python sm.ols change format of summary to avoid scientific notation

Question

I am running an ols model and I need to know all the coefficients so I can use them in my analysis. How can I display/save the coefficients in a different format than scientific notation?

model = sm.ols(formula="sales ~ product_category + quantity_bought + quantity_ordered + quantity_returned + season", data=final_email).fit()
print model.summary()

OLS Regression Results                            
==============================================================================
Dep. Variable:                sales   R-squared:                       0.974
Model:                            OLS   Adj. R-squared:                  0.938
Method:                 Least Squares   F-statistic:                     27.26
Date:                Tue, 18 Apr 2017   Prob (F-statistic):           5.39e-13
Time:                        11:43:36   Log-Likelihood:                -806.04
No. Observations:                  60   AIC:                             1682.
Df Residuals:                      25   BIC:                             1755.
Df Model:                          34                                         
Covariance Type:            nonrobust                                         
======================================================================================
                         coef    std err          t      P>|t|      [95.0% Conf. Int.]
--------------------------------------------------------------------------------------
Intercept            -2.79e+05   2.883e+05     -0.987      0.333     -8.92e+05  3.14e+05
Product_category[A]   4.343e+04   2.456e+05      0.186      0.854     -4.95e+05  5.93e+05
Product_category[B]   2.784e+05    1.23e+05      1.128      0.270     -1.68e+05  5.75e+05
quantity_bought       -74678      1.754e+05     -0.048      0.962      -3.4e+05  3.24e+05
quantity_ordered      3.543e+05   1.363e+05      1.827      0.080     -4.21e+04  7.05e+05
quantity_returned     1.285e+05   2.154e+05      0.512      0.613     -4.61e+05  7.66e+05
season               -1.983e+04   1.76e+05     -0.133      0.895     -2.69e+05  

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.19e-29. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.

This didn't help:

pd.set_option('display.float_format', lambda x: '%3.f' % x)

Answer 1

So that is something that is hardcoded into the statsmodels source. But the best way to get the coefficients out would be with model.params . Take a look at the source for RegressionResults , specifically all of the attributes and that will show you how to access all of the pertinent information for you model fit.

Answer 2

As of version 0.10.2, there is an experimental function summary2() which takes a float_format .

Here is the docstring of that function from the source code :

    def summary2(self, yname=None, xname=None, title=None, alpha=.05,
                 float_format="%.4f"):
        """
        Experimental summary function to summarize the regression results.
        Parameters
        ----------
        yname : str
            The name of the dependent variable (optional).
        xname : list[str], optional
            Names for the exogenous variables. Default is `var_##` for ## in
            the number of regressors. Must match the number of parameters
            in the model.
        title : str, optional
            Title for the top table. If not None, then this replaces the
            default title.
        alpha : float
            The significance level for the confidence intervals.
        float_format : str
            The format for floats in parameters summary.
        Returns
        -------
        Summary
            Instance holding the summary tables and text, which can be printed
            or converted to various output formats.

Answer 3

You cannot make it by now with the statsmodels version 0.8.0 , because RegressionResultsWrapper.summary() method haven't got a good support for this feature. Only xname, yname, alpha, title are available.

So, there is a experiental function called RegressionResultsWrapper.summary() which has a parameter float_format which may let you realize what you need to do.

But since this is an experimental function. Bugs may occur when you use it. When you specify the float_format , some results may not behave as you expected.

I have reviewed the source code, finding that some format of the result are hard-coded . So if float_format doesn't work. Edit the source file may be your last option. Don't be worry, it may not difficult as you thought. Feel free to ask more.