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Improve to_dask_dataframe performance #7844

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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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16 changes: 14 additions & 2 deletions xarray/core/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6403,7 +6403,14 @@ def to_dask_dataframe(
columns.extend(k for k in self.coords if k not in self.dims)
columns.extend(self.data_vars)

has_many_dims = len(ordered_dims) > 1
if has_many_dims:
ds_chunks = self.chunks
else:
ds_chunks = {}

series_list = []
df_meta = pd.DataFrame()
for name in columns:
try:
var = self.variables[name]
Expand All @@ -6422,8 +6429,13 @@ 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])
if has_many_dims:
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Is this really that impactful, can we optimize set_dims instead?

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I'll think I'll save the has_many_dims paths for a future PR. I think it might introduce bugs if we don't consistently chunk with the same shape.

# Broadcast then flatten the array:
var_new_dims = var.set_dims(ordered_dims).chunk(ds_chunks)
dask_array = var_new_dims._data.reshape(-1)
else:
dask_array = var._data
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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