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‎.pre-commit-config.yaml

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hooks:
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- id: black
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.0.285
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rev: v0.0.287
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hooks:
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- id: ruff
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args: [--exit-non-zero-on-fix]
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alias: ruff-selected-autofixes
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args: [--select, "ANN001,ANN204", --fix-only, --exit-non-zero-on-fix]
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- repo: https://github.com/jendrikseipp/vulture
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rev: 'v2.7'
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rev: 'v2.9.1'
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hooks:
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- id: vulture
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entry: python scripts/run_vulture.py
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'--filter=-readability/casting,-runtime/int,-build/include_subdir,-readability/fn_size'
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]
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- repo: https://github.com/pylint-dev/pylint
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rev: v3.0.0a6
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rev: v3.0.0a7
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hooks:
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- id: pylint
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stages: [manual]
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types: [text] # overwrite types: [rst]
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types_or: [python, rst]
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- repo: https://github.com/sphinx-contrib/sphinx-lint
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rev: v0.6.7
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rev: v0.6.8
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- id: sphinx-lint
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- repo: local

‎asv_bench/benchmarks/array.py

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self.array[i] = "foo"
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def time_setitem_list(self, multiple_chunks):
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indexer = list(range(0, 50)) + list(range(-1000, 0, 50))
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indexer = list(range(50)) + list(range(-1000, 0, 50))
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self.array[indexer] = ["foo"] * len(indexer)
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def time_setitem_slice(self, multiple_chunks):

‎asv_bench/benchmarks/frame_methods.py

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self.df.sort_values(by="A", ascending=ascending)
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class SortIndexByColumns:
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def setup(self):
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class SortMultiKey:
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params = [True, False]
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param_names = ["monotonic"]
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def setup(self, monotonic):
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N = 10000
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K = 10
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self.df = DataFrame(
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df = DataFrame(
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{
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"key1": tm.makeStringIndex(N).values.repeat(K),
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"key2": tm.makeStringIndex(N).values.repeat(K),
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"value": np.random.randn(N * K),
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}
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)
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if monotonic:
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df = df.sort_values(["key1", "key2"])
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self.df_by_columns = df
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self.df_by_index = df.set_index(["key1", "key2"])
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def time_sort_values(self, monotonic):
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self.df_by_columns.sort_values(by=["key1", "key2"])
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def time_frame_sort_values_by_columns(self):
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self.df.sort_values(by=["key1", "key2"])
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def time_sort_index(self, monotonic):
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self.df_by_index.sort_index()
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class Quantile:

‎asv_bench/benchmarks/join_merge.py

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def setup(self):
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self.left_object = DataFrame(
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{
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"X": np.random.choice(range(0, 10), size=(10000,)),
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"X": np.random.choice(range(10), size=(10000,)),
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"Y": np.random.choice(["one", "two", "three"], size=(10000,)),
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}
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)
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self.right_object = DataFrame(
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{
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"X": np.random.choice(range(0, 10), size=(10000,)),
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"X": np.random.choice(range(10), size=(10000,)),
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"Z": np.random.choice(["jjj", "kkk", "sss"], size=(10000,)),
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}
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)

