Linear regression - reduce degrees of freedom

Question

I have a Pandas dataframe with columns like

Order     Balance     Profit cum (%)

I'm doing a linear regression

model_profit_tr = pd.ols(y=df_closed['Profit cum (%)'], x=df_closed['Order'])

The problem with this is that standard model is like (equation of a line that does not pass through the origin)

y = a * x + b

There is 2 degrees of freedom (a and b)

slope (a):

a=model_profit_tr.beta['x']

and intercept (b):

b=model_profit_tr.beta['intercept']

I'd like to reduce degree of freedom for my model (from 2 to 1) and I 'd like to have a model like

y = a * x

Answer 1

Use the intercept keyword argument:

model_profit_tr = pd.ols(y=df_closed['Profit cum (%)'], 
                         x=df_closed['Order'], 
                         intercept=False)

From docs:

In [65]: help(pandas.ols) 
Help on function ols in module pandas.stats.interface:

ols(**kwargs)

    [snip]

    Parameters
    ----------
    y: Series or DataFrame
        See above for types
    x: Series, DataFrame, dict of Series, dict of DataFrame, Panel
    weights : Series or ndarray
        The weights are presumed to be (proportional to) the inverse of the
        variance of the observations.  That is, if the variables are to be
        transformed by 1/sqrt(W) you must supply weights = 1/W
    intercept: bool
        True if you want an intercept.  Defaults to True.
    nw_lags: None or int
        Number of Newey-West lags.  Defaults to None.

    [snip]