CLN: remove versionadded:: 0.20 (#29126)

Author: simonjayhawkins

doc/source/development/contributing.rst

@@ -1197,8 +1197,6 @@ submitting a pull request.
 
 For more, see the `pytest <http://docs.pytest.org/en/latest/>`_ documentation.
 
-    .. versionadded:: 0.20.0
-
 Furthermore one can run
 
 .. code-block:: python

doc/source/getting_started/basics.rst

@@ -172,8 +172,6 @@ You are highly encouraged to install both libraries. See the section
 
 These are both enabled to be used by default, you can control this by setting the options:
 
-.. versionadded:: 0.20.0
-
 .. code-block:: python
 
    pd.set_option('compute.use_bottleneck', False)
@@ -891,8 +889,6 @@ functionality.
 Aggregation API
 ~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 The aggregation API allows one to express possibly multiple aggregation operations in a single concise way.
 This API is similar across pandas objects, see :ref:`groupby API <groupby.aggregate>`, the
 :ref:`window functions API <stats.aggregate>`, and the :ref:`resample API <timeseries.aggregate>`.
@@ -1030,8 +1026,6 @@ to the built in :ref:`describe function <basics.describe>`.
 Transform API
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 The :meth:`~DataFrame.transform` method returns an object that is indexed the same (same size)
 as the original. This API allows you to provide *multiple* operations at the same
 time rather than one-by-one. Its API is quite similar to the ``.agg`` API.

doc/source/user_guide/advanced.rst

@@ -206,8 +206,6 @@ highly performant. If you want to see only the used levels, you can use the
 To reconstruct the ``MultiIndex`` with only the used levels, the
 :meth:`~MultiIndex.remove_unused_levels` method may be used.
 
-.. versionadded:: 0.20.0
-
 .. ipython:: python
 
    new_mi = df[['foo', 'qux']].columns.remove_unused_levels()
@@ -928,8 +926,6 @@ If you need integer based selection, you should use ``iloc``:
 IntervalIndex
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 :class:`IntervalIndex` together with its own dtype, :class:`~pandas.api.types.IntervalDtype`
 as well as the :class:`Interval` scalar type,  allow first-class support in pandas
 for interval notation.

doc/source/user_guide/categorical.rst

@@ -874,8 +874,6 @@ The below raises ``TypeError`` because the categories are ordered and not identi
    Out[3]:
    TypeError: to union ordered Categoricals, all categories must be the same
 
-.. versionadded:: 0.20.0
-
 Ordered categoricals with different categories or orderings can be combined by
 using the ``ignore_ordered=True`` argument.
 

doc/source/user_guide/computation.rst

@@ -471,8 +471,6 @@ default of the index) in a DataFrame.
 Rolling window endpoints
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 The inclusion of the interval endpoints in rolling window calculations can be specified with the ``closed``
 parameter:
 

doc/source/user_guide/groupby.rst

@@ -311,8 +311,6 @@ Grouping with multiple levels is supported.
    s
    s.groupby(level=['first', 'second']).sum()
 
-.. versionadded:: 0.20
-
 Index level names may be supplied as keys.
 
 .. ipython:: python
@@ -353,8 +351,6 @@ Index levels may also be specified by name.
 
    df.groupby([pd.Grouper(level='second'), 'A']).sum()
 
-.. versionadded:: 0.20
-
 Index level names may be specified as keys directly to ``groupby``.
 
 .. ipython:: python
@@ -1274,8 +1270,6 @@ To see the order in which each row appears within its group, use the
 Enumerate groups
 ~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.2
-
 To see the ordering of the groups (as opposed to the order of rows
 within a group given by ``cumcount``) you can use
 :meth:`~pandas.core.groupby.DataFrameGroupBy.ngroup`.

doc/source/user_guide/io.rst

@@ -163,9 +163,6 @@ dtype : Type name or dict of column -> type, default ``None``
   (unsupported with ``engine='python'``). Use `str` or `object` together
   with suitable ``na_values`` settings to preserve and
   not interpret dtype.
-
-  .. versionadded:: 0.20.0 support for the Python parser.
-
 engine : {``'c'``, ``'python'``}
   Parser engine to use. The C engine is faster while the Python engine is
   currently more feature-complete.
@@ -417,10 +414,6 @@ However, if you wanted for all the data to be coerced, no matter the type, then
 using the ``converters`` argument of :func:`~pandas.read_csv` would certainly be
 worth trying.
 
