Calculate Product Moment Correlation Coefficient (Pearson Coefficient, r)

pearson.py

import math
import random

class Buffer:
    def __init__(self, one, two):
        self.one = str(one)
        self.two = str(two)
        self.length = max((len(self.one), len(self.two))) + 1

    def __call__(self, obj):
        return " " * (self.length - len(str(obj)))

class Bivariate:
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.data = []

    def add_point(self, x, y):
        self.data.append((x, y))

    def pearson(self):
        n = len(self.data)

        sig_x    = sum((d[0] for d in self.data))
        sig_x_sq = sum((d[0] ** 2 for d in self.data))

        sig_y    = sum((d[1] for d in self.data))
        sig_y_sq = sum((d[1] ** 2 for d in self.data))

        sig_x_y  = sum((d[0] * d[1] for d in self.data))

        Sxx = sig_x_sq - ((sig_x ** 2) / n)
        Syy = sig_y_sq - ((sig_y ** 2) / n)
        Sxy = sig_x_y  - ((sig_x * sig_y) / n)

        r = Sxy / math.sqrt(Sxx * Syy)
        return r

    def pretty_print(self):
        row1 = self.x + Buffer(self.x, self.y)(self.x)
        row2 = self.y + Buffer(self.x, self.y)(self.y)

        for datum in self.data:
            b = Buffer(datum[0], datum[1])
            row1 += str(datum[0]) + b(datum[0])
            row2 += str(datum[1]) + b(datum[1])

        print(row1)
        print(row2)

## Answer Question

data = Bivariate("Rainfall", "Discharge")

data.add_point(1.3, 2.9)
data.add_point(1.9, 2.7)
data.add_point(4.2, 7.9)
data.add_point(3.7, 6.8)
data.add_point(1.8, 2.8)
data.add_point(2.5, 3.9)
data.add_point(2.6, 5.2)
data.add_point(0.5, 1.9)

data.pretty_print()
print("r = {}".format(data.pearson()))