Add files using upload-large-folder tool

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 code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/_libs/window/aggregations.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
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code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas-2.3.3.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas-2.3.3.dist-info/LICENSE

@@ -0,0 +1,1250 @@
+BSD 3-Clause License
+
+Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
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+--------------------------------------------------------------------------------
+dateutil - Extensions to the standard Python datetime module.
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+Copyright (c) 2012-2014 - Tomi Pieviläinen <tomi.pievilainen@iki.fi>
+Copyright (c) 2014-2016 - Yaron de Leeuw <me@jarondl.net>
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+modification, are permitted provided that the following conditions are met:
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+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+Based on http://opensource.org/licenses/MIT
+
+This is a template. Complete and ship as file LICENSE the following 2
+lines (only)
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+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
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+included in all copies or substantial portions of the Software.
+
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+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+The MIT License
+
+Copyright (c) 2008-     Attractive Chaos <attractor@live.co.uk>
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.musl as a whole is licensed under the following standard MIT license:
+
+----------------------------------------------------------------------
+Copyright © 2005-2020 Rich Felker, et al.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
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+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+----------------------------------------------------------------------
+
+Authors/contributors include:
+
+A. Wilcox
+Ada Worcester
+Alex Dowad
+Alex Suykov
+Alexander Monakov
+Andre McCurdy
+Andrew Kelley
+Anthony G. Basile
+Aric Belsito
+Arvid Picciani
+Bartosz Brachaczek
+Benjamin Peterson
+Bobby Bingham
+Boris Brezillon
+Brent Cook
+Chris Spiegel
+Clément Vasseur
+Daniel Micay
+Daniel Sabogal
+Daurnimator
+David Carlier
+David Edelsohn
+Denys Vlasenko
+Dmitry Ivanov
+Dmitry V. Levin
+Drew DeVault
+Emil Renner Berthing
+Fangrui Song
+Felix Fietkau
+Felix Janda
+Gianluca Anzolin
+Hauke Mehrtens
+He X
+Hiltjo Posthuma
+Isaac Dunham
+Jaydeep Patil
+Jens Gustedt
+Jeremy Huntwork
+Jo-Philipp Wich
+Joakim Sindholt
+John Spencer
+Julien Ramseier
+Justin Cormack
+Kaarle Ritvanen
+Khem Raj
+Kylie McClain
+Leah Neukirchen
+Luca Barbato
+Luka Perkov
+M Farkas-Dyck (Strake)
+Mahesh Bodapati
+Markus Wichmann
+Masanori Ogino
+Michael Clark
+Michael Forney
+Mikhail Kremnyov
+Natanael Copa
+Nicholas J. Kain
+orc
+Pascal Cuoq
+Patrick Oppenlander
+Petr Hosek
+Petr Skocik
+Pierre Carrier
+Reini Urban
+Rich Felker
+Richard Pennington
+Ryan Fairfax
+Samuel Holland
+Segev Finer
+Shiz
+sin
+Solar Designer
+Stefan Kristiansson
+Stefan O'Rear
+Szabolcs Nagy
+Timo Teräs
+Trutz Behn
+Valentin Ochs
+Will Dietz
+William Haddon
+William Pitcock
+
+Portions of this software are derived from third-party works licensed
+under terms compatible with the above MIT license:
+
+The TRE regular expression implementation (src/regex/reg* and
+src/regex/tre*) is Copyright © 2001-2008 Ville Laurikari and licensed
+under a 2-clause BSD license (license text in the source files). The
+included version has been heavily modified by Rich Felker in 2012, in
+the interests of size, simplicity, and namespace cleanliness.
+
+Much of the math library code (src/math/* and src/complex/*) is
+Copyright © 1993,2004 Sun Microsystems or
+Copyright © 2003-2011 David Schultz or
+Copyright © 2003-2009 Steven G. Kargl or
+Copyright © 2003-2009 Bruce D. Evans or
+Copyright © 2008 Stephen L. Moshier or
+Copyright © 2017-2018 Arm Limited
+and labelled as such in comments in the individual source files. All
+have been licensed under extremely permissive terms.
+
+The ARM memcpy code (src/string/arm/memcpy.S) is Copyright © 2008
+The Android Open Source Project and is licensed under a two-clause BSD
+license. It was taken from Bionic libc, used on Android.
+
+The AArch64 memcpy and memset code (src/string/aarch64/*) are
+Copyright © 1999-2019, Arm Limited.
+
+The implementation of DES for crypt (src/crypt/crypt_des.c) is
+Copyright © 1994 David Burren. It is licensed under a BSD license.
+
+The implementation of blowfish crypt (src/crypt/crypt_blowfish.c) was
+originally written by Solar Designer and placed into the public
+domain. The code also comes with a fallback permissive license for use
+in jurisdictions that may not recognize the public domain.
+
+The smoothsort implementation (src/stdlib/qsort.c) is Copyright © 2011
+Valentin Ochs and is licensed under an MIT-style license.
+
+The x86_64 port was written by Nicholas J. Kain and is licensed under
+the standard MIT terms.
+
+The mips and microblaze ports were originally written by Richard
+Pennington for use in the ellcc project. The original code was adapted
+by Rich Felker for build system and code conventions during upstream
+integration. It is licensed under the standard MIT terms.
+
+The mips64 port was contributed by Imagination Technologies and is
+licensed under the standard MIT terms.
+
+The powerpc port was also originally written by Richard Pennington,
+and later supplemented and integrated by John Spencer. It is licensed
+under the standard MIT terms.
+
+All other files which have no copyright comments are original works
+produced specifically for use as part of this library, written either
+by Rich Felker, the main author of the library, or by one or more
+contibutors listed above. Details on authorship of individual files
+can be found in the git version control history of the project. The
+omission of copyright and license comments in each file is in the
+interest of source tree size.
+
+In addition, permission is hereby granted for all public header files
+(include/* and arch/*/bits/*) and crt files intended to be linked into
+applications (crt/*, ldso/dlstart.c, and arch/*/crt_arch.h) to omit
+the copyright notice and permission notice otherwise required by the
+license, and to use these files without any requirement of
+attribution. These files include substantial contributions from:
+
+Bobby Bingham
+John Spencer
+Nicholas J. Kain
+Rich Felker
+Richard Pennington
+Stefan Kristiansson
+Szabolcs Nagy
+
+all of whom have explicitly granted such permission.
+
+This file previously contained text expressing a belief that most of
+the files covered by the above exception were sufficiently trivial not
+to be subject to copyright, resulting in confusion over whether it
+negated the permissions granted in the license. In the spirit of
+permissive licensing, and of not having licensing issues being an
+obstacle to adoption, that text has been removed.Copyright (c) 2005-2023, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
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+      any Contribution intentionally submitted for inclusion in the Work
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+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
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+      the Work or Derivative Works thereof, You may choose to offer,
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+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+
+Copyright (c) Donald Stufft and individual contributors.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+    1. Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.A. HISTORY OF THE SOFTWARE
+==========================
+
+Python was created in the early 1990s by Guido van Rossum at Stichting
+Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands
+as a successor of a language called ABC.  Guido remains Python's
+principal author, although it includes many contributions from others.
+
+In 1995, Guido continued his work on Python at the Corporation for
+National Research Initiatives (CNRI, see https://www.cnri.reston.va.us)
+in Reston, Virginia where he released several versions of the
+software.
+
+In May 2000, Guido and the Python core development team moved to
+BeOpen.com to form the BeOpen PythonLabs team.  In October of the same
+year, the PythonLabs team moved to Digital Creations, which became
+Zope Corporation.  In 2001, the Python Software Foundation (PSF, see
+https://www.python.org/psf/) was formed, a non-profit organization
+created specifically to own Python-related Intellectual Property.
+Zope Corporation was a sponsoring member of the PSF.
+
+All Python releases are Open Source (see https://opensource.org for
+the Open Source Definition).  Historically, most, but not all, Python
+releases have also been GPL-compatible; the table below summarizes
+the various releases.
+
+    Release         Derived     Year        Owner       GPL-
+                    from                                compatible? (1)
+
+    0.9.0 thru 1.2              1991-1995   CWI         yes
+    1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
+    1.6             1.5.2       2000        CNRI        no
+    2.0             1.6         2000        BeOpen.com  no
+    1.6.1           1.6         2001        CNRI        yes (2)
+    2.1             2.0+1.6.1   2001        PSF         no
+    2.0.1           2.0+1.6.1   2001        PSF         yes
+    2.1.1           2.1+2.0.1   2001        PSF         yes
+    2.1.2           2.1.1       2002        PSF         yes
+    2.1.3           2.1.2       2002        PSF         yes
+    2.2 and above   2.1.1       2001-now    PSF         yes
+
+Footnotes:
+
+(1) GPL-compatible doesn't mean that we're distributing Python under
+    the GPL.  All Python licenses, unlike the GPL, let you distribute
+    a modified version without making your changes open source.  The
+    GPL-compatible licenses make it possible to combine Python with
+    other software that is released under the GPL; the others don't.
+
+(2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
+    because its license has a choice of law clause.  According to
+    CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
+    is "not incompatible" with the GPL.
+
+Thanks to the many outside volunteers who have worked under Guido's
+direction to make these releases possible.
+
+
+B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+===============================================================
+
+Python software and documentation are licensed under the
+Python Software Foundation License Version 2.
+
+Starting with Python 3.8.6, examples, recipes, and other code in
+the documentation are dual licensed under the PSF License Version 2
+and the Zero-Clause BSD license.
+
+Some software incorporated into Python is under different licenses.
+The licenses are listed with code falling under that license.
+
+
+PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+--------------------------------------------
+
+1. This LICENSE AGREEMENT is between the Python Software Foundation
+("PSF"), and the Individual or Organization ("Licensee") accessing and
+otherwise using this software ("Python") in source or binary form and
+its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, PSF hereby
+grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+analyze, test, perform and/or display publicly, prepare derivative works,
+distribute, and otherwise use Python alone or in any derivative version,
+provided, however, that PSF's License Agreement and PSF's notice of copyright,
+i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 Python Software Foundation;
+All Rights Reserved" are retained in Python alone or in any derivative version
+prepared by Licensee.
+
+3. In the event Licensee prepares a derivative work that is based on
+or incorporates Python or any part thereof, and wants to make
+the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to Python.
+
+4. PSF is making Python available to Licensee on an "AS IS"
+basis.  PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
+OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. Nothing in this License Agreement shall be deemed to create any
+relationship of agency, partnership, or joint venture between PSF and
+Licensee.  This License Agreement does not grant permission to use PSF
+trademarks or trade name in a trademark sense to endorse or promote
+products or services of Licensee, or any third party.
+
+8. By copying, installing or otherwise using Python, Licensee
+agrees to be bound by the terms and conditions of this License
+Agreement.
+
+
+BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
+-------------------------------------------
+
+BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
+
+1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
+office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
+Individual or Organization ("Licensee") accessing and otherwise using
+this software in source or binary form and its associated
+documentation ("the Software").
+
+2. Subject to the terms and conditions of this BeOpen Python License
+Agreement, BeOpen hereby grants Licensee a non-exclusive,
+royalty-free, world-wide license to reproduce, analyze, test, perform
+and/or display publicly, prepare derivative works, distribute, and
+otherwise use the Software alone or in any derivative version,
+provided, however, that the BeOpen Python License is retained in the
+Software, alone or in any derivative version prepared by Licensee.
+
+3. BeOpen is making the Software available to Licensee on an "AS IS"
+basis.  BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
+SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
+AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
+DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+5. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+6. This License Agreement shall be governed by and interpreted in all
+respects by the law of the State of California, excluding conflict of
+law provisions.  Nothing in this License Agreement shall be deemed to
+create any relationship of agency, partnership, or joint venture
+between BeOpen and Licensee.  This License Agreement does not grant
+permission to use BeOpen trademarks or trade names in a trademark
+sense to endorse or promote products or services of Licensee, or any
+third party.  As an exception, the "BeOpen Python" logos available at
+http://www.pythonlabs.com/logos.html may be used according to the
+permissions granted on that web page.
+
+7. By copying, installing or otherwise using the software, Licensee
+agrees to be bound by the terms and conditions of this License
+Agreement.
+
+
+CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
+---------------------------------------
+
+1. This LICENSE AGREEMENT is between the Corporation for National
+Research Initiatives, having an office at 1895 Preston White Drive,
+Reston, VA 20191 ("CNRI"), and the Individual or Organization
+("Licensee") accessing and otherwise using Python 1.6.1 software in
+source or binary form and its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, CNRI
+hereby grants Licensee a nonexclusive, royalty-free, world-wide
+license to reproduce, analyze, test, perform and/or display publicly,
+prepare derivative works, distribute, and otherwise use Python 1.6.1
+alone or in any derivative version, provided, however, that CNRI's
+License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
+1995-2001 Corporation for National Research Initiatives; All Rights
+Reserved" are retained in Python 1.6.1 alone or in any derivative
+version prepared by Licensee.  Alternately, in lieu of CNRI's License
+Agreement, Licensee may substitute the following text (omitting the
+quotes): "Python 1.6.1 is made available subject to the terms and
+conditions in CNRI's License Agreement.  This Agreement together with
+Python 1.6.1 may be located on the internet using the following
+unique, persistent identifier (known as a handle): 1895.22/1013.  This
+Agreement may also be obtained from a proxy server on the internet
+using the following URL: http://hdl.handle.net/1895.22/1013".
+
+3. In the event Licensee prepares a derivative work that is based on
+or incorporates Python 1.6.1 or any part thereof, and wants to make
+the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to Python 1.6.1.
+
+4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
+basis.  CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
+OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. This License Agreement shall be governed by the federal
+intellectual property law of the United States, including without
+limitation the federal copyright law, and, to the extent such
+U.S. federal law does not apply, by the law of the Commonwealth of
+Virginia, excluding Virginia's conflict of law provisions.
+Notwithstanding the foregoing, with regard to derivative works based
+on Python 1.6.1 that incorporate non-separable material that was
+previously distributed under the GNU General Public License (GPL), the
+law of the Commonwealth of Virginia shall govern this License
+Agreement only as to issues arising under or with respect to
+Paragraphs 4, 5, and 7 of this License Agreement.  Nothing in this
+License Agreement shall be deemed to create any relationship of
+agency, partnership, or joint venture between CNRI and Licensee.  This
+License Agreement does not grant permission to use CNRI trademarks or
+trade name in a trademark sense to endorse or promote products or
+services of Licensee, or any third party.
+
+8. By clicking on the "ACCEPT" button where indicated, or by copying,
+installing or otherwise using Python 1.6.1, Licensee agrees to be
+bound by the terms and conditions of this License Agreement.
+
+        ACCEPT
+
+
+CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
+--------------------------------------------------
+
+Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
+The Netherlands.  All rights reserved.
+
+Permission to use, copy, modify, and distribute this software and its
+documentation for any purpose and without fee is hereby granted,
+provided that the above copyright notice appear in all copies and that
+both that copyright notice and this permission notice appear in
+supporting documentation, and that the name of Stichting Mathematisch
+Centrum or CWI not be used in advertising or publicity pertaining to
+distribution of the software without specific, written prior
+permission.
+
+STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
+THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
+FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION
+----------------------------------------------------------------------
+
+Permission to use, copy, modify, and/or distribute this software for any
+purpose with or without fee is hereby granted.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+PERFORMANCE OF THIS SOFTWARE.
+Copyright (c) 2014, Al Sweigart
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the {organization} nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Copyright (c) 2017 Anthony Sottile
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.Copyright (c) 2015-2019 Jared Hobbs
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.Developed by ESN, an Electronic Arts Inc. studio.
+Copyright (c) 2014, Electronic Arts Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+* Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the above copyright
+notice, this list of conditions and the following disclaimer in the
+documentation and/or other materials provided with the distribution.
+* Neither the name of ESN, Electronic Arts Inc. nor the
+names of its contributors may be used to endorse or promote products
+derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL ELECTRONIC ARTS INC. BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+----
+
+Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
+https://github.com/client9/stringencoders
+
+  Copyright 2005, 2006, 2007
+  Nick Galbreath -- nickg [at] modp [dot] com
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
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+----
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code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas-2.3.3.dist-info/METADATA

@@ -0,0 +1,1574 @@
+Metadata-Version: 2.1
+Name: pandas
+Version: 2.3.3
+Summary: Powerful data structures for data analysis, time series, and statistics
+Author-Email: The Pandas Development Team <pandas-dev@python.org>
+License: BSD 3-Clause License
+         
+         Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
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+         CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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+         Copyright (c) 2010-2019 Keith Goodman
+         Copyright (c) 2019 Bottleneck Developers
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
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+         
+             * Redistributions of source code must retain the above copyright notice,
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+             * Redistributions in binary form must reproduce the above copyright
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+         CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+         ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+         POSSIBILITY OF SUCH DAMAGE.Copyright 2017- Paul Ganssle <paul@ganssle.io>
+         Copyright 2017- dateutil contributors (see AUTHORS file)
+         
+            Licensed under the Apache License, Version 2.0 (the "License");
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+         
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+         --------------------------------------------------------------------------------
+         dateutil - Extensions to the standard Python datetime module.
+         
+         Copyright (c) 2003-2011 - Gustavo Niemeyer <gustavo@niemeyer.net>
+         Copyright (c) 2012-2014 - Tomi Pieviläinen <tomi.pievilainen@iki.fi>
+         Copyright (c) 2014-2016 - Yaron de Leeuw <me@jarondl.net>
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+         
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+         
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+         License: MIT + file LICENSE
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+         EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+         MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+         NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+         LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+         OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+         WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+         The MIT License
+         
+         Copyright (c) 2008-     Attractive Chaos <attractor@live.co.uk>
+         
+         Permission is hereby granted, free of charge, to any person obtaining
+         a copy of this software and associated documentation files (the
+         "Software"), to deal in the Software without restriction, including
+         without limitation the rights to use, copy, modify, merge, publish,
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+         permit persons to whom the Software is furnished to do so, subject to
+         the following conditions:
+         
+         The above copyright notice and this permission notice shall be
+         included in all copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+         EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+         MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+         NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+         BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+         ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+         CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+         SOFTWARE.musl as a whole is licensed under the following standard MIT license:
+         
+         ----------------------------------------------------------------------
+         Copyright © 2005-2020 Rich Felker, et al.
+         
+         Permission is hereby granted, free of charge, to any person obtaining
+         a copy of this software and associated documentation files (the
+         "Software"), to deal in the Software without restriction, including
+         without limitation the rights to use, copy, modify, merge, publish,
+         distribute, sublicense, and/or sell copies of the Software, and to
+         permit persons to whom the Software is furnished to do so, subject to
+         the following conditions:
+         
+         The above copyright notice and this permission notice shall be
+         included in all copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+         EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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+         IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+         CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+         TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+         SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+         ----------------------------------------------------------------------
+         
+         Authors/contributors include:
+         
+         A. Wilcox
+         Ada Worcester
+         Alex Dowad
+         Alex Suykov
+         Alexander Monakov
+         Andre McCurdy
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+         Aric Belsito
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+         William Pitcock
+         
+         Portions of this software are derived from third-party works licensed
+         under terms compatible with the above MIT license:
+         
+         The TRE regular expression implementation (src/regex/reg* and
+         src/regex/tre*) is Copyright © 2001-2008 Ville Laurikari and licensed
+         under a 2-clause BSD license (license text in the source files). The
+         included version has been heavily modified by Rich Felker in 2012, in
+         the interests of size, simplicity, and namespace cleanliness.
+         
+         Much of the math library code (src/math/* and src/complex/*) is
+         Copyright © 1993,2004 Sun Microsystems or
+         Copyright © 2003-2011 David Schultz or
+         Copyright © 2003-2009 Steven G. Kargl or
+         Copyright © 2003-2009 Bruce D. Evans or
+         Copyright © 2008 Stephen L. Moshier or
+         Copyright © 2017-2018 Arm Limited
+         and labelled as such in comments in the individual source files. All
+         have been licensed under extremely permissive terms.
+         
+         The ARM memcpy code (src/string/arm/memcpy.S) is Copyright © 2008
+         The Android Open Source Project and is licensed under a two-clause BSD
+         license. It was taken from Bionic libc, used on Android.
+         
+         The AArch64 memcpy and memset code (src/string/aarch64/*) are
+         Copyright © 1999-2019, Arm Limited.
+         
+         The implementation of DES for crypt (src/crypt/crypt_des.c) is
+         Copyright © 1994 David Burren. It is licensed under a BSD license.
+         
+         The implementation of blowfish crypt (src/crypt/crypt_blowfish.c) was
+         originally written by Solar Designer and placed into the public
+         domain. The code also comes with a fallback permissive license for use
+         in jurisdictions that may not recognize the public domain.
+         
+         The smoothsort implementation (src/stdlib/qsort.c) is Copyright © 2011
+         Valentin Ochs and is licensed under an MIT-style license.
+         
+         The x86_64 port was written by Nicholas J. Kain and is licensed under
+         the standard MIT terms.
+         
+         The mips and microblaze ports were originally written by Richard
+         Pennington for use in the ellcc project. The original code was adapted
+         by Rich Felker for build system and code conventions during upstream
+         integration. It is licensed under the standard MIT terms.
+         
+         The mips64 port was contributed by Imagination Technologies and is
+         licensed under the standard MIT terms.
+         
+         The powerpc port was also originally written by Richard Pennington,
+         and later supplemented and integrated by John Spencer. It is licensed
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+         
+         All other files which have no copyright comments are original works
+         produced specifically for use as part of this library, written either
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+         contibutors listed above. Details on authorship of individual files
+         can be found in the git version control history of the project. The
+         omission of copyright and license comments in each file is in the
+         interest of source tree size.
+         
+         In addition, permission is hereby granted for all public header files
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+         
+         Bobby Bingham
+         John Spencer
+         Nicholas J. Kain
+         Rich Felker
+         Richard Pennington
+         Stefan Kristiansson
+         Szabolcs Nagy
+         
+         all of whom have explicitly granted such permission.
+         
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+         to be subject to copyright, resulting in confusion over whether it
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+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
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+             1. Redistributions of source code must retain the above copyright notice,
+                this list of conditions and the following disclaimer.
+         
+             2. Redistributions in binary form must reproduce the above copyright
+                notice, this list of conditions and the following disclaimer in the
+                documentation and/or other materials provided with the distribution.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+         ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+         WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+         DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+         FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+         DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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+         CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+         OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+         OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.A. HISTORY OF THE SOFTWARE
+         ==========================
+         
+         Python was created in the early 1990s by Guido van Rossum at Stichting
+         Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands
+         as a successor of a language called ABC.  Guido remains Python's
+         principal author, although it includes many contributions from others.
+         
+         In 1995, Guido continued his work on Python at the Corporation for
+         National Research Initiatives (CNRI, see https://www.cnri.reston.va.us)
+         in Reston, Virginia where he released several versions of the
+         software.
+         
+         In May 2000, Guido and the Python core development team moved to
+         BeOpen.com to form the BeOpen PythonLabs team.  In October of the same
+         year, the PythonLabs team moved to Digital Creations, which became
+         Zope Corporation.  In 2001, the Python Software Foundation (PSF, see
+         https://www.python.org/psf/) was formed, a non-profit organization
+         created specifically to own Python-related Intellectual Property.
+         Zope Corporation was a sponsoring member of the PSF.
+         
+         All Python releases are Open Source (see https://opensource.org for
+         the Open Source Definition).  Historically, most, but not all, Python
+         releases have also been GPL-compatible; the table below summarizes
+         the various releases.
+         
+             Release         Derived     Year        Owner       GPL-
+                             from                                compatible? (1)
+         
+             0.9.0 thru 1.2              1991-1995   CWI         yes
+             1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
+             1.6             1.5.2       2000        CNRI        no
+             2.0             1.6         2000        BeOpen.com  no
+             1.6.1           1.6         2001        CNRI        yes (2)
+             2.1             2.0+1.6.1   2001        PSF         no
+             2.0.1           2.0+1.6.1   2001        PSF         yes
+             2.1.1           2.1+2.0.1   2001        PSF         yes
+             2.1.2           2.1.1       2002        PSF         yes
+             2.1.3           2.1.2       2002        PSF         yes
+             2.2 and above   2.1.1       2001-now    PSF         yes
+         
+         Footnotes:
+         
+         (1) GPL-compatible doesn't mean that we're distributing Python under
+             the GPL.  All Python licenses, unlike the GPL, let you distribute
+             a modified version without making your changes open source.  The
+             GPL-compatible licenses make it possible to combine Python with
+             other software that is released under the GPL; the others don't.
+         
+         (2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
+             because its license has a choice of law clause.  According to
+             CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
+             is "not incompatible" with the GPL.
+         
+         Thanks to the many outside volunteers who have worked under Guido's
+         direction to make these releases possible.
+         
+         
+         B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+         ===============================================================
+         
+         Python software and documentation are licensed under the
+         Python Software Foundation License Version 2.
+         
+         Starting with Python 3.8.6, examples, recipes, and other code in
+         the documentation are dual licensed under the PSF License Version 2
+         and the Zero-Clause BSD license.
+         
+         Some software incorporated into Python is under different licenses.
+         The licenses are listed with code falling under that license.
+         
+         
+         PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+         --------------------------------------------
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+         
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+         -------------------------------------------
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+         CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
+         ---------------------------------------
+         
+         1. This LICENSE AGREEMENT is between the Corporation for National
+         Research Initiatives, having an office at 1895 Preston White Drive,
+         Reston, VA 20191 ("CNRI"), and the Individual or Organization
+         ("Licensee") accessing and otherwise using Python 1.6.1 software in
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+         
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+         prepare derivative works, distribute, and otherwise use Python 1.6.1
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+         6. This License Agreement will automatically terminate upon a material
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+         
+         7. This License Agreement shall be governed by the federal
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+         Notwithstanding the foregoing, with regard to derivative works based
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+         8. By clicking on the "ACCEPT" button where indicated, or by copying,
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+         
+                 ACCEPT
+         
+         
+         CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
+         --------------------------------------------------
+         
+         Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
+         The Netherlands.  All rights reserved.
+         
+         Permission to use, copy, modify, and distribute this software and its
+         documentation for any purpose and without fee is hereby granted,
+         provided that the above copyright notice appear in all copies and that
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+         STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
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+         
+         ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION
+         ----------------------------------------------------------------------
+         
+         Permission to use, copy, modify, and/or distribute this software for any
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+         THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+         REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+         AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+         INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+         LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+         OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+         PERFORMANCE OF THIS SOFTWARE.
+         Copyright (c) 2014, Al Sweigart
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         
+         * Redistributions of source code must retain the above copyright notice, this
+           list of conditions and the following disclaimer.
+         
+         * Redistributions in binary form must reproduce the above copyright notice,
+           this list of conditions and the following disclaimer in the documentation
+           and/or other materials provided with the distribution.
+         
+         * Neither the name of the {organization} nor the names of its
+           contributors may be used to endorse or promote products derived from
+           this software without specific prior written permission.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+         AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+         IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+         DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+         FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+         DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+         SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+         CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+         OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+         OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Copyright (c) 2017 Anthony Sottile
+         
+         Permission is hereby granted, free of charge, to any person obtaining a copy
+         of this software and associated documentation files (the "Software"), to deal
+         in the Software without restriction, including without limitation the rights
+         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+         copies of the Software, and to permit persons to whom the Software is
+         furnished to do so, subject to the following conditions:
+         
+         The above copyright notice and this permission notice shall be included in
+         all copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+         THE SOFTWARE.Copyright (c) 2015-2019 Jared Hobbs
+         
+         Permission is hereby granted, free of charge, to any person obtaining a copy of
+         this software and associated documentation files (the "Software"), to deal in
+         the Software without restriction, including without limitation the rights to
+         use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+         of the Software, and to permit persons to whom the Software is furnished to do
+         so, subject to the following conditions:
+         
+         The above copyright notice and this permission notice shall be included in all
+         copies or substantial portions of the Software.
+         
+         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+         SOFTWARE.Developed by ESN, an Electronic Arts Inc. studio.
+         Copyright (c) 2014, Electronic Arts Inc.
+         All rights reserved.
+         
+         Redistribution and use in source and binary forms, with or without
+         modification, are permitted provided that the following conditions are met:
+         * Redistributions of source code must retain the above copyright
+         notice, this list of conditions and the following disclaimer.
+         * Redistributions in binary form must reproduce the above copyright
+         notice, this list of conditions and the following disclaimer in the
+         documentation and/or other materials provided with the distribution.
+         * Neither the name of ESN, Electronic Arts Inc. nor the
+         names of its contributors may be used to endorse or promote products
+         derived from this software without specific prior written permission.
+         
+         THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+         ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+         WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+         DISCLAIMED. IN NO EVENT SHALL ELECTRONIC ARTS INC. BE LIABLE
+         FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+         (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+         LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+         ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+         (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+         SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+         
+         ----
+         
+         Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
+         https://github.com/client9/stringencoders
+         
+           Copyright 2005, 2006, 2007
+           Nick Galbreath -- nickg [at] modp [dot] com
+           All rights reserved.
+         
+           Redistribution and use in source and binary forms, with or without
+           modification, are permitted provided that the following conditions are
+           met:
+         
+             Redistributions of source code must retain the above copyright
+             notice, this list of conditions and the following disclaimer.
+         
+             Redistributions in binary form must reproduce the above copyright
+             notice, this list of conditions and the following disclaimer in the
+             documentation and/or other materials provided with the distribution.
+         
+             Neither the name of the modp.com nor the names of its
+             contributors may be used to endorse or promote products derived from
+             this software without specific prior written permission.
+         
+           THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+           "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+           LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+           A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+           OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+           SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+           LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+           DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+           THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+           (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+           OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+         
+           This is the standard "new" BSD license:
+           http://www.opensource.org/licenses/bsd-license.php
+         
+         https://github.com/client9/stringencoders/blob/cfd5c1507325ae497ea9bacdacba12c0ffd79d30/COPYING
+         
+         ----
+         
+         Numeric decoder derived from from TCL library
+         https://opensource.apple.com/source/tcl/tcl-14/tcl/license.terms
+          * Copyright (c) 1988-1993 The Regents of the University of California.
+          * Copyright (c) 1994 Sun Microsystems, Inc.
+         
+           This software is copyrighted by the Regents of the University of
+           California, Sun Microsystems, Inc., Scriptics Corporation, ActiveState
+           Corporation and other parties.  The following terms apply to all files
+           associated with the software unless explicitly disclaimed in
+           individual files.
+         
+           The authors hereby grant permission to use, copy, modify, distribute,
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+           license, or royalty fee is required for any of the authorized uses.
+           Modifications to this software may be copyrighted by their authors
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+         
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+
+<div align="center">
+  <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
+</div>
+
+-----------------
+
+# pandas: powerful Python data analysis toolkit
+
+| | |
+| --- | --- |
+| Testing | [![CI - Test](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml) [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) |
+| Package | [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) [![PyPI Downloads](https://img.shields.io/pypi/dm/pandas.svg?label=PyPI%20downloads)](https://pypi.org/project/pandas/) [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/conda-forge/pandas) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pandas.svg?label=Conda%20downloads)](https://anaconda.org/conda-forge/pandas) |
+| Meta | [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![License - BSD 3-Clause](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [![Slack](https://img.shields.io/badge/join_Slack-information-brightgreen.svg?logo=slack)](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) |
+
+
+## What is it?
+
+**pandas** is a Python package that provides fast, flexible, and expressive data
+structures designed to make working with "relational" or "labeled" data both
+easy and intuitive. It aims to be the fundamental high-level building block for
+doing practical, **real world** data analysis in Python. Additionally, it has
+the broader goal of becoming **the most powerful and flexible open source data
+analysis / manipulation tool available in any language**. It is already well on
+its way towards this goal.
+
+## Table of Contents
+
+- [Main Features](#main-features)
+- [Where to get it](#where-to-get-it)
+- [Dependencies](#dependencies)
+- [Installation from sources](#installation-from-sources)
+- [License](#license)
+- [Documentation](#documentation)
+- [Background](#background)
+- [Getting Help](#getting-help)
+- [Discussion and Development](#discussion-and-development)
+- [Contributing to pandas](#contributing-to-pandas)
+
+## Main Features
+Here are just a few of the things that pandas does well:
+
+  - Easy handling of [**missing data**][missing-data] (represented as
+    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
+  - Size mutability: columns can be [**inserted and
+    deleted**][insertion-deletion] from DataFrame and higher dimensional
+    objects
+  - Automatic and explicit [**data alignment**][alignment]: objects can
+    be explicitly aligned to a set of labels, or the user can simply
+    ignore the labels and let `Series`, `DataFrame`, etc. automatically
+    align the data for you in computations
+  - Powerful, flexible [**group by**][groupby] functionality to perform
+    split-apply-combine operations on data sets, for both aggregating
+    and transforming data
+  - Make it [**easy to convert**][conversion] ragged,
+    differently-indexed data in other Python and NumPy data structures
+    into DataFrame objects
+  - Intelligent label-based [**slicing**][slicing], [**fancy
+    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
+    large data sets
+  - Intuitive [**merging**][merging] and [**joining**][joining] data
+    sets
+  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
+    data sets
+  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
+    labels per tick)
+  - Robust IO tools for loading data from [**flat files**][flat-files]
+    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
+    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
+  - [**Time series**][timeseries]-specific functionality: date range
+    generation and frequency conversion, moving window statistics,
+    date shifting and lagging
+
+
+   [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
+   [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
+   [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
+   [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
+   [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
+   [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
+   [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
+   [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
+   [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
+   [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
+   [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
+   [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
+   [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
+   [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
+   [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
+   [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
+   [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
+   [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality
+
+## Where to get it
+The source code is currently hosted on GitHub at:
+https://github.com/pandas-dev/pandas
+
+Binary installers for the latest released version are available at the [Python
+Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).
+
+```sh
+# conda
+conda install -c conda-forge pandas
+```
+
+```sh
+# or PyPI
+pip install pandas
+```
+
+The list of changes to pandas between each release can be found
+[here](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). For full
+details, see the commit logs at https://github.com/pandas-dev/pandas.
+
+## Dependencies
+- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
+- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
+- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)
+
+See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.
+
+## Installation from sources
+To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
+dependencies above. Cython can be installed from PyPI:
+
+```sh
+pip install cython
+```
+
+In the `pandas` directory (same one where you found this file after
+cloning the git repo), execute:
+
+```sh
+pip install .
+```
+
+or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):
+
+
+```sh
+python -m pip install -ve . --no-build-isolation --config-settings=editable-verbose=true
+```
+
+See the full instructions for [installing from source](https://pandas.pydata.org/docs/dev/development/contributing_environment.html).
+
+## License
+[BSD 3](LICENSE)
+
+## Documentation
+The official documentation is hosted on [PyData.org](https://pandas.pydata.org/pandas-docs/stable/).
+
+## Background
+Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
+has been under active development since then.
+
+## Getting Help
+
+For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
+Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).
+
+## Discussion and Development
+Most development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https://github.com/pandas-dev/pandas/issues).
+
+Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions.
+
+There are also frequent [community meetings](https://pandas.pydata.org/docs/dev/development/community.html#community-meeting) for project maintainers open to the community as well as monthly [new contributor meetings](https://pandas.pydata.org/docs/dev/development/community.html#new-contributor-meeting) to help support new contributors.
+
+Additional information on the communication channels can be found on the [contributor community](https://pandas.pydata.org/docs/development/community.html) page.
+
+## Contributing to pandas
+
+[![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas)
+
+All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
+
+A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**.
+
+If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) where you could start out.
+
+You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas).
+
+Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!
+
+Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack).
+
+As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)
+
+<hr>
+
+[Go to Top](#table-of-contents)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas-2.3.3.dist-info/WHEEL

@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: meson
+Root-Is-Purelib: false
+Tag: cp310-cp310-manylinux_2_24_x86_64
+Tag: cp310-cp310-manylinux_2_28_x86_64
+

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas-2.3.3.dist-info/entry_points.txt

@@ -0,0 +1,3 @@
+[pandas_plotting_backends]
+matplotlib = pandas:plotting._matplotlib
+

