NumPy Arithmetic Operations
Add, Subtract, Multiply, Divide Arrays

Introduction

When you work with data in NumPy, it's often not about manipulating a single value—it's about performing calculations across entire arrays. Whether you're adding a constant, subtracting arrays element-wise, or calculating percentages, NumPy's arithmetic operations make it simple and fast.

Why Use NumPy for Arithmetic?

Standard Python lists require looping for element-wise calculations, which is inefficient. NumPy arrays are optimized for vectorized operations—this means you can write cleaner code that runs significantly faster.

Basic Arithmetic Operations

Let’s explore the foundational arithmetic operations in NumPy. These include:

• Addition
• Subtraction
• Multiplication
• Division
• Modulus (remainder)
• Exponentiation (power)

Step-by-Step Examples

1. Setup: Import NumPy

import numpy as np

2. Create Sample Arrays

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

These two arrays have the same shape and can be used for element-wise operations.

3. Addition

result = a + b
print(result)

Output: [11 22 33]

Each element of a is added to the corresponding element in b.

4. Subtraction

result = a - b
print(result)

Output: [ 9 18 27]

Works element by element, just like basic algebra.

5. Multiplication

result = a * b
print(result)

Output: [10 40 90]

Performs element-wise multiplication, not matrix multiplication.

6. Division

result = a / b
print(result)

Output: [10. 10. 10.]

Division always returns a float array, even if all numbers are integers.

7. Modulus (Remainder)

result = a % b
print(result)

Output: [0 0 0]

Shows the remainder of division element-wise. All elements divide evenly here.

8. Exponentiation

result = a ** b
print(result)

Output: [ 10 400 27000]

Raises each element of a to the power of the corresponding element in b.

Broadcasting: Arithmetic with Scalars

result = a + 5
print(result)

Output: [15 25 35]

NumPy automatically broadcasts the scalar (5) across the entire array.

Precautions and Tips

• Always ensure arrays are compatible in shape before element-wise operations. Mismatched shapes raise a ValueError.
• Division by zero returns inf or nan, not a crash—NumPy is designed for numerical robustness.
• Use np.seterr() to configure how floating-point errors are handled (e.g., ignore, warn, or raise).

Verification Tips

• Check array.shape before performing operations.
• Use np.allclose() to compare arrays with floating-point values.
• Always print and validate results during learning or debugging.

Conclusion

Arithmetic operations in NumPy are intuitive and incredibly powerful. By mastering these basics, you unlock the true performance of numerical Python. Whether you're building machine learning models, analyzing data, or just crunching numbers, these operations form the foundation of every NumPy workflow.

Next Steps

In the next topic, we'll dive deeper into Broadcasting—one of NumPy’s most powerful features that lets you operate on arrays of different shapes. Stay tuned!