High-level graph layer for a simple shuffle operation in which each output partition depends on all input partitions.

Parameters

name : str

Name of new shuffled output collection.

column : str or list of str

Column(s) to be used to map rows to output partitions (by hashing).

npartitions : int

Number of output partitions.

npartitions_input : int

Number of partitions in the original (un-shuffled) DataFrame.

ignore_index: bool, default False :

Ignore index during shuffle. If True , performance may improve, but index values will not be preserved.

name_input : str

Name of input collection.

meta_input : pd.DataFrame-like object

Empty metadata of input collection.

parts_out : list of int (optional)

List of required output-partition indices.

annotations : dict (optional)

Layer annotations

Simple HighLevelGraph Shuffle layer

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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File: /dask/layers.py#364
type: <class 'abc.ABCMeta'>
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