import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
import patsy
print(patsy.__version__)

nobs = 51
x = np.linspace(-1, 1, nobs)
exog = x[:, None] * np.arange(4)
y = exog.sum(1)
exog[5, 1] = np.nan
endog = y + np.random.randn(nobs)
weights = np.ones(nobs)

data = dict(y=endog, x0=exog[:, 0], x1=exog[:, 1], x2=exog[:, 2], x3=exog[:, 3])


res1 = smf.wls('y~x0+x1', data=data).fit()
print(res1.summary())

res1 = smf.wls('y~x0+x1', data=data, weights=weights).fit()
print(res1.summary())