One of the most powerful features of NumPy is its ufuncs, short for universal functions. These are vectorized wrappers for simple functions that allow you to perform element-wise operations on NumPy arrays with high performance and clean syntax.

In this article, you'll learn:

• ✅ What are ufuncs?

• How to use built-in ufuncs in NumPy

• Performing element-wise operations

• ⚙️ Creating your own ufuncs

• Full code examples

• Tips and Common pitfalls

What is a ufunc?

A ufunc (universal function) is a function that operates on ndarrays in an element-by-element fashion, supporting broadcasting, type casting, and array-like input/output.

Ufuncs are faster than regular Python loops and more concise than list comprehensions.

Why Use ufuncs?

Common NumPy ufuncs Categories

Basic Usage of ufuncs

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

# Element-wise addition
result = np.add(arr1, arr2)
print(result)

Output:

[5 7 9]

You can achieve the same using operators (thanks to ufunc overloading):

print(arr1 + arr2)

More Arithmetic ufunc Examples

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

print("Add:", np.add(a, b))
print("Subtract:", np.subtract(a, b))
print("Multiply:", np.multiply(a, b))
print("Divide:", np.divide(a, b))
print("Power:", np.power(a, b))

Trigonometric Functions

angles = np.array([0, np.pi/2, np.pi])
print("Sine:", np.sin(angles))
print("Cosine:", np.cos(angles))

Rounding and Floor/Ceil

arr = np.array([1.1, 2.5, 3.8])

print("Rounded:", np.around(arr))
print("Floor:", np.floor(arr))
print("Ceil:", np.ceil(arr))

Comparison Functions

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

print("x > y:", np.greater(x, y))
print("x == y:", np.equal(x, y))

⚙️ Creating Your Own ufunc with np.frompyfunc()

You can convert any Python function into a ufunc:

def custom_add(x, y):
    return x + y

ufunc_add = np.frompyfunc(custom_add, 2, 1)

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(ufunc_add(arr1, arr2))

Output:

[5 7 9]

⚠️ Note: frompyfunc() always returns an array of Python objects.

Full Working Example

import numpy as np

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

print("Addition:", np.add(a, b))
print("Multiplication:", np.multiply(a, b))
print("Sine of a:", np.sin(a))
print("Logarithm of b:", np.log(b))
print("Comparison (a < b):", np.less(a, b))

# Custom ufunc
def multiply_add(x, y):
    return x * y + 1

custom_func = np.frompyfunc(multiply_add, 2, 1)
print("Custom ufunc:", custom_func(a, b))

Tips for Using ufuncs

Common Pitfalls

Conclusion

Ufuncs are the secret weapon of NumPy. They allow you to perform fast, efficient, and readable array operations using vectorized code. Whether you're working with simple arithmetic or advanced scientific calculations, ufuncs are the backbone of high-performance computing in Python.

What’s Next?

• NumPy Array Broadcasting

• NumPy Mathematical Functions

• Data Aggregation with NumPy

• Vectorization vs Loops in Python