How do I sort a list of dictionaries by a value of the dictionary?

The sorted() function takes a key= parameter

newlist = sorted(list_to_be_sorted, key=lambda d: d['name']) 

Alternatively, you can use operator.itemgetter instead of defining the function yourself

from operator import itemgetter
newlist = sorted(list_to_be_sorted, key=itemgetter('name')) 

For completeness, add reverse=True to sort in descending order

newlist = sorted(list_to_be_sorted, key=itemgetter('name'), reverse=True)

import operator

To sort the list of dictionaries by key='name':

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

To sort the list of dictionaries by key='age':

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

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

my_list.sort(lambda x,y : cmp(x['name'], y['name']))

my_list will now be what you want.

Or better:

Since Python 2.4, there's a key argument is both more efficient and neater:

my_list = sorted(my_list, key=lambda k: k['name'])

...the lambda is, IMO, easier to understand than operator.itemgetter, but your mileage may vary.

import operator
a_list_of_dicts.sort(key=operator.itemgetter('name'))

'key' is used to sort by an arbitrary value and 'itemgetter' sets that value to each item's 'name' attribute.

I guess you've meant:

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

This would be sorted like this:

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

You could use a custom comparison function, or you could pass in a function that calculates a custom sort key. That's usually more efficient as the key is only calculated once per item, while the comparison function would be called many more times.

You could do it this way:

def mykey(adict): return adict['name']
x = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age':10}]
sorted(x, key=mykey)

But the standard library contains a generic routine for getting items of arbitrary objects: itemgetter. So try this instead:

from operator import itemgetter
x = [{'name': 'Homer', 'age': 39}, {'name': 'Bart', 'age':10}]
sorted(x, key=itemgetter('name'))

You have to implement your own comparison function that will compare the dictionaries by values of name keys. See Sorting Mini-HOW TO from PythonInfo Wiki

I have been a big fan of a filter with lambda. However, it is not best option if you consider time complexity.

First option

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

Second option

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

Fast comparison of execution times

# 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 loops, best of 3: 0.736 µsec per loop

# 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 loops, best of 3: 0.438 µsec per loop

If performance is a concern, I would use operator.itemgetter instead of lambda as built-in functions perform faster than hand-crafted functions. The itemgetter function seems to perform approximately 20% faster than lambda based on my testing.

From https://wiki.python.org/moin/PythonSpeed:

Likewise, the builtin functions run faster than hand-built equivalents. For example, map(operator.add, v1, v2) is faster than map(lambda x,y: x+y, v1, v2).

Here is a comparison of sorting speed using lambda vs itemgetter.

import random
import operator

# Create a list of 100 dicts with random 8-letter names and random ages from 0 to 100.
l = [{'name': ''.join(random.choices(string.ascii_lowercase, k=8)), 'age': random.randint(0, 100)} for i in range(100)]

# Test the performance with a lambda function sorting on name
%timeit sorted(l, key=lambda x: x['name'])
13 µs ± 388 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

# Test the performance with itemgetter sorting on name
%timeit sorted(l, key=operator.itemgetter('name'))
10.7 µs ± 38.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

# Check that each technique produces the same sort order
sorted(l, key=lambda x: x['name']) == sorted(l, key=operator.itemgetter('name'))
True

Both techniques sort the list in the same order (verified by execution of the final statement in the code block), but the first one is a little faster.

As indicated by @Claudiu to @monojohnny in comment section of this answer,
given:

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

to sort the list of dictionaries by key 'age', 'name'
(like in SQL statement ORDER BY age, name), you can use:

newlist = sorted( list_to_be_sorted, key=lambda k: (k['age'], k['name']) )

or, likewise

import operator
newlist = sorted( list_to_be_sorted, key=operator.itemgetter('age','name') )

print(newlist)

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

sorting by multiple columns, while in descending order on some of them: the cmps array is global to the cmp function, containing field names and inv == -1 for desc 1 for asc

def cmpfun(a, b):
    for (name, inv) in cmps:
        res = cmp(a[name], b[name])
        if res != 0:
            return res * inv
    return 0

data = [
    dict(name='alice', age=10), 
    dict(name='baruch', age=9), 
    dict(name='alice', age=11),
]

all_cmps = [
    [('name', 1), ('age', -1)], 
    [('name', 1), ('age', 1)], 
    [('name', -1), ('age', 1)],]

print 'data:', data
for cmps in all_cmps: print 'sort:', cmps; print sorted(data, cmpfun)