#!/usr/bin/env python

import pandas as pd
import matplotlib.pylab as plt
import numpy as np

# see also http://www.wired.com/wiredscience/2011/01/linear-regression-with-pylab/

data = [
(0.2, 1.3),
(1.3, 3.9),
(2.1, 4.8),
(2.9,5.5),
(3.3,6.9)
]

df = pd.DataFrame(data, columns=['X', 'Y'])

print(df)

model_with_intercept = pd.ols(y=df['Y'], x=df['X'], intercept=True)
df['Y_fit_with_intercept'] = model_with_intercept.y_fitted

model_no_intercept = pd.ols(y=df['Y'], x=df['X'], intercept=False)
df['Y_fit_no_intercept'] = model_no_intercept.y_fitted

df.plot(x='X', y=['Y', 'Y_fit_with_intercept', 'Y_fit_no_intercept'])

plt.show()