DOC: Cleaned references to pandas <v0.12 in docs

doc/source/basics.rst

@@ -251,8 +251,8 @@ replace NaN with some other value using ``fillna`` if you wish).
 Flexible Comparisons
 ~~~~~~~~~~~~~~~~~~~~
 
-Starting in v0.8, pandas introduced binary comparison methods eq, ne, lt, gt,
-le, and ge to Series and DataFrame whose behavior is analogous to the binary
+Series and DataFrame have the binary comparison methods ``eq``, ``ne``, ``lt``, ``gt``,
+``le``, and ``ge`` whose behavior is analogous to the binary
 arithmetic operations described above:
 
 .. ipython:: python
@@ -1908,7 +1908,7 @@ each type in a ``DataFrame``:
 
    dft.get_dtype_counts()
 
-Numeric dtypes will propagate and can coexist in DataFrames (starting in v0.11.0).
+Numeric dtypes will propagate and can coexist in DataFrames.
 If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``,
 or a passed ``Series``, then it will be preserved in DataFrame operations. Furthermore,
 different numeric dtypes will **NOT** be combined. The following example will give you a taste.
@@ -2137,7 +2137,7 @@ gotchas
 ~~~~~~~
 
 Performing selection operations on ``integer`` type data can easily upcast the data to ``floating``.
-The dtype of the input data will be preserved in cases where ``nans`` are not introduced (starting in 0.11.0)
+The dtype of the input data will be preserved in cases where ``nans`` are not introduced.
 See also :ref:`Support for integer NA <gotchas.intna>`
 
 .. ipython:: python

doc/source/dsintro.rst

@@ -73,7 +73,7 @@ index is passed, one will be created having values ``[0, ..., len(data) - 1]``.
 
 .. note::
 
-    Starting in v0.8.0, pandas supports non-unique index values. If an operation
+    pandas supports non-unique index values. If an operation
     that does not support duplicate index values is attempted, an exception
     will be raised at that time. The reason for being lazy is nearly all performance-based
     (there are many instances in computations, like parts of GroupBy, where the index
@@ -698,7 +698,7 @@ DataFrame in tabular form, though it won't always fit the console width:
 
    print(baseball.iloc[-20:, :12].to_string())
 
-New since 0.10.0, wide DataFrames will now be printed across multiple rows by
+Wide DataFrames will be printed across multiple rows by
 default:
 
 .. ipython:: python
@@ -845,19 +845,16 @@ DataFrame objects with mixed-type columns, all of the data will get upcasted to
 
 .. note::
 
-   Unfortunately Panel, being less commonly used than Series and DataFrame,
+   Panel, being less commonly used than Series and DataFrame,
    has been slightly neglected feature-wise. A number of methods and options
-   available in DataFrame are not available in Panel. This will get worked
-   on, of course, in future releases. And faster if you join me in working on
-   the codebase.
+   available in DataFrame are not available in Panel.
 
 .. _dsintro.to_panel:
 
 From DataFrame using ``to_panel`` method
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-This method was introduced in v0.7 to replace ``LongPanel.to_long``, and converts
-a DataFrame with a two-level index to a Panel.
+``to_panel`` converts a DataFrame with a two-level index to a Panel.
 
 .. ipython:: python
    :okwarning:

doc/source/groupby.rst

@@ -140,7 +140,7 @@ columns:
 
     In [5]: grouped = df.groupby(get_letter_type, axis=1)
 
-Starting with 0.8, pandas Index objects now support duplicate values. If a
+pandas Index objects support duplicate values. If a
 non-unique index is used as the group key in a groupby operation, all values
 for the same index value will be considered to be in one group and thus the
 output of aggregation functions will only contain unique index values:
@@ -288,8 +288,6 @@ chosen level:
 
    s.sum(level='second')
 
-.. versionadded:: 0.6
-
 Grouping with multiple levels is supported.
 
 .. ipython:: python

doc/source/indexing.rst

@@ -66,8 +66,6 @@ See the :ref:`cookbook<cookbook.selection>` for some advanced strategies
 Different Choices for Indexing
 ------------------------------
 
-.. versionadded:: 0.11.0
-
 Object selection has had a number of user-requested additions in order to
 support more explicit location based indexing. Pandas now supports three types
 of multi-axis indexing.

doc/source/io.rst

@@ -364,7 +364,7 @@ warn_bad_lines : boolean, default ``True``
 Specifying column data types
 ''''''''''''''''''''''''''''
 
-Starting with v0.10, you can indicate the data type for the whole DataFrame or
+You can indicate the data type for the whole DataFrame or
 individual columns:
 
 .. ipython:: python
@@ -3346,7 +3346,7 @@ Read/Write API
 ''''''''''''''
 
 ``HDFStore`` supports an top-level API using  ``read_hdf`` for reading and ``to_hdf`` for writing,
-similar to how ``read_csv`` and ``to_csv`` work. (new in 0.11.0)
+similar to how ``read_csv`` and ``to_csv`` work.
 
 .. ipython:: python
 
@@ -3791,7 +3791,7 @@ indexed dimension as the ``where``.
 
 .. note::
 
-   Indexes are automagically created (starting ``0.10.1``) on the indexables
+   Indexes are automagically created on the indexables
    and any data columns you specify. This behavior can be turned off by passing
    ``index=False`` to ``append``.
 
