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Remove .. versonadded:: 0.18 (#27463)
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doc/source/getting_started/basics.rst

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@@ -1422,8 +1422,6 @@ The :meth:`~DataFrame.rename` method also provides an ``inplace`` named
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parameter that is by default ``False`` and copies the underlying data. Pass
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``inplace=True`` to rename the data in place.
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Finally, :meth:`~Series.rename` also accepts a scalar or list-like
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for altering the ``Series.name`` attribute.
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doc/source/getting_started/dsintro.rst

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@@ -251,8 +251,6 @@ Series can also have a ``name`` attribute:
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The Series ``name`` will be assigned automatically in many cases, in particular
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when taking 1D slices of DataFrame as you will see below.
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You can rename a Series with the :meth:`pandas.Series.rename` method.
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.. ipython:: python

doc/source/user_guide/enhancingperf.rst

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@@ -601,8 +601,6 @@ This allows for *formulaic evaluation*. The assignment target can be a
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new column name or an existing column name, and it must be a valid Python
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identifier.
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The ``inplace`` keyword determines whether this assignment will performed
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on the original ``DataFrame`` or return a copy with the new column.
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df['a'] = 1
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df
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The ``query`` method gained the ``inplace`` keyword which determines
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The ``query`` method has a ``inplace`` keyword which determines
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whether the query modifies the original frame.
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.. ipython:: python

doc/source/user_guide/groupby.rst

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.. _groupby.transform.window_resample:
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New syntax to window and resample operations
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Window and resample operations
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Working with the resample, expanding or rolling operations on the groupby
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level used to require the application of helper functions. However,
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now it is possible to use ``resample()``, ``expanding()`` and
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It is possible to use ``resample()``, ``expanding()`` and
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``rolling()`` as methods on groupbys.
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The example below will apply the ``rolling()`` method on the samples of

doc/source/user_guide/indexing.rst

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* A ``callable`` function with one argument (the calling Series or DataFrame) and
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that returns valid output for indexing (one of the above).
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See more at :ref:`Selection by Label <indexing.label>`.
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* ``.iloc`` is primarily integer position based (from ``0`` to
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Selection by callable
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---------------------
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``.loc``, ``.iloc``, and also ``[]`` indexing can accept a ``callable`` as indexer.
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The ``callable`` must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.
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doc/source/user_guide/io.rst

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If this option is set to ``True``, nothing should be passed in for the
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``delimiter`` parameter.
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Column and index locations and names
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++++++++++++++++++++++++++++++++++++
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doc/source/user_guide/reshaping.rst

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df3
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df3.unstack()
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Alternatively, unstack takes an optional ``fill_value`` argument, for specifying
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the value of missing data.
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from_dict = pd.get_dummies(df, prefix={'B': 'from_B', 'A': 'from_A'})
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from_dict
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Sometimes it will be useful to only keep k-1 levels of a categorical
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variable to avoid collinearity when feeding the result to statistical models.
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You can switch to this mode by turn on ``drop_first``.

doc/source/user_guide/text.rst

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String ``Index`` also supports ``get_dummies`` which returns a ``MultiIndex``.
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idx = pd.Index(['a', 'a|b', np.nan, 'a|c'])

doc/source/user_guide/timeseries.rst

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Assembling datetime from multiple DataFrame columns
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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You can also pass a ``DataFrame`` of integer or string columns to assemble into a ``Series`` of ``Timestamps``.
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.. ipython:: python
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Custom business hour
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~~~~~~~~~~~~~~~~~~~~
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The ``CustomBusinessHour`` is a mixture of ``BusinessHour`` and ``CustomBusinessDay`` which
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allows you to specify arbitrary holidays. ``CustomBusinessHour`` works as the same
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pandas/core/frame.py

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See the documentation for :func:`eval` for complete details
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on the keyword arguments accepted by :meth:`DataFrame.query`.
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Returns
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DataFrame
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DataFrame
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The indexes ``i`` and ``j`` are now optional, and default to
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"""
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result = self.copy()
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* nearest: `i` or `j` whichever is nearest.
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* midpoint: (`i` + `j`) / 2.
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Returns
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Series or DataFrame

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