The following data supports the cookbook below. The data provides information about the institution’s enrollment numbers and the tuition costs. This cookbook’s fictional example data relates to college and universities. For Euclidian distance, we’ll replicate those results a second way with an assist from SciPy. I’ll show how to calculate three distance measures from scratch including Euclidian, hamming, and Jaccard. Likewise, we will use SciPy’s zscore function. We will also need to calculate square roots. For example, we will use Pandas and Numpy. import pandas as pd import numpy as np from math import sqrt from scipy.spatial import distance from scipy.stats import zscoreįrom these imports, you can infer what lies ahead. The first bit of code you will need are standard imports. ![]() Department of Education data on institutions of higher learning (stay tuned for that). ![]() A subsequent article will show these techniques with U.S. At the bottom of this article is a link to a supporting notebook scratch space. Throughout this cookbook are code snippets. ![]() It is helpful to know which other observations are objectively and empirically similar - so you can compare apples to apples and not apples to oranges. Identifying observations that are like a reference observation is a useful exercise when your analytical goal is to compare the reference observation with other observations. Under the hood, this math is an important component of clustering, factor analysis, component analysis, and other techniques. Using measures of distance to measure similarity is not novel. This coding cookbook explores how to use measures of distance when looking to measure similarity between and among one or more observation. How far is it? In hyperspaces, proximity indicate similarity.
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