v Spearman's Rank Correlation - Statistics

Spearman's Rank Correlation

Preliminaries

import numpy as np
import pandas as pd
import scipy.stats

Create Data

# Create two lists of random values
x = [1,2,3,4,5,6,7,8,9]
y = [2,1,2,4.5,7,6.5,6,9,9.5]

Calculate Spearman's Rank Correlation

Spearman's rank correlation is the Pearson's correlation coefficient of the ranked version of the variables.

# Create a function that takes in x's and y's
def spearmans_rank_correlation(xs, ys):

    # Calculate the rank of x's
    xranks = pd.Series(xs).rank()

    # Caclulate the ranking of the y's
    yranks = pd.Series(ys).rank()

    # Calculate Pearson's correlation coefficient on the ranked versions of the data
    return scipy.stats.pearsonr(xranks, yranks)
# Run the function
spearmans_rank_correlation(x, y)[0]
0.90377360145618091

Calculate Spearman's Correlation Using SciPy

# Just to check our results, here it Spearman's using Scipy
scipy.stats.spearmanr(x, y)[0]
0.90377360145618102