def quantile(self, q=0.5, interpolation='linear'):

        self._check_percentile(q)

        if _np_version_under1p9:
            if interpolation != 'linear':
                raise ValueError("Interpolation methods other than linear "
                                 "are not supported in numpy < 1.9.")

        kwargs = dict()
        if not _np_version_under1p9:
            kwargs.update({'interpolation': interpolation})

        result = self._data.quantile(qs=q, **kwargs)

        if com.is_list_like(result):
            # explicitly use Float64Index to coerce empty result to float dtype
            index = Float64Index(q)
            return self._constructor(result, index=index, name=self.name)
        else:
            # scalar
            return result
Write a simple model as a pickle file
dataset/ETHPy150Open pydata/pandas/pandas/core/series.py/Series.quantile--crossover with--dataset/ETHPy150Open rrenaud/Gibberish-Detector/gib_detect_train.py/train