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Fix default name conversion in ToFrame #1044

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merged 10 commits into from
May 2, 2024
Merged

Fix default name conversion in ToFrame #1044

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a0e0e43
fix subtle issue with renaming before a merge
rjzamora Apr 26, 2024
b73938d
check formatting
rjzamora Apr 26, 2024
aea6002
Update dask_expr/tests/test_merge.py
rjzamora Apr 26, 2024
9ee4059
Merge remote-tracking branch 'upstream/main' into fix-rename-then-merge
rjzamora Apr 29, 2024
d46447d
test index case
rjzamora Apr 29, 2024
83adc93
Merge remote-tracking branch 'upstream/main' into fix-rename-then-merge
rjzamora May 1, 2024
c15e5d7
handle tuples
rjzamora May 1, 2024
9a63d29
Add test
phofl May 2, 2024
de1e678
Add test
phofl May 2, 2024
4977083
Update
phofl May 2, 2024
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9 changes: 8 additions & 1 deletion dask_expr/_expr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1613,7 +1613,14 @@ class ToFrame(Elemwise):
_keyword_only = ["name"]
operation = M.to_frame
_filter_passthrough = True
_preserves_partitioning_information = True

@functools.cached_property
def unique_partition_mapping_columns_from_shuffle(self):
unique_mapping = self.frame.unique_partition_mapping_columns_from_shuffle
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I don't see how this test covers the added function here? Could you elaborate?

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@rjzamora rjzamora Apr 29, 2024 •
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Yeah, that's a fair question. This problem is still a bit confusing to me :)

When an un-named Series is shuffled, and then converted to a DataFrame, it's unique_partition_mapping_columns_from_shuffle result will be something like {None} instead of a set containing the real (default) column name ({0}). This results in a KeyError when RenameFrame tries to select the None key instead of 0.

There seem to be several ways to avoid the error. However, I think the root problem is that ToFrame must properly account for the name of the column it creates.

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Yes agree, but this check might not properly account for that

set(self.frame.columns) == unique_mapping

unique_mapping could be a tuple of one column, I think we have to be a bit more elaborate here

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Okay, I could use your help here if the current solution is wrong/incomplete. I was thinking that the only case we need to catch here is when we are converting to a dataframe from an unnamed Index or Series, but I didn't dig into the Index case at all. Is that what you have in mind?

unique_mapping could be a tuple of one column

Doesn't unique_partition_mapping_columns_from_shuffle always return a set?

if set(self.frame.columns) == unique_mapping:
# Account for default name conversion
return set(self.columns)
return unique_mapping


class ToFrameIndex(Elemwise):
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15 changes: 15 additions & 0 deletions dask_expr/tests/test_merge.py
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Expand Up @@ -1011,3 +1011,18 @@ def test_merge_suffix_projections():
expected = df.merge(df, on="a")
expected = expected[expected["c_x"] == "A"]["c_y"]
assert_eq(result, expected)


def test_merge_after_rename():
pleft = pd.Series(range(10))
pleft = pleft.drop_duplicates().to_frame()
pleft.columns = ["a"]

left = from_pandas(pd.Series(range(10)), npartitions=2)
left = left.drop_duplicates().to_frame()
left.columns = ["a"]

right = pd.DataFrame({"a": [1, 2] * 5})
expected = pleft.merge(right, how="inner")
result = left.merge(right, how="inner")
assert_eq(result, expected, check_index=False)
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