@@ -3878,7 +3878,7 @@ create a new table!)
 Iterator
 ++++++++
 
-Starting in ``0.11.0``, you can pass, ``iterator=True`` or ``chunksize=number_in_a_chunk``
+You can pass ``iterator=True`` or ``chunksize=number_in_a_chunk``
 to ``select`` and ``select_as_multiple`` to return an iterator on the results.
 The default is 50,000 rows returned in a chunk.
 
@@ -3986,8 +3986,8 @@ of rows in an object.
 Multiple Table Queries
 ++++++++++++++++++++++
 
-New in 0.10.1 are the methods ``append_to_multiple`` and
-``select_as_multiple``, that can perform appending/selecting from
+The methods ``append_to_multiple`` and
+``select_as_multiple`` can perform appending/selecting from
 multiple tables at once. The idea is to have one table (call it the
 selector table) that you index most/all of the columns, and perform your
 queries. The other table(s) are data tables with an index matching the
@@ -4291,7 +4291,7 @@ Pass ``min_itemsize`` on the first table creation to a-priori specify the minimu
 ``min_itemsize`` can be an integer, or a dict mapping a column name to an integer. You can pass ``values`` as a key to
 allow all *indexables* or *data_columns* to have this min_itemsize.
 
-Starting in 0.11.0, passing a ``min_itemsize`` dict will cause all passed columns to be created as *data_columns* automatically.
+Passing a ``min_itemsize`` dict will cause all passed columns to be created as *data_columns* automatically.
 
 .. note::
 
@@ -4419,44 +4419,6 @@ Now you can import the ``DataFrame`` into R:
    starting point if you have stored multiple ``DataFrame`` objects to a
    single HDF5 file.
 
-Backwards Compatibility
-'''''''''''''''''''''''
-
-0.10.1 of ``HDFStore`` can read tables created in a prior version of pandas,
-however query terms using the
-prior (undocumented) methodology are unsupported. ``HDFStore`` will
-issue a warning if you try to use a legacy-format file. You must
-read in the entire file and write it out using the new format, using the
-method ``copy`` to take advantage of the updates. The group attribute
-``pandas_version`` contains the version information. ``copy`` takes a
-number of options, please see the docstring.
-
-
-.. ipython:: python
-   :suppress:
-
-   import os
-   legacy_file_path = os.path.abspath('source/_static/legacy_0.10.h5')
-
-.. ipython:: python
-   :okwarning:
-
-   # a legacy store
-   legacy_store = pd.HDFStore(legacy_file_path,'r')
-   legacy_store
-
-   # copy (and return the new handle)
-   new_store = legacy_store.copy('store_new.h5')
-   new_store
-   new_store.close()
-
-.. ipython:: python
-   :suppress:
-
-   legacy_store.close()
-   import os
-   os.remove('store_new.h5')
-
 
 Performance
 '''''''''''

doc/source/missing_data.rst

@@ -67,9 +67,8 @@ arise and we wish to also consider that "missing" or "not available" or "NA".
 
 .. note::
 
-   Prior to version v0.10.0 ``inf`` and ``-inf`` were also
-   considered to be "NA" in computations. This is no longer the case by
-   default; use the ``mode.use_inf_as_na`` option to recover it.
+   If you want to consider ``inf`` and ``-inf``
+   to be "NA" in computations, you can use the ``mode.use_inf_as_na`` option to archieve it.
 
 .. _missing.isna:
 
@@ -485,8 +484,8 @@ respectively:
 
 Replacing Generic Values
 ~~~~~~~~~~~~~~~~~~~~~~~~
-Often times we want to replace arbitrary values with other values. New in v0.8
-is the ``replace`` method in Series/DataFrame that provides an efficient yet
+Often times we want to replace arbitrary values with other values. The
+``replace`` method in Series/DataFrame provides an efficient yet
 flexible way to perform such replacements.
 
 For a Series, you can replace a single value or a list of values by another

doc/source/timeseries.rst

@@ -1069,8 +1069,7 @@ Offset Aliases
 ~~~~~~~~~~~~~~
 
 A number of string aliases are given to useful common time series
-frequencies. We will refer to these aliases as *offset aliases*
-(referred to as *time rules* prior to v0.8.0).
+frequencies. We will refer to these aliases as *offset aliases*.
 
 .. csv-table::
     :header: "Alias", "Description"

doc/source/visualization.rst

@@ -306,8 +306,6 @@ subplots:
    df.diff().hist(color='k', alpha=0.5, bins=50)
 
 
-.. versionadded:: 0.10.0
-
 The ``by`` keyword can be specified to plot grouped histograms:
 
 .. ipython:: python
@@ -831,8 +829,6 @@ and take a :class:`Series` or :class:`DataFrame` as an argument.
 Scatter Matrix Plot
 ~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.7.3
-
 You can create a scatter plot matrix using the
 ``scatter_matrix`` method in ``pandas.plotting``:
 
@@ -859,8 +855,6 @@ You can create a scatter plot matrix using the
 Density Plot
 ~~~~~~~~~~~~
 
-.. versionadded:: 0.8.0
-
 You can create density plots using the :meth:`Series.plot.kde` and :meth:`DataFrame.plot.kde` methods.
 
 .. ipython:: python