Зміст курсу
Ultimate NumPy
Ultimate NumPy
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 (Optional)
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. The innermost 1D arrays lie along axis 2 (e.g., [1, 2, 3]
), where each small cube with a side length of 1
is a particular element (number).
All the elements of a 3D array are stored inside these innermost 1D arrays. The cube is just a visual representation to make things clear. The total number of elements (small cubes) is 27
(the volume of the cube).
However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.
Завдання
Create a 2D array named array_2d
:
- Use the correct function to create a
numpy
2D array; - Create a 2D array based on two lists (the first argument):
[24, 41]
and[32, 25]
in this order; - Set the data type of its elements to
np.int8
via specifying the second argument.
Дякуємо за ваш відгук!
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 (Optional)
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. The innermost 1D arrays lie along axis 2 (e.g., [1, 2, 3]
), where each small cube with a side length of 1
is a particular element (number).
All the elements of a 3D array are stored inside these innermost 1D arrays. The cube is just a visual representation to make things clear. The total number of elements (small cubes) is 27
(the volume of the cube).
However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.
Завдання
Create a 2D array named array_2d
:
- Use the correct function to create a
numpy
2D array; - Create a 2D array based on two lists (the first argument):
[24, 41]
and[32, 25]
in this order; - Set the data type of its elements to
np.int8
via specifying the second argument.
Дякуємо за ваш відгук!
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 (Optional)
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. The innermost 1D arrays lie along axis 2 (e.g., [1, 2, 3]
), where each small cube with a side length of 1
is a particular element (number).
All the elements of a 3D array are stored inside these innermost 1D arrays. The cube is just a visual representation to make things clear. The total number of elements (small cubes) is 27
(the volume of the cube).
However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.
Завдання
Create a 2D array named array_2d
:
- Use the correct function to create a
numpy
2D array; - Create a 2D array based on two lists (the first argument):
[24, 41]
and[32, 25]
in this order; - Set the data type of its elements to
np.int8
via specifying the second argument.
Дякуємо за ваш відгук!
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 (Optional)
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. The innermost 1D arrays lie along axis 2 (e.g., [1, 2, 3]
), where each small cube with a side length of 1
is a particular element (number).
All the elements of a 3D array are stored inside these innermost 1D arrays. The cube is just a visual representation to make things clear. The total number of elements (small cubes) is 27
(the volume of the cube).
However, in practice, the approach to handling 3D and higher-dimensional arrays is no different from handling 2D arrays.
Завдання
Create a 2D array named array_2d
:
- Use the correct function to create a
numpy
2D array; - Create a 2D array based on two lists (the first argument):
[24, 41]
and[32, 25]
in this order; - Set the data type of its elements to
np.int8
via specifying the second argument.