Pythonic way to sorting list of namedtuples by field name

Question

I want to sort a list of named tuples without having to remember the index of the fieldname. My solution seems rather awkward and was hoping someone would have a more elegan

Answer 1

I tested the two alternatives given here for speed, since @zenpoy was concerned about performance.

Testing script:

import random
from collections import namedtuple
from timeit import timeit
from operator import attrgetter

runs = 10000
size = 10000
random.seed = 42
Person = namedtuple('Person', 'name,age')
seq = [Person(str(random.randint(0, 10 ** 10)), random.randint(0, 100)) for _ in range(size)]

def attrgetter_test_name():
    return sorted(seq.copy(), key=attrgetter('name'))

def attrgetter_test_age():
    return sorted(seq.copy(), key=attrgetter('age'))

def lambda_test_name():
    return sorted(seq.copy(), key=lambda x: x.name)

def lambda_test_age():
    return sorted(seq.copy(), key=lambda x: x.age)

print('attrgetter_test_name', timeit(stmt=attrgetter_test_name, number=runs))
print('attrgetter_test_age', timeit(stmt=attrgetter_test_age, number=runs))
print('lambda_test_name', timeit(stmt=lambda_test_name, number=runs))
print('lambda_test_age', timeit(stmt=lambda_test_age, number=runs))

Results:

attrgetter_test_name 44.26793992166096
attrgetter_test_age 31.98247099677627
lambda_test_name 47.97959511074551
lambda_test_age 35.69356267603864

Using lambda was indeed slower. Up to 10% slower.

EDIT:

Further testing shows the results when sorting using multiple attributes. Added the following two test cases with the same setup:

def attrgetter_test_both():
    return sorted(seq.copy(), key=attrgetter('age', 'name'))

def lambda_test_both():
    return sorted(seq.copy(), key=lambda x: (x.age, x.name))

print('attrgetter_test_both', timeit(stmt=attrgetter_test_both, number=runs))
print('lambda_test_both', timeit(stmt=lambda_test_both, number=runs))

Results:

attrgetter_test_both 92.80101586919373
lambda_test_both 96.85089983147456

Lambda still underperforms, but less so. Now about 5% slower.

Testing is done on Python 3.6.0.