Prev Next

ufunc Intro

Universal functions (ufuncs) in NumPy are functions that operate element-wise on arrays, enabling fast vectorized operations. These functions simplify mathematical, logical, and statistical operations on arrays, making them efficient and concise.

Key Topics

Why Use ufuncs?

ufuncs allow you to perform operations on entire arrays without needing explicit loops, significantly improving performance for large datasets.

Example

# Example of a ufunc (square root)
import numpy as np

arr = np.array([4, 9, 16, 25])
result = np.sqrt(arr)

print("Square roots:", result)

Output

Square roots: [2. 3. 4. 5.]

Explanation: The sqrt ufunc computes the square root of each element in the array, demonstrating its element-wise operation.

Common ufuncs

Some common ufuncs include add, subtract, multiply, divide, and log. These can be applied to arrays for various mathematical computations.

Example

# Using the add ufunc
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = np.add(arr1, arr2)

print("Sum of arrays:", result)

Output

Sum of arrays: [5 7 9]

Explanation: The add ufunc adds corresponding elements of two arrays, showcasing its simplicity and efficiency.

Key Takeaways

  • Element-wise Operations: ufuncs perform operations on arrays element by element.
  • Performance: They are faster and more efficient than traditional loops for array computations.
  • Applications: Used in mathematics, statistics, and machine learning tasks.
Prev Next