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)
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.