Single and Multi-Dimensional Arrays

Single-Dimensional Arrays

A single-dimensional array is a linear collection of elements, meaning it has only one row of data. Each element is accessed using a single index.

Think of it like a list of numbers in a straight line:

[10, 20, 30, 40, 50]

Each item in this array is accessed using a single index.

Example Using Array Module

import array

# Creating a single-dimensional array of integers
numbers = array.array('i', [10, 20, 30, 40, 50])

# Accessing elements using their index
print(numbers[0])  # Output: 10
print(numbers[3])  # Output: 40

Multi-Dimensional Arrays

A multi-dimensional array is an array containing other arrays inside it. The most common type is a 2D array, which resembles a table with rows and columns.

For example, a 2D array can be visualised as:

[
  [1, 2, 3], 
  [4, 5, 6], 
  [7, 8, 9]
]

Each element is accessed using two indices: one for the row and one for the column.

2D Multidimensional Array

Example Using NumPy Module

Python’s built-in array module only supports single-dimensional arrays, so we use NumPy for multi-dimensional arrays.

import numpy as np

# Creating a 2D array (3 rows, 3 columns)
matrix = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])

# Accessing elements using row and column indices
print(matrix[0][1])  # Output: 2 (row 0, column 1)
print(matrix[2][2])  # Output: 9 (row 2, column 2)

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Key Differences

Feature	Single-Dimensional Array	Multi-Dimensional Array
Structure	A single row of elements	A grid (rows and columns)
Indexing	Uses one index (e.g., arr[2])	Uses multiple indices (e.g., arr[1][2])
Usage	Used for simple lists of data	Used for tables, matrices, images, etc.
Library	Can use array module or lists	Requires numpy for efficient handling