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.github/workflows/unit-tests.yml

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@@ -181,8 +181,7 @@ jobs:
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timeout-minutes: 90
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strategy:
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matrix:
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# Note: Don't use macOS latest since macos 14 appears to be arm64 only
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os: [macos-13, macos-14, windows-2025]
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os: [macos-15-intel, macos-15, windows-2025]
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env_file: [actions-311.yaml, actions-312.yaml, actions-313.yaml]
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fail-fast: false
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runs-on: ${{ matrix.os }}
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pyodide build
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- name: Set up Node.js
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uses: actions/setup-node@v5
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uses: actions/setup-node@v6
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with:
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node-version: '20'
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.github/workflows/wheels.yml

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- [ubuntu-24.04, musllinux_x86_64]
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- [ubuntu-24.04-arm, manylinux_aarch64]
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- [ubuntu-24.04-arm, musllinux_aarch64]
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- [macos-13, macosx_x86_64]
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# Note: M1 images on Github Actions start from macOS 14
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- [macos-14, macosx_arm64]
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- [windows-2022, win_amd64]
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- [macos-15-intel, macosx_x86_64]
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- [macos-15, macosx_arm64]
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- [windows-2025, win_amd64]
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- [windows-11-arm, win_arm64]
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python: [["cp311", "3.11"], ["cp312", "3.12"], ["cp313", "3.13"], ["cp313t", "3.13"], ["cp314", "3.14"], ["cp314t", "3.14"]]
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include:

asv_bench/benchmarks/ctors.py

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def arr_dict(arr):
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return dict(zip(range(len(arr)), arr))
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return dict(zip(range(len(arr)), arr, strict=True))
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def list_of_tuples(arr):

asv_bench/benchmarks/series_methods.py

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self.idx = date_range(
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start=datetime(2015, 10, 26), end=datetime(2016, 1, 1), freq="50s"
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)
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self.data = dict(zip(self.idx, range(len(self.idx))))
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self.data = dict(zip(self.idx, range(len(self.idx)), strict=True))
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self.array = np.array([1, 2, 3])
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self.idx2 = Index(["a", "b", "c"])
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self.to_replace_list = np.random.choice(self.arr, num_to_replace)
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self.values_list = np.random.choice(self.arr1, num_to_replace)
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self.replace_dict = dict(zip(self.to_replace_list, self.values_list))
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self.replace_dict = dict(
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zip(self.to_replace_list, self.values_list, strict=True)
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)
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def time_replace_dict(self, num_to_replace):
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self.ser.replace(self.replace_dict)

ci/code_checks.sh

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@@ -73,10 +73,12 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Period.freq GL08" \
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-i "pandas.Period.ordinal GL08" \
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-i "pandas.errors.IncompatibleFrequency SA01,SS06,EX01" \
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-i "pandas.errors.InvalidVersion GL08" \
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-i "pandas.api.extensions.ExtensionArray.value_counts EX01,RT03,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
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-i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
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-i "pandas.core.resample.Resampler.quantile PR01,PR07" \
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-i "pandas.api.typing.DataFrameGroupBy.plot PR02" \
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-i "pandas.api.typing.SeriesGroupBy.plot PR02" \
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-i "pandas.api.typing.Resampler.quantile PR01,PR07" \
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-i "pandas.arrays.NumpyExtensionArray GL08" \
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-i "pandas.tseries.offsets.BDay PR02,SA01" \
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-i "pandas.tseries.offsets.BHalfYearBegin.is_on_offset GL08" \
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-i "pandas.tseries.offsets.BHalfYearBegin.n GL08" \

doc/source/reference/groupby.rst

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=======
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GroupBy
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=======
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.. currentmodule:: pandas.core.groupby
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.. currentmodule:: pandas.api.typing
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:class:`pandas.api.typing.DataFrameGroupBy` and :class:`pandas.api.typing.SeriesGroupBy`
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instances are returned by groupby calls :func:`pandas.DataFrame.groupby` and
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NamedAgg
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.. currentmodule:: pandas.core.groupby
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.. currentmodule:: pandas.api.typing
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Function application
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--------------------

doc/source/reference/resampling.rst

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==========
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Resampling
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==========
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.. currentmodule:: pandas.core.resample
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.. currentmodule:: pandas.api.typing
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:class:`pandas.api.typing.Resampler` instances are returned by
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resample calls: :func:`pandas.DataFrame.resample`, :func:`pandas.Series.resample`.

doc/source/reference/window.rst

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Rolling window functions
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------------------------
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.. currentmodule:: pandas.api.typing
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.. autosummary::
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:toctree: api/
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Weighted window functions
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.. currentmodule:: pandas.core.window.rolling
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.. currentmodule:: pandas.api.typing
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.. autosummary::
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:toctree: api/
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Expanding window functions
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.. currentmodule:: pandas.core.window.expanding
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Exponentially-weighted window functions
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doc/source/user_guide/basics.rst

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df.select_dtypes(include=["number", "bool"], exclude=["unsignedinteger"])
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To select string columns you must use the ``object`` dtype:
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To select string columns include ``str``:
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.. ipython:: python
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df.select_dtypes(include=["object"])
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df.select_dtypes(include=[str])
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.. note::
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This is a change in pandas 3.0. Previously strings were stored in ``object``
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dtype columns, so would be selected with ``include=[object]``. See
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:ref:`the migration guide <string_migration.select_dtypes>` for details on
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how to write code that works with both versions.
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To see all the child dtypes of a generic ``dtype`` like ``numpy.number`` you
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can define a function that returns a tree of child dtypes:

doc/source/user_guide/enhancingperf.rst

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:func:`~pandas.eval` is many orders of magnitude slower for
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smaller expressions or objects than plain Python. A good rule of thumb is
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to only use :func:`~pandas.eval` when you have a
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:class:`~pandas.DataFrame` with more than 10,000 rows.
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Supported syntax
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~~~~~~~~~~~~~~~~

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