This is a dictionary-backed instance of BlockwiseDep
. The purpose of this class is to simplify the construction of IO-based Blockwise Layers with block/partition-dependent function arguments that are difficult to calculate at graph-materialization time.
Dictionary-based Blockwise-IO argument
Specify an IO-based function for the Blockwise Layer. Note that the function will be passed a single input object when the task is executed (e.g. a single tuple
or dict
):
>>> import pandas as pd
... func = lambda x: pd.read_csv(**x)
Use BlockwiseDepDict
to define the input argument to func
for each block/partition:
>>> dep = BlockwiseDepDict(
... mapping={
... (0,) : {
... "filepath_or_buffer": "data.csv",
... "skiprows": 1,
... "nrows": 2,
... "names": ["a", "b"],
... },
... (1,) : {
... "filepath_or_buffer": "data.csv",
... "skiprows": 3,
... "nrows": 2,
... "names": ["a", "b"],
... },
... }
... )
Construct a Blockwise Layer with dep
speficied in the indices
list:
>>> layer = Blockwise(See :
... output="collection-name",
... output_indices="i",
... dsk={"collection-name": (func, '_0')},
... indices=[(dep, "i")],
... numblocks={},
... )
The following pages refer to to this document either explicitly or contain code examples using this.
dask.blockwise.BlockwiseDepDict
dask.blockwise.BlockwiseDep
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