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_isin.py

@@ -0,0 +1,252 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core import algorithms
+from pandas.core.arrays import PeriodArray
+
+
+class TestSeriesIsIn:
+    def test_isin(self):
+        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
+
+        result = s.isin(["A", "C"])
+        expected = Series([True, False, True, False, False, False, True, True])
+        tm.assert_series_equal(result, expected)
+
+        # GH#16012
+        # This specific issue has to have a series over 1e6 in len, but the
+        # comparison array (in_list) must be large enough so that numpy doesn't
+        # do a manual masking trick that will avoid this issue altogether
+        s = Series(list("abcdefghijk" * 10**5))
+        # If numpy doesn't do the manual comparison/mask, these
+        # unorderable mixed types are what cause the exception in numpy
+        in_list = [-1, "a", "b", "G", "Y", "Z", "E", "K", "E", "S", "I", "R", "R"] * 6
+
+        assert s.isin(in_list).sum() == 200000
+
+    def test_isin_with_string_scalar(self):
+        # GH#4763
+        s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
+        msg = (
+            r"only list-like objects are allowed to be passed to isin\(\), "
+            r"you passed a `str`"
+        )
+        with pytest.raises(TypeError, match=msg):
+            s.isin("a")
+
+        s = Series(["aaa", "b", "c"])
+        with pytest.raises(TypeError, match=msg):
+            s.isin("aaa")
+
+    def test_isin_datetimelike_mismatched_reso(self):
+        expected = Series([True, True, False, False, False])
+
+        ser = Series(date_range("jan-01-2013", "jan-05-2013"))
+
+        # fails on dtype conversion in the first place
+        day_values = np.asarray(ser[0:2].values).astype("datetime64[D]")
+        result = ser.isin(day_values)
+        tm.assert_series_equal(result, expected)
+
+        dta = ser[:2]._values.astype("M8[s]")
+        result = ser.isin(dta)
+        tm.assert_series_equal(result, expected)
+
+    def test_isin_datetimelike_mismatched_reso_list(self):
+        expected = Series([True, True, False, False, False])
+
+        ser = Series(date_range("jan-01-2013", "jan-05-2013"))
+
+        dta = ser[:2]._values.astype("M8[s]")
+        result = ser.isin(list(dta))
+        tm.assert_series_equal(result, expected)
+
+    def test_isin_with_i8(self):
+        # GH#5021
+
+        expected = Series([True, True, False, False, False])
+        expected2 = Series([False, True, False, False, False])
+
+        # datetime64[ns]
+        s = Series(date_range("jan-01-2013", "jan-05-2013"))
+
+        result = s.isin(s[0:2])
+        tm.assert_series_equal(result, expected)
+
+        result = s.isin(s[0:2].values)
+        tm.assert_series_equal(result, expected)
+
+        result = s.isin([s[1]])
+        tm.assert_series_equal(result, expected2)
+
+        result = s.isin([np.datetime64(s[1])])
+        tm.assert_series_equal(result, expected2)
+
+        result = s.isin(set(s[0:2]))
+        tm.assert_series_equal(result, expected)
+
+        # timedelta64[ns]
+        s = Series(pd.to_timedelta(range(5), unit="d"))
+        result = s.isin(s[0:2])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
+    def test_isin_empty(self, empty):
+        # see GH#16991
+        s = Series(["a", "b"])
+        expected = Series([False, False])
+
+        result = s.isin(empty)
+        tm.assert_series_equal(expected, result)
+
+    def test_isin_read_only(self):
+        # https://github.com/pandas-dev/pandas/issues/37174
+        arr = np.array([1, 2, 3])
+        arr.setflags(write=False)
+        s = Series([1, 2, 3])
+        result = s.isin(arr)
+        expected = Series([True, True, True])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [object, None])
+    def test_isin_dt64_values_vs_ints(self, dtype):
+        # GH#36621 dont cast integers to datetimes for isin
+        dti = date_range("2013-01-01", "2013-01-05")
+        ser = Series(dti)
+
+        comps = np.asarray([1356998400000000000], dtype=dtype)
+
+        res = dti.isin(comps)
+        expected = np.array([False] * len(dti), dtype=bool)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = ser.isin(comps)
+        tm.assert_series_equal(res, Series(expected))
+
+        res = pd.core.algorithms.isin(ser, comps)
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_isin_tzawareness_mismatch(self):
+        dti = date_range("2013-01-01", "2013-01-05")
+        ser = Series(dti)
+
+        other = dti.tz_localize("UTC")
+
+        res = dti.isin(other)
+        expected = np.array([False] * len(dti), dtype=bool)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = ser.isin(other)
+        tm.assert_series_equal(res, Series(expected))
+
+        res = pd.core.algorithms.isin(ser, other)
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_isin_period_freq_mismatch(self):
+        dti = date_range("2013-01-01", "2013-01-05")
+        pi = dti.to_period("M")
+        ser = Series(pi)
+
+        # We construct another PeriodIndex with the same i8 values
+        #  but different dtype
+        dtype = dti.to_period("Y").dtype
+        other = PeriodArray._simple_new(pi.asi8, dtype=dtype)
+
+        res = pi.isin(other)
+        expected = np.array([False] * len(pi), dtype=bool)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = ser.isin(other)
+        tm.assert_series_equal(res, Series(expected))
+
+        res = pd.core.algorithms.isin(ser, other)
+        tm.assert_numpy_array_equal(res, expected)
+
+    @pytest.mark.parametrize("values", [[-9.0, 0.0], [-9, 0]])
+    def test_isin_float_in_int_series(self, values):
+        # GH#19356 GH#21804
+        ser = Series(values)
+        result = ser.isin([-9, -0.5])
+        expected = Series([True, False])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["boolean", "Int64", "Float64"])
+    @pytest.mark.parametrize(
+        "data,values,expected",
+        [
+            ([0, 1, 0], [1], [False, True, False]),
+            ([0, 1, 0], [1, pd.NA], [False, True, False]),
+            ([0, pd.NA, 0], [1, 0], [True, False, True]),
+            ([0, 1, pd.NA], [1, pd.NA], [False, True, True]),
+            ([0, 1, pd.NA], [1, np.nan], [False, True, False]),
+            ([0, pd.NA, pd.NA], [np.nan, pd.NaT, None], [False, False, False]),
+        ],
+    )
+    def test_isin_masked_types(self, dtype, data, values, expected):
+        # GH#42405
+        ser = Series(data, dtype=dtype)
+
+        result = ser.isin(values)
+        expected = Series(expected, dtype="boolean")
+
+        tm.assert_series_equal(result, expected)
+
+
+def test_isin_large_series_mixed_dtypes_and_nan(monkeypatch):
+    # https://github.com/pandas-dev/pandas/issues/37094
+    # combination of object dtype for the values
+    # and > _MINIMUM_COMP_ARR_LEN elements
+    min_isin_comp = 5
+    ser = Series([1, 2, np.nan] * min_isin_comp)
+    with monkeypatch.context() as m:
+        m.setattr(algorithms, "_MINIMUM_COMP_ARR_LEN", min_isin_comp)
+        result = ser.isin({"foo", "bar"})
+    expected = Series([False] * 3 * min_isin_comp)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "array,expected",
+    [
+        (
+            [0, 1j, 1j, 1, 1 + 1j, 1 + 2j, 1 + 1j],
+            Series([False, True, True, False, True, True, True], dtype=bool),
+        )
+    ],
+)
+def test_isin_complex_numbers(array, expected):
+    # GH 17927
+    result = Series(array).isin([1j, 1 + 1j, 1 + 2j])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data,is_in",
+    [([1, [2]], [1]), (["simple str", [{"values": 3}]], ["simple str"])],
+)
+def test_isin_filtering_with_mixed_object_types(data, is_in):
+    # GH 20883
+
+    ser = Series(data)
+    result = ser.isin(is_in)
+    expected = Series([True, False])
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("data", [[1, 2, 3], [1.0, 2.0, 3.0]])
+@pytest.mark.parametrize("isin", [[1, 2], [1.0, 2.0]])
+def test_isin_filtering_on_iterable(data, isin):
+    # GH 50234
+
+    ser = Series(data)
+    result = ser.isin(i for i in isin)
+    expected_result = Series([True, True, False])
+
+    tm.assert_series_equal(result, expected_result)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_isna.py

@@ -0,0 +1,35 @@
+"""
+We also test Series.notna in this file.
+"""
+import numpy as np
+
+from pandas import (
+    Period,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestIsna:
+    def test_isna_period_dtype(self):
+        # GH#13737
+        ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")])
+
+        expected = Series([False, True])
+
+        result = ser.isna()
+        tm.assert_series_equal(result, expected)
+
+        result = ser.notna()
+        tm.assert_series_equal(result, ~expected)
+
+    def test_isna(self):
+        ser = Series([0, 5.4, 3, np.nan, -0.001])
+        expected = Series([False, False, False, True, False])
+        tm.assert_series_equal(ser.isna(), expected)
+        tm.assert_series_equal(ser.notna(), ~expected)
+
+        ser = Series(["hi", "", np.nan])
+        expected = Series([False, False, True])
+        tm.assert_series_equal(ser.isna(), expected)
+        tm.assert_series_equal(ser.notna(), ~expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_item.py

@@ -0,0 +1,59 @@
+"""
+Series.item method, mainly testing that we get python scalars as opposed to
+numpy scalars.
+"""
+import pytest
+
+from pandas import (
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+
+
+class TestItem:
+    def test_item(self):
+        # We are testing that we get python scalars as opposed to numpy scalars
+        ser = Series([1])
+        result = ser.item()
+        assert result == 1
+        assert result == ser.iloc[0]
+        assert isinstance(result, int)  # i.e. not np.int64
+
+        ser = Series([0.5], index=[3])
+        result = ser.item()
+        assert isinstance(result, float)
+        assert result == 0.5
+
+        ser = Series([1, 2])
+        msg = "can only convert an array of size 1"
+        with pytest.raises(ValueError, match=msg):
+            ser.item()
+
+        dti = date_range("2016-01-01", periods=2)
+        with pytest.raises(ValueError, match=msg):
+            dti.item()
+        with pytest.raises(ValueError, match=msg):
+            Series(dti).item()
+
+        val = dti[:1].item()
+        assert isinstance(val, Timestamp)
+        val = Series(dti)[:1].item()
+        assert isinstance(val, Timestamp)
+
+        tdi = dti - dti
+        with pytest.raises(ValueError, match=msg):
+            tdi.item()
+        with pytest.raises(ValueError, match=msg):
+            Series(tdi).item()
+
+        val = tdi[:1].item()
+        assert isinstance(val, Timedelta)
+        val = Series(tdi)[:1].item()
+        assert isinstance(val, Timedelta)
+
+        # Case where ser[0] would not work
+        ser = Series(dti, index=[5, 6])
+        val = ser.iloc[:1].item()
+        assert val == dti[0]

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_map.py

@@ -0,0 +1,604 @@
+from collections import (
+    Counter,
+    defaultdict,
+)
+from decimal import Decimal
+import math
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    bdate_range,
+    date_range,
+    isna,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+def test_series_map_box_timedelta():
+    # GH#11349
+    ser = Series(timedelta_range("1 day 1 s", periods=5, freq="h"))
+
+    def f(x):
+        return x.total_seconds()
+
+    ser.map(f)
+
+
+def test_map_callable(datetime_series):
+    with np.errstate(all="ignore"):
+        tm.assert_series_equal(datetime_series.map(np.sqrt), np.sqrt(datetime_series))
+
+    # map function element-wise
+    tm.assert_series_equal(datetime_series.map(math.exp), np.exp(datetime_series))
+
+    # empty series
+    s = Series(dtype=object, name="foo", index=Index([], name="bar"))
+    rs = s.map(lambda x: x)
+    tm.assert_series_equal(s, rs)
+
+    # check all metadata (GH 9322)
+    assert s is not rs
+    assert s.index is rs.index
+    assert s.dtype == rs.dtype
+    assert s.name == rs.name
+
+    # index but no data
+    s = Series(index=[1, 2, 3], dtype=np.float64)
+    rs = s.map(lambda x: x)
+    tm.assert_series_equal(s, rs)
+
+
+def test_map_same_length_inference_bug():
+    s = Series([1, 2])
+
+    def f(x):
+        return (x, x + 1)
+
+    s = Series([1, 2, 3])
+    result = s.map(f)
+    expected = Series([(1, 2), (2, 3), (3, 4)])
+    tm.assert_series_equal(result, expected)
+
+    s = Series(["foo,bar"])
+    result = s.map(lambda x: x.split(","))
+    expected = Series([("foo", "bar")])
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_map_box_timestamps():
+    # GH#2689, GH#2627
+    ser = Series(date_range("1/1/2000", periods=3))
+
+    def func(x):
+        return (x.hour, x.day, x.month)
+
+    result = ser.map(func)
+    expected = Series([(0, 1, 1), (0, 2, 1), (0, 3, 1)])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_series_stringdtype(any_string_dtype, using_infer_string):
+    # map test on StringDType, GH#40823
+    ser1 = Series(
+        data=["cat", "dog", "rabbit"],
+        index=["id1", "id2", "id3"],
+        dtype=any_string_dtype,
+    )
+    ser2 = Series(["id3", "id2", "id1", "id7000"], dtype=any_string_dtype)
+    result = ser2.map(ser1)
+
+    item = pd.NA
+    if ser2.dtype == object:
+        item = np.nan
+
+    expected = Series(data=["rabbit", "dog", "cat", item], dtype=any_string_dtype)
+    if using_infer_string and any_string_dtype == "object":
+        expected = expected.astype("str")
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, expected_dtype",
+    [(["1-1", "1-1", np.nan], "category"), (["1-1", "1-2", np.nan], "str")],
+)
+def test_map_categorical_with_nan_values(data, expected_dtype):
+    # GH 20714 bug fixed in: GH 24275
+    def func(val):
+        return val.split("-")[0]
+
+    s = Series(data, dtype="category")
+
+    result = s.map(func, na_action="ignore")
+    expected = Series(["1", "1", np.nan], dtype=expected_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_empty_integer_series():
+    # GH52384
+    s = Series([], dtype=int)
+    result = s.map(lambda x: x)
+    tm.assert_series_equal(result, s)
+
+
+def test_map_empty_integer_series_with_datetime_index():
+    # GH 21245
+    s = Series([], index=date_range(start="2018-01-01", periods=0), dtype=int)
+    result = s.map(lambda x: x)
+    tm.assert_series_equal(result, s)
+
+
+@pytest.mark.parametrize("func", [str, lambda x: str(x)])
+def test_map_simple_str_callables_same_as_astype(
+    string_series, func, using_infer_string
+):
+    # test that we are evaluating row-by-row first
+    # before vectorized evaluation
+    result = string_series.map(func)
+    expected = string_series.astype(str if not using_infer_string else "str")
+    tm.assert_series_equal(result, expected)
+
+
+def test_list_raises(string_series):
+    with pytest.raises(TypeError, match="'list' object is not callable"):
+        string_series.map([lambda x: x])
+
+
+def test_map():
+    data = {
+        "A": [0.0, 1.0, 2.0, 3.0, 4.0],
+        "B": [0.0, 1.0, 0.0, 1.0, 0.0],
+        "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
+        "D": bdate_range("1/1/2009", periods=5),
+    }
+
+    source = Series(data["B"], index=data["C"])
+    target = Series(data["C"][:4], index=data["D"][:4])
+
+    merged = target.map(source)
+
+    for k, v in merged.items():
+        assert v == source[target[k]]
+
+    # input could be a dict
+    merged = target.map(source.to_dict())
+
+    for k, v in merged.items():
+        assert v == source[target[k]]
+
+
+def test_map_datetime(datetime_series):
+    # function
+    result = datetime_series.map(lambda x: x * 2)
+    tm.assert_series_equal(result, datetime_series * 2)
+
+
+def test_map_category():
+    # GH 10324
+    a = Series([1, 2, 3, 4])
+    b = Series(["even", "odd", "even", "odd"], dtype="category")
+    c = Series(["even", "odd", "even", "odd"])
+
+    exp = Series(["odd", "even", "odd", np.nan], dtype="category")
+    tm.assert_series_equal(a.map(b), exp)
+    exp = Series(["odd", "even", "odd", np.nan])
+    tm.assert_series_equal(a.map(c), exp)
+
+
+def test_map_category_numeric():
+    a = Series(["a", "b", "c", "d"])
+    b = Series([1, 2, 3, 4], index=pd.CategoricalIndex(["b", "c", "d", "e"]))
+    c = Series([1, 2, 3, 4], index=Index(["b", "c", "d", "e"]))
+
+    exp = Series([np.nan, 1, 2, 3])
+    tm.assert_series_equal(a.map(b), exp)
+    exp = Series([np.nan, 1, 2, 3])
+    tm.assert_series_equal(a.map(c), exp)
+
+
+def test_map_category_string():
+    a = Series(["a", "b", "c", "d"])
+    b = Series(
+        ["B", "C", "D", "E"],
+        dtype="category",
+        index=pd.CategoricalIndex(["b", "c", "d", "e"]),
+    )
+    c = Series(["B", "C", "D", "E"], index=Index(["b", "c", "d", "e"]))
+
+    exp = Series(
+        pd.Categorical([np.nan, "B", "C", "D"], categories=["B", "C", "D", "E"])
+    )
+    tm.assert_series_equal(a.map(b), exp)
+    exp = Series([np.nan, "B", "C", "D"])
+    tm.assert_series_equal(a.map(c), exp)
+
+
+@pytest.mark.filterwarnings(r"ignore:Dtype inference:FutureWarning")
+def test_map_empty(request, index):
+    if isinstance(index, MultiIndex):
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="Initializing a Series from a MultiIndex is not supported"
+            )
+        )
+
+    s = Series(index)
+    result = s.map({})
+
+    expected = Series(np.nan, index=s.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_compat():
+    # related GH 8024
+    s = Series([True, True, False], index=[1, 2, 3])
+    result = s.map({True: "foo", False: "bar"})
+    expected = Series(["foo", "foo", "bar"], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_int():
+    left = Series({"a": 1.0, "b": 2.0, "c": 3.0, "d": 4})
+    right = Series({1: 11, 2: 22, 3: 33})
+
+    assert left.dtype == np.float64
+    assert issubclass(right.dtype.type, np.integer)
+
+    merged = left.map(right)
+    assert merged.dtype == np.float64
+    assert isna(merged["d"])
+    assert not isna(merged["c"])
+
+
+def test_map_type_inference():
+    s = Series(range(3))
+    s2 = s.map(lambda x: np.where(x == 0, 0, 1))
+    assert issubclass(s2.dtype.type, np.integer)
+
+
+def test_map_decimal(string_series):
+    result = string_series.map(lambda x: Decimal(str(x)))
+    assert result.dtype == np.object_
+    assert isinstance(result.iloc[0], Decimal)
+
+
+def test_map_na_exclusion():
+    s = Series([1.5, np.nan, 3, np.nan, 5])
+
+    result = s.map(lambda x: x * 2, na_action="ignore")
+    exp = s * 2
+    tm.assert_series_equal(result, exp)
+
+
+def test_map_dict_with_tuple_keys():
+    """
+    Due to new MultiIndex-ing behaviour in v0.14.0,
+    dicts with tuple keys passed to map were being
+    converted to a multi-index, preventing tuple values
+    from being mapped properly.
+    """
+    # GH 18496
+    df = DataFrame({"a": [(1,), (2,), (3, 4), (5, 6)]})
+    label_mappings = {(1,): "A", (2,): "B", (3, 4): "A", (5, 6): "B"}
+
+    df["labels"] = df["a"].map(label_mappings)
+    df["expected_labels"] = Series(["A", "B", "A", "B"], index=df.index)
+    # All labels should be filled now
+    tm.assert_series_equal(df["labels"], df["expected_labels"], check_names=False)
+
+
+def test_map_counter():
+    s = Series(["a", "b", "c"], index=[1, 2, 3])
+    counter = Counter()
+    counter["b"] = 5
+    counter["c"] += 1
+    result = s.map(counter)
+    expected = Series([0, 5, 1], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_defaultdict():
+    s = Series([1, 2, 3], index=["a", "b", "c"])
+    default_dict = defaultdict(lambda: "blank")
+    default_dict[1] = "stuff"
+    result = s.map(default_dict)
+    expected = Series(["stuff", "blank", "blank"], index=["a", "b", "c"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_dict_na_key():
+    # https://github.com/pandas-dev/pandas/issues/17648
+    # Checks that np.nan key is appropriately mapped
+    s = Series([1, 2, np.nan])
+    expected = Series(["a", "b", "c"])
+    result = s.map({1: "a", 2: "b", np.nan: "c"})
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_defaultdict_na_key(na_action):
+    # GH 48813
+    s = Series([1, 2, np.nan])
+    default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"})
+    result = s.map(default_map, na_action=na_action)
+    expected = Series({0: "a", 1: "b", 2: "c" if na_action is None else np.nan})
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_defaultdict_missing_key(na_action):
+    # GH 48813
+    s = Series([1, 2, np.nan])
+    default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", 3: "c"})
+    result = s.map(default_map, na_action=na_action)
+    expected = Series({0: "a", 1: "b", 2: "missing" if na_action is None else np.nan})
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_defaultdict_unmutated(na_action):
+    # GH 48813
+    s = Series([1, 2, np.nan])
+    default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"})
+    expected_default_map = default_map.copy()
+    s.map(default_map, na_action=na_action)
+    assert default_map == expected_default_map
+
+
+@pytest.mark.parametrize("arg_func", [dict, Series])
+def test_map_dict_ignore_na(arg_func):
+    # GH#47527
+    mapping = arg_func({1: 10, np.nan: 42})
+    ser = Series([1, np.nan, 2])
+    result = ser.map(mapping, na_action="ignore")
+    expected = Series([10, np.nan, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_defaultdict_ignore_na():
+    # GH#47527
+    mapping = defaultdict(int, {1: 10, np.nan: 42})
+    ser = Series([1, np.nan, 2])
+    result = ser.map(mapping)
+    expected = Series([10, 42, 0])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "na_action, expected",
+    [(None, Series([10.0, 42.0, np.nan])), ("ignore", Series([10, np.nan, np.nan]))],
+)
+def test_map_categorical_na_ignore(na_action, expected):
+    # GH#47527
+    values = pd.Categorical([1, np.nan, 2], categories=[10, 1, 2])
+    ser = Series(values)
+    result = ser.map({1: 10, np.nan: 42}, na_action=na_action)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_dict_subclass_with_missing():
+    """
+    Test Series.map with a dictionary subclass that defines __missing__,
+    i.e. sets a default value (GH #15999).
+    """
+
+    class DictWithMissing(dict):
+        def __missing__(self, key):
+            return "missing"
+
+    s = Series([1, 2, 3])
+    dictionary = DictWithMissing({3: "three"})
+    result = s.map(dictionary)
+    expected = Series(["missing", "missing", "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_dict_subclass_without_missing():
+    class DictWithoutMissing(dict):
+        pass
+
+    s = Series([1, 2, 3])
+    dictionary = DictWithoutMissing({3: "three"})
+    result = s.map(dictionary)
+    expected = Series([np.nan, np.nan, "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_abc_mapping(non_dict_mapping_subclass):
+    # https://github.com/pandas-dev/pandas/issues/29733
+    # Check collections.abc.Mapping support as mapper for Series.map
+    s = Series([1, 2, 3])
+    not_a_dictionary = non_dict_mapping_subclass({3: "three"})
+    result = s.map(not_a_dictionary)
+    expected = Series([np.nan, np.nan, "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_abc_mapping_with_missing(non_dict_mapping_subclass):
+    # https://github.com/pandas-dev/pandas/issues/29733
+    # Check collections.abc.Mapping support as mapper for Series.map
+    class NonDictMappingWithMissing(non_dict_mapping_subclass):
+        def __missing__(self, key):
+            return "missing"
+
+    s = Series([1, 2, 3])
+    not_a_dictionary = NonDictMappingWithMissing({3: "three"})
+    result = s.map(not_a_dictionary)
+    # __missing__ is a dict concept, not a Mapping concept,
+    # so it should not change the result!
+    expected = Series([np.nan, np.nan, "three"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_box_dt64(unit):
+    vals = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]
+    ser = Series(vals).dt.as_unit(unit)
+    assert ser.dtype == f"datetime64[{unit}]"
+    # boxed value must be Timestamp instance
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
+    exp = Series(["Timestamp_1_None", "Timestamp_2_None"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_map_box_dt64tz(unit):
+    vals = [
+        pd.Timestamp("2011-01-01", tz="US/Eastern"),
+        pd.Timestamp("2011-01-02", tz="US/Eastern"),
+    ]
+    ser = Series(vals).dt.as_unit(unit)
+    assert ser.dtype == f"datetime64[{unit}, US/Eastern]"
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}")
+    exp = Series(["Timestamp_1_US/Eastern", "Timestamp_2_US/Eastern"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_map_box_td64(unit):
+    # timedelta
+    vals = [pd.Timedelta("1 days"), pd.Timedelta("2 days")]
+    ser = Series(vals).dt.as_unit(unit)
+    assert ser.dtype == f"timedelta64[{unit}]"
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.days}")
+    exp = Series(["Timedelta_1", "Timedelta_2"])
+    tm.assert_series_equal(res, exp)
+
+
+def test_map_box_period():
+    # period
+    vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")]
+    ser = Series(vals)
+    assert ser.dtype == "Period[M]"
+    res = ser.map(lambda x: f"{type(x).__name__}_{x.freqstr}")
+    exp = Series(["Period_M", "Period_M"])
+    tm.assert_series_equal(res, exp)
+
+
+@pytest.mark.parametrize("na_action", [None, "ignore"])
+def test_map_categorical(na_action, using_infer_string):
+    values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
+    s = Series(values, name="XX", index=list("abcdefg"))
+
+    result = s.map(lambda x: x.lower(), na_action=na_action)
+    exp_values = pd.Categorical(list("abbabcd"), categories=list("dcba"), ordered=True)
+    exp = Series(exp_values, name="XX", index=list("abcdefg"))
+    tm.assert_series_equal(result, exp)
+    tm.assert_categorical_equal(result.values, exp_values)
+
+    result = s.map(lambda x: "A", na_action=na_action)
+    exp = Series(["A"] * 7, name="XX", index=list("abcdefg"))
+    tm.assert_series_equal(result, exp)
+    assert result.dtype == object if not using_infer_string else "str"
+
+
+@pytest.mark.parametrize(
+    "na_action, expected",
+    (
+        [None, Series(["A", "B", "nan"], name="XX")],
+        [
+            "ignore",
+            Series(
+                ["A", "B", np.nan],
+                name="XX",
+                dtype=pd.CategoricalDtype(list("DCBA"), True),
+            ),
+        ],
+    ),
+)
+def test_map_categorical_na_action(na_action, expected):
+    dtype = pd.CategoricalDtype(list("DCBA"), ordered=True)
+    values = pd.Categorical(list("AB") + [np.nan], dtype=dtype)
+    s = Series(values, name="XX")
+    result = s.map(str, na_action=na_action)
+    tm.assert_series_equal(result, expected)
+
+
+def test_map_datetimetz():
+    values = date_range("2011-01-01", "2011-01-02", freq="h").tz_localize("Asia/Tokyo")
+    s = Series(values, name="XX")
+
+    # keep tz
+    result = s.map(lambda x: x + pd.offsets.Day())
+    exp_values = date_range("2011-01-02", "2011-01-03", freq="h").tz_localize(
+        "Asia/Tokyo"
+    )
+    exp = Series(exp_values, name="XX")
+    tm.assert_series_equal(result, exp)
+
+    result = s.map(lambda x: x.hour)
+    exp = Series(list(range(24)) + [0], name="XX", dtype=np.int64)
+    tm.assert_series_equal(result, exp)
+
+    # not vectorized
+    def f(x):
+        if not isinstance(x, pd.Timestamp):
+            raise ValueError
+        return str(x.tz)
+
+    result = s.map(f)
+    exp = Series(["Asia/Tokyo"] * 25, name="XX")
+    tm.assert_series_equal(result, exp)
+
+
+@pytest.mark.parametrize(
+    "vals,mapping,exp",
+    [
+        (list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]),
+        (list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3),
+        (list(range(3)), {0: 42}, [42] + [np.nan] * 3),
+    ],
+)
+def test_map_missing_mixed(vals, mapping, exp):
+    # GH20495
+    s = Series(vals + [np.nan])
+    result = s.map(mapping)
+    exp = Series(exp)
+    tm.assert_series_equal(result, exp)
+
+
+def test_map_scalar_on_date_time_index_aware_series():
+    # GH 25959
+    # Calling map on a localized time series should not cause an error
+    series = Series(
+        np.arange(10, dtype=np.float64),
+        index=date_range("2020-01-01", periods=10, tz="UTC"),
+        name="ts",
+    )
+    result = Series(series.index).map(lambda x: 1)
+    tm.assert_series_equal(result, Series(np.ones(len(series)), dtype="int64"))
+
+
+def test_map_float_to_string_precision():
+    # GH 13228
+    ser = Series(1 / 3)
+    result = ser.map(lambda val: str(val)).to_dict()
+    expected = {0: "0.3333333333333333"}
+    assert result == expected
+
+
+def test_map_to_timedelta():
+    list_of_valid_strings = ["00:00:01", "00:00:02"]
+    a = pd.to_timedelta(list_of_valid_strings)
+    b = Series(list_of_valid_strings).map(pd.to_timedelta)
+    tm.assert_series_equal(Series(a), b)
+
+    list_of_strings = ["00:00:01", np.nan, pd.NaT, pd.NaT]
+
+    a = pd.to_timedelta(list_of_strings)
+    ser = Series(list_of_strings)
+    b = ser.map(pd.to_timedelta)
+    tm.assert_series_equal(Series(a), b)
+
+
+def test_map_type():
+    # GH 46719
+    s = Series([3, "string", float], index=["a", "b", "c"])
+    result = s.map(type)
+    expected = Series([int, str, type], index=["a", "b", "c"])
+    tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_matmul.py

@@ -0,0 +1,82 @@
+import operator
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestMatmul:
+    def test_matmul(self):
+        # matmul test is for GH#10259
+        a = Series(
+            np.random.default_rng(2).standard_normal(4), index=["p", "q", "r", "s"]
+        )
+        b = DataFrame(
+            np.random.default_rng(2).standard_normal((3, 4)),
+            index=["1", "2", "3"],
+            columns=["p", "q", "r", "s"],
+        ).T
+
+        # Series @ DataFrame -> Series
+        result = operator.matmul(a, b)
+        expected = Series(np.dot(a.values, b.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        # DataFrame @ Series -> Series
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        # Series @ Series -> scalar
+        result = operator.matmul(a, a)
+        expected = np.dot(a.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # vector (1D np.array) @ Series (__rmatmul__)
+        result = operator.matmul(a.values, a)
+        expected = np.dot(a.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # vector (1D list) @ Series (__rmatmul__)
+        result = operator.matmul(a.values.tolist(), a)
+        expected = np.dot(a.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # matrix (2D np.array) @ Series (__rmatmul__)
+        result = operator.matmul(b.T.values, a)
+        expected = np.dot(b.T.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # GH#21530
+        # matrix (2D nested lists) @ Series (__rmatmul__)
+        result = operator.matmul(b.T.values.tolist(), a)
+        expected = np.dot(b.T.values, a.values)
+        tm.assert_almost_equal(result, expected)
+
+        # mixed dtype DataFrame @ Series
+        a["p"] = int(a.p)
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        # different dtypes DataFrame @ Series
+        a = a.astype(int)
+        result = operator.matmul(b.T, a)
+        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
+        tm.assert_series_equal(result, expected)
+
+        msg = r"Dot product shape mismatch, \(4,\) vs \(3,\)"
+        # exception raised is of type Exception
+        with pytest.raises(Exception, match=msg):
+            a.dot(a.values[:3])
+        msg = "matrices are not aligned"
+        with pytest.raises(ValueError, match=msg):
+            a.dot(b.T)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py

@@ -0,0 +1,248 @@
+"""
+Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo"
+but are implicitly also testing nsmallest_foo.
+"""
+from itertools import product
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+main_dtypes = [
+    "datetime",
+    "datetimetz",
+    "timedelta",
+    "int8",
+    "int16",
+    "int32",
+    "int64",
+    "float32",
+    "float64",
+    "uint8",
+    "uint16",
+    "uint32",
+    "uint64",
+]
+
+
+@pytest.fixture
+def s_main_dtypes():
+    """
+    A DataFrame with many dtypes
+
+    * datetime
+    * datetimetz
+    * timedelta
+    * [u]int{8,16,32,64}
+    * float{32,64}
+
+    The columns are the name of the dtype.
+    """
+    df = pd.DataFrame(
+        {
+            "datetime": pd.to_datetime(["2003", "2002", "2001", "2002", "2005"]),
+            "datetimetz": pd.to_datetime(
+                ["2003", "2002", "2001", "2002", "2005"]
+            ).tz_localize("US/Eastern"),
+            "timedelta": pd.to_timedelta(["3d", "2d", "1d", "2d", "5d"]),
+        }
+    )
+
+    for dtype in [
+        "int8",
+        "int16",
+        "int32",
+        "int64",
+        "float32",
+        "float64",
+        "uint8",
+        "uint16",
+        "uint32",
+        "uint64",
+    ]:
+        df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype)
+
+    return df
+
+
+@pytest.fixture(params=main_dtypes)
+def s_main_dtypes_split(request, s_main_dtypes):
+    """Each series in s_main_dtypes."""
+    return s_main_dtypes[request.param]
+
+
+def assert_check_nselect_boundary(vals, dtype, method):
+    # helper function for 'test_boundary_{dtype}' tests
+    ser = Series(vals, dtype=dtype)
+    result = getattr(ser, method)(3)
+    expected_idxr = [0, 1, 2] if method == "nsmallest" else [3, 2, 1]
+    expected = ser.loc[expected_idxr]
+    tm.assert_series_equal(result, expected)
+
+
+class TestSeriesNLargestNSmallest:
+    @pytest.mark.parametrize(
+        "r",
+        [
+            Series([3.0, 2, 1, 2, "5"], dtype="object"),
+            Series([3.0, 2, 1, 2, 5], dtype="object"),
+            # not supported on some archs
+            # Series([3., 2, 1, 2, 5], dtype='complex256'),
+            Series([3.0, 2, 1, 2, 5], dtype="complex128"),
+            Series(list("abcde")),
+            Series(list("abcde"), dtype="category"),
+        ],
+    )
+    def test_nlargest_error(self, r):
+        dt = r.dtype
+        msg = f"Cannot use method 'n(largest|smallest)' with dtype {dt}"
+        args = 2, len(r), 0, -1
+        methods = r.nlargest, r.nsmallest
+        for method, arg in product(methods, args):
+            with pytest.raises(TypeError, match=msg):
+                method(arg)
+
+    def test_nsmallest_nlargest(self, s_main_dtypes_split):
+        # float, int, datetime64 (use i8), timedelts64 (same),
+        # object that are numbers, object that are strings
+        ser = s_main_dtypes_split
+
+        tm.assert_series_equal(ser.nsmallest(2), ser.iloc[[2, 1]])
+        tm.assert_series_equal(ser.nsmallest(2, keep="last"), ser.iloc[[2, 3]])
+
+        empty = ser.iloc[0:0]
+        tm.assert_series_equal(ser.nsmallest(0), empty)
+        tm.assert_series_equal(ser.nsmallest(-1), empty)
+        tm.assert_series_equal(ser.nlargest(0), empty)
+        tm.assert_series_equal(ser.nlargest(-1), empty)
+
+        tm.assert_series_equal(ser.nsmallest(len(ser)), ser.sort_values())
+        tm.assert_series_equal(ser.nsmallest(len(ser) + 1), ser.sort_values())
+        tm.assert_series_equal(ser.nlargest(len(ser)), ser.iloc[[4, 0, 1, 3, 2]])
+        tm.assert_series_equal(ser.nlargest(len(ser) + 1), ser.iloc[[4, 0, 1, 3, 2]])
+
+    def test_nlargest_misc(self):
+        ser = Series([3.0, np.nan, 1, 2, 5])
+        result = ser.nlargest()
+        expected = ser.iloc[[4, 0, 3, 2, 1]]
+        tm.assert_series_equal(result, expected)
+        result = ser.nsmallest()
+        expected = ser.iloc[[2, 3, 0, 4, 1]]
+        tm.assert_series_equal(result, expected)
+
+        msg = 'keep must be either "first", "last"'
+        with pytest.raises(ValueError, match=msg):
+            ser.nsmallest(keep="invalid")
+        with pytest.raises(ValueError, match=msg):
+            ser.nlargest(keep="invalid")
+
+        # GH#15297
+        ser = Series([1] * 5, index=[1, 2, 3, 4, 5])
+        expected_first = Series([1] * 3, index=[1, 2, 3])
+        expected_last = Series([1] * 3, index=[5, 4, 3])
+
+        result = ser.nsmallest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nsmallest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+        result = ser.nlargest(3)
+        tm.assert_series_equal(result, expected_first)
+
+        result = ser.nlargest(3, keep="last")
+        tm.assert_series_equal(result, expected_last)
+
+    @pytest.mark.parametrize("n", range(1, 5))
+    def test_nlargest_n(self, n):
+        # GH 13412
+        ser = Series([1, 4, 3, 2], index=[0, 0, 1, 1])
+        result = ser.nlargest(n)
+        expected = ser.sort_values(ascending=False).head(n)
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(n)
+        expected = ser.sort_values().head(n)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_boundary_integer(self, nselect_method, any_int_numpy_dtype):
+        # GH#21426
+        dtype_info = np.iinfo(any_int_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val, min_val + 1, max_val - 1, max_val]
+        assert_check_nselect_boundary(vals, any_int_numpy_dtype, nselect_method)
+
+    def test_nlargest_boundary_float(self, nselect_method, float_numpy_dtype):
+        # GH#21426
+        dtype_info = np.finfo(float_numpy_dtype)
+        min_val, max_val = dtype_info.min, dtype_info.max
+        min_2nd, max_2nd = np.nextafter([min_val, max_val], 0, dtype=float_numpy_dtype)
+        vals = [min_val, min_2nd, max_2nd, max_val]
+        assert_check_nselect_boundary(vals, float_numpy_dtype, nselect_method)
+
+    @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+    def test_nlargest_boundary_datetimelike(self, nselect_method, dtype):
+        # GH#21426
+        # use int64 bounds and +1 to min_val since true minimum is NaT
+        # (include min_val/NaT at end to maintain same expected_idxr)
+        dtype_info = np.iinfo("int64")
+        min_val, max_val = dtype_info.min, dtype_info.max
+        vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val]
+        assert_check_nselect_boundary(vals, dtype, nselect_method)
+
+    def test_nlargest_duplicate_keep_all_ties(self):
+        # see GH#16818
+        ser = Series([10, 9, 8, 7, 7, 7, 7, 6])
+        result = ser.nlargest(4, keep="all")
+        expected = Series([10, 9, 8, 7, 7, 7, 7])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.nsmallest(2, keep="all")
+        expected = Series([6, 7, 7, 7, 7], index=[7, 3, 4, 5, 6])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data,expected", [([True, False], [True]), ([True, False, True, True], [True])]
+    )
+    def test_nlargest_boolean(self, data, expected):
+        # GH#26154 : ensure True > False
+        ser = Series(data)
+        result = ser.nlargest(1)
+        expected = Series(expected)
+        tm.assert_series_equal(result, expected)
+
+    def test_nlargest_nullable(self, any_numeric_ea_dtype):
+        # GH#42816
+        dtype = any_numeric_ea_dtype
+        if dtype.startswith("UInt"):
+            # Can't cast from negative float to uint on some platforms
+            arr = np.random.default_rng(2).integers(1, 10, 10)
+        else:
+            arr = np.random.default_rng(2).standard_normal(10)
+        arr = arr.astype(dtype.lower(), copy=False)
+
+        ser = Series(arr.copy(), dtype=dtype)
+        ser[1] = pd.NA
+        result = ser.nlargest(5)
+
+        expected = (
+            Series(np.delete(arr, 1), index=ser.index.delete(1))
+            .nlargest(5)
+            .astype(dtype)
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_nsmallest_nan_when_keep_is_all(self):
+        # GH#46589
+        s = Series([1, 2, 3, 3, 3, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1.0, 2.0, 3.0, 3.0, 3.0])
+        tm.assert_series_equal(result, expected)
+
+        s = Series([1, 2, None, None, None])
+        result = s.nsmallest(3, keep="all")
+        expected = Series([1, 2, None, None, None])
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_nunique.py

