Explain the Python Arrays

ICSM Computer 500 29 Sep 2025

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Updated 29 Sep 2025

ICSM Computer 29 Sep 2025

In Python, when people say “arrays,” they might mean two different things:

Lists (built-in)
- Most common “array-like” structure in Python.
- Can store items of mixed types (e.g., integers, strings, objects).

numbers = [1, 2, 3, 4, 5]
mixed = [1, "hello", 3.14, True]

Array module (array library)
- Provides a true array type (from the array module).
- Stores elements of a single data type only (like C-style arrays).
- More memory-efficient than lists for large collections of numbers.

import array

nums = array.array('i', [1, 2, 3, 4, 5])  # 'i' means signed integer
nums.append(6)
print(nums)

NumPy arrays (numpy.ndarray)
- Most powerful, used in data science, machine learning, and scientific computing.
- Supports multi-dimensional arrays (matrices, tensors).
- Provides fast vectorized operations.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr * 2)  # vectorized operation

Feature	List	`array` module	NumPy Array
Stores mixed types?	Yes	No (one type only)	No (one type only)
Performance	Slower	Faster than list	Fastest (C backend)
Dimensions	1D, nested	1D only	Multi-dimensional
Use case	General usage	Memory efficiency	Data science, math

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