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Merge remote-tracking branch 'upstream/main' into series-sum-attrs
2 parents 742e3b1 + 2cdb97e commit 5178113

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36 files changed

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.circleci/config.yml

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@@ -92,7 +92,13 @@ jobs:
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no_output_timeout: 30m # Sometimes the tests won't generate any output, make sure the job doesn't get killed by that
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command: |
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pip3 install cibuildwheel==2.20.0
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cibuildwheel --output-dir wheelhouse
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if [[ $CIBW_BUILD == cp313t* ]]; then
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# TODO: temporarily run 3.13 free threaded builds without build isolation
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# since we need pre-release cython
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CIBW_BUILD_FRONTEND="pip; args: --no-build-isolation" cibuildwheel --output-dir wheelhouse
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else
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cibuildwheel --output-dir wheelhouse
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fi
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environment:
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CIBW_BUILD: << parameters.cibw-build >>
@@ -141,6 +147,10 @@ workflows:
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cibw-build: ["cp310-manylinux_aarch64",
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"cp311-manylinux_aarch64",
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"cp312-manylinux_aarch64",
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"cp313-manylinux_aarch64",
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"cp313t-manylinux_aarch64",
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"cp310-musllinux_aarch64",
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"cp311-musllinux_aarch64",
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"cp312-musllinux_aarch64",]
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"cp312-musllinux_aarch64",
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"cp313-musllinux_aarch64",
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"cp313t-musllinux_aarch64"]

.github/workflows/wheels.yml

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@@ -102,9 +102,7 @@ jobs:
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python: [["cp310", "3.10"], ["cp311", "3.11"], ["cp312", "3.12"], ["cp313", "3.13"], ["cp313t", "3.13"]]
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include:
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# TODO: Remove this plus installing build deps in cibw_before_build.sh
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# and test deps in cibw_before_test.sh after pandas can be built with a released NumPy/Cython
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- python: ["cp313", "3.13"]
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cibw_build_frontend: 'pip; args: --no-build-isolation'
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# after pandas can be built with a released NumPy/Cython
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- python: ["cp313t", "3.13"]
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cibw_build_frontend: 'pip; args: --no-build-isolation'
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# Build Pyodide wheels and upload them to Anaconda.org
@@ -187,11 +185,9 @@ jobs:
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- name: Test Windows Wheels
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if: ${{ matrix.buildplat[1] == 'win_amd64' }}
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shell: pwsh
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# TODO: Remove NumPy nightly install when there's a 3.13 wheel on PyPI
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run: |
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$TST_CMD = @"
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python -m pip install hypothesis>=6.84.0 pytest>=7.3.2 pytest-xdist>=3.4.0;
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${{ matrix.python[1] == '3.13' && 'python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy;' }}
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python -m pip install `$(Get-Item pandas\wheelhouse\*.whl);
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python -c `'import pandas as pd; pd.test(extra_args=[`\"--no-strict-data-files`\", `\"-m not clipboard and not single_cpu and not slow and not network and not db`\"])`';
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"@

MANIFEST.in

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@@ -65,4 +65,3 @@ graft pandas/_libs/include
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# Include cibw script in sdist since it's needed for building wheels
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include scripts/cibw_before_build.sh
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include scripts/cibw_before_test.sh

