Utilisation de la syntaxe décorateur

Vous avez créé un décorateur appelé print_args qui affiche tous les arguments et leurs valeurs chaque fois qu'une fonction qu'il décore est appelée.

Cet exercice fait partie du cours

<cours>Écrire des fonctions en Python</cours>

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

def my_function(a, b, c):
  print(a + b + c)

# Decorate my_function() with the print_args() decorator
my_function = ____

my_function(1, 2, 3)

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Écrire des fonctions en Python</cours>

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The goal of this course is to transform you into a Python expert, and so the first chapter starts off with best practices when writing functions. You'll cover docstrings and why they matter and how to know when you need to turn a chunk of code into a function. You will also learn the details of how Python passes arguments to functions, as well as some common gotchas that can cause debugging headaches when calling functions.

Exercise 1: Docstrings Exercise 2: Crafting a docstring Exercise 3: Retrieving docstrings Exercise 4: Docstrings to the rescue!Exercise 5: DRY and "Do One Thing"Exercise 6: Extract a function Exercise 7: Split up a function Exercise 8: Pass by assignment Exercise 9: Mutable or immutable?Exercise 10: Best practice for default arguments

If you've ever seen the "with" keyword in Python and wondered what its deal was, then this is the chapter for you! Context managers are a convenient way to provide connections in Python and guarantee that those connections get cleaned up when you are done using them. This chapter will show you how to use context managers, as well as how to write your own.

Exercise 1: Using context managers Exercise 2: The number of cats Exercise 3: The speed of cats Exercise 4: Writing context managers Exercise 5: The timer() context manager Exercise 6: A read-only open() context manager Exercise 7: Advanced topics Exercise 8: Context manager use cases Exercise 9: Scraping the NASDAQ Exercise 10: Changing the working directory

Decorators are an extremely powerful concept in Python. They allow you to modify the behavior of a function without changing the code of the function itself. This chapter will lay the foundational concepts needed to thoroughly understand decorators (functions as objects, scope, and closures), and give you a good introduction into how decorators are used and defined. This deep dive into Python internals will set you up to be a superstar Pythonista.

Exercise 1: Les fonctions sont des objets Exercise 2: Développement d'une application de données en ligne de commande Exercise 3: Révision du code de vos collègues Exercise 4: Fonctions de retour pour un jeu mathématique Exercise 5: Périmètre Exercise 6: Comprendre la portée Exercise 7: Modification de variables en dehors de la portée locale Exercise 8: Fermetures Exercise 9: Vérification de la fermeture Exercise 10: Les fermetures assurent la sécurité de vos valeurs Exercise 11: Décorateurs Exercise 12: Utilisation de la syntaxe décorateur

Exercice actuel

Exercise 13: Définition d'un décorateur

Now that you understand how decorators work under the hood, this chapter gives you a bunch of real-world examples of when and how you would write decorators in your own code. You will also learn advanced decorator concepts like how to preserve the metadata of your decorated functions and how to write decorators that take arguments.

Exercise 1: Real-world examples Exercise 2: Print the return type Exercise 3: Counter Exercise 4: Decorators and metadata Exercise 5: Preserving docstrings when decorating functions Exercise 6: Measuring decorator overhead Exercise 7: Decorators that take arguments Exercise 8: Run_n_times()Exercise 9: HTML Generator Exercise 10: Timeout(): a real world example Exercise 11: Tag your functions Exercise 12: Check the return type Exercise 13: Great job!