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Merged
Illviljan merged 5 commits into from
May 25, 2023
Merged

Improve to_dask_dataframe performance #7844

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e68c168
Improve to_dask_dataframe performance
Illviljan May 15, 2023
fa781de
Add ASV test
Illviljan May 20, 2023
ff5a364
Merge branch 'main' into improve_dask_dataframe_performance
Illviljan May 20, 2023
62ad6b4
Update pandas.py
Illviljan May 21, 2023
8162a9a
Update dataset.py
Illviljan May 21, 2023
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19 changes: 11 additions & 8 deletions asv_bench/benchmarks/pandas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,19 +29,20 @@ def time_from_series(self, dtype, subset):
class ToDataFrame:
def setup(self, *args, **kwargs):
xp = kwargs.get("xp", np)
nvars = kwargs.get("nvars", 1)
random_kws = kwargs.get("random_kws", {})
method = kwargs.get("method", "to_dataframe")

dim1 = 10_000
dim2 = 10_000

var = xr.Variable(
dims=("dim1", "dim2"), data=xp.random.random((dim1, dim2), **random_kws)
)
data_vars = {f"long_name_{v}": (("dim1", "dim2"), var) for v in range(nvars)}

ds = xr.Dataset(
{
"x": xr.DataArray(
data=xp.random.random((dim1, dim2), **random_kws),
dims=["dim1", "dim2"],
coords={"dim1": np.arange(0, dim1), "dim2": np.arange(0, dim2)},
)
}
data_vars, coords={"dim1": np.arange(0, dim1), "dim2": np.arange(0, dim2)}
)
self.to_frame = getattr(ds, method)

Expand All @@ -58,4 +59,6 @@ def setup(self, *args, **kwargs):

import dask.array as da

super().setup(xp=da, random_kws=dict(chunks=5000), method="to_dask_dataframe")
super().setup(
xp=da, random_kws=dict(chunks=5000), method="to_dask_dataframe", nvars=500
)
10 changes: 8 additions & 2 deletions xarray/core/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6465,7 +6465,10 @@ def to_dask_dataframe(
columns.extend(k for k in self.coords if k not in self.dims)
columns.extend(self.data_vars)

ds_chunks = self.chunks

series_list = []
df_meta = pd.DataFrame()
for name in columns:
try:
var = self.variables[name]
Expand All @@ -6484,8 +6487,11 @@ def to_dask_dataframe(
if not is_duck_dask_array(var._data):
var = var.chunk()

dask_array = var.set_dims(ordered_dims).chunk(self.chunks).data
series = dd.from_array(dask_array.reshape(-1), columns=[name])
# Broadcast then flatten the array:
var_new_dims = var.set_dims(ordered_dims).chunk(ds_chunks)
dask_array = var_new_dims._data.reshape(-1)

series = dd.from_dask_array(dask_array, columns=name, meta=df_meta)
series_list.append(series)

df = dd.concat(series_list, axis=1)
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