DOC: Clean-up references to v12 to v14 (both included)

doc/source/advanced.rst

@@ -270,9 +270,6 @@ Passing a list of labels or tuples works similar to reindexing:
 Using slicers
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.14.0
-
-In 0.14.0 we added a new way to slice multi-indexed objects.
 You can slice a multi-index by providing multiple indexers.
 
 You can provide any of the selectors as if you are indexing by label, see :ref:`Selection by Label <indexing.label>`,
@@ -384,7 +381,7 @@ selecting data at a particular level of a MultiIndex easier.
 
 .. ipython:: python
 
-   # using the slicers (new in 0.14.0)
+   # using the slicers
    df.loc[(slice(None),'one'),:]
 
 You can also select on the columns with :meth:`~pandas.MultiIndex.xs`, by
@@ -397,7 +394,7 @@ providing the axis argument
 
 .. ipython:: python
 
-   # using the slicers (new in 0.14.0)
+   # using the slicers
    df.loc[:,(slice(None),'one')]
 
 :meth:`~pandas.MultiIndex.xs` also allows selection with multiple keys
@@ -408,11 +405,9 @@ providing the axis argument
 
 .. ipython:: python
 
-   # using the slicers (new in 0.14.0)
+   # using the slicers
    df.loc[:,('bar','one')]
 
-.. versionadded:: 0.13.0
-
 You can pass ``drop_level=False`` to :meth:`~pandas.MultiIndex.xs` to retain
 the level that was selected
 
@@ -743,16 +738,6 @@ Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``ND
 Float64Index
 ~~~~~~~~~~~~
 
-.. note::
-
-   As of 0.14.0, ``Float64Index`` is backed by a native ``float64`` dtype
-   array. Prior to 0.14.0, ``Float64Index`` was backed by an ``object`` dtype
-   array. Using a ``float64`` dtype in the backend speeds up arithmetic
-   operations by about 30x and boolean indexing operations on the
-   ``Float64Index`` itself are about 2x as fast.
-
-.. versionadded:: 0.13.0
-
 By default a ``Float64Index`` will be automatically created when passing floating, or mixed-integer-floating values in index creation.
 This enables a pure label-based slicing paradigm that makes ``[],ix,loc`` for scalar indexing and slicing work exactly the
 same.

doc/source/basics.rst

@@ -347,7 +347,7 @@ That is because NaNs do not compare as equals:
 
    np.nan == np.nan
 
-So, as of v0.13.1, NDFrames (such as Series, DataFrames, and Panels)
+So, NDFrames (such as Series, DataFrames, and Panels)
 have an :meth:`~DataFrame.equals` method for testing equality, with NaNs in
 corresponding locations treated as equal.
 
@@ -1104,10 +1104,6 @@ Applying with a ``Panel`` will pass a ``Series`` to the applied function. If the
 function returns a ``Series``, the result of the application will be a ``Panel``. If the applied function
 reduces to a scalar, the result of the application will be a ``DataFrame``.
 
-.. note::
-
-   Prior to 0.13.1 ``apply`` on a ``Panel`` would only work on ``ufuncs`` (e.g. ``np.sum/np.max``).
-
 .. ipython:: python
 
    import pandas.util.testing as tm
@@ -1800,8 +1796,6 @@ Series has the :meth:`~Series.searchsorted` method, which works similar to
 smallest / largest values
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.14.0
-
 ``Series`` has the :meth:`~Series.nsmallest` and :meth:`~Series.nlargest` methods which return the
 smallest or largest :math:`n` values. For a large ``Series`` this can be much
 faster than sorting the entire Series and calling ``head(n)`` on the result.
@@ -2168,8 +2162,6 @@ Selecting columns based on ``dtype``
 
 .. _basics.selectdtypes:
 
-.. versionadded:: 0.14.1
-
 The :meth:`~DataFrame.select_dtypes` method implements subsetting of columns
 based on their ``dtype``.
 

doc/source/comparison_with_r.rst

@@ -247,8 +247,6 @@ For more details and examples see :ref:`the reshaping documentation
 |subset|_
 ~~~~~~~~~~
 
-.. versionadded:: 0.13
-
 The :meth:`~pandas.DataFrame.query` method is similar to the base R ``subset``
 function. In R you might want to get the rows of a ``data.frame`` where one
 column's values are less than another column's values:
@@ -277,8 +275,6 @@ For more details and examples see :ref:`the query documentation
 |with|_
 ~~~~~~~~
 
-.. versionadded:: 0.13
-
 An expression using a data.frame called ``df`` in R with the columns ``a`` and
 ``b`` would be evaluated using ``with`` like so:
 

doc/source/cookbook.rst

@@ -818,7 +818,7 @@ The :ref:`Concat <merging.concatenation>` docs. The :ref:`Join <merging.join>` d
    df1 = pd.DataFrame(np.random.randn(6, 3), index=rng, columns=['A', 'B', 'C'])
    df2 = df1.copy()
 
-ignore_index is needed in pandas < v0.13, and depending on df construction
+Depending on df construction, ``ignore_index`` may be needed
 
 .. ipython:: python
 

doc/source/enhancingperf.rst

@@ -213,17 +213,18 @@ the rows, applying our ``integrate_f_typed``, and putting this in the zeros arra
 
 .. warning::
 
-   In 0.13.0 since ``Series`` has internaly been refactored to no longer sub-class ``ndarray``
-   but instead subclass ``NDFrame``, you can **not pass** a ``Series`` directly as a ``ndarray`` typed parameter
-   to a cython function. Instead pass the actual ``ndarray`` using the ``.values`` attribute of the Series.
+   You can **not pass** a ``Series`` directly as a ``ndarray`` typed parameter
+   to a cython function. Instead pass the actual ``ndarray`` using the
+   ``.values`` attribute of the Series. The reason is that the cython
+   definition is specific to an ndarray and not the passed Series.
 
-   Prior to 0.13.0
+   So, do not do this:
 
    .. code-block:: python
 
         apply_integrate_f(df['a'], df['b'], df['N'])
 
-   Use ``.values`` to get the underlying ``ndarray``
+   But rather, use ``.values`` to get the underlying ``ndarray``
 
    .. code-block:: python
 
@@ -399,10 +400,8 @@ Read more in the `numba docs <http://numba.pydata.org/>`__.
 
 .. _enhancingperf.eval:
 
-Expression Evaluation via :func:`~pandas.eval` (Experimental)
--------------------------------------------------------------
-
-.. versionadded:: 0.13
+Expression Evaluation via :func:`~pandas.eval`
+-----------------------------------------------
 
 The top-level function :func:`pandas.eval` implements expression evaluation of
 :class:`~pandas.Series` and :class:`~pandas.DataFrame` objects.
@@ -539,10 +538,8 @@ Now let's do the same thing but with comparisons:
    of type ``bool`` or ``np.bool_``. Again, you should perform these kinds of
    operations in plain Python.
 
-The ``DataFrame.eval`` method (Experimental)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. versionadded:: 0.13
+The ``DataFrame.eval`` method
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In addition to the top level :func:`pandas.eval` function you can also
 evaluate an expression in the "context" of a :class:`~pandas.DataFrame`.
@@ -646,19 +643,6 @@ whether the query modifies the original frame.
 Local Variables
 ~~~~~~~~~~~~~~~
 
-In pandas version 0.14 the local variable API has changed. In pandas 0.13.x,
-you could refer to local variables the same way you would in standard Python.
-For example,
-
-.. code-block:: python
-
-   df = pd.DataFrame(np.random.randn(5, 2), columns=['a', 'b'])
-   newcol = np.random.randn(len(df))
-   df.eval('b + newcol')
-
-   UndefinedVariableError: name 'newcol' is not defined
-
-As you can see from the exception generated, this syntax is no longer allowed.
 You must *explicitly reference* any local variable that you want to use in an
 expression by placing the ``@`` character in front of the name. For example,
 

doc/source/groupby.rst

@@ -766,8 +766,6 @@ missing values with the ``ffill()`` method.
 Filtration
 ----------
 
-.. versionadded:: 0.12
-
 The ``filter`` method returns a subset of the original object. Suppose we
 want to take only elements that belong to groups with a group sum greater
 than 2.
@@ -858,8 +856,6 @@ In this example, we chopped the collection of time series into yearly chunks
 then independently called :ref:`fillna <missing_data.fillna>` on the
 groups.
 
