DOC: Remove ..versionchanged less than version 2.0

doc/source/user_guide/io.rst

@@ -303,8 +303,6 @@ compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``'zstd'
   As an example, the following could be passed for faster compression and to
   create a reproducible gzip archive:
   ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
-
-  .. versionchanged:: 1.2.0 Previous versions forwarded dict entries for 'gzip' to ``gzip.open``.
 thousands : str, default ``None``
   Thousands separator.
 decimal : str, default ``'.'``
@@ -1472,7 +1470,7 @@ rather than reading the entire file into memory, such as the following:
    table
 
 
-By specifying a ``chunksize`` to ``read_csv``, the return
+By specifying a ``chunksize`` to :func:`read_csv` as a context manager, the return
 value will be an iterable object of type ``TextFileReader``:
 
 .. ipython:: python
@@ -1482,10 +1480,6 @@ value will be an iterable object of type ``TextFileReader``:
        for chunk in reader:
            print(chunk)
 
-.. versionchanged:: 1.2
-
-  ``read_csv/json/sas`` return a context-manager when iterating through a file.
-
 Specifying ``iterator=True`` will also return the ``TextFileReader`` object:
 
 .. ipython:: python

doc/source/user_guide/visualization.rst

@@ -326,8 +326,6 @@ The ``by`` keyword can be specified to plot grouped histograms:
 
 In addition, the ``by`` keyword can also be specified in :meth:`DataFrame.plot.hist`.
 
-.. versionchanged:: 1.4.0
-
 .. ipython:: python
 
    data = pd.DataFrame(
@@ -480,8 +478,6 @@ columns:
 
 You could also create groupings with :meth:`DataFrame.plot.box`, for instance:
 
-.. versionchanged:: 1.4.0
-
 .. ipython:: python
    :suppress:
 

pandas/core/arrays/categorical.py

@@ -2545,10 +2545,6 @@ def unique(self) -> Self:
         Return the ``Categorical`` which ``categories`` and ``codes`` are
         unique.
 
-        .. versionchanged:: 1.3.0
-
-            Previously, unused categories were dropped from the new categories.
-
         Returns
         -------
         Categorical

pandas/core/arrays/masked.py

@@ -1686,8 +1686,6 @@ def any(
         missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
         is used as for logical operations.
 
-        .. versionchanged:: 1.4.0
-
         Parameters
         ----------
         skipna : bool, default True
@@ -1774,8 +1772,6 @@ def all(
         missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
         is used as for logical operations.
 
-        .. versionchanged:: 1.4.0
-
         Parameters
         ----------
         skipna : bool, default True

pandas/core/arrays/string_.py

@@ -573,9 +573,7 @@ class StringArray(BaseStringArray, NumpyExtensionArray):  # type: ignore[misc]
            :meth:`pandas.array` with ``dtype="string"`` for a stable way of
            creating a `StringArray` from any sequence.
 
-        .. versionchanged:: 1.5.0
-
-           StringArray now accepts array-likes containing
+           StringArray accepts array-likes containing
            nan-likes(``None``, ``np.nan``) for the ``values`` parameter
            in addition to strings and :attr:`pandas.NA`
 

pandas/core/frame.py

@@ -554,8 +554,6 @@ class DataFrame(NDFrame, OpsMixin):
         If data is a dict containing one or more Series (possibly of different dtypes),
         ``copy=False`` will ensure that these inputs are not copied.
 
-        .. versionchanged:: 1.3.0
-
     See Also
     --------
     DataFrame.from_records : Constructor from tuples, also record arrays.
@@ -2686,17 +2684,13 @@ def to_stata(
             8 characters and values are repeated.
         {compression_options}
 
-            .. versionchanged:: 1.4.0 Zstandard support.
-
         {storage_options}
 
         value_labels : dict of dicts
             Dictionary containing columns as keys and dictionaries of column value
             to labels as values. Labels for a single variable must be 32,000
             characters or smaller.
 
-            .. versionadded:: 1.4.0
-
         Raises
         ------
         NotImplementedError
@@ -3534,8 +3528,6 @@ def to_xml(
             scripts and not later versions is currently supported.
         {compression_options}
 
-            .. versionchanged:: 1.4.0 Zstandard support.
-
         {storage_options}
 
         Returns
@@ -9487,13 +9479,6 @@ def groupby(
             when the result's index (and column) labels match the inputs, and
             are included otherwise.
 
