Python sort objects by value

How do I sort a list of objects based on an attribute of the objects?

apart from operator.attrgetter(‘attribute_name’) you can also use functors as key like object_list.sort(key=my_sorting_functor(‘my_key’)), leaving the implementation out intentionally.

9 Answers 9

To sort the list in place:

orig_list.sort(key=lambda x: x.count, reverse=True) 

To return a new list, use sorted :

new_list = sorted(orig_list, key=lambda x: x.count, reverse=True) 

No problem. btw, if muhuk is right and it’s a list of Django objects, you should consider his solution. However, for the general case of sorting objects, my solution is probably best practice.

On large lists you will get better performance using operator.attrgetter(‘count’) as your key. This is just an optimized (lower level) form of the lambda function in this answer.

Thanks for the great answer. In case if it is a list of dictionaries and ‘count’ is one of its key then it needs to be changed like below : ut.sort(key=lambda x: x[‘count’], reverse=True)

I suppose it deserves the following update: if there is a need to sort by multiple fields, it could be achieved by consecutive calls to sort(), because python is using stable sort algorithm.

Thanks @KenanBanks, you were right. Annoyingly outlook was doing some weird things with calendar timezones so that some came through without the timezone details. no idea why!

A way that can be fastest, especially if your list has a lot of records, is to use operator.attrgetter(«count») . However, this might run on an pre-operator version of Python, so it would be nice to have a fallback mechanism. You might want to do the following, then:

try: import operator except ImportError: keyfun= lambda x: x.count # use a lambda if no operator module else: keyfun= operator.attrgetter("count") # use operator since it's faster than lambda ut.sort(key=keyfun, reverse=True) # sort in-place 

Here I would use the variable name «keyfun» instead of «cmpfun» to avoid confusion. The sort() method does accept a comparison function through the cmp= argument as well.

This doesn’t seems to work if the object has dynamically added attributes, (if you’ve done self.__dict__ = <'some':'dict'>after the __init__ method). I don’t know why it sould be different, though.

@tutuca: I’ve never replaced the instance __dict__ . Note that «an object having dynamically added attributes» and «setting an object’s __dict__ attribute» are almost orthogonal concepts. I’m saying that because your comment seems to imply that setting the __dict__ attribute is a requirement for dynamically adding attributes.

@tzot: if I understand the use of operator.attrgetter , I could supply a function with any property name and return a sorted collection.

Readers should notice that the key= method:

ut.sort(key=lambda x: x.count, reverse=True) 

is many times faster than adding rich comparison operators to the objects. I was surprised to read this (page 485 of «Python in a Nutshell»). You can confirm this by running tests on this little program:

#!/usr/bin/env python import random class C: def __init__(self,count): self.count = count def __cmp__(self,other): return cmp(self.count,other.count) longList = [C(random.random()) for i in xrange(1000000)] #about 6.1 secs longList2 = longList[:] longList.sort() #about 52 - 6.1 = 46 secs longList2.sort(key = lambda c: c.count) #about 9 - 6.1 = 3 secs 

My, very minimal, tests show the first sort is more than 10 times slower, but the book says it is only about 5 times slower in general. The reason they say is due to the highly optimizes sort algorithm used in python (timsort).

Still, its very odd that .sort(lambda) is faster than plain old .sort(). I hope they fix that.

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Sorting HOW TO¶

Python lists have a built-in list.sort() method that modifies the list in-place. There is also a sorted() built-in function that builds a new sorted list from an iterable.

In this document, we explore the various techniques for sorting data using Python.

Sorting Basics¶

A simple ascending sort is very easy: just call the sorted() function. It returns a new sorted list:

>>> sorted([5, 2, 3, 1, 4]) [1, 2, 3, 4, 5] 

You can also use the list.sort() method. It modifies the list in-place (and returns None to avoid confusion). Usually it’s less convenient than sorted() — but if you don’t need the original list, it’s slightly more efficient.

>>> a = [5, 2, 3, 1, 4] >>> a.sort() >>> a [1, 2, 3, 4, 5] 

Another difference is that the list.sort() method is only defined for lists. In contrast, the sorted() function accepts any iterable.

>>> sorted(1: 'D', 2: 'B', 3: 'B', 4: 'E', 5: 'A'>) [1, 2, 3, 4, 5] 

Key Functions¶

Both list.sort() and sorted() have a key parameter to specify a function (or other callable) to be called on each list element prior to making comparisons.

For example, here’s a case-insensitive string comparison:

>>> sorted("This is a test string from Andrew".split(), key=str.lower) ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This'] 

The value of the key parameter should be a function (or other callable) that takes a single argument and returns a key to use for sorting purposes. This technique is fast because the key function is called exactly once for each input record.

A common pattern is to sort complex objects using some of the object’s indices as keys. For example:

>>> student_tuples = [ . ('john', 'A', 15), . ('jane', 'B', 12), . ('dave', 'B', 10), . ] >>> sorted(student_tuples, key=lambda student: student[2]) # sort by age [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] 

The same technique works for objects with named attributes. For example:

>>> class Student: . def __init__(self, name, grade, age): . self.name = name . self.grade = grade . self.age = age . def __repr__(self): . return repr((self.name, self.grade, self.age)) >>> student_objects = [ . Student('john', 'A', 15), . Student('jane', 'B', 12), . Student('dave', 'B', 10), . ] >>> sorted(student_objects, key=lambda student: student.age) # sort by age [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] 

Operator Module Functions¶

The key-function patterns shown above are very common, so Python provides convenience functions to make accessor functions easier and faster. The operator module has itemgetter() , attrgetter() , and a methodcaller() function.

Using those functions, the above examples become simpler and faster:

>>> from operator import itemgetter, attrgetter >>> sorted(student_tuples, key=itemgetter(2)) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] >>> sorted(student_objects, key=attrgetter('age')) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] 

The operator module functions allow multiple levels of sorting. For example, to sort by grade then by age:

>>> sorted(student_tuples, key=itemgetter(1,2)) [('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)] >>> sorted(student_objects, key=attrgetter('grade', 'age')) [('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)] 

Ascending and Descending¶

Both list.sort() and sorted() accept a reverse parameter with a boolean value. This is used to flag descending sorts. For example, to get the student data in reverse age order:

>>> sorted(student_tuples, key=itemgetter(2), reverse=True) [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)] >>> sorted(student_objects, key=attrgetter('age'), reverse=True) [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)] 

Sort Stability and Complex Sorts¶

Sorts are guaranteed to be stable. That means that when multiple records have the same key, their original order is preserved.

>>> data = [('red', 1), ('blue', 1), ('red', 2), ('blue', 2)] >>> sorted(data, key=itemgetter(0)) [('blue', 1), ('blue', 2), ('red', 1), ('red', 2)] 

Notice how the two records for blue retain their original order so that (‘blue’, 1) is guaranteed to precede (‘blue’, 2) .

This wonderful property lets you build complex sorts in a series of sorting steps. For example, to sort the student data by descending grade and then ascending age, do the age sort first and then sort again using grade:

>>> s = sorted(student_objects, key=attrgetter('age')) # sort on secondary key >>> sorted(s, key=attrgetter('grade'), reverse=True) # now sort on primary key, descending [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] 

This can be abstracted out into a wrapper function that can take a list and tuples of field and order to sort them on multiple passes.

>>> def multisort(xs, specs): . for key, reverse in reversed(specs): . xs.sort(key=attrgetter(key), reverse=reverse) . return xs >>> multisort(list(student_objects), (('grade', True), ('age', False))) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] 

The Timsort algorithm used in Python does multiple sorts efficiently because it can take advantage of any ordering already present in a dataset.

Decorate-Sort-Undecorate¶

This idiom is called Decorate-Sort-Undecorate after its three steps:

  • First, the initial list is decorated with new values that control the sort order.
  • Second, the decorated list is sorted.
  • Finally, the decorations are removed, creating a list that contains only the initial values in the new order.

For example, to sort the student data by grade using the DSU approach:

>>> decorated = [(student.grade, i, student) for i, student in enumerate(student_objects)] >>> decorated.sort() >>> [student for grade, i, student in decorated] # undecorate [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)] 

This idiom works because tuples are compared lexicographically; the first items are compared; if they are the same then the second items are compared, and so on.

It is not strictly necessary in all cases to include the index i in the decorated list, but including it gives two benefits:

  • The sort is stable – if two items have the same key, their order will be preserved in the sorted list.
  • The original items do not have to be comparable because the ordering of the decorated tuples will be determined by at most the first two items. So for example the original list could contain complex numbers which cannot be sorted directly.

Another name for this idiom is Schwartzian transform, after Randal L. Schwartz, who popularized it among Perl programmers.

Now that Python sorting provides key-functions, this technique is not often needed.

Comparison Functions¶

Unlike key functions that return an absolute value for sorting, a comparison function computes the relative ordering for two inputs.

For example, a balance scale compares two samples giving a relative ordering: lighter, equal, or heavier. Likewise, a comparison function such as cmp(a, b) will return a negative value for less-than, zero if the inputs are equal, or a positive value for greater-than.

It is common to encounter comparison functions when translating algorithms from other languages. Also, some libraries provide comparison functions as part of their API. For example, locale.strcoll() is a comparison function.

To accommodate those situations, Python provides functools.cmp_to_key to wrap the comparison function to make it usable as a key function:

sorted(words, key=cmp_to_key(strcoll)) # locale-aware sort order 

Odds and Ends¶

  • For locale aware sorting, use locale.strxfrm() for a key function or locale.strcoll() for a comparison function. This is necessary because “alphabetical” sort orderings can vary across cultures even if the underlying alphabet is the same.
  • The reverse parameter still maintains sort stability (so that records with equal keys retain the original order). Interestingly, that effect can be simulated without the parameter by using the builtin reversed() function twice:
>>> data = [('red', 1), ('blue', 1), ('red', 2), ('blue', 2)] >>> standard_way = sorted(data, key=itemgetter(0), reverse=True) >>> double_reversed = list(reversed(sorted(reversed(data), key=itemgetter(0)))) >>> assert standard_way == double_reversed >>> standard_way [('red', 1), ('red', 2), ('blue', 1), ('blue', 2)] 
>>> Student.__lt__ = lambda self, other: self.age  other.age >>> sorted(student_objects) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] 
>>> students = ['dave', 'john', 'jane'] >>> newgrades = 'john': 'F', 'jane':'A', 'dave': 'C'> >>> sorted(students, key=newgrades.__getitem__) ['jane', 'dave', 'john'] 

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