pop(k) for k in drop_keys)',
          "pop inplace - create list (drop_keys)")
    timer('d = dict.fromkeys(range(dsize))\n'
          'for k in drop_keys: del d[k]', "del inplace")
    timer('d = dict.fromkeys(range(dsize));'
          'drop_keys = [k for k in d if k not in keep_keys]\n'
          'for k in drop_keys: del d[k]', "del inplace (drop_keys)")
    timer("""d = dict.fromkeys(range(dsize))
{k:v for k,v in d.items() if k in keep_keys}""", "copy dict comprehension")
    timer("""keep_keys=random.sample(range(dsize), 5)
d = dict.fromkeys(range(dsize))
{k:v for k,v in d.items() if k in keep_keys}""",
          "copy dict comprehension - small keep_keys")

if __name__ == '__main__':
    _time_filter_dict()

结果:

Empty statement 8.375600000000427e-05
pop inplace - exhaust iterator 1.046749841
pop inplace - exhaust iterator (drop_keys) 1.830537424
pop inplace - create list 1.1531293939999987
pop inplace - create list (drop_keys) 1.4512304149999995
del inplace 0.8008298079999996
del inplace (drop_keys) 1.1573763689999979
copy dict comprehension 1.1982901489999982
copy dict comprehension - small keep_keys 1.4407784069999998

因此，如果我们想要在适当的地方更新，似乎del是赢家-字典理解解决方案取决于正在创建的字典的大小，当然，删除一半的键已经太慢了-所以避免创建一个新的字典，如果你可以在适当的地方过滤。

编辑来解决@mpen的评论-我从keep_keys中计算了drop key(假设我们没有drop key) -我假设keep_keys/drop_keys是这个迭代的集合，或者会花很长时间。有了这些假设，del仍然更快——但要确定的是:如果你有一个(set, list, tuple)的下拉键，使用del

我们也可以通过稍微更优雅的字典理解来实现这一点:

my_dict = {"a":1,"b":2,"c":3,"d":4}

filtdict = {k: v for k, v in my_dict.items() if k.startswith('a')}
print(filtdict)