# dask.dataframe.map_overlap

dask.dataframe.map_overlap(func, df, before, after, *args, meta=<no_default>, enforce_metadata=True, transform_divisions=True, align_dataframes=True, **kwargs)

  Apply a function to each partition, sharing rows with adjacent partitions.

  Parameters:
    func function

      The function applied to each partition. If this function accepts the special `partition_info` keyword argument, it will receive information on the partition’s relative location within the dataframe.

    df: dd.DataFrame, dd.Series
    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.

    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`.

    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 pd.DataFrame, pd.Series, dict, iterable, tuple, optional

      An empty `pd.DataFrame` or `pd.Series` that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a `DataFrame`, a `dict` of `{name: dtype}` or iterable of `(name, dtype)` can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of `(name, dtype)` can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing `meta` is recommended. For more information, see `dask.dataframe.utils.make_meta`.

  See also

  `dd.DataFrame.map_overlap`