我测试了各种方法来遍历字典列表并返回键x具有特定值的字典。

结果:

速度:列表推导>生成器表达式>>常规列表迭代>>>过滤器。 所有的比例都与列表中的字典数量线性(10x列表大小-> 10x时间)。 对于大量(数千个)键，每个字典的键不会显著影响速度。请看我计算的图表:https://imgur.com/a/quQzv(方法名称见下文)。

所有测试均使用Python 3.6.4, W7x64完成。

from random import randint
from timeit import timeit


list_dicts = []
for _ in range(1000):     # number of dicts in the list
    dict_tmp = {}
    for i in range(10):   # number of keys for each dict
        dict_tmp[f"key{i}"] = randint(0,50)
    list_dicts.append( dict_tmp )



def a():
    # normal iteration over all elements
    for dict_ in list_dicts:
        if dict_["key3"] == 20:
            pass

def b():
    # use 'generator'
    for dict_ in (x for x in list_dicts if x["key3"] == 20):
        pass

def c():
    # use 'list'
    for dict_ in [x for x in list_dicts if x["key3"] == 20]:
        pass

def d():
    # use 'filter'
    for dict_ in filter(lambda x: x['key3'] == 20, list_dicts):
        pass

结果:

1.7303 # normal list iteration 
1.3849 # generator expression 
1.3158 # list comprehension 
7.7848 # filter

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

我会像这样创建一个字典的字典:

names = ["Tom", "Mark", "Pam"]
ages = [10, 5, 7]
my_d = {}

for i, j in zip(names, ages):
    my_d[i] = {"name": i, "age": j}

或者，使用与问题中完全相同的信息:

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

for d in info_list:
    my_d[d["name"]] = d

然后你可以执行my_d["Pam"]，得到{"name": "Pam"， "age": 7}

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'}

你可以使用生成器表达式:

>>> dicts = [
...     { "name": "Tom", "age": 10 },
...     { "name": "Mark", "age": 5 },
...     { "name": "Pam", "age": 7 },
...     { "name": "Dick", "age": 12 }
... ]

>>> next(item for item in dicts if item["name"] == "Pam")
{'age': 7, 'name': 'Pam'}

如果你需要处理不存在的项，那么你可以做用户Matt在他的评论中建议的事情，并使用稍微不同的API提供默认值:

next((item for item in dicts if item["name"] == "Pam"), None)

并且要查找项目的索引，而不是项目本身，您可以枚举()列表:

next((i for i, item in enumerate(dicts) if item["name"] == "Pam"), None)

为@FrédéricHamidi添加一点点。

如果你不确定字典列表中是否有键，这样做会有帮助:

next((item for item in dicts if item.get("name") and item["name"] == "Pam"), None)