regression OLS in python

Question

I have some questions about multiple regression models in python:

1. Why is it necessary to apply a “dummy intercept” vector of ones to start for the Least Square method (OLS)? (I am refering to the use of X = sm.add_constant(X). I know that the Least square method is a system of derivatives equal to zero. Is it computed with some iterative method that make a “dummy intercept” necessary? Where can I find some informative material about the detail of the algorithm est = sm.OLS(y, X).fit()?

2. As far as I understood, scale.fit_transform produce a normalization of the data. Usually a normalization do not produce value higher than 1. Why, once scaled I see value that exceed 1?

3. Where is it possible to find a official documentation about python functions?

Thanks in advance