code: def _na_for_min_count(values, axis):
      
      # 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
docs: 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.