Re-shaping of an Array & Element-wise Operations

Reshaping an Array

reshape() changes an array's dimensions without changing its data — the total number of elements must stay the same.

import numpy as np

a = np.arange(1, 13)         # [1 2 3 4 5 6 7 8 9 10 11 12]
print(a.shape)                  # (12,)

b = a.reshape(3, 4)               # reshape to 3 rows x 4 columns
print(b)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]

c = a.reshape(4, 3)
print(c)

d = a.reshape(2, 2, 3)             # 3D array: 2 blocks of 2x3
print(d.shape)                       # (2, 2, 3)

flat = b.flatten()                    # back to 1D
print(flat)                             # [1 2 3 4 5 6 7 8 9 10 11 12]

reshape() fails with a ValueError if the requested shape doesn't match the total element count (e.g. reshaping 12 elements into (3, 5)).

Element-wise Operations

NumPy applies arithmetic operators to every element automatically (no loop needed) — called vectorization.

a = np.array([1, 2, 3, 4])
b = np.array([10, 20, 30, 40])

print(a + b)     # [11 22 33 44]
print(a - b)      # [-9 -18 -27 -36]
print(a * b)       # [10 40 90 160]
print(b / a)         # [10. 10. 10. 10.]
print(a ** 2)          # [1 4 9 16]

Operations with a scalar (broadcasting)

a = np.array([1, 2, 3, 4])
print(a + 10)     # [11 12 13 14]
print(a * 2)       # [2 4 6 8]
print(a > 2)         # [False False True True]  -- element-wise boolean comparison

Element-wise math functions

a = np.array([1, 4, 9, 16])
print(np.sqrt(a))     # [1. 2. 3. 4.]
print(np.exp([1, 2]))  # [2.718... 7.389...]
print(np.log(a))         # natural log of each element