Function repartition

dask/dataframe/dask_expr/_collection.py:5955–5995

Repartition dataframe along new divisions Dask.DataFrame objects are partitioned along their index. Often when multiple dataframes interact we need to align these partitionings. The ``repartition`` function constructs a new DataFrame object holding the same data but partitioned on

(df, divisions, force=False)

def repartition(df, divisions, force=False):
    """Repartition dataframe along new divisions

    Dask.DataFrame objects are partitioned along their index.  Often when
    multiple dataframes interact we need to align these partitionings.  The
    ``repartition`` function constructs a new DataFrame object holding the same
    data but partitioned on different values.  It does this by performing a
    sequence of ``loc`` and ``concat`` calls to split and merge the previous
    generation of partitions.

    Parameters
    ----------

    divisions : list
        List of partitions to be used
    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.

    Examples
    --------

    >>> df = df.repartition([0, 5, 10, 20])  # doctest: +SKIP

    Also works on Pandas objects

    >>> ddf = dd.repartition(df, [0, 5, 10, 20])  # doctest: +SKIP
    """
    if isinstance(df, FrameBase):
        return df.repartition(divisions=divisions, force=force)
    elif is_dataframe_like(df) or is_series_like(df):
        return new_collection(
            FromPandasDivisions(
                _BackendData(df),
                divisions=divisions,
                pyarrow_strings_enabled=pyarrow_strings_enabled(),
            )
        )
    else:
        raise NotImplementedError(f"repartition is not implemented for {type(df)}.")


def pivot_table(df, index, columns, values, aggfunc="mean"):