De snelheid van katten

Je werkt aan een nieuwe webservice die Instagram-feeds verwerkt om te bepalen welke foto's katten bevatten (vraag niet waarom — dit is het internet). De code die de data verwerkt is trager dan je zou willen, dus je probeert hem te tunen zodat hij sneller draait. Gegeven een afbeelding, image, heb je twee functies die deze kunnen verwerken:

process_with_numpy(image)
process_with_pytorch(image)

Je collega heeft een contextmanager, timer(), geschreven die uitprint hoe lang de code binnen het contextblok erover doet. Ze stelt voor om die te gebruiken om te zien welke van de twee opties sneller is. Meet de tijd van elke functie om te bepalen welke je in je webservice moet gebruiken.

Deze oefening maakt deel uit van de cursus

Functies schrijven in Python

Cursus bekijken

Oefeninstructies

Gebruik de contextmanager timer() om te meten hoe lang process_with_numpy(image) erover doet.
Gebruik de contextmanager timer() om te meten hoe lang process_with_pytorch(image) erover doet.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Functies schrijven in Python

SkillTag.level.intermediateSkillTag.label

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Begin de cursus 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: Contextmanagers gebruiken Exercise 2: Het aantal katten Exercise 3: De snelheid van katten

Huidige oefening

Exercise 4: Contextmanagers schrijven Exercise 5: De contextmanager timer()Exercise 6: Een contextmanager voor alleen-lezen open()Exercise 7: Geavanceerde onderwerpen Exercise 8: Gebruikssituaties voor contextmanagers Exercise 9: De NASDAQ scrapen Exercise 10: De werkmap wijzigen

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!