Using Python Scipy's Distance Functions
When working with machine learning, data analysis, or computational geometry, calculating the distance between points in a multi-dimensional space is a crucial task. Distances are used in clustering algorithms, anomaly detection, and many other applications. Python’s SciPy library provides several distance functions that allow you to calculate different types of distances efficiently. In this blog post, we'll explore three popular distance metrics—Euclidean, Manhattan, and Hamming—using SciPy’s distance functions.