diff --git a/doc/source/development/contributing.rst b/doc/source/development/contributing.rst
index 949b6bd475319a..62e582dffae476 100644
--- a/doc/source/development/contributing.rst
+++ b/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
diff --git a/doc/source/getting_started/basics.rst b/doc/source/getting_started/basics.rst
index 36a7166f350e5b..9b97aa25a92403 100644
--- a/doc/source/getting_started/basics.rst
+++ b/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.
diff --git a/doc/source/user_guide/advanced.rst b/doc/source/user_guide/advanced.rst
index 4949dd580414f3..c6eadd2adadce4 100644
--- a/doc/source/user_guide/advanced.rst
+++ b/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.
diff --git a/doc/source/user_guide/categorical.rst b/doc/source/user_guide/categorical.rst
index 8ca96ba0daa5ee..6651f656ae45d3 100644
--- a/doc/source/user_guide/categorical.rst
+++ b/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.
 
diff --git a/doc/source/user_guide/computation.rst b/doc/source/user_guide/computation.rst
index 4beac5e035efc2..bc00cd7f13e13a 100644
--- a/doc/source/user_guide/computation.rst
+++ b/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:
 
diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 141d1708d882d6..8cd229070e365f 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/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`.
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 6b23c814843e13..173bcf7537154d 100644
--- a/doc/source/user_guide/io.rst
+++ b/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.
 
diff --git a/doc/source/user_guide/merging.rst b/doc/source/user_guide/merging.rst
index 4c0d3b75a4f790..7bedc9515abb21 100644
--- a/doc/source/user_guide/merging.rst
+++ b/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.
diff --git a/doc/source/user_guide/options.rst b/doc/source/user_guide/options.rst
index a6491c66456132..5817efb31814e7 100644
--- a/doc/source/user_guide/options.rst
+++ b/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:
diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst
index b2ee252495f239..8583a9312b6907 100644
--- a/doc/source/user_guide/reshaping.rst
+++ b/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.::
 
diff --git a/doc/source/user_guide/text.rst b/doc/source/user_guide/text.rst
index 789ff2a65355b5..d521c745ccfe54 100644
--- a/doc/source/user_guide/text.rst
+++ b/doc/source/user_guide/text.rst
@@ -228,8 +228,6 @@ and ``repl`` must be strings:
     dollars.str.replace(r'-\$', '-')
     dollars.str.replace('-$', '-', regex=False)
 
-.. versionadded:: 0.20.0
-
 The ``replace`` method can also take a callable as replacement. It is called
 on every ``pat`` using :func:`re.sub`. The callable should expect one
 positional argument (a regex object) and return a string.
@@ -254,8 +252,6 @@ positional argument (a regex object) and return a string.
    pd.Series(['Foo Bar Baz', np.nan],
              dtype="string").str.replace(pat, repl)
 
-.. versionadded:: 0.20.0
-
 The ``replace`` method also accepts a compiled regular expression object
 from :func:`re.compile` as a pattern. All flags should be included in the
 compiled regular expression object.
diff --git a/doc/source/user_guide/timedeltas.rst b/doc/source/user_guide/timedeltas.rst
index 3e46140d79b8e6..3439a0a4c13c75 100644
--- a/doc/source/user_guide/timedeltas.rst
+++ b/doc/source/user_guide/timedeltas.rst
@@ -327,8 +327,6 @@ similarly to the ``Series``. These are the *displayed* values of the ``Timedelta
 You can convert a ``Timedelta`` to an `ISO 8601 Duration`_ string with the
 ``.isoformat`` method
 
-.. versionadded:: 0.20.0
-
 .. ipython:: python
 
     pd.Timedelta(days=6, minutes=50, seconds=3,
diff --git a/doc/source/user_guide/timeseries.rst b/doc/source/user_guide/timeseries.rst
index 0894edd69c2ae4..17b02374050d29 100644
--- a/doc/source/user_guide/timeseries.rst
+++ b/doc/source/user_guide/timeseries.rst
@@ -376,8 +376,6 @@ We subtract the epoch (midnight at January 1, 1970 UTC) and then floor divide by
 Using the ``origin`` Parameter
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 Using the ``origin`` parameter, one can specify an alternative starting point for creation
 of a ``DatetimeIndex``. For example, to use 1960-01-01 as the starting date:
 
