Return values at the given quantile over requested axis.

        Parameters
        ----------
        q : float or array-like, default 0.5 (50% quantile)
            Value between 0 <= q <= 1, the quantile(s) to compute.
        axis : {0, 1, 'index', 'columns'} (default 0)
            Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
        numeric_only : bool, default True
            If False, the quantile of datetime and timedelta data will be
            computed as well.
        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
            This optional parameter specifies the interpolation method to use,
            when the desired quantile lies between two data points `i` and `j`:

            * linear: `i + (j - i) * fraction`, where `fraction` is the
              fractional part of the index surrounded by `i` and `j`.
            * lower: `i`.
            * higher: `j`.
            * nearest: `i` or `j` whichever is nearest.
            * midpoint: (`i` + `j`) / 2.

            .. versionadded:: 0.18.0

        Returns
        -------
        Series or DataFrame

            If ``q`` is an array, a DataFrame will be returned where the
              index is ``q``, the columns are the columns of self, and the
              values are the quantiles.
            If ``q`` is a float, a Series will be returned where the
              index is the columns of self and the values are the quantiles.

        See Also
        --------
        core.window.Rolling.quantile: Rolling quantile.
        numpy.percentile: Numpy function to compute the percentile.

        Examples
        --------
        >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
        ...                   columns=['a', 'b'])
        >>> df.quantile(.1)
        a    1.3
        b    3.7
        Name: 0.1, dtype: float64
        >>> df.quantile([.1, .5])
               a     b
        0.1  1.3   3.7
        0.5  2.5  55.0

        Specifying `numeric_only=False` will also compute the quantile of
        datetime and timedelta data.

        >>> df = pd.DataFrame({'A': [1, 2],
        ...                    'B': [pd.Timestamp('2010'),
        ...                          pd.Timestamp('2011')],
        ...                    'C': [pd.Timedelta('1 days'),
        ...                          pd.Timedelta('2 days')]})
        >>> df.quantile(0.5, numeric_only=False)
        A                    1.5
        B    2010-07-02 12:00:00
        C        1 days 12:00:00
        Name: 0.5, dtype: object

def quantile(self, q=0.5, axis=0, numeric_only=True,
                 interpolation='linear'):
        """
        Return values at the given quantile over requested axis.

        Parameters
        ----------
        q : float or array-like, default 0.5 (50% quantile)
            Value between 0 <= q <= 1, the quantile(s) to compute.
        axis : {0, 1, 'index', 'columns'} (default 0)
            Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
        numeric_only : bool, default True
            If False, the quantile of datetime and timedelta data will be
            computed as well.
        interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
            This optional parameter specifies the interpolation method to use,
            when the desired quantile lies between two data points `i` and `j`:

            * linear: `i + (j - i) * fraction`, where `fraction` is the
              fractional part of the index surrounded by `i` and `j`.
            * lower: `i`.
            * higher: `j`.
            * nearest: `i` or `j` whichever is nearest.
            * midpoint: (`i` + `j`) / 2.

            .. versionadded:: 0.18.0

        Returns
        -------
        Series or DataFrame

            If ``q`` is an array, a DataFrame will be returned where the
              index is ``q``, the columns are the columns of self, and the
              values are the quantiles.
            If ``q`` is a float, a Series will be returned where the
              index is the columns of self and the values are the quantiles.

        See Also
        --------
        core.window.Rolling.quantile: Rolling quantile.
        numpy.percentile: Numpy function to compute the percentile.

        Examples
        --------
        >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
        ...                   columns=['a', 'b'])
        >>> df.quantile(.1)
        a    1.3
        b    3.7
        Name: 0.1, dtype: float64
        >>> df.quantile([.1, .5])
               a     b
        0.1  1.3   3.7
        0.5  2.5  55.0

        Specifying `numeric_only=False` will also compute the quantile of
        datetime and timedelta data.

        >>> df = pd.DataFrame({'A': [1, 2],
        ...                    'B': [pd.Timestamp('2010'),
        ...                          pd.Timestamp('2011')],
        ...                    'C': [pd.Timedelta('1 days'),
        ...                          pd.Timedelta('2 days')]})
        >>> df.quantile(0.5, numeric_only=False)
        A                    1.5
        B    2010-07-02 12:00:00
        C        1 days 12:00:00
        Name: 0.5, dtype: object
        """
        self._check_percentile(q)

        data = self._get_numeric_data() if numeric_only else self
        axis = self._get_axis_number(axis)
        is_transposed = axis == 1

        if is_transposed:
            data = data.T

        result = data._data.quantile(qs=q,
                                     axis=1,
                                     interpolation=interpolation,
                                     transposed=is_transposed)

        if result.ndim == 2:
            result = self._constructor(result)
        else:
            result = self._constructor_sliced(result, name=q)

        if is_transposed:
            result = result.T

        return result