Un context manager open() in sola lettura

Hai un sacco di file di dati per il tuo prossimo progetto di deep learning che ti hanno richiesto mesi per essere raccolti e ripuliti. Sarebbe terribile sovrascriverne uno per errore mentre provi a leggerlo per l'addestramento, quindi decidi di creare una versione in sola lettura del context manager open() da usare nel tuo progetto.

Il context manager open() standard:

accetta un nome di file e una modalità ('r' per leggere, 'w' per scrivere, oppure 'a' per accodare)
apre il file in lettura, scrittura o in modalità append
restituisce il controllo al contesto, insieme a un riferimento al file
attende che il contesto termini
e poi chiude il file prima di uscire

Il tuo context manager farà la stessa cosa, tranne che accetterà solo il nome del file come argomento e aprirà il file esclusivamente in lettura.

Questo esercizio fa parte del corso

Scrivere funzioni in Python

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Istruzioni dell'esercizio

Restituisci il controllo da open_read_only() al blocco di contesto, assicurandoti che l'oggetto read_only_file venga assegnato a my_file.
Usa il metodo .close() di read_only_file per assicurarti di non lasciare file aperti in giro.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

@contextlib.contextmanager
def open_read_only(filename):
  """Open a file in read-only mode.

  Args:
    filename (str): The location of the file to read

  Yields:
    file object
  """
  read_only_file = open(filename, mode='r')
  # Yield read_only_file so it can be assigned to my_file
  ____ ____
  # Close read_only_file
  ____.____()

with open_read_only('my_file.txt') as my_file:
  print(my_file.read())

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Scrivere funzioni in Python

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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: Usare i context manager Exercise 2: Il numero di volte che compare "cat"Exercise 3: La velocità dei gatti Exercise 4: Scrivere context manager Exercise 5: Il context manager timer()Exercise 6: Un context manager open() in sola lettura

Esercizio in corso

Exercise 7: Argomenti avanzati Exercise 8: Casi d'uso dei context manager Exercise 9: Scraping del NASDAQ Exercise 10: Cambiare la directory di lavoro

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: Functions are objects Exercise 2: Building a command line data app Exercise 3: Reviewing your co-worker's code Exercise 4: Returning functions for a math game Exercise 5: Scope Exercise 6: Understanding scope Exercise 7: Modifying variables outside local scope Exercise 8: Closures Exercise 9: Checking for closure Exercise 10: Closures keep your values safe Exercise 11: Decorators Exercise 12: Using decorator syntax Exercise 13: Defining a decorator

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!