diff --git a/doc/source/user_guide/visualization.rst b/doc/source/user_guide/visualization.rst
index fa16b2f2166105..609969b6667268 100644
--- a/doc/source/user_guide/visualization.rst
+++ b/doc/source/user_guide/visualization.rst
@@ -1247,8 +1247,6 @@ in ``pandas.plotting.plot_params`` can be used in a `with statement`:
 Automatic date tick adjustment
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 ``TimedeltaIndex`` now uses the native matplotlib
 tick locator methods, it is useful to call the automatic
 date tick adjustment from matplotlib for figures whose ticklabels overlap.
diff --git a/pandas/_libs/interval.pyx b/pandas/_libs/interval.pyx
index 3c7ec70fb1f88f..6a3f20928f64bc 100644
--- a/pandas/_libs/interval.pyx
+++ b/pandas/_libs/interval.pyx
@@ -191,8 +191,6 @@ cdef class Interval(IntervalMixin):
     """
     Immutable object implementing an Interval, a bounded slice-like interval.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     left : orderable scalar
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 3d267b0114695c..8435f1cd7d7322 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -1157,8 +1157,6 @@ cdef class _Timedelta(timedelta):
         ``P[n]Y[n]M[n]DT[n]H[n]M[n]S``, where the ``[n]`` s are replaced by the
         values. See https://en.wikipedia.org/wiki/ISO_8601#Durations.
 
-        .. versionadded:: 0.20.0
-
         Returns
         -------
         formatted : str
diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index bd1ed0bb7d3189..f2176f573207ce 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -199,8 +199,6 @@ def union_categoricals(to_union, sort_categories=False, ignore_order=False):
         If true, the ordered attribute of the Categoricals will be ignored.
         Results in an unordered categorical.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     result : Categorical
diff --git a/pandas/core/dtypes/inference.py b/pandas/core/dtypes/inference.py
index 461b5cc6232cdc..e69e703f3a96c0 100644
--- a/pandas/core/dtypes/inference.py
+++ b/pandas/core/dtypes/inference.py
@@ -162,8 +162,6 @@ def is_file_like(obj):
     Note: file-like objects must be iterable, but
     iterable objects need not be file-like.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     obj : The object to check
@@ -281,8 +279,6 @@ def is_nested_list_like(obj):
     Check if the object is list-like, and that all of its elements
     are also list-like.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     obj : The object to check
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 7f36826a893dd3..f2074bab276ac8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2082,8 +2082,6 @@ def to_feather(self, fname):
         """
         Write out the binary feather-format for DataFrames.
 
-        .. versionadded:: 0.20.0
-
         Parameters
         ----------
         fname : str
@@ -7868,8 +7866,6 @@ def nunique(self, axis=0, dropna=True):
         Return Series with number of distinct observations. Can ignore NaN
         values.
 
-        .. versionadded:: 0.20.0
-
         Parameters
         ----------
         axis : {0 or 'index', 1 or 'columns'}, default 0
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index a300748ee5bc81..61af22c6e92b34 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -897,8 +897,6 @@ def squeeze(self, axis=None):
             A specific axis to squeeze. By default, all length-1 axes are
             squeezed.
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         DataFrame, Series, or scalar
@@ -2163,8 +2161,6 @@ def _repr_data_resource_(self):
         Specifies the one-based bottommost row and rightmost column that
         is to be frozen.
 
-        .. versionadded:: 0.20.0.
-
     See Also
     --------
     to_csv : Write DataFrame to a comma-separated values (csv) file.
@@ -2756,8 +2752,6 @@ def to_pickle(self, path, compression="infer", protocol=pickle.HIGHEST_PROTOCOL)
         default 'infer'
             A string representing the compression to use in the output file. By
             default, infers from the file extension in specified path.
-
-            .. versionadded:: 0.20.0
         protocol : int
             Int which indicates which protocol should be used by the pickler,
             default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
@@ -3032,22 +3026,15 @@ def to_latex(
         multicolumn : bool, default True
             Use \multicolumn to enhance MultiIndex columns.
             The default will be read from the config module.
-
-            .. versionadded:: 0.20.0
         multicolumn_format : str, default 'l'
             The alignment for multicolumns, similar to `column_format`
             The default will be read from the config module.
-
-            .. versionadded:: 0.20.0
         multirow : bool, default False
             Use \multirow to enhance MultiIndex rows. Requires adding a
             \usepackage{multirow} to your LaTeX preamble. Will print
             centered labels (instead of top-aligned) across the contained
             rows, separating groups via clines. The default will be read
             from the pandas config module.
-
-            .. versionadded:: 0.20.0
-
         caption : str, optional
             The LaTeX caption to be placed inside ``\caption{}`` in the output.
 
