# dask.dataframe.DataFrame.sort_values

DataFrame.sort_values(by: str | list[str], npartitions: int | Literal['auto'] | None = None, ascending: bool = True, na_position: Literal['first', 'last'] = 'last', sort_function: Callable[[DataFrame], DataFrame] | None = None, sort_function_kwargs: Mapping[str, Any] | None = None, **kwargs) → DataFrame

  Sort the dataset by a single column.

  Sorting a parallel dataset requires expensive shuffles and is generally not recommended. See `set_index` for implementation details.

  Parameters:
    by: str or list[str]

      Column(s) to sort by.

    npartitions: int, None, or ‘auto’

      The ideal number of output partitions. If None, use the same as the input. If ‘auto’ then decide by memory use.

    ascending: bool, optional

      Sort ascending vs. descending. Defaults to True.

    na_position: {‘last’, ‘first’}, optional

      Puts NaNs at the beginning if ‘first’, puts NaN at the end if ‘last’. Defaults to ‘last’.

    sort_function: function, optional

      Sorting function to use when sorting underlying partitions. If None, defaults to `M.sort_values` (the partition library’s implementation of `sort_values`).

    sort_function_kwargs: dict, optional

      Additional keyword arguments to pass to the partition sorting function. By default, `by`, `ascending`, and `na_position` are provided.

  Examples

      >>> df2 = df.sort_values('x')