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Lära Introduction to NumPy | NumPy Basics
Ultimate NumPy
course content

Kursinnehåll

Ultimate NumPy

Ultimate NumPy

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

book
Introduction 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 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.

Uppgift

Swipe to start coding

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

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 1. Kapitel 1
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book
Introduction 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 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.

Uppgift

Swipe to start coding

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

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 1. Kapitel 1
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Vi beklagar att något gick fel. Vad hände?
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