@@ -5133,8 +5120,6 @@ def pipe(self, func, *args, **kwargs):
     Call ``func`` on self producing a %(klass)s with transformed values
     and that has the same axis length as self.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     func : function, str, list or dict
@@ -5805,8 +5790,6 @@ def astype(self, dtype, copy=True, errors="raise"):
             - ``raise`` : allow exceptions to be raised
             - ``ignore`` : suppress exceptions. On error return original object.
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         casted : same type as caller
@@ -7946,8 +7929,6 @@ def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
             Value to use for missing values, applied during upsampling (note
             this does not fill NaNs that already were present).
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         converted : same type as caller
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index a78857423e7e0a..5c7c56e2a31df0 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1692,8 +1692,6 @@ def nunique(self, dropna=True):
         Return DataFrame with number of distinct observations per group for
         each column.
 
-        .. versionadded:: 0.20.0
-
         Parameters
         ----------
         dropna : bool, default True
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index f622480cfe4b7f..f88f2e21bd5950 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1942,8 +1942,6 @@ def ngroup(self, ascending=True):
         would be seen when iterating over the groupby object, not the
         order they are first observed.
 
-        .. versionadded:: 0.20.2
-
         Parameters
         ----------
         ascending : bool, default True
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 9d6487f7a8ae4a..4c15e4b26ed46d 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1897,8 +1897,6 @@ def isna(self):
         empty strings `''` or :attr:`numpy.inf` are not considered NA values
         (unless you set ``pandas.options.mode.use_inf_as_na = True``).
 
-        .. versionadded:: 0.20.0
-
         Returns
         -------
         numpy.ndarray
@@ -1956,8 +1954,6 @@ def notna(self):
         NA values, such as None or :attr:`numpy.NaN`, get mapped to ``False``
         values.
 
-        .. versionadded:: 0.20.0
-
         Returns
         -------
         numpy.ndarray
@@ -3420,8 +3416,6 @@ def _reindex_non_unique(self, target):
             Sort the join keys lexicographically in the result Index. If False,
             the order of the join keys depends on the join type (how keyword)
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         join_index, (left_indexer, right_indexer)
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index fda5c78a61e530..74dbcd4067ec00 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -1823,8 +1823,6 @@ def _lexsort_depth(self) -> int:
 
     def _sort_levels_monotonic(self):
         """
-        .. versionadded:: 0.20.0
-
         This is an *internal* function.
 
         Create a new MultiIndex from the current to monotonically sorted
@@ -1901,8 +1899,6 @@ def remove_unused_levels(self):
         appearance, meaning the same .values and ordering. It will also
         be .equals() to the original.
 
-        .. versionadded:: 0.20.0
-
         Returns
         -------
         MultiIndex
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index d4ae3767f61577..13cb0f9aed303c 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -822,8 +822,6 @@ def asfreq(self, fill_value=None):
             Value to use for missing values, applied during upsampling (note
             this does not fill NaNs that already were present).
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         DataFrame or Series
diff --git a/pandas/core/reshape/melt.py b/pandas/core/reshape/melt.py
index 6f2e264f1a4d01..c85050bc4232b1 100644
--- a/pandas/core/reshape/melt.py
+++ b/pandas/core/reshape/melt.py
@@ -220,9 +220,6 @@ def wide_to_long(df, stubnames, i, j, sep="", suffix=r"\d+"):
         in the wide format, to be stripped from the names in the long format.
         For example, if your column names are A-suffix1, A-suffix2, you
         can strip the hyphen by specifying `sep='-'`
-
-        .. versionadded:: 0.20.0
-
     suffix : str, default '\\d+'
         A regular expression capturing the wanted suffixes. '\\d+' captures
         numeric suffixes. Suffixes with no numbers could be specified with the
@@ -231,8 +228,6 @@ def wide_to_long(df, stubnames, i, j, sep="", suffix=r"\d+"):
         A-one, B-two,.., and you have an unrelated column A-rating, you can
         ignore the last one by specifying `suffix='(!?one|two)'`
 
-        .. versionadded:: 0.20.0
-
         .. versionchanged:: 0.23.0
             When all suffixes are numeric, they are cast to int64/float64.
 
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index d4aed24eaa26ac..b557e1389a21b2 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -364,8 +364,6 @@ def merge_asof(
     direction : 'backward' (default), 'forward', or 'nearest'
         Whether to search for prior, subsequent, or closest matches.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     merged : DataFrame
diff --git a/pandas/core/reshape/tile.py b/pandas/core/reshape/tile.py
index 6942a5797a7f0f..2cc9f8927effb5 100644
--- a/pandas/core/reshape/tile.py
+++ b/pandas/core/reshape/tile.py
@@ -300,8 +300,6 @@ def qcut(x, q, labels=None, retbins=False, precision=3, duplicates="raise"):
     duplicates : {default 'raise', 'drop'}, optional
         If bin edges are not unique, raise ValueError or drop non-uniques.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     out : Categorical or Series or array of integers if labels is False
diff --git a/pandas/core/strings.py b/pandas/core/strings.py
index e50da168af4d2c..fcbb000acc2560 100644
--- a/pandas/core/strings.py
+++ b/pandas/core/strings.py
@@ -491,18 +491,10 @@ def str_replace(arr, pat, repl, n=-1, case=None, flags=0, regex=True):
     ----------
     pat : str or compiled regex
         String can be a character sequence or regular expression.
-
-        .. versionadded:: 0.20.0
-            `pat` also accepts a compiled regex.
-
     repl : str or callable
         Replacement string or a callable. The callable is passed the regex
         match object and must return a replacement string to be used.
         See :func:`re.sub`.
-
-        .. versionadded:: 0.20.0
-            `repl` also accepts a callable.
-
     n : int, default -1 (all)
         Number of replacements to make from start.
     case : bool, default None
diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py
index ea7a09881ef875..70143e4603a4b2 100644
--- a/pandas/core/tools/datetimes.py
+++ b/pandas/core/tools/datetimes.py
@@ -644,8 +644,6 @@ def to_datetime(
           at noon on January 1, 4713 BC.
         - If Timestamp convertible, origin is set to Timestamp identified by
           origin.
-
-        .. versionadded:: 0.20.0
     cache : bool, default True
         If True, use a cache of unique, converted dates to apply the datetime
         conversion. May produce significant speed-up when parsing duplicate
diff --git a/pandas/core/util/hashing.py b/pandas/core/util/hashing.py
index ca5279e93f678a..e3617d53b000ae 100644
--- a/pandas/core/util/hashing.py
+++ b/pandas/core/util/hashing.py
@@ -76,8 +76,6 @@ def hash_pandas_object(
         Whether to first categorize object arrays before hashing. This is more
         efficient when the array contains duplicate values.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     Series of uint64, same length as the object
@@ -146,8 +144,6 @@ def hash_tuples(vals, encoding="utf8", hash_key=None):
     """
     Hash an MultiIndex / list-of-tuples efficiently
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     vals : MultiIndex, list-of-tuples, or single tuple
@@ -262,8 +258,6 @@ def hash_array(vals, encoding: str = "utf8", hash_key=None, categorize: bool = T
         Whether to first categorize object arrays before hashing. This is more
         efficient when the array contains duplicate values.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     1d uint64 numpy array of hash values, same length as the vals
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index 3e75340ed3bcf7..68eb1f630bfc32 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -649,8 +649,6 @@ class Window(_Window):
         For fixed windows, defaults to 'both'. Remaining cases not implemented
         for fixed windows.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     a Window or Rolling sub-classed for the particular operation
diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py
index a85fc8bfb14142..883af5c2e62f05 100644
--- a/pandas/errors/__init__.py
+++ b/pandas/errors/__init__.py
@@ -25,8 +25,6 @@ class UnsortedIndexError(KeyError):
     """
     Error raised when attempting to get a slice of a MultiIndex,
     and the index has not been lexsorted. Subclass of `KeyError`.
-
-    .. versionadded:: 0.20.0
     """
 
