11. Combining & Splitting Arrays

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])

np.concatenate([a, b], axis=0)   # stack rows -> 3x2
np.vstack([a, b])                # same as concatenate axis=0
np.hstack([a, a])                # side by side -> 2x4
np.column_stack([[1,2,3],[4,5,6]])  # -> [[1 4],[2 5],[3 6]]
np.stack([np.array([1,2]), np.array([3,4])])  # NEW axis -> shape (2,2)

m = np.arange(6)
np.split(m, 3)          # [array([0,1]), array([2,3]), array([4,5])]
np.array_split(m, 4)    # uneven split allowed

⚠️ Common Mistake: concatenate requires matching shapes on all axes except the join axis. Mismatched dimensions → ValueError.

⭐ Interview Question: stack vs concatenate? concatenate keeps ndim the same; stack adds a new dimension.