-.. versionadded:: 0.14.1
-
 The ``nlargest`` and ``nsmallest`` methods work on ``Series`` style groupbys:
 
 .. ipython:: python
@@ -1048,19 +1044,6 @@ Just like for a DataFrame or Series you can call head and tail on a groupby:
 
 This shows the first or last n rows from each group.
 
-.. warning::
-
-   Before 0.14.0 this was implemented with a fall-through apply,
-   so the result would incorrectly respect the as_index flag:
-
-   .. code-block:: python
-
-       >>> g.head(1):  # was equivalent to g.apply(lambda x: x.head(1))
-             A  B
-        A
-        1 0  1  2
-        5 2  5  6
-
 .. _groupby.nth:
 
 Taking the nth row of each group
@@ -1113,8 +1096,6 @@ You can also select multiple rows from each group by specifying multiple nth val
 Enumerate group items
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.13.0
-
 To see the order in which each row appears within its group, use the
 ``cumcount`` method:
 

doc/source/indexing.rst

@@ -248,8 +248,6 @@ as an attribute:
    - In any of these cases, standard indexing will still work, e.g. ``s['1']``, ``s['min']``, and ``s['index']`` will
      access the corresponding element or column.
 
-   - The ``Series/Panel`` accesses are available starting in 0.13.0.
-
 If you are using the IPython environment, you may also use tab-completion to
 see these accessible attributes.
 
@@ -529,7 +527,6 @@ Out of range slice indexes are handled gracefully just as in Python/Numpy.
 .. ipython:: python
 
     # these are allowed in python/numpy.
-    # Only works in Pandas starting from v0.14.0.
     x = list('abcdef')
     x
     x[4:10]
@@ -539,14 +536,8 @@ Out of range slice indexes are handled gracefully just as in Python/Numpy.
     s.iloc[4:10]
     s.iloc[8:10]
 
-.. note::
-
-    Prior to v0.14.0, ``iloc`` would not accept out of bounds indexers for
-    slices, e.g. a value that exceeds the length of the object being indexed.
-
-
-Note that this could result in an empty axis (e.g. an empty DataFrame being
-returned)
+Note that using slices that go out of bounds can result in
+an empty axis (e.g. an empty DataFrame being returned)
 
 .. ipython:: python
 
@@ -745,8 +736,6 @@ Finally, one can also set a seed for ``sample``'s random number generator using
 Setting With Enlargement
 ------------------------
 
-.. versionadded:: 0.13
-
 The ``.loc/[]`` operations can perform enlargement when setting a non-existant key for that axis.
 
 In the ``Series`` case this is effectively an appending operation
@@ -1020,8 +1009,6 @@ partial setting via ``.loc`` (but on the contents rather than the axis labels)
    df2[ df2[1:4] > 0 ] = 3
    df2
 
-.. versionadded:: 0.13
-
 Where can also accept ``axis`` and ``level`` parameters to align the input when
 performing the ``where``.
 
@@ -1064,8 +1051,6 @@ as condition and ``other`` argument.
 The :meth:`~pandas.DataFrame.query` Method (Experimental)
 ---------------------------------------------------------
 
-.. versionadded:: 0.13
-
 :class:`~pandas.DataFrame` objects have a :meth:`~pandas.DataFrame.query`
 method that allows selection using an expression.
 
@@ -1506,8 +1491,6 @@ The name, if set, will be shown in the console display:
 Setting metadata
 ~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.13.0
-
 Indexes are "mostly immutable", but it is possible to set and change their
 metadata, like the index ``name`` (or, for ``MultiIndex``, ``levels`` and
 ``labels``).
@@ -1790,7 +1773,7 @@ Evaluation order matters
 
 Furthermore, in chained expressions, the order may determine whether a copy is returned or not.
 If an expression will set values on a copy of a slice, then a ``SettingWithCopy``
-exception will be raised (this raise/warn behavior is new starting in 0.13.0)
+warning will be issued.
 
 You can control the action of a chained assignment via the option ``mode.chained_assignment``,
 which can take the values ``['raise','warn',None]``, where showing a warning is the default.

doc/source/install.rst

@@ -107,7 +107,7 @@ following command::
 
 To install a specific pandas version::
 
-    conda install pandas=0.13.1
+    conda install pandas=0.20.3
 
 To install other packages, IPython for example::