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BUG: DataFrame.update not operating in-place for datetime64[ns, UTC] dtype #56228

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Merged
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Nov 29, 2023
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BUG: DataFrame.update not operating in-place for datetime64[ns, UTC] dtype #56228

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8e4b65c
inplace update
MarcoGorelli Nov 28, 2023
04eb8db
copy-on-write fixups
MarcoGorelli Nov 28, 2023
fed72ec
Update doc/source/whatsnew/v2.2.0.rst
MarcoGorelli Nov 28, 2023
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v2.2.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -520,7 +520,7 @@ Indexing

Missing
^^^^^^^
-
- Bug in :meth:`DataFrame.update` wasn't updating in-place for tz-aware datetime64 dtypes (:issue:`56227`)
-

MultiIndex
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14 changes: 6 additions & 8 deletions pandas/core/frame.py
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Expand Up @@ -8838,14 +8838,14 @@ def update(
in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
... 'B': [400., 500., 600.]})
Comment on lines 8840 to +8841
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Changing these to floats, because otherwise we'd get

<ipython-input-3-3ae759fa2c19>:1: FutureWarning: Downcasting behavior in Series and DataFrame methods 'where', 'mask', and 'clip' is deprecated. In a future version this will not infer object dtypes or cast all-round floats to integers. Instead call result.infer_objects(copy=False) for object inference, or cast round floats explicitly. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`

which looks correct because we're trying to update an int column df['B'] with a float one new_df['B']

>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
A B
0 1 4
1 2 500
2 3 6
A B
0 1 4.0
1 2 500.0
2 3 6.0
"""
if not PYPY and using_copy_on_write():
if sys.getrefcount(self) <= REF_COUNT:
Expand All @@ -8862,8 +8862,6 @@ def update(
stacklevel=2,
)

from pandas.core.computation import expressions

# TODO: Support other joins
if join != "left": # pragma: no cover
raise NotImplementedError("Only left join is supported")
Expand Down Expand Up @@ -8897,7 +8895,7 @@ def update(
if mask.all():
continue

self.loc[:, col] = expressions.where(mask, this, that)
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So I suppose numexpr generally will prevent inplace operations?

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Being inplace here is generally not a good idea since non inplace methods are faster if you replace the whole column

self.loc[:, col] = self[col].where(mask, that)

# ----------------------------------------------------------------------
# Data reshaping
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16 changes: 16 additions & 0 deletions pandas/tests/frame/methods/test_update.py
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Expand Up @@ -140,6 +140,22 @@ def test_update_datetime_tz(self):
expected = DataFrame([pd.Timestamp("2019", tz="UTC")])
tm.assert_frame_equal(result, expected)

def test_update_datetime_tz_in_place(self, using_copy_on_write, warn_copy_on_write):
# https://github.com/pandas-dev/pandas/issues/56227
result = DataFrame([pd.Timestamp("2019", tz="UTC")])
orig = result.copy()
view = result[:]
with tm.assert_produces_warning(
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Can you use assert_cow_warning here? That's optimised for these types of warnings and requires only warn_copy_on_write as input

FutureWarning if warn_copy_on_write else None, match="Setting a value"
):
result.update(result + pd.Timedelta(days=1))
expected = DataFrame([pd.Timestamp("2019-01-02", tz="UTC")])
tm.assert_frame_equal(result, expected)
if not using_copy_on_write:
tm.assert_frame_equal(view, expected)
else:
tm.assert_frame_equal(view, orig)

def test_update_with_different_dtype(self, using_copy_on_write):
# GH#3217
df = DataFrame({"a": [1, 3], "b": [np.nan, 2]})
Expand Down