Spearman Rank Correlation
Spearman Rank Correlation [Simply explained]
import numpy as np from scipy import stats import pandas as pd Example 1 Manual # Step 1: Grab the data hits = np.array([150, 180, 120, 210, 160]) rbis = np.array([75, 90, 50, 110, 85]) # Step 2: Rank the data hits_rank = stats.rankdata(hits) rbis_rank = stats.rankdata(rbis) # Step 3: Calculate the rank differences and their squares d = hits_rank - rbis_rank d_squared = d ** 2 # Step 4: Apply the Spearman formula n = len(hits) spearman_manual = 1 - (6 * np.sum(d_squared)) / (n * (n**2 - 1)) # Print the manually calculated Spearman correlation print(f"Manual Spearman Correlation: {spearman_manual}") 
Example 2 Fast wth scipy
# Step 1: Calculate Spearman's rank correlation using scipy spearman_corr, p_value = stats.spearmanr(hits, rbis) # Print the Spearman correlation coefficient and p-value print(f"Spearman Correlation: {spearman_corr}") 
Example 3 Ordinal Data with pandas data = { 'Hours_Studied': [10, 15, 5, 18], 'Grades': ['C', 'A', 'D', 'B'] } # Create DataFrame df = pd.DataFrame(data) # Convert Grades to numerical ordinal data grade_mapping = {'A': 4, 'B': 3, 'C': 2, 'D': 1} df['Grades_Ordinal'] = df['Grades'].map(grade_mapping) # Rank the data using pandas df['Hours_Rank'] = df['Hours_Studied'].rank() df['Grades_Rank'] = df['Grades_Ordinal'].rank() df.head(5) 
spearman_corr = df[['Hours_Rank', 'Grades_Rank']].corr(method='spearman').iloc[0, 1] print(f"Spearman correlation: {spearman_corr:.3f}") 
