def _na_for_min_count(values, axis):
<EOL>if is_numeric_dtype(values):<EOL><INDENT>values = values.astype('<STR_LIT>')<EOL><DEDENT>fill_value = na_value_for_dtype(values.dtype)<EOL>if values.ndim == <NUM_LIT:1>:<EOL><INDENT>return fill_value<EOL><DEDENT>else:<EOL><INDENT>result_shape = (values.shape[:axis] +<EOL>values.shape[axis + <NUM_LIT:1>:])<EOL>result = np.empty(result_shape, dtype=values.dtype)<EOL>result.fill(fill_value)<EOL>return result<EOL><DEDENT>
Return the missing value for `values`

    Parameters
    ----------
    values : ndarray
    axis : int or None
        axis for the reduction

    Returns
    -------
    result : scalar or ndarray
        For 1-D values, returns a scalar of the correct missing type.
        For 2-D values, returns a 1-D array where each element is missing.