parameters: (values, axis=None, skipna=True, mask=None)
return_statement: return result
docstring: Parameters
      ----------
      values : ndarray
      axis: int, optional
      skipna : bool, default True
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      --------
      result : int
          The index of min value in specified axis or -1 in the NA case
  
      Examples
      --------
      >>> import pandas.core.nanops as nanops
      >>> s = pd.Series([1, 2, 3, np.nan, 4])
      >>> nanops.nanargmin(s)
      0
docstring_summary: Parameters
      ----------
      values : ndarray
      axis: int, optional
      skipna : bool, default True
      mask : ndarray[bool], optional
          nan-mask if known
docstring_tokens: Parameters ---------- values : ndarray axis : int optional skipna : bool default True mask : ndarray [ bool ] optional nan - mask if known
function: def nanargmin(values, axis=None, skipna=True, mask=None):
      """
      Parameters
      ----------
      values : ndarray
      axis: int, optional
      skipna : bool, default True
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      --------
      result : int
          The index of min value in specified axis or -1 in the NA case
  
      Examples
      --------
      >>> import pandas.core.nanops as nanops
      >>> s = pd.Series([1, 2, 3, np.nan, 4])
      >>> nanops.nanargmin(s)
      0
      """
      values, mask, dtype, _, _ = _get_values(
          values, skipna, fill_value_typ='+inf', mask=mask)
      result = values.argmin(axis)
      result = _maybe_arg_null_out(result, axis, mask, skipna)
      return result
function_tokens: def nanargmin ( values , axis = None , skipna = True , mask = None ) : values , mask , dtype , _ , _ = _get_values ( values , skipna , fill_value_typ = '+inf' , mask = mask ) result = values . argmin ( axis ) result = _maybe_arg_null_out ( result , axis , mask , skipna ) return result