La méthode de tableau de liste de tri Python - Explication croissante et décroissante avec des exemples

Si vous souhaitez apprendre à utiliser la sort()méthode dans vos projets Python, cet article est pour vous. Cette méthode est très puissante et vous pouvez la personnaliser en fonction de vos besoins, voyons donc comment elle fonctionne en détail.

Tu vas apprendre:

  • Comment utiliser cette méthode et personnaliser ses fonctionnalités.
  • Quand l'utiliser et quand ne pas l'utiliser.
  • Comment l'appeler en passant différentes combinaisons d'arguments.
  • Comment trier une liste par ordre croissant et décroissant.
  • Comment comparer les éléments d'une liste en fonction de valeurs intermédiaires.
  • Comment vous pouvez passer des fonctions lambda à cette méthode.
  • Comment cette méthode se compare à la sorted()fonction.
  • Pourquoi la sort()méthode effectue un tri stable.
  • Comment le processus de mutation fonctionne dans les coulisses.

Es-tu prêt? Commençons! ⭐

🔹 Objectif et cas d'utilisation

Avec la sort()méthode, vous pouvez trier une liste soit:

  • Ordre croissant
  • Ordre décroissant

Cette méthode est utilisée pour trier une liste sur place, ce qui signifie qu'elle la mute ou la modifie directement sans créer de copies supplémentaires, alors rappelez-vous:

Vous en apprendrez plus sur la mutation dans cet article (je le promets!), Mais pour l'instant il est très important que vous sachiez que la sort()méthode modifie la liste, donc sa version originale est perdue.

Pour cette raison, vous ne devez utiliser cette méthode que si:

  • Vous souhaitez modifier (trier) la liste de manière permanente.
  • Vous n'avez pas besoin de conserver la version originale de la liste.

Si cela correspond à vos besoins, la .sort()méthode correspond exactement à ce que vous recherchez.

🔸 Syntaxe et arguments

Voyons comment vous pouvez appeler .sort()pour profiter de sa pleine puissance.

C'est l'appel le plus basique (sans arguments):

Si vous ne passez aucun argument, par défaut:

  • La liste sera triée par ordre croissant.
  • Les éléments de la liste seront comparés directement à l'aide de leurs valeurs avec l' <opérateur.

Par exemple:

>>> b = [6, 3, 8, 2, 7, 3, 9] >>> b.sort() >>> b [2, 3, 3, 6, 7, 8, 9] # Sorted!

Arguments personnalisés  

Pour personnaliser le fonctionnement de la sort()méthode, vous pouvez transmettre deux arguments facultatifs:

  • Clé
  • Inverser

Voyons comment ils changent le comportement de cette méthode. Ici, nous avons un appel de méthode avec ces deux arguments:

Avant d'expliquer leur fonctionnement, j'aimerais expliquer quelque chose que vous avez probablement remarqué dans le diagramme ci-dessus - dans l'appel de méthode, les noms des paramètres doivent être inclus avant leurs valeurs correspondantes, comme ceci:

  • key=
  • reverse=

En effet, ce sont des arguments de mots clés uniquement . Si vous leur passez une valeur personnalisée, leurs noms doivent être spécifiés dans l'appel de méthode, suivis d'un signe égal =et de leurs valeurs correspondantes, comme ceci:

Sinon, si vous essayez de passer les arguments directement comme nous le faisons normalement pour les paramètres positionnels, vous verrez cette erreur car la fonction ne saura pas quel argument correspond à quel paramètre:

TypeError: sort() takes no positional arguments

Inverser

Maintenant que vous savez ce que sont les arguments de mots clés uniquement, commençons par reverse.

La valeur de reversepeut être soit Truesoit False:

  • False signifie que la liste sera triée par ordre croissant.
  • True signifie que la liste sera triée par ordre décroissant (inverse).

💡 Astuce: par défaut, sa valeur est False- si vous ne passez aucun argument pour ce paramètre, la liste est triée par ordre croissant.

Voici quelques exemples:

# List of Integers >>> b = [6, 3, 8, 2, 7, 3, 9] >>> b.sort() >>> b [2, 3, 3, 6, 7, 8, 9] # List of Strings >>> c = ["A", "Z", "D", "T", "U"] >>> c.sort() >>> c ['A', 'D', 'T', 'U', 'Z'] 

💡 Conseil: si les éléments de la liste sont des chaînes, ils sont triés par ordre alphabétique.

# List of Integers >>> b = [6, 3, 8, 2, 7, 3, 9] >>> b.sort(reverse=True) >>> b [9, 8, 7, 6, 3, 3, 2] # List of Strings >>> c = ["A", "Z", "D", "T", "U"] >>> c.sort(reverse=True) >>> c ['Z', 'U', 'T', 'D', 'A']

💡 Conseil: notez comment la liste est triée par ordre décroissant si reversec'est le cas True.

Clé

Maintenant que vous savez comment travailler avec le reverseparamètre, voyons le keyparamètre.

Ce paramètre est un peu plus détaillé car il détermine comment les éléments de la liste sont comparés pendant le processus de tri.

La valeur de keyest soit:

  • None, which means that the elements of the list will be compared directly. For example, in a list of integers, the integers themselves can be used for the comparison.
  • Afunction of one argument that generates an intermediate value for each element. This intermediate value is calculated only once and it's used to make the comparisons during the entire sorting process. We use this when we don't want to compare the elements directly, for example, when we want to compare strings based on their length (the intermediate value).

💡 Tip: By default, the value of key is None, so the elements are compared directly.

For example:

Let's say that we want to sort a list of strings based on their length, from the shortest string to the longest string. We can pass the function len as the value of key, like this:

>>> d = ["aaa", "bb", "c"] >>> d.sort(key=len) >>> d ['c', 'bb', 'aaa']

💡 Tip: Notice that we are only passing the name of the function (len) without parenthesis because we are not calling the function. This is very important.

Notice the difference between comparing the elements directly and comparing their length (see below). Using the default value of key (None) would have sorted the strings alphabetically (left), but now we are sorting them based on their length (right):

What happens behind the scenes? Each element is passed as an argument to the len() function, and the value returned by this function call is used to perform the comparisons during the sorting process:

This results in a list with a different sorting criteria: length.

Here we have another example:

Another interesting example is sorting a list of strings as if they were all written in lowercase letters (for example, making "Aa" equivalent to "aa").

According to lexicographical order, capital letters come before lowercase letters:

>>> "E" < "e" True

So the string "Emma" would come before "emily" in a sorted list, even if their lowercase versions would be in the opposite order:

>>> "Emma" >> "emma" < "emily" False

To avoid distinguishing between capital and lowercase letters, we can pass the function str.lower as key. This will generate a lowercase version of the strings that will be used for the comparisons:

>>> e = ["Emma", "emily", "Amy", "Jason"] >>> e.sort(key=str.lower) >>> e ['Amy', 'emily', 'Emma', 'Jason']

Notice that now, "emily" comes before "Emma" in the sorted list, which is exactly what we wanted.

💡 Tip: if we had used the default sorting process, all the strings that started with an uppercase letter would have come before all the strings that started with a lowercase letter:

>>> e = ["Emma", "emily", "Amy", "Jason"] >>> e.sort() >>> e ['Amy', 'Emma', 'Jason', 'emily']

Here is an example using Object-Oriented Programming (OOP):

If we have this very simple Python class:

>>> class Client: def __init__(self, age): self.age = age

And we create four instances:

>>> client1 = Client(67) >>> client2 = Client(23) >>> client3 = Client(13) >>> client4 = Client(35)

We can make a list that references them:

>>> clients = [client1, client2, client3, client4]

Then, if we define a function to get the age of these instances:

>>> def get_age(client): return client.age

We can sort the list based on their age by passing the get_age function an an argument:

>>> clients.sort(key=get_age)

This is the final, sorted version of the list. We use a for loop to print the age of the instances in the order that they appear in the list:

>>> for client in clients: print(client.age) 13 23 35 67

Exactly what we wanted – now the list is sorted in ascending order based on the age of the instances.

💡 Tip: Instead of defining a get_age function, we could have used a lambda function to get the age of each instance, like this:

>>> clients.sort(key=lambda x: x.age)

Lambda functions are small and simple anonymous functions, which means that they don't have a name. They are very helpful for these scenarios when we only want to use them in particular places for a very short period of time.

This is the basic structure of the lambda function that we are using to sort the list:

Passing Both Arguments

Awesome! Now you know to customize the functionality of the sort() method. But you can take your skills to a whole new level by combining the effect of key and reverse in the same method call:

>>> f = ["A", "a", "B", "b", "C", "c"] >>> f.sort(key=str.lower, reverse=True) >>> f ['C', 'c', 'B', 'b', 'A', 'a']

These are the different combinations of the arguments and their effect:

The Order of Keyword-Only Arguments Doesn't Matter

Since we are specifying the names of the arguments, we already know which value corresponds to which parameter, so we can include either key or reverse first in the list and the effect will be exactly the same.

So this method call:

Is equivalent to:

This is an example:

>>> a = ["Zz", "c", "y", "o", "F"] >>> a.sort(key=str.lower, reverse=True) >>> a ['Zz', 'y', 'o', 'F', 'c']

If we change the order of the arguments, we get the exact same result:

>>> a = ["Zz", "c", "y", "o", "F"] >>> a.sort(reverse=True, key=str.lower) >>> a ['Zz', 'y', 'o', 'F', 'c']

🔹 Return Value

Parlons maintenant un peu de la valeur de retour de cette méthode. La sort()méthode renvoie None- elle ne renvoie pas une version triée de la liste, comme on pourrait s'y attendre intuitivement.

Selon la documentation Python:

Pour rappeler aux utilisateurs qu'il fonctionne par effet secondaire, il ne renvoie pas la séquence triée.

Fondamentalement, cela est utilisé pour nous rappeler que nous modifions la liste d'origine en mémoire, pas en générant une nouvelle copie de la liste.