@@ -0,0 +1,24 @@
+import numpy as np
+
+from pandas import (
+    Categorical,
+    Series,
+)
+
+
+def test_nunique():
+    # basics.rst doc example
+    series = Series(np.random.default_rng(2).standard_normal(500))
+    series[20:500] = np.nan
+    series[10:20] = 5000
+    result = series.nunique()
+    assert result == 11
+
+
+def test_nunique_categorical():
+    # GH#18051
+    ser = Series(Categorical([]))
+    assert ser.nunique() == 0
+
+    ser = Series(Categorical([np.nan]))
+    assert ser.nunique() == 0

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_pct_change.py

@@ -0,0 +1,128 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesPctChange:
+    def test_pct_change(self, datetime_series):
+        msg = (
+            "The 'fill_method' keyword being not None and the 'limit' keyword in "
+            "Series.pct_change are deprecated"
+        )
+
+        rs = datetime_series.pct_change(fill_method=None)
+        tm.assert_series_equal(rs, datetime_series / datetime_series.shift(1) - 1)
+
+        rs = datetime_series.pct_change(2)
+        filled = datetime_series.ffill()
+        tm.assert_series_equal(rs, filled / filled.shift(2) - 1)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = datetime_series.pct_change(fill_method="bfill", limit=1)
+        filled = datetime_series.bfill(limit=1)
+        tm.assert_series_equal(rs, filled / filled.shift(1) - 1)
+
+        rs = datetime_series.pct_change(freq="5D")
+        filled = datetime_series.ffill()
+        tm.assert_series_equal(
+            rs, (filled / filled.shift(freq="5D") - 1).reindex_like(filled)
+        )
+
+    def test_pct_change_with_duplicate_axis(self):
+        # GH#28664
+        common_idx = date_range("2019-11-14", periods=5, freq="D")
+        result = Series(range(5), common_idx).pct_change(freq="B")
+
+        # the reason that the expected should be like this is documented at PR 28681
+        expected = Series([np.nan, np.inf, np.nan, np.nan, 3.0], common_idx)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_pct_change_shift_over_nas(self):
+        s = Series([1.0, 1.5, np.nan, 2.5, 3.0])
+
+        msg = "The default fill_method='pad' in Series.pct_change is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            chg = s.pct_change()
+
+        expected = Series([np.nan, 0.5, 0.0, 2.5 / 1.5 - 1, 0.2])
+        tm.assert_series_equal(chg, expected)
+
+    @pytest.mark.parametrize(
+        "freq, periods, fill_method, limit",
+        [
+            ("5B", 5, None, None),
+            ("3B", 3, None, None),
+            ("3B", 3, "bfill", None),
+            ("7B", 7, "pad", 1),
+            ("7B", 7, "bfill", 3),
+            ("14B", 14, None, None),
+        ],
+    )
+    def test_pct_change_periods_freq(
+        self, freq, periods, fill_method, limit, datetime_series
+    ):
+        msg = (
+            "The 'fill_method' keyword being not None and the 'limit' keyword in "
+            "Series.pct_change are deprecated"
+        )
+
+        # GH#7292
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_freq = datetime_series.pct_change(
+                freq=freq, fill_method=fill_method, limit=limit
+            )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_periods = datetime_series.pct_change(
+                periods, fill_method=fill_method, limit=limit
+            )
+        tm.assert_series_equal(rs_freq, rs_periods)
+
+        empty_ts = Series(index=datetime_series.index, dtype=object)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_freq = empty_ts.pct_change(
+                freq=freq, fill_method=fill_method, limit=limit
+            )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs_periods = empty_ts.pct_change(
+                periods, fill_method=fill_method, limit=limit
+            )
+        tm.assert_series_equal(rs_freq, rs_periods)
+
+
+@pytest.mark.parametrize("fill_method", ["pad", "ffill", None])
+def test_pct_change_with_duplicated_indices(fill_method):
+    # GH30463
+    s = Series([np.nan, 1, 2, 3, 9, 18], index=["a", "b"] * 3)
+
+    warn = None if fill_method is None else FutureWarning
+    msg = (
+        "The 'fill_method' keyword being not None and the 'limit' keyword in "
+        "Series.pct_change are deprecated"
+    )
+    with tm.assert_produces_warning(warn, match=msg):
+        result = s.pct_change(fill_method=fill_method)
+
+    expected = Series([np.nan, np.nan, 1.0, 0.5, 2.0, 1.0], index=["a", "b"] * 3)
+    tm.assert_series_equal(result, expected)
+
+
+def test_pct_change_no_warning_na_beginning():
+    # GH#54981
+    ser = Series([None, None, 1, 2, 3])
+    result = ser.pct_change()
+    expected = Series([np.nan, np.nan, np.nan, 1, 0.5])
+    tm.assert_series_equal(result, expected)
+
+
+def test_pct_change_empty():
+    # GH 57056
+    ser = Series([], dtype="float64")
+    expected = ser.copy()
+    result = ser.pct_change(periods=0)
+    tm.assert_series_equal(expected, result)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_pop.py

@@ -0,0 +1,13 @@
+from pandas import Series
+import pandas._testing as tm
+
+
+def test_pop():
+    # GH#6600
+    ser = Series([0, 4, 0], index=["A", "B", "C"], name=4)
+
+    result = ser.pop("B")
+    assert result == 4
+
+    expected = Series([0, 0], index=["A", "C"], name=4)
+    tm.assert_series_equal(ser, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_quantile.py

@@ -0,0 +1,247 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_integer
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.indexes.datetimes import Timestamp
+
+
+class TestSeriesQuantile:
+    def test_quantile(self, datetime_series):
+        q = datetime_series.quantile(0.1)
+        assert q == np.percentile(datetime_series.dropna(), 10)
+
+        q = datetime_series.quantile(0.9)
+        assert q == np.percentile(datetime_series.dropna(), 90)
+
+        # object dtype
+        q = Series(datetime_series, dtype=object).quantile(0.9)
+        assert q == np.percentile(datetime_series.dropna(), 90)
+
+        # datetime64[ns] dtype
+        dts = datetime_series.index.to_series()
+        q = dts.quantile(0.2)
+        assert q == Timestamp("2000-01-10 19:12:00")
+
+        # timedelta64[ns] dtype
+        tds = dts.diff()
+        q = tds.quantile(0.25)
+        assert q == pd.to_timedelta("24:00:00")
+
+        # GH7661
+        result = Series([np.timedelta64("NaT")]).sum()
+        assert result == pd.Timedelta(0)
+
+        msg = "percentiles should all be in the interval \\[0, 1\\]"
+        for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
+            with pytest.raises(ValueError, match=msg):
+                datetime_series.quantile(invalid)
+
+        s = Series(np.random.default_rng(2).standard_normal(100))
+        percentile_array = [-0.5, 0.25, 1.5]
+        with pytest.raises(ValueError, match=msg):
+            s.quantile(percentile_array)
+
+    def test_quantile_multi(self, datetime_series, unit):
+        datetime_series.index = datetime_series.index.as_unit(unit)
+        qs = [0.1, 0.9]
+        result = datetime_series.quantile(qs)
+        expected = Series(
+            [
+                np.percentile(datetime_series.dropna(), 10),
+                np.percentile(datetime_series.dropna(), 90),
+            ],
+            index=qs,
+            name=datetime_series.name,
+        )
+        tm.assert_series_equal(result, expected)
+
+        dts = datetime_series.index.to_series()
+        dts.name = "xxx"
+        result = dts.quantile((0.2, 0.2))
+        expected = Series(
+            [Timestamp("2000-01-10 19:12:00"), Timestamp("2000-01-10 19:12:00")],
+            index=[0.2, 0.2],
+            name="xxx",
+            dtype=f"M8[{unit}]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = datetime_series.quantile([])
+        expected = Series(
+            [], name=datetime_series.name, index=Index([], dtype=float), dtype="float64"
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_interpolation(self, datetime_series):
+        # see gh-10174
+
+        # interpolation = linear (default case)
+        q = datetime_series.quantile(0.1, interpolation="linear")
+        assert q == np.percentile(datetime_series.dropna(), 10)
+        q1 = datetime_series.quantile(0.1)
+        assert q1 == np.percentile(datetime_series.dropna(), 10)
+
+        # test with and without interpolation keyword
+        assert q == q1
+
+    def test_quantile_interpolation_dtype(self):
+        # GH #10174
+
+        # interpolation = linear (default case)
+        q = Series([1, 3, 4]).quantile(0.5, interpolation="lower")
+        assert q == np.percentile(np.array([1, 3, 4]), 50)
+        assert is_integer(q)
+
+        q = Series([1, 3, 4]).quantile(0.5, interpolation="higher")
+        assert q == np.percentile(np.array([1, 3, 4]), 50)
+        assert is_integer(q)
+
+    def test_quantile_nan(self):
+        # GH 13098
+        ser = Series([1, 2, 3, 4, np.nan])
+        result = ser.quantile(0.5)
+        expected = 2.5
+        assert result == expected
+
+        # all nan/empty
+        s1 = Series([], dtype=object)
+        cases = [s1, Series([np.nan, np.nan])]
+
+        for ser in cases:
+            res = ser.quantile(0.5)
+            assert np.isnan(res)
+
+            res = ser.quantile([0.5])
+            tm.assert_series_equal(res, Series([np.nan], index=[0.5]))
+
+            res = ser.quantile([0.2, 0.3])
+            tm.assert_series_equal(res, Series([np.nan, np.nan], index=[0.2, 0.3]))
+
+    @pytest.mark.parametrize(
+        "case",
+        [
+            [
+                Timestamp("2011-01-01"),
+                Timestamp("2011-01-02"),
+                Timestamp("2011-01-03"),
+            ],
+            [
+                Timestamp("2011-01-01", tz="US/Eastern"),
+                Timestamp("2011-01-02", tz="US/Eastern"),
+                Timestamp("2011-01-03", tz="US/Eastern"),
+            ],
+            [pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")],
+            # NaT
+            [
+                Timestamp("2011-01-01"),
+                Timestamp("2011-01-02"),
+                Timestamp("2011-01-03"),
+                pd.NaT,
+            ],
+            [
+                Timestamp("2011-01-01", tz="US/Eastern"),
+                Timestamp("2011-01-02", tz="US/Eastern"),
+                Timestamp("2011-01-03", tz="US/Eastern"),
+                pd.NaT,
+            ],
+            [
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+                pd.Timedelta("3 days"),
+                pd.NaT,
+            ],
+        ],
+    )
+    def test_quantile_box(self, case):
+        ser = Series(case, name="XXX")
+        res = ser.quantile(0.5)
+        assert res == case[1]
+
+        res = ser.quantile([0.5])
+        exp = Series([case[1]], index=[0.5], name="XXX")
+        tm.assert_series_equal(res, exp)
+
+    def test_datetime_timedelta_quantiles(self):
+        # covers #9694
+        assert pd.isna(Series([], dtype="M8[ns]").quantile(0.5))
+        assert pd.isna(Series([], dtype="m8[ns]").quantile(0.5))
+
+    def test_quantile_nat(self):
+        res = Series([pd.NaT, pd.NaT]).quantile(0.5)
+        assert res is pd.NaT
+
+        res = Series([pd.NaT, pd.NaT]).quantile([0.5])
+        tm.assert_series_equal(res, Series([pd.NaT], index=[0.5]))
+
+    @pytest.mark.parametrize(
+        "values, dtype",
+        [([0, 0, 0, 1, 2, 3], "Sparse[int]"), ([0.0, None, 1.0, 2.0], "Sparse[float]")],
+    )
+    def test_quantile_sparse(self, values, dtype):
+        ser = Series(values, dtype=dtype)
+        result = ser.quantile([0.5])
+        expected = Series(np.asarray(ser)).quantile([0.5]).astype("Sparse[float]")
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_empty_float64(self):
+        # floats
+        ser = Series([], dtype="float64")
+
+        res = ser.quantile(0.5)
+        assert np.isnan(res)
+
+        res = ser.quantile([0.5])
+        exp = Series([np.nan], index=[0.5])
+        tm.assert_series_equal(res, exp)
+
+    def test_quantile_empty_int64(self):
+        # int
+        ser = Series([], dtype="int64")
+
+        res = ser.quantile(0.5)
+        assert np.isnan(res)
+
+        res = ser.quantile([0.5])
+        exp = Series([np.nan], index=[0.5])
+        tm.assert_series_equal(res, exp)
+
+    def test_quantile_empty_dt64(self):
+        # datetime
+        ser = Series([], dtype="datetime64[ns]")
+
+        res = ser.quantile(0.5)
+        assert res is pd.NaT
+
+        res = ser.quantile([0.5])
+        exp = Series([pd.NaT], index=[0.5], dtype=ser.dtype)
+        tm.assert_series_equal(res, exp)
+
+    @pytest.mark.parametrize("dtype", [int, float, "Int64"])
+    def test_quantile_dtypes(self, dtype):
+        result = Series([1, 2, 3], dtype=dtype).quantile(np.arange(0, 1, 0.25))
+        expected = Series(np.arange(1, 3, 0.5), index=np.arange(0, 1, 0.25))
+        if dtype == "Int64":
+            expected = expected.astype("Float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_all_na(self, any_int_ea_dtype):
+        # GH#50681
+        ser = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
+        with tm.assert_produces_warning(None):
+            result = ser.quantile([0.1, 0.5])
+        expected = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype, index=[0.1, 0.5])
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_dtype_size(self, any_int_ea_dtype):
+        # GH#50681
+        ser = Series([pd.NA, pd.NA, 1], dtype=any_int_ea_dtype)
+        result = ser.quantile([0.1, 0.5])
+        expected = Series([1, 1], dtype=any_int_ea_dtype, index=[0.1, 0.5])
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_rank.py

@@ -0,0 +1,563 @@
+from itertools import chain
+import operator
+
+import numpy as np
+import pytest
+
+from pandas._libs.algos import (
+    Infinity,
+    NegInfinity,
+)
+import pandas.util._test_decorators as td
+
+from pandas import (
+    NA,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype
+
+
+@pytest.fixture
+def ser():
+    return Series([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])
+
+
+@pytest.fixture(
+    params=[
+        ["average", np.array([1.5, 5.5, 7.0, 3.5, np.nan, 3.5, 1.5, 8.0, np.nan, 5.5])],
+        ["min", np.array([1, 5, 7, 3, np.nan, 3, 1, 8, np.nan, 5])],
+        ["max", np.array([2, 6, 7, 4, np.nan, 4, 2, 8, np.nan, 6])],
+        ["first", np.array([1, 5, 7, 3, np.nan, 4, 2, 8, np.nan, 6])],
+        ["dense", np.array([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])],
+    ],
+    ids=lambda x: x[0],
+)
+def results(request):
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        "object",
+        "float64",
+        "int64",
+        "Float64",
+        "Int64",
+        pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        pytest.param("int64[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")),
+        "string[python]",
+        "str",
+    ]
+)
+def dtype(request):
+    return request.param
+
+
+def expected_dtype(dtype, method, pct=False):
+    exp_dtype = "float64"
+    # elif dtype in ["Int64", "Float64", "string[pyarrow]", "string[python]"]:
+    if dtype in ["string[pyarrow]"]:
+        exp_dtype = "Float64"
+    elif dtype in ["float64[pyarrow]", "int64[pyarrow]"]:
+        if method == "average" or pct:
+            exp_dtype = "double[pyarrow]"
+        else:
+            exp_dtype = "uint64[pyarrow]"
+
+    return exp_dtype
+
+
+class TestSeriesRank:
+    def test_rank(self, datetime_series):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        datetime_series[::2] = np.nan
+        datetime_series[:10:3] = 4.0
+
+        ranks = datetime_series.rank()
+        oranks = datetime_series.astype("O").rank()
+
+        tm.assert_series_equal(ranks, oranks)
+
+        mask = np.isnan(datetime_series)
+        filled = datetime_series.fillna(np.inf)
+
+        # rankdata returns a ndarray
+        exp = Series(sp_stats.rankdata(filled), index=filled.index, name="ts")
+        exp[mask] = np.nan
+
+        tm.assert_series_equal(ranks, exp)
+
+        iseries = Series(np.arange(5).repeat(2))
+
+        iranks = iseries.rank()
+        exp = iseries.astype(float).rank()
+        tm.assert_series_equal(iranks, exp)
+        iseries = Series(np.arange(5)) + 1.0
+        exp = iseries / 5.0
+        iranks = iseries.rank(pct=True)
+
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series(np.repeat(1, 100))
+        exp = Series(np.repeat(0.505, 100))
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        # Explicit cast to float to avoid implicit cast when setting nan
+        iseries = iseries.astype("float")
+        iseries[1] = np.nan
+        exp = Series(np.repeat(50.0 / 99.0, 100))
+        exp[1] = np.nan
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series(np.arange(5)) + 1.0
+        iseries[4] = np.nan
+        exp = iseries / 4.0
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series(np.repeat(np.nan, 100))
+        exp = iseries.copy()
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        # Explicit cast to float to avoid implicit cast when setting nan
+        iseries = Series(np.arange(5), dtype="float") + 1
+        iseries[4] = np.nan
+        exp = iseries / 4.0
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        rng = date_range("1/1/1990", periods=5)
+        # Explicit cast to float to avoid implicit cast when setting nan
+        iseries = Series(np.arange(5), rng, dtype="float") + 1
+        iseries.iloc[4] = np.nan
+        exp = iseries / 4.0
+        iranks = iseries.rank(pct=True)
+        tm.assert_series_equal(iranks, exp)
+
+        iseries = Series([1e-50, 1e-100, 1e-20, 1e-2, 1e-20 + 1e-30, 1e-1])
+        exp = Series([2, 1, 3, 5, 4, 6.0])
+        iranks = iseries.rank()
+        tm.assert_series_equal(iranks, exp)
+
+        # GH 5968
+        iseries = Series(["3 day", "1 day 10m", "-2 day", NaT], dtype="m8[ns]")
+        exp = Series([3, 2, 1, np.nan])
+        iranks = iseries.rank()
+        tm.assert_series_equal(iranks, exp)
+
+        values = np.array(
+            [-50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40],
+            dtype="float64",
+        )
+        random_order = np.random.default_rng(2).permutation(len(values))
+        iseries = Series(values[random_order])
+        exp = Series(random_order + 1.0, dtype="float64")
+        iranks = iseries.rank()
+        tm.assert_series_equal(iranks, exp)
+
+    def test_rank_categorical(self):
+        # GH issue #15420 rank incorrectly orders ordered categories
+
+        # Test ascending/descending ranking for ordered categoricals
+        exp = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
+        exp_desc = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
+        ordered = Series(
+            ["first", "second", "third", "fourth", "fifth", "sixth"]
+        ).astype(
+            CategoricalDtype(
+                categories=["first", "second", "third", "fourth", "fifth", "sixth"],
+                ordered=True,
+            )
+        )
+        tm.assert_series_equal(ordered.rank(), exp)
+        tm.assert_series_equal(ordered.rank(ascending=False), exp_desc)
+
+        # Unordered categoricals should be ranked as objects
+        unordered = Series(
+            ["first", "second", "third", "fourth", "fifth", "sixth"]
+        ).astype(
+            CategoricalDtype(
+                categories=["first", "second", "third", "fourth", "fifth", "sixth"],
+                ordered=False,
+            )
+        )
+        exp_unordered = Series([2.0, 4.0, 6.0, 3.0, 1.0, 5.0])
+        res = unordered.rank()
+        tm.assert_series_equal(res, exp_unordered)
+
+        unordered1 = Series([1, 2, 3, 4, 5, 6]).astype(
+            CategoricalDtype([1, 2, 3, 4, 5, 6], False)
+        )
+        exp_unordered1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
+        res1 = unordered1.rank()
+        tm.assert_series_equal(res1, exp_unordered1)
+
+        # Test na_option for rank data
+        na_ser = Series(
+            ["first", "second", "third", "fourth", "fifth", "sixth", np.nan]
+        ).astype(
+            CategoricalDtype(
+                ["first", "second", "third", "fourth", "fifth", "sixth", "seventh"],
+                True,
+            )
+        )
+
+        exp_top = Series([2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0])
+        exp_bot = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
+        exp_keep = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, np.nan])
+
+        tm.assert_series_equal(na_ser.rank(na_option="top"), exp_top)
+        tm.assert_series_equal(na_ser.rank(na_option="bottom"), exp_bot)
+        tm.assert_series_equal(na_ser.rank(na_option="keep"), exp_keep)
+
+        # Test na_option for rank data with ascending False
+        exp_top = Series([7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
+        exp_bot = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 7.0])
+        exp_keep = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, np.nan])
+
+        tm.assert_series_equal(na_ser.rank(na_option="top", ascending=False), exp_top)
+        tm.assert_series_equal(
+            na_ser.rank(na_option="bottom", ascending=False), exp_bot
+        )
+        tm.assert_series_equal(na_ser.rank(na_option="keep", ascending=False), exp_keep)
+
+        # Test invalid values for na_option
+        msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+
+        with pytest.raises(ValueError, match=msg):
+            na_ser.rank(na_option="bad", ascending=False)
+
+        # invalid type
+        with pytest.raises(ValueError, match=msg):
+            na_ser.rank(na_option=True, ascending=False)
+
+        # Test with pct=True
+        na_ser = Series(["first", "second", "third", "fourth", np.nan]).astype(
+            CategoricalDtype(["first", "second", "third", "fourth"], True)
+        )
+        exp_top = Series([0.4, 0.6, 0.8, 1.0, 0.2])
+        exp_bot = Series([0.2, 0.4, 0.6, 0.8, 1.0])
+        exp_keep = Series([0.25, 0.5, 0.75, 1.0, np.nan])
+
+        tm.assert_series_equal(na_ser.rank(na_option="top", pct=True), exp_top)
+        tm.assert_series_equal(na_ser.rank(na_option="bottom", pct=True), exp_bot)
+        tm.assert_series_equal(na_ser.rank(na_option="keep", pct=True), exp_keep)
+
+    def test_rank_signature(self):
+        s = Series([0, 1])
+        s.rank(method="average")
+        msg = "No axis named average for object type Series"
+        with pytest.raises(ValueError, match=msg):
+            s.rank("average")
+
+    def test_rank_tie_methods(self, ser, results, dtype, using_infer_string):
+        method, exp = results
+        if (
+            dtype == "int64"
+            or dtype == "Int64"
+            or (not using_infer_string and dtype == "str")
+        ):
+            pytest.skip("int64/str does not support NaN")
+
+        ser = ser if dtype is None else ser.astype(dtype)
+        result = ser.rank(method=method)
+        tm.assert_series_equal(result, Series(exp, dtype=expected_dtype(dtype, method)))
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    @pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
+    @pytest.mark.parametrize("na_option", ["top", "bottom", "keep"])
+    @pytest.mark.parametrize(
+        "dtype, na_value, pos_inf, neg_inf",
+        [
+            ("object", None, Infinity(), NegInfinity()),
+            ("float64", np.nan, np.inf, -np.inf),
+            ("Float64", NA, np.inf, -np.inf),
+            pytest.param(
+                "float64[pyarrow]",
+                NA,
+                np.inf,
+                -np.inf,
+                marks=td.skip_if_no("pyarrow"),
+            ),
+        ],
+    )
+    def test_rank_tie_methods_on_infs_nans(
+        self, method, na_option, ascending, dtype, na_value, pos_inf, neg_inf
+    ):
+        pytest.importorskip("scipy")
+        if dtype == "float64[pyarrow]":
+            if method == "average":
+                exp_dtype = "float64[pyarrow]"
+            else:
+                exp_dtype = "uint64[pyarrow]"
+        else:
+            exp_dtype = "float64"
+
+        chunk = 3
+        in_arr = [neg_inf] * chunk + [na_value] * chunk + [pos_inf] * chunk
+        iseries = Series(in_arr, dtype=dtype)
+        exp_ranks = {
+            "average": ([2, 2, 2], [5, 5, 5], [8, 8, 8]),
+            "min": ([1, 1, 1], [4, 4, 4], [7, 7, 7]),
+            "max": ([3, 3, 3], [6, 6, 6], [9, 9, 9]),
+            "first": ([1, 2, 3], [4, 5, 6], [7, 8, 9]),
+            "dense": ([1, 1, 1], [2, 2, 2], [3, 3, 3]),
+        }
+        ranks = exp_ranks[method]
+        if na_option == "top":
+            order = [ranks[1], ranks[0], ranks[2]]
+        elif na_option == "bottom":
+            order = [ranks[0], ranks[2], ranks[1]]
+        else:
+            order = [ranks[0], [np.nan] * chunk, ranks[1]]
+        expected = order if ascending else order[::-1]
+        expected = list(chain.from_iterable(expected))
+        result = iseries.rank(method=method, na_option=na_option, ascending=ascending)
+        tm.assert_series_equal(result, Series(expected, dtype=exp_dtype))
+
+    def test_rank_desc_mix_nans_infs(self):
+        # GH 19538
+        # check descending ranking when mix nans and infs
+        iseries = Series([1, np.nan, np.inf, -np.inf, 25])
+        result = iseries.rank(ascending=False)
+        exp = Series([3, np.nan, 1, 4, 2], dtype="float64")
+        tm.assert_series_equal(result, exp)
+
+    @pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
+    @pytest.mark.parametrize(
+        "op, value",
+        [
+            [operator.add, 0],
+            [operator.add, 1e6],
+            [operator.mul, 1e-6],
+        ],
+    )
+    def test_rank_methods_series(self, method, op, value):
+        sp_stats = pytest.importorskip("scipy.stats")
+
+        xs = np.random.default_rng(2).standard_normal(9)
+        xs = np.concatenate([xs[i:] for i in range(0, 9, 2)])  # add duplicates
+        np.random.default_rng(2).shuffle(xs)
+
+        index = [chr(ord("a") + i) for i in range(len(xs))]
+        vals = op(xs, value)
+        ts = Series(vals, index=index)
+        result = ts.rank(method=method)
+        sprank = sp_stats.rankdata(vals, method if method != "first" else "ordinal")
+        expected = Series(sprank, index=index).astype("float64")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ser, exp",
+        [
+            ([1], [1]),
+            ([2], [1]),
+            ([0], [1]),
+            ([2, 2], [1, 1]),
+            ([1, 2, 3], [1, 2, 3]),
+            ([4, 2, 1], [3, 2, 1]),
+            ([1, 1, 5, 5, 3], [1, 1, 3, 3, 2]),
+            ([-5, -4, -3, -2, -1], [1, 2, 3, 4, 5]),
+        ],
+    )
+    def test_rank_dense_method(self, dtype, ser, exp):
+        if ser[0] < 0 and dtype.startswith("str"):
+            exp = exp[::-1]
+        s = Series(ser).astype(dtype)
+        result = s.rank(method="dense")
+        expected = Series(exp).astype(expected_dtype(dtype, "dense"))
+        tm.assert_series_equal(result, expected)
+
+    def test_rank_descending(self, ser, results, dtype, using_infer_string):
+        method, _ = results
+        if dtype == "int64" or (not using_infer_string and dtype == "str"):
+            s = ser.dropna()
+        else:
+            s = ser.astype(dtype)
+
+        res = s.rank(ascending=False)
+        if dtype.startswith("str"):
+            expected = (s.astype("float64").max() - s.astype("float64")).rank()
+        else:
+            expected = (s.max() - s).rank()
+        tm.assert_series_equal(res, expected.astype(expected_dtype(dtype, "average")))
+
+        if dtype.startswith("str"):
+            expected = (s.astype("float64").max() - s.astype("float64")).rank(
+                method=method
+            )
+        else:
+            expected = (s.max() - s).rank(method=method)
+        res2 = s.rank(method=method, ascending=False)
+        tm.assert_series_equal(res2, expected.astype(expected_dtype(dtype, method)))
+
+    def test_rank_int(self, ser, results):
+        method, exp = results
+        s = ser.dropna().astype("i8")
+
+        result = s.rank(method=method)
+        expected = Series(exp).dropna()
+        expected.index = result.index
+        tm.assert_series_equal(result, expected)
+
+    def test_rank_object_bug(self):
+        # GH 13445
+
+        # smoke tests
+        Series([np.nan] * 32).astype(object).rank(ascending=True)
+        Series([np.nan] * 32).astype(object).rank(ascending=False)
+
+    def test_rank_modify_inplace(self):
+        # GH 18521
+        # Check rank does not mutate series
+        s = Series([Timestamp("2017-01-05 10:20:27.569000"), NaT])
+        expected = s.copy()
+
+        s.rank()
+        result = s
+        tm.assert_series_equal(result, expected)
+
+    def test_rank_ea_small_values(self):
+        # GH#52471
+        ser = Series(
+            [5.4954145e29, -9.791984e-21, 9.3715776e-26, NA, 1.8790257e-28],
+            dtype="Float64",
+        )
+        result = ser.rank(method="min")
+        expected = Series([4, 1, 3, np.nan, 2])
+        tm.assert_series_equal(result, expected)
+
+
+# GH15630, pct should be on 100% basis when method='dense'
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0, 1.0]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 2, 2.0 / 2, 2.0 / 2]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.0 / 3, 1.0 / 3, 3.0 / 3, 3.0 / 3, 2.0 / 3]),
+        ([1, 1, 3, 3, 5, 5], [1.0 / 3, 1.0 / 3, 2.0 / 3, 2.0 / 3, 3.0 / 3, 3.0 / 3]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_dense_pct(dtype, ser, exp):
+    if ser[0] < 0 and dtype.startswith("str"):
+        exp = exp[::-1]
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="dense", pct=True)
+    expected = Series(exp).astype(expected_dtype(dtype, "dense", pct=True))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0 / 2, 1.0 / 2]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 2.0 / 3, 2.0 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.0 / 5, 1.0 / 5, 4.0 / 5, 4.0 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [1.0 / 6, 1.0 / 6, 3.0 / 6, 3.0 / 6, 5.0 / 6, 5.0 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_min_pct(dtype, ser, exp):
+    if ser[0] < 0 and dtype.startswith("str"):
+        exp = exp[::-1]
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="min", pct=True)
+    expected = Series(exp).astype(expected_dtype(dtype, "min", pct=True))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0, 1.0]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 3.0 / 3, 3.0 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [2.0 / 5, 2.0 / 5, 5.0 / 5, 5.0 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [2.0 / 6, 2.0 / 6, 4.0 / 6, 4.0 / 6, 6.0 / 6, 6.0 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_max_pct(dtype, ser, exp):
+    if ser[0] < 0 and dtype.startswith("str"):
+        exp = exp[::-1]
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="max", pct=True)
+    expected = Series(exp).astype(expected_dtype(dtype, "max", pct=True))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.5 / 2, 1.5 / 2]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 2.5 / 3, 2.5 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.5 / 5, 1.5 / 5, 4.5 / 5, 4.5 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [1.5 / 6, 1.5 / 6, 3.5 / 6, 3.5 / 6, 5.5 / 6, 5.5 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_average_pct(dtype, ser, exp):
+    if ser[0] < 0 and dtype.startswith("str"):
+        exp = exp[::-1]
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="average", pct=True)
+    expected = Series(exp).astype(expected_dtype(dtype, "average", pct=True))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "ser, exp",
+    [
+        ([1], [1.0]),
+        ([1, 2], [1.0 / 2, 2.0 / 2]),
+        ([2, 2], [1.0 / 2, 2.0 / 2.0]),
+        ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([1, 2, 2], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
+        ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
+        ([1, 1, 5, 5, 3], [1.0 / 5, 2.0 / 5, 4.0 / 5, 5.0 / 5, 3.0 / 5]),
+        ([1, 1, 3, 3, 5, 5], [1.0 / 6, 2.0 / 6, 3.0 / 6, 4.0 / 6, 5.0 / 6, 6.0 / 6]),
+        ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]),
+    ],
+)
+def test_rank_first_pct(dtype, ser, exp):
+    if ser[0] < 0 and dtype.startswith("str"):
+        exp = exp[::-1]
+    s = Series(ser).astype(dtype)
+    result = s.rank(method="first", pct=True)
+    expected = Series(exp).astype(expected_dtype(dtype, "first", pct=True))
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.single_cpu
+def test_pct_max_many_rows():
+    # GH 18271
+    s = Series(np.arange(2**24 + 1))
+    result = s.rank(pct=True).max()
+    assert result == 1

