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Create benchmark for groupby #5772

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merged 4 commits into from
Sep 20, 2021
Merged

Create benchmark for groupby #5772

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076e0ac
Create groupby.py
Illviljan Sep 6, 2021
d41fda7
Update groupby.py
Illviljan Sep 7, 2021
26ff12f
Update groupby.py
Illviljan Sep 7, 2021
bc93320
Update asv_bench/benchmarks/groupby.py
dcherian Sep 14, 2021
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39 changes: 39 additions & 0 deletions asv_bench/benchmarks/groupby.py
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import numpy as np

import xarray as xr

from . import parameterized, requires_dask


class GroupBy:
def setup(self, *args, **kwargs):
self.ds = xr.Dataset(
{
"a": xr.DataArray(np.r_[np.arange(500.0), np.arange(500.0)]),
"b": xr.DataArray(np.arange(1000.0)),
}
)

@parameterized(["method"], [("sum", "mean")])
def agg(self, method):
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return getattr(self.ds.groupby("a"), method)()


class GroupByDask(GroupBy):
def setup(self, *args, **kwargs):
requires_dask()
super().setup(**kwargs)
self.ds = self.ds.chunk({"dim_0": 50})


class GroupByDataFrame(GroupBy):
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I'm not sure we really need the dataframe tests.

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I think it's good to have some kind of "competitor" comparison, because performance complaints will always be relative to them anyway. It might not be needed for more than just a few of the (future) tests though.

def setup(self, *args, **kwargs):
super().setup(**kwargs)
self.ds = self.ds.to_dataframe()


class GroupByDaskDataFrame(GroupBy):
def setup(self, *args, **kwargs):
requires_dask()
super().setup(**kwargs)
self.ds = self.ds.chunk({"dim_0": 50}).to_dataframe()