‎doc/cheatsheet/README.md

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# Pandas Cheat Sheet
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The Pandas Cheat Sheet was created using Microsoft Powerpoint 2013.
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To create the PDF version, within Powerpoint, simply do a "Save As"
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and pick "PDF" as the format.
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This cheat sheet, originally written by Irv Lustig, [Princeton Consultants](https://www.princetonoptimization.com/), was inspired by the [RStudio Data Wrangling Cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf).
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| Topic | PDF | PPT |
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|------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Pandas_Cheat_Sheet | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
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| Pandas_Cheat_Sheet_JA | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet_JA.pptx" target="_parent"><img src="https://img.shields.io/badge/Open in PPT-B7472A?style=flat-square&logo=microsoft-powerpoint&logoColor=white"/></a> |
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**Alternative**
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Alternatively, if you want to complement your learning, you can use the Pandas Cheat sheets
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developed by [DataCamp](https://www.datacamp.com/) in "PDF", "Google Colab" and "Streamlit" formats.
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| Topic | PDF | Streamlit | Google Colab |
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|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Pandas | <a href="https://github.com/fralfaro/DS-Cheat-Sheets/blob/main/docs/files/pandas_cs.pdf" target="_parent"><img src="https://img.shields.io/badge/Open in PDF-%23FF0000.svg?style=flat-square&logo=adobe&logoColor=white"/></a> | <a href="https://ds-cheat-sheets-pandas.streamlit.app/" target="_parent"><img src="https://static.streamlit.io/badges/streamlit_badge_black_white.svg"/></a> | <a href="https://colab.research.google.com/github/fralfaro/DS-Cheat-Sheets/blob/main/docs/examples/pandas/pandas.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |

‎doc/cheatsheet/README.txt

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This file was deleted.

‎doc/make.py

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Open the rst file `page` and extract its title.
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"""
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fname = os.path.join(SOURCE_PATH, f"{page}.rst")
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option_parser = docutils.frontend.OptionParser(
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components=(docutils.parsers.rst.Parser,)
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doc = docutils.utils.new_document(
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"<doc>",
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docutils.frontend.get_default_settings(docutils.parsers.rst.Parser),
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)
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doc = docutils.utils.new_document("<doc>", option_parser.get_default_values())
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with open(fname, encoding="utf-8") as f:
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data = f.read()
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‎doc/source/development/debugging_extensions.rst

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pip install -ve . --no-build-isolation --config-settings=builddir="debug" --config-settings=setup-args="-Dbuildtype=debug"
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.. note::
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conda environements update CFLAGS/CPPFLAGS with flags that are geared towards generating releases. If using conda, you may need to set ``CFLAGS="$CFLAGS -O0"`` and ``CPPFLAGS="$CPPFLAGS -O0"`` to ensure optimizations are turned off for debugging
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By specifying ``builddir="debug"`` all of the targets will be built and placed in the debug directory relative to the project root. This helps to keep your debug and release artifacts separate; you are of course able to choose a different directory name or omit altogether if you do not care to separate build types.
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Editor support
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--------------
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The meson build system generates a `compilation database <https://clang.llvm.org/docs/JSONCompilationDatabase.html>`_ automatically and places it in the build directory. Many language servers and IDEs can use this information to provide code-completion, go-to-defintion and error checking support as you type.
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The meson build system generates a `compilation database <https://clang.llvm.org/docs/JSONCompilationDatabase.html>`_ automatically and places it in the build directory. Many language servers and IDEs can use this information to provide code-completion, go-to-definition and error checking support as you type.
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How each language server / IDE chooses to look for the compilation database may vary. When in doubt you may want to create a symlink at the root of the project that points to the compilation database in your build directory. Assuming you used *debug* as your directory name, you can run::
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‎doc/source/getting_started/intro_tutorials/01_table_oriented.rst

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</ul>
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.. note::
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If you are familiar to Python
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If you are familiar with Python
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:ref:`dictionaries <python:tut-dictionaries>`, the selection of a
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single column is very similar to selection of dictionary values based on
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single column is very similar to the selection of dictionary values based on
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the key.
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You can create a ``Series`` from scratch as well:

‎doc/source/user_guide/io.rst

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A ``Series`` or ``DataFrame`` can be converted to a valid JSON string. Use ``to_json``
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with optional parameters:
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1814-
* ``path_or_buf`` : the pathname or buffer to write the output
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This can be ``None`` in which case a JSON string is returned
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* ``path_or_buf`` : the pathname or buffer to write the output.
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This can be ``None`` in which case a JSON string is returned.
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* ``orient`` :
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``Series``:

‎doc/source/whatsnew/index.rst

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.. toctree::
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:maxdepth: 2
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v2.1.1
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v2.1.0
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Version 2.0