ci/code_checks.sh

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@@ -70,10 +70,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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--format=actions \
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-i ES01 `# For now it is ok if docstrings are missing the extended summary` \
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-i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
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-i "pandas.NA SA01" \
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-i "pandas.Period.freq GL08" \
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-i "pandas.Period.ordinal GL08" \
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-i "pandas.PeriodDtype.freq SA01" \
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-i "pandas.RangeIndex.from_range PR01,SA01" \
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-i "pandas.RangeIndex.step SA01" \
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-i "pandas.Series.cat.add_categories PR01,PR02" \
@@ -103,12 +101,10 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Series.sparse.from_coo PR07,SA01" \
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-i "pandas.Series.sparse.npoints SA01" \
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-i "pandas.Series.sparse.sp_values SA01" \
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-i "pandas.Timedelta.components SA01" \
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-i "pandas.Timedelta.max PR02" \
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-i "pandas.Timedelta.min PR02" \
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-i "pandas.Timedelta.resolution PR02" \
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-i "pandas.Timedelta.to_timedelta64 SA01" \
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-i "pandas.Timedelta.total_seconds SA01" \
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-i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
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-i "pandas.Timestamp.max PR02" \
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-i "pandas.Timestamp.min PR02" \
@@ -117,14 +113,12 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Timestamp.tzinfo GL08" \
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-i "pandas.Timestamp.year GL08" \
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-i "pandas.api.types.is_dict_like PR07,SA01" \
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-i "pandas.api.types.is_extension_array_dtype SA01" \
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-i "pandas.api.types.is_file_like PR07,SA01" \
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-i "pandas.api.types.is_float PR01,SA01" \
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-i "pandas.api.types.is_float_dtype SA01" \
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-i "pandas.api.types.is_hashable PR01,RT03,SA01" \
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-i "pandas.api.types.is_int64_dtype SA01" \
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-i "pandas.api.types.is_integer PR01,SA01" \
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-i "pandas.api.types.is_integer_dtype SA01" \
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-i "pandas.api.types.is_interval_dtype SA01" \
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-i "pandas.api.types.is_iterator PR07,SA01" \
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-i "pandas.api.types.is_list_like SA01" \
@@ -136,7 +130,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
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-i "pandas.arrays.BooleanArray SA01" \
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-i "pandas.arrays.DatetimeArray SA01" \
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-i "pandas.arrays.FloatingArray SA01" \
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-i "pandas.arrays.IntegerArray SA01" \
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-i "pandas.arrays.IntervalArray.left SA01" \
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-i "pandas.arrays.IntervalArray.length SA01" \
@@ -151,7 +144,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.hist RT03" \
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-i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
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-i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
@@ -198,7 +190,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.errors.IntCastingNaNError SA01" \
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-i "pandas.errors.InvalidIndexError SA01" \
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-i "pandas.errors.InvalidVersion SA01" \
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-i "pandas.errors.MergeError SA01" \
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-i "pandas.errors.NullFrequencyError SA01" \
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-i "pandas.errors.NumExprClobberingError SA01" \
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-i "pandas.errors.NumbaUtilError SA01" \
@@ -391,7 +382,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.tseries.offsets.Week.n GL08" \
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-i "pandas.tseries.offsets.Week.normalize GL08" \
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-i "pandas.tseries.offsets.Week.weekday GL08" \
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-i "pandas.tseries.offsets.WeekOfMonth SA01" \
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-i "pandas.tseries.offsets.WeekOfMonth.is_on_offset GL08" \
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-i "pandas.tseries.offsets.WeekOfMonth.n GL08" \
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-i "pandas.tseries.offsets.WeekOfMonth.normalize GL08" \

doc/source/conf.py

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@@ -254,7 +254,9 @@
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"json_url": "https://pandas.pydata.org/versions.json",
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"version_match": switcher_version,
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},
257-
"show_version_warning_banner": True,
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# This shows a warning for patch releases since the
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# patch version doesn't compare as equal (e.g. 2.2.1 != 2.2.0 but it should be)
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"show_version_warning_banner": False,
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"icon_links": [
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{
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"name": "Mastodon",

doc/source/whatsnew/index.rst

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.. toctree::
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:maxdepth: 2
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v2.2.3
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v2.2.2
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v2.2.1
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v2.2.0

doc/source/whatsnew/v2.2.2.rst

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@@ -56,4 +56,4 @@ Other
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Contributors
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~~~~~~~~~~~~
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.. contributors:: v2.2.1..v2.2.2|HEAD
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.. contributors:: v2.2.1..v2.2.2

