Lists, arrays, and structured storage

Python Lists & NumPy Arrays

Physics simulations handle collections like particle positions or time series. Python provides lists while NumPy provides arrays.

Python lists are ordered, mutable, and can hold mixed types. They use 0-indexing: temperatures[0] gets the first item, and len() gives the count.

NumPy arrays are homogeneous, fast, and support vectorised operations. Functions like np.linspace generate points, and operations apply element-wise.

Tip: Use np.array for numerical computation and Python list for heterogeneous or small collections.

Slicing, Speed & Pitfalls

Slicing extracts subsets using start:stop:step syntax. For example, data[1:4] returns indices 1, 2, and 3, while data[::2] takes every second element.

Broadcasting allows math operations like a * 2 to multiply every element without writing an explicit loop.

Common pitfall: Assigning a list copies a reference, not the data. After b = a, mutating b mutates a too. Always use explicit copies like list(a) or slicing to fork data.