Function from_dask_array

dask/dataframe/dask_expr/_collection.py:5087–5136

Create a Dask DataFrame from a Dask Array. Converts a 2d array into a DataFrame and a 1d array into a Series. Parameters ---------- x : da.Array columns : list or string list of column names if DataFrame, single string if Series index : dask.dataframe.Index, optiona

(x, columns=None, index=None, meta=None)

def from_dask_array(x, columns=None, index=None, meta=None) -> DataFrame:
    """Create a Dask DataFrame from a Dask Array.

    Converts a 2d array into a DataFrame and a 1d array into a Series.

    Parameters
    ----------
    x : da.Array
    columns : list or string
        list of column names if DataFrame, single string if Series
    index : dask.dataframe.Index, optional
        An optional *dask* Index to use for the output Series or DataFrame.

        The default output index depends on whether `x` has any unknown
        chunks. If there are any unknown chunks, the output has ``None``
        for all the divisions (one per chunk). If all the chunks are known,
        a default index with known divisions is created.

        Specifying `index` can be useful if you're conforming a Dask Array
        to an existing dask Series or DataFrame, and you would like the
        indices to match.
    meta : object, optional
        An optional `meta` parameter can be passed for dask
        to specify the concrete dataframe type to be returned.
        By default, pandas DataFrame is used.

    Examples
    --------
    >>> import dask.array as da
    >>> import dask.dataframe as dd
    >>> x = da.ones((4, 2), chunks=(2, 2))
    >>> df = dd.io.from_dask_array(x, columns=['a', 'b'])
    >>> df.compute()
         a    b
    0  1.0  1.0
    1  1.0  1.0
    2  1.0  1.0
    3  1.0  1.0

    See Also
    --------
    dask.bag.to_dataframe: from dask.bag
    dask.dataframe.DataFrame.values: Reverse conversion
    dask.dataframe.DataFrame.to_records: Reverse conversion
    """
    from dask.dataframe.io import from_dask_array

    if columns is not None and isinstance(columns, list) and not len(columns):
        columns = None
    return from_dask_array(x, columns=columns, index=index, meta=meta)


@dataframe_creation_dispatch.register_inplace("pandas")