dask.dataframe.from_delayed(dfs: Delayed | distributed.Future | Iterable[Delayed | distributed.Future], meta=None, divisions: tuple | Literal['sorted'] | None = None, prefix: str = 'from-delayed', verify_meta: bool = True) DataFrame | Series[source]

Create Dask DataFrame from many Dask Delayed objects


A dask.delayed.Delayed, a distributed.Future, or an iterable of either of these objects, e.g. returned by client.submit. These comprise the individual partitions of the resulting dataframe. If a single object is provided (not an iterable), then the resulting dataframe will have only one partition.

metapd.DataFrame, pd.Series, dict, iterable, tuple, optional

An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.


Partition boundaries along the index. For tuple, see https://docs.dask.org/en/latest/dataframe-design.html#partitions For string ‘sorted’ will compute the delayed values to find index values. Assumes that the indexes are mutually sorted. If None, then won’t use index information


Prefix to prepend to the keys.


If True check that the partitions have consistent metadata, defaults to True.