In this article, I’m going to explain the full details about matrix and How to create the NXNXN Matrix using the Python program.

**What is NxNxN?**

NxNxN is pronounced by **N by N by N** also called as **NxNxN Cube** or **NxNxN Puzzle**. It represents a cube with the same dimensions, which means that the cube has the same length, width and height.

The **NxNxN** puzzles that fit under this category include the **2x2x2 cube**, the **Rubik’s cube**, the **4x4x4 cube**, the **5x5x5 cube**, etc. The **1x1x1** cube also belongs in this category, even if it is not a twisty puzzle because it does complete the **NxNxN** set.

**How to Create NxNxN Matrix in Python?**

lets us learn how to create the nxnxn matrix in Python using different ways with examples.

**Create NxN Matrix in Python 3 with Non Duplicating numbers**

The below code is to create an **nxn **matrix in Python, and it does not repeat the numbers row-wise and column-wise. These are mainly used in Puzzles like Sudoko.

```
# Python Program to create nxn Matrix
import numpy as np
# Provide the value of N
N = 5
# returns evenly spaced values
row = np.arange(N)
# create an new array filled with zeros of given shape and type
result = np.zeros((N, N))
# Logic to roll array elements of given axis
for i in row:
result[i] = np.roll(row, i)
print(result)
```

**Output**

```
[[0. 1. 2. 3. 4.]
[4. 0. 1. 2. 3.]
[3. 4. 0. 1. 2.]
[2. 3. 4. 0. 1.]
[1. 2. 3. 4. 0.]]
```

**Create NXNXN Matrix Program 3 in Python using Numpy**

The below code is to create an **nxnxn **matrix Programm in Python 3. Just change the value of N based on the requirement and the shape that you need to generate. For a standard Rubik’s cube, it would be 3x3x3, so the value of n would be 3.

**Example: **

```
# Program to nxnxn matrix python 3
import numpy as np
# Provide the value of nxnxn
n = 3
a = np.arange(n)
b = np.array([a]*n)
matrix = np.array([b]*n)
#creating an array containg n-dimensional points
flat_mat = matrix.reshape((int(matrix.size/n),n))
#just a random matrix we will use as a rotation
rotate = np.eye(n) + 2
#apply the rotation on each n-dimensional point
result = np.array([rotate.dot(x) for x in flat_mat])
#return to original shape
result=result.reshape((n,n,n))
print(result)
```

**Output**

```
[[[6. 7. 8.]
[6. 7. 8.]
[6. 7. 8.]]
[[6. 7. 8.]
[6. 7. 8.]
[6. 7. 8.]]
[[6. 7. 8.]
[6. 7. 8.]
[6. 7. 8.]]]
```