Pearson Correlation Coefficient, Covariance

pearson_corr.py

def main():
    n = int(raw_input().strip())
    X = map(float, raw_input().strip().split(' '))
    Y = map(float, raw_input().strip().split(' '))
    x_mean = sum(X) / len(X)
    y_mean = sum(Y) / len(Y)
    s_devX = std(X, x_mean)
    s_devY = std(Y, y_mean)
    covariance = cov(X, Y, x_mean, y_mean)
    print round(covariance / (s_devX * s_devY), 3)

# standard deviation
def std(arr, mean):
    return pow(sum([pow((x - mean), 2) for x in arr])/len(arr), 0.5)

# covariance
def cov(X, Y, x_mean, y_mean):
    return (sum([ (x-x_mean)*(y-y_mean) for x,y in zip(X, Y)]) / len(X))

main()