Python has a few ways to work with arrays, depending on what you’re trying to do. Let’s break it down 🧠🐍

🧺 1. Using Lists as Arrays

Python’s built-in lists are the most common way to store sequences of items.

fruits = ["apple", "banana", "cherry"]
print(fruits[0])  # Output: apple

• Lists can hold mixed data types.
• They’re dynamic, so you can add/remove items easily.
• You can loop through them, slice them, and use tons of built-in methods.

🧪 2. Using the array Module

If you want a more memory-efficient array with only one data type, use the array module.

import array as arr

numbers = arr.array('i', [1, 2, 3, 4])  # 'i' stands for integer
numbers.append(5)
print(numbers)

• Type codes like 'i' (int), 'f' (float) define the data type.
• Great for large numeric datasets when memory matters.

🔬 3. Using NumPy Arrays

For scientific computing or multi-dimensional arrays, NumPy is the go-to.

import numpy as np

a = np.array([1, 2, 3])
print(a * 2)  # Output: [2 4 6]

• Supports multi-dimensional arrays (like matrices).
• Super fast and efficient for math-heavy operations.
• Ideal for data science, machine learning, and simulations.

🧠 Quick Comparison