 
diff --git a/pandas/io/excel/_base.py b/pandas/io/excel/_base.py
index 039a0560af627c..6eb1b9e950dfdc 100644
--- a/pandas/io/excel/_base.py
+++ b/pandas/io/excel/_base.py
@@ -107,9 +107,6 @@
     Use `object` to preserve data as stored in Excel and not interpret dtype.
     If converters are specified, they will be applied INSTEAD
     of dtype conversion.
-
-    .. versionadded:: 0.20.0
-
 engine : str, default None
     If io is not a buffer or path, this must be set to identify io.
     Acceptable values are None, "xlrd", "openpyxl" or "odf".
diff --git a/pandas/io/feather_format.py b/pandas/io/feather_format.py
index 25a6db675265d3..dd6519275ad150 100644
--- a/pandas/io/feather_format.py
+++ b/pandas/io/feather_format.py
@@ -71,8 +71,6 @@ def read_feather(path, columns=None, use_threads=True):
     """
     Load a feather-format object from the file path.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     path : str, path object or file-like object
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index 0200b78e02fd2b..abf2caf3914e06 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -491,8 +491,6 @@ def render(self, **kwargs):
             This is useful when you need to provide
             additional variables for a custom template.
 
-            .. versionadded:: 0.20
-
         Returns
         -------
         rendered : str
@@ -1200,9 +1198,6 @@ def bar(
             - 'mid' : the center of the cell is at (max-min)/2, or
               if values are all negative (positive) the zero is aligned
               at the right (left) of the cell.
-
-              .. versionadded:: 0.20.0
-
         vmin : float, optional
             Minimum bar value, defining the left hand limit
             of the bar drawing range, lower values are clipped to `vmin`.
diff --git a/pandas/io/json/_normalize.py b/pandas/io/json/_normalize.py
index 24a255c78f3c07..cf8b9d901eda24 100644
--- a/pandas/io/json/_normalize.py
+++ b/pandas/io/json/_normalize.py
@@ -46,9 +46,6 @@ def nested_to_record(
     sep : str, default '.'
         Nested records will generate names separated by sep,
         e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
-
-        .. versionadded:: 0.20.0
-
     level: int, optional, default: 0
         The number of levels in the json string.
 
@@ -146,15 +143,9 @@ def json_normalize(
           always present.
         * 'raise' : will raise KeyError if keys listed in meta are not
           always present.
-
-        .. versionadded:: 0.20.0
-
     sep : str, default '.'
         Nested records will generate names separated by sep.
         e.g., for sep='.', {'foo': {'bar': 0}} -> foo.bar.
-
-        .. versionadded:: 0.20.0
-
     max_level : int, default None
         Max number of levels(depth of dict) to normalize.
         if None, normalizes all levels.
diff --git a/pandas/io/pickle.py b/pandas/io/pickle.py
index 4b9a52a1fb8f33..621e8e09230b76 100644
--- a/pandas/io/pickle.py
+++ b/pandas/io/pickle.py
@@ -23,8 +23,6 @@ def to_pickle(obj, path, compression="infer", protocol=pickle.HIGHEST_PROTOCOL):
     compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
         A string representing the compression to use in the output file. By
         default, infers from the file extension in specified path.
-
-        .. versionadded:: 0.20.0
     protocol : int
         Int which indicates which protocol should be used by the pickler,
         default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
@@ -99,8 +97,6 @@ def read_pickle(path, compression="infer"):
         or '.zip' respectively, and no decompression otherwise.
         Set to None for no decompression.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     unpickled : same type as object stored in file
diff --git a/pandas/plotting/_misc.py b/pandas/plotting/_misc.py
index 426ca9632af293..815c69bc27d7ad 100644
--- a/pandas/plotting/_misc.py
+++ b/pandas/plotting/_misc.py
@@ -353,8 +353,6 @@ def parallel_coordinates(
         Options to be passed to axvline method for vertical lines.
     sort_labels : bool, default False
         Sort class_column labels, useful when assigning colors.
-
-        .. versionadded:: 0.20.0
     **kwargs
         Options to pass to matplotlib plotting method.