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DOC: doc typos, xref #15838
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ci/requirements-3.6_DOC.run

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beautifulsoup4
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html5lib
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pytables
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python-snappy
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openpyxl
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xlrd
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xlwt

doc/source/install.rst

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* `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
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* `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage. Version 3.0.0 or higher required, Version 3.2.1 or higher highly recommended.
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* `Feather Format <https://github.com/wesm/feather>`__: necessary for feather-based storage, version 0.3.1 or higher.
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* ``Apache Parquet Format``, either `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.4.1) or `fastparquet <https://fastparquet.readthedocs.io/en/latest/necessary>`__ (>= 0.0.6) for parquet-based storage. The `snappy <https://pypi.python.org/pypi/python-snappy>`__ and `brotli <https://pypi.python.org/pypi/brotlipy>`__ are available for compression support.
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* `Apache Parquet <https://parquet.apache.org/>`__, either `pyarrow <http://arrow.apache.org/docs/python/>`__ (>= 0.4.1) or `fastparquet <https://fastparquet.readthedocs.io/en/latest/necessary>`__ (>= 0.0.6) for parquet-based storage. The `snappy <https://pypi.python.org/pypi/python-snappy>`__ and `brotli <https://pypi.python.org/pypi/brotlipy>`__ are available for compression support.
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* `SQLAlchemy <http://www.sqlalchemy.org>`__: for SQL database support. Version 0.8.1 or higher recommended. Besides SQLAlchemy, you also need a database specific driver. You can find an overview of supported drivers for each SQL dialect in the `SQLAlchemy docs <http://docs.sqlalchemy.org/en/latest/dialects/index.html>`__. Some common drivers are:
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* `psycopg2 <http://initd.org/psycopg/>`__: for PostgreSQL

doc/source/io.rst

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.. versionadded:: 0.21.0
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`Parquet <https://parquet.apache.org/`__ provides a partitioned binary columnar serialization for data frames. It is designed to
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`Apache Parquet <https://parquet.apache.org/>`__ provides a partitioned binary columnar serialization for data frames. It is designed to
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make reading and writing data frames efficient, and to make sharing data across data analysis
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languages easy. Parquet can use a variety of compression techniques to shrink the file size as much as possible
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while still maintaining good read performance.
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If the engine is NOT specified, then the ``pd.options.io.parquet.engine`` option is checked; if this is also ``auto``, then
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then ``pyarrow`` is tried, and falling back to ``fastparquet``.
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See the documentation for `pyarrow <http://arrow.apache.org/docs/python/`__ and `fastparquet <https://fastparquet.readthedocs.io/en/latest/>`__
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See the documentation for `pyarrow <http://arrow.apache.org/docs/python/>`__ and `fastparquet <https://fastparquet.readthedocs.io/en/latest/>`__
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.. note::
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doc/source/whatsnew/v0.21.0.txt

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Highlights include:
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- Integration with `Apache Parquet <https://parquet.apache.org/>`__, including a new top-level :func:`pd.read_parquet` and :func:`DataFrame.to_parquet` method, see :ref:`here <io.parquet>`.
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Check the :ref:`API Changes <whatsnew_0210.api_breaking>` and :ref:`deprecations <whatsnew_0210.deprecations>` before updating.
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.. contents:: What's new in v0.21.0
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- :func:`DataFrame.select_dtypes` now accepts scalar values for include/exclude as well as list-like. (:issue:`16855`)
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- :func:`date_range` now accepts 'YS' in addition to 'AS' as an alias for start of year (:issue:`9313`)
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- :func:`date_range` now accepts 'Y' in addition to 'A' as an alias for end of year (:issue:`9313`)
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- Integration with Apache Parquet, including a new top-level ``pd.read_parquet()`` and ``DataFrame.to_parquet()`` method, see :ref:`here <io.parquet>`.
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- Integration with `Apache Parquet <https://parquet.apache.org/>`__, including a new top-level :func:`pd.read_parquet` and :func:`DataFrame.to_parquet` method, see :ref:`here <io.parquet>`.
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.. _whatsnew_0210.api_breaking:
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