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Merge branch 'main' into Test_issue_57930
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‎.pre-commit-config.yaml‎

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skip: [pyright, mypy]
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repos:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.8.1
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rev: v0.8.6
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hooks:
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- id: ruff
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args: [--exit-non-zero-on-fix]
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- id: ruff-format
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exclude: ^scripts|^pandas/tests/frame/test_query_eval.py
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- repo: https://github.com/jendrikseipp/vulture
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rev: 'v2.13'
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rev: 'v2.14'
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hooks:
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- id: vulture
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entry: python scripts/run_vulture.py
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hooks:
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- id: isort
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- repo: https://github.com/asottile/pyupgrade
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rev: v3.19.0
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rev: v3.19.1
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hooks:
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- id: pyupgrade
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args: [--py310-plus]
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- id: sphinx-lint
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args: ["--enable", "all", "--disable", "line-too-long"]
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- repo: https://github.com/pre-commit/mirrors-clang-format
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rev: v19.1.4
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rev: v19.1.6
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hooks:
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- id: clang-format
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files: ^pandas/_libs/src|^pandas/_libs/include

‎LICENSE‎

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Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
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All rights reserved.
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Copyright (c) 2011-2024, Open source contributors.
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Copyright (c) 2011-2025, Open source contributors.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:

‎ci/code_checks.sh‎

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-i "pandas.Timestamp.resolution PR02" \
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-i "pandas.Timestamp.tzinfo GL08" \
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-i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
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-i "pandas.arrays.NumpyExtensionArray SA01" \
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-i "pandas.arrays.TimedeltaArray PR07,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.max PR01,RT03,SA01" \
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-i "pandas.core.resample.Resampler.mean SA01" \
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-i "pandas.core.resample.Resampler.min PR01,RT03,SA01" \
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-i "pandas.core.resample.Resampler.prod SA01" \
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-i "pandas.core.resample.Resampler.quantile PR01,PR07" \
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-i "pandas.core.resample.Resampler.std SA01" \
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-i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
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-i "pandas.core.resample.Resampler.var SA01" \
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-i "pandas.errors.ValueLabelTypeMismatch SA01" \
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-i "pandas.plotting.andrews_curves RT03,SA01" \
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-i "pandas.tseries.offsets.BDay PR02,SA01" \
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-i "pandas.tseries.offsets.BQuarterBegin.is_on_offset GL08" \

‎doc/cheatsheet/Pandas_Cheat_Sheet_FA.pdf‎

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‎doc/cheatsheet/Pandas_Cheat_Sheet_FA.pptx‎

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‎doc/cheatsheet/README.md‎

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This cheat sheet, originally written by Irv Lustig, [Princeton Consultants](https://www.princetonoptimization.com/), was inspired by the [RStudio Data Wrangling Cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf).
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| Topic | PDF | PPT |
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|------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Pandas_Cheat_Sheet | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
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| Pandas_Cheat_Sheet_JA | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
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| Topic | Language | PDF | PPT |
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|------------------------|-------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Pandas_Cheat_Sheet | English | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
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| Pandas_Cheat_Sheet_JA | Japanese | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
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| Pandas_Cheat_Sheet_FA | Persian | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_FA.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_FA.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
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‎doc/source/user_guide/indexing.rst‎

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.. warning::
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``iloc`` supports two kinds of boolean indexing. If the indexer is a boolean ``Series``,
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an error will be raised. For instance, in the following example, ``df.iloc[s.values, 1]`` is ok.
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The boolean indexer is an array. But ``df.iloc[s, 1]`` would raise ``ValueError``.
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While ``loc`` supports two kinds of boolean indexing, ``iloc`` only supports indexing with a
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boolean array. If the indexer is a boolean ``Series``, an error will be raised. For instance,
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in the following example, ``df.iloc[s.values, 1]`` is ok. The boolean indexer is an array.
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.. ipython:: python
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‎doc/source/whatsnew/v3.0.0.rst‎

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- :meth:`Series.map` can now accept kwargs to pass on to func (:issue:`59814`)
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- :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
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- :meth:`str.get_dummies` now accepts a ``dtype`` parameter to specify the dtype of the resulting DataFrame (:issue:`47872`)
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- Implemented :meth:`Series.str.isascii` and :meth:`Series.str.isascii` (:issue:`59091`)
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- Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
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- Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
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- Support passing a :class:`Iterable[Hashable]` input to :meth:`DataFrame.drop_duplicates` (:issue:`59237`)

‎pandas/_libs/index.pyx‎

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bint uses_na
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def __init__(self, ndarray values, na_value):
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self.uses_na = na_value is C_NA
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cdef bint _checknull(self, object val):
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if self.uses_na:
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return val is C_NA
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else:
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return util.is_nan(val)
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‎pandas/core/arrays/_arrow_string_mixins.py‎

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def _str_isascii(self):
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result = pc.string_is_ascii(self._pa_array)
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return self._convert_bool_result(result)
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def _str_isdecimal(self):
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result = pc.utf8_is_decimal(self._pa_array)
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