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

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Challenge: Implementing Benchmarking

Task

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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 unfamiliarβ€”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.

Solution

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SectionΒ 1. ChapterΒ 4
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book
Challenge: Implementing Benchmarking

Task

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 unfamiliarβ€”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.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
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