Function concat

dask/dataframe/dask_expr/_collection.py:5403–5531

Concatenate DataFrames along rows. - When axis=0 (default), concatenate DataFrames row-wise: - If all divisions are known and ordered, concatenate DataFrames keeping divisions. When divisions are not ordered, specifying interleave_partition=True allows concatenate divisio

(
    dfs,
    axis=0,
    join="outer",
    ignore_unknown_divisions=False,
    ignore_order=False,
    interleave_partitions=False,
    **kwargs,
)

def concat(
    dfs,
    axis=0,
    join="outer",
    ignore_unknown_divisions=False,
    ignore_order=False,
    interleave_partitions=False,
    **kwargs,
):
    """Concatenate DataFrames along rows.

    - When axis=0 (default), concatenate DataFrames row-wise:

      - If all divisions are known and ordered, concatenate DataFrames keeping
        divisions. When divisions are not ordered, specifying
        interleave_partition=True allows concatenate divisions each by each.

      - If any of division is unknown, concatenate DataFrames resetting its
        division to unknown (None)

    - When axis=1, concatenate DataFrames column-wise:

      - Allowed if all divisions are known.

      - If any of division is unknown, it raises ValueError.

    Parameters
    ----------
    dfs : list
        List of dask.DataFrames to be concatenated
    axis : {0, 1, 'index', 'columns'}, default 0
        The axis to concatenate along
    join : {'inner', 'outer'}, default 'outer'
        How to handle indexes on other axis
    interleave_partitions : bool, default False
        Whether to concatenate DataFrames ignoring its order. If True, every
        divisions are concatenated each by each.
    ignore_unknown_divisions : bool, default False
        By default a warning is raised if any input has unknown divisions.
        Set to True to disable this warning.
    ignore_order : bool, default False
        Whether to ignore order when doing the union of categoricals.

    Notes
    -----
    This differs in from ``pd.concat`` in the when concatenating Categoricals
    with different categories. Pandas currently coerces those to objects
    before concatenating. Coercing to objects is very expensive for large
    arrays, so dask preserves the Categoricals by taking the union of
    the categories.

    Examples
    --------
    If all divisions are known and ordered, divisions are kept.

    >>> import dask.dataframe as dd
    >>> a                                               # doctest: +SKIP
    dd.DataFrame<x, divisions=(1, 3, 5)>