Voici un exemple de la valeur de retour de sort():

>>> nums = [6.5, 2.4, 7.3, 3.5, 2.6, 7.4] # Assign the return value to this variable: >>> val = nums.sort() # Check the return value: >>> print(val) None

Voir? Nonea été renvoyé par l'appel de méthode.

💡 Tip: It is very important not to confuse the sort() method with the sorted() function, which is a function that works very similarly, but doesn't modify the original list. Instead sorted() generates and returns a new copy of the list, already sorted.

This is an example that we can use to compare them:

# The sort() method returns None >>> nums = [6.5, 2.4, 7.3, 3.5, 2.6, 7.4] >>> val = nums.sort() >>> print(val) None
# sorted() returns a new sorted copy of the original list >>> nums = [6.5, 2.4, 7.3, 3.5, 2.6, 7.4] >>> val = sorted(nums) >>> val [2.4, 2.6, 3.5, 6.5, 7.3, 7.4] # But it doesn't modify the original list >>> nums [6.5, 2.4, 7.3, 3.5, 2.6, 7.4]

This is very important because their effect is very different. Using the sort() method when you intended to use sorted() can introduce serious bugs into your program because you might not realize that the list is being mutated.

🔸 The sort() Method Performs a Stable Sort

Now let's talk a little bit about the characteristics of the sorting algorithm used by sort().

Cette méthode effectue un tri stable car elle fonctionne avec une implémentation de TimSort, un algorithme de tri très efficace et stable.

Selon la documentation Python:

Un tri est stable s'il garantit de ne pas changer l'ordre relatif des éléments qui se comparent égaux - cela est utile pour le tri en plusieurs passes (par exemple, trier par département, puis par classe de salaire).

Cela signifie que si deux éléments ont la même valeur ou valeur intermédiaire (clé), ils sont garantis de rester dans le même ordre l'un par rapport à l'autre.

Voyons ce que je veux dire par là. Veuillez jeter un œil à cet exemple pendant quelques instants:

>>> d = ["BB", "AA", "CC", "A", "B", "AAA", "BBB"] >>> d.sort(key=len) >>> d ['A', 'B', 'BB', 'AA', 'CC', 'AAA', 'BBB']

Nous comparons les éléments en fonction de leur longueur car nous avons passé la lenfonction comme argument pour key.

We can see that there are three elements with length 2: "BB", "AA", and "CC" in that order.

Now, notice that these three elements are in the same relative order in the final sorted list:

This is because the algorithm is guaranteed to be stable and the three of them had the same intermediate value (key) during the sorting process (their length was 2, so their key was 2).

💡 Tip: The same happened with "A" and "B" (length 1) and "AAA" and "BBB" (length 3), their original order relative to each other was preserved.

Now you know how the sort() method works, so let's dive into mutation and how it can affect your program.

🔹 Mutation and Risks

As promised, let's see how the process of mutation works behind the scenes:

When you define a list in Python, like this:

a = [1, 2, 3, 4]

You create an object at a specific memory location. This location is called the "memory address" of the object, represented by a unique integer called an id.

You can think of an id as a "tag" used to identify a specific place in memory:

You can access a list's id using the id() function, passing the list as argument:

>>> a = [1, 2, 3, 4] >>> id(a) 60501512

When you mutate the list, you change it directly in memory. You may ask, why is this so risky?

It's risky because it affects every single line of code that uses the list after the mutation, so you may be writing code to work with a list that is completely different from the actual list that exists in memory after the mutation.

This is why you need to be very careful with methods that cause mutation.

In particular, the sort() method mutates the list. This is an example of its effect:

Here is an example:

# Define a list >>> a = [7, 3, 5, 1] # Check its id >>> id(a) 67091624 # Sort the list using .sort() >>> a.sort() # Check its id (it's the same, so the list is the same object in memory) >>> id(a) 67091624 # Now the list is sorted. It has been mutated! >>> a [1, 3, 5, 7]

The list was mutated after calling .sort().

Every single line of code that works with list a after the mutation has occurred will use the new, sorted version of the list. If this was not what you intended, you may not realize that other parts of your program are working with the new version of the list.

Here is another example of the risks of mutation within a function:

# List >>> a = [7, 3, 5, 1] # Function that prints the elements of the list in ascending order. >>> def print_sorted(x): x.sort() for elem in x: print(elem) # Call the function passing 'a' as argument >>> print_sorted(a) 1 3 5 7 # Oops! The original list was mutated. >>> a [1, 3, 5, 7]

The list a that was passed as argument was mutated, even if that wasn't what you intended when you initially wrote the function.

💡 Tip: If a function mutates an argument, it should be clearly stated to avoid introducing bugs into other parts of your program.

🔸 Summary of the sort() Method

  • The sort() method lets you sort a list in ascending or descending order.
  • It takes two keyword-only arguments: key and reverse.
  • reverse determines if the list is sorted in ascending or descending order.
  • key is a function that generates an intermediate value for each element, and this value is used to do the comparisons during the sorting process.
  • The sort() method mutates the list, causing permanent changes. You need to be very careful and only use it if you do not need the original version of the list.

I really hope that you liked my article and found it helpful. Now you can work with the sort() method in your Python projects. Check out my online courses. Follow me on Twitter. ⭐️