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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _na_for_min_count(values, axis):
 """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. """

    # we either return np.nan or pd.NaT
    if is_numeric_dtype(values):
        values = values.astype('float64')
    fill_value = na_value_for_dtype(values.dtype)

    if values.ndim == 1:
        return fill_value
    else:
        result_shape = (values.shape[:axis] +
                        values.shape[axis + 1:])
        result = np.empty(result_shape, dtype=values.dtype)
        result.fill(fill_value)
        return result