doc/source/whatsnew/v2.2.3.rst

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.. _whatsnew_223:
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What's new in 2.2.3 (September 20, 2024)
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----------------------------------------
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These are the changes in pandas 2.2.3. See :ref:`release` for a full changelog
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including other versions of pandas.
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{{ header }}
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.. ---------------------------------------------------------------------------
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.. _whatsnew_220.py13_compat:
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Pandas 2.2.3 is now compatible with Python 3.13
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Pandas 2.2.3 is the first version of pandas that is generally compatible with the upcoming
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Python 3.13, and both wheels for free-threaded and normal Python 3.13 will be uploaded for
20+
this release.
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As usual please report any bugs discovered to our `issue tracker <https://github.com/pandas-dev/pandas/issues/new/choose>`_
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.. ---------------------------------------------------------------------------
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.. _whatsnew_223.bug_fixes:
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Bug fixes
28+
~~~~~~~~~
29+
- Bug in :func:`eval` on :class:`complex` including division ``/`` discards imaginary part. (:issue:`21374`)
30+
- Minor fixes for numpy 2.1 compatibility. (:issue:`59444`)
31+
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.. ---------------------------------------------------------------------------
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.. _whatsnew_223.other:
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Other
36+
~~~~~
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- Missing licenses for 3rd party dependencies were added back into the wheels. (:issue:`58632`)
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.. ---------------------------------------------------------------------------
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.. _whatsnew_223.contributors:
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Contributors
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~~~~~~~~~~~~
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.. contributors:: v2.2.2..v2.2.3|HEAD

doc/source/whatsnew/v3.0.0.rst

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@@ -630,6 +630,7 @@ I/O
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- Bug in :meth:`read_csv` where the order of the ``na_values`` makes an inconsistency when ``na_values`` is a list non-string values. (:issue:`59303`)
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- Bug in :meth:`read_excel` raising ``ValueError`` when passing array of boolean values when ``dtype="boolean"``. (:issue:`58159`)
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- Bug in :meth:`read_json` not validating the ``typ`` argument to not be exactly ``"frame"`` or ``"series"`` (:issue:`59124`)
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- Bug in :meth:`read_json` where extreme value integers in string format were incorrectly parsed as a different integer number (:issue:`20608`)
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- Bug in :meth:`read_stata` raising ``KeyError`` when input file is stored in big-endian format and contains strL data. (:issue:`58638`)
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- Bug in :meth:`read_stata` where extreme value integers were incorrectly interpreted as missing for format versions 111 and prior (:issue:`58130`)
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- Bug in :meth:`read_stata` where the missing code for double was not recognised for format versions 105 and prior (:issue:`58149`)
@@ -693,7 +694,6 @@ Other
693694
^^^^^
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- Bug in :class:`DataFrame` when passing a ``dict`` with a NA scalar and ``columns`` that would always return ``np.nan`` (:issue:`57205`)
695696
- Bug in :func:`eval` on :class:`ExtensionArray` on including division ``/`` failed with a ``TypeError``. (:issue:`58748`)
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- Bug in :func:`eval` on :class:`complex` including division ``/`` discards imaginary part. (:issue:`21374`)
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- Bug in :func:`eval` where the names of the :class:`Series` were not preserved when using ``engine="numexpr"``. (:issue:`10239`)
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- Bug in :func:`unique` on :class:`Index` not always returning :class:`Index` (:issue:`57043`)
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- Bug in :meth:`DataFrame.apply` where passing ``engine="numba"`` ignored ``args`` passed to the applied function (:issue:`58712`)

pandas/_libs/missing.pyx

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@@ -347,6 +347,14 @@ class NAType(C_NAType):
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The NA singleton is a missing value indicator defined by pandas. It is
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used in certain new extension dtypes (currently the "string" dtype).
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350+
See Also
351+
--------
352+
numpy.nan : Floating point representation of Not a Number (NaN) for numerical data.
353+
isna : Detect missing values for an array-like object.
354+
notna : Detect non-missing values for an array-like object.
355+
DataFrame.fillna : Fill missing values in a DataFrame.
356+
Series.fillna : Fill missing values in a Series.
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Examples
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--------
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>>> pd.NA

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