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_reindex.py

@@ -0,0 +1,443 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    NA,
+    Categorical,
+    Float64Dtype,
+    Index,
+    MultiIndex,
+    NaT,
+    Period,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+
+
+def test_reindex(datetime_series, string_series):
+    identity = string_series.reindex(string_series.index)
+
+    assert tm.shares_memory(string_series.index, identity.index)
+
+    assert identity.index.is_(string_series.index)
+    assert identity.index.identical(string_series.index)
+
+    subIndex = string_series.index[10:20]
+    subSeries = string_series.reindex(subIndex)
+
+    for idx, val in subSeries.items():
+        assert val == string_series[idx]
+
+    subIndex2 = datetime_series.index[10:20]
+    subTS = datetime_series.reindex(subIndex2)
+
+    for idx, val in subTS.items():
+        assert val == datetime_series[idx]
+    stuffSeries = datetime_series.reindex(subIndex)
+
+    assert np.isnan(stuffSeries).all()
+
+    # This is extremely important for the Cython code to not screw up
+    nonContigIndex = datetime_series.index[::2]
+    subNonContig = datetime_series.reindex(nonContigIndex)
+    for idx, val in subNonContig.items():
+        assert val == datetime_series[idx]
+
+    # return a copy the same index here
+    result = datetime_series.reindex()
+    assert result is not datetime_series
+
+
+def test_reindex_nan():
+    ts = Series([2, 3, 5, 7], index=[1, 4, np.nan, 8])
+
+    i, j = [np.nan, 1, np.nan, 8, 4, np.nan], [2, 0, 2, 3, 1, 2]
+    tm.assert_series_equal(ts.reindex(i), ts.iloc[j])
+
+    ts.index = ts.index.astype("object")
+
+    # reindex coerces index.dtype to float, loc/iloc doesn't
+    tm.assert_series_equal(ts.reindex(i), ts.iloc[j], check_index_type=False)
+
+
+def test_reindex_series_add_nat():
+    rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s")
+    series = Series(rng)
+
+    result = series.reindex(range(15))
+    assert np.issubdtype(result.dtype, np.dtype("M8[ns]"))
+
+    mask = result.isna()
+    assert mask[-5:].all()
+    assert not mask[:-5].any()
+
+
+def test_reindex_with_datetimes():
+    rng = date_range("1/1/2000", periods=20)
+    ts = Series(np.random.default_rng(2).standard_normal(20), index=rng)
+
+    result = ts.reindex(list(ts.index[5:10]))
+    expected = ts[5:10]
+    expected.index = expected.index._with_freq(None)
+    tm.assert_series_equal(result, expected)
+
+    result = ts[list(ts.index[5:10])]
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_corner(datetime_series):
+    # (don't forget to fix this) I think it's fixed
+    empty = Series(index=[])
+    empty.reindex(datetime_series.index, method="pad")  # it works
+
+    # corner case: pad empty series
+    reindexed = empty.reindex(datetime_series.index, method="pad")
+
+    # pass non-Index
+    reindexed = datetime_series.reindex(list(datetime_series.index))
+    datetime_series.index = datetime_series.index._with_freq(None)
+    tm.assert_series_equal(datetime_series, reindexed)
+
+    # bad fill method
+    ts = datetime_series[::2]
+    msg = (
+        r"Invalid fill method\. Expecting pad \(ffill\), backfill "
+        r"\(bfill\) or nearest\. Got foo"
+    )
+    with pytest.raises(ValueError, match=msg):
+        ts.reindex(datetime_series.index, method="foo")
+
+
+def test_reindex_pad():
+    s = Series(np.arange(10), dtype="int64")
+    s2 = s[::2]
+
+    reindexed = s2.reindex(s.index, method="pad")
+    reindexed2 = s2.reindex(s.index, method="ffill")
+    tm.assert_series_equal(reindexed, reindexed2)
+
+    expected = Series([0, 0, 2, 2, 4, 4, 6, 6, 8, 8])
+    tm.assert_series_equal(reindexed, expected)
+
+
+def test_reindex_pad2():
+    # GH4604
+    s = Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"])
+    new_index = ["a", "g", "c", "f"]
+    expected = Series([1, 1, 3, 3], index=new_index)
+
+    # this changes dtype because the ffill happens after
+    result = s.reindex(new_index).ffill()
+    tm.assert_series_equal(result, expected.astype("float64"))
+
+    msg = "The 'downcast' keyword in ffill is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s.reindex(new_index).ffill(downcast="infer")
+    tm.assert_series_equal(result, expected)
+
+    expected = Series([1, 5, 3, 5], index=new_index)
+    result = s.reindex(new_index, method="ffill")
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_inference():
+    # inference of new dtype
+    s = Series([True, False, False, True], index=list("abcd"))
+    new_index = "agc"
+    msg = "Downcasting object dtype arrays on"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s.reindex(list(new_index)).ffill()
+    expected = Series([True, True, False], index=list(new_index))
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_downcasting():
+    # GH4618 shifted series downcasting
+    s = Series(False, index=range(5))
+    msg = "Downcasting object dtype arrays on"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = s.shift(1).bfill()
+    expected = Series(False, index=range(5))
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_nearest():
+    s = Series(np.arange(10, dtype="int64"))
+    target = [0.1, 0.9, 1.5, 2.0]
+    result = s.reindex(target, method="nearest")
+    expected = Series(np.around(target).astype("int64"), target)
+    tm.assert_series_equal(expected, result)
+
+    result = s.reindex(target, method="nearest", tolerance=0.2)
+    expected = Series([0, 1, np.nan, 2], target)
+    tm.assert_series_equal(expected, result)
+
+    result = s.reindex(target, method="nearest", tolerance=[0.3, 0.01, 0.4, 3])
+    expected = Series([0, np.nan, np.nan, 2], target)
+    tm.assert_series_equal(expected, result)
+
+
+def test_reindex_int(datetime_series):
+    ts = datetime_series[::2]
+    int_ts = Series(np.zeros(len(ts), dtype=int), index=ts.index)
+
+    # this should work fine
+    reindexed_int = int_ts.reindex(datetime_series.index)
+
+    # if NaNs introduced
+    assert reindexed_int.dtype == np.float64
+
+    # NO NaNs introduced
+    reindexed_int = int_ts.reindex(int_ts.index[::2])
+    assert reindexed_int.dtype == np.dtype(int)
+
+
+def test_reindex_bool(datetime_series):
+    # A series other than float, int, string, or object
+    ts = datetime_series[::2]
+    bool_ts = Series(np.zeros(len(ts), dtype=bool), index=ts.index)
+
+    # this should work fine
+    reindexed_bool = bool_ts.reindex(datetime_series.index)
+
+    # if NaNs introduced
+    assert reindexed_bool.dtype == np.object_
+
+    # NO NaNs introduced
+    reindexed_bool = bool_ts.reindex(bool_ts.index[::2])
+    assert reindexed_bool.dtype == np.bool_
+
+
+def test_reindex_bool_pad(datetime_series):
+    # fail
+    ts = datetime_series[5:]
+    bool_ts = Series(np.zeros(len(ts), dtype=bool), index=ts.index)
+    filled_bool = bool_ts.reindex(datetime_series.index, method="pad")
+    assert isna(filled_bool[:5]).all()
+
+
+def test_reindex_categorical():
+    index = date_range("20000101", periods=3)
+
+    # reindexing to an invalid Categorical
+    s = Series(["a", "b", "c"], dtype="category")
+    result = s.reindex(index)
+    expected = Series(
+        Categorical(values=[np.nan, np.nan, np.nan], categories=["a", "b", "c"])
+    )
+    expected.index = index
+    tm.assert_series_equal(result, expected)
+
+    # partial reindexing
+    expected = Series(Categorical(values=["b", "c"], categories=["a", "b", "c"]))
+    expected.index = [1, 2]
+    result = s.reindex([1, 2])
+    tm.assert_series_equal(result, expected)
+
+    expected = Series(Categorical(values=["c", np.nan], categories=["a", "b", "c"]))
+    expected.index = [2, 3]
+    result = s.reindex([2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_astype_order_consistency():
+    # GH#17444
+    ser = Series([1, 2, 3], index=[2, 0, 1])
+    new_index = [0, 1, 2]
+    temp_dtype = "category"
+    new_dtype = str
+    result = ser.reindex(new_index).astype(temp_dtype).astype(new_dtype)
+    expected = ser.astype(temp_dtype).reindex(new_index).astype(new_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_fill_value():
+    # -----------------------------------------------------------
+    # floats
+    floats = Series([1.0, 2.0, 3.0])
+    result = floats.reindex([1, 2, 3])
+    expected = Series([2.0, 3.0, np.nan], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+    result = floats.reindex([1, 2, 3], fill_value=0)
+    expected = Series([2.0, 3.0, 0], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+    # -----------------------------------------------------------
+    # ints
+    ints = Series([1, 2, 3])
+
+    result = ints.reindex([1, 2, 3])
+    expected = Series([2.0, 3.0, np.nan], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+    # don't upcast
+    result = ints.reindex([1, 2, 3], fill_value=0)
+    expected = Series([2, 3, 0], index=[1, 2, 3])
+    assert issubclass(result.dtype.type, np.integer)
+    tm.assert_series_equal(result, expected)
+
+    # -----------------------------------------------------------
+    # objects
+    objects = Series([1, 2, 3], dtype=object)
+
+    result = objects.reindex([1, 2, 3])
+    expected = Series([2, 3, np.nan], index=[1, 2, 3], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    result = objects.reindex([1, 2, 3], fill_value="foo")
+    expected = Series([2, 3, "foo"], index=[1, 2, 3], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    # ------------------------------------------------------------
+    # bools
+    bools = Series([True, False, True])
+
+    result = bools.reindex([1, 2, 3])
+    expected = Series([False, True, np.nan], index=[1, 2, 3], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+    result = bools.reindex([1, 2, 3], fill_value=False)
+    expected = Series([False, True, False], index=[1, 2, 3])
+    tm.assert_series_equal(result, expected)
+
+
+@td.skip_array_manager_not_yet_implemented
+@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+@pytest.mark.parametrize("fill_value", ["string", 0, Timedelta(0)])
+def test_reindex_fill_value_datetimelike_upcast(dtype, fill_value, using_array_manager):
+    # https://github.com/pandas-dev/pandas/issues/42921
+    if dtype == "timedelta64[ns]" and fill_value == Timedelta(0):
+        # use the scalar that is not compatible with the dtype for this test
+        fill_value = Timestamp(0)
+
+    ser = Series([NaT], dtype=dtype)
+
+    result = ser.reindex([0, 1], fill_value=fill_value)
+    expected = Series([NaT, fill_value], index=[0, 1], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_datetimeindexes_tz_naive_and_aware():
+    # GH 8306
+    idx = date_range("20131101", tz="America/Chicago", periods=7)
+    newidx = date_range("20131103", periods=10, freq="h")
+    s = Series(range(7), index=idx)
+    msg = (
+        r"Cannot compare dtypes datetime64\[ns, America/Chicago\] "
+        r"and datetime64\[ns\]"
+    )
+    with pytest.raises(TypeError, match=msg):
+        s.reindex(newidx, method="ffill")
+
+
+def test_reindex_empty_series_tz_dtype():
+    # GH 20869
+    result = Series(dtype="datetime64[ns, UTC]").reindex([0, 1])
+    expected = Series([NaT] * 2, dtype="datetime64[ns, UTC]")
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "p_values, o_values, values, expected_values",
+    [
+        (
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")],
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC"), "All"],
+            [1.0, 1.0],
+            [1.0, 1.0, np.nan],
+        ),
+        (
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")],
+            [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")],
+            [1.0, 1.0],
+            [1.0, 1.0],
+        ),
+    ],
+)
+def test_reindex_periodindex_with_object(p_values, o_values, values, expected_values):
+    # GH#28337
+    period_index = PeriodIndex(p_values)
+    object_index = Index(o_values)
+
+    ser = Series(values, index=period_index)
+    result = ser.reindex(object_index)
+    expected = Series(expected_values, index=object_index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_too_many_args():
+    # GH 40980
+    ser = Series([1, 2])
+    msg = r"reindex\(\) takes from 1 to 2 positional arguments but 3 were given"
+    with pytest.raises(TypeError, match=msg):
+        ser.reindex([2, 3], False)
+
+
+def test_reindex_double_index():
+    # GH 40980
+    ser = Series([1, 2])
+    msg = r"reindex\(\) got multiple values for argument 'index'"
+    with pytest.raises(TypeError, match=msg):
+        ser.reindex([2, 3], index=[3, 4])
+
+
+def test_reindex_no_posargs():
+    # GH 40980
+    ser = Series([1, 2])
+    result = ser.reindex(index=[1, 0])
+    expected = Series([2, 1], index=[1, 0])
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("values", [[["a"], ["x"]], [[], []]])
+def test_reindex_empty_with_level(values):
+    # GH41170
+    ser = Series(
+        range(len(values[0])), index=MultiIndex.from_arrays(values), dtype="object"
+    )
+    result = ser.reindex(np.array(["b"]), level=0)
+    expected = Series(
+        index=MultiIndex(levels=[["b"], values[1]], codes=[[], []]), dtype="object"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_missing_category():
+    # GH#18185
+    ser = Series([1, 2, 3, 1], dtype="category")
+    msg = r"Cannot setitem on a Categorical with a new category \(-1\)"
+    with pytest.raises(TypeError, match=msg):
+        ser.reindex([1, 2, 3, 4, 5], fill_value=-1)
+
+
+def test_reindexing_with_float64_NA_log():
+    # GH 47055
+    s = Series([1.0, NA], dtype=Float64Dtype())
+    s_reindex = s.reindex(range(3))
+    result = s_reindex.values._data
+    expected = np.array([1, np.nan, np.nan])
+    tm.assert_numpy_array_equal(result, expected)
+    with tm.assert_produces_warning(None):
+        result_log = np.log(s_reindex)
+        expected_log = Series([0, np.nan, np.nan], dtype=Float64Dtype())
+        tm.assert_series_equal(result_log, expected_log)
+
+
+@pytest.mark.parametrize("dtype", ["timedelta64", "datetime64"])
+def test_reindex_expand_nonnano_nat(dtype):
+    # GH 53497
+    ser = Series(np.array([1], dtype=f"{dtype}[s]"))
+    result = ser.reindex(RangeIndex(2))
+    expected = Series(
+        np.array([1, getattr(np, dtype)("nat", "s")], dtype=f"{dtype}[s]")
+    )
+    tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_reindex_like.py

@@ -0,0 +1,41 @@
+from datetime import datetime
+
+import numpy as np
+
+from pandas import Series
+import pandas._testing as tm
+
+
+def test_reindex_like(datetime_series):
+    other = datetime_series[::2]
+    tm.assert_series_equal(
+        datetime_series.reindex(other.index), datetime_series.reindex_like(other)
+    )
+
+    # GH#7179
+    day1 = datetime(2013, 3, 5)
+    day2 = datetime(2013, 5, 5)
+    day3 = datetime(2014, 3, 5)
+
+    series1 = Series([5, None, None], [day1, day2, day3])
+    series2 = Series([None, None], [day1, day3])
+
+    result = series1.reindex_like(series2, method="pad")
+    expected = Series([5, np.nan], index=[day1, day3])
+    tm.assert_series_equal(result, expected)
+
+
+def test_reindex_like_nearest():
+    ser = Series(np.arange(10, dtype="int64"))
+
+    target = [0.1, 0.9, 1.5, 2.0]
+    other = ser.reindex(target, method="nearest")
+    expected = Series(np.around(target).astype("int64"), target)
+
+    result = ser.reindex_like(other, method="nearest")
+    tm.assert_series_equal(expected, result)
+
+    result = ser.reindex_like(other, method="nearest", tolerance=1)
+    tm.assert_series_equal(expected, result)
+    result = ser.reindex_like(other, method="nearest", tolerance=[1, 2, 3, 4])
+    tm.assert_series_equal(expected, result)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_rename.py

@@ -0,0 +1,184 @@
+from datetime import datetime
+import re
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+    array,
+)
+import pandas._testing as tm
+
+
+class TestRename:
+    def test_rename(self, datetime_series):
+        ts = datetime_series
+        renamer = lambda x: x.strftime("%Y%m%d")
+        renamed = ts.rename(renamer)
+        assert renamed.index[0] == renamer(ts.index[0])
+
+        # dict
+        rename_dict = dict(zip(ts.index, renamed.index))
+        renamed2 = ts.rename(rename_dict)
+        tm.assert_series_equal(renamed, renamed2)
+
+    def test_rename_partial_dict(self):
+        # partial dict
+        ser = Series(np.arange(4), index=["a", "b", "c", "d"], dtype="int64")
+        renamed = ser.rename({"b": "foo", "d": "bar"})
+        tm.assert_index_equal(renamed.index, Index(["a", "foo", "c", "bar"]))
+
+    def test_rename_retain_index_name(self):
+        # index with name
+        renamer = Series(
+            np.arange(4), index=Index(["a", "b", "c", "d"], name="name"), dtype="int64"
+        )
+        renamed = renamer.rename({})
+        assert renamed.index.name == renamer.index.name
+
+    def test_rename_by_series(self):
+        ser = Series(range(5), name="foo")
+        renamer = Series({1: 10, 2: 20})
+        result = ser.rename(renamer)
+        expected = Series(range(5), index=[0, 10, 20, 3, 4], name="foo")
+        tm.assert_series_equal(result, expected)
+
+    def test_rename_set_name(self, using_infer_string):
+        ser = Series(range(4), index=list("abcd"))
+        for name in ["foo", 123, 123.0, datetime(2001, 11, 11), ("foo",)]:
+            result = ser.rename(name)
+            assert result.name == name
+            if using_infer_string:
+                tm.assert_extension_array_equal(result.index.values, ser.index.values)
+            else:
+                tm.assert_numpy_array_equal(result.index.values, ser.index.values)
+            assert ser.name is None
+
+    def test_rename_set_name_inplace(self, using_infer_string):
+        ser = Series(range(3), index=list("abc"))
+        for name in ["foo", 123, 123.0, datetime(2001, 11, 11), ("foo",)]:
+            ser.rename(name, inplace=True)
+            assert ser.name == name
+            exp = np.array(["a", "b", "c"], dtype=np.object_)
+            if using_infer_string:
+                exp = array(exp, dtype="str")
+                tm.assert_extension_array_equal(ser.index.values, exp)
+            else:
+                tm.assert_numpy_array_equal(ser.index.values, exp)
+
+    def test_rename_axis_supported(self):
+        # Supporting axis for compatibility, detailed in GH-18589
+        ser = Series(range(5))
+        ser.rename({}, axis=0)
+        ser.rename({}, axis="index")
+
+        with pytest.raises(ValueError, match="No axis named 5"):
+            ser.rename({}, axis=5)
+
+    def test_rename_inplace(self, datetime_series):
+        renamer = lambda x: x.strftime("%Y%m%d")
+        expected = renamer(datetime_series.index[0])
+
+        datetime_series.rename(renamer, inplace=True)
+        assert datetime_series.index[0] == expected
+
+    def test_rename_with_custom_indexer(self):
+        # GH 27814
+        class MyIndexer:
+            pass
+
+        ix = MyIndexer()
+        ser = Series([1, 2, 3]).rename(ix)
+        assert ser.name is ix
+
+    def test_rename_with_custom_indexer_inplace(self):
+        # GH 27814
+        class MyIndexer:
+            pass
+
+        ix = MyIndexer()
+        ser = Series([1, 2, 3])
+        ser.rename(ix, inplace=True)
+        assert ser.name is ix
+
+    def test_rename_callable(self):
+        # GH 17407
+        ser = Series(range(1, 6), index=Index(range(2, 7), name="IntIndex"))
+        result = ser.rename(str)
+        expected = ser.rename(lambda i: str(i))
+        tm.assert_series_equal(result, expected)
+
+        assert result.name == expected.name
+
+    def test_rename_none(self):
+        # GH 40977
+        ser = Series([1, 2], name="foo")
+        result = ser.rename(None)
+        expected = Series([1, 2])
+        tm.assert_series_equal(result, expected)
+
+    def test_rename_series_with_multiindex(self):
+        # issue #43659
+        arrays = [
+            ["bar", "baz", "baz", "foo", "qux"],
+            ["one", "one", "two", "two", "one"],
+        ]
+
+        index = MultiIndex.from_arrays(arrays, names=["first", "second"])
+        ser = Series(np.ones(5), index=index)
+        result = ser.rename(index={"one": "yes"}, level="second", errors="raise")
+
+        arrays_expected = [
+            ["bar", "baz", "baz", "foo", "qux"],
+            ["yes", "yes", "two", "two", "yes"],
+        ]
+
+        index_expected = MultiIndex.from_arrays(
+            arrays_expected, names=["first", "second"]
+        )
+        series_expected = Series(np.ones(5), index=index_expected)
+
+        tm.assert_series_equal(result, series_expected)
+
+    def test_rename_series_with_multiindex_keeps_ea_dtypes(self):
+        # GH21055
+        arrays = [
+            Index([1, 2, 3], dtype="Int64").astype("category"),
+            Index([1, 2, 3], dtype="Int64"),
+        ]
+        mi = MultiIndex.from_arrays(arrays, names=["A", "B"])
+        ser = Series(1, index=mi)
+        result = ser.rename({1: 4}, level=1)
+
+        arrays_expected = [
+            Index([1, 2, 3], dtype="Int64").astype("category"),
+            Index([4, 2, 3], dtype="Int64"),
+        ]
+        mi_expected = MultiIndex.from_arrays(arrays_expected, names=["A", "B"])
+        expected = Series(1, index=mi_expected)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_rename_error_arg(self):
+        # GH 46889
+        ser = Series(["foo", "bar"])
+        match = re.escape("[2] not found in axis")
+        with pytest.raises(KeyError, match=match):
+            ser.rename({2: 9}, errors="raise")
+
+    def test_rename_copy_false(self, using_copy_on_write, warn_copy_on_write):
+        # GH 46889
+        ser = Series(["foo", "bar"])
+        ser_orig = ser.copy()
+        shallow_copy = ser.rename({1: 9}, copy=False)
+        with tm.assert_cow_warning(warn_copy_on_write):
+            ser[0] = "foobar"
+        if using_copy_on_write:
+            assert ser_orig[0] == shallow_copy[0]
+            assert ser_orig[1] == shallow_copy[9]
+        else:
+            assert ser[0] == shallow_copy[0]
+            assert ser[1] == shallow_copy[9]

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_rename_axis.py

@@ -0,0 +1,47 @@
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesRenameAxis:
+    def test_rename_axis_mapper(self):
+        # GH 19978
+        mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"])
+        ser = Series(list(range(len(mi))), index=mi)
+
+        result = ser.rename_axis(index={"ll": "foo"})
+        assert result.index.names == ["foo", "nn"]
+
+        result = ser.rename_axis(index=str.upper, axis=0)
+        assert result.index.names == ["LL", "NN"]
+
+        result = ser.rename_axis(index=["foo", "goo"])
+        assert result.index.names == ["foo", "goo"]
+
+        with pytest.raises(TypeError, match="unexpected"):
+            ser.rename_axis(columns="wrong")
+
+    def test_rename_axis_inplace(self, datetime_series):
+        # GH 15704
+        expected = datetime_series.rename_axis("foo")
+        result = datetime_series
+        no_return = result.rename_axis("foo", inplace=True)
+
+        assert no_return is None
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("kwargs", [{"mapper": None}, {"index": None}, {}])
+    def test_rename_axis_none(self, kwargs):
+        # GH 25034
+        index = Index(list("abc"), name="foo")
+        ser = Series([1, 2, 3], index=index)
+
+        result = ser.rename_axis(**kwargs)
+        expected_index = index.rename(None) if kwargs else index
+        expected = Series([1, 2, 3], index=expected_index)
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_repeat.py

@@ -0,0 +1,40 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestRepeat:
+    def test_repeat(self):
+        ser = Series(np.random.default_rng(2).standard_normal(3), index=["a", "b", "c"])
+
+        reps = ser.repeat(5)
+        exp = Series(ser.values.repeat(5), index=ser.index.values.repeat(5))
+        tm.assert_series_equal(reps, exp)
+
+        to_rep = [2, 3, 4]
+        reps = ser.repeat(to_rep)
+        exp = Series(ser.values.repeat(to_rep), index=ser.index.values.repeat(to_rep))
+        tm.assert_series_equal(reps, exp)
+
+    def test_numpy_repeat(self):
+        ser = Series(np.arange(3), name="x")
+        expected = Series(
+            ser.values.repeat(2), name="x", index=ser.index.values.repeat(2)
+        )
+        tm.assert_series_equal(np.repeat(ser, 2), expected)
+
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.repeat(ser, 2, axis=0)
+
+    def test_repeat_with_multiindex(self):
+        # GH#9361, fixed by  GH#7891
+        m_idx = MultiIndex.from_tuples([(1, 2), (3, 4), (5, 6), (7, 8)])
+        data = ["a", "b", "c", "d"]
+        m_df = Series(data, index=m_idx)
+        assert m_df.repeat(3).shape == (3 * len(data),)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_replace.py

@@ -0,0 +1,819 @@
+import re
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
+
+
+class TestSeriesReplace:
+    def test_replace_explicit_none(self):
+        # GH#36984 if the user explicitly passes value=None, give it to them
+        ser = pd.Series([0, 0, ""], dtype=object)
+        result = ser.replace("", None)
+        expected = pd.Series([0, 0, None], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        # Cast column 2 to object to avoid implicit cast when setting entry to ""
+        df = pd.DataFrame(np.zeros((3, 3))).astype({2: object})
+        df.iloc[2, 2] = ""
+        result = df.replace("", None)
+        expected = pd.DataFrame(
+            {
+                0: np.zeros(3),
+                1: np.zeros(3),
+                2: np.array([0.0, 0.0, None], dtype=object),
+            }
+        )
+        assert expected.iloc[2, 2] is None
+        tm.assert_frame_equal(result, expected)
+
+        # GH#19998 same thing with object dtype
+        ser = pd.Series([10, 20, 30, "a", "a", "b", "a"])
+        result = ser.replace("a", None)
+        expected = pd.Series([10, 20, 30, None, None, "b", None])
+        assert expected.iloc[-1] is None
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_noop_doesnt_downcast(self):
+        # GH#44498
+        ser = pd.Series([None, None, pd.Timestamp("2021-12-16 17:31")], dtype=object)
+        res = ser.replace({np.nan: None})  # should be a no-op
+        tm.assert_series_equal(res, ser)
+        assert res.dtype == object
+
+        # same thing but different calling convention
+        res = ser.replace(np.nan, None)
+        tm.assert_series_equal(res, ser)
+        assert res.dtype == object
+
+    def test_replace(self):
+        N = 50
+        ser = pd.Series(np.random.default_rng(2).standard_normal(N))
+        ser[0:4] = np.nan
+        ser[6:10] = 0
+
+        # replace list with a single value
+        return_value = ser.replace([np.nan], -1, inplace=True)
+        assert return_value is None
+
+        exp = ser.fillna(-1)
+        tm.assert_series_equal(ser, exp)
+
+        rs = ser.replace(0.0, np.nan)
+        ser[ser == 0.0] = np.nan
+        tm.assert_series_equal(rs, ser)
+
+        ser = pd.Series(
+            np.fabs(np.random.default_rng(2).standard_normal(N)),
+            pd.date_range("2020-01-01", periods=N),
+            dtype=object,
+        )
+        ser[:5] = np.nan
+        ser[6:10] = "foo"
+        ser[20:30] = "bar"
+
+        # replace list with a single value
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace([np.nan, "foo", "bar"], -1)
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -1).all()
+        assert (rs[20:30] == -1).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -2).all()
+        assert (rs[20:30] == -3).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values with 2 lists
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
+        tm.assert_series_equal(rs, rs2)
+
+        # replace inplace
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
+        assert return_value is None
+
+        assert (ser[:5] == -1).all()
+        assert (ser[6:10] == -1).all()
+        assert (ser[20:30] == -1).all()
+
+    def test_replace_nan_with_inf(self):
+        ser = pd.Series([np.nan, 0, np.inf])
+        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
+
+        ser = pd.Series([np.nan, 0, "foo", "bar", np.inf, None, pd.NaT])
+        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
+        filled = ser.copy()
+        filled[4] = 0
+        tm.assert_series_equal(ser.replace(np.inf, 0), filled)
+
+    def test_replace_listlike_value_listlike_target(self, datetime_series):
+        ser = pd.Series(datetime_series.index)
+        tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0))
+
+        # malformed
+        msg = r"Replacement lists must match in length\. Expecting 3 got 2"
+        with pytest.raises(ValueError, match=msg):
+            ser.replace([1, 2, 3], [np.nan, 0])
+
+        # ser is dt64 so can't hold 1 or 2, so this replace is a no-op
+        result = ser.replace([1, 2], [np.nan, 0])
+        tm.assert_series_equal(result, ser)
+
+        ser = pd.Series([0, 1, 2, 3, 4])
+        result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])
+        tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0]))
+
+    def test_replace_gh5319(self):
+        # API change from 0.12?
+        # GH 5319
+        ser = pd.Series([0, np.nan, 2, 3, 4])
+        expected = ser.ffill()
+        msg = (
+            "Series.replace without 'value' and with non-dict-like "
+            "'to_replace' is deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.replace([np.nan])
+        tm.assert_series_equal(result, expected)
+
+        ser = pd.Series([0, np.nan, 2, 3, 4])
+        expected = ser.ffill()
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.replace(np.nan)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_datetime64(self):
+        # GH 5797
+        ser = pd.Series(pd.date_range("20130101", periods=5))
+        expected = ser.copy()
+        expected.loc[2] = pd.Timestamp("20120101")
+        result = ser.replace({pd.Timestamp("20130103"): pd.Timestamp("20120101")})
+        tm.assert_series_equal(result, expected)
+        result = ser.replace(pd.Timestamp("20130103"), pd.Timestamp("20120101"))
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_nat_with_tz(self):
+        # GH 11792: Test with replacing NaT in a list with tz data
+        ts = pd.Timestamp("2015/01/01", tz="UTC")
+        s = pd.Series([pd.NaT, pd.Timestamp("2015/01/01", tz="UTC")])
+        result = s.replace([np.nan, pd.NaT], pd.Timestamp.min)
+        expected = pd.Series([pd.Timestamp.min, ts], dtype=object)
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_timedelta_td64(self):
+        tdi = pd.timedelta_range(0, periods=5)
+        ser = pd.Series(tdi)
+
+        # Using a single dict argument means we go through replace_list
+        result = ser.replace({ser[1]: ser[3]})
+
+        expected = pd.Series([ser[0], ser[3], ser[2], ser[3], ser[4]])
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_with_single_list(self):
+        ser = pd.Series([0, 1, 2, 3, 4])
+        msg2 = (
+            "Series.replace without 'value' and with non-dict-like "
+            "'to_replace' is deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            result = ser.replace([1, 2, 3])
+        tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4]))
+
+        s = ser.copy()
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            return_value = s.replace([1, 2, 3], inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4]))
+
+        # make sure things don't get corrupted when fillna call fails
+        s = ser.copy()
+        msg = (
+            r"Invalid fill method\. Expecting pad \(ffill\) or backfill "
+            r"\(bfill\)\. Got crash_cymbal"
+        )
+        msg3 = "The 'method' keyword in Series.replace is deprecated"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg3):
+                return_value = s.replace([1, 2, 3], inplace=True, method="crash_cymbal")
+            assert return_value is None
+        tm.assert_series_equal(s, ser)
+
+    def test_replace_mixed_types(self):
+        ser = pd.Series(np.arange(5), dtype="int64")
+
+        def check_replace(to_rep, val, expected):
+            sc = ser.copy()
+            result = ser.replace(to_rep, val)
+            return_value = sc.replace(to_rep, val, inplace=True)
+            assert return_value is None
+            tm.assert_series_equal(expected, result)
+            tm.assert_series_equal(expected, sc)
+
+        # 3.0 can still be held in our int64 series, so we do not upcast GH#44940
+        tr, v = [3], [3.0]
+        check_replace(tr, v, ser)
+        # Note this matches what we get with the scalars 3 and 3.0
+        check_replace(tr[0], v[0], ser)
+
+        # MUST upcast to float
+        e = pd.Series([0, 1, 2, 3.5, 4])
+        tr, v = [3], [3.5]
+        check_replace(tr, v, e)
+
+        # casts to object
+        e = pd.Series([0, 1, 2, 3.5, "a"])
+        tr, v = [3, 4], [3.5, "a"]
+        check_replace(tr, v, e)
+
+        # again casts to object
+        e = pd.Series([0, 1, 2, 3.5, pd.Timestamp("20130101")])
+        tr, v = [3, 4], [3.5, pd.Timestamp("20130101")]
+        check_replace(tr, v, e)
+
+        # casts to object
+        e = pd.Series([0, 1, 2, 3.5, True], dtype="object")
+        tr, v = [3, 4], [3.5, True]
+        check_replace(tr, v, e)
+
+        # test an object with dates + floats + integers + strings
+        dr = pd.Series(pd.date_range("1/1/2001", "1/10/2001", freq="D"))
+        result = dr.astype(object).replace([dr[0], dr[1], dr[2]], [1.0, 2, "a"])
+        expected = pd.Series([1.0, 2, "a"] + dr[3:].tolist(), dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_bool_with_string_no_op(self):
+        s = pd.Series([True, False, True])
+        result = s.replace("fun", "in-the-sun")
+        tm.assert_series_equal(s, result)
+
+    def test_replace_bool_with_string(self):
+        # nonexistent elements
+        s = pd.Series([True, False, True])
+        result = s.replace(True, "2u")
+        expected = pd.Series(["2u", False, "2u"])
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_bool_with_bool(self):
+        s = pd.Series([True, False, True])
+        result = s.replace(True, False)
+        expected = pd.Series([False] * len(s))
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_with_dict_with_bool_keys(self):
+        s = pd.Series([True, False, True])
+        result = s.replace({"asdf": "asdb", True: "yes"})
+        expected = pd.Series(["yes", False, "yes"])
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_Int_with_na(self, any_int_ea_dtype):
+        # GH 38267
+        result = pd.Series([0, None], dtype=any_int_ea_dtype).replace(0, pd.NA)
+        expected = pd.Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
+        tm.assert_series_equal(result, expected)
+        result = pd.Series([0, 1], dtype=any_int_ea_dtype).replace(0, pd.NA)
+        result.replace(1, pd.NA, inplace=True)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace2(self):
+        N = 50
+        ser = pd.Series(
+            np.fabs(np.random.default_rng(2).standard_normal(N)),
+            pd.date_range("2020-01-01", periods=N),
+            dtype=object,
+        )
+        ser[:5] = np.nan
+        ser[6:10] = "foo"
+        ser[20:30] = "bar"
+
+        # replace list with a single value
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace([np.nan, "foo", "bar"], -1)
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -1).all()
+        assert (rs[20:30] == -1).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3})
+
+        assert (rs[:5] == -1).all()
+        assert (rs[6:10] == -2).all()
+        assert (rs[20:30] == -3).all()
+        assert (pd.isna(ser[:5])).all()
+
+        # replace with different values with 2 lists
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3])
+        tm.assert_series_equal(rs, rs2)
+
+        # replace inplace
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True)
+        assert return_value is None
+        assert (ser[:5] == -1).all()
+        assert (ser[6:10] == -1).all()
+        assert (ser[20:30] == -1).all()
+
+    @pytest.mark.parametrize("inplace", [True, False])
+    def test_replace_cascade(self, inplace):
+        # Test that replaced values are not replaced again
+        # GH #50778
+        ser = pd.Series([1, 2, 3])
+        expected = pd.Series([2, 3, 4])
+
+        res = ser.replace([1, 2, 3], [2, 3, 4], inplace=inplace)
+        if inplace:
+            tm.assert_series_equal(ser, expected)
+        else:
+            tm.assert_series_equal(res, expected)
+
+    def test_replace_with_dictlike_and_string_dtype(self, nullable_string_dtype):
+        # GH 32621, GH#44940
+        ser = pd.Series(["one", "two", np.nan], dtype=nullable_string_dtype)
+        expected = pd.Series(["1", "2", np.nan], dtype=nullable_string_dtype)
+        result = ser.replace({"one": "1", "two": "2"})
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_with_empty_dictlike(self):
+        # GH 15289
+        s = pd.Series(list("abcd"))
+        tm.assert_series_equal(s, s.replace({}))
+
+        empty_series = pd.Series([])
+        tm.assert_series_equal(s, s.replace(empty_series))
+
+    def test_replace_string_with_number(self):
+        # GH 15743
+        s = pd.Series([1, 2, 3])
+        result = s.replace("2", np.nan)
+        expected = pd.Series([1, 2, 3])
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_replacer_equals_replacement(self):
+        # GH 20656
+        # make sure all replacers are matching against original values
+        s = pd.Series(["a", "b"])
+        expected = pd.Series(["b", "a"])
+        result = s.replace({"a": "b", "b": "a"})
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_unicode_with_number(self):
+        # GH 15743
+        s = pd.Series([1, 2, 3])
+        result = s.replace("2", np.nan)
+        expected = pd.Series([1, 2, 3])
+        tm.assert_series_equal(expected, result)
+
+    def test_replace_mixed_types_with_string(self):
+        # Testing mixed
+        s = pd.Series([1, 2, 3, "4", 4, 5])
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = s.replace([2, "4"], np.nan)
+        expected = pd.Series([1, np.nan, 3, np.nan, 4, 5])
+        tm.assert_series_equal(expected, result)
+
+    @pytest.mark.parametrize(
+        "categorical, numeric",
+        [
+            (pd.Categorical(["A"], categories=["A", "B"]), [1]),
+            (pd.Categorical(["A", "B"], categories=["A", "B"]), [1, 2]),
+        ],
+    )
+    def test_replace_categorical(self, categorical, numeric, using_infer_string):
+        # GH 24971, GH#23305
+        ser = pd.Series(categorical)
+        msg = "Downcasting behavior in `replace`"
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = ser.replace({"A": 1, "B": 2})
+        expected = pd.Series(numeric).astype("category")
+        if 2 not in expected.cat.categories:
+            # i.e. categories should be [1, 2] even if there are no "B"s present
+            # GH#44940
+            expected = expected.cat.add_categories(2)
+        tm.assert_series_equal(expected, result)
+
+    @pytest.mark.parametrize(
+        "data, data_exp", [(["a", "b", "c"], ["b", "b", "c"]), (["a"], ["b"])]
+    )
+    def test_replace_categorical_inplace(self, data, data_exp):
+        # GH 53358
+        result = pd.Series(data, dtype="category")
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result.replace(to_replace="a", value="b", inplace=True)
+        expected = pd.Series(data_exp, dtype="category")
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_categorical_single(self):
+        # GH 26988
+        dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
+        s = pd.Series(dti)
+        c = s.astype("category")
+
+        expected = c.copy()
+        expected = expected.cat.add_categories("foo")
+        expected[2] = "foo"
+        expected = expected.cat.remove_unused_categories()
+        assert c[2] != "foo"
+
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = c.replace(c[2], "foo")
+        tm.assert_series_equal(expected, result)
+        assert c[2] != "foo"  # ensure non-inplace call does not alter original
+
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = c.replace(c[2], "foo", inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(expected, c)
+
+        first_value = c[0]
+        msg = "with CategoricalDtype is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            return_value = c.replace(c[1], c[0], inplace=True)
+        assert return_value is None
+        assert c[0] == c[1] == first_value  # test replacing with existing value
+
+    def test_replace_with_no_overflowerror(self):
+        # GH 25616
+        # casts to object without Exception from OverflowError
+        s = pd.Series([0, 1, 2, 3, 4])
+        result = s.replace([3], ["100000000000000000000"])
+        expected = pd.Series([0, 1, 2, "100000000000000000000", 4])
+        tm.assert_series_equal(result, expected)
+
+        s = pd.Series([0, "100000000000000000000", "100000000000000000001"])
+        result = s.replace(["100000000000000000000"], [1])
+        expected = pd.Series([0, 1, "100000000000000000001"])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ser, to_replace, exp",
+        [
+            ([1, 2, 3], {1: 2, 2: 3, 3: 4}, [2, 3, 4]),
+            (["1", "2", "3"], {"1": "2", "2": "3", "3": "4"}, ["2", "3", "4"]),
+        ],
+    )
+    def test_replace_commutative(self, ser, to_replace, exp):
+        # GH 16051
+        # DataFrame.replace() overwrites when values are non-numeric
+
+        series = pd.Series(ser)
+
+        expected = pd.Series(exp)
+        result = series.replace(to_replace)
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ser, exp", [([1, 2, 3], [1, True, 3]), (["x", 2, 3], ["x", True, 3])]
+    )
+    def test_replace_no_cast(self, ser, exp):
+        # GH 9113
+        # BUG: replace int64 dtype with bool coerces to int64
+
+        series = pd.Series(ser)
+        result = series.replace(2, True)
+        expected = pd.Series(exp)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_invalid_to_replace(self):
+        # GH 18634
+        # API: replace() should raise an exception if invalid argument is given
+        series = pd.Series(["a", "b", "c "])
+        msg = (
+            r"Expecting 'to_replace' to be either a scalar, array-like, "
+            r"dict or None, got invalid type.*"
+        )
+        msg2 = (
+            "Series.replace without 'value' and with non-dict-like "
+            "'to_replace' is deprecated"
+        )
+        with pytest.raises(TypeError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                series.replace(lambda x: x.strip())
+
+    @pytest.mark.parametrize("frame", [False, True])
+    def test_replace_nonbool_regex(self, frame):
+        obj = pd.Series(["a", "b", "c "])
+        if frame:
+            obj = obj.to_frame()
+
+        msg = "'to_replace' must be 'None' if 'regex' is not a bool"
+        with pytest.raises(ValueError, match=msg):
+            obj.replace(to_replace=["a"], regex="foo")
+
+    @pytest.mark.parametrize("frame", [False, True])
+    def test_replace_empty_copy(self, frame):
+        obj = pd.Series([], dtype=np.float64)
+        if frame:
+            obj = obj.to_frame()
+
+        res = obj.replace(4, 5, inplace=True)
+        assert res is None
+
+        res = obj.replace(4, 5, inplace=False)
+        tm.assert_equal(res, obj)
+        assert res is not obj
+
+    def test_replace_only_one_dictlike_arg(self, fixed_now_ts):
+        # GH#33340
+
+        ser = pd.Series([1, 2, "A", fixed_now_ts, True])
+        to_replace = {0: 1, 2: "A"}
+        value = "foo"
+        msg = "Series.replace cannot use dict-like to_replace and non-None value"
+        with pytest.raises(ValueError, match=msg):
+            ser.replace(to_replace, value)
+
+        to_replace = 1
+        value = {0: "foo", 2: "bar"}
+        msg = "Series.replace cannot use dict-value and non-None to_replace"
+        with pytest.raises(ValueError, match=msg):
+            ser.replace(to_replace, value)
+
+    def test_replace_extension_other(self, frame_or_series):
+        # https://github.com/pandas-dev/pandas/issues/34530
+        obj = frame_or_series(pd.array([1, 2, 3], dtype="Int64"))
+        result = obj.replace("", "")  # no exception
+        # should not have changed dtype
+        tm.assert_equal(obj, result)
+
+    def _check_replace_with_method(self, ser: pd.Series):
+        df = ser.to_frame()
+
+        msg1 = "The 'method' keyword in Series.replace is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg1):
+            res = ser.replace(ser[1], method="pad")
+        expected = pd.Series([ser[0], ser[0]] + list(ser[2:]), dtype=ser.dtype)
+        tm.assert_series_equal(res, expected)
+
+        msg2 = "The 'method' keyword in DataFrame.replace is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            res_df = df.replace(ser[1], method="pad")
+        tm.assert_frame_equal(res_df, expected.to_frame())
+
+        ser2 = ser.copy()
+        with tm.assert_produces_warning(FutureWarning, match=msg1):
+            res2 = ser2.replace(ser[1], method="pad", inplace=True)
+        assert res2 is None
+        tm.assert_series_equal(ser2, expected)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg2):
+            res_df2 = df.replace(ser[1], method="pad", inplace=True)
+        assert res_df2 is None
+        tm.assert_frame_equal(df, expected.to_frame())
+
+    def test_replace_ea_dtype_with_method(self, any_numeric_ea_dtype):
+        arr = pd.array([1, 2, pd.NA, 4], dtype=any_numeric_ea_dtype)
+        ser = pd.Series(arr)
+
+        self._check_replace_with_method(ser)
+
+    @pytest.mark.parametrize("as_categorical", [True, False])
+    def test_replace_interval_with_method(self, as_categorical):
+        # in particular interval that can't hold NA
+
+        idx = pd.IntervalIndex.from_breaks(range(4))
+        ser = pd.Series(idx)
+        if as_categorical:
+            ser = ser.astype("category")
+
+        self._check_replace_with_method(ser)
+
+    @pytest.mark.parametrize("as_period", [True, False])
+    @pytest.mark.parametrize("as_categorical", [True, False])
+    def test_replace_datetimelike_with_method(self, as_period, as_categorical):
+        idx = pd.date_range("2016-01-01", periods=5, tz="US/Pacific")
+        if as_period:
+            idx = idx.tz_localize(None).to_period("D")
+
+        ser = pd.Series(idx)
+        ser.iloc[-2] = pd.NaT
+        if as_categorical:
+            ser = ser.astype("category")
+
+        self._check_replace_with_method(ser)
+
+    def test_replace_with_compiled_regex(self):
+        # https://github.com/pandas-dev/pandas/issues/35680
+        s = pd.Series(["a", "b", "c"])
+        regex = re.compile("^a$")
+        result = s.replace({regex: "z"}, regex=True)
+        expected = pd.Series(["z", "b", "c"])
+        tm.assert_series_equal(result, expected)
+
+    def test_pandas_replace_na(self):
+        # GH#43344
+        # GH#56599
+        ser = pd.Series(["AA", "BB", "CC", "DD", "EE", "", pd.NA, "AA"], dtype="string")
+        regex_mapping = {
+            "AA": "CC",
+            "BB": "CC",
+            "EE": "CC",
+            "CC": "CC-REPL",
+        }
+        result = ser.replace(regex_mapping, regex=True)
+        exp = pd.Series(
+            ["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA, "CC"], dtype="string"
+        )
+        tm.assert_series_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "dtype, input_data, to_replace, expected_data",
+        [
+            ("bool", [True, False], {True: False}, [False, False]),
+            ("int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
+            ("Int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
+            ("float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
+            ("Float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
+            ("string", ["one", "two"], {"one": "1", "two": "2"}, ["1", "2"]),
+            (
+                pd.IntervalDtype("int64"),
+                IntervalArray([pd.Interval(1, 2), pd.Interval(2, 3)]),
+                {pd.Interval(1, 2): pd.Interval(10, 20)},
+                IntervalArray([pd.Interval(10, 20), pd.Interval(2, 3)]),
+            ),
+            (
+                pd.IntervalDtype("float64"),
+                IntervalArray([pd.Interval(1.0, 2.7), pd.Interval(2.8, 3.1)]),
+                {pd.Interval(1.0, 2.7): pd.Interval(10.6, 20.8)},
+                IntervalArray([pd.Interval(10.6, 20.8), pd.Interval(2.8, 3.1)]),
+            ),
+            (
+                pd.PeriodDtype("M"),
+                [pd.Period("2020-05", freq="M")],
+                {pd.Period("2020-05", freq="M"): pd.Period("2020-06", freq="M")},
+                [pd.Period("2020-06", freq="M")],
+            ),
+        ],
+    )
+    def test_replace_dtype(self, dtype, input_data, to_replace, expected_data):
+        # GH#33484
+        ser = pd.Series(input_data, dtype=dtype)
+        result = ser.replace(to_replace)
+        expected = pd.Series(expected_data, dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_string_dtype(self):
+        # GH#40732, GH#44940
+        ser = pd.Series(["one", "two", np.nan], dtype="string")
+        res = ser.replace({"one": "1", "two": "2"})
+        expected = pd.Series(["1", "2", np.nan], dtype="string")
+        tm.assert_series_equal(res, expected)
+
+        # GH#31644
+        ser2 = pd.Series(["A", np.nan], dtype="string")
+        res2 = ser2.replace("A", "B")
+        expected2 = pd.Series(["B", np.nan], dtype="string")
+        tm.assert_series_equal(res2, expected2)
+
+        ser3 = pd.Series(["A", "B"], dtype="string")
+        res3 = ser3.replace("A", pd.NA)
+        expected3 = pd.Series([pd.NA, "B"], dtype="string")
+        tm.assert_series_equal(res3, expected3)
+
+    def test_replace_string_dtype_list_to_replace(self):
+        # GH#41215, GH#44940
+        ser = pd.Series(["abc", "def"], dtype="string")
+        res = ser.replace(["abc", "any other string"], "xyz")
+        expected = pd.Series(["xyz", "def"], dtype="string")
+        tm.assert_series_equal(res, expected)
+
+    def test_replace_string_dtype_regex(self):
+        # GH#31644
+        ser = pd.Series(["A", "B"], dtype="string")
+        res = ser.replace(r".", "C", regex=True)
+        expected = pd.Series(["C", "C"], dtype="string")
+        tm.assert_series_equal(res, expected)
+
+    def test_replace_nullable_numeric(self):
+        # GH#40732, GH#44940
+
+        floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype())
+        assert floats.replace({1.0: 9}).dtype == floats.dtype
+        assert floats.replace(1.0, 9).dtype == floats.dtype
+        assert floats.replace({1.0: 9.0}).dtype == floats.dtype
+        assert floats.replace(1.0, 9.0).dtype == floats.dtype
+
+        res = floats.replace(to_replace=[1.0, 2.0], value=[9.0, 10.0])
+        assert res.dtype == floats.dtype
+
+        ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype())
+        assert ints.replace({1: 9}).dtype == ints.dtype
+        assert ints.replace(1, 9).dtype == ints.dtype
+        assert ints.replace({1: 9.0}).dtype == ints.dtype
+        assert ints.replace(1, 9.0).dtype == ints.dtype
+
+        # nullable (for now) raises instead of casting
+        with pytest.raises(TypeError, match="Invalid value"):
+            ints.replace({1: 9.5})
+        with pytest.raises(TypeError, match="Invalid value"):
+            ints.replace(1, 9.5)
+
+    @pytest.mark.parametrize("regex", [False, True])
+    def test_replace_regex_dtype_series(self, regex):
+        # GH-48644
+        series = pd.Series(["0"], dtype=object)
+        expected = pd.Series([1])
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = series.replace(to_replace="0", value=1, regex=regex)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("regex", [False, True])
+    def test_replace_regex_dtype_series_string(self, regex):
+        series = pd.Series(["0"], dtype="str")
+        expected = pd.Series([1], dtype="int64")
+        msg = "Downcasting behavior in `replace`"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = series.replace(to_replace="0", value=1, regex=regex)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_different_int_types(self, any_int_numpy_dtype):
+        # GH#45311
+        labs = pd.Series([1, 1, 1, 0, 0, 2, 2, 2], dtype=any_int_numpy_dtype)
+
+        maps = pd.Series([0, 2, 1], dtype=any_int_numpy_dtype)
+        map_dict = dict(zip(maps.values, maps.index))
+
+        result = labs.replace(map_dict)
+        expected = labs.replace({0: 0, 2: 1, 1: 2})
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("val", [2, np.nan, 2.0])
+    def test_replace_value_none_dtype_numeric(self, val):
+        # GH#48231
+        ser = pd.Series([1, val])
+        result = ser.replace(val, None)
+        expected = pd.Series([1, None], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_replace_change_dtype_series(self):
+        # GH#25797
+        df = pd.DataFrame({"Test": ["0.5", True, "0.6"]}, dtype=object)
+        df["Test"] = df["Test"].replace([True], [np.nan])
+        expected = pd.DataFrame({"Test": ["0.5", np.nan, "0.6"]}, dtype=object)
+        tm.assert_frame_equal(df, expected)
+
+        df = pd.DataFrame({"Test": ["0.5", None, "0.6"]}, dtype=object)
+        df["Test"] = df["Test"].replace([None], [np.nan])
+        tm.assert_frame_equal(df, expected)
+
+        df = pd.DataFrame({"Test": ["0.5", None, "0.6"]}, dtype=object)
+        df["Test"] = df["Test"].fillna(np.nan)
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize("dtype", ["object", "Int64"])
+    def test_replace_na_in_obj_column(self, dtype):
+        # GH#47480
+        ser = pd.Series([0, 1, pd.NA], dtype=dtype)
+        expected = pd.Series([0, 2, pd.NA], dtype=dtype)
+        result = ser.replace(to_replace=1, value=2)
+        tm.assert_series_equal(result, expected)
+
+        ser.replace(to_replace=1, value=2, inplace=True)
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize("val", [0, 0.5])
+    def test_replace_numeric_column_with_na(self, val):
+        # GH#50758
+        ser = pd.Series([val, 1])
+        expected = pd.Series([val, pd.NA])
+        result = ser.replace(to_replace=1, value=pd.NA)
+        tm.assert_series_equal(result, expected)
+
+        ser.replace(to_replace=1, value=pd.NA, inplace=True)
+        tm.assert_series_equal(ser, expected)
+
+    def test_replace_ea_float_with_bool(self):
+        # GH#55398
+        ser = pd.Series([0.0], dtype="Float64")
+        expected = ser.copy()
+        result = ser.replace(False, 1.0)
+        tm.assert_series_equal(result, expected)
+
+        ser = pd.Series([False], dtype="boolean")
+        expected = ser.copy()
+        result = ser.replace(0.0, True)
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_reset_index.py

