El método de matriz de lista de ordenación de Python: ascendente y descendente explicado con ejemplos

Programación

Si desea aprender a trabajar con el sort()método en sus proyectos de Python, este artículo es para usted. Este método es muy poderoso y puede personalizarlo para que se adapte a sus necesidades, así que veamos cómo funciona en detalle.

Aprenderás:

  • Cómo utilizar este método y personalizar su funcionalidad.
  • Cuándo usarlo y cuándo no usarlo.
  • Cómo llamarlo pasando diferentes combinaciones de argumentos.
  • Cómo ordenar una lista en orden ascendente y descendente.
  • Cómo comparar los elementos de una lista basada en valores intermedios.
  • Cómo puede pasar funciones lambda a este método.
  • Cómo se compara este método con la sorted()función.
  • Por qué el sort()método realiza una clasificación estable.
  • Cómo funciona el proceso de mutación entre bastidores.

¿Estás listo? ¡Vamos a empezar! ⭐

? Propósito y casos de uso

Con el sort()método, puede ordenar una lista en:

  • Orden ascendente
  • Orden descendiente

Este método se utiliza para ordenar una lista en su lugar, lo que significa que la muta o la modifica directamente sin crear copias adicionales, así que recuerde:

Aprenderá más sobre la mutación en este artículo (¡lo prometo!), Pero por ahora es muy importante que sepa que el sort()método modifica la lista, por lo que se pierde su versión original.

Por este motivo, solo debe utilizar este método si:

  • Quiere modificar (ordenar) la lista de forma permanente.
  • No es necesario que conserve la versión original de la lista.

Si esto se ajusta a sus necesidades, entonces el .sort()método es exactamente lo que está buscando.

? Sintaxis y argumentos

Veamos cómo puedes llamar .sort()para aprovechar todo su poder.

Esta es la llamada más básica (sin argumentos):

Si no pasa ningún argumento, por defecto:

  • La lista se ordenará en orden ascendente.
  • Los elementos de la lista se compararán directamente usando sus valores con el <operador.

Por ejemplo:

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

Argumentos personalizados  

Para personalizar cómo funciona el sort()método, puede pasar dos argumentos opcionales:

  • Llave
  • Contrarrestar

Veamos cómo cambian el comportamiento de este método. Aquí tenemos una llamada al método con estos dos argumentos:

Antes de explicar cómo funcionan, me gustaría explicar algo que probablemente haya notado en el diagrama anterior: en la llamada al método, los nombres de los parámetros deben incluirse antes de sus valores correspondientes, así:

  • key=
  • reverse=

Esto se debe a que son argumentos de solo palabras clave . Si está pasando un valor personalizado para ellos, sus nombres deben especificarse en la llamada al método, seguidos de un signo igual =y sus valores correspondientes, así:

De lo contrario, si intenta pasar los argumentos directamente como lo hacemos normalmente para los parámetros posicionales, verá este error porque la función no sabrá qué argumento corresponde a qué parámetro:

TypeError: sort() takes no positional arguments

Contrarrestar

Ahora que sabe qué son los argumentos de solo palabras clave, comencemos con reverse.

El valor de reversepuede ser Trueo False:

  • False significa que la lista se ordenará en orden ascendente.
  • True significa que la lista se ordenará en orden descendente (inverso).

? Sugerencia: De forma predeterminada, su valor es False: si no pasa ningún argumento para este parámetro, la lista se ordena en orden ascendente.

Aquí tenemos algunos ejemplos:

# 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']

? Sugerencia: si los elementos de la lista son cadenas, se ordenan alfabéticamente.

# 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']

? Sugerencia: observe cómo la lista está ordenada en orden descendente si lo reverseestá True.

Llave

Ahora que sabe cómo trabajar con el reverseparámetro, veamos el keyparámetro.

Este parámetro es un poco más detallado porque determina cómo se compararán los elementos de la lista durante el proceso de clasificación.

El valor de keyes:

  • 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

Ahora hablemos un poco sobre el valor de retorno de este método. El sort()método regresa None, no devuelve una versión ordenada de la lista, como podríamos esperar intuitivamente.

Según la documentación de Python:

Para recordar a los usuarios que funciona por efecto secundario, no devuelve la secuencia ordenada.

Básicamente, esto se usa para recordarnos que estamos modificando la lista original en la memoria, no generando una nueva copia de la lista.

Este es un ejemplo del valor de retorno 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

¿Ver? Nonefue devuelto por la llamada al método.

? 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().

Este método realiza una clasificación estable porque funciona con una implementación de TimSort, un algoritmo de clasificación muy eficiente y estable.

Según la documentación de Python:

Una clasificación es estable si garantiza que no se cambiará el orden relativo de los elementos que se comparan de la misma manera ; esto es útil para clasificar en varias pasadas (por ejemplo, ordenar por departamento y luego por grado salarial).

Esto significa que si dos elementos tienen el mismo valor o valor intermedio (clave), se garantiza que permanecerán en el mismo orden entre sí.

Veamos a qué me refiero con esto. Por favor, eche un vistazo a este ejemplo por unos momentos:

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

Estamos comparando los elementos en función de su longitud porque pasamos la lenfunción como argumento para 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. ⭐️