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Lære Challenge: Implementing Benchmarking | Understanding and Measuring Performance
Optimization Techniques in Python

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Challenge: Implementing Benchmarking

Opgave

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Let's practice benchmarking by comparing two approaches to squaring the elements of a NumPy array. The first approach, the slower one, uses a for loop to square each element individually, while the second approach leverages vectorization. Don't worry if this concept sounds unfamiliar—we'll discuss it later in the course.

Your task for now is the following:

  1. Define two functions:
    • The first, named square_array_slow, should take a single parameter array;
    • The second, named square_array_fast, should also take the same parameter.
  2. Decorate both functions with the timeit_decorator and set number to 100.

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Challenge: Implementing Benchmarking

Opgave

Swipe to start coding

Let's practice benchmarking by comparing two approaches to squaring the elements of a NumPy array. The first approach, the slower one, uses a for loop to square each element individually, while the second approach leverages vectorization. Don't worry if this concept sounds unfamiliar—we'll discuss it later in the course.

Your task for now is the following:

  1. Define two functions:
    • The first, named square_array_slow, should take a single parameter array;
    • The second, named square_array_fast, should also take the same parameter.
  2. Decorate both functions with the timeit_decorator and set number to 100.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

close

Awesome!

Completion rate improved to 7.69

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