@@ -0,0 +1,225 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+    date_range,
+    option_context,
+)
+import pandas._testing as tm
+
+
+class TestResetIndex:
+    def test_reset_index_dti_round_trip(self):
+        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
+        d1 = DataFrame({"v": np.random.default_rng(2).random(len(dti))}, index=dti)
+        d2 = d1.reset_index()
+        assert d2.dtypes.iloc[0] == np.dtype("M8[ns]")
+        d3 = d2.set_index("index")
+        tm.assert_frame_equal(d1, d3, check_names=False)
+
+        # GH#2329
+        stamp = datetime(2012, 11, 22)
+        df = DataFrame([[stamp, 12.1]], columns=["Date", "Value"])
+        df = df.set_index("Date")
+
+        assert df.index[0] == stamp
+        assert df.reset_index()["Date"].iloc[0] == stamp
+
+    def test_reset_index(self):
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        )[:5]
+        ser = df.stack(future_stack=True)
+        ser.index.names = ["hash", "category"]
+
+        ser.name = "value"
+        df = ser.reset_index()
+        assert "value" in df
+
+        df = ser.reset_index(name="value2")
+        assert "value2" in df
+
+        # check inplace
+        s = ser.reset_index(drop=True)
+        s2 = ser
+        return_value = s2.reset_index(drop=True, inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(s, s2)
+
+        # level
+        index = MultiIndex(
+            levels=[["bar"], ["one", "two", "three"], [0, 1]],
+            codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+        )
+        s = Series(np.random.default_rng(2).standard_normal(6), index=index)
+        rs = s.reset_index(level=1)
+        assert len(rs.columns) == 2
+
+        rs = s.reset_index(level=[0, 2], drop=True)
+        tm.assert_index_equal(rs.index, Index(index.get_level_values(1)))
+        assert isinstance(rs, Series)
+
+    def test_reset_index_name(self):
+        s = Series([1, 2, 3], index=Index(range(3), name="x"))
+        assert s.reset_index().index.name is None
+        assert s.reset_index(drop=True).index.name is None
+
+    def test_reset_index_level(self):
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
+
+        for levels in ["A", "B"], [0, 1]:
+            # With MultiIndex
+            s = df.set_index(["A", "B"])["C"]
+
+            result = s.reset_index(level=levels[0])
+            tm.assert_frame_equal(result, df.set_index("B"))
+
+            result = s.reset_index(level=levels[:1])
+            tm.assert_frame_equal(result, df.set_index("B"))
+
+            result = s.reset_index(level=levels)
+            tm.assert_frame_equal(result, df)
+
+            result = df.set_index(["A", "B"]).reset_index(level=levels, drop=True)
+            tm.assert_frame_equal(result, df[["C"]])
+
+            with pytest.raises(KeyError, match="Level E "):
+                s.reset_index(level=["A", "E"])
+
+            # With single-level Index
+            s = df.set_index("A")["B"]
+
+            result = s.reset_index(level=levels[0])
+            tm.assert_frame_equal(result, df[["A", "B"]])
+
+            result = s.reset_index(level=levels[:1])
+            tm.assert_frame_equal(result, df[["A", "B"]])
+
+            result = s.reset_index(level=levels[0], drop=True)
+            tm.assert_series_equal(result, df["B"])
+
+            with pytest.raises(IndexError, match="Too many levels"):
+                s.reset_index(level=[0, 1, 2])
+
+        # Check that .reset_index([],drop=True) doesn't fail
+        result = Series(range(4)).reset_index([], drop=True)
+        expected = Series(range(4))
+        tm.assert_series_equal(result, expected)
+
+    def test_reset_index_range(self):
+        # GH 12071
+        s = Series(range(2), name="A", dtype="int64")
+        series_result = s.reset_index()
+        assert isinstance(series_result.index, RangeIndex)
+        series_expected = DataFrame(
+            [[0, 0], [1, 1]], columns=["index", "A"], index=RangeIndex(stop=2)
+        )
+        tm.assert_frame_equal(series_result, series_expected)
+
+    def test_reset_index_drop_errors(self):
+        #  GH 20925
+
+        # KeyError raised for series index when passed level name is missing
+        s = Series(range(4))
+        with pytest.raises(KeyError, match="does not match index name"):
+            s.reset_index("wrong", drop=True)
+        with pytest.raises(KeyError, match="does not match index name"):
+            s.reset_index("wrong")
+
+        # KeyError raised for series when level to be dropped is missing
+        s = Series(range(4), index=MultiIndex.from_product([[1, 2]] * 2))
+        with pytest.raises(KeyError, match="not found"):
+            s.reset_index("wrong", drop=True)
+
+    def test_reset_index_with_drop(self):
+        arrays = [
+            ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
+            ["one", "two", "one", "two", "one", "two", "one", "two"],
+        ]
+        tuples = zip(*arrays)
+        index = MultiIndex.from_tuples(tuples)
+        data = np.random.default_rng(2).standard_normal(8)
+        ser = Series(data, index=index)
+        ser.iloc[3] = np.nan
+
+        deleveled = ser.reset_index()
+        assert isinstance(deleveled, DataFrame)
+        assert len(deleveled.columns) == len(ser.index.levels) + 1
+        assert deleveled.index.name == ser.index.name
+
+        deleveled = ser.reset_index(drop=True)
+        assert isinstance(deleveled, Series)
+        assert deleveled.index.name == ser.index.name
+
+    def test_reset_index_inplace_and_drop_ignore_name(self):
+        # GH#44575
+        ser = Series(range(2), name="old")
+        ser.reset_index(name="new", drop=True, inplace=True)
+        expected = Series(range(2), name="old")
+        tm.assert_series_equal(ser, expected)
+
+    def test_reset_index_drop_infer_string(self):
+        # GH#56160
+        pytest.importorskip("pyarrow")
+        ser = Series(["a", "b", "c"], dtype=object)
+        with option_context("future.infer_string", True):
+            result = ser.reset_index(drop=True)
+        tm.assert_series_equal(result, ser)
+
+
+@pytest.mark.parametrize(
+    "array, dtype",
+    [
+        (["a", "b"], object),
+        (
+            pd.period_range("12-1-2000", periods=2, freq="Q-DEC"),
+            pd.PeriodDtype(freq="Q-DEC"),
+        ),
+    ],
+)
+def test_reset_index_dtypes_on_empty_series_with_multiindex(
+    array, dtype, using_infer_string
+):
+    # GH 19602 - Preserve dtype on empty Series with MultiIndex
+    idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], array])
+    result = Series(dtype=object, index=idx)[:0].reset_index().dtypes
+    exp = "str" if using_infer_string else object
+    expected = Series(
+        {
+            "level_0": np.int64,
+            "level_1": np.float64,
+            "level_2": exp if dtype == object else dtype,
+            0: object,
+        }
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "names, expected_names",
+    [
+        (["A", "A"], ["A", "A"]),
+        (["level_1", None], ["level_1", "level_1"]),
+    ],
+)
+@pytest.mark.parametrize("allow_duplicates", [False, True])
+def test_column_name_duplicates(names, expected_names, allow_duplicates):
+    # GH#44755 reset_index with duplicate column labels
+    s = Series([1], index=MultiIndex.from_arrays([[1], [1]], names=names))
+    if allow_duplicates:
+        result = s.reset_index(allow_duplicates=True)
+        expected = DataFrame([[1, 1, 1]], columns=expected_names + [0])
+        tm.assert_frame_equal(result, expected)
+    else:
+        with pytest.raises(ValueError, match="cannot insert"):
+            s.reset_index()

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_round.py

@@ -0,0 +1,81 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestSeriesRound:
+    def test_round(self, datetime_series):
+        datetime_series.index.name = "index_name"
+        result = datetime_series.round(2)
+        expected = Series(
+            np.round(datetime_series.values, 2), index=datetime_series.index, name="ts"
+        )
+        tm.assert_series_equal(result, expected)
+        assert result.name == datetime_series.name
+
+    def test_round_numpy(self, any_float_dtype):
+        # See GH#12600
+        ser = Series([1.53, 1.36, 0.06], dtype=any_float_dtype)
+        out = np.round(ser, decimals=0)
+        expected = Series([2.0, 1.0, 0.0], dtype=any_float_dtype)
+        tm.assert_series_equal(out, expected)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.round(ser, decimals=0, out=ser)
+
+    def test_round_numpy_with_nan(self, any_float_dtype):
+        # See GH#14197
+        ser = Series([1.53, np.nan, 0.06], dtype=any_float_dtype)
+        with tm.assert_produces_warning(None):
+            result = ser.round()
+        expected = Series([2.0, np.nan, 0.0], dtype=any_float_dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_round_builtin(self, any_float_dtype):
+        ser = Series(
+            [1.123, 2.123, 3.123],
+            index=range(3),
+            dtype=any_float_dtype,
+        )
+        result = round(ser)
+        expected_rounded0 = Series(
+            [1.0, 2.0, 3.0], index=range(3), dtype=any_float_dtype
+        )
+        tm.assert_series_equal(result, expected_rounded0)
+
+        decimals = 2
+        expected_rounded = Series(
+            [1.12, 2.12, 3.12], index=range(3), dtype=any_float_dtype
+        )
+        result = round(ser, decimals)
+        tm.assert_series_equal(result, expected_rounded)
+
+    @pytest.mark.parametrize("method", ["round", "floor", "ceil"])
+    @pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"])
+    def test_round_nat(self, method, freq, unit):
+        # GH14940, GH#56158
+        ser = Series([pd.NaT], dtype=f"M8[{unit}]")
+        expected = Series(pd.NaT, dtype=f"M8[{unit}]")
+        round_method = getattr(ser.dt, method)
+        result = round_method(freq)
+        tm.assert_series_equal(result, expected)
+
+    def test_round_ea_boolean(self):
+        # GH#55936
+        ser = Series([True, False], dtype="boolean")
+        expected = ser.copy()
+        result = ser.round(2)
+        tm.assert_series_equal(result, expected)
+        result.iloc[0] = False
+        tm.assert_series_equal(ser, expected)
+
+    def test_round_dtype_object(self):
+        # GH#61206
+        ser = Series([0.2], dtype="object")
+        msg = "Expected numeric dtype, got object instead."
+        with pytest.raises(TypeError, match=msg):
+            ser.round()

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_searchsorted.py

@@ -0,0 +1,77 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.api.types import is_scalar
+
+
+class TestSeriesSearchSorted:
+    def test_searchsorted(self):
+        ser = Series([1, 2, 3])
+
+        result = ser.searchsorted(1, side="left")
+        assert is_scalar(result)
+        assert result == 0
+
+        result = ser.searchsorted(1, side="right")
+        assert is_scalar(result)
+        assert result == 1
+
+    def test_searchsorted_numeric_dtypes_scalar(self):
+        ser = Series([1, 2, 90, 1000, 3e9])
+        res = ser.searchsorted(30)
+        assert is_scalar(res)
+        assert res == 2
+
+        res = ser.searchsorted([30])
+        exp = np.array([2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_numeric_dtypes_vector(self):
+        ser = Series([1, 2, 90, 1000, 3e9])
+        res = ser.searchsorted([91, 2e6])
+        exp = np.array([3, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_datetime64_scalar(self):
+        ser = Series(date_range("20120101", periods=10, freq="2D"))
+        val = Timestamp("20120102")
+        res = ser.searchsorted(val)
+        assert is_scalar(res)
+        assert res == 1
+
+    def test_searchsorted_datetime64_scalar_mixed_timezones(self):
+        # GH 30086
+        ser = Series(date_range("20120101", periods=10, freq="2D", tz="UTC"))
+        val = Timestamp("20120102", tz="America/New_York")
+        res = ser.searchsorted(val)
+        assert is_scalar(res)
+        assert res == 1
+
+    def test_searchsorted_datetime64_list(self):
+        ser = Series(date_range("20120101", periods=10, freq="2D"))
+        vals = [Timestamp("20120102"), Timestamp("20120104")]
+        res = ser.searchsorted(vals)
+        exp = np.array([1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_sorter(self):
+        # GH8490
+        ser = Series([3, 1, 2])
+        res = ser.searchsorted([0, 3], sorter=np.argsort(ser))
+        exp = np.array([0, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_searchsorted_dataframe_fail(self):
+        # GH#49620
+        ser = Series([1, 2, 3, 4, 5])
+        vals = pd.DataFrame([[1, 2], [3, 4]])
+        msg = "Value must be 1-D array-like or scalar, DataFrame is not supported"
+        with pytest.raises(ValueError, match=msg):
+            ser.searchsorted(vals)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_set_name.py

@@ -0,0 +1,21 @@
+from datetime import datetime
+
+from pandas import Series
+
+
+class TestSetName:
+    def test_set_name(self):
+        ser = Series([1, 2, 3])
+        ser2 = ser._set_name("foo")
+        assert ser2.name == "foo"
+        assert ser.name is None
+        assert ser is not ser2
+
+    def test_set_name_attribute(self):
+        ser = Series([1, 2, 3])
+        ser2 = Series([1, 2, 3], name="bar")
+        for name in [7, 7.0, "name", datetime(2001, 1, 1), (1,), "\u05D0"]:
+            ser.name = name
+            assert ser.name == name
+            ser2.name = name
+            assert ser2.name == name

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_size.py

@@ -0,0 +1,22 @@
+import pytest
+
+from pandas import Series
+
+
+@pytest.mark.parametrize(
+    "data, index, expected",
+    [
+        ([1, 2, 3], None, 3),
+        ({"a": 1, "b": 2, "c": 3}, None, 3),
+        ([1, 2, 3], ["x", "y", "z"], 3),
+        ([1, 2, 3, 4, 5], ["x", "y", "z", "w", "n"], 5),
+        ([1, 2, 3], None, 3),
+        ([1, 2, 3], ["x", "y", "z"], 3),
+        ([1, 2, 3, 4], ["x", "y", "z", "w"], 4),
+    ],
+)
+def test_series(data, index, expected):
+    # GH#52897
+    ser = Series(data, index=index)
+    assert ser.size == expected
+    assert isinstance(ser.size, int)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_sort_index.py

@@ -0,0 +1,337 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    IntervalIndex,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(params=["quicksort", "mergesort", "heapsort", "stable"])
+def sort_kind(request):
+    return request.param
+
+
+class TestSeriesSortIndex:
+    def test_sort_index_name(self, datetime_series):
+        result = datetime_series.sort_index(ascending=False)
+        assert result.name == datetime_series.name
+
+    def test_sort_index(self, datetime_series):
+        datetime_series.index = datetime_series.index._with_freq(None)
+
+        rindex = list(datetime_series.index)
+        np.random.default_rng(2).shuffle(rindex)
+
+        random_order = datetime_series.reindex(rindex)
+        sorted_series = random_order.sort_index()
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        # descending
+        sorted_series = random_order.sort_index(ascending=False)
+        tm.assert_series_equal(
+            sorted_series, datetime_series.reindex(datetime_series.index[::-1])
+        )
+
+        # compat on level
+        sorted_series = random_order.sort_index(level=0)
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        # compat on axis
+        sorted_series = random_order.sort_index(axis=0)
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        msg = "No axis named 1 for object type Series"
+        with pytest.raises(ValueError, match=msg):
+            random_order.sort_values(axis=1)
+
+        sorted_series = random_order.sort_index(level=0, axis=0)
+        tm.assert_series_equal(sorted_series, datetime_series)
+
+        with pytest.raises(ValueError, match=msg):
+            random_order.sort_index(level=0, axis=1)
+
+    def test_sort_index_inplace(self, datetime_series):
+        datetime_series.index = datetime_series.index._with_freq(None)
+
+        # For GH#11402
+        rindex = list(datetime_series.index)
+        np.random.default_rng(2).shuffle(rindex)
+
+        # descending
+        random_order = datetime_series.reindex(rindex)
+        result = random_order.sort_index(ascending=False, inplace=True)
+
+        assert result is None
+        expected = datetime_series.reindex(datetime_series.index[::-1])
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(random_order, expected)
+
+        # ascending
+        random_order = datetime_series.reindex(rindex)
+        result = random_order.sort_index(ascending=True, inplace=True)
+
+        assert result is None
+        expected = datetime_series.copy()
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(random_order, expected)
+
+    def test_sort_index_level(self):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        res = s.sort_index(level="A")
+        tm.assert_series_equal(backwards, res)
+
+        res = s.sort_index(level=["A", "B"])
+        tm.assert_series_equal(backwards, res)
+
+        res = s.sort_index(level="A", sort_remaining=False)
+        tm.assert_series_equal(s, res)
+
+        res = s.sort_index(level=["A", "B"], sort_remaining=False)
+        tm.assert_series_equal(s, res)
+
+    @pytest.mark.parametrize("level", ["A", 0])  # GH#21052
+    def test_sort_index_multiindex(self, level):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        # implicit sort_remaining=True
+        res = s.sort_index(level=level)
+        tm.assert_series_equal(backwards, res)
+
+        # GH#13496
+        # sort has no effect without remaining lvls
+        res = s.sort_index(level=level, sort_remaining=False)
+        tm.assert_series_equal(s, res)
+
+    def test_sort_index_kind(self, sort_kind):
+        # GH#14444 & GH#13589:  Add support for sort algo choosing
+        series = Series(index=[3, 2, 1, 4, 3], dtype=object)
+        expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(kind=sort_kind)
+        tm.assert_series_equal(expected_series, index_sorted_series)
+
+    def test_sort_index_na_position(self):
+        series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object)
+        expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="first")
+        tm.assert_series_equal(expected_series_first, index_sorted_series)
+
+        expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="last")
+        tm.assert_series_equal(expected_series_last, index_sorted_series)
+
+    def test_sort_index_intervals(self):
+        s = Series(
+            [np.nan, 1, 2, 3], IntervalIndex.from_arrays([0, 1, 2, 3], [1, 2, 3, 4])
+        )
+
+        result = s.sort_index()
+        expected = s
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(ascending=False)
+        expected = Series(
+            [3, 2, 1, np.nan], IntervalIndex.from_arrays([3, 2, 1, 0], [4, 3, 2, 1])
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("inplace", [True, False])
+    @pytest.mark.parametrize(
+        "original_list, sorted_list, ascending, ignore_index, output_index",
+        [
+            ([2, 3, 6, 1], [2, 3, 6, 1], True, True, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [2, 3, 6, 1], True, False, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [1, 6, 3, 2], False, True, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [1, 6, 3, 2], False, False, [3, 2, 1, 0]),
+        ],
+    )
+    def test_sort_index_ignore_index(
+        self, inplace, original_list, sorted_list, ascending, ignore_index, output_index
+    ):
+        # GH 30114
+        ser = Series(original_list)
+        expected = Series(sorted_list, index=output_index)
+        kwargs = {
+            "ascending": ascending,
+            "ignore_index": ignore_index,
+            "inplace": inplace,
+        }
+
+        if inplace:
+            result_ser = ser.copy()
+            result_ser.sort_index(**kwargs)
+        else:
+            result_ser = ser.sort_index(**kwargs)
+
+        tm.assert_series_equal(result_ser, expected)
+        tm.assert_series_equal(ser, Series(original_list))
+
+    def test_sort_index_ascending_list(self):
+        # GH#16934
+
+        # Set up a Series with a three level MultiIndex
+        arrays = [
+            ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+            ["one", "two", "one", "two", "one", "two", "one", "two"],
+            [4, 3, 2, 1, 4, 3, 2, 1],
+        ]
+        tuples = zip(*arrays)
+        mi = MultiIndex.from_tuples(tuples, names=["first", "second", "third"])
+        ser = Series(range(8), index=mi)
+
+        # Sort with boolean ascending
+        result = ser.sort_index(level=["third", "first"], ascending=False)
+        expected = ser.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]
+        tm.assert_series_equal(result, expected)
+
+        # Sort with list of boolean ascending
+        result = ser.sort_index(level=["third", "first"], ascending=[False, True])
+        expected = ser.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ascending",
+        [
+            None,
+            (True, None),
+            (False, "True"),
+        ],
+    )
+    def test_sort_index_ascending_bad_value_raises(self, ascending):
+        ser = Series(range(10), index=[0, 3, 2, 1, 4, 5, 7, 6, 8, 9])
+        match = 'For argument "ascending" expected type bool'
+        with pytest.raises(ValueError, match=match):
+            ser.sort_index(ascending=ascending)
+
+
+class TestSeriesSortIndexKey:
+    def test_sort_index_multiindex_key(self):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        result = s.sort_index(level="C", key=lambda x: -x)
+        tm.assert_series_equal(s, result)
+
+        result = s.sort_index(level="C", key=lambda x: x)  # nothing happens
+        tm.assert_series_equal(backwards, result)
+
+    def test_sort_index_multiindex_key_multi_level(self):
+        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
+        s = Series([1, 2], mi)
+        backwards = s.iloc[[1, 0]]
+
+        result = s.sort_index(level=["A", "C"], key=lambda x: -x)
+        tm.assert_series_equal(s, result)
+
+        result = s.sort_index(level=["A", "C"], key=lambda x: x)  # nothing happens
+        tm.assert_series_equal(backwards, result)
+
+    def test_sort_index_key(self):
+        series = Series(np.arange(6, dtype="int64"), index=list("aaBBca"))
+
+        result = series.sort_index()
+        expected = series.iloc[[2, 3, 0, 1, 5, 4]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_index(key=lambda x: x.str.lower())
+        expected = series.iloc[[0, 1, 5, 2, 3, 4]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_index(key=lambda x: x.str.lower(), ascending=False)
+        expected = series.iloc[[4, 2, 3, 0, 1, 5]]
+        tm.assert_series_equal(result, expected)
+
+    def test_sort_index_key_int(self):
+        series = Series(np.arange(6, dtype="int64"), index=np.arange(6, dtype="int64"))
+
+        result = series.sort_index()
+        tm.assert_series_equal(result, series)
+
+        result = series.sort_index(key=lambda x: -x)
+        expected = series.sort_index(ascending=False)
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_index(key=lambda x: 2 * x)
+        tm.assert_series_equal(result, series)
+
+    def test_sort_index_kind_key(self, sort_kind, sort_by_key):
+        # GH #14444 & #13589:  Add support for sort algo choosing
+        series = Series(index=[3, 2, 1, 4, 3], dtype=object)
+        expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(kind=sort_kind, key=sort_by_key)
+        tm.assert_series_equal(expected_series, index_sorted_series)
+
+    def test_sort_index_kind_neg_key(self, sort_kind):
+        # GH #14444 & #13589:  Add support for sort algo choosing
+        series = Series(index=[3, 2, 1, 4, 3], dtype=object)
+        expected_series = Series(index=[4, 3, 3, 2, 1], dtype=object)
+
+        index_sorted_series = series.sort_index(kind=sort_kind, key=lambda x: -x)
+        tm.assert_series_equal(expected_series, index_sorted_series)
+
+    def test_sort_index_na_position_key(self, sort_by_key):
+        series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object)
+        expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="first", key=sort_by_key)
+        tm.assert_series_equal(expected_series_first, index_sorted_series)
+
+        expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object)
+
+        index_sorted_series = series.sort_index(na_position="last", key=sort_by_key)
+        tm.assert_series_equal(expected_series_last, index_sorted_series)
+
+    def test_changes_length_raises(self):
+        s = Series([1, 2, 3])
+        with pytest.raises(ValueError, match="change the shape"):
+            s.sort_index(key=lambda x: x[:1])
+
+    def test_sort_values_key_type(self):
+        s = Series([1, 2, 3], DatetimeIndex(["2008-10-24", "2008-11-23", "2007-12-22"]))
+
+        result = s.sort_index(key=lambda x: x.month)
+        expected = s.iloc[[0, 1, 2]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(key=lambda x: x.day)
+        expected = s.iloc[[2, 1, 0]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(key=lambda x: x.year)
+        expected = s.iloc[[2, 0, 1]]
+        tm.assert_series_equal(result, expected)
+
+        result = s.sort_index(key=lambda x: x.month_name())
+        expected = s.iloc[[2, 1, 0]]
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "ascending",
+        [
+            [True, False],
+            [False, True],
+        ],
+    )
+    def test_sort_index_multi_already_monotonic(self, ascending):
+        # GH 56049
+        mi = MultiIndex.from_product([[1, 2], [3, 4]])
+        ser = Series(range(len(mi)), index=mi)
+        result = ser.sort_index(ascending=ascending)
+        if ascending == [True, False]:
+            expected = ser.take([1, 0, 3, 2])
+        elif ascending == [False, True]:
+            expected = ser.take([2, 3, 0, 1])
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_sort_values.py

