Creating Higher Dimensional Arrays
2D Arrays
Let's now create a higher dimensional array, namely a 2D array:
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}')
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:
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}')
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.
Swipe to start coding
Create a 2D array using lists. This array can have any number of rows and columns, with arbitrary values.
Solution
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