Spearman implementation In Python

Spearman.py

from __future__ import division
import numpy as np
''' Spearman Correlation '''
x = [73, 76, 78, 65, 86, 82, 91]
y = [77, 78, 79, 80, 86, 89, 95]


col = [list(a) for a in zip(x, y)]
xy = sorted(col, key=lambda x: x[0], reverse=True)
for i, row in enumerate(xy):
	# appending x_ value
	row.append(i+1)
	
_xy = sorted(xy, key=lambda x: x[1], reverse=True)
for i, row in enumerate(_xy):
	# appending y_ value
	row.append(i+1)

	# appending d = (x_ - y_)
	row.append(row[2] - row[3])

	# appending (x_ - y_)^2
	row.append((row[2] - row[3]) ** 2)

_xy = np.array(_xy)
n = len(_xy)

d = np.sum(_xy[:,4])
d2 = np.sum(_xy[:,5])

print (1 - (6*d2) / (n*(n**2 - 1)))*100