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Optimization Techniques in Python
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Conteúdo do Curso

Optimization Techniques in Python

Optimization Techniques in Python

1. Understanding and Measuring Performance
Introduction to Python PerformanceTiming and Benchmarking BasicsMeasuring Function PerformanceChallenge: Implementing Benchmarking
2. Efficient Use of Data Structures
Lists and NumPy ArraysSets and TuplesChallenge: Choosing Optimal Data StructuresUsing the collections Module
3. Optimizing with Python's Built-in Features
Leveraging map() and List ComprehensionsHandling Large FilesMaximizing Sorting Efficiency

bookChallenge: Implementing Benchmarking

Tarefa

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 1. Capítulo 4
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bookChallenge: Implementing Benchmarking

Tarefa

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 1. Capítulo 4
toggle bottom row

bookChallenge: Implementing Benchmarking

Tarefa

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Tarefa

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 1. Capítulo 4
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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