Python Arrays

Lists Are Python’s Default "Array"

For most beginner and everyday tasks, a regular list already behaves like a dynamic array: it grows automatically and can be indexed, sliced, and looped over. Most Python code never needs anything more specialised.

scores = [85, 92, 78, 90]
scores.append(88)
print(scores[2])   # 78

The array Module

The built-in array module gives you a true fixed-type array. Every element must be the same numeric type, declared with a one-letter typecode, which saves memory compared to a list of the same size.

import array

nums = array.array("i", [1, 2, 3, 4])  # 'i' = signed int
nums.append(5)
print(nums)         # array('i', [1, 2, 3, 4, 5])
print(nums[0])      # 1

Common Typecodes

NumPy Arrays

For serious numerical work — data analysis, machine learning, image processing — the third-party NumPy library’s ndarray is the standard choice. It supports fast vectorised math, multiple dimensions, and a huge ecosystem of tools.

import numpy as np

grid = np.array([[1, 2], [3, 4]])
print(grid.shape)   # (2, 2)
print(grid * 2)     # element-wise multiplication

pip install numpy

Which One Should You Use?

• List — default choice for everyday Python code, mixed types, small to medium data.
• array module — large collections of one numeric type, when you need lower memory use and have no extra dependencies.
• NumPy array — numeric/scientific computing, matrices, and any project already using data-analysis libraries.