Lists vs Arrays

Now the focus on arrays in the syllabus may cause a bit of confusion, as Python tends to use lists instead.

The following breaks down the differences between lists and arrays in Python.

Feature	Lists	Arrays (array module or NumPy)
Data Type	Can store mixed data types	Typically stores a single data type
Flexibility	Can hold numbers, strings, other lists	Requires all elements to be of the same type
Performance	Slower for numerical operations	Faster for numerical operations
Memory Usage	Uses more memory	More memory-efficient for large data sets
Indexing	Supports indexing and slicing	Supports indexing and slicing (NumPy has more advanced indexing)
Operations	General-purpose operations	Supports mathematical and vectorised operations (NumPy)
Library Needed?	No (built-in)	Yes (array module or numpy)

Example of a List

my_list = [1, "apple", 3.14, [5, 6]]  # Stores different types of data
print(my_list[1])  # Output: apple

Example of an Array (array module)

import array
my_array = array.array('i', [1, 2, 3, 4])  # Only stores integers
print(my_array[1])  # Output: 2

When to Use Each?

Use lists when working with different types of data.

Use arrays when working with large numerical datasets for better performance.