@@ -0,0 +1,246 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesSortValues:
+    def test_sort_values(self, datetime_series, using_copy_on_write):
+        # check indexes are reordered corresponding with the values
+        ser = Series([3, 2, 4, 1], ["A", "B", "C", "D"])
+        expected = Series([1, 2, 3, 4], ["D", "B", "A", "C"])
+        result = ser.sort_values()
+        tm.assert_series_equal(expected, result)
+
+        ts = datetime_series.copy()
+        ts[:5] = np.nan
+        vals = ts.values
+
+        result = ts.sort_values()
+        assert np.isnan(result[-5:]).all()
+        tm.assert_numpy_array_equal(result[:-5].values, np.sort(vals[5:]))
+
+        # na_position
+        result = ts.sort_values(na_position="first")
+        assert np.isnan(result[:5]).all()
+        tm.assert_numpy_array_equal(result[5:].values, np.sort(vals[5:]))
+
+        # something object-type
+        ser = Series(["A", "B"], [1, 2])
+        # no failure
+        ser.sort_values()
+
+        # ascending=False
+        ordered = ts.sort_values(ascending=False)
+        expected = np.sort(ts.dropna().values)[::-1]
+        tm.assert_almost_equal(expected, ordered.dropna().values)
+        ordered = ts.sort_values(ascending=False, na_position="first")
+        tm.assert_almost_equal(expected, ordered.dropna().values)
+
+        # ascending=[False] should behave the same as ascending=False
+        ordered = ts.sort_values(ascending=[False])
+        expected = ts.sort_values(ascending=False)
+        tm.assert_series_equal(expected, ordered)
+        ordered = ts.sort_values(ascending=[False], na_position="first")
+        expected = ts.sort_values(ascending=False, na_position="first")
+        tm.assert_series_equal(expected, ordered)
+
+        msg = 'For argument "ascending" expected type bool, received type NoneType.'
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=None)
+        msg = r"Length of ascending \(0\) must be 1 for Series"
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=[])
+        msg = r"Length of ascending \(3\) must be 1 for Series"
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=[1, 2, 3])
+        msg = r"Length of ascending \(2\) must be 1 for Series"
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending=[False, False])
+        msg = 'For argument "ascending" expected type bool, received type str.'
+        with pytest.raises(ValueError, match=msg):
+            ts.sort_values(ascending="foobar")
+
+        # inplace=True
+        ts = datetime_series.copy()
+        return_value = ts.sort_values(ascending=False, inplace=True)
+        assert return_value is None
+        tm.assert_series_equal(ts, datetime_series.sort_values(ascending=False))
+        tm.assert_index_equal(
+            ts.index, datetime_series.sort_values(ascending=False).index
+        )
+
+        # GH#5856/5853
+        # Series.sort_values operating on a view
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+        s = df.iloc[:, 0]
+
+        msg = (
+            "This Series is a view of some other array, to sort in-place "
+            "you must create a copy"
+        )
+        if using_copy_on_write:
+            s.sort_values(inplace=True)
+            tm.assert_series_equal(s, df.iloc[:, 0].sort_values())
+        else:
+            with pytest.raises(ValueError, match=msg):
+                s.sort_values(inplace=True)
+
+    def test_sort_values_categorical(self):
+        c = Categorical(["a", "b", "b", "a"], ordered=False)
+        cat = Series(c.copy())
+
+        # sort in the categories order
+        expected = Series(
+            Categorical(["a", "a", "b", "b"], ordered=False), index=[0, 3, 1, 2]
+        )
+        result = cat.sort_values()
+        tm.assert_series_equal(result, expected)
+
+        cat = Series(Categorical(["a", "c", "b", "d"], ordered=True))
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+
+        cat = Series(
+            Categorical(
+                ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True
+            )
+        )
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+
+        res = cat.sort_values(ascending=False)
+        exp = np.array(["d", "c", "b", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+
+        raw_cat1 = Categorical(
+            ["a", "b", "c", "d"], categories=["a", "b", "c", "d"], ordered=False
+        )
+        raw_cat2 = Categorical(
+            ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True
+        )
+        s = ["a", "b", "c", "d"]
+        df = DataFrame(
+            {"unsort": raw_cat1, "sort": raw_cat2, "string": s, "values": [1, 2, 3, 4]}
+        )
+
+        # Cats must be sorted in a dataframe
+        res = df.sort_values(by=["string"], ascending=False)
+        exp = np.array(["d", "c", "b", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(res["sort"].values.__array__(), exp)
+        assert res["sort"].dtype == "category"
+
+        res = df.sort_values(by=["sort"], ascending=False)
+        exp = df.sort_values(by=["string"], ascending=True)
+        tm.assert_series_equal(res["values"], exp["values"])
+        assert res["sort"].dtype == "category"
+        assert res["unsort"].dtype == "category"
+
+        # unordered cat, but we allow this
+        df.sort_values(by=["unsort"], ascending=False)
+
+        # multi-columns sort
+        # GH#7848
+        df = DataFrame(
+            {"id": [6, 5, 4, 3, 2, 1], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
+        )
+        df["grade"] = Categorical(df["raw_grade"], ordered=True)
+        df["grade"] = df["grade"].cat.set_categories(["b", "e", "a"])
+
+        # sorts 'grade' according to the order of the categories
+        result = df.sort_values(by=["grade"])
+        expected = df.iloc[[1, 2, 5, 0, 3, 4]]
+        tm.assert_frame_equal(result, expected)
+
+        # multi
+        result = df.sort_values(by=["grade", "id"])
+        expected = df.iloc[[2, 1, 5, 4, 3, 0]]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("inplace", [True, False])
+    @pytest.mark.parametrize(
+        "original_list, sorted_list, ignore_index, output_index",
+        [
+            ([2, 3, 6, 1], [6, 3, 2, 1], True, [0, 1, 2, 3]),
+            ([2, 3, 6, 1], [6, 3, 2, 1], False, [2, 1, 0, 3]),
+        ],
+    )
+    def test_sort_values_ignore_index(
+        self, inplace, original_list, sorted_list, ignore_index, output_index
+    ):
+        # GH 30114
+        ser = Series(original_list)
+        expected = Series(sorted_list, index=output_index)
+        kwargs = {"ignore_index": ignore_index, "inplace": inplace}
+
+        if inplace:
+            result_ser = ser.copy()
+            result_ser.sort_values(ascending=False, **kwargs)
+        else:
+            result_ser = ser.sort_values(ascending=False, **kwargs)
+
+        tm.assert_series_equal(result_ser, expected)
+        tm.assert_series_equal(ser, Series(original_list))
+
+    def test_mergesort_descending_stability(self):
+        # GH 28697
+        s = Series([1, 2, 1, 3], ["first", "b", "second", "c"])
+        result = s.sort_values(ascending=False, kind="mergesort")
+        expected = Series([3, 2, 1, 1], ["c", "b", "first", "second"])
+        tm.assert_series_equal(result, expected)
+
+    def test_sort_values_validate_ascending_for_value_error(self):
+        # GH41634
+        ser = Series([23, 7, 21])
+
+        msg = 'For argument "ascending" expected type bool, received type str.'
+        with pytest.raises(ValueError, match=msg):
+            ser.sort_values(ascending="False")
+
+    @pytest.mark.parametrize("ascending", [False, 0, 1, True])
+    def test_sort_values_validate_ascending_functional(self, ascending):
+        # GH41634
+        ser = Series([23, 7, 21])
+        expected = np.sort(ser.values)
+
+        sorted_ser = ser.sort_values(ascending=ascending)
+        if not ascending:
+            expected = expected[::-1]
+
+        result = sorted_ser.values
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestSeriesSortingKey:
+    def test_sort_values_key(self):
+        series = Series(np.array(["Hello", "goodbye"]))
+
+        result = series.sort_values(axis=0)
+        expected = series
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_values(axis=0, key=lambda x: x.str.lower())
+        expected = series[::-1]
+        tm.assert_series_equal(result, expected)
+
+    def test_sort_values_key_nan(self):
+        series = Series(np.array([0, 5, np.nan, 3, 2, np.nan]))
+
+        result = series.sort_values(axis=0)
+        expected = series.iloc[[0, 4, 3, 1, 2, 5]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_values(axis=0, key=lambda x: x + 5)
+        expected = series.iloc[[0, 4, 3, 1, 2, 5]]
+        tm.assert_series_equal(result, expected)
+
+        result = series.sort_values(axis=0, key=lambda x: -x, ascending=False)
+        expected = series.iloc[[0, 4, 3, 1, 2, 5]]
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_to_csv.py

@@ -0,0 +1,179 @@
+from datetime import datetime
+from io import StringIO
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series
+import pandas._testing as tm
+
+from pandas.io.common import get_handle
+
+
+class TestSeriesToCSV:
+    def read_csv(self, path, **kwargs):
+        params = {"index_col": 0, "header": None}
+        params.update(**kwargs)
+
+        header = params.get("header")
+        out = pd.read_csv(path, **params).squeeze("columns")
+
+        if header is None:
+            out.name = out.index.name = None
+
+        return out
+
+    def test_from_csv(self, datetime_series, string_series):
+        # freq doesn't round-trip
+        datetime_series.index = datetime_series.index._with_freq(None)
+
+        with tm.ensure_clean() as path:
+            datetime_series.to_csv(path, header=False)
+            ts = self.read_csv(path, parse_dates=True)
+            tm.assert_series_equal(datetime_series, ts, check_names=False)
+
+            assert ts.name is None
+            assert ts.index.name is None
+
+            # see gh-10483
+            datetime_series.to_csv(path, header=True)
+            ts_h = self.read_csv(path, header=0)
+            assert ts_h.name == "ts"
+
+            string_series.to_csv(path, header=False)
+            series = self.read_csv(path)
+            tm.assert_series_equal(string_series, series, check_names=False)
+
+            assert series.name is None
+            assert series.index.name is None
+
+            string_series.to_csv(path, header=True)
+            series_h = self.read_csv(path, header=0)
+            assert series_h.name == "series"
+
+            with open(path, "w", encoding="utf-8") as outfile:
+                outfile.write("1998-01-01|1.0\n1999-01-01|2.0")
+
+            series = self.read_csv(path, sep="|", parse_dates=True)
+            check_series = Series(
+                {datetime(1998, 1, 1): 1.0, datetime(1999, 1, 1): 2.0}
+            )
+            tm.assert_series_equal(check_series, series)
+
+            series = self.read_csv(path, sep="|", parse_dates=False)
+            check_series = Series({"1998-01-01": 1.0, "1999-01-01": 2.0})
+            tm.assert_series_equal(check_series, series)
+
+    def test_to_csv(self, datetime_series):
+        with tm.ensure_clean() as path:
+            datetime_series.to_csv(path, header=False)
+
+            with open(path, newline=None, encoding="utf-8") as f:
+                lines = f.readlines()
+            assert lines[1] != "\n"
+
+            datetime_series.to_csv(path, index=False, header=False)
+            arr = np.loadtxt(path)
+            tm.assert_almost_equal(arr, datetime_series.values)
+
+    def test_to_csv_unicode_index(self):
+        buf = StringIO()
+        s = Series(["\u05d0", "d2"], index=["\u05d0", "\u05d1"])
+
+        s.to_csv(buf, encoding="UTF-8", header=False)
+        buf.seek(0)
+
+        s2 = self.read_csv(buf, index_col=0, encoding="UTF-8")
+        tm.assert_series_equal(s, s2)
+
+    def test_to_csv_float_format(self):
+        with tm.ensure_clean() as filename:
+            ser = Series([0.123456, 0.234567, 0.567567])
+            ser.to_csv(filename, float_format="%.2f", header=False)
+
+            rs = self.read_csv(filename)
+            xp = Series([0.12, 0.23, 0.57])
+            tm.assert_series_equal(rs, xp)
+
+    def test_to_csv_list_entries(self):
+        s = Series(["jack and jill", "jesse and frank"])
+
+        split = s.str.split(r"\s+and\s+")
+
+        buf = StringIO()
+        split.to_csv(buf, header=False)
+
+    def test_to_csv_path_is_none(self):
+        # GH 8215
+        # Series.to_csv() was returning None, inconsistent with
+        # DataFrame.to_csv() which returned string
+        s = Series([1, 2, 3])
+        csv_str = s.to_csv(path_or_buf=None, header=False)
+        assert isinstance(csv_str, str)
+
+    @pytest.mark.parametrize(
+        "s,encoding",
+        [
+            (
+                Series([0.123456, 0.234567, 0.567567], index=["A", "B", "C"], name="X"),
+                None,
+            ),
+            # GH 21241, 21118
+            (Series(["abc", "def", "ghi"], name="X"), "ascii"),
+            (Series(["123", "你好", "世界"], name="中文"), "gb2312"),
+            (
+                Series(["123", "Γειά σου", "Κόσμε"], name="Ελληνικά"),  # noqa: RUF001
+                "cp737",
+            ),
+        ],
+    )
+    def test_to_csv_compression(self, s, encoding, compression):
+        with tm.ensure_clean() as filename:
+            s.to_csv(filename, compression=compression, encoding=encoding, header=True)
+            # test the round trip - to_csv -> read_csv
+            result = pd.read_csv(
+                filename,
+                compression=compression,
+                encoding=encoding,
+                index_col=0,
+            ).squeeze("columns")
+            tm.assert_series_equal(s, result)
+
+            # test the round trip using file handle - to_csv -> read_csv
+            with get_handle(
+                filename, "w", compression=compression, encoding=encoding
+            ) as handles:
+                s.to_csv(handles.handle, encoding=encoding, header=True)
+
+            result = pd.read_csv(
+                filename,
+                compression=compression,
+                encoding=encoding,
+                index_col=0,
+            ).squeeze("columns")
+            tm.assert_series_equal(s, result)
+
+            # explicitly ensure file was compressed
+            with tm.decompress_file(filename, compression) as fh:
+                text = fh.read().decode(encoding or "utf8")
+                assert s.name in text
+
+            with tm.decompress_file(filename, compression) as fh:
+                tm.assert_series_equal(
+                    s,
+                    pd.read_csv(fh, index_col=0, encoding=encoding).squeeze("columns"),
+                )
+
+    def test_to_csv_interval_index(self, using_infer_string):
+        # GH 28210
+        s = Series(["foo", "bar", "baz"], index=pd.interval_range(0, 3))
+
+        with tm.ensure_clean("__tmp_to_csv_interval_index__.csv") as path:
+            s.to_csv(path, header=False)
+            result = self.read_csv(path, index_col=0)
+
+            # can't roundtrip intervalindex via read_csv so check string repr (GH 23595)
+            expected = s
+            expected.index = expected.index.astype("str")
+            tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_to_dict.py

@@ -0,0 +1,38 @@
+import collections
+
+import numpy as np
+import pytest
+
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestSeriesToDict:
+    @pytest.mark.parametrize(
+        "mapping", (dict, collections.defaultdict(list), collections.OrderedDict)
+    )
+    def test_to_dict(self, mapping, datetime_series):
+        # GH#16122
+        result = Series(datetime_series.to_dict(into=mapping), name="ts")
+        expected = datetime_series.copy()
+        expected.index = expected.index._with_freq(None)
+        tm.assert_series_equal(result, expected)
+
+        from_method = Series(datetime_series.to_dict(into=collections.Counter))
+        from_constructor = Series(collections.Counter(datetime_series.items()))
+        tm.assert_series_equal(from_method, from_constructor)
+
+    @pytest.mark.parametrize(
+        "input",
+        (
+            {"a": np.int64(64), "b": 10},
+            {"a": np.int64(64), "b": 10, "c": "ABC"},
+            {"a": np.uint64(64), "b": 10, "c": "ABC"},
+        ),
+    )
+    def test_to_dict_return_types(self, input):
+        # GH25969
+
+        d = Series(input).to_dict()
+        assert isinstance(d["a"], int)
+        assert isinstance(d["b"], int)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_to_frame.py

@@ -0,0 +1,63 @@
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestToFrame:
+    def test_to_frame_respects_name_none(self):
+        # GH#44212 if we explicitly pass name=None, then that should be respected,
+        #  not changed to 0
+        # GH-45448 this is first deprecated & enforced in 2.0
+        ser = Series(range(3))
+        result = ser.to_frame(None)
+
+        exp_index = Index([None], dtype=object)
+        tm.assert_index_equal(result.columns, exp_index)
+
+        result = ser.rename("foo").to_frame(None)
+        exp_index = Index([None], dtype=object)
+        tm.assert_index_equal(result.columns, exp_index)
+
+    def test_to_frame(self, datetime_series):
+        datetime_series.name = None
+        rs = datetime_series.to_frame()
+        xp = DataFrame(datetime_series.values, index=datetime_series.index)
+        tm.assert_frame_equal(rs, xp)
+
+        datetime_series.name = "testname"
+        rs = datetime_series.to_frame()
+        xp = DataFrame(
+            {"testname": datetime_series.values}, index=datetime_series.index
+        )
+        tm.assert_frame_equal(rs, xp)
+
+        rs = datetime_series.to_frame(name="testdifferent")
+        xp = DataFrame(
+            {"testdifferent": datetime_series.values}, index=datetime_series.index
+        )
+        tm.assert_frame_equal(rs, xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
+    )
+    def test_to_frame_expanddim(self):
+        # GH#9762
+
+        class SubclassedSeries(Series):
+            @property
+            def _constructor_expanddim(self):
+                return SubclassedFrame
+
+        class SubclassedFrame(DataFrame):
+            pass
+
+        ser = SubclassedSeries([1, 2, 3], name="X")
+        result = ser.to_frame()
+        assert isinstance(result, SubclassedFrame)
+        expected = SubclassedFrame({"X": [1, 2, 3]})
+        tm.assert_frame_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_to_numpy.py

@@ -0,0 +1,49 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    NA,
+    Series,
+    Timedelta,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("dtype", ["int64", "float64"])
+def test_to_numpy_na_value(dtype):
+    # GH#48951
+    ser = Series([1, 2, NA, 4])
+    result = ser.to_numpy(dtype=dtype, na_value=0)
+    expected = np.array([1, 2, 0, 4], dtype=dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_to_numpy_cast_before_setting_na():
+    # GH#50600
+    ser = Series([1])
+    result = ser.to_numpy(dtype=np.float64, na_value=np.nan)
+    expected = np.array([1.0])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_to_numpy_arrow_dtype_given():
+    # GH#57121
+    ser = Series([1, NA], dtype="int64[pyarrow]")
+    result = ser.to_numpy(dtype="float64")
+    expected = np.array([1.0, np.nan])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_ea_int_to_td_ts():
+    # GH#57093
+    ser = Series([1, None], dtype="Int64")
+    result = ser.astype("m8[ns]")
+    expected = Series([1, Timedelta("nat")], dtype="m8[ns]")
+    tm.assert_series_equal(result, expected)
+
+    result = ser.astype("M8[ns]")
+    expected = Series([1, Timedelta("nat")], dtype="M8[ns]")
+    tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_tolist.py

@@ -0,0 +1,36 @@
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    Interval,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+
+
+@pytest.mark.parametrize(
+    "values, dtype, expected_dtype",
+    (
+        ([1], "int64", int),
+        ([1], "Int64", int),
+        ([1.0], "float64", float),
+        ([1.0], "Float64", float),
+        (["abc"], "object", str),
+        (["abc"], "string", str),
+        ([Interval(1, 3)], "interval", Interval),
+        ([Period("2000-01-01", "D")], "period[D]", Period),
+        ([Timedelta(days=1)], "timedelta64[ns]", Timedelta),
+        ([Timestamp("2000-01-01")], "datetime64[ns]", Timestamp),
+        pytest.param([1], "int64[pyarrow]", int, marks=td.skip_if_no("pyarrow")),
+        pytest.param([1.0], "float64[pyarrow]", float, marks=td.skip_if_no("pyarrow")),
+        pytest.param(["abc"], "string[pyarrow]", str, marks=td.skip_if_no("pyarrow")),
+    ),
+)
+def test_tolist_scalar_dtype(values, dtype, expected_dtype):
+    # GH49890
+    ser = Series(values, dtype=dtype)
+    result_dtype = type(ser.tolist()[0])
+    assert result_dtype == expected_dtype

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_truncate.py

@@ -0,0 +1,67 @@
+from datetime import datetime
+
+import pytest
+
+import pandas as pd
+from pandas import (
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestTruncate:
+    def test_truncate_datetimeindex_tz(self):
+        # GH 9243
+        idx = date_range("4/1/2005", "4/30/2005", freq="D", tz="US/Pacific")
+        s = Series(range(len(idx)), index=idx)
+        with pytest.raises(TypeError, match="Cannot compare tz-naive"):
+            # GH#36148 as of 2.0 we require tzawareness compat
+            s.truncate(datetime(2005, 4, 2), datetime(2005, 4, 4))
+
+        lb = idx[1]
+        ub = idx[3]
+        result = s.truncate(lb.to_pydatetime(), ub.to_pydatetime())
+        expected = Series([1, 2, 3], index=idx[1:4])
+        tm.assert_series_equal(result, expected)
+
+    def test_truncate_periodindex(self):
+        # GH 17717
+        idx1 = pd.PeriodIndex(
+            [pd.Period("2017-09-02"), pd.Period("2017-09-02"), pd.Period("2017-09-03")]
+        )
+        series1 = Series([1, 2, 3], index=idx1)
+        result1 = series1.truncate(after="2017-09-02")
+
+        expected_idx1 = pd.PeriodIndex(
+            [pd.Period("2017-09-02"), pd.Period("2017-09-02")]
+        )
+        tm.assert_series_equal(result1, Series([1, 2], index=expected_idx1))
+
+        idx2 = pd.PeriodIndex(
+            [pd.Period("2017-09-03"), pd.Period("2017-09-02"), pd.Period("2017-09-03")]
+        )
+        series2 = Series([1, 2, 3], index=idx2)
+        result2 = series2.sort_index().truncate(after="2017-09-02")
+
+        expected_idx2 = pd.PeriodIndex([pd.Period("2017-09-02")])
+        tm.assert_series_equal(result2, Series([2], index=expected_idx2))
+
+    def test_truncate_one_element_series(self):
+        # GH 35544
+        series = Series([0.1], index=pd.DatetimeIndex(["2020-08-04"]))
+        before = pd.Timestamp("2020-08-02")
+        after = pd.Timestamp("2020-08-04")
+
+        result = series.truncate(before=before, after=after)
+
+        # the input Series and the expected Series are the same
+        tm.assert_series_equal(result, series)
+
+    def test_truncate_index_only_one_unique_value(self):
+        # GH 42365
+        obj = Series(0, index=date_range("2021-06-30", "2021-06-30")).repeat(5)
+
+        truncated = obj.truncate("2021-06-28", "2021-07-01")
+
+        tm.assert_series_equal(truncated, obj)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_tz_localize.py

@@ -0,0 +1,123 @@
+from datetime import timezone
+
+import pytest
+import pytz
+
+from pandas._libs.tslibs import timezones
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestTZLocalize:
+    def test_series_tz_localize_ambiguous_bool(self):
+        # make sure that we are correctly accepting bool values as ambiguous
+
+        # GH#14402
+        ts = Timestamp("2015-11-01 01:00:03")
+        expected0 = Timestamp("2015-11-01 01:00:03-0500", tz="US/Central")
+        expected1 = Timestamp("2015-11-01 01:00:03-0600", tz="US/Central")
+
+        ser = Series([ts])
+        expected0 = Series([expected0])
+        expected1 = Series([expected1])
+
+        with tm.external_error_raised(pytz.AmbiguousTimeError):
+            ser.dt.tz_localize("US/Central")
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=True)
+        tm.assert_series_equal(result, expected0)
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=[True])
+        tm.assert_series_equal(result, expected0)
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=False)
+        tm.assert_series_equal(result, expected1)
+
+        result = ser.dt.tz_localize("US/Central", ambiguous=[False])
+        tm.assert_series_equal(result, expected1)
+
+    def test_series_tz_localize_matching_index(self):
+        # Matching the index of the result with that of the original series
+        # GH 43080
+        dt_series = Series(
+            date_range(start="2021-01-01T02:00:00", periods=5, freq="1D"),
+            index=[2, 6, 7, 8, 11],
+            dtype="category",
+        )
+        result = dt_series.dt.tz_localize("Europe/Berlin")
+        expected = Series(
+            date_range(
+                start="2021-01-01T02:00:00", periods=5, freq="1D", tz="Europe/Berlin"
+            ),
+            index=[2, 6, 7, 8, 11],
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "method, exp",
+        [
+            ["shift_forward", "2015-03-29 03:00:00"],
+            ["shift_backward", "2015-03-29 01:59:59.999999999"],
+            ["NaT", NaT],
+            ["raise", None],
+            ["foo", "invalid"],
+        ],
+    )
+    def test_tz_localize_nonexistent(self, warsaw, method, exp, unit):
+        # GH 8917
+        tz = warsaw
+        n = 60
+        dti = date_range(start="2015-03-29 02:00:00", periods=n, freq="min", unit=unit)
+        ser = Series(1, index=dti)
+        df = ser.to_frame()
+
+        if method == "raise":
+            with tm.external_error_raised(pytz.NonExistentTimeError):
+                dti.tz_localize(tz, nonexistent=method)
+            with tm.external_error_raised(pytz.NonExistentTimeError):
+                ser.tz_localize(tz, nonexistent=method)
+            with tm.external_error_raised(pytz.NonExistentTimeError):
+                df.tz_localize(tz, nonexistent=method)
+
+        elif exp == "invalid":
+            msg = (
+                "The nonexistent argument must be one of "
+                "'raise', 'NaT', 'shift_forward', 'shift_backward' "
+                "or a timedelta object"
+            )
+            with pytest.raises(ValueError, match=msg):
+                dti.tz_localize(tz, nonexistent=method)
+            with pytest.raises(ValueError, match=msg):
+                ser.tz_localize(tz, nonexistent=method)
+            with pytest.raises(ValueError, match=msg):
+                df.tz_localize(tz, nonexistent=method)
+
+        else:
+            result = ser.tz_localize(tz, nonexistent=method)
+            expected = Series(1, index=DatetimeIndex([exp] * n, tz=tz).as_unit(unit))
+            tm.assert_series_equal(result, expected)
+
+            result = df.tz_localize(tz, nonexistent=method)
+            expected = expected.to_frame()
+            tm.assert_frame_equal(result, expected)
+
+            res_index = dti.tz_localize(tz, nonexistent=method)
+            tm.assert_index_equal(res_index, expected.index)
+
+    @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_series_tz_localize_empty(self, tzstr):
+        # GH#2248
+        ser = Series(dtype=object)
+
+        ser2 = ser.tz_localize("utc")
+        assert ser2.index.tz == timezone.utc
+
+        ser2 = ser.tz_localize(tzstr)
+        timezones.tz_compare(ser2.index.tz, timezones.maybe_get_tz(tzstr))

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_unique.py

@@ -0,0 +1,76 @@
+import numpy as np
+
+from pandas import (
+    Categorical,
+    IntervalIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestUnique:
+    def test_unique_uint64(self):
+        ser = Series([1, 2, 2**63, 2**63], dtype=np.uint64)
+        res = ser.unique()
+        exp = np.array([1, 2, 2**63], dtype=np.uint64)
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_unique_data_ownership(self):
+        # it works! GH#1807
+        Series(Series(["a", "c", "b"]).unique()).sort_values()
+
+    def test_unique(self):
+        # GH#714 also, dtype=float
+        ser = Series([1.2345] * 100)
+        ser[::2] = np.nan
+        result = ser.unique()
+        assert len(result) == 2
+
+        # explicit f4 dtype
+        ser = Series([1.2345] * 100, dtype="f4")
+        ser[::2] = np.nan
+        result = ser.unique()
+        assert len(result) == 2
+
+    def test_unique_nan_object_dtype(self):
+        # NAs in object arrays GH#714
+        ser = Series(["foo"] * 100, dtype="O")
+        ser[::2] = np.nan
+        result = ser.unique()
+        assert len(result) == 2
+
+    def test_unique_none(self):
+        # decision about None
+        ser = Series([1, 2, 3, None, None, None], dtype=object)
+        result = ser.unique()
+        expected = np.array([1, 2, 3, None], dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_unique_categorical(self):
+        # GH#18051
+        cat = Categorical([])
+        ser = Series(cat)
+        result = ser.unique()
+        tm.assert_categorical_equal(result, cat)
+
+        cat = Categorical([np.nan])
+        ser = Series(cat)
+        result = ser.unique()
+        tm.assert_categorical_equal(result, cat)
+
+    def test_tz_unique(self):
+        # GH 46128
+        dti1 = date_range("2016-01-01", periods=3)
+        ii1 = IntervalIndex.from_breaks(dti1)
+        ser1 = Series(ii1)
+        uni1 = ser1.unique()
+        tm.assert_interval_array_equal(ser1.array, uni1)
+
+        dti2 = date_range("2016-01-01", periods=3, tz="US/Eastern")
+        ii2 = IntervalIndex.from_breaks(dti2)
+        ser2 = Series(ii2)
+        uni2 = ser2.unique()
+        tm.assert_interval_array_equal(ser2.array, uni2)
+
+        assert uni1.dtype != uni2.dtype

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_unstack.py

@@ -0,0 +1,169 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+def test_unstack_preserves_object():
+    mi = MultiIndex.from_product([["bar", "foo"], ["one", "two"]])
+
+    ser = Series(np.arange(4.0), index=mi, dtype=object)
+
+    res1 = ser.unstack()
+    assert (res1.dtypes == object).all()
+
+    res2 = ser.unstack(level=0)
+    assert (res2.dtypes == object).all()
+
+
+def test_unstack():
+    index = MultiIndex(
+        levels=[["bar", "foo"], ["one", "three", "two"]],
+        codes=[[1, 1, 0, 0], [0, 1, 0, 2]],
+    )
+
+    s = Series(np.arange(4.0), index=index)
+    unstacked = s.unstack()
+
+    expected = DataFrame(
+        [[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]],
+        index=["bar", "foo"],
+        columns=["one", "three", "two"],
+    )
+
+    tm.assert_frame_equal(unstacked, expected)
+
+    unstacked = s.unstack(level=0)
+    tm.assert_frame_equal(unstacked, expected.T)
+
+    index = MultiIndex(
+        levels=[["bar"], ["one", "two", "three"], [0, 1]],
+        codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+    )
+    s = Series(np.random.default_rng(2).standard_normal(6), index=index)
+    exp_index = MultiIndex(
+        levels=[["one", "two", "three"], [0, 1]],
+        codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+    )
+    expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0)
+    unstacked = s.unstack(0).sort_index()
+    tm.assert_frame_equal(unstacked, expected)
+
+    # GH5873
+    idx = MultiIndex.from_arrays([[101, 102], [3.5, np.nan]])
+    ts = Series([1, 2], index=idx)
+    left = ts.unstack()
+    right = DataFrame(
+        [[np.nan, 1], [2, np.nan]], index=[101, 102], columns=[np.nan, 3.5]
+    )
+    tm.assert_frame_equal(left, right)
+
+    idx = MultiIndex.from_arrays(
+        [
+            ["cat", "cat", "cat", "dog", "dog"],
+            ["a", "a", "b", "a", "b"],
+            [1, 2, 1, 1, np.nan],
+        ]
+    )
+    ts = Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)
+    right = DataFrame(
+        [[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]],
+        columns=["cat", "dog"],
+    )
+    tpls = [("a", 1), ("a", 2), ("b", np.nan), ("b", 1)]
+    right.index = MultiIndex.from_tuples(tpls)
+    tm.assert_frame_equal(ts.unstack(level=0), right)
+
+
+def test_unstack_tuplename_in_multiindex():
+    # GH 19966
+    idx = MultiIndex.from_product(
+        [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")]
+    )
+    ser = Series(1, index=idx)
+    result = ser.unstack(("A", "a"))
+
+    expected = DataFrame(
+        [[1, 1, 1], [1, 1, 1], [1, 1, 1]],
+        columns=MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]),
+        index=Index([1, 2, 3], name=("B", "b")),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "unstack_idx, expected_values, expected_index, expected_columns",
+    [
+        (
+            ("A", "a"),
+            [[1, 1], [1, 1], [1, 1], [1, 1]],
+            MultiIndex.from_tuples([(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]),
+            MultiIndex.from_tuples([("a",), ("b",)], names=[("A", "a")]),
+        ),
+        (
+            (("A", "a"), "B"),
+            [[1, 1, 1, 1], [1, 1, 1, 1]],
+            Index([3, 4], name="C"),
+            MultiIndex.from_tuples(
+                [("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"]
+            ),
+        ),
+    ],
+)
+def test_unstack_mixed_type_name_in_multiindex(
+    unstack_idx, expected_values, expected_index, expected_columns
+):
+    # GH 19966
+    idx = MultiIndex.from_product(
+        [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"]
+    )
+    ser = Series(1, index=idx)
+    result = ser.unstack(unstack_idx)
+
+    expected = DataFrame(
+        expected_values, columns=expected_columns, index=expected_index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_multi_index_categorical_values():
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((10, 4)),
+        columns=Index(list("ABCD")),
+        index=date_range("2000-01-01", periods=10, freq="B"),
+    )
+    mi = df.stack(future_stack=True).index.rename(["major", "minor"])
+    ser = Series(["foo"] * len(mi), index=mi, name="category", dtype="category")
+
+    result = ser.unstack()
+
+    dti = ser.index.levels[0]
+    c = pd.Categorical(["foo"] * len(dti))
+    expected = DataFrame(
+        {"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
+        columns=Index(list("ABCD"), name="minor"),
+        index=dti.rename("major"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_unstack_mixed_level_names():
+    # GH#48763
+    arrays = [["a", "a"], [1, 2], ["red", "blue"]]
+    idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y"))
+    ser = Series([1, 2], index=idx)
+    result = ser.unstack("x")
+    expected = DataFrame(
+        [[1], [2]],
+        columns=Index(["a"], name="x"),
+        index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]),
+    )
+    tm.assert_frame_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_update.py

@@ -0,0 +1,143 @@
+import numpy as np
+import pytest
+
+from pandas.compat import WARNING_CHECK_DISABLED
+import pandas.util._test_decorators as td
+
+from pandas import (
+    CategoricalDtype,
+    DataFrame,
+    NaT,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestUpdate:
+    def test_update(self, using_copy_on_write):
+        s = Series([1.5, np.nan, 3.0, 4.0, np.nan])
+        s2 = Series([np.nan, 3.5, np.nan, 5.0])
+        s.update(s2)
+
+        expected = Series([1.5, 3.5, 3.0, 5.0, np.nan])
+        tm.assert_series_equal(s, expected)
+
+        # GH 3217
+        df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])
+        df["c"] = np.nan
+        # Cast to object to avoid upcast when setting "foo"
+        df["c"] = df["c"].astype(object)
+        df_orig = df.copy()
+
+        if using_copy_on_write:
+            with tm.raises_chained_assignment_error():
+                df["c"].update(Series(["foo"], index=[0]))
+            expected = df_orig
+        else:
+            with tm.assert_produces_warning(
+                FutureWarning if not WARNING_CHECK_DISABLED else None,
+                match="inplace method",
+            ):
+                df["c"].update(Series(["foo"], index=[0]))
+            expected = DataFrame(
+                [[1, np.nan, "foo"], [3, 2.0, np.nan]], columns=["a", "b", "c"]
+            )
+            expected["c"] = expected["c"].astype(object)
+        tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize(
+        "other, dtype, expected, warn",
+        [
+            # other is int
+            ([61, 63], "int32", Series([10, 61, 12], dtype="int32"), None),
+            ([61, 63], "int64", Series([10, 61, 12]), None),
+            ([61, 63], float, Series([10.0, 61.0, 12.0]), None),
+            ([61, 63], object, Series([10, 61, 12], dtype=object), None),
+            # other is float, but can be cast to int
+            ([61.0, 63.0], "int32", Series([10, 61, 12], dtype="int32"), None),
+            ([61.0, 63.0], "int64", Series([10, 61, 12]), None),
+            ([61.0, 63.0], float, Series([10.0, 61.0, 12.0]), None),
+            ([61.0, 63.0], object, Series([10, 61.0, 12], dtype=object), None),
+            # others is float, cannot be cast to int
+            ([61.1, 63.1], "int32", Series([10.0, 61.1, 12.0]), FutureWarning),
+            ([61.1, 63.1], "int64", Series([10.0, 61.1, 12.0]), FutureWarning),
+            ([61.1, 63.1], float, Series([10.0, 61.1, 12.0]), None),
+            ([61.1, 63.1], object, Series([10, 61.1, 12], dtype=object), None),
+            # other is object, cannot be cast
+            ([(61,), (63,)], "int32", Series([10, (61,), 12]), FutureWarning),
+            ([(61,), (63,)], "int64", Series([10, (61,), 12]), FutureWarning),
+            ([(61,), (63,)], float, Series([10.0, (61,), 12.0]), FutureWarning),
+            ([(61,), (63,)], object, Series([10, (61,), 12]), None),
+        ],
+    )
+    def test_update_dtypes(self, other, dtype, expected, warn):
+        ser = Series([10, 11, 12], dtype=dtype)
+        other = Series(other, index=[1, 3])
+        with tm.assert_produces_warning(warn, match="item of incompatible dtype"):
+            ser.update(other)
+
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize(
+        "series, other, expected",
+        [
+            # update by key
+            (
+                Series({"a": 1, "b": 2, "c": 3, "d": 4}),
+                {"b": 5, "c": np.nan},
+                Series({"a": 1, "b": 5, "c": 3, "d": 4}),
+            ),
+            # update by position
+            (Series([1, 2, 3, 4]), [np.nan, 5, 1], Series([1, 5, 1, 4])),
+        ],
+    )
+    def test_update_from_non_series(self, series, other, expected):
+        # GH 33215
+        series.update(other)
+        tm.assert_series_equal(series, expected)
+
+    @pytest.mark.parametrize(
+        "data, other, expected, dtype",
+        [
+            (["a", None], [None, "b"], ["a", "b"], "string[python]"),
+            pytest.param(
+                ["a", None],
+                [None, "b"],
+                ["a", "b"],
+                "string[pyarrow]",
+                marks=td.skip_if_no("pyarrow"),
+            ),
+            ([1, None], [None, 2], [1, 2], "Int64"),
+            ([True, None], [None, False], [True, False], "boolean"),
+            (
+                ["a", None],
+                [None, "b"],
+                ["a", "b"],
+                CategoricalDtype(categories=["a", "b"]),
+            ),
+            (
+                [Timestamp(year=2020, month=1, day=1, tz="Europe/London"), NaT],
+                [NaT, Timestamp(year=2020, month=1, day=1, tz="Europe/London")],
+                [Timestamp(year=2020, month=1, day=1, tz="Europe/London")] * 2,
+                "datetime64[ns, Europe/London]",
+            ),
+        ],
+    )
+    def test_update_extension_array_series(self, data, other, expected, dtype):
+        result = Series(data, dtype=dtype)
+        other = Series(other, dtype=dtype)
+        expected = Series(expected, dtype=dtype)
+
+        result.update(other)
+        tm.assert_series_equal(result, expected)
+
+    def test_update_with_categorical_type(self):
+        # GH 25744
+        dtype = CategoricalDtype(["a", "b", "c", "d"])
+        s1 = Series(["a", "b", "c"], index=[1, 2, 3], dtype=dtype)
+        s2 = Series(["b", "a"], index=[1, 2], dtype=dtype)
+        s1.update(s2)
+        result = s1
+        expected = Series(["b", "a", "c"], index=[1, 2, 3], dtype=dtype)
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_value_counts.py

