Skip to content

BUG: Fix regression when using Series with arrow string array #52076

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Mar 22, 2023
Merged

BUG: Fix regression when using Series with arrow string array #52076

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
eb75042
BUG: Fix regression when using Series with arrow string array
phofl Mar 19, 2023
9166337
Move
phofl Mar 21, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 5 additions & 2 deletions pandas/_libs/lib.pyx
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -752,8 +752,11 @@ cpdef ndarray[object] ensure_string_array(
out = arr.astype(str).astype(object)
out[arr.isna()] = na_value
return out

arr = arr.to_numpy()
if hasattr(arr, "type"):
# pyarrow array
arr = np.array(arr)
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

  1. can we handle this at a higher level? 2) why doesnt the pyarrow object's to_numpy work here?

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

It sets zero_copy_only to True which causes raising here.

we have to do a check like this somewhere as long as arrow is not a hard dependency, can do it at a higher level as well

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

moved it to a higher level, not sure if it is better though

else:
arr = arr.to_numpy()
elif not util.is_array(arr):
arr = np.array(arr, dtype="object")

Expand Down
2 changes: 2 additions & 0 deletions pandas/core/arrays/string_arrow.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -151,6 +151,8 @@ def _from_sequence(cls, scalars, dtype: Dtype | None = None, copy: bool = False)
result = scalars._data
result = lib.ensure_string_array(result, copy=copy, convert_na_value=False)
return cls(pa.array(result, mask=na_values, type=pa.string()))
elif isinstance(scalars, (pa.Array, pa.ChunkedArray)):
return cls(pc.cast(scalars, pa.string()))

# convert non-na-likes to str
result = lib.ensure_string_array(scalars, copy=copy)
Expand Down
8 changes: 8 additions & 0 deletions pandas/tests/extension/test_arrow.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2353,6 +2353,14 @@ def test_concat_empty_arrow_backed_series(dtype):
tm.assert_series_equal(result, expected)


@pytest.mark.parametrize("dtype", ["string", "string[pyarrow]"])
def test_series_from_string_array(dtype):
arr = pa.array("the quick brown fox".split())
ser = pd.Series(arr, dtype=dtype)
expected = pd.Series(ArrowExtensionArray(arr), dtype=dtype)
tm.assert_series_equal(ser, expected)


# _data was renamed to _pa_data
class OldArrowExtensionArray(ArrowExtensionArray):
def __getstate__(self):
Expand Down