Multidimensional Arrays in Python

Until now, you have worked with one-dimensional arrays, where all the data was stored in a single line or row. Now, let’s see what a multidimensional array is.

A multidimensional array is an array of arrays. For example, a two-dimensional array would be an array with each element as a one-dimensional array. 

1-D array : [1, 2, 3, 4, 5]
2-D array : [ [1, 2, 3, 4, 5], [6, 7, 8, 9, 10] ]

Similarly, a three-dimensional array can be thought of as an array with each element as a two-dimensional array. To create multidimensional arrays, you can give a multidimensional list as an input to the np.array function. 

Let’s hear from Vaidehi as she explains the concept of two-dimensional arrays.

# [00:28] There is an error in the brackets of 3-D Array. A 3-D array would look like this:

[[[a, b, c], [d, e, f], [g, h, i]], 

  [[1, 2, 3], [4, 5, 6], [7, 8, 9]], 

 [[A, B, C], [D, E, F], [G, H, I]]]

In NumPy, the dimension is called axis. In NumPy terminology, for 2-D arrays:

• axis = 0 - refers to the rows
• axis = 1 - refers to the columns

Multidimensional arrays are indexed using as many indices as the number of dimensions or axes. For instance, to index a 2-D array, you need two indices: array[x, y]. Each axis has an index starting at 0. The figure provided above shows the axes and their indices for a 2-D array.
 

In the next video, you will learn how to subset and operate over multidimensional arrays.

In the next segment, you will learn how to initialise fixed-length one-dimensional NumPy arrays using different functions.