Arrays - operations

NumPy - The Basics

2 min read

Published Sep 22 2025

NumPyPython

Arithmetic Operations

+, -, *, /, ** → element-wise addition, subtraction, multiplication, division, power.
Works element-wise, so array shapes must be compatible.

Examples:

import numpy as np

# Create arrays

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

print(a + 5)

# [6 7 8]

print(a * 5)

# [ 5 10 15]

print("Addition:", a + b)

# Addition: [5 7 9]

print("Subtraction:", a - b)

# Subtraction: [-3 -3 -3]

print("Multiplication:", a * b)

# Multiplication: [ 4 10 18]

print("Division:", b / a)

# Division: [4. 2.5 2. ]

print("Power:", a ** 2)

# Power: [1 4 9]

Arithmetic operations also support broadcasting:

Broadcasting allows NumPy to perform element-wise operations on arrays of different shapes.
NumPy “stretches” the smaller array across the larger one without actually copying data.
Rules for broadcasting:
1. If arrays have different dimensions, prepend 1s to the smaller shape.
2. Arrays are compatible if in every dimension they are equal or one of them is 1.
3. NumPy stretches the dimension with size 1 to match the other array.

Examples:

import numpy as np

# 1D array

a = np.array([1, 2, 3])

# 2D array (3 rows, 3 columns)

b = np.array([[10, 20, 30],

[40, 50, 60],

[70, 80, 90]])

# Broadcasting in arithmetic operations

print("Addition:\n", b + a)

# → [[11 22 33]

# [41 52 63]

# [71 82 93]]

print("Subtraction:\n", b - a)

# → [[ 9 18 27]

# [39 48 57]

# [69 78 87]]

print("Multiplication:\n", b * a)

# → [[10 40 90]

# [40 100 180]

# [70 160 270]]

print("Division:\n", b / a)

# → [[10. 10. 10.]

# [40. 25. 20.]

# [70. 40. 30.]]

Comparison Operations

Comparison operations are element-wise checks between arrays, or between an array and a scalar.
They return a boolean array (True / False) with the same shape as the input arrays which can be used for masking, filtering, or conditional operations.
Works for arrays of same shape, or using broadcasting if shapes are compatible.

Comparison operators: