Summary  
This chapter covers creating higher-dimensional arrays by passing nested lists to NumPy’s array function, demonstrating how to implement code for 2D and 3D ndarrays.  

General domain of usage  
scientific computing

## 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.


Unlock the full potential of Python's most essential library for numerical computing, NumPy. This comprehensive course is designed to take you from a beginner's understanding to an advanced level of proficiency in NumPy. Whether you're a data scientist, engineer, researcher, or developer, mastering NumPy is essential for efficient data manipulation, scientific computing, and machine learning.

First of all, we will discuss the applications of NumPy and why it is popular. Afterwards you will learn various possible options to create different arrays in NumPy.

It's now time to learn how to use index notation to retrieve specific elements or subsets of data from arrays. You will also learn how to use indices to retrieve data based on certain conditions and handle missing values.

There are quite few of common operations on arrays which you may want to perform. Luckily, NumPy has built-in functions and methods for this purpose which you will learn here. 

It's time to discover how to efficiently use mathematical operations on NumPy arrays and apply them in some real-world problems.

Creating Higher Dimensional Arrays

2D Arrays

3D Array

Solution

Awesome!

Creating Higher Dimensional Arrays

2D Arrays

3D Array

Solution

Awesome!