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.github/workflows/unit-tests.yml

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moto:
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image: motoserver/moto:5.0.27
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env:
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AWS_ACCESS_KEY_ID: foobar_key
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AWS_SECRET_ACCESS_KEY: foobar_secret
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ports:
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- 5000:5000
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AUTHORS.md

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led by Wes McKinney. AQR released the source under this license in 2009.
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* Copyright (c) 2011-2012, Lambda Foundry, Inc.
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Wes is now an employee of Lambda Foundry, and remains the pandas project
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Wes became an employee of Lambda Foundry, and remained the pandas project
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lead.
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* Copyright (c) 2011-2012, PyData Development Team
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The PyData Development Team is the collection of developers of the PyData
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project. This includes all of the PyData sub-projects, including pandas. The
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project. This includes all of the PyData sub-projects, such as pandas. The
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core team that coordinates development on GitHub can be found here:
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https://github.com/pydata.
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PyData uses a shared copyright model. Each contributor maintains copyright
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over their contributions to PyData. However, it is important to note that
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these contributions are typically only changes to the repositories. Thus,
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these contributions are typically limited to changes to the repositories. Thus,
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the PyData source code, in its entirety, is not the copyright of any single
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person or institution. Instead, it is the collective copyright of the
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entire PyData Development Team. If individual contributors want to maintain
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a record of what changes/contributions they have specific copyright on,
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a record of the specific changes or contributions they hold copyright to,
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they should indicate their copyright in the commit message of the change
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when they commit the change to one of the PyData repositories.
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License
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=======
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pandas is distributed under a 3-clause ("Simplified" or "New") BSD
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pandas is distributed under the 3-clause ("Simplified" or "New") BSD
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license. Parts of NumPy, SciPy, numpydoc, bottleneck, which all have
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BSD-compatible licenses, are included. Their licenses follow the pandas
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BSD-compatible licenses, are included. Their licenses are compatible with the pandas
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license.

ci/deps/actions-310-minimum_versions.yaml

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- qtpy=2.3.0
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- openpyxl=3.1.2
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- psycopg2=2.9.6
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- pyarrow=10.0.1
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- pyarrow=12.0.1
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- pyiceberg=0.7.1
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- pymysql=1.1.0
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- pyqt=5.15.9

ci/deps/actions-310.yaml

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- qtpy>=2.3.0
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- openpyxl>=3.1.2
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- psycopg2>=2.9.6
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- pyarrow>=10.0.1
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- pyarrow>=12.0.1
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- pyiceberg>=0.7.1
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- pymysql>=1.1.0
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- pyqt>=5.15.9

ci/deps/actions-311-downstream_compat.yaml

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- qtpy>=2.3.0
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- openpyxl>=3.1.2
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- psycopg2>=2.9.6
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- pyarrow>=10.0.1
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- pyarrow>=12.0.1
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- pytz>=2023.4
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- pyxlsb>=1.0.10
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- s3fs>=2023.12.2
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- scipy>=1.12.0
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# TEMP upper pin for scipy (https://github.com/statsmodels/statsmodels/issues/9584)
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- scipy>=1.12.0,<1.16
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- sqlalchemy>=2.0.0
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- tabulate>=0.9.0
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- xarray>=2024.1.1

ci/deps/actions-311.yaml

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- pyarrow>=12.0.1
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ci/deps/actions-312.yaml

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- pyarrow>=12.0.1
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- pyiceberg>=0.7.1
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ci/deps/actions-313.yaml

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doc/source/getting_started/install.rst

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`PyTables <https://github.com/PyTables/PyTables>`__ 3.8.0 hdf5 HDF5-based reading / writing
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`zlib <https://github.com/madler/zlib>`__ hdf5 Compression for HDF5
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`fastparquet <https://github.com/dask/fastparquet>`__ 2024.2.0 - Parquet reading / writing (pyarrow is default)
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`pyarrow <https://github.com/apache/arrow>`__ 10.0.1 parquet, feather Parquet, ORC, and feather reading / writing
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`pyarrow <https://github.com/apache/arrow>`__ 12.0.1 parquet, feather Parquet, ORC, and feather reading / writing
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`PyIceberg <https://py.iceberg.apache.org/>`__ 0.7.1 iceberg Apache Iceberg reading / writing
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`pyreadstat <https://github.com/Roche/pyreadstat>`__ 1.2.6 spss SPSS files (.sav) reading
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`odfpy <https://github.com/eea/odfpy>`__ 1.4.1 excel Open document format (.odf, .ods, .odt) reading / writing

doc/source/reference/indexing.rst

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:toctree: api/
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Index.astype
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Index.infer_objects
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Index.item
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Index.map
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Index.ravel

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