将接受的答案放在函数中，以便于重用

def get_item(collection, key, target):
    return next((item for item in collection if item[key] == target), None)

也可以写成

   get_item_lambda = lambda collection, key, target : next((item for item in collection if item[key] == target), None)

结果

    key = "name"
    target = "Pam"
    print(get_item(target_list, key, target))
    print(get_item_lambda(target_list, key, target))

    #{'name': 'Pam', 'age': 7}
    #{'name': 'Pam', 'age': 7}

如果键可能不在目标字典中，请使用dict。get和避免KeyError

def get_item(collection, key, target):
    return next((item for item in collection if item.get(key, None) == target), None)

get_item_lambda = lambda collection, key, target : next((item for item in collection if item.get(key, None) == target), None)

我的第一个想法是，你可能想要考虑为这些字典创建一个字典……举个例子，如果你要多次搜索它。

然而，这可能是一个不成熟的优化。有什么问题:

def get_records(key, store=dict()):
    '''Return a list of all records containing name==key from our store
    '''
    assert key is not None
    return [d for d in store if d['name']==key]

你必须遍历列表中的所有元素。没有捷径!

除非在其他地方保存了一个包含指向列表项的名称的字典，但这时必须处理从列表中弹出元素的后果。

你试过熊猫套餐吗?它非常适合这类搜索任务，也进行了优化。

import pandas as pd

listOfDicts = [
{"name": "Tom", "age": 10},
{"name": "Mark", "age": 5},
{"name": "Pam", "age": 7}
]

# Create a data frame, keys are used as column headers.
# Dict items with the same key are entered into the same respective column.
df = pd.DataFrame(listOfDicts)

# The pandas dataframe allows you to pick out specific values like so:

df2 = df[ (df['name'] == 'Pam') & (df['age'] == 7) ]

# Alternate syntax, same thing

df2 = df[ (df.name == 'Pam') & (df.age == 7) ]

我在下面添加了一些基准测试，以说明熊猫在更大范围内(即10万+条目)的更快运行时间:

setup_large = 'dicts = [];\
[dicts.extend(({ "name": "Tom", "age": 10 },{ "name": "Mark", "age": 5 },\
{ "name": "Pam", "age": 7 },{ "name": "Dick", "age": 12 })) for _ in range(25000)];\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(dicts);'

setup_small = 'dicts = [];\
dicts.extend(({ "name": "Tom", "age": 10 },{ "name": "Mark", "age": 5 },\
{ "name": "Pam", "age": 7 },{ "name": "Dick", "age": 12 }));\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(dicts);'

method1 = '[item for item in dicts if item["name"] == "Pam"]'
method2 = 'df[df["name"] == "Pam"]'

import timeit
t = timeit.Timer(method1, setup_small)
print('Small Method LC: ' + str(t.timeit(100)))
t = timeit.Timer(method2, setup_small)
print('Small Method Pandas: ' + str(t.timeit(100)))

t = timeit.Timer(method1, setup_large)
print('Large Method LC: ' + str(t.timeit(100)))
t = timeit.Timer(method2, setup_large)
print('Large Method Pandas: ' + str(t.timeit(100)))

#Small Method LC: 0.000191926956177
#Small Method Pandas: 0.044392824173
#Large Method LC: 1.98827004433
#Large Method Pandas: 0.324505090714

这里提出的大多数(如果不是全部)实现都有两个缺陷:

他们假设只传递一个键来进行搜索，而对于复杂的字典，有更多的键可能是有趣的 它们假定所有传递用于搜索的键都存在于字典中，因此当KeyError不存在时，它们不会正确处理。

更新后的命题:

def find_first_in_list(objects, **kwargs):
    return next((obj for obj in objects if
                 len(set(obj.keys()).intersection(kwargs.keys())) > 0 and
                 all([obj[k] == v for k, v in kwargs.items() if k in obj.keys()])),
                None)

也许不是最python化的，但至少更安全一点。

用法:

>>> obj1 = find_first_in_list(list_of_dict, name='Pam', age=7)
>>> obj2 = find_first_in_list(list_of_dict, name='Pam', age=27)
>>> obj3 = find_first_in_list(list_of_dict, name='Pam', address='nowhere')
>>> 
>>> print(obj1, obj2, obj3)
{"name": "Pam", "age": 7}, None, {"name": "Pam", "age": 7}

要点。

简单地使用列表理解:

[i for i in dct if i['name'] == 'Pam'][0]

示例代码:

dct = [
    {'name': 'Tom', 'age': 10},
    {'name': 'Mark', 'age': 5},
    {'name': 'Pam', 'age': 7}
]

print([i for i in dct if i['name'] == 'Pam'][0])

> {'age': 7, 'name': 'Pam'}