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Lära Creating Higher Dimensional Arrays | NumPy Basics
Ultimate NumPy
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Kursinnehåll

Ultimate NumPy

Ultimate NumPy

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

book
Creating Higher Dimensional Arrays

2D Arrays

Let's now create a higher dimensional array, namely a 2D array:

1234
import numpy as np # Creating a 2D array array_2d = np.array([[1, 2, 3], [4, 5, 6]]) print(f'2-dimensional array: \n{array_2d}')
copy

Basically, creating a higher-dimensional NumPy array involves passing a higher-dimensional list as the argument of the array() function.

Note

Any NumPy array object is called an ndarray.

Here is a visualization of our 2D array:

We can think of it as a 2x3 matrix.

3D Array

Creating 3D arrays is nearly identical to creating 2D arrays. The only difference is that we now need to pass a 3D list as an argument:

12345678
import numpy as np # Creating a 3D array array_3d = np.array([ [[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]] ]) print(f'3-dimensional array: \n{array_3d}')
copy

However, visualizing a 3D array is a bit more complex, but it can still be done:

The array is 3x3x3, which is why we have a cube with each side equal to 3.

In practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.

Uppgift

Swipe to start coding

Create a 2D array using lists. This array can have any number of rows and columns, with arbitrary values.

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 3
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book
Creating Higher Dimensional Arrays

2D Arrays

Let's now create a higher dimensional array, namely a 2D array:

1234
import numpy as np # Creating a 2D array array_2d = np.array([[1, 2, 3], [4, 5, 6]]) print(f'2-dimensional array: \n{array_2d}')
copy

Basically, creating a higher-dimensional NumPy array involves passing a higher-dimensional list as the argument of the array() function.

Note

Any NumPy array object is called an ndarray.

Here is a visualization of our 2D array:

We can think of it as a 2x3 matrix.

3D Array

Creating 3D arrays is nearly identical to creating 2D arrays. The only difference is that we now need to pass a 3D list as an argument:

12345678
import numpy as np # Creating a 3D array array_3d = np.array([ [[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]] ]) print(f'3-dimensional array: \n{array_3d}')
copy

However, visualizing a 3D array is a bit more complex, but it can still be done:

The array is 3x3x3, which is why we have a cube with each side equal to 3.

In practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.

Uppgift

Swipe to start coding

Create a 2D array using lists. This array can have any number of rows and columns, with arbitrary values.

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 3
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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