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statsmodels logistic regression odds ratio

>>> import statsmodels.api as sm
>>> import numpy as np
>>> X = np.random.normal(0, 1, (100, 3))
>>> y = np.random.choice([0, 1], 100)
>>> res = sm.Logit(y, X).fit()
Optimization terminated successfully.
         Current function value: 0.683158
         Iterations 4
>>> res.summary()
<class 'statsmodels.iolib.summary.Summary'>
"""
                           Logit Regression Results                           
==============================================================================
Dep. Variable:                      y   No. Observations:                  100
Model:                          Logit   Df Residuals:                       97
Method:                           MLE   Df Model:                            2
Date:                Sun, 05 Jun 2016   Pseudo R-squ.:                0.009835
Time:                        23:25:06   Log-Likelihood:                -68.316
converged:                       True   LL-Null:                       -68.994
                                        LLR p-value:                    0.5073
==============================================================================
                 coef    std err          z      P>|z|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1            -0.0033      0.181     -0.018      0.985        -0.359     0.352
x2             0.0565      0.213      0.265      0.791        -0.362     0.475
x3             0.2985      0.216      1.380      0.168        -0.125     0.723
==============================================================================
"""
>>>

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