NumPy is a powerful library in Python used for numerical operations and data analysis. Reshaping arrays is a common operation in NumPy, and it allows you to change the dimensions of an array without changing its data. In this article, we’ll discuss how to reshape a 2D NumPy array into a 3D array.

Understanding 2D and 3D Arrays

• A 2D array is a collection of data points arranged in rows and columns, forming a matrix. It can be visualized as a spreadsheet or a grid.
• A 3D array is an extension of a 2D array, where an additional dimension is added, typically representing depth or volume. It can be visualized as a stack of 2D arrays.

Reshaping a 2D Array to 3D using reshape() method

To reshape a 2D NumPy array into a 3D array, you can use the reshape() method. The reshape() method takes two arguments:

• The desired shape of the 3D array as a tuple
• The original 2D array

Example 1

import numpy as np
# Create a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("Original 2D array:")
print(array_2d)

# Reshape the 2D array into a 3D array with shape (3, 3, 1)
array_3d = array_2d.reshape((3, 3, 1))
print("\nReshaped 3D array:")
print(array_3d)

Output:

Original 2D array:
[[1 2 3]
 [4 5 6]
 [7 8 9]]
Reshaped 3D array:
[[[1]
  [2]
  [3]]
 [[4]
  [5]
  [6]]
 [[7]
  [8]
  [9]]]

In this example, the original 2D array has three rows and three columns. We reshaped it into a 3D array with the shape (3, 3, 1). The resulting 3D array has three 2D slices, each with a shape of (3, 3). The additional dimension (the third dimension) has a size of 1, indicating that each 2D slice has a depth of 1.

Example 2

import numpy as np

# Create a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])

# Reshape the 2D array into a 3D array with shape (2, 2, 3)
array_3d = array_2d.reshape((2, 2, 3))

print("Original 2D array:")
print(array_2d)
print("\nReshaped 3D array:")
print(array_3d)

Output:

Original 2D array:
[[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
Reshaped 3D array:
[[[ 1  2  3]
  [ 4  5  6]]
 [[ 7  8  9]
  [10 11 12]]]

The original 2D array is reshaped into a 3D array with a shape of (2, 2, 3).

Conclusion

Reshaping arrays is a fundamental operation in NumPy, and it allows you to manipulate data in various ways. Reshaping a 2D array into a 3D array can be useful for representing data with an additional dimension, such as depth or volume. Additionally, reshaping a flattened array into a 3D array can help organize data into a structured format. By understanding how to reshape arrays, you can effectively work with multidimensional data in NumPy.

Numpy Reshape 2D To 3D Array – FAQs

How to Reshape NumPy Array from 2D to 3D

To reshape a 2D array into a 3D array, you can use the reshape() method, specifying the desired new shape. The total number of elements must remain constant:

import numpy as np

# Create a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])

# Reshape it to 3D (for example, 2 layers of 3x1 matrices)
array_3d = array_2d.reshape((2, 3, 1))
print(array_3d)

How to Create a 3 Dimensional Array in NumPy

You can directly create a 3D array by specifying the desired shape and using functions like np.zeros, np.ones, or np.random.rand:

# Create a 3D array of shape (2, 3, 4) filled with random numbers
array_3d = np.random.rand(2, 3, 4)
print(array_3d)

How to Reshape 2D Array to 1D in NumPy

To flatten a 2D array to a 1D array, use the reshape() method or the flatten() method, which is simpler and more readable:

# Flatten the 2D array to 1D
array_1d = array_2d.flatten()
print(array_1d)

Is reshape(-1) the Same as flatten()?

reshape(-1) changes the shape of an array to 1D by inferring the total number of elements. It does not necessarily create a copy of the array; it depends on the order of the array and the reshaping operation. On the other hand, flatten() always returns a copy of the array flattened to one dimension:

import numpy as np

# Create a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])

# Reshape it to 3D (for example, 2 layers of 3x1 matrices)
array_3d = array_2d.reshape((2, 3, 1))
print(array_3d)

What Does reshape() Do?

The reshape() function changes the shape of an array without changing its data. It allows you to specify new dimensions as long as the total number of elements remains the same:

# Example of reshape
reshaped_array = np.arange(8).reshape((2, 4))
print(reshaped_array)

What is rot90 in NumPy?

The rot90() function in NumPy rotates an array by 90 degrees in the plane specified by two axes. By default, it rotates the array counterclockwise:

# Create a 2D array
array_2d = np.array([[1, 2], [3, 4]])

# Rotate the array 90 degrees counterclockwise
rotated_array = np.rot90(array_2d)
print(rotated_array)