Function from_dict

dask/dataframe/dask_expr/_collection.py:5035–5084

Construct a Dask DataFrame from a Python Dictionary Parameters ---------- data : dict Of the form {field : array-like} or {field : dict}. npartitions : int The number of partitions of the index to create. Note that depending on the size and index of the

(
    data,
    npartitions,
    orient="columns",
    dtype=None,
    columns=None,
    constructor=pd.DataFrame,
)

@dataframe_creation_dispatch.register_inplace("pandas")
def from_dict(
    data,
    npartitions,
    orient="columns",
    dtype=None,
    columns=None,
    constructor=pd.DataFrame,
):
    """
    Construct a Dask DataFrame from a Python Dictionary

    Parameters
    ----------
    data : dict
        Of the form {field : array-like} or {field : dict}.
    npartitions : int
        The number of partitions of the index to create. Note that depending on
        the size and index of the dataframe, the output may have fewer
        partitions than requested.
    orient : {'columns', 'index', 'tight'}, default 'columns'
        The "orientation" of the data. If the keys of the passed dict
        should be the columns of the resulting DataFrame, pass 'columns'
        (default). Otherwise if the keys should be rows, pass 'index'.
        If 'tight', assume a dict with keys
        ['index', 'columns', 'data', 'index_names', 'column_names'].
    dtype: bool
        Data type to force, otherwise infer.
    columns: string, optional
        Column labels to use when ``orient='index'``. Raises a ValueError
        if used with ``orient='columns'`` or ``orient='tight'``.
    constructor: class, default pd.DataFrame
        Class with which ``from_dict`` should be called with.

    Examples
    --------
    >>> import dask.dataframe as dd
    >>> ddf = dd.from_dict({"num1": [1, 2, 3, 4], "num2": [7, 8, 9, 10]}, npartitions=2)
    """

    collection_types = {type(v) for v in data.values() if is_dask_collection(v)}
    if collection_types:
        raise NotImplementedError(
            "from_dict doesn't currently support Dask collections as inputs. "
            f"Objects of type {collection_types} were given in the input dict."
        )

    return from_pandas(
        constructor.from_dict(data, orient, dtype, columns),
        npartitions,
    )


def from_dask_array(x, columns=None, index=None, meta=None) -> DataFrame: