Returns the float values converted into strings using
        the parameters given at initialisation, as a numpy array

def get_result_as_array(self):
        """
        Returns the float values converted into strings using
        the parameters given at initialisation, as a numpy array
        """

        if self.formatter is not None:
            return np.array([self.formatter(x) for x in self.values])

        if self.fixed_width:
            threshold = get_option("display.chop_threshold")
        else:
            threshold = None

        # if we have a fixed_width, we'll need to try different float_format
        def format_values_with(float_format):
            formatter = self._value_formatter(float_format, threshold)

            # default formatter leaves a space to the left when formatting
            # floats, must be consistent for left-justifying NaNs (GH #25061)
            if self.justify == 'left':
                na_rep = ' ' + self.na_rep
            else:
                na_rep = self.na_rep

            # separate the wheat from the chaff
            values = self.values
            is_complex = is_complex_dtype(values)
            mask = isna(values)
            if hasattr(values, 'to_dense'):  # sparse numpy ndarray
                values = values.to_dense()
            values = np.array(values, dtype='object')
            values[mask] = na_rep
            imask = (~mask).ravel()
            values.flat[imask] = np.array([formatter(val)
                                           for val in values.ravel()[imask]])

            if self.fixed_width:
                if is_complex:
                    return _trim_zeros_complex(values, na_rep)
                else:
                    return _trim_zeros_float(values, na_rep)

            return values

        # There is a special default string when we are fixed-width
        # The default is otherwise to use str instead of a formatting string
        if self.float_format is None:
            if self.fixed_width:
                float_format = partial('{value: .{digits:d}f}'.format,
                                       digits=self.digits)
            else:
                float_format = self.float_format
        else:
            float_format = lambda value: self.float_format % value

        formatted_values = format_values_with(float_format)

        if not self.fixed_width:
            return formatted_values

        # we need do convert to engineering format if some values are too small
        # and would appear as 0, or if some values are too big and take too
        # much space

        if len(formatted_values) > 0:
            maxlen = max(len(x) for x in formatted_values)
            too_long = maxlen > self.digits + 6
        else:
            too_long = False

        with np.errstate(invalid='ignore'):
            abs_vals = np.abs(self.values)
            # this is pretty arbitrary for now
            # large values: more that 8 characters including decimal symbol
            # and first digit, hence > 1e6
            has_large_values = (abs_vals > 1e6).any()
            has_small_values = ((abs_vals < 10**(-self.digits)) &
                                (abs_vals > 0)).any()

        if has_small_values or (too_long and has_large_values):
            float_format = partial('{value: .{digits:d}e}'.format,
                                   digits=self.digits)
            formatted_values = format_values_with(float_format)

        return formatted_values