How do you stack, concatenate, and split NumPy arrays?

Combining and splitting arrays is a frequent operation in data preprocessing — assembling feature matrices from multiple sources, or splitting a dataset into folds for cross-validation.

import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])

# --- Concatenating along existing axes ---
np.concatenate([a, b], axis=0)  # stack rows (vertical)
# [[1 2]
#  [3 4]
#  [5 6]
#  [7 8]]

np.concatenate([a, b], axis=1)  # stack columns (horizontal)
# [[1 2 5 6]
#  [3 4 7 8]]

# --- Convenience stacking functions ---
np.vstack([a, b])   # vertical stack — same as axis=0
np.hstack([a, b])   # horizontal stack — same as axis=1 for 2-D
np.dstack([a, b])   # depth stack (creates a 3rd axis)

# stack — creates a NEW axis (different from concatenate!)
np.stack([a, b], axis=0)   # shape (2, 2, 2)
np.stack([a, b], axis=2)   # shape (2, 2, 2) — depth

# --- Splitting ---
big = np.arange(12).reshape(6, 2)
parts = np.vsplit(big, 3)    # split into 3 equal arrays along axis 0
# [array([[0,1]]), array([[2,3]]), ... ]

# Split at specific indices
parts = np.split(big, [2, 4], axis=0)  # [0:2], [2:4], [4:]

# Tile — repeat an array
np.tile(a, (2, 3))   # repeat a 2 times along rows, 3 times along cols

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