-  .. versionadded:: 0.20.0 support for the Python parser.
-
-     The ``dtype`` option is supported by the 'python' engine.
-
 .. note::
    In some cases, reading in abnormal data with columns containing mixed dtypes
    will result in an inconsistent dataset. If you rely on pandas to infer the
@@ -616,8 +609,6 @@ Filtering columns (``usecols``)
 The ``usecols`` argument allows you to select any subset of the columns in a
 file, either using the column names, position numbers or a callable:
 
-.. versionadded:: 0.20.0 support for callable `usecols` arguments
-
 .. ipython:: python
 
     data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'
@@ -1447,8 +1438,6 @@ is whitespace).
    df = pd.read_fwf('bar.csv', header=None, index_col=0)
    df
 
-.. versionadded:: 0.20.0
-
 ``read_fwf`` supports the ``dtype`` parameter for specifying the types of
 parsed columns to be different from the inferred type.
 
@@ -2221,8 +2210,6 @@ For line-delimited json files, pandas can also return an iterator which reads in
 Table schema
 ''''''''''''
 
-.. versionadded:: 0.20.0
-
 `Table Schema`_ is a spec for describing tabular datasets as a JSON
 object. The JSON includes information on the field names, types, and
 other attributes. You can use the orient ``table`` to build
@@ -3071,8 +3058,6 @@ missing data to recover integer dtype:
 Dtype specifications
 ++++++++++++++++++++
 
-.. versionadded:: 0.20
-
 As an alternative to converters, the type for an entire column can
 be specified using the `dtype` keyword, which takes a dictionary
 mapping column names to types.  To interpret data with
@@ -3345,8 +3330,6 @@ any pickled pandas object (or any other pickled object) from file:
 Compressed pickle files
 '''''''''''''''''''''''
 
-.. versionadded:: 0.20.0
-
 :func:`read_pickle`, :meth:`DataFrame.to_pickle` and :meth:`Series.to_pickle` can read
 and write compressed pickle files. The compression types of ``gzip``, ``bz2``, ``xz`` are supported for reading and writing.
 The ``zip`` file format only supports reading and must contain only one data file
@@ -4323,8 +4306,6 @@ control compression: ``complevel`` and ``complib``.
              - `bzip2 <http://bzip.org/>`_: Good compression rates.
              - `blosc <http://www.blosc.org/>`_: Fast compression and decompression.
 
-             .. versionadded:: 0.20.2
-
                 Support for alternative blosc compressors:
 
                 - `blosc:blosclz <http://www.blosc.org/>`_ This is the
@@ -4651,8 +4632,6 @@ Performance
 Feather
 -------
 
-.. versionadded:: 0.20.0
-
 Feather provides binary columnar serialization for data frames. It is designed to make reading and writing data
 frames efficient, and to make sharing data across data analysis languages easy.
 

doc/source/user_guide/merging.rst

@@ -843,8 +843,6 @@ resulting dtype will be upcast.
    pd.merge(left, right, how='outer', on='key')
    pd.merge(left, right, how='outer', on='key').dtypes
 
-.. versionadded:: 0.20.0
-
 Merging will preserve ``category`` dtypes of the mergands. See also the section on :ref:`categoricals <categorical.merge>`.
 
 The left frame.

doc/source/user_guide/options.rst

@@ -561,8 +561,6 @@ However, setting this option incorrectly for your terminal will cause these char
 Table schema display
 --------------------
 
-.. versionadded:: 0.20.0
-
 ``DataFrame`` and ``Series`` will publish a Table Schema representation
 by default. False by default, this can be enabled globally with the
 ``display.html.table_schema`` option:

doc/source/user_guide/reshaping.rst

@@ -539,8 +539,6 @@ Alternatively we can specify custom bin-edges:
    c = pd.cut(ages, bins=[0, 18, 35, 70])
    c
 
-.. versionadded:: 0.20.0
-
 If the ``bins`` keyword is an ``IntervalIndex``, then these will be
 used to bin the passed data.::