Lists, arrays, and structured storage

Physics simulations deal with collections of data: time series, particle positions, field values. Python provides lists and NumPy provides arrays.

Python lists — Ordered, mutable, can hold mixed types:

temperatures = [20.1, 21.3, 19.8, 22.0]
temperatures.append(23.5)       # add element
print(temperatures[0])          # → 20.1 (0-indexed)
print(len(temperatures))        # → 5

NumPy arrays — Homogeneous, fast, support vectorised operations:

import numpy as np

t = np.linspace(0, 10, 1000)      # 1000 points from 0 to 10
x = np.sin(2 * np.pi * t)         # element-wise sine

mean_x = np.mean(x)               # statistical operations

Why arrays over lists?

• Speed: NumPy operations run in compiled C, not interpreted Python.
• Broadcasting: a * 2 multiplies every element — no loop needed.
• Memory: Arrays store raw numbers; lists store Python objects with overhead.

Slicing

data = np.array([10, 20, 30, 40, 50])
data[1:4]    # → [20, 30, 40]   (indices 1, 2, 3)
data[::2]    # → [10, 30, 50]   (every 2nd element)

Tip: Use np.array for numerical computation and Python list for heterogeneous or small collections. Never loop over array elements when a vectorised operation exists.

Common pitfall: Assigning a list copies a reference, not the data: after b = a, mutating b mutates a too. Deliberate copies (list(a), slicing) are how you actually fork data.