NumPy Reshaping & Combining Arrays

Last Updated: 09 Nov 2025

Reshaping means changing the shape or dimensions of an array without changing its data. We often reshape arrays before feeding them into ML models or matrix operations.

Hinglish Tip: “Reshape ka matlab hota hai — same data, bas arrangement change karna.”

np.reshape()

  • Total elements must match: new shape = old size.
  • Use -1 for one unknown dimension only.
import numpy as np

arr = np.arange(12)
print(arr)
print("Shape:", arr.shape)

reshaped = arr.reshape(3, 4)
print(reshaped)
print("Reshaped Shape:", reshaped.shape)

# Unknown dimension
reshaped = arr.reshape(3, -1)   # -1 → auto-calculate
print(reshaped)
print("Reshaped Shape:", reshaped.shape)

Flattening Arrays

np.ravel() → View (changes affect original)

arr = np.array([[1, 2], [3, 4]])
flat = arr.ravel()
print(flat)
flat[0] = 99
print(arr)   # original changed!

np.flatten() → Copy (original safe)

arr = np.array([[1, 2], [3, 4]])
flat = arr.flatten()
flat[0] = 99
print(arr)   # original NOT changed

np.ravel() = reshape(-1) → faster, memory-efficient

Transpose → .T

arr = np.array([[1, 2, 3],
                [4, 5, 6]])
transposed = arr.T
print(transposed)
# [[1 4]
#  [2 5]
#  [3 6]]

Hinglish Tip: “Transpose = rows ban jate hain columns, columns ban jate hain rows.”

Stacking Arrays

np.concatenate() — along existing axis

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
print(np.concatenate((a, b), axis=0))   # vertical

np.vstack() — Vertical stack

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

np.hstack() — Horizontal stack

print(np.hstack((a, b)))   # [1 2 3 4 5 6]

np.dstack() — Depth (3rd axis)

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

Splitting Arrays

np.split() — into N equal parts

arr = np.arange(10)
print(np.split(arr, 2))   # 2 parts → [[0..4], [5..9]]

np.hsplit() — split columns

arr = np.arange(16).reshape(4, 4)
print(np.hsplit(arr, 2))  # two 4×2 arrays

np.vsplit() — split rows

print(np.vsplit(arr, 2))  # two 2×4 arrays

Use Cases

# Prepare image batch: 100 images → (100, 28, 28) to (100, 784)
images = np.random.rand(100, 28, 28)
flattened = images.reshape(100, -1)   # ML model input
print(flattened.shape)   # (100, 784)
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