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.
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 of a list. 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.
Another difference is that the list.sort()
method is only defined for
lists. In contrast, the sorted()
function accepts any iterable.
Starting with Python 2.4, both list.sort()
and sorted()
added a
key parameter to specify a function to be called on each list element prior to
making comparisons.
For example, here's a case-insensitive string comparison:
[UNKNOWN NODE doctest_block]The value of the key parameter should be a function 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:
[UNKNOWN NODE doctest_block]The same technique works for objects with named attributes. For example:
[UNKNOWN NODE doctest_block][UNKNOWN NODE doctest_block]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 operator.itemgetter()
, operator.attrgetter()
, and
starting in Python 2.5 an operator.methodcaller()
function.
Using those functions, the above examples become simpler and faster:
[UNKNOWN NODE doctest_block][UNKNOWN NODE doctest_block][UNKNOWN NODE doctest_block]The operator module functions allow multiple levels of sorting. For example, to sort by grade then by age:
[UNKNOWN NODE doctest_block][UNKNOWN NODE doctest_block]The operator.methodcaller()
function makes method calls with fixed
parameters for each object being sorted. For example, the str.count()
method could be used to compute message priority by counting the
number of exclamation marks in a message:
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:
Starting with Python 2.2, sorts are guaranteed to be stable. That means that when multiple records have the same key, their original order is preserved.
[UNKNOWN NODE doctest_block]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:
[UNKNOWN NODE doctest_block]The Timsort algorithm used in Python does multiple sorts efficiently because it can take advantage of any ordering already present in a dataset.
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:
[UNKNOWN NODE doctest_block]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.
For large lists and lists where the comparison information is expensive to calculate, and Python versions before 2.4, DSU is likely to be the fastest way to sort the list. For 2.4 and later, key functions provide the same functionality.
Many constructs given in this HOWTO assume Python 2.4 or later. Before that,
there was no sorted()
builtin and list.sort()
took no keyword
arguments. Instead, all of the Py2.x versions supported a cmp parameter to
handle user specified comparison functions.
In Python 3, the cmp parameter was removed entirely (as part of a larger effort to
simplify and unify the language, eliminating the conflict between rich
comparisons and the __cmp__()
magic method).
In Python 2, sort()
allowed an optional function which can be called for doing the
comparisons. That function should take two arguments to be compared and then
return a negative value for less-than, return zero if they are equal, or return
a positive value for greater-than. For example, we can do:
Or you can reverse the order of comparison with:
[UNKNOWN NODE doctest_block]When porting code from Python 2.x to 3.x, the situation can arise when you have the user supplying a comparison function and you need to convert that to a key function. The following wrapper makes that easy to do:
def cmp_to_key(mycmp):
'Convert a cmp= function into a key= function'
class K(object):
def __init__(self, obj, *args):
self.obj = obj
def __lt__(self, other):
return mycmp(self.obj, other.obj) < 0
def __gt__(self, other):
return mycmp(self.obj, other.obj) > 0
def __eq__(self, other):
return mycmp(self.obj, other.obj) == 0
def __le__(self, other):
return mycmp(self.obj, other.obj) <= 0
def __ge__(self, other):
return mycmp(self.obj, other.obj) >= 0
def __ne__(self, other):
return mycmp(self.obj, other.obj) != 0
return K
To convert to a key function, just wrap the old comparison function:
>>> sorted([5, 2, 4, 1, 3], key=cmp_to_key(reverse_numeric))
[5, 4, 3, 2, 1]
In Python 2.7, the functools.cmp_to_key()
function was added to the
functools module.
- For locale aware sorting, use
locale.strxfrm()
for a key function orlocale.strcoll()
for a comparison function. The reverse parameter still maintains sort stability (so that records with equal keys retain their original order). Interestingly, that effect can be simulated without the parameter by using the builtin
[UNKNOWN NODE doctest_block]reversed()
function twice:To create a standard sort order for a class, just add the appropriate rich comparison methods:
[UNKNOWN NODE doctest_block]For general purpose comparisons, the recommended approach is to define all six rich comparison operators. The
functools.total_ordering()
class decorator makes this easy to implement.Key functions need not depend directly on the objects being sorted. A key function can also access external resources. For instance, if the student grades are stored in a dictionary, they can be used to sort a separate list of student names:
[UNKNOWN NODE doctest_block]