Method repartition

dask/dataframe/dask_expr/_collection.py:1288–1383

Repartition a collection Exactly one of `divisions`, `npartitions` or `partition_size` should be specified. A ``ValueError`` will be raised when that is not the case. Parameters ---------- divisions : list, optional The "dividing lines" used to s

(
        self,
        divisions: tuple | None = None,
        npartitions: int | None = None,
        partition_size: str | None = None,
        freq=None,
        force: bool = False,
    )

        )

    def repartition(
        self,
        divisions: tuple | None = None,
        npartitions: int | None = None,
        partition_size: str | None = None,
        freq=None,
        force: bool = False,
    ):
        """Repartition a collection

        Exactly one of `divisions`, `npartitions` or `partition_size` should be
        specified. A ``ValueError`` will be raised when that is not the case.

        Parameters
        ----------
        divisions : list, optional
            The "dividing lines" used to split the dataframe into partitions.
            For ``divisions=[0, 10, 50, 100]``, there would be three output partitions,
            where the new index contained [0, 10), [10, 50), and [50, 100), respectively.
            See https://docs.dask.org/en/latest/dataframe-design.html#partitions.
        npartitions : int, Callable, optional
            Approximate number of partitions of output. The number of
            partitions used may be slightly lower than npartitions depending
            on data distribution, but will never be higher.
            The Callable gets the number of partitions of the input as an argument
            and should return an int.
        partition_size : str, optional
            Max number of bytes of memory for each partition. Use numbers or strings
            like 5MB. If specified npartitions and divisions will be ignored. Note that
            the size reflects the number of bytes used as computed by
            pandas.DataFrame.memory_usage, which will not necessarily match the size
            when storing to disk.

            .. warning::

               This keyword argument triggers computation to determine
               the memory size of each partition, which may be expensive.

        force : bool, default False
            Allows the expansion of the existing divisions.
            If False then the new divisions' lower and upper bounds must be
            the same as the old divisions'.
        freq : str, pd.Timedelta
            A period on which to partition timeseries data like ``'7D'`` or
            ``'12h'`` or ``pd.Timedelta(hours=12)``.  Assumes a datetime index.

        Notes
        -----
        Exactly one of `divisions`, `npartitions`, `partition_size`, or `freq`
        should be specified. A ``ValueError`` will be raised when that is
        not the case.

        Also note that ``len(divisions)`` is equal to ``npartitions + 1``. This is because ``divisions``
        represents the upper and lower bounds of each partition. The first item is the
        lower bound of the first partition, the second item is the lower bound of the
        second partition and the upper bound of the first partition, and so on.
        The second-to-last item is the lower bound of the last partition, and the last
        (extra) item is the upper bound of the last partition.