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)

Medicinal

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.

Singley

model.params works without scientific notification; thank you – Medicinal

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.

Carter

you can import is as: from statsmodels.iolib import summary2 and simply call is on the result of the fit function – Phanerozoic

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.

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