Method map_partitions

dask/dataframe/dask_expr/_collection.py:991–1133

Apply a Python function to each partition Parameters ---------- func : function Function applied to each partition. args, kwargs : Arguments and keywords to pass to the function. Arguments and keywords may contain ``FrameBase`` or

(
        self,
        func,
        *args,
        meta=no_default,
        enforce_metadata=True,
        transform_divisions=True,
        clear_divisions=False,
        align_dataframes=False,
        parent_meta=None,
        required_columns=None,
        **kwargs,
    )

    @insert_meta_param_description(pad=12)
    def map_partitions(
        self,
        func,
        *args,
        meta=no_default,
        enforce_metadata=True,
        transform_divisions=True,
        clear_divisions=False,
        align_dataframes=False,
        parent_meta=None,
        required_columns=None,
        **kwargs,
    ):
        """Apply a Python function to each partition

        Parameters
        ----------
        func : function
            Function applied to each partition.
        args, kwargs :
            Arguments and keywords to pass to the function. Arguments and
            keywords may contain ``FrameBase`` or regular python objects.
            DataFrame-like args (both dask and pandas) must have the same
            number of partitions as ``self`` or comprise a single partition.
            Key-word arguments, Single-partition arguments, and general
            python-object arguments will be broadcasted to all partitions.
        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.
        clear_divisions : bool, default False
            Whether divisions should be cleared. If True, `transform_divisions`
            will be ignored.
        required_columns : list or None, default None
            List of columns that ``func`` requires for execution. These columns
            must belong to the first DataFrame argument (in ``args``). If None
            is specified (the default), the query optimizer will assume that
            all input columns are required.
        $META

        Examples
        --------
        Given a DataFrame, Series, or Index, such as:

        >>> import pandas as pd
        >>> import dask.dataframe as dd
        >>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5],
        ...                    'y': [1., 2., 3., 4., 5.]})
        >>> ddf = dd.from_pandas(df, npartitions=2)

        One can use ``map_partitions`` to apply a function on each partition.
        Extra arguments and keywords can optionally be provided, and will be
        passed to the function after the partition.