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Merge branch 'main' into add-raise-to-series-map
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‎.circleci/config.yml

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fi
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python -m pip install --no-build-isolation -ve . -Csetup-args="--werror"
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PATH=$HOME/miniconda3/envs/pandas-dev/bin:$HOME/miniconda3/condabin:$PATH
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sudo apt-get update && sudo apt-get install -y libegl1 libopengl0
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ci/run_tests.sh
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test-linux-musl:
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docker:

‎.github/workflows/unit-tests.yml

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nogil: true
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- name: Build Environment
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# TODO: Once numpy 2.2.1 is out, don't install nightly version
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# Tests segfault with numpy 2.2.0: https://github.com/numpy/numpy/pull/27955
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run: |
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python --version
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python -m pip install --upgrade pip setuptools wheel numpy meson[ninja]==1.2.1 meson-python==0.13.1
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python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple cython
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python -m pip install --upgrade pip setuptools wheel meson[ninja]==1.2.1 meson-python==0.13.1
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python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple cython numpy
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python -m pip install versioneer[toml]
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python -m pip install python-dateutil pytz tzdata hypothesis>=6.84.0 pytest>=7.3.2 pytest-xdist>=3.4.0 pytest-cov
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python -m pip install -ve . --no-build-isolation --no-index --no-deps -Csetup-args="--werror"

‎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.Period.freq GL08" \
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-i "pandas.Period.ordinal GL08" \
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-i "pandas.RangeIndex.from_range PR01,SA01" \
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-i "pandas.Series.dt.unit GL08" \
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-i "pandas.Series.pad PR01,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.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.IntervalArray.length 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.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.nunique SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
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-i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
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-i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
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-i "pandas.core.resample.Resampler.get_group RT03,SA01" \
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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.sem SA01" \
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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.NullFrequencyError SA01" \
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-i "pandas.errors.NumbaUtilError SA01" \
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-i "pandas.errors.PerformanceWarning SA01" \
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-i "pandas.errors.UndefinedVariableError PR01,SA01" \
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-i "pandas.errors.ValueLabelTypeMismatch SA01" \
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-i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
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-i "pandas.plotting.andrews_curves RT03,SA01" \
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-i "pandas.plotting.scatter_matrix PR07,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" \
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-i "pandas.tseries.offsets.BQuarterBegin.n 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/development/maintaining.rst

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for reference):
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- The pandas-dev and pydata mailing lists
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- Twitter, Mastodon, Telegram and LinkedIn
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- X, Mastodon, Telegram and LinkedIn
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7. Update this release instructions to fix anything incorrect and to update about any
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‎doc/source/reference/frame.rst

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DataFrame.duplicated
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DataFrame.filter
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DataFrame.head
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DataFrame.idxmax
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DataFrame.sample
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‎doc/source/user_guide/cookbook.rst

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df.groupby("animal").apply(lambda subf: subf["size"][subf["weight"].idxmax()])
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expected_df = gb.apply(GrowUp, include_groups=False)
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expected_df = gb.apply(GrowUp)
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`Expanding apply

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