‎doc/source/whatsnew/v0.15.2.rst

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- Indexing in ``MultiIndex`` beyond lex-sort depth is now supported, though
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a lexically sorted index will have a better performance. (:issue:`2646`)
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.. ipython:: python
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:okexcept:
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:okwarning:
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.. code-block:: ipython
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In [1]: df = pd.DataFrame({'jim':[0, 0, 1, 1],
30+
...: 'joe':['x', 'x', 'z', 'y'],
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...: 'jolie':np.random.rand(4)}).set_index(['jim', 'joe'])
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...:
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df = pd.DataFrame({'jim':[0, 0, 1, 1],
32-
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df
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Out[2]:
36+
jolie
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jim joe
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0 x 0.126970
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x 0.966718
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1 z 0.260476
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y 0.897237
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[4 rows x 1 columns]
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Out[3]: 1
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# in prior versions this would raise a KeyError
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# will now show a PerformanceWarning
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df.loc[(1, 'z')]
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Out[4]:
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jolie
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jim joe
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1 z 0.260476
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[1 rows x 1 columns]
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df2
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Out[6]:
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jolie
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jim joe
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0 x 0.126970
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x 0.966718
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1 y 0.897237
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z 0.260476
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[4 rows x 1 columns]
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In [7]: df2.index.lexsort_depth
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Out[7]: 2
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Out[8]:
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jolie
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jim joe
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[1 rows x 1 columns]
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- Bug in unique of Series with ``category`` dtype, which returned all categories regardless
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whether they were "used" or not (see :issue:`8559` for the discussion).

‎doc/source/whatsnew/v0.24.0.rst

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from io import StringIO
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‎doc/source/whatsnew/v2.1.1.rst

