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‎.github/workflows/wheels.yml‎

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buildplat:
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- [ubuntu-22.04, manylinux_x86_64]
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- [ubuntu-22.04, musllinux_x86_64]
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- [macos-12, macosx_x86_64]
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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]

‎.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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- 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
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args: [-i]
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types_or: [c, c++]
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- repo: https://github.com/trim21/pre-commit-mirror-meson
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rev: v1.6.1
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hooks:
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- id: meson-fmt
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args: ['--inplace']
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- repo: local
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- id: pyright

‎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:

‎asv_bench/benchmarks/io/csv.py‎

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self.StringIO_input = StringIO(data)
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def time_read_csv_index_col(self):
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read_csv(self.StringIO_input, index_col="a")
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read_csv(self.data(self.StringIO_input), index_col="a")
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class ReadCSVDatePyarrowEngine(StringIORewind):
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def time_read_csv_index_col(self):
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read_csv(
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self.StringIO_input,
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self.data(self.StringIO_input),
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parse_dates=["a"],
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engine="pyarrow",
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dtype_backend="pyarrow",

‎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/user_guide/io.rst‎

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.. code-block:: ipython
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In [320]: url = "https://www.fdic.gov/resources/resolutions/bank-failures/failed-bank-list"
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[ Bank NameBank CityCity StateSt ... Acquiring InstitutionAI Closing DateClosing FundFund
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In [324]: headers = {
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In [326]: 'Accept':'application/json',
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.....: 'User-Agent':'Mozilla Firefox v14.0',
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.....: 'Accept':'application/json',
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.....: 'Connection':'keep-alive',
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.....: 'Auth':'Bearer 2*/f3+fe68df*4'
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.....: }
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In [325]: pd.read_html(url, storage_options=headers)
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[ 0 1
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0 Remote Socket: 51.15.105.256:51760
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1 Protocol Version: HTTP/1.1

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