@@ -0,0 +1,271 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestSeriesValueCounts:
+    def test_value_counts_datetime(self, unit):
+        # most dtypes are tested in tests/base
+        values = [
+            pd.Timestamp("2011-01-01 09:00"),
+            pd.Timestamp("2011-01-01 10:00"),
+            pd.Timestamp("2011-01-01 11:00"),
+            pd.Timestamp("2011-01-01 09:00"),
+            pd.Timestamp("2011-01-01 09:00"),
+            pd.Timestamp("2011-01-01 11:00"),
+        ]
+
+        exp_idx = pd.DatetimeIndex(
+            ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],
+            name="xxx",
+        ).as_unit(unit)
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx").dt.as_unit(unit)
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check DatetimeIndex outputs the same result
+        idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit)
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_datetime_tz(self, unit):
+        values = [
+            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 10:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
+            pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
+        ]
+
+        exp_idx = pd.DatetimeIndex(
+            ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],
+            tz="US/Eastern",
+            name="xxx",
+        ).as_unit(unit)
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx").dt.as_unit(unit)
+        tm.assert_series_equal(ser.value_counts(), exp)
+        idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit)
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_period(self):
+        values = [
+            pd.Period("2011-01", freq="M"),
+            pd.Period("2011-02", freq="M"),
+            pd.Period("2011-03", freq="M"),
+            pd.Period("2011-01", freq="M"),
+            pd.Period("2011-01", freq="M"),
+            pd.Period("2011-03", freq="M"),
+        ]
+
+        exp_idx = pd.PeriodIndex(
+            ["2011-01", "2011-03", "2011-02"], freq="M", name="xxx"
+        )
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx")
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check DatetimeIndex outputs the same result
+        idx = pd.PeriodIndex(values, name="xxx")
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_categorical_ordered(self):
+        # most dtypes are tested in tests/base
+        values = Categorical([1, 2, 3, 1, 1, 3], ordered=True)
+
+        exp_idx = CategoricalIndex(
+            [1, 3, 2], categories=[1, 2, 3], ordered=True, name="xxx"
+        )
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx")
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check CategoricalIndex outputs the same result
+        idx = CategoricalIndex(values, name="xxx")
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_categorical_not_ordered(self):
+        values = Categorical([1, 2, 3, 1, 1, 3], ordered=False)
+
+        exp_idx = CategoricalIndex(
+            [1, 3, 2], categories=[1, 2, 3], ordered=False, name="xxx"
+        )
+        exp = Series([3, 2, 1], index=exp_idx, name="count")
+
+        ser = Series(values, name="xxx")
+        tm.assert_series_equal(ser.value_counts(), exp)
+        # check CategoricalIndex outputs the same result
+        idx = CategoricalIndex(values, name="xxx")
+        tm.assert_series_equal(idx.value_counts(), exp)
+
+        # normalize
+        exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion")
+        tm.assert_series_equal(ser.value_counts(normalize=True), exp)
+        tm.assert_series_equal(idx.value_counts(normalize=True), exp)
+
+    def test_value_counts_categorical(self):
+        # GH#12835
+        cats = Categorical(list("abcccb"), categories=list("cabd"))
+        ser = Series(cats, name="xxx")
+        res = ser.value_counts(sort=False)
+
+        exp_index = CategoricalIndex(
+            list("cabd"), categories=cats.categories, name="xxx"
+        )
+        exp = Series([3, 1, 2, 0], name="count", index=exp_index)
+        tm.assert_series_equal(res, exp)
+
+        res = ser.value_counts(sort=True)
+
+        exp_index = CategoricalIndex(
+            list("cbad"), categories=cats.categories, name="xxx"
+        )
+        exp = Series([3, 2, 1, 0], name="count", index=exp_index)
+        tm.assert_series_equal(res, exp)
+
+        # check object dtype handles the Series.name as the same
+        # (tested in tests/base)
+        ser = Series(["a", "b", "c", "c", "c", "b"], name="xxx")
+        res = ser.value_counts()
+        exp = Series([3, 2, 1], name="count", index=Index(["c", "b", "a"], name="xxx"))
+        tm.assert_series_equal(res, exp)
+
+    def test_value_counts_categorical_with_nan(self):
+        # see GH#9443
+
+        # sanity check
+        ser = Series(["a", "b", "a"], dtype="category")
+        exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count")
+
+        res = ser.value_counts(dropna=True)
+        tm.assert_series_equal(res, exp)
+
+        res = ser.value_counts(dropna=True)
+        tm.assert_series_equal(res, exp)
+
+        # same Series via two different constructions --> same behaviour
+        series = [
+            Series(["a", "b", None, "a", None, None], dtype="category"),
+            Series(
+                Categorical(["a", "b", None, "a", None, None], categories=["a", "b"])
+            ),
+        ]
+
+        for ser in series:
+            # None is a NaN value, so we exclude its count here
+            exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count")
+            res = ser.value_counts(dropna=True)
+            tm.assert_series_equal(res, exp)
+
+            # we don't exclude the count of None and sort by counts
+            exp = Series(
+                [3, 2, 1], index=CategoricalIndex([np.nan, "a", "b"]), name="count"
+            )
+            res = ser.value_counts(dropna=False)
+            tm.assert_series_equal(res, exp)
+
+            # When we aren't sorting by counts, and np.nan isn't a
+            # category, it should be last.
+            exp = Series(
+                [2, 1, 3], index=CategoricalIndex(["a", "b", np.nan]), name="count"
+            )
+            res = ser.value_counts(dropna=False, sort=False)
+            tm.assert_series_equal(res, exp)
+
+    @pytest.mark.parametrize(
+        "ser, dropna, exp",
+        [
+            (
+                Series([False, True, True, pd.NA]),
+                False,
+                Series([2, 1, 1], index=[True, False, pd.NA], name="count"),
+            ),
+            (
+                Series([False, True, True, pd.NA]),
+                True,
+                Series([2, 1], index=Index([True, False], dtype=object), name="count"),
+            ),
+            (
+                Series(range(3), index=[True, False, np.nan]).index,
+                False,
+                Series([1, 1, 1], index=[True, False, np.nan], name="count"),
+            ),
+        ],
+    )
+    def test_value_counts_bool_with_nan(self, ser, dropna, exp):
+        # GH32146
+        out = ser.value_counts(dropna=dropna)
+        tm.assert_series_equal(out, exp)
+
+    @pytest.mark.parametrize(
+        "input_array,expected",
+        [
+            (
+                [1 + 1j, 1 + 1j, 1, 3j, 3j, 3j],
+                Series(
+                    [3, 2, 1],
+                    index=Index([3j, 1 + 1j, 1], dtype=np.complex128),
+                    name="count",
+                ),
+            ),
+            (
+                np.array([1 + 1j, 1 + 1j, 1, 3j, 3j, 3j], dtype=np.complex64),
+                Series(
+                    [3, 2, 1],
+                    index=Index([3j, 1 + 1j, 1], dtype=np.complex64),
+                    name="count",
+                ),
+            ),
+        ],
+    )
+    def test_value_counts_complex_numbers(self, input_array, expected):
+        # GH 17927
+        result = Series(input_array).value_counts()
+        tm.assert_series_equal(result, expected)
+
+    def test_value_counts_masked(self):
+        # GH#54984
+        dtype = "Int64"
+        ser = Series([1, 2, None, 2, None, 3], dtype=dtype)
+        result = ser.value_counts(dropna=False)
+        expected = Series(
+            [2, 2, 1, 1],
+            index=Index([2, None, 1, 3], dtype=dtype),
+            dtype=dtype,
+            name="count",
+        )
+        tm.assert_series_equal(result, expected)
+
+        result = ser.value_counts(dropna=True)
+        expected = Series(
+            [2, 1, 1], index=Index([2, 1, 3], dtype=dtype), dtype=dtype, name="count"
+        )
+        tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_values.py

@@ -0,0 +1,29 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    IntervalIndex,
+    Series,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestValues:
+    @pytest.mark.parametrize(
+        "data",
+        [
+            period_range("2000", periods=4),
+            IntervalIndex.from_breaks([1, 2, 3, 4]),
+        ],
+    )
+    def test_values_object_extension_dtypes(self, data):
+        # https://github.com/pandas-dev/pandas/issues/23995
+        result = Series(data).values
+        expected = np.array(data.astype(object))
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_values(self, datetime_series):
+        tm.assert_almost_equal(
+            datetime_series.values, list(datetime_series), check_dtype=False
+        )

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/series/methods/test_view.py

@@ -0,0 +1,61 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    Series,
+    array,
+    date_range,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Series.view is deprecated and will be removed in a future version.:FutureWarning"  # noqa: E501
+)
+
+
+class TestView:
+    def test_view_i8_to_datetimelike(self):
+        dti = date_range("2000", periods=4, tz="US/Central")
+        ser = Series(dti.asi8)
+
+        result = ser.view(dti.dtype)
+        tm.assert_datetime_array_equal(result._values, dti._data._with_freq(None))
+
+        pi = dti.tz_localize(None).to_period("D")
+        ser = Series(pi.asi8)
+        result = ser.view(pi.dtype)
+        tm.assert_period_array_equal(result._values, pi._data)
+
+    def test_view_tz(self):
+        # GH#24024
+        ser = Series(date_range("2000", periods=4, tz="US/Central"))
+        result = ser.view("i8")
+        expected = Series(
+            [
+                946706400000000000,
+                946792800000000000,
+                946879200000000000,
+                946965600000000000,
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "first", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]
+    )
+    @pytest.mark.parametrize(
+        "second", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]
+    )
+    @pytest.mark.parametrize("box", [Series, Index, array])
+    def test_view_between_datetimelike(self, first, second, box):
+        dti = date_range("2016-01-01", periods=3)
+
+        orig = box(dti)
+        obj = orig.view(first)
+        assert obj.dtype == first
+        tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)
+
+        res = obj.view(second)
+        assert res.dtype == second
+        tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/strings/__init__.py

@@ -0,0 +1,23 @@
+import numpy as np
+
+import pandas as pd
+
+
+def is_object_or_nan_string_dtype(dtype):
+    """
+    Check if string-like dtype is following NaN semantics, i.e. is object
+    dtype or a NaN-variant of the StringDtype.
+    """
+    return (isinstance(dtype, np.dtype) and dtype == "object") or (
+        dtype.na_value is np.nan
+    )
+
+
+def _convert_na_value(ser, expected):
+    if ser.dtype != object:
+        if ser.dtype.na_value is np.nan:
+            expected = expected.fillna(np.nan)
+        else:
+            # GH#18463
+            expected = expected.fillna(pd.NA)
+    return expected

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/strings/conftest.py

@@ -0,0 +1,132 @@
+import pytest
+
+from pandas import Series
+from pandas.core.strings.accessor import StringMethods
+
+_any_string_method = [
+    ("cat", (), {"sep": ","}),
+    ("cat", (Series(list("zyx")),), {"sep": ",", "join": "left"}),
+    ("center", (10,), {}),
+    ("contains", ("a",), {}),
+    ("count", ("a",), {}),
+    ("decode", ("UTF-8",), {}),
+    ("encode", ("UTF-8",), {}),
+    ("endswith", ("a",), {}),
+    ("endswith", ((),), {}),
+    ("endswith", (("a",),), {}),
+    ("endswith", (("a", "b"),), {}),
+    ("endswith", (("a", "MISSING"),), {}),
+    ("endswith", ("a",), {"na": True}),
+    ("endswith", ("a",), {"na": False}),
+    ("extract", ("([a-z]*)",), {"expand": False}),
+    ("extract", ("([a-z]*)",), {"expand": True}),
+    ("extractall", ("([a-z]*)",), {}),
+    ("find", ("a",), {}),
+    ("findall", ("a",), {}),
+    ("get", (0,), {}),
+    # because "index" (and "rindex") fail intentionally
+    # if the string is not found, search only for empty string
+    ("index", ("",), {}),
+    ("join", (",",), {}),
+    ("ljust", (10,), {}),
+    ("match", ("a",), {}),
+    ("fullmatch", ("a",), {}),
+    ("normalize", ("NFC",), {}),
+    ("pad", (10,), {}),
+    ("partition", (" ",), {"expand": False}),
+    ("partition", (" ",), {"expand": True}),
+    ("repeat", (3,), {}),
+    ("replace", ("a", "z"), {}),
+    ("rfind", ("a",), {}),
+    ("rindex", ("",), {}),
+    ("rjust", (10,), {}),
+    ("rpartition", (" ",), {"expand": False}),
+    ("rpartition", (" ",), {"expand": True}),
+    ("slice", (0, 1), {}),
+    ("slice_replace", (0, 1, "z"), {}),
+    ("split", (" ",), {"expand": False}),
+    ("split", (" ",), {"expand": True}),
+    ("startswith", ("a",), {}),
+    ("startswith", (("a",),), {}),
+    ("startswith", (("a", "b"),), {}),
+    ("startswith", (("a", "MISSING"),), {}),
+    ("startswith", ((),), {}),
+    ("startswith", ("a",), {"na": True}),
+    ("startswith", ("a",), {"na": False}),
+    ("removeprefix", ("a",), {}),
+    ("removesuffix", ("a",), {}),
+    # translating unicode points of "a" to "d"
+    ("translate", ({97: 100},), {}),
+    ("wrap", (2,), {}),
+    ("zfill", (10,), {}),
+] + list(
+    zip(
+        [
+            # methods without positional arguments: zip with empty tuple and empty dict
+            "capitalize",
+            "cat",
+            "get_dummies",
+            "isalnum",
+            "isalpha",
+            "isdecimal",
+            "isdigit",
+            "islower",
+            "isnumeric",
+            "isspace",
+            "istitle",
+            "isupper",
+            "len",
+            "lower",
+            "lstrip",
+            "partition",
+            "rpartition",
+            "rsplit",
+            "rstrip",
+            "slice",
+            "slice_replace",
+            "split",
+            "strip",
+            "swapcase",
+            "title",
+            "upper",
+            "casefold",
+        ],
+        [()] * 100,
+        [{}] * 100,
+    )
+)
+ids, _, _ = zip(*_any_string_method)  # use method name as fixture-id
+missing_methods = {f for f in dir(StringMethods) if not f.startswith("_")} - set(ids)
+
+# test that the above list captures all methods of StringMethods
+assert not missing_methods
+
+
+@pytest.fixture(params=_any_string_method, ids=ids)
+def any_string_method(request):
+    """
+    Fixture for all public methods of `StringMethods`
+
+    This fixture returns a tuple of the method name and sample arguments
+    necessary to call the method.
+
+    Returns
+    -------
+    method_name : str
+        The name of the method in `StringMethods`
+    args : tuple
+        Sample values for the positional arguments
+    kwargs : dict
+        Sample values for the keyword arguments
+
+    Examples
+    --------
+    >>> def test_something(any_string_method):
+    ...     s = Series(['a', 'b', np.nan, 'd'])
+    ...
+    ...     method_name, args, kwargs = any_string_method
+    ...     method = getattr(s.str, method_name)
+    ...     # will not raise
+    ...     method(*args, **kwargs)
+    """
+    return request.param

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/strings/test_api.py

@@ -0,0 +1,205 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalDtype,
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    _testing as tm,
+    option_context,
+)
+from pandas.core.strings.accessor import StringMethods
+
+# subset of the full set from pandas/conftest.py
+_any_allowed_skipna_inferred_dtype = [
+    ("string", ["a", np.nan, "c"]),
+    ("bytes", [b"a", np.nan, b"c"]),
+    ("empty", [np.nan, np.nan, np.nan]),
+    ("empty", []),
+    ("mixed-integer", ["a", np.nan, 2]),
+]
+ids, _ = zip(*_any_allowed_skipna_inferred_dtype)  # use inferred type as id
+
+
+@pytest.fixture(params=_any_allowed_skipna_inferred_dtype, ids=ids)
+def any_allowed_skipna_inferred_dtype(request):
+    """
+    Fixture for all (inferred) dtypes allowed in StringMethods.__init__
+
+    The covered (inferred) types are:
+    * 'string'
+    * 'empty'
+    * 'bytes'
+    * 'mixed'
+    * 'mixed-integer'
+
+    Returns
+    -------
+    inferred_dtype : str
+        The string for the inferred dtype from _libs.lib.infer_dtype
+    values : np.ndarray
+        An array of object dtype that will be inferred to have
+        `inferred_dtype`
+
+    Examples
+    --------
+    >>> from pandas._libs import lib
+    >>>
+    >>> def test_something(any_allowed_skipna_inferred_dtype):
+    ...     inferred_dtype, values = any_allowed_skipna_inferred_dtype
+    ...     # will pass
+    ...     assert lib.infer_dtype(values, skipna=True) == inferred_dtype
+    ...
+    ...     # constructor for .str-accessor will also pass
+    ...     Series(values).str
+    """
+    inferred_dtype, values = request.param
+    values = np.array(values, dtype=object)  # object dtype to avoid casting
+
+    # correctness of inference tested in tests/dtypes/test_inference.py
+    return inferred_dtype, values
+
+
+def test_api(any_string_dtype):
+    # GH 6106, GH 9322
+    assert Series.str is StringMethods
+    assert isinstance(Series([""], dtype=any_string_dtype).str, StringMethods)
+
+
+def test_api_mi_raises():
+    # GH 23679
+    mi = MultiIndex.from_arrays([["a", "b", "c"]])
+    msg = "Can only use .str accessor with Index, not MultiIndex"
+    with pytest.raises(AttributeError, match=msg):
+        mi.str
+    assert not hasattr(mi, "str")
+
+
+@pytest.mark.parametrize("dtype", [object, "category"])
+def test_api_per_dtype(index_or_series, dtype, any_skipna_inferred_dtype):
+    # one instance of parametrized fixture
+    box = index_or_series
+    inferred_dtype, values = any_skipna_inferred_dtype
+
+    t = box(values, dtype=dtype)  # explicit dtype to avoid casting
+
+    types_passing_constructor = [
+        "string",
+        "unicode",
+        "empty",
+        "bytes",
+        "mixed",
+        "mixed-integer",
+    ]
+    if inferred_dtype in types_passing_constructor:
+        # GH 6106
+        assert isinstance(t.str, StringMethods)
+    else:
+        # GH 9184, GH 23011, GH 23163
+        msg = "Can only use .str accessor with string values.*"
+        with pytest.raises(AttributeError, match=msg):
+            t.str
+        assert not hasattr(t, "str")
+
+
+@pytest.mark.parametrize("dtype", [object, "category"])
+def test_api_per_method(
+    index_or_series,
+    dtype,
+    any_allowed_skipna_inferred_dtype,
+    any_string_method,
+    request,
+    using_infer_string,
+):
+    # this test does not check correctness of the different methods,
+    # just that the methods work on the specified (inferred) dtypes,
+    # and raise on all others
+    box = index_or_series
+
+    # one instance of each parametrized fixture
+    inferred_dtype, values = any_allowed_skipna_inferred_dtype
+    method_name, args, kwargs = any_string_method
+
+    reason = None
+    if box is Index and values.size == 0:
+        if method_name in ["partition", "rpartition"] and kwargs.get("expand", True):
+            raises = TypeError
+            reason = "Method cannot deal with empty Index"
+        elif method_name == "split" and kwargs.get("expand", None):
+            raises = TypeError
+            reason = "Split fails on empty Series when expand=True"
+        elif method_name == "get_dummies":
+            raises = ValueError
+            reason = "Need to fortify get_dummies corner cases"
+
+    elif (
+        box is Index
+        and inferred_dtype == "empty"
+        and dtype == object
+        and method_name == "get_dummies"
+    ):
+        raises = ValueError
+        reason = "Need to fortify get_dummies corner cases"
+
+    if reason is not None:
+        mark = pytest.mark.xfail(raises=raises, reason=reason)
+        request.applymarker(mark)
+
+    t = box(values, dtype=dtype)  # explicit dtype to avoid casting
+    method = getattr(t.str, method_name)
+
+    if using_infer_string and dtype == "category":
+        string_allowed = method_name not in ["decode"]
+    else:
+        string_allowed = True
+    bytes_allowed = method_name in ["decode", "get", "len", "slice"]
+    # as of v0.23.4, all methods except 'cat' are very lenient with the
+    # allowed data types, just returning NaN for entries that error.
+    # This could be changed with an 'errors'-kwarg to the `str`-accessor,
+    # see discussion in GH 13877
+    mixed_allowed = method_name not in ["cat"]
+
+    allowed_types = (
+        ["empty"]
+        + ["string", "unicode"] * string_allowed
+        + ["bytes"] * bytes_allowed
+        + ["mixed", "mixed-integer"] * mixed_allowed
+    )
+
+    if inferred_dtype in allowed_types:
+        # xref GH 23555, GH 23556
+        with option_context("future.no_silent_downcasting", True):
+            method(*args, **kwargs)  # works!
+    else:
+        # GH 23011, GH 23163
+        msg = (
+            f"Cannot use .str.{method_name} with values of "
+            f"inferred dtype {repr(inferred_dtype)}."
+            "|a bytes-like object is required, not 'str'"
+        )
+        with pytest.raises(TypeError, match=msg):
+            method(*args, **kwargs)
+
+
+def test_api_for_categorical(any_string_method, any_string_dtype):
+    # https://github.com/pandas-dev/pandas/issues/10661
+    s = Series(list("aabb"), dtype=any_string_dtype)
+    s = s + " " + s
+    c = s.astype("category")
+    c = c.astype(CategoricalDtype(c.dtype.categories.astype("object")))
+    assert isinstance(c.str, StringMethods)
+
+    method_name, args, kwargs = any_string_method
+
+    result = getattr(c.str, method_name)(*args, **kwargs)
+    expected = getattr(s.astype("object").str, method_name)(*args, **kwargs)
+
+    if isinstance(result, DataFrame):
+        tm.assert_frame_equal(result, expected)
+    elif isinstance(result, Series):
+        tm.assert_series_equal(result, expected)
+    else:
+        # str.cat(others=None) returns string, for example
+        assert result == expected

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/strings/test_case_justify.py

@@ -0,0 +1,423 @@
+from datetime import datetime
+import operator
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    _testing as tm,
+)
+
+
+def test_title(any_string_dtype):
+    s = Series(["FOO", "BAR", np.nan, "Blah", "blurg"], dtype=any_string_dtype)
+    result = s.str.title()
+    expected = Series(["Foo", "Bar", np.nan, "Blah", "Blurg"], dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_title_mixed_object():
+    s = Series(["FOO", np.nan, "bar", True, datetime.today(), "blah", None, 1, 2.0])
+    result = s.str.title()
+    expected = Series(
+        ["Foo", np.nan, "Bar", np.nan, np.nan, "Blah", None, np.nan, np.nan],
+        dtype=object,
+    )
+    tm.assert_almost_equal(result, expected)
+
+
+def test_lower_upper(any_string_dtype):
+    s = Series(["om", np.nan, "nom", "nom"], dtype=any_string_dtype)
+
+    result = s.str.upper()
+    expected = Series(["OM", np.nan, "NOM", "NOM"], dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = result.str.lower()
+    tm.assert_series_equal(result, s)
+
+
+def test_lower_upper_mixed_object():
+    s = Series(["a", np.nan, "b", True, datetime.today(), "foo", None, 1, 2.0])
+
+    result = s.str.upper()
+    expected = Series(
+        ["A", np.nan, "B", np.nan, np.nan, "FOO", None, np.nan, np.nan], dtype=object
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.lower()
+    expected = Series(
+        ["a", np.nan, "b", np.nan, np.nan, "foo", None, np.nan, np.nan], dtype=object
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, expected",
+    [
+        (
+            ["FOO", "BAR", np.nan, "Blah", "blurg"],
+            ["Foo", "Bar", np.nan, "Blah", "Blurg"],
+        ),
+        (["a", "b", "c"], ["A", "B", "C"]),
+        (["a b", "a bc. de"], ["A b", "A bc. de"]),
+    ],
+)
+def test_capitalize(data, expected, any_string_dtype):
+    s = Series(data, dtype=any_string_dtype)
+    result = s.str.capitalize()
+    expected = Series(expected, dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_capitalize_mixed_object():
+    s = Series(["FOO", np.nan, "bar", True, datetime.today(), "blah", None, 1, 2.0])
+    result = s.str.capitalize()
+    expected = Series(
+        ["Foo", np.nan, "Bar", np.nan, np.nan, "Blah", None, np.nan, np.nan],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_swapcase(any_string_dtype):
+    s = Series(["FOO", "BAR", np.nan, "Blah", "blurg"], dtype=any_string_dtype)
+    result = s.str.swapcase()
+    expected = Series(["foo", "bar", np.nan, "bLAH", "BLURG"], dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_swapcase_mixed_object():
+    s = Series(["FOO", np.nan, "bar", True, datetime.today(), "Blah", None, 1, 2.0])
+    result = s.str.swapcase()
+    expected = Series(
+        ["foo", np.nan, "BAR", np.nan, np.nan, "bLAH", None, np.nan, np.nan],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_casefold():
+    # GH25405
+    expected = Series(["ss", np.nan, "case", "ssd"])
+    s = Series(["ß", np.nan, "case", "ßd"])
+    result = s.str.casefold()
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_casemethods(any_string_dtype):
+    values = ["aaa", "bbb", "CCC", "Dddd", "eEEE"]
+    s = Series(values, dtype=any_string_dtype)
+    assert s.str.lower().tolist() == [v.lower() for v in values]
+    assert s.str.upper().tolist() == [v.upper() for v in values]
+    assert s.str.title().tolist() == [v.title() for v in values]
+    assert s.str.capitalize().tolist() == [v.capitalize() for v in values]
+    assert s.str.swapcase().tolist() == [v.swapcase() for v in values]
+
+
+def test_pad(any_string_dtype):
+    s = Series(["a", "b", np.nan, "c", np.nan, "eeeeee"], dtype=any_string_dtype)
+
+    result = s.str.pad(5, side="left")
+    expected = Series(
+        ["    a", "    b", np.nan, "    c", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.pad(5, side="right")
+    expected = Series(
+        ["a    ", "b    ", np.nan, "c    ", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.pad(5, side="both")
+    expected = Series(
+        ["  a  ", "  b  ", np.nan, "  c  ", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_pad_mixed_object():
+    s = Series(["a", np.nan, "b", True, datetime.today(), "ee", None, 1, 2.0])
+
+    result = s.str.pad(5, side="left")
+    expected = Series(
+        ["    a", np.nan, "    b", np.nan, np.nan, "   ee", None, np.nan, np.nan],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.pad(5, side="right")
+    expected = Series(
+        ["a    ", np.nan, "b    ", np.nan, np.nan, "ee   ", None, np.nan, np.nan],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.pad(5, side="both")
+    expected = Series(
+        ["  a  ", np.nan, "  b  ", np.nan, np.nan, "  ee ", None, np.nan, np.nan],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_pad_fillchar(any_string_dtype):
+    s = Series(["a", "b", np.nan, "c", np.nan, "eeeeee"], dtype=any_string_dtype)
+
+    result = s.str.pad(5, side="left", fillchar="X")
+    expected = Series(
+        ["XXXXa", "XXXXb", np.nan, "XXXXc", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.pad(5, side="right", fillchar="X")
+    expected = Series(
+        ["aXXXX", "bXXXX", np.nan, "cXXXX", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.pad(5, side="both", fillchar="X")
+    expected = Series(
+        ["XXaXX", "XXbXX", np.nan, "XXcXX", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_pad_fillchar_bad_arg_raises(any_string_dtype):
+    s = Series(["a", "b", np.nan, "c", np.nan, "eeeeee"], dtype=any_string_dtype)
+
+    msg = "fillchar must be a character, not str"
+    with pytest.raises(TypeError, match=msg):
+        s.str.pad(5, fillchar="XY")
+
+    msg = "fillchar must be a character, not int"
+    with pytest.raises(TypeError, match=msg):
+        s.str.pad(5, fillchar=5)
+
+
+@pytest.mark.parametrize("method_name", ["center", "ljust", "rjust", "zfill", "pad"])
+def test_pad_width_bad_arg_raises(method_name, any_string_dtype):
+    # see gh-13598
+    s = Series(["1", "22", "a", "bb"], dtype=any_string_dtype)
+    op = operator.methodcaller(method_name, "f")
+
+    msg = "width must be of integer type, not str"
+    with pytest.raises(TypeError, match=msg):
+        op(s.str)
+
+
+def test_center_ljust_rjust(any_string_dtype):
+    s = Series(["a", "b", np.nan, "c", np.nan, "eeeeee"], dtype=any_string_dtype)
+
+    result = s.str.center(5)
+    expected = Series(
+        ["  a  ", "  b  ", np.nan, "  c  ", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.ljust(5)
+    expected = Series(
+        ["a    ", "b    ", np.nan, "c    ", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.rjust(5)
+    expected = Series(
+        ["    a", "    b", np.nan, "    c", np.nan, "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_center_ljust_rjust_mixed_object():
+    s = Series(["a", np.nan, "b", True, datetime.today(), "c", "eee", None, 1, 2.0])
+
+    result = s.str.center(5)
+    expected = Series(
+        [
+            "  a  ",
+            np.nan,
+            "  b  ",
+            np.nan,
+            np.nan,
+            "  c  ",
+            " eee ",
+            None,
+            np.nan,
+            np.nan,
+        ],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.ljust(5)
+    expected = Series(
+        [
+            "a    ",
+            np.nan,
+            "b    ",
+            np.nan,
+            np.nan,
+            "c    ",
+            "eee  ",
+            None,
+            np.nan,
+            np.nan,
+        ],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = s.str.rjust(5)
+    expected = Series(
+        [
+            "    a",
+            np.nan,
+            "    b",
+            np.nan,
+            np.nan,
+            "    c",
+            "  eee",
+            None,
+            np.nan,
+            np.nan,
+        ],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_center_ljust_rjust_fillchar(any_string_dtype):
+    # GH#54533, GH#54792
+    s = Series(["a", "bb", "cccc", "ddddd", "eeeeee"], dtype=any_string_dtype)
+
+    result = s.str.center(5, fillchar="X")
+    expected = Series(
+        ["XXaXX", "XXbbX", "Xcccc", "ddddd", "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+    expected = np.array([v.center(5, "X") for v in np.array(s)], dtype=np.object_)
+    tm.assert_numpy_array_equal(np.array(result, dtype=np.object_), expected)
+
+    result = s.str.ljust(5, fillchar="X")
+    expected = Series(
+        ["aXXXX", "bbXXX", "ccccX", "ddddd", "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+    expected = np.array([v.ljust(5, "X") for v in np.array(s)], dtype=np.object_)
+    tm.assert_numpy_array_equal(np.array(result, dtype=np.object_), expected)
+
+    result = s.str.rjust(5, fillchar="X")
+    expected = Series(
+        ["XXXXa", "XXXbb", "Xcccc", "ddddd", "eeeeee"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+    expected = np.array([v.rjust(5, "X") for v in np.array(s)], dtype=np.object_)
+    tm.assert_numpy_array_equal(np.array(result, dtype=np.object_), expected)
+
+
+def test_center_ljust_rjust_fillchar_bad_arg_raises(any_string_dtype):
+    s = Series(["a", "bb", "cccc", "ddddd", "eeeeee"], dtype=any_string_dtype)
+
+    # If fillchar is not a character, normal str raises TypeError
+    # 'aaa'.ljust(5, 'XY')
+    # TypeError: must be char, not str
+    template = "fillchar must be a character, not {dtype}"
+
+    with pytest.raises(TypeError, match=template.format(dtype="str")):
+        s.str.center(5, fillchar="XY")
+
+    with pytest.raises(TypeError, match=template.format(dtype="str")):
+        s.str.ljust(5, fillchar="XY")
+
+    with pytest.raises(TypeError, match=template.format(dtype="str")):
+        s.str.rjust(5, fillchar="XY")
+
+    with pytest.raises(TypeError, match=template.format(dtype="int")):
+        s.str.center(5, fillchar=1)
+
+    with pytest.raises(TypeError, match=template.format(dtype="int")):
+        s.str.ljust(5, fillchar=1)
+
+    with pytest.raises(TypeError, match=template.format(dtype="int")):
+        s.str.rjust(5, fillchar=1)
+
+
+def test_zfill(any_string_dtype):
+    s = Series(["1", "22", "aaa", "333", "45678"], dtype=any_string_dtype)
+
+    result = s.str.zfill(5)
+    expected = Series(
+        ["00001", "00022", "00aaa", "00333", "45678"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+    expected = np.array([v.zfill(5) for v in np.array(s)], dtype=np.object_)
+    tm.assert_numpy_array_equal(np.array(result, dtype=np.object_), expected)
+
+    result = s.str.zfill(3)
+    expected = Series(["001", "022", "aaa", "333", "45678"], dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+    expected = np.array([v.zfill(3) for v in np.array(s)], dtype=np.object_)
+    tm.assert_numpy_array_equal(np.array(result, dtype=np.object_), expected)
+
+    s = Series(["1", np.nan, "aaa", np.nan, "45678"], dtype=any_string_dtype)
+    result = s.str.zfill(5)
+    expected = Series(
+        ["00001", np.nan, "00aaa", np.nan, "45678"], dtype=any_string_dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_wrap(any_string_dtype):
+    # test values are: two words less than width, two words equal to width,
+    # two words greater than width, one word less than width, one word
+    # equal to width, one word greater than width, multiple tokens with
+    # trailing whitespace equal to width
+    s = Series(
+        [
+            "hello world",
+            "hello world!",
+            "hello world!!",
+            "abcdefabcde",
+            "abcdefabcdef",
+            "abcdefabcdefa",
+            "ab ab ab ab ",
+            "ab ab ab ab a",
+            "\t",
+        ],
+        dtype=any_string_dtype,
+    )
+
+    # expected values
+    expected = Series(
+        [
+            "hello world",
+            "hello world!",
+            "hello\nworld!!",
+            "abcdefabcde",
+            "abcdefabcdef",
+            "abcdefabcdef\na",
+            "ab ab ab ab",
+            "ab ab ab ab\na",
+            "",
+        ],
+        dtype=any_string_dtype,
+    )
+
+    result = s.str.wrap(12, break_long_words=True)
+    tm.assert_series_equal(result, expected)
+
+
+def test_wrap_unicode(any_string_dtype):
+    # test with pre and post whitespace (non-unicode), NaN, and non-ascii Unicode
+    s = Series(
+        ["  pre  ", np.nan, "\xac\u20ac\U00008000 abadcafe"], dtype=any_string_dtype
+    )
+    expected = Series(
+        ["  pre", np.nan, "\xac\u20ac\U00008000 ab\nadcafe"], dtype=any_string_dtype
+    )
+    result = s.str.wrap(6)
+    tm.assert_series_equal(result, expected)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/strings/test_cat.py

@@ -0,0 +1,427 @@
+import re
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    _testing as tm,
+    concat,
+    option_context,
+)
+
+
+@pytest.mark.parametrize("other", [None, Series, Index])
+def test_str_cat_name(index_or_series, other):
+    # GH 21053
+    box = index_or_series
+    values = ["a", "b"]
+    if other:
+        other = other(values)
+    else:
+        other = values
+    result = box(values, name="name").str.cat(other, sep=",")
+    assert result.name == "name"
+
+
+@pytest.mark.parametrize(
+    "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+)
+def test_str_cat(index_or_series, infer_string):
+    with option_context("future.infer_string", infer_string):
+        box = index_or_series
+        # test_cat above tests "str_cat" from ndarray;
+        # here testing "str.cat" from Series/Index to ndarray/list
+        s = box(["a", "a", "b", "b", "c", np.nan])
+
+        # single array
+        result = s.str.cat()
+        expected = "aabbc"
+        assert result == expected
+
+        result = s.str.cat(na_rep="-")
+        expected = "aabbc-"
+        assert result == expected
+
+        result = s.str.cat(sep="_", na_rep="NA")
+        expected = "a_a_b_b_c_NA"
+        assert result == expected
+
+        t = np.array(["a", np.nan, "b", "d", "foo", np.nan], dtype=object)
+        expected = box(["aa", "a-", "bb", "bd", "cfoo", "--"])
+
+        # Series/Index with array
+        result = s.str.cat(t, na_rep="-")
+        tm.assert_equal(result, expected)
+
+        # Series/Index with list
+        result = s.str.cat(list(t), na_rep="-")
+        tm.assert_equal(result, expected)
+
+        # errors for incorrect lengths
+        rgx = r"If `others` contains arrays or lists \(or other list-likes.*"
+        z = Series(["1", "2", "3"])
+
+        with pytest.raises(ValueError, match=rgx):
+            s.str.cat(z.values)
+
+        with pytest.raises(ValueError, match=rgx):
+            s.str.cat(list(z))
+
+
+def test_str_cat_raises_intuitive_error(index_or_series):
+    # GH 11334
+    box = index_or_series
+    s = box(["a", "b", "c", "d"])
+    message = "Did you mean to supply a `sep` keyword?"
+    with pytest.raises(ValueError, match=message):
+        s.str.cat("|")
+    with pytest.raises(ValueError, match=message):
+        s.str.cat("    ")
+
+
+@pytest.mark.parametrize(
+    "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+)
+@pytest.mark.parametrize("sep", ["", None])
+@pytest.mark.parametrize("dtype_target", ["object", "category"])
+@pytest.mark.parametrize("dtype_caller", ["object", "category"])
+def test_str_cat_categorical(
+    index_or_series, dtype_caller, dtype_target, sep, infer_string
+):
+    box = index_or_series
+
+    with option_context("future.infer_string", infer_string):
+        s = Index(["a", "a", "b", "a"], dtype=dtype_caller)
+        s = s if box == Index else Series(s, index=s, dtype=s.dtype)
+        t = Index(["b", "a", "b", "c"], dtype=dtype_target)
+
+        expected = Index(
+            ["ab", "aa", "bb", "ac"], dtype=object if dtype_caller == "object" else None
+        )
+        expected = (
+            expected
+            if box == Index
+            else Series(
+                expected, index=Index(s, dtype=dtype_caller), dtype=expected.dtype
+            )
+        )
+
+        # Series/Index with unaligned Index -> t.values
+        result = s.str.cat(t.values, sep=sep)
+        tm.assert_equal(result, expected)
+
+        # Series/Index with Series having matching Index
+        t = Series(t.values, index=Index(s, dtype=dtype_caller))
+        result = s.str.cat(t, sep=sep)
+        tm.assert_equal(result, expected)
+
+        # Series/Index with Series.values
+        result = s.str.cat(t.values, sep=sep)
+        tm.assert_equal(result, expected)
+
+        # Series/Index with Series having different Index
+        t = Series(t.values, index=t.values)
+        expected = Index(
+            ["aa", "aa", "bb", "bb", "aa"],
+            dtype=object if dtype_caller == "object" else None,
+        )
+        dtype = object if dtype_caller == "object" else s.dtype.categories.dtype
+        expected = (
+            expected
+            if box == Index
+            else Series(
+                expected,
+                index=Index(expected.str[:1], dtype=dtype),
+                dtype=expected.dtype,
+            )
+        )
+
+        result = s.str.cat(t, sep=sep)
+        tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [[1, 2, 3], [0.1, 0.2, 0.3], [1, 2, "b"]],
+    ids=["integers", "floats", "mixed"],
+)
+# without dtype=object, np.array would cast [1, 2, 'b'] to ['1', '2', 'b']
+@pytest.mark.parametrize(
+    "box",
+    [Series, Index, list, lambda x: np.array(x, dtype=object)],
+    ids=["Series", "Index", "list", "np.array"],
+)
+def test_str_cat_wrong_dtype_raises(box, data):
+    # GH 22722
+    s = Series(["a", "b", "c"])
+    t = box(data)
+
+    msg = "Concatenation requires list-likes containing only strings.*"
+    with pytest.raises(TypeError, match=msg):
+        # need to use outer and na_rep, as otherwise Index would not raise
+        s.str.cat(t, join="outer", na_rep="-")
+
+
+def test_str_cat_mixed_inputs(index_or_series):
+    box = index_or_series
+    s = Index(["a", "b", "c", "d"])
+    s = s if box == Index else Series(s, index=s)
+
+    t = Series(["A", "B", "C", "D"], index=s.values)
+    d = concat([t, Series(s, index=s)], axis=1)
+
+    expected = Index(["aAa", "bBb", "cCc", "dDd"])
+    expected = expected if box == Index else Series(expected.values, index=s.values)
+
+    # Series/Index with DataFrame
+    result = s.str.cat(d)
+    tm.assert_equal(result, expected)
+
+    # Series/Index with two-dimensional ndarray
+    result = s.str.cat(d.values)
+    tm.assert_equal(result, expected)
+
+    # Series/Index with list of Series
+    result = s.str.cat([t, s])
+    tm.assert_equal(result, expected)
+
+    # Series/Index with mixed list of Series/array
+    result = s.str.cat([t, s.values])
+    tm.assert_equal(result, expected)
+
+    # Series/Index with list of Series; different indexes
+    t.index = ["b", "c", "d", "a"]
+    expected = box(["aDa", "bAb", "cBc", "dCd"])
+    expected = expected if box == Index else Series(expected.values, index=s.values)
+    result = s.str.cat([t, s])
+    tm.assert_equal(result, expected)
+
+    # Series/Index with mixed list; different index
+    result = s.str.cat([t, s.values])
+    tm.assert_equal(result, expected)
+
+    # Series/Index with DataFrame; different indexes
+    d.index = ["b", "c", "d", "a"]
+    expected = box(["aDd", "bAa", "cBb", "dCc"])
+    expected = expected if box == Index else Series(expected.values, index=s.values)
+    result = s.str.cat(d)
+    tm.assert_equal(result, expected)
+
+    # errors for incorrect lengths
+    rgx = r"If `others` contains arrays or lists \(or other list-likes.*"
+    z = Series(["1", "2", "3"])
+    e = concat([z, z], axis=1)
+
+    # two-dimensional ndarray
+    with pytest.raises(ValueError, match=rgx):
+        s.str.cat(e.values)
+
+    # list of list-likes
+    with pytest.raises(ValueError, match=rgx):
+        s.str.cat([z.values, s.values])
+
+    # mixed list of Series/list-like
+    with pytest.raises(ValueError, match=rgx):
+        s.str.cat([z.values, s])
+
+    # errors for incorrect arguments in list-like
+    rgx = "others must be Series, Index, DataFrame,.*"
+    # make sure None/NaN do not crash checks in _get_series_list
+    u = Series(["a", np.nan, "c", None])
+
+    # mix of string and Series
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat([u, "u"])
+
+    # DataFrame in list
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat([u, d])
+
+    # 2-dim ndarray in list
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat([u, d.values])
+
+    # nested lists
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat([u, [u, d]])
+
+    # forbidden input type: set
+    # GH 23009
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat(set(u))
+
+    # forbidden input type: set in list
+    # GH 23009
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat([u, set(u)])
+
+    # other forbidden input type, e.g. int
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat(1)
+
+    # nested list-likes
+    with pytest.raises(TypeError, match=rgx):
+        s.str.cat(iter([t.values, list(s)]))
+
+
+@pytest.mark.parametrize("join", ["left", "outer", "inner", "right"])
+def test_str_cat_align_indexed(index_or_series, join):
+    # https://github.com/pandas-dev/pandas/issues/18657
+    box = index_or_series
+
+    s = Series(["a", "b", "c", "d"], index=["a", "b", "c", "d"])
+    t = Series(["D", "A", "E", "B"], index=["d", "a", "e", "b"])
+    sa, ta = s.align(t, join=join)
+    # result after manual alignment of inputs
+    expected = sa.str.cat(ta, na_rep="-")
+
+    if box == Index:
+        s = Index(s)
+        sa = Index(sa)
+        expected = Index(expected)
+
+    result = s.str.cat(t, join=join, na_rep="-")
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("join", ["left", "outer", "inner", "right"])
+def test_str_cat_align_mixed_inputs(join):
+    s = Series(["a", "b", "c", "d"])
+    t = Series(["d", "a", "e", "b"], index=[3, 0, 4, 1])
+    d = concat([t, t], axis=1)
+
+    expected_outer = Series(["aaa", "bbb", "c--", "ddd", "-ee"])
+    expected = expected_outer.loc[s.index.join(t.index, how=join)]
+
+    # list of Series
+    result = s.str.cat([t, t], join=join, na_rep="-")
+    tm.assert_series_equal(result, expected)
+
+    # DataFrame
+    result = s.str.cat(d, join=join, na_rep="-")
+    tm.assert_series_equal(result, expected)
+
+    # mixed list of indexed/unindexed
+    u = np.array(["A", "B", "C", "D"])
+    expected_outer = Series(["aaA", "bbB", "c-C", "ddD", "-e-"])
+    # joint index of rhs [t, u]; u will be forced have index of s
+    rhs_idx = (
+        t.index.intersection(s.index)
+        if join == "inner"
+        else t.index.union(s.index)
+        if join == "outer"
+        else t.index.append(s.index.difference(t.index))
+    )
+
+    expected = expected_outer.loc[s.index.join(rhs_idx, how=join)]
+    result = s.str.cat([t, u], join=join, na_rep="-")
+    tm.assert_series_equal(result, expected)
+
+    with pytest.raises(TypeError, match="others must be Series,.*"):
+        # nested lists are forbidden
+        s.str.cat([t, list(u)], join=join)
+
+    # errors for incorrect lengths
+    rgx = r"If `others` contains arrays or lists \(or other list-likes.*"
+    z = Series(["1", "2", "3"]).values
+
+    # unindexed object of wrong length
+    with pytest.raises(ValueError, match=rgx):
+        s.str.cat(z, join=join)
+
+    # unindexed object of wrong length in list
+    with pytest.raises(ValueError, match=rgx):
+        s.str.cat([t, z], join=join)
+
+
+def test_str_cat_all_na(index_or_series, index_or_series2):
+    # GH 24044
+    box = index_or_series
+    other = index_or_series2
+
+    # check that all NaNs in caller / target work
+    s = Index(["a", "b", "c", "d"])
+    s = s if box == Index else Series(s, index=s)
+    t = other([np.nan] * 4, dtype=object)
+    # add index of s for alignment
+    t = t if other == Index else Series(t, index=s)
+
+    # all-NA target
+    if box == Series:
+        expected = Series([np.nan] * 4, index=s.index, dtype=s.dtype)
+    else:  # box == Index
+        # TODO: Strimg option, this should return string dtype
+        expected = Index([np.nan] * 4, dtype=object)
+    result = s.str.cat(t, join="left")
+    tm.assert_equal(result, expected)
+
+    # all-NA caller (only for Series)
+    if other == Series:
+        expected = Series([np.nan] * 4, dtype=object, index=t.index)
+        result = t.str.cat(s, join="left")
+        tm.assert_series_equal(result, expected)
+
+
+def test_str_cat_special_cases():
+    s = Series(["a", "b", "c", "d"])
+    t = Series(["d", "a", "e", "b"], index=[3, 0, 4, 1])
+
+    # iterator of elements with different types
+    expected = Series(["aaa", "bbb", "c-c", "ddd", "-e-"])
+    result = s.str.cat(iter([t, s.values]), join="outer", na_rep="-")
+    tm.assert_series_equal(result, expected)
+
+    # right-align with different indexes in others
+    expected = Series(["aa-", "d-d"], index=[0, 3])
+    result = s.str.cat([t.loc[[0]], t.loc[[3]]], join="right", na_rep="-")
+    tm.assert_series_equal(result, expected)
+
+
+def test_cat_on_filtered_index():
+    df = DataFrame(
+        index=MultiIndex.from_product(
+            [[2011, 2012], [1, 2, 3]], names=["year", "month"]
+        )
+    )
+
+    df = df.reset_index()
+    df = df[df.month > 1]
+
+    str_year = df.year.astype("str")
+    str_month = df.month.astype("str")
+    str_both = str_year.str.cat(str_month, sep=" ")
+
+    assert str_both.loc[1] == "2011 2"
+
+    str_multiple = str_year.str.cat([str_month, str_month], sep=" ")
+
+    assert str_multiple.loc[1] == "2011 2 2"
+
+
+@pytest.mark.parametrize("klass", [tuple, list, np.array, Series, Index])
+def test_cat_different_classes(klass):
+    # https://github.com/pandas-dev/pandas/issues/33425
+    s = Series(["a", "b", "c"])
+    result = s.str.cat(klass(["x", "y", "z"]))
+    expected = Series(["ax", "by", "cz"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_cat_on_series_dot_str():
+    # GH 28277
+    ps = Series(["AbC", "de", "FGHI", "j", "kLLLm"])
+
+    message = re.escape(
+        "others must be Series, Index, DataFrame, np.ndarray "
+        "or list-like (either containing only strings or "
+        "containing only objects of type Series/Index/"
+        "np.ndarray[1-dim])"
+    )
+    with pytest.raises(TypeError, match=message):
+        ps.str.cat(others=ps.str)

code/LaDi-RL-old-qwen-cod/LaDi-RL-old-qwen-cod/venv/lib64/python3.10/site-packages/pandas/tests/strings/test_extract.py

@@ -0,0 +1,724 @@
+from datetime import datetime
+import re
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import ArrowDtype
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    _testing as tm,
+)
+
+
+def test_extract_expand_kwarg_wrong_type_raises(any_string_dtype):
+    # TODO: should this raise TypeError
+    values = Series(["fooBAD__barBAD", np.nan, "foo"], dtype=any_string_dtype)
+    with pytest.raises(ValueError, match="expand must be True or False"):
+        values.str.extract(".*(BAD[_]+).*(BAD)", expand=None)
+
+
+def test_extract_expand_kwarg(any_string_dtype):
+    s = Series(["fooBAD__barBAD", np.nan, "foo"], dtype=any_string_dtype)
+    expected = DataFrame(["BAD__", np.nan, np.nan], dtype=any_string_dtype)
+
+    result = s.str.extract(".*(BAD[_]+).*")
+    tm.assert_frame_equal(result, expected)
+
+    result = s.str.extract(".*(BAD[_]+).*", expand=True)
+    tm.assert_frame_equal(result, expected)
+
+    expected = DataFrame(
+        [["BAD__", "BAD"], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    result = s.str.extract(".*(BAD[_]+).*(BAD)", expand=False)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_expand_False_mixed_object():
+    ser = Series(
+        ["aBAD_BAD", np.nan, "BAD_b_BAD", True, datetime.today(), "foo", None, 1, 2.0]
+    )
+
+    # two groups
+    result = ser.str.extract(".*(BAD[_]+).*(BAD)", expand=False)
+    er = [np.nan, np.nan]  # empty row
+    expected = DataFrame(
+        [["BAD_", "BAD"], er, ["BAD_", "BAD"], er, er, er, er, er, er], dtype=object
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # single group
+    result = ser.str.extract(".*(BAD[_]+).*BAD", expand=False)
+    expected = Series(
+        ["BAD_", np.nan, "BAD_", np.nan, np.nan, np.nan, None, np.nan, np.nan],
+        dtype=object,
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_extract_expand_index_raises():
+    # GH9980
+    # Index only works with one regex group since
+    # multi-group would expand to a frame
+    idx = Index(["A1", "A2", "A3", "A4", "B5"])
+    msg = "only one regex group is supported with Index"
+    with pytest.raises(ValueError, match=msg):
+        idx.str.extract("([AB])([123])", expand=False)
+
+
+def test_extract_expand_no_capture_groups_raises(index_or_series, any_string_dtype):
+    s_or_idx = index_or_series(["A1", "B2", "C3"], dtype=any_string_dtype)
+    msg = "pattern contains no capture groups"
+
+    # no groups
+    with pytest.raises(ValueError, match=msg):
+        s_or_idx.str.extract("[ABC][123]", expand=False)
+
+    # only non-capturing groups
+    with pytest.raises(ValueError, match=msg):
+        s_or_idx.str.extract("(?:[AB]).*", expand=False)
+
+
+def test_extract_expand_single_capture_group(index_or_series, any_string_dtype):
+    # single group renames series/index properly
+    s_or_idx = index_or_series(["A1", "A2"], dtype=any_string_dtype)
+    result = s_or_idx.str.extract(r"(?P<uno>A)\d", expand=False)
+
+    expected = index_or_series(["A", "A"], name="uno", dtype=any_string_dtype)
+    if index_or_series == Series:
+        tm.assert_series_equal(result, expected)
+    else:
+        tm.assert_index_equal(result, expected)
+
+
+def test_extract_expand_capture_groups(any_string_dtype):
+    s = Series(["A1", "B2", "C3"], dtype=any_string_dtype)
+    # one group, no matches
+    result = s.str.extract("(_)", expand=False)
+    expected = Series([np.nan, np.nan, np.nan], dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+    # two groups, no matches
+    result = s.str.extract("(_)(_)", expand=False)
+    expected = DataFrame(
+        [[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one group, some matches
+    result = s.str.extract("([AB])[123]", expand=False)
+    expected = Series(["A", "B", np.nan], dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+    # two groups, some matches
+    result = s.str.extract("([AB])([123])", expand=False)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one named group
+    result = s.str.extract("(?P<letter>[AB])", expand=False)
+    expected = Series(["A", "B", np.nan], name="letter", dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+    # two named groups
+    result = s.str.extract("(?P<letter>[AB])(?P<number>[123])", expand=False)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]],
+        columns=["letter", "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # mix named and unnamed groups
+    result = s.str.extract("([AB])(?P<number>[123])", expand=False)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]],
+        columns=[0, "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one normal group, one non-capturing group
+    result = s.str.extract("([AB])(?:[123])", expand=False)
+    expected = Series(["A", "B", np.nan], dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+    # two normal groups, one non-capturing group
+    s = Series(["A11", "B22", "C33"], dtype=any_string_dtype)
+    result = s.str.extract("([AB])([123])(?:[123])", expand=False)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one optional group followed by one normal group
+    s = Series(["A1", "B2", "3"], dtype=any_string_dtype)
+    result = s.str.extract("(?P<letter>[AB])?(?P<number>[123])", expand=False)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, "3"]],
+        columns=["letter", "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one normal group followed by one optional group
+    s = Series(["A1", "B2", "C"], dtype=any_string_dtype)
+    result = s.str.extract("(?P<letter>[ABC])(?P<number>[123])?", expand=False)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], ["C", np.nan]],
+        columns=["letter", "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_expand_capture_groups_index(index, any_string_dtype):
+    # https://github.com/pandas-dev/pandas/issues/6348
+    # not passing index to the extractor
+    data = ["A1", "B2", "C"]
+
+    if len(index) == 0:
+        pytest.skip("Test requires len(index) > 0")
+    while len(index) < len(data):
+        index = index.repeat(2)
+
+    index = index[: len(data)]
+    ser = Series(data, index=index, dtype=any_string_dtype)
+
+    result = ser.str.extract(r"(\d)", expand=False)
+    expected = Series(["1", "2", np.nan], index=index, dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+    result = ser.str.extract(r"(?P<letter>\D)(?P<number>\d)?", expand=False)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], ["C", np.nan]],
+        columns=["letter", "number"],
+        index=index,
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_single_series_name_is_preserved(any_string_dtype):
+    s = Series(["a3", "b3", "c2"], name="bob", dtype=any_string_dtype)
+    result = s.str.extract(r"(?P<sue>[a-z])", expand=False)
+    expected = Series(["a", "b", "c"], name="sue", dtype=any_string_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_extract_expand_True(any_string_dtype):
+    # Contains tests like those in test_match and some others.
+    s = Series(["fooBAD__barBAD", np.nan, "foo"], dtype=any_string_dtype)
+
+    result = s.str.extract(".*(BAD[_]+).*(BAD)", expand=True)
+    expected = DataFrame(
+        [["BAD__", "BAD"], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_expand_True_mixed_object():
+    er = [np.nan, np.nan]  # empty row
+    mixed = Series(
+        [
+            "aBAD_BAD",
+            np.nan,
+            "BAD_b_BAD",
+            True,
+            datetime.today(),
+            "foo",
+            None,
+            1,
+            2.0,
+        ]
+    )
+
+    result = mixed.str.extract(".*(BAD[_]+).*(BAD)", expand=True)
+    expected = DataFrame(
+        [["BAD_", "BAD"], er, ["BAD_", "BAD"], er, er, er, er, er, er], dtype=object
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_expand_True_single_capture_group_raises(
+    index_or_series, any_string_dtype
+):
+    # these should work for both Series and Index
+    # no groups
+    s_or_idx = index_or_series(["A1", "B2", "C3"], dtype=any_string_dtype)
+    msg = "pattern contains no capture groups"
+    with pytest.raises(ValueError, match=msg):
+        s_or_idx.str.extract("[ABC][123]", expand=True)
+
+    # only non-capturing groups
+    with pytest.raises(ValueError, match=msg):
+        s_or_idx.str.extract("(?:[AB]).*", expand=True)
+
+
+def test_extract_expand_True_single_capture_group(index_or_series, any_string_dtype):
+    # single group renames series/index properly
+    s_or_idx = index_or_series(["A1", "A2"], dtype=any_string_dtype)
+    result = s_or_idx.str.extract(r"(?P<uno>A)\d", expand=True)
+    expected = DataFrame({"uno": ["A", "A"]}, dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("name", [None, "series_name"])
+def test_extract_series(name, any_string_dtype):
+    # extract should give the same result whether or not the series has a name.
+    s = Series(["A1", "B2", "C3"], name=name, dtype=any_string_dtype)
+
+    # one group, no matches
+    result = s.str.extract("(_)", expand=True)
+    expected = DataFrame([np.nan, np.nan, np.nan], dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+    # two groups, no matches
+    result = s.str.extract("(_)(_)", expand=True)
+    expected = DataFrame(
+        [[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one group, some matches
+    result = s.str.extract("([AB])[123]", expand=True)
+    expected = DataFrame(["A", "B", np.nan], dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+    # two groups, some matches
+    result = s.str.extract("([AB])([123])", expand=True)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one named group
+    result = s.str.extract("(?P<letter>[AB])", expand=True)
+    expected = DataFrame({"letter": ["A", "B", np.nan]}, dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+    # two named groups
+    result = s.str.extract("(?P<letter>[AB])(?P<number>[123])", expand=True)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]],
+        columns=["letter", "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # mix named and unnamed groups
+    result = s.str.extract("([AB])(?P<number>[123])", expand=True)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]],
+        columns=[0, "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one normal group, one non-capturing group
+    result = s.str.extract("([AB])(?:[123])", expand=True)
+    expected = DataFrame(["A", "B", np.nan], dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_optional_groups(any_string_dtype):
+    # two normal groups, one non-capturing group
+    s = Series(["A11", "B22", "C33"], dtype=any_string_dtype)
+    result = s.str.extract("([AB])([123])(?:[123])", expand=True)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one optional group followed by one normal group
+    s = Series(["A1", "B2", "3"], dtype=any_string_dtype)
+    result = s.str.extract("(?P<letter>[AB])?(?P<number>[123])", expand=True)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], [np.nan, "3"]],
+        columns=["letter", "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one normal group followed by one optional group
+    s = Series(["A1", "B2", "C"], dtype=any_string_dtype)
+    result = s.str.extract("(?P<letter>[ABC])(?P<number>[123])?", expand=True)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], ["C", np.nan]],
+        columns=["letter", "number"],
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_dataframe_capture_groups_index(index, any_string_dtype):
+    # GH6348
+    # not passing index to the extractor
+
+    data = ["A1", "B2", "C"]
+
+    if len(index) < len(data):
+        pytest.skip(f"Index needs more than {len(data)} values")
+
+    index = index[: len(data)]
+    s = Series(data, index=index, dtype=any_string_dtype)
+
+    result = s.str.extract(r"(\d)", expand=True)
+    expected = DataFrame(["1", "2", np.nan], index=index, dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+    result = s.str.extract(r"(?P<letter>\D)(?P<number>\d)?", expand=True)
+    expected = DataFrame(
+        [["A", "1"], ["B", "2"], ["C", np.nan]],
+        columns=["letter", "number"],
+        index=index,
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extract_single_group_returns_frame(any_string_dtype):
+    # GH11386 extract should always return DataFrame, even when
+    # there is only one group. Prior to v0.18.0, extract returned
+    # Series when there was only one group in the regex.
+    s = Series(["a3", "b3", "c2"], name="series_name", dtype=any_string_dtype)
+    result = s.str.extract(r"(?P<letter>[a-z])", expand=True)
+    expected = DataFrame({"letter": ["a", "b", "c"]}, dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extractall(any_string_dtype):
+    data = [
+        "dave@google.com",
+        "tdhock5@gmail.com",
+        "maudelaperriere@gmail.com",
+        "rob@gmail.com some text steve@gmail.com",
+        "a@b.com some text c@d.com and e@f.com",
+        np.nan,
+        "",
+    ]
+    expected_tuples = [
+        ("dave", "google", "com"),
+        ("tdhock5", "gmail", "com"),
+        ("maudelaperriere", "gmail", "com"),
+        ("rob", "gmail", "com"),
+        ("steve", "gmail", "com"),
+        ("a", "b", "com"),
+        ("c", "d", "com"),
+        ("e", "f", "com"),
+    ]
+    pat = r"""
+    (?P<user>[a-z0-9]+)
+    @
+    (?P<domain>[a-z]+)
+    \.
+    (?P<tld>[a-z]{2,4})
+    """
+    expected_columns = ["user", "domain", "tld"]
+    s = Series(data, dtype=any_string_dtype)
+    # extractall should return a DataFrame with one row for each match, indexed by the
+    # subject from which the match came.
+    expected_index = MultiIndex.from_tuples(
+        [(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 0), (4, 1), (4, 2)],
+        names=(None, "match"),
+    )
+    expected = DataFrame(
+        expected_tuples, expected_index, expected_columns, dtype=any_string_dtype
+    )
+    result = s.str.extractall(pat, flags=re.VERBOSE)
+    tm.assert_frame_equal(result, expected)
+
+    # The index of the input Series should be used to construct the index of the output
+    # DataFrame:
+    mi = MultiIndex.from_tuples(
+        [
+            ("single", "Dave"),
+            ("single", "Toby"),
+            ("single", "Maude"),
+            ("multiple", "robAndSteve"),
+            ("multiple", "abcdef"),
+            ("none", "missing"),
+            ("none", "empty"),
+        ]
+    )
+    s = Series(data, index=mi, dtype=any_string_dtype)
+    expected_index = MultiIndex.from_tuples(
+        [
+            ("single", "Dave", 0),
+            ("single", "Toby", 0),
+            ("single", "Maude", 0),
+            ("multiple", "robAndSteve", 0),
+            ("multiple", "robAndSteve", 1),
+            ("multiple", "abcdef", 0),
+            ("multiple", "abcdef", 1),
+            ("multiple", "abcdef", 2),
+        ],
+        names=(None, None, "match"),
+    )
+    expected = DataFrame(
+        expected_tuples, expected_index, expected_columns, dtype=any_string_dtype
+    )
+    result = s.str.extractall(pat, flags=re.VERBOSE)
+    tm.assert_frame_equal(result, expected)
+
+    # MultiIndexed subject with names.
+    s = Series(data, index=mi, dtype=any_string_dtype)
+    s.index.names = ("matches", "description")
+    expected_index.names = ("matches", "description", "match")
+    expected = DataFrame(
+        expected_tuples, expected_index, expected_columns, dtype=any_string_dtype
+    )
+    result = s.str.extractall(pat, flags=re.VERBOSE)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "pat,expected_names",
+    [
+        # optional groups.
+        ("(?P<letter>[AB])?(?P<number>[123])", ["letter", "number"]),
+        # only one of two groups has a name.
+        ("([AB])?(?P<number>[123])", [0, "number"]),
+    ],
+)
+def test_extractall_column_names(pat, expected_names, any_string_dtype):
+    s = Series(["", "A1", "32"], dtype=any_string_dtype)
+
+    result = s.str.extractall(pat)
+    expected = DataFrame(
+        [("A", "1"), (np.nan, "3"), (np.nan, "2")],
+        index=MultiIndex.from_tuples([(1, 0), (2, 0), (2, 1)], names=(None, "match")),
+        columns=expected_names,
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extractall_single_group(any_string_dtype):
+    s = Series(["a3", "b3", "d4c2"], name="series_name", dtype=any_string_dtype)
+    expected_index = MultiIndex.from_tuples(
+        [(0, 0), (1, 0), (2, 0), (2, 1)], names=(None, "match")
+    )
+
+    # extractall(one named group) returns DataFrame with one named column.
+    result = s.str.extractall(r"(?P<letter>[a-z])")
+    expected = DataFrame(
+        {"letter": ["a", "b", "d", "c"]}, index=expected_index, dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # extractall(one un-named group) returns DataFrame with one un-named column.
+    result = s.str.extractall(r"([a-z])")
+    expected = DataFrame(
+        ["a", "b", "d", "c"], index=expected_index, dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extractall_single_group_with_quantifier(any_string_dtype):
+    # GH#13382
+    # extractall(one un-named group with quantifier) returns DataFrame with one un-named
+    # column.
+    s = Series(["ab3", "abc3", "d4cd2"], name="series_name", dtype=any_string_dtype)
+    result = s.str.extractall(r"([a-z]+)")
+    expected = DataFrame(
+        ["ab", "abc", "d", "cd"],
+        index=MultiIndex.from_tuples(
+            [(0, 0), (1, 0), (2, 0), (2, 1)], names=(None, "match")
+        ),
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, names",
+    [
+        ([], (None,)),
+        ([], ("i1",)),
+        ([], (None, "i2")),
+        ([], ("i1", "i2")),
+        (["a3", "b3", "d4c2"], (None,)),
+        (["a3", "b3", "d4c2"], ("i1", "i2")),
+        (["a3", "b3", "d4c2"], (None, "i2")),
+        (["a3", "b3", "d4c2"], ("i1", "i2")),
+    ],
+)
+def test_extractall_no_matches(data, names, any_string_dtype):
+    # GH19075 extractall with no matches should return a valid MultiIndex
+    n = len(data)
+    if len(names) == 1:
+        index = Index(range(n), name=names[0])
+    else:
+        tuples = (tuple([i] * (n - 1)) for i in range(n))
+        index = MultiIndex.from_tuples(tuples, names=names)
+    s = Series(data, name="series_name", index=index, dtype=any_string_dtype)
+    expected_index = MultiIndex.from_tuples([], names=(names + ("match",)))
+
+    # one un-named group.
+    result = s.str.extractall("(z)")
+    expected = DataFrame(columns=[0], index=expected_index, dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+    # two un-named groups.
+    result = s.str.extractall("(z)(z)")
+    expected = DataFrame(columns=[0, 1], index=expected_index, dtype=any_string_dtype)
+    tm.assert_frame_equal(result, expected)
+
+    # one named group.
+    result = s.str.extractall("(?P<first>z)")
+    expected = DataFrame(
+        columns=["first"], index=expected_index, dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # two named groups.
+    result = s.str.extractall("(?P<first>z)(?P<second>z)")
+    expected = DataFrame(
+        columns=["first", "second"], index=expected_index, dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # one named, one un-named.
+    result = s.str.extractall("(z)(?P<second>z)")
+    expected = DataFrame(
+        columns=[0, "second"], index=expected_index, dtype=any_string_dtype
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extractall_stringindex(any_string_dtype):
+    s = Series(["a1a2", "b1", "c1"], name="xxx", dtype=any_string_dtype)
+    result = s.str.extractall(r"[ab](?P<digit>\d)")
+    expected = DataFrame(
+        {"digit": ["1", "2", "1"]},
+        index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0)], names=[None, "match"]),
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # index should return the same result as the default index without name thus
+    # index.name doesn't affect to the result
+    if any_string_dtype == "object":
+        for idx in [
+            Index(["a1a2", "b1", "c1"], dtype=object),
+            Index(["a1a2", "b1", "c1"], name="xxx", dtype=object),
+        ]:
+            result = idx.str.extractall(r"[ab](?P<digit>\d)")
+            tm.assert_frame_equal(result, expected)
+
+    s = Series(
+        ["a1a2", "b1", "c1"],
+        name="s_name",
+        index=Index(["XX", "yy", "zz"], name="idx_name"),
+        dtype=any_string_dtype,
+    )
+    result = s.str.extractall(r"[ab](?P<digit>\d)")
+    expected = DataFrame(
+        {"digit": ["1", "2", "1"]},
+        index=MultiIndex.from_tuples(
+            [("XX", 0), ("XX", 1), ("yy", 0)], names=["idx_name", "match"]
+        ),
+        dtype=any_string_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_extractall_no_capture_groups_raises(any_string_dtype):
+    # Does not make sense to use extractall with a regex that has no capture groups.
+    # (it returns DataFrame with one column for each capture group)
+    s = Series(["a3", "b3", "d4c2"], name="series_name", dtype=any_string_dtype)
+    with pytest.raises(ValueError, match="no capture groups"):
+        s.str.extractall(r"[a-z]")
+
+
+def test_extract_index_one_two_groups():
+    s = Series(["a3", "b3", "d4c2"], index=["A3", "B3", "D4"], name="series_name")
+    r = s.index.str.extract(r"([A-Z])", expand=True)
+    e = DataFrame(["A", "B", "D"])
+    tm.assert_frame_equal(r, e)
+
+    # Prior to v0.18.0, index.str.extract(regex with one group)
+    # returned Index. With more than one group, extract raised an
+    # error (GH9980). Now extract always returns DataFrame.
+    r = s.index.str.extract(r"(?P<letter>[A-Z])(?P<digit>[0-9])", expand=True)
+    e_list = [("A", "3"), ("B", "3"), ("D", "4")]
+    e = DataFrame(e_list, columns=["letter", "digit"])
+    tm.assert_frame_equal(r, e)
+
+
+def test_extractall_same_as_extract(any_string_dtype):
+    s = Series(["a3", "b3", "c2"], name="series_name", dtype=any_string_dtype)
+
+    pattern_two_noname = r"([a-z])([0-9])"
+    extract_two_noname = s.str.extract(pattern_two_noname, expand=True)
+    has_multi_index = s.str.extractall(pattern_two_noname)
+    no_multi_index = has_multi_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_two_noname, no_multi_index)
+
+    pattern_two_named = r"(?P<letter>[a-z])(?P<digit>[0-9])"
+    extract_two_named = s.str.extract(pattern_two_named, expand=True)
+    has_multi_index = s.str.extractall(pattern_two_named)
+    no_multi_index = has_multi_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_two_named, no_multi_index)
+
+    pattern_one_named = r"(?P<group_name>[a-z])"
+    extract_one_named = s.str.extract(pattern_one_named, expand=True)
+    has_multi_index = s.str.extractall(pattern_one_named)
+    no_multi_index = has_multi_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_one_named, no_multi_index)
+
+    pattern_one_noname = r"([a-z])"
+    extract_one_noname = s.str.extract(pattern_one_noname, expand=True)
+    has_multi_index = s.str.extractall(pattern_one_noname)
+    no_multi_index = has_multi_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_one_noname, no_multi_index)
+
+
+def test_extractall_same_as_extract_subject_index(any_string_dtype):
+    # same as above tests, but s has an MultiIndex.
+    mi = MultiIndex.from_tuples(
+        [("A", "first"), ("B", "second"), ("C", "third")],
+        names=("capital", "ordinal"),
+    )
+    s = Series(["a3", "b3", "c2"], index=mi, name="series_name", dtype=any_string_dtype)
+
+    pattern_two_noname = r"([a-z])([0-9])"
+    extract_two_noname = s.str.extract(pattern_two_noname, expand=True)
+    has_match_index = s.str.extractall(pattern_two_noname)
+    no_match_index = has_match_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_two_noname, no_match_index)
+
+    pattern_two_named = r"(?P<letter>[a-z])(?P<digit>[0-9])"
+    extract_two_named = s.str.extract(pattern_two_named, expand=True)
+    has_match_index = s.str.extractall(pattern_two_named)
+    no_match_index = has_match_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_two_named, no_match_index)
+
+    pattern_one_named = r"(?P<group_name>[a-z])"
+    extract_one_named = s.str.extract(pattern_one_named, expand=True)
+    has_match_index = s.str.extractall(pattern_one_named)
+    no_match_index = has_match_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_one_named, no_match_index)
+
+    pattern_one_noname = r"([a-z])"
+    extract_one_noname = s.str.extract(pattern_one_noname, expand=True)
+    has_match_index = s.str.extractall(pattern_one_noname)
+    no_match_index = has_match_index.xs(0, level="match")
+    tm.assert_frame_equal(extract_one_noname, no_match_index)
+
+
+def test_extractall_preserves_dtype():
+    # Ensure that when extractall is called on a series with specific dtypes set, that
+    # the dtype is preserved in the resulting DataFrame's column.
+    pa = pytest.importorskip("pyarrow")
+
+    result = Series(["abc", "ab"], dtype=ArrowDtype(pa.string())).str.extractall("(ab)")
+    assert result.dtypes[0] == "string[pyarrow]"