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Python list sort using lambda

Photo by Martin Sanchez on UnsplashWhen it comes to sorting, the most-used Python functions are sorted() and sort().Although there are some differences in their usage, memory usage, and operation speed, as discussed in a previous Medium article, both functions can be used to sort a list. Below is a simple example.Basic Sorting ExamplesIn the above example, there are a few things to highlight for those who are less familiar with the sorted() and sort() functions.The sort() function is actually an instance method of the list data type, such that the usage is list.sort().The sorting operation by sort() is in place and returns None. Thus, if you check the type by running type(numbers.sort()), the output will be NoneType.Unlike the sort() function, the sorted() function has better flexibility by allowing the argument to be a list or any other iterable objects, like the tuple shown in the example.For the sorted() function, the return value is a list even if we pass in a non-list data like a tuple.The default sorting order is ascending. If we specify that reverse=True, the sorting order will be descending.Essentially, both sorted() and sort() functions are comparable when a list is to be sorted with the sorted() function has better flexibility. Therefore, for the following demonstration, we'll just use the sorted() function.Beyond the basic sorting operations on int and str, what can we do if we want to sort a list of dictionaries, like the one called activities below? List of DictionariesIf we sort the variable activities, the following error will occur, as the interpreter doesn't know how to compare instances of dict. What should we do then?Lambdas come to the rescue! If you recall, a lambda in Python is just an anonymous function that has one or more arguments, but only one expression.In the example below, we create a lambda and assign it to the variable add_ten. As a lambda is a function, the assigned variable add_ten is a function, and thus, we call it as a regular function using the parentheses.We have some basic ideas of how a lambda works, and we can now see how we can use it to sort the dictionaries.Sort a list of dictionariesAs shown in the above example, we create a lambda and pass it in as the key argument. Now, the interpreter doesn't complain that the list of dicts can't be sorted. Hooray!Actually, we can write a lambda that is a little more complicated if we want the activities to be sorted by more than one keys.Sort by day and activityAs shown above, the list is now sorted by day and activity. The same effect can be achieved by using the itemgetter() function as shown below.Actually, the operator module has additional operations, such as attrgetter(), which is handy in sorting custom objects.From the above example, you may have noticed that for the key argument, we can simply pass in a function, which gives us more flexibility in how we can customize the sorting. Here's an example of this.Sort by a custom functionWe have a function called activity_sorting, which is used as the key for the sorted() function. From the output, you can tell that the activities variable is now sorted by the activity.This tutorial has introduced how we can use lambdas and custom functions to sort a list of dictionaries in Python. These approaches can also be applied to lists of custom objects.If the sorting key involves just one expression, you may want to consider using lambdas without writing a full function outside the sorted() or sort() function. 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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