parameters: (values, axis=None, skipna=True, min_count=0, mask=None)
return_statement: return _maybe_null_out(result, axis, mask, values.shape, min_count=min_count)
docstring: Parameters
      ----------
      values : ndarray[dtype]
      axis: int, optional
      skipna : bool, default True
      min_count: int, default 0
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      -------
      result : dtype
  
      Examples
      --------
      >>> import pandas.core.nanops as nanops
      >>> s = pd.Series([1, 2, 3, np.nan])
      >>> nanops.nanprod(s)
      6.0
  
      Returns
      -------
      The product of all elements on a given axis. ( NaNs are treated as 1)
docstring_summary: Parameters
      ----------
      values : ndarray[dtype]
      axis: int, optional
      skipna : bool, default True
      min_count: int, default 0
      mask : ndarray[bool], optional
          nan-mask if known
docstring_tokens: Parameters ---------- values : ndarray [ dtype ] axis : int optional skipna : bool default True min_count : int default 0 mask : ndarray [ bool ] optional nan - mask if known
function: def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
      """
      Parameters
      ----------
      values : ndarray[dtype]
      axis: int, optional
      skipna : bool, default True
      min_count: int, default 0
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      -------
      result : dtype
  
      Examples
      --------
      >>> import pandas.core.nanops as nanops
      >>> s = pd.Series([1, 2, 3, np.nan])
      >>> nanops.nanprod(s)
      6.0
  
      Returns
      -------
      The product of all elements on a given axis. ( NaNs are treated as 1)
      """
      mask = _maybe_get_mask(values, skipna, mask)
  
      if skipna and mask is not None:
          values = values.copy()
          values[mask] = 1
      result = values.prod(axis)
      return _maybe_null_out(result, axis, mask, values.shape, min_count=min_count)
function_tokens: def nanprod ( values , axis = None , skipna = True , min_count = 0 , mask = None ) : mask = _maybe_get_mask ( values , skipna , mask ) if skipna and mask is not None : values = values . copy ( ) values [ mask ] = 1 result = values . prod ( axis ) return _maybe_null_out ( result , axis , mask , values . shape , min_count = min_count )