-            .. versionchanged:: 1.5.0
-
-               Warns that ``group_keys`` will no longer be ignored when the
-               result from ``apply`` is a like-indexed Series or DataFrame.
-               Specify ``group_keys`` explicitly to include the group keys or
-               not.
-
             .. versionchanged:: 2.0.0
 
                ``group_keys`` now defaults to ``True``.

pandas/core/generic.py

@@ -2415,8 +2415,6 @@ def to_json(
             list-like.
         {compression_options}
 
-            .. versionchanged:: 1.4.0 Zstandard support.
-
         index : bool or None, default None
             The index is only used when 'orient' is 'split', 'index', 'column',
             or 'table'. Of these, 'index' and 'column' do not support
@@ -3850,12 +3848,6 @@ def to_csv(
             The newline character or character sequence to use in the output
             file. Defaults to `os.linesep`, which depends on the OS in which
             this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).
-
-            .. versionchanged:: 1.5.0
-
-                Previously was line_terminator, changed for consistency with
-                read_csv and the standard library 'csv' module.
-
         chunksize : int or None
             Rows to write at a time.
         date_format : str, default None
@@ -5859,11 +5851,6 @@ def sample(
             If int, array-like, or BitGenerator, seed for random number generator.
             If np.random.RandomState or np.random.Generator, use as given.
             Default ``None`` results in sampling with the current state of np.random.
-
-            .. versionchanged:: 1.4.0
-
-                np.random.Generator objects now accepted
-
         axis : {0 or 'index', 1 or 'columns', None}, default None
             Axis to sample. Accepts axis number or name. Default is stat axis
             for given data type. For `Series` this parameter is unused and defaults to `None`.

pandas/core/groupby/generic.py

@@ -251,9 +251,7 @@ def _get_data_to_aggregate(
     1        1        2
     2        3        4
 
-    .. versionchanged:: 1.3.0
-
-        The resulting dtype will reflect the return value of the aggregating function.
+    The resulting dtype will reflect the return value of the aggregating function.
 
     >>> s.groupby([1, 1, 2, 2]).agg(lambda x: x.astype(float).min())
     1    1.0
@@ -307,11 +305,8 @@ def apply(self, func, *args, **kwargs) -> Series:
 
         Notes
         -----
-
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the passed ``func``,
-            see the examples below.
+        The resulting dtype will reflect the return value of the passed ``func``,
+        see the examples below.
 
         Functions that mutate the passed object can produce unexpected
         behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
@@ -332,9 +327,7 @@ def apply(self, func, *args, **kwargs) -> Series:
         its argument and returns a Series.  `apply` combines the result for
         each group together into a new Series.
 
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the passed ``func``.
+        The resulting dtype will reflect the return value of the passed ``func``.
 
         >>> g1.apply(lambda x: x * 2 if x.name == "a" else x / 2)
         a    0.0
@@ -455,10 +448,8 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
         behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
         for more details.
 
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the passed ``func``,
-            see the examples below.
+        The resulting dtype will reflect the return value of the passed ``func``,
+        see the examples below.
 
         Examples
         --------
@@ -497,10 +488,8 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
         1        1        2
         2        3        4
 
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the aggregating
-            function.
+        The resulting dtype will reflect the return value of the aggregating
+        function.
 
         >>> s.groupby([1, 1, 2, 2]).agg(lambda x: x.astype(float).min())
         1    1.0
@@ -705,8 +694,6 @@ def _wrap_applied_output(
     Parrot     25.0
     Name: Max Speed, dtype: float64
 
-    .. versionchanged:: 1.3.0
-
     The resulting dtype will reflect the return value of the passed ``func``,
     for example:
 
@@ -1788,9 +1775,7 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
 
     See :ref:`groupby.aggregate.named` for more.
 
-    .. versionchanged:: 1.3.0
-
-        The resulting dtype will reflect the return value of the aggregating function.
+    The resulting dtype will reflect the return value of the aggregating function.
 
     >>> df.groupby("A")[["B"]].agg(lambda x: x.astype(float).min())
           B
@@ -1881,10 +1866,8 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
         behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
         for more details.
 
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the passed ``func``,
-            see the examples below.
+        The resulting dtype will reflect the return value of the passed ``func``,
+        see the examples below.
 
         Examples
         --------
@@ -1964,10 +1947,8 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
 
         See :ref:`groupby.aggregate.named` for more.
 
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the aggregating
-            function.
+        The resulting dtype will reflect the return value of the aggregating
+        function.
 
         >>> df.groupby("A")[["B"]].agg(lambda x: x.astype(float).min())
               B
@@ -2326,8 +2307,6 @@ def _transform_general(self, func, engine, engine_kwargs, *args, **kwargs):
     4  3.666667  4.0
     5  4.000000  5.0
 
-    .. versionchanged:: 1.3.0
-
     The resulting dtype will reflect the return value of the passed ``func``,
     for example:
 

pandas/core/groupby/groupby.py

@@ -406,10 +406,8 @@ class providing the base-class of operations.
 The group data and group index will be passed as numpy arrays to the JITed
 user defined function, and no alternative execution attempts will be tried.
 
-.. versionchanged:: 1.3.0
-
-    The resulting dtype will reflect the return value of the passed ``func``,
-    see the examples below.
+The resulting dtype will reflect the return value of the passed ``func``,
+see the examples below.
 
 .. versionchanged:: 2.0.0
 
@@ -1518,11 +1516,8 @@ def apply(self, func, *args, include_groups: bool = False, **kwargs) -> NDFrameT
 
         Notes
         -----
-
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the passed ``func``,
-            see the examples below.
+        The resulting dtype will reflect the return value of the passed ``func``,
+        see the examples below.
 
         Functions that mutate the passed object can produce unexpected
         behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
@@ -1562,9 +1557,7 @@ def apply(self, func, *args, include_groups: bool = False, **kwargs) -> NDFrameT
         its argument and returns a Series.  `apply` combines the result for
         each group together into a new DataFrame.
 
-        .. versionchanged:: 1.3.0
-
-            The resulting dtype will reflect the return value of the passed ``func``.
+        The resulting dtype will reflect the return value of the passed ``func``.
 
         >>> g1[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min())
              B    C
@@ -5563,10 +5556,6 @@ def sample(
             If np.random.RandomState or np.random.Generator, use as given.
             Default ``None`` results in sampling with the current state of np.random.
 
-            .. versionchanged:: 1.4.0
-
-                np.random.Generator objects now accepted
-
         Returns
         -------
         Series or DataFrame

pandas/core/indexes/base.py

@@ -1953,18 +1953,13 @@ def set_names(self, names, *, level=None, inplace: bool = False) -> Self | None:
 
         Parameters
         ----------
-
         names : Hashable or a sequence of the previous or dict-like for MultiIndex
             Name(s) to set.
 
-            .. versionchanged:: 1.3.0
-
         level : int, Hashable or a sequence of the previous, optional
             If the index is a MultiIndex and names is not dict-like, level(s) to set
             (None for all levels). Otherwise level must be None.
 
-            .. versionchanged:: 1.3.0
-
         inplace : bool, default False
             Modifies the object directly, instead of creating a new Index or
             MultiIndex.

pandas/core/series.py

@@ -5928,8 +5928,6 @@ def between(
         inclusive : {"both", "neither", "left", "right"}
             Include boundaries. Whether to set each bound as closed or open.
 
-            .. versionchanged:: 1.3.0
-
         Returns
         -------
         Series

pandas/core/shared_docs.py

@@ -139,13 +139,6 @@
     when the result's index (and column) labels match the inputs, and
     are included otherwise.
 
-    .. versionchanged:: 1.5.0
-
-       Warns that ``group_keys`` will no longer be ignored when the
-       result from ``apply`` is a like-indexed Series or DataFrame.
-       Specify ``group_keys`` explicitly to include the group keys or
-       not.
-
     .. versionchanged:: 2.0.0
 
        ``group_keys`` now defaults to ``True``.
@@ -620,9 +613,6 @@
     4    None
     dtype: object
 
-        .. versionchanged:: 1.4.0
-            Previously the explicit ``None`` was silently ignored.
-
     When ``regex=True``, ``value`` is not ``None`` and `to_replace` is a string,
     the replacement will be applied in all columns of the DataFrame.