Missing Data

All of the models can handle missing data. For performance reasons, the default is not to do any checking for missing data. If, however, you would like for missing data to be handled internally, you can do so by using the missing keyword argument. The default is to do nothing

In [1]: import statsmodels.api as sm

In [2]: data = sm.datasets.longley.load(as_pandas=False)

In [3]: data.exog = sm.add_constant(data.exog)

# add in some missing data
In [4]: missing_idx = np.array([False] * len(data.endog))

In [5]: missing_idx[[4, 10, 15]] = True

In [6]: data.endog[missing_idx] = np.nan

In [7]: ols_model = sm.OLS(data.endog, data.exog)

In [8]: ols_fit = ols_model.fit()

In [9]: print(ols_fit.params)
[nan nan nan nan nan nan nan]

This silently fails and all of the model parameters are NaN, which is probably not what you expected. If you are not sure whether or not you have missing data you can use missing = ‘raise’. This will raise a MissingDataError during model instantiation if missing data is present so that you know something was wrong in your input data.

In [10]: ols_model = sm.OLS(data.endog, data.exog, missing='raise')
---------------------------------------------------------------------------
MissingDataError                          Traceback (most recent call last)
<ipython-input-10-5debd60362bf> in <module>
----> 1 ols_model = sm.OLS(data.endog, data.exog, missing='raise')

/usr/lib/python3/dist-packages/statsmodels/regression/linear_model.py in __init__(self, endog, exog, missing, hasconst, **kwargs)
    856     def __init__(self, endog, exog=None, missing='none', hasconst=None,
    857                  **kwargs):
--> 858         super(OLS, self).__init__(endog, exog, missing=missing,
    859                                   hasconst=hasconst, **kwargs)
    860         if "weights" in self._init_keys:

/usr/lib/python3/dist-packages/statsmodels/regression/linear_model.py in __init__(self, endog, exog, weights, missing, hasconst, **kwargs)
    699         else:
    700             weights = weights.squeeze()
--> 701         super(WLS, self).__init__(endog, exog, missing=missing,
    702                                   weights=weights, hasconst=hasconst, **kwargs)
    703         nobs = self.exog.shape[0]

/usr/lib/python3/dist-packages/statsmodels/regression/linear_model.py in __init__(self, endog, exog, **kwargs)
    188     """
    189     def __init__(self, endog, exog, **kwargs):
--> 190         super(RegressionModel, self).__init__(endog, exog, **kwargs)
    191         self._data_attr.extend(['pinv_wexog', 'wendog', 'wexog', 'weights'])
    192 

/usr/lib/python3/dist-packages/statsmodels/base/model.py in __init__(self, endog, exog, **kwargs)
    234 
    235     def __init__(self, endog, exog=None, **kwargs):
--> 236         super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
    237         self.initialize()
    238 

/usr/lib/python3/dist-packages/statsmodels/base/model.py in __init__(self, endog, exog, **kwargs)
     74         missing = kwargs.pop('missing', 'none')
     75         hasconst = kwargs.pop('hasconst', None)
---> 76         self.data = self._handle_data(endog, exog, missing, hasconst,
     77                                       **kwargs)
     78         self.k_constant = self.data.k_constant

/usr/lib/python3/dist-packages/statsmodels/base/model.py in _handle_data(self, endog, exog, missing, hasconst, **kwargs)
     98 
     99     def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
--> 100         data = handle_data(endog, exog, missing, hasconst, **kwargs)
    101         # kwargs arrays could have changed, easier to just attach here
    102         for key in kwargs:

/usr/lib/python3/dist-packages/statsmodels/base/data.py in handle_data(endog, exog, missing, hasconst, **kwargs)
    669 
    670     klass = handle_data_class_factory(endog, exog)
--> 671     return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
    672                  **kwargs)

/usr/lib/python3/dist-packages/statsmodels/base/data.py in __init__(self, endog, exog, missing, hasconst, **kwargs)
     69             self.formula = kwargs.pop('formula')
     70         if missing != 'none':
---> 71             arrays, nan_idx = self.handle_missing(endog, exog, missing,
     72                                                   **kwargs)
     73             self.missing_row_idx = nan_idx

/usr/lib/python3/dist-packages/statsmodels/base/data.py in handle_missing(cls, endog, exog, missing, **kwargs)
    283 
    284         elif missing == 'raise':
--> 285             raise MissingDataError("NaNs were encountered in the data")
    286 
    287         elif missing == 'drop':

MissingDataError: NaNs were encountered in the data

If you want statsmodels to handle the missing data by dropping the observations, use missing = ‘drop’.

In [11]: ols_model = sm.OLS(data.endog, data.exog, missing='drop')

We are considering adding a configuration framework so that you can set the option with a global setting.