La velocità dei gatti

Stai lavorando a un nuovo servizio web che elabora i feed di Instagram per identificare quali foto contengono gatti (non chiedere perché — è internet). Il codice che elabora i dati è più lento di quanto vorresti, quindi stai cercando di ottimizzarlo per farlo girare più veloce. Data un'immagine, image, hai due funzioni che possono elaborarla:

process_with_numpy(image)
process_with_pytorch(image)

La tua collega ha scritto un context manager, timer(), che stampa quanto tempo impiega a essere eseguito il codice all'interno del blocco di contesto. Ti suggerisce di usarlo per vedere quale delle due opzioni è più veloce. Misura il tempo di ciascuna funzione per decidere quale usare nel tuo servizio web.

Questo esercizio fa parte del corso

Scrivere funzioni in Python

Visualizza il corso

Istruzioni dell'esercizio

Usa il context manager timer() per misurare quanto impiega process_with_numpy(image) a essere eseguita.
Usa il context manager timer() per misurare quanto impiega process_with_pytorch(image) a essere eseguita.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

image = get_image_from_instagram()

# Time how long process_with_numpy(image) takes to run
____ ____:
  print('Numpy version')
  process_with_numpy(image)

# Time how long process_with_pytorch(image) takes to run
____ ____:
  print('Pytorch version')
  process_with_pytorch(image)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Scrivere funzioni in Python

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

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

Esercizio in corso

Exercise 4: Scrivere context manager Exercise 5: Il context manager timer()Exercise 6: Un context manager open() in sola lettura 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!