Method map_overlap

dask/dataframe/dask_expr/_collection.py:1132–1286

Apply a function to each partition, sharing rows with adjacent partitions. This can be useful for implementing windowing functions such as ``df.rolling(...).mean()`` or ``df.diff()``. Parameters ---------- func : function Function applied to each

(
        self,
        func,
        before,
        after,
        *args,
        meta=no_default,
        enforce_metadata=True,
        transform_divisions=True,
        clear_divisions=False,
        align_dataframes=False,
        **kwargs,
    )

    @insert_meta_param_description(pad=12)
    def map_overlap(
        self,
        func,
        before,
        after,
        *args,
        meta=no_default,
        enforce_metadata=True,
        transform_divisions=True,
        clear_divisions=False,
        align_dataframes=False,
        **kwargs,
    ):
        """Apply a function to each partition, sharing rows with adjacent partitions.

        This can be useful for implementing windowing functions such as
        ``df.rolling(...).mean()`` or ``df.diff()``.

        Parameters
        ----------
        func : function
            Function applied to each partition.
        before : int, timedelta or string timedelta
            The rows to prepend to partition ``i`` from the end of
            partition ``i - 1``.
        after : int, timedelta or string timedelta
            The rows to append to partition ``i`` from the beginning
            of partition ``i + 1``.
        args, kwargs :
            Positional and keyword arguments to pass to the function.
            Positional arguments are computed on a per-partition basis, while
            keyword arguments are shared across all partitions. The partition
            itself will be the first positional argument, with all other
            arguments passed *after*. Arguments can be ``Scalar``, ``Delayed``,
            or regular Python objects. DataFrame-like args (both dask and
            pandas) will be repartitioned to align (if necessary) before
            applying the function; see ``align_dataframes`` to control this
            behavior.
        enforce_metadata : bool, default True
            Whether to enforce at runtime that the structure of the DataFrame
            produced by ``func`` actually matches the structure of ``meta``.
            This will rename and reorder columns for each partition,
            and will raise an error if this doesn't work,
            but it won't raise if dtypes don't match.
        transform_divisions : bool, default True
            Whether to apply the function onto the divisions and apply those
            transformed divisions to the output.
        align_dataframes : bool, default True
            Whether to repartition DataFrame- or Series-like args
            (both dask and pandas) so their divisions align before applying
            the function. This requires all inputs to have known divisions.
            Single-partition inputs will be split into multiple partitions.

            If False, all inputs must have either the same number of partitions
            or a single partition. Single-partition inputs will be broadcast to
            every partition of multi-partition inputs.
        $META