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.. _whatsnew_211:
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What's new in 2.1.1 (September XX, 2023)
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----------------------------------------
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These are the changes in pandas 2.1.1. See :ref:`release` for a full changelog
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including other versions of pandas.
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{{ header }}
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.. ---------------------------------------------------------------------------
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.. _whatsnew_211.regressions:
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Fixed regressions
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~~~~~~~~~~~~~~~~~
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- Fixed regression in :func:`concat` when :class:`DataFrame` 's have two different extension dtypes (:issue:`54848`)
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- Fixed regression in :func:`merge` when merging over a PyArrow string index (:issue:`54894`)
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- Fixed regression in :func:`read_csv` when ``usecols`` is given and ``dtypes`` is a dict for ``engine="python"`` (:issue:`54868`)
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- Fixed regression in :func:`read_csv` when ``delim_whitespace`` is True (:issue:`54918`, :issue:`54931`)
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- Fixed regression in :meth:`.GroupBy.get_group` raising for ``axis=1`` (:issue:`54858`)
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- Fixed regression in :meth:`DataFrame.__setitem__` raising ``AssertionError`` when setting a :class:`Series` with a partial :class:`MultiIndex` (:issue:`54875`)
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- Fixed regression in :meth:`DataFrame.filter` not respecting the order of elements for ``filter`` (:issue:`54980`)
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- Fixed regression in :meth:`DataFrame.to_sql` not roundtripping datetime columns correctly for sqlite (:issue:`54877`)
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- Fixed regression in :meth:`MultiIndex.append` raising when appending overlapping :class:`IntervalIndex` levels (:issue:`54934`)
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- Fixed regression in :meth:`Series.drop_duplicates` for PyArrow strings (:issue:`54904`)
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- Fixed regression in :meth:`Series.interpolate` raising when ``fill_value`` was given (:issue:`54920`)
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- Fixed regression in :meth:`Series.value_counts` raising for numeric data if ``bins`` was specified (:issue:`54857`)
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- Fixed regression when comparing a :class:`Series` with ``datetime64`` dtype with ``None`` (:issue:`54870`)
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.. ---------------------------------------------------------------------------
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.. _whatsnew_211.bug_fixes:
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Bug fixes
34+
~~~~~~~~~
35+
- Fixed bug for :class:`ArrowDtype` raising ``NotImplementedError`` for fixed-size list (:issue:`55000`)
36+
- Fixed bug in :meth:`DataFrame.stack` with ``future_stack=True`` and columns a non-:class:`MultiIndex` consisting of tuples (:issue:`54948`)
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- Fixed bug in :meth:`Series.pct_change` and :meth:`DataFrame.pct_change` showing unnecessary ``FutureWarning`` (:issue:`54981`)
38+
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.. ---------------------------------------------------------------------------
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.. _whatsnew_211.other:
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Other
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~~~~~
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-
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.. ---------------------------------------------------------------------------
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.. _whatsnew_211.contributors:
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Contributors
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~~~~~~~~~~~~

‎doc/source/whatsnew/v2.2.0.rst

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- Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_parquet` except ``path``. (:issue:`54229`)
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- Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_pickle` except ``path``. (:issue:`54229`)
147147
- Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_string` except ``buf``. (:issue:`54229`)
148+
- Deprecated downcasting behavior in :meth:`Series.where`, :meth:`DataFrame.where`, :meth:`Series.mask`, :meth:`DataFrame.mask`, :meth:`Series.clip`, :meth:`DataFrame.clip`; in a future version these will not infer object-dtype columns to non-object dtype, or all-round floats to integer dtype. Call ``result.infer_objects(copy=False)`` on the result for object inference, or explicitly cast floats to ints. To opt in to the future version, use ``pd.set_option("future.downcasting", True)`` (:issue:`53656`)
148149
- Deprecated not passing a tuple to :class:`DataFrameGroupBy.get_group` or :class:`SeriesGroupBy.get_group` when grouping by a length-1 list-like (:issue:`25971`)
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- Deprecated strings ``S``, ``U``, and ``N`` denoting units in :func:`to_timedelta` (:issue:`52536`)
150151
- Deprecated strings ``T``, ``S``, ``L``, ``U``, and ``N`` denoting frequencies in :class:`Minute`, :class:`Second`, :class:`Milli`, :class:`Micro`, :class:`Nano` (:issue:`52536`)
@@ -157,19 +158,21 @@ Deprecations
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158159
Performance improvements
159160
~~~~~~~~~~~~~~~~~~~~~~~~
161+
- Performance improvement in :func:`to_dict` on converting DataFrame to dictionary (:issue:`50990`)
162+
- Performance improvement in :meth:`DataFrame.sort_index` and :meth:`Series.sort_index` when indexed by a :class:`MultiIndex` (:issue:`54835`)
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- Performance improvement when indexing with more than 4 keys (:issue:`54550`)
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-
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.. ---------------------------------------------------------------------------
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.. _whatsnew_220.bug_fixes:
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Bug fixes
167169
~~~~~~~~~
168170
- Bug in :class:`AbstractHolidayCalendar` where timezone data was not propagated when computing holiday observances (:issue:`54580`)
171+
- Bug in :class:`pandas.core.window.Rolling` where duplicate datetimelike indexes are treated as consecutive rather than equal with ``closed='left'`` and ``closed='neither'`` (:issue:`20712`)
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170173
Categorical
171174
^^^^^^^^^^^
172-
-
175+
- :meth:`Categorical.isin` raising ``InvalidIndexError`` for categorical containing overlapping :class:`Interval` values (:issue:`34974`)
173176
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Reshaping
245248
^^^^^^^^^
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-
249+
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-
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Sparse

‎pandas/_libs/meson.build

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'interval': {'sources': ['interval.pyx', _intervaltree_helper]},
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'interval': {'sources': ['interval.pyx', _intervaltree_helper],
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'deps': _khash_primitive_helper_dep},
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'deps': _khash_primitive_helper_dep},
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'lib': {'sources': ['lib.pyx', 'src/parser/tokenizer.c']},

‎pandas/_libs/src/parser/tokenizer.c

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// applied when in a field
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#define IS_DELIMITER(c) \
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#define _TOKEN_CLEANUP() \
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self->stream_len = slen; \

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