title: python sm.ols change format of summary to avoid scientific notation
body: <p>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?</p>
  
  <pre><code>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&gt;|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.
  </code></pre>
  
  <p>This didn't help:</p>
  
  <pre><code>pd.set_option('display.float_format', lambda x: '%3.f' % x)
  </code></pre>