Title: Spearman's Rank Correlation
Slug: spearmans_rank_correlation
Summary: Spearman's Rank Correlation in Python.
Date: 2016-02-08 12:00
Category: Statistics
Tags: Basics Authors: Chris Albon

Preliminaries



In [1]:

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

Create Data



In [2]:

    
# 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.



In [3]:

    
# 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)



In [4]:

    
# Run the function
spearmans_rank_correlation(x, y)[0]









    Out[4]:





0.90377360145618091

Calculate Spearman's Correlation Using SciPy



In [5]:

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









    Out[5]:





0.90377360145618102