Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Introduction to NumPy | NumPy Basics
Ultimate NumPy
course content

Conteúdo do Curso

Ultimate NumPy

Ultimate NumPy

1. NumPy Basics
2. Indexing and Slicing
3. Commonly used NumPy Functions
4. Math with NumPy

bookIntroduction to NumPy

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Tarefa

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

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 1
toggle bottom row

bookIntroduction to NumPy

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Tarefa

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

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 1
toggle bottom row

bookIntroduction to NumPy

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Tarefa

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

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!

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Tarefa

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

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