DOC: Remove ..versionadded directives before 2.0

doc/source/user_guide/duplicates.rst

@@ -109,8 +109,6 @@ with the same label.
 Disallowing Duplicate Labels
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 1.2.0
-
 As noted above, handling duplicates is an important feature when reading in raw
 data. That said, you may want to avoid introducing duplicates as part of a data
 processing pipeline (from methods like :meth:`pandas.concat`,

doc/source/user_guide/groupby.rst

@@ -1264,8 +1264,6 @@ with
 Numba accelerated routines
 --------------------------
 
-.. versionadded:: 1.1
-
 If `Numba <https://numba.pydata.org/>`__ is installed as an optional dependency, the ``transform`` and
 ``aggregate`` methods support ``engine='numba'`` and ``engine_kwargs`` arguments.
 See :ref:`enhancing performance with Numba <enhancingperf.numba>` for general usage of the arguments

doc/source/user_guide/io.rst

@@ -158,12 +158,6 @@ dtype : Type name or dict of column -> type, default ``None``
   and not interpret dtype. If converters are specified, they will be applied INSTEAD
   of dtype conversion.
 
-  .. versionadded:: 1.5.0
-
-     Support for defaultdict was added. Specify a defaultdict as input where
-     the default determines the dtype of the columns which are not explicitly
-     listed.
-
 dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames
   Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
   arrays, nullable dtypes are used for all dtypes that have a nullable
@@ -177,12 +171,8 @@ dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFram
 engine : {``'c'``, ``'python'``, ``'pyarrow'``}
   Parser engine to use. The C and pyarrow engines are faster, while the python engine
   is currently more feature-complete. Multithreading is currently only supported by
-  the pyarrow engine.
-
-  .. versionadded:: 1.4.0
-
-     The "pyarrow" engine was added as an *experimental* engine, and some features
-     are unsupported, or may not work correctly, with this engine.
+  the pyarrow engine.The "pyarrow" engine was added as an *experimental* engine,
+  and some features are unsupported, or may not work correctly, with this engine.
 converters : dict, default ``None``
   Dict of functions for converting values in certain columns. Keys can either be
   integers or column labels.
@@ -357,8 +347,6 @@ on_bad_lines : {{'error', 'warn', 'skip'}}, default 'error'
     - 'warn', print a warning when a bad line is encountered and skip that line.
     - 'skip', skip bad lines without raising or warning when they are encountered.
 
-    .. versionadded:: 1.3.0
-
 .. _io.dtypes:
 
 Specifying column data types
@@ -937,8 +925,6 @@ DD/MM/YYYY instead. For convenience, a ``dayfirst`` keyword is provided:
 Writing CSVs to binary file objects
 +++++++++++++++++++++++++++++++++++
 
-.. versionadded:: 1.2.0
-
 ``df.to_csv(..., mode="wb")`` allows writing a CSV to a file object
 opened binary mode. In most cases, it is not necessary to specify
 ``mode`` as pandas will auto-detect whether the file object is
@@ -1124,8 +1110,6 @@ You can elect to skip bad lines:
     data = "a,b,c\n1,2,3\n4,5,6,7\n8,9,10"
     pd.read_csv(StringIO(data), on_bad_lines="skip")
 
-.. versionadded:: 1.4.0
-
 Or pass a callable function to handle the bad line if ``engine="python"``.
 The bad line will be a list of strings that was split by the ``sep``:
 
@@ -1553,8 +1537,6 @@ functions - the following example shows reading a CSV file:
 
    df = pd.read_csv("https://download.bls.gov/pub/time.series/cu/cu.item", sep="\t")
 
-.. versionadded:: 1.3.0
-
 A custom header can be sent alongside HTTP(s) requests by passing a dictionary
 of header key value mappings to the ``storage_options`` keyword argument as shown below:
 
@@ -1606,8 +1588,6 @@ More sample configurations and documentation can be found at `S3Fs documentation
 If you do *not* have S3 credentials, you can still access public
 data by specifying an anonymous connection, such as
 
-.. versionadded:: 1.2.0
-
 .. code-block:: python
 
    pd.read_csv(
@@ -2541,8 +2521,6 @@ Links can be extracted from cells along with the text using ``extract_links="all
     df[("GitHub", None)]
     df[("GitHub", None)].str[1]
 
-.. versionadded:: 1.5.0
-
 .. _io.html:
 
 Writing to HTML files
@@ -2732,8 +2710,6 @@ parse HTML tables in the top-level pandas io function ``read_html``.
 LaTeX
 -----
 
-.. versionadded:: 1.3.0
-
 Currently there are no methods to read from LaTeX, only output methods.
 
 Writing to LaTeX files
@@ -2772,8 +2748,6 @@ XML
 Reading XML
 '''''''''''
 
-.. versionadded:: 1.3.0
-
 The top-level :func:`~pandas.io.xml.read_xml` function can accept an XML
 string/file/URL and will parse nodes and attributes into a pandas ``DataFrame``.
 
@@ -3099,8 +3073,6 @@ supports parsing such sizeable files using `lxml's iterparse`_ and `etree's iter
 which are memory-efficient methods to iterate through an XML tree and extract specific elements and attributes.
 without holding entire tree in memory.
 
-.. versionadded:: 1.5.0
-
 .. _`lxml's iterparse`: https://lxml.de/3.2/parsing.html#iterparse-and-iterwalk
 .. _`etree's iterparse`: https://docs.python.org/3/library/xml.etree.elementtree.html#xml.etree.ElementTree.iterparse
 
@@ -3139,8 +3111,6 @@ of reading in Wikipedia's very large (12 GB+) latest article data dump.
 Writing XML
 '''''''''''
 
-.. versionadded:: 1.3.0
-
 ``DataFrame`` objects have an instance method ``to_xml`` which renders the
 contents of the ``DataFrame`` as an XML document.
 

doc/source/user_guide/reshaping.rst

@@ -478,8 +478,6 @@ The values can be cast to a different type using the ``dtype`` argument.
 
     pd.get_dummies(df, dtype=np.float32).dtypes
 
-.. versionadded:: 1.5.0
-
 :func:`~pandas.from_dummies` converts the output of :func:`~pandas.get_dummies` back into
 a :class:`Series` of categorical values from indicator values.
 

doc/source/user_guide/text.rst

@@ -335,8 +335,6 @@ regular expression object will raise a ``ValueError``.
 ``removeprefix`` and ``removesuffix`` have the same effect as ``str.removeprefix`` and ``str.removesuffix`` added in
 `Python 3.9 <https://docs.python.org/3/library/stdtypes.html#str.removeprefix>`__:
 
-.. versionadded:: 1.4.0
-
 .. ipython:: python
 
    s = pd.Series(["str_foo", "str_bar", "no_prefix"])

doc/source/user_guide/timeseries.rst

@@ -1964,8 +1964,6 @@ Note the use of ``'start'`` for ``origin`` on the last example. In that case, ``
 Backward resample
 ~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 1.3.0
-
 Instead of adjusting the beginning of bins, sometimes we need to fix the end of the bins to make a backward resample with a given ``freq``. The backward resample sets ``closed`` to ``'right'`` by default since the last value should be considered as the edge point for the last bin.
 
 We can set ``origin`` to ``'end'``. The value for a specific ``Timestamp`` index stands for the resample result from the current ``Timestamp`` minus ``freq`` to the current ``Timestamp`` with a right close.

doc/source/user_guide/visualization.rst

@@ -649,8 +649,6 @@ each point:
 
 If a categorical column is passed to ``c``, then a discrete colorbar will be produced:
 
-.. versionadded:: 1.3.0
-
 .. ipython:: python
 
    @savefig scatter_plot_categorical.png

doc/source/user_guide/window.rst

@@ -76,9 +76,6 @@ which will first group the data by the specified keys and then perform a windowi
     <https://en.wikipedia.org/wiki/Kahan_summation_algorithm>`__ is used
     to compute the rolling sums to preserve accuracy as much as possible.
 
-
-.. versionadded:: 1.3.0
-
 Some windowing operations also support the ``method='table'`` option in the constructor which
 performs the windowing operation over an entire :class:`DataFrame` instead of a single column at a time.
 This can provide a useful performance benefit for a :class:`DataFrame` with many columns
@@ -100,8 +97,6 @@ be calculated with :meth:`~Rolling.apply` by specifying a separate column of wei
    df = pd.DataFrame([[1, 2, 0.6], [2, 3, 0.4], [3, 4, 0.2], [4, 5, 0.7]])
    df.rolling(2, method="table", min_periods=0).apply(weighted_mean, raw=True, engine="numba")  # noqa: E501
 
-.. versionadded:: 1.3
-
 Some windowing operations also support an ``online`` method after constructing a windowing object
 which returns a new object that supports passing in new :class:`DataFrame` or :class:`Series` objects
 to continue the windowing calculation with the new values (i.e. online calculations).
@@ -182,8 +177,6 @@ By default the labels are set to the right edge of the window, but a
 
 This can also be applied to datetime-like indices.
 
-.. versionadded:: 1.3.0
-
 .. ipython:: python
 
     df = pd.DataFrame(
@@ -363,11 +356,8 @@ Numba will be applied in potentially two routines:
 The ``engine_kwargs`` argument is a dictionary of keyword arguments that will be passed into the
 `numba.jit decorator <https://numba.readthedocs.io/en/stable/user/jit.html>`__.
 These keyword arguments will be applied to *both* the passed function (if a standard Python function)
-and the apply for loop over each window.
-
-.. versionadded:: 1.3.0
-
-``mean``, ``median``, ``max``, ``min``, and ``sum`` also support the ``engine`` and ``engine_kwargs`` arguments.
+and the apply for loop over each window. ``mean``, ``median``, ``max``, ``min``, and ``sum``
+also support the ``engine`` and ``engine_kwargs`` arguments.
 
 .. _window.cov_corr:
 

pandas/_testing/asserters.py

@@ -931,14 +931,10 @@ def assert_series_equal(
         assertion message.
     check_index : bool, default True
         Whether to check index equivalence. If False, then compare only values.
-
-        .. versionadded:: 1.3.0
     check_like : bool, default False
         If True, ignore the order of the index. Must be False if check_index is False.
         Note: same labels must be with the same data.
 
-        .. versionadded:: 1.5.0
-
     See Also
     --------
     testing.assert_index_equal : Check that two Indexes are equal.

pandas/core/algorithms.py

@@ -698,8 +698,7 @@ def factorize(
         NaN values will be encoded as non-negative integers and will not drop the
         NaN from the uniques of the values.
 
-        .. versionadded:: 1.5.0
-    {size_hint}\
+    {size_hint}
 
     Returns
     -------
@@ -731,7 +730,7 @@ def factorize(
     ``pd.factorize(values)``. The results are identical for methods like
     :meth:`Series.factorize`.
 
-    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"))
+    >>> codes, uniques = pd.factorize(np.array(["b", "b", "a", "c", "b"], dtype="O"))
     >>> codes
     array([0, 0, 1, 2, 0])
     >>> uniques
@@ -740,8 +739,9 @@ def factorize(
     With ``sort=True``, the `uniques` will be sorted, and `codes` will be
     shuffled so that the relationship is the maintained.
 
-    >>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"),
-    ...                               sort=True)
+    >>> codes, uniques = pd.factorize(
+    ...     np.array(["b", "b", "a", "c", "b"], dtype="O"), sort=True
+    ... )
     >>> codes
     array([1, 1, 0, 2, 1])
     >>> uniques
@@ -751,7 +751,7 @@ def factorize(
     the `codes` with the sentinel value ``-1`` and missing values are not
     included in `uniques`.
 
-    >>> codes, uniques = pd.factorize(np.array(['b', None, 'a', 'c', 'b'], dtype="O"))
+    >>> codes, uniques = pd.factorize(np.array(["b", None, "a", "c", "b"], dtype="O"))
     >>> codes
     array([ 0, -1,  1,  2,  0])
     >>> uniques
@@ -761,7 +761,7 @@ def factorize(
     NumPy arrays). When factorizing pandas objects, the type of `uniques`
     will differ. For Categoricals, a `Categorical` is returned.
 
-    >>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])
+    >>> cat = pd.Categorical(["a", "a", "c"], categories=["a", "b", "c"])
     >>> codes, uniques = pd.factorize(cat)
     >>> codes
     array([0, 0, 1])
@@ -775,7 +775,7 @@ def factorize(
     For all other pandas objects, an Index of the appropriate type is
     returned.
 
-    >>> cat = pd.Series(['a', 'a', 'c'])
+    >>> cat = pd.Series(["a", "a", "c"])
     >>> codes, uniques = pd.factorize(cat)
     >>> codes
     array([0, 0, 1])

pandas/core/arrays/base.py

@@ -1574,8 +1574,6 @@ def factorize(
             NaN values will be encoded as non-negative integers and will not drop the
             NaN from the uniques of the values.
 
-            .. versionadded:: 1.5.0
-
         Returns
         -------
         codes : ndarray

pandas/core/common.py

@@ -639,8 +639,6 @@ def fill_missing_names(names: Sequence[Hashable | None]) -> list[Hashable]:
     """
     If a name is missing then replace it by level_n, where n is the count
 
-    .. versionadded:: 1.4.0
-
     Parameters
     ----------
     names : list-like