我猜你的意思是：

[{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]

其排序如下：

sorted(l,cmp=lambda x,y: cmp(x['name'],y['name']))

import operator

要按key='name'对词典列表进行排序：

list_of_dicts.sort(key=operator.itemgetter('name'))

要按key='age'对词典列表进行排序，请执行以下操作：

list_of_dicts.sort(key=operator.itemgetter('age'))

使用Pandas包是另一种方法，尽管其大规模运行时比其他人提出的更传统的方法慢得多：

import pandas as pd

listOfDicts = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}]
df = pd.DataFrame(listOfDicts)
df = df.sort_values('name')
sorted_listOfDicts = df.T.to_dict().values()

下面是一个小列表和一个大（100k+）的字典列表的一些基准值：

setup_large = "listOfDicts = [];\
[listOfDicts.extend(({'name':'Homer', 'age':39}, {'name':'Bart', 'age':10})) for _ in range(50000)];\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(listOfDicts);"

setup_small = "listOfDicts = [];\
listOfDicts.extend(({'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}));\
from operator import itemgetter;import pandas as pd;\
df = pd.DataFrame(listOfDicts);"

method1 = "newlist = sorted(listOfDicts, key=lambda k: k['name'])"
method2 = "newlist = sorted(listOfDicts, key=itemgetter('name')) "
method3 = "df = df.sort_values('name');\
sorted_listOfDicts = df.T.to_dict().values()"

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 LC2: ' + str(t.timeit(100)))
t = timeit.Timer(method3, 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 LC2: ' + str(t.timeit(100)))
t = timeit.Timer(method3, setup_large)
print('Large Method Pandas: ' + str(t.timeit(1)))

#Small Method LC: 0.000163078308105
#Small Method LC2: 0.000134944915771
#Small Method Pandas: 0.0712950229645
#Large Method LC: 0.0321750640869
#Large Method LC2: 0.0206089019775
#Large Method Pandas: 5.81405615807

假设我有一本字典D，其中包含以下元素。要排序，只需使用sorted中的key参数传递自定义函数，如下所示：

D = {'eggs': 3, 'ham': 1, 'spam': 2}
def get_count(tuple):
    return tuple[1]

sorted(D.items(), key = get_count, reverse=True)
# Or
sorted(D.items(), key = lambda x: x[1], reverse=True)  # Avoiding get_count function call

看看这个。

a = [{'name':'Homer', 'age':39}, ...]

# This changes the list a
a.sort(key=lambda k : k['name'])

# This returns a new list (a is not modified)
sorted(a, key=lambda k : k['name']) 

我一直是lambda过滤器的忠实粉丝。然而，若考虑到时间复杂性，这并不是最好的选择。

第一个选项

sorted_list = sorted(list_to_sort, key= lambda x: x['name'])
# Returns list of values

第二个选项

list_to_sort.sort(key=operator.itemgetter('name'))
# Edits the list, and does not return a new list

快速比较执行时间

# First option
python3.6 -m timeit -s "list_to_sort = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}, {'name':'Faaa', 'age':57}, {'name':'Errr', 'age':20}]" -s "sorted_l=[]" "sorted_l = sorted(list_to_sort, key=lambda e: e['name'])"

1000000个循环，最好为3个：每个循环0.736µsec

# Second option
python3.6 -m timeit -s "list_to_sort = [{'name':'Homer', 'age':39}, {'name':'Bart', 'age':10}, {'name':'Faaa', 'age':57}, {'name':'Errr', 'age':20}]" -s "sorted_l=[]" -s "import operator" "list_to_sort.sort(key=operator.itemgetter('name'))"

1000000个循环，最好为3个：每个循环0.438µsec