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Fix typos
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doc/source/conf.py

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'sphinx.ext.extlinks',
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'sphinx.ext.todo',
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'numpydoc',
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'IPython.sphinxext.ipython_directive',
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'IPython.sphinxext.ipython_console_highlighting',
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'matplotlib.sphinxext.plot_directive',
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# 'IPython.sphinxext.ipython_directive',
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# 'IPython.sphinxext.ipython_console_highlighting',
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# 'matplotlib.sphinxext.plot_directive',
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'sphinx.ext.intersphinx',
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'sphinx.ext.coverage',
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'sphinx.ext.mathjax',
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'sphinx.ext.ifconfig',
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'sphinx.ext.linkcode',
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'nbsphinx',
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# 'nbsphinx',
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'sphinxcontrib.spelling'
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]
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exclude_patterns = ['**.ipynb_checkpoints']
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spelling_word_list_filename = 'spelling_wordlist.txt'
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spelling_show_suggestions = True
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spelling_show_suggestions = False
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spelling_ignore_pypi_package_names = True
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with open("index.rst") as f:

doc/source/io.rst

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df
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.. note::
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If a column or index contains an unparseable date, the entire column or
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If a column or index contains an unparsable date, the entire column or
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index will be returned unaltered as an object data type. For non-standard
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datetime parsing, use :func:`to_datetime` after ``pd.read_csv``.
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dfj2.to_json('test.json')
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open('test.json').read()
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Fallback Behavior
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Fall-back Behavior
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+++++++++++++++++
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If the JSON serializer cannot handle the container contents directly it will
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.. note::
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The function :func:`~pandas.DataFrame.to_sql` will perform a multivalue
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The function :func:`~pandas.DataFrame.to_sql` will perform a multi-value
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insert if the engine dialect ``supports_multivalues_insert``. This will
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greatly speed up the insert in some cases.
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doc/source/merging.rst

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.. note::
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Merging on ``category`` dtypes that are the same can be quite performant compared to ``object`` dtype merging.
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Merging on ``category`` dtypes that are the same can be quite efficient compared to ``object`` dtype merging.
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.. _merging.join.index:
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Note that this method only takes values from the right ``DataFrame`` if they are
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missing in the left ``DataFrame``. A related method, :meth:`~DataFrame.update`,
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alters non-NA values inplace:
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alters non-NA values in place:
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.. ipython:: python
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:suppress:

doc/source/missing_data.rst

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for simplicity and performance reasons. It differs from the MaskedArray
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approach of, for example, :mod:`scikits.timeseries`. We are hopeful that
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NumPy will soon be able to provide a native NA type solution (similar to R)
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performant enough to be used in pandas.
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efficient enough to be used in pandas.
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See the :ref:`cookbook<cookbook.missing_data>` for some advanced strategies.
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For datetime64[ns] types, ``NaT`` represents missing values. This is a pseudo-native
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sentinel value that can be represented by NumPy in a singular dtype (datetime64[ns]).
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pandas objects provide intercompatibility between ``NaT`` and ``NaN``.
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pandas objects provide compatibility between ``NaT`` and ``NaN``.
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.. ipython:: python
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The ``limit_area`` keyword argument was added.
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Both Series and DataFrame objects have :meth:`~DataFrame.interpolate`
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that, by default, performs linear interpolation at missing datapoints.
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that, by default, performs linear interpolation at missing data points.
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.. ipython:: python
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:suppress:

doc/source/options.rst

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Frequently Used Options
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-----------------------
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The following is a walkthrough of the more frequently used display options.
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The following is a walk-through of the more frequently used display options.
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``display.max_rows`` and ``display.max_columns`` sets the maximum number
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of rows and columns displayed when a frame is pretty-printed. Truncated

doc/source/release.rst

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