DOC: Remove notes to old Python/package versions (#52640)

Author: mroeschke

doc/source/development/extending.rst

@@ -450,7 +450,7 @@ Below is an example to define two original properties, "internal_cache" as a tem
 Plotting backends
 -----------------
 
-Starting in 0.25 pandas can be extended with third-party plotting backends. The
+pandas can be extended with third-party plotting backends. The
 main idea is letting users select a plotting backend different than the provided
 one based on Matplotlib. For example:
 

doc/source/getting_started/install.rst

@@ -149,14 +149,6 @@ to install pandas with the optional dependencies to read Excel files.
 
 The full list of extras that can be installed can be found in the :ref:`dependency section.<install.optional_dependencies>`
 
-Installing with ActivePython
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Installation instructions for
-`ActivePython <https://www.activestate.com/products/python/>`__ can be found
-`here <https://www.activestate.com/products/python/>`__. Versions
-2.7, 3.5 and 3.6 include pandas.
-
 Installing using your Linux distribution's package manager.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

doc/source/user_guide/advanced.rst

@@ -918,7 +918,7 @@ If you select a label *contained* within an interval, this will also select the
    df.loc[2.5]
    df.loc[[2.5, 3.5]]
 
-Selecting using an ``Interval`` will only return exact matches (starting from pandas 0.25.0).
+Selecting using an ``Interval`` will only return exact matches.
 
 .. ipython:: python
 

doc/source/user_guide/io.rst

@@ -3999,7 +3999,7 @@ any pickled pandas object (or any other pickled object) from file:
 
 .. warning::
 
-   :func:`read_pickle` is only guaranteed backwards compatible back to pandas version 0.20.3
+   :func:`read_pickle` is only guaranteed backwards compatible back to a few minor release.
 
 .. _io.pickle.compression:
 
@@ -5922,11 +5922,6 @@ And then issue the following queries:
 Google BigQuery
 ---------------
 
-.. warning::
-
-   Starting in 0.20.0, pandas has split off Google BigQuery support into the
-   separate package ``pandas-gbq``. You can ``pip install pandas-gbq`` to get it.
-
 The ``pandas-gbq`` package provides functionality to read/write from Google BigQuery.
 
 pandas integrates with this external package. if ``pandas-gbq`` is installed, you can
@@ -6114,7 +6109,7 @@ SAS formats
 -----------
 
 The top-level function :func:`read_sas` can read (but not write) SAS
-XPORT (.xpt) and (since *v0.18.0*) SAS7BDAT (.sas7bdat) format files.
+XPORT (.xpt) and SAS7BDAT (.sas7bdat) format files.
 
 SAS files only