Skip to content

BUG: fix convert_dtypes to preserve timezone from tz-aware pyarrow timestamp dtype #60304

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

Closed
wants to merge 14 commits into from
Closed

BUG: fix convert_dtypes to preserve timezone from tz-aware pyarrow timestamp dtype #60304

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
14 commits
Select commit Hold shift + click to select a range
6c99f92
removed if blocks, added test, and added to whats new
road-1 Nov 13, 2024
eb5f8cf
Merge branch 'main' into 60237
road-1 Nov 15, 2024
b578b6f
added duration if statement
road-1 Nov 15, 2024
7d079ac
fixing line length in test file
road-1 Nov 15, 2024
cab7b55
re-remove if
road-1 Nov 19, 2024
d1bfc53
changing test location
road-1 Nov 19, 2024
5ae19ae
changing test location to test_arrow.py
road-1 Nov 19, 2024
4cb581d
Merge branch 'main' into 60237
road-1 Nov 19, 2024
ae52904
added if statement to pre-existing if statements to check for tz
road-1 Nov 20, 2024
67f55f3
simplified if statements
road-1 Nov 20, 2024
a96e3fa
Merge branch 'main' into 60237
road-1 Nov 20, 2024
adfcf40
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Nov 20, 2024
4422307
Merge branch 'main' into 60237
road-1 Dec 2, 2024
f7d5b6a
Merge branch 'main' into 60237
road-1 Dec 3, 2024
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
1 change: 1 addition & 0 deletions doc/source/whatsnew/v3.0.0.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -649,6 +649,7 @@ Conversion
- Bug in :meth:`DataFrame.update` bool dtype being converted to object (:issue:`55509`)
- Bug in :meth:`Series.astype` might modify read-only array inplace when casting to a string dtype (:issue:`57212`)
- Bug in :meth:`Series.reindex` not maintaining ``float32`` type when a ``reindex`` introduces a missing value (:issue:`45857`)
- Bug in :meth: 'Series.convert_dtype' strips the timezone on an already Timezone aware pyarrow timestamp dtype (:issue:'60237')

Strings
^^^^^^^
Expand Down
12 changes: 0 additions & 12 deletions pandas/core/dtypes/dtypes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2277,18 +2277,6 @@ def name(self) -> str: # type: ignore[override]
@cache_readonly
def numpy_dtype(self) -> np.dtype:
"""Return an instance of the related numpy dtype"""
if pa.types.is_timestamp(self.pyarrow_dtype):
# pa.timestamp(unit).to_pandas_dtype() returns ns units
# regardless of the pyarrow timestamp units.
# This can be removed if/when pyarrow addresses it:
# https://github.com/apache/arrow/issues/34462
return np.dtype(f"datetime64[{self.pyarrow_dtype.unit}]")
if pa.types.is_duration(self.pyarrow_dtype):
# pa.duration(unit).to_pandas_dtype() returns ns units
# regardless of the pyarrow duration units
# This can be removed if/when pyarrow addresses it:
# https://github.com/apache/arrow/issues/34462
return np.dtype(f"timedelta64[{self.pyarrow_dtype.unit}]")
Copy link
Member

Choose a reason for hiding this comment

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

By removing this, it will go through np.dtype(self.pyarrow_dtype.to_pandas_dtype()), which I think will raise a type error? (because to_pandas_dtype returns a DatetimeTZDtype, and np.dtype(..) does not recognize that) And so this will start to return object dtype instead of datetime64?

Copy link

Choose a reason for hiding this comment

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

For a bit of context from my linked issue, in pandas 2.0, an ArrowDtype for a pyarrow timestamp with a non-null timezone did return a numpy object dtype

The if statements that are removed in this pull request were added in pandas 2.1.0rc0 (#51800) to fix the other issue you pointed out with the datetime unit being lost in older pyarrow versions (fix for that was added in pyarrow 14 apache/arrow#35656). It wasn't noted in the pull request that added them that it was intended to change the semantics for tz-aware types as well, so I think it was just an unintentional side effect that they started returning a numpy dtype of datetime64 instead of object.

I noted in the issue that the pyarrow table in the pandas arrays, scalars, and data types section of the API docs seems to indicate that a pyarrow timestamp should map to a pandas DatetimeTZDtype and a numpy datetime64 dtype (which does match how pyarrow itself handles converting a tz-aware array to pandas and numpy respectively)

Quoting from my comment on the issue

I would definitely defer to someone else's judgement on whether that is correct, or if there should be a distinction in that table linked between a pa.timestamp() type with and without timezone

You are pretty much exactly the person I had in mind of who would be best suited to make that judgement call. As far as I can tell, a tz-aware pyarrow timestamp is the only instance in that table that can lose information when it maps to the shown numpy dtype

if pa.types.is_string(self.pyarrow_dtype) or pa.types.is_large_string(
self.pyarrow_dtype
):
Expand Down
8 changes: 8 additions & 0 deletions pandas/tests/frame/methods/test_convert_dtypes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -196,3 +196,11 @@ def test_convert_dtypes_from_arrow(self):
result = df.convert_dtypes()
expected = df.astype({"a": "string[python]"})
tm.assert_frame_equal(result, expected)

def test_convert_dtypes_timezone_series(self):
# GH#60237
ser = pd.Series(pd.date_range(start='2020-01-01', periods=5, freq='h', tz='UTC'))
ser = ser.astype("timestamp[ns, tz=UTC][pyarrow]")
expected = ser
result = ser.convert_dtypes(dtype_backend="pyarrow")
tm.assert_series_equal(result, expected)
Loading