The speed of cats

You're working on a new web service that processes Instagram feeds to identify which pictures contain cats (don't ask why -- it's the internet). The code that processes the data is slower than you would like it to be, so you are working on tuning it up to run faster. Given an image, image, you have two functions that can process it:

process_with_numpy(image)
process_with_pytorch(image)

Your colleague wrote a context manager, timer(), that will print out how long the code inside the context block takes to run. She is suggesting you use it to see which of the two options is faster. Time each function to determine which one to use in your web service.

This exercise is part of the course

Writing Functions in Python

Exercise instructions

Use the timer() context manager to time how long process_with_numpy(image) takes to run.
Use the timer() context manager to time how long process_with_pytorch(image) takes to run.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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)

Edit and Run Code

This exercise is part of the course

Writing Functions in Python

IntermediateSkill Level

4.8+

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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

Current Exercise

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: 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!