Module 1 · Lesson 5

Combining & Splitting Arrays

• np.concatenate([a, b], axis=...) joins arrays along an existing axis (shapes must match on every other axis).
• np.vstack / np.hstack are convenience wrappers for concatenating along axis 0 / axis 1.
• np.stack([a, b], axis=...) joins arrays along a new axis instead — use this when you want to go from several (n,) arrays to one (2, n) or (n, 2) array.
• np.split(arr, n, axis=...) divides an array into n equal pieces along an axis.

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

print(np.concatenate([a, b]))          # (6,)
print(np.stack([a, b]))                # (2, 3) — new axis 0
print(np.stack([a, b], axis=1))        # (3, 2) — new axis 1

m = np.arange(8).reshape(2, 4)
left, right = np.split(m, 2, axis=1)
print(left)
print(right)

Output:

[1 2 3 4 5 6]
[[1 2 3]
 [4 5 6]]
[[1 4]
 [2 5]
 [3 6]]
[[0 1]
 [4 5]]
[[2 3]
 [6 7]]

def interleave(a, b):
    return np.stack([a, b], axis=1).ravel()

🎯 Key Takeaways

• NumPy arrays beat Python lists for numeric work because they’re contiguous, fixed-type, and support vectorized operations in compiled code.
• Pick the right creation function for the job: np.array from existing data, np.zeros/ones/full/eye for placeholders, np.arange/ np.linspace for ranges.
• .shape, .ndim, .size, .dtype tell you everything about an array’s layout; .astype converts between dtypes (and can silently lose data).
• .reshape, .ravel, .flatten, .T, and np.newaxis rearrange data without changing its values.
• np.concatenate/vstack/hstack join along an existing axis; np.stack creates a new one.

Next up: Module 2 — Indexing, Slicing & Views, where you’ll learn how to select exactly the data you want — and which selections give you a view vs. a copy.