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Arrays

Arrays are collections of items of the same type, commonly used for numerical computation. In Python, the list is general-purpose; numpy arrays provide efficient numeric arrays. Matlab uses arrays (matrices) as its core data structure.

# Python lists (general)
lst = [1, 2, 3]

# Numpy arrays (numeric)
import numpy as np
a = np.array([1, 2, 3])
b = a * 2  # element-wise

# Shape and indexing
a.shape
a[0]

% Create arrays/matrices
A = [1 2 3];       % row vector
M = [1 2; 3 4];    % 2x2 matrix

% Element-wise operations
B = A .* 2;

% Size and indexing
s = size(M);
v = M(1,2);

Gotchas

  • Numpy arrays enforce homogeneous types; mixing types will coerce to a common type.
  • Matlab is 1-based indexing; Python/NumPy are 0-based.
  • Be careful with shapes (row vs column) when multiplying matrices.