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DOC/CLN: remove versionadded/changed:: 0.23 (#36338)
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doc/source/development/extending.rst

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Extension types
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---------------
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.. versionadded:: 0.23.0
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.. warning::
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The :class:`pandas.api.extensions.ExtensionDtype` and :class:`pandas.api.extensions.ExtensionArray` APIs are new and

doc/source/getting_started/install.rst

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One of the following combinations of libraries is needed to use the
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top-level :func:`~pandas.read_html` function:
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* `BeautifulSoup4`_ and `html5lib`_
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* `BeautifulSoup4`_ and `lxml`_
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* `BeautifulSoup4`_ and `html5lib`_ and `lxml`_

doc/source/user_guide/advanced.rst

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pd.interval_range(start=0, end=4, closed='neither')
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Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
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intervals from ``start`` to ``end`` inclusively, with ``periods`` number of elements
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in the resulting ``IntervalIndex``:

doc/source/user_guide/basics.rst

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By indexes and values
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~~~~~~~~~~~~~~~~~~~~~
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Strings passed as the ``by`` parameter to :meth:`DataFrame.sort_values` may
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refer to either columns or index level names.
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doc/source/user_guide/categorical.rst

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df['B']
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.. versionadded:: 0.23.0
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Analogously, all columns in an existing ``DataFrame`` can be batch converted using :meth:`DataFrame.astype`:
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.. ipython:: python

doc/source/user_guide/dsintro.rst

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of one argument to be called on the ``DataFrame``. A *copy* of the original
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DataFrame is returned, with the new values inserted.
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Starting with Python 3.6 the order of ``**kwargs`` is preserved. This allows
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for *dependent* assignment, where an expression later in ``**kwargs`` can refer
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to a column created earlier in the same :meth:`~DataFrame.assign`.

doc/source/user_guide/io.rst

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then ``level_<i>`` is used.
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``read_json`` also accepts ``orient='table'`` as an argument. This allows for
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the preservation of metadata such as dtypes and index names in a
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round-trippable manner.

doc/source/user_guide/merging.rst

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.. warning::
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The default behavior with ``join='outer'`` is to sort the other axis
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(columns in this case). In a future version of pandas, the default will
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be to not sort. We specified ``sort=False`` to opt in to the new
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Merging on a combination of columns and index levels
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Strings passed as the ``on``, ``left_on``, and ``right_on`` parameters
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may refer to either column names or index level names. This enables merging
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``DataFrame`` instances on a combination of index levels and columns without

doc/source/user_guide/missing_data.rst

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Interpolation
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~~~~~~~~~~~~~
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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 data points.
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ser.interpolate(limit_direction='both')
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By default, ``NaN`` values are filled whether they are inside (surrounded by)
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existing valid values, or outside existing valid values. Introduced in v0.23
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the ``limit_area`` parameter restricts filling to either inside or outside values.
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existing valid values, or outside existing valid values. The ``limit_area``
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parameter restricts filling to either inside or outside values.
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.. ipython:: python
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doc/source/user_guide/reshaping.rst

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pd.get_dummies(df, dtype=bool).dtypes
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.. _reshaping.factorize:
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doc/source/user_guide/text.rst

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# We need to escape the special character (for >1 len patterns)
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dollars.str.replace(r'-\$', '-')
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If you do want literal replacement of a string (equivalent to
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:meth:`str.replace`), you can set the optional ``regex`` parameter to
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``False``, rather than escaping each character. In this case both ``pat``
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Concatenating a Series and something array-like into a Series
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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The parameter ``others`` can also be two-dimensional. In this case, the number or rows must match the lengths of the calling ``Series`` (or ``Index``).
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Concatenating a Series and an indexed object into a Series, with alignment
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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For concatenation with a ``Series`` or ``DataFrame``, it is possible to align the indexes before concatenation by setting
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the ``join``-keyword.
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doc/source/user_guide/timedeltas.rst

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Parsing
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-------
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You can construct a ``Timedelta`` scalar through various arguments:
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You can construct a ``Timedelta`` scalar through various arguments, including `ISO 8601 Duration`_ strings.
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pd.Timedelta('P0DT0H1M0S')
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pd.Timedelta('P0DT0H0M0.000000123S')
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:ref:`DateOffsets<timeseries.offsets>` (``Day, Hour, Minute, Second, Milli, Micro, Nano``) can also be used in construction.
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pd.timedelta_range(start='1 days', periods=5, freq='2D5H')
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Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
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timedeltas from ``start`` to ``end`` inclusively, with ``periods`` number of elements
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in the resulting ``TimedeltaIndex``:

doc/source/user_guide/timeseries.rst

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pd.bdate_range(start=start, periods=20)
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Specifying ``start``, ``end``, and ``periods`` will generate a range of evenly spaced
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dates from ``start`` to ``end`` inclusively, with ``periods`` number of elements in the
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resulting ``DatetimeIndex``:
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Slice vs. exact match
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The same string used as an indexing parameter can be treated either as a slice or as an exact match depending on the resolution of the index. If the string is less accurate than the index, it will be treated as a slice, otherwise as an exact match.
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Consider a ``Series`` object with a minute resolution index:

pandas/_libs/tslibs/nattype.pyx

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month_name : string
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string
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""",
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)
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string
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""",
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# _nat_methods

pandas/_libs/tslibs/timestamps.pyx

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string
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year, month, day : int
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hour, minute, second, microsecond : int, optional, default 0
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nanosecond : int, optional, default 0
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tzinfo : datetime.tzinfo, optional, default None
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fold : {0, 1}, default None, keyword-only
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pandas/core/algorithms.py

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"""
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Parameters
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pandas/core/arrays/base.py

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with a custom type and will not attempt to coerce them to objects. They
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pandas/core/arrays/categorical.py

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pandas/core/arrays/datetimes.py

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pandas/core/arrays/interval.py

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neither.
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%(name)s\
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pandas/core/dtypes/base.py

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See Also
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extensions.register_extension_dtype

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