Python list sort cmp lambda

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Python list sort cmp lambda

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples/Code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }] [{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }] The easiest way to sort is with the sorted(list) function, which takes a list and returns a new list with those elements in sorted order. The original list is not changed. a = [5, 1, 4, 3] print sorted(a) ## [1, 3, 4, 5] print a ## [5, 1, 4, 3] It's most common to pass a list into the sorted() function, but in fact it can take as input any sort of iterable collection. The older list.sort() method is an alternative detailed below. The sorted() function seems easier to use compared to sort(), so I recommend using sorted(). The sorted() function can be customized through optional arguments. The sorted() optional argument reverse=True, e.g. sorted(list, reverse=True), makes it sort backwards. strs = ['aa', 'BB', 'zz', 'CC'] print sorted(strs) ## ['BB', 'CC', 'aa', 'zz'] (case sensitive) print sorted(strs, reverse=True) ## ['zz', 'aa', 'CC', 'BB'] Custom Sorting With key= For more complex custom sorting, sorted() takes an optional "key=" specifying a "key" function that transforms each element before comparison. The key function takes in 1 value and returns 1 value, and the returned "proxy" value is used for the comparisons within the sort. For example with a list of strings, specifying key=len (the built in len() function) sorts the strings by length, from shortest to longest. The sort calls len() for each string to get the list of proxy length values, and then sorts with those proxy values. strs = ['ccc', 'aaaa', 'd', 'bb'] print sorted(strs, key=len) ## ['d', 'bb', 'ccc', 'aaaa'] As another example, specifying "str.lower" as the key function is a way to force the sorting to treat uppercase and lowercase the same: ## "key" argument specifying str.lower function to use for sorting print sorted(strs, key=str.lower) ## ['aa', 'BB', 'CC', 'zz'] You can also pass in your own MyFn as the key function, like this: ## Say we have a list of strings we want to sort by the last letter of the string. strs = ['xc', 'zb', 'yd' ,'wa'] ## Write a little function that takes a string, and returns its last letter. ## This will be the key function (takes in 1 value, returns 1 value). def MyFn(s): return s[-1] ## Now pass key=MyFn to sorted() to sort by the last letter: print sorted(strs, key=MyFn) ## ['wa', 'zb', 'xc', 'yd'] To use key= custom sorting, remember that you provide a function that takes one value and returns the proxy value to guide the sorting. There is also an optional argument "cmp=cmpFn" to sorted() that specifies a traditional two-argument comparison function that takes two values from the list and returns negative/0/positive to indicate their ordering. The built in comparison function for strings, ints, ... is cmp(a, b), so often you want to call cmp() in your custom comparator. The newer one argument key= sorting is generally preferable. sort() method As an alternative to sorted(), the sort() method on a list sorts that list into ascending order, e.g. list.sort(). The sort() method changes the underlying list and returns None, so use it like this: alist.sort() ## correct alist = blist.sort() ## NO incorrect, sort() returns None The above is a very common misunderstanding with sort() -- it *does not return* the sorted list. The sort() method must be called on a list; it does not work on any enumerable collection (but the sorted() function above works on anything). The sort() method predates the sorted() function, so you will likely see it in older code. The sort() method does not need to create a new list, so it can be a little faster in the case that the elements to sort are already in a list. Tuples A tuple is a fixed size grouping of elements, such as an (x, y) co-ordinate. Tuples are like lists, except they are immutable and do not change size (tuples are not strictly immutable since one of the contained elements could be mutable). Tuples play a sort of "struct" role in Python -- a convenient way to pass around a little logical, fixed size bundle of values. A function that needs to return multiple values can just return a tuple of the values. For example, if I wanted to have a list of 3-d coordinates, the natural python representation would be a list of tuples, where each tuple is size 3 holding one (x, y, z) group. To create a tuple, just list the values within parenthesis separated by commas. The "empty" tuple is just an empty pair of parenthesis. Accessing the elements in a tuple is just like a list -- len(), [ ], for, in, etc. all work the same. tuple = (1, 2, 'hi') print len(tuple) ## 3 print tuple[2] ## hi tuple[2] = 'bye' ## NO, tuples cannot be changed tuple = (1, 2, 'bye') ## this works To create a size-1 tuple, the lone element must be followed by a comma. tuple = ('hi',) ## size-1 tuple It's a funny case in the syntax, but the comma is necessary to distinguish the tuple from the ordinary case of putting an expression in parentheses. In some cases you can omit the parenthesis and Python will see from the commas that you intend a tuple. Assigning a tuple to an identically sized tuple of variable names assigns all the corresponding values. If the tuples are not the same size, it throws an error. This feature works for lists too. (x, y, z) = (42, 13, "hike") print z ## hike (err_string, err_code) = Foo() ## Foo() returns a length-2 tuple List Comprehensions (optional) List comprehensions are a more advanced feature which is nice for some cases but is not needed for the exercises and is not something you need to learn at first (i.e. you can skip this section). A list comprehension is a compact way to write an expression that expands to a whole list. Suppose we have a list nums [1, 2, 3, 4], here is the list comprehension to compute a list of their squares [1, 4, 9, 16]: nums = [1, 2, 3, 4] squares = [ n * n for n in nums ] ## [1, 4, 9, 16] The syntax is [ expr for var in list ] -- the for var in list looks like a regular for-loop, but without the colon (:). The expr to its left is evaluated once for each element to give the values for the new list. Here is an example with strings, where each string is changed to upper case with '!!!' appended: strs = ['hello', 'and', 'goodbye'] shouting = [ s.upper() + '!!!' for s in strs ] ## ['HELLO!!!', 'AND!!!', 'GOODBYE!!!'] You can add an if test to the right of the for-loop to narrow the result. The if test is evaluated for each element, including only the elements where the test is true. ## Select values To practice the material in this section, try later problems in list1.py that use sorting and tuples (in the Basic Exercises). Author Andrew Dalke and Raymond Hettinger Release 0.1 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. 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] 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)] 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)] 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)] 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. 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. 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 Py3.0, 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 Py2.x, 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: >>> def numeric_compare(x, y): ... return x - y >>> sorted([5, 2, 4, 1, 3], cmp=numeric_compare) [1, 2, 3, 4, 5] Or you can reverse the order of comparison with: >>> def reverse_numeric(x, y): ... return y - x >>> sorted([5, 2, 4, 1, 3], cmp=reverse_numeric) [5, 4, 3, 2, 1] 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: 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 __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 3.2, the functools.cmp_to_key() function was added to the functools module in the standard library. Odd and Ends? For locale aware sorting, use locale.strxfrm() for a key function or locale.strcoll() for a comparison function. 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)] The sort routines are guaranteed to use __lt__() when making comparisons between two objects. So, it is easy to add a standard sort order to a class by defining an __lt__() method: >>> Student.__lt__ = lambda self, other: self.age < other.age >>> sorted(student_objects) [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)] 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: >>> students = ['dave', 'john', 'jane'] >>> newgrades = {'john': 'F', 'jane':'A', 'dave': 'C'} >>> sorted(students, key=newgrades.__getitem__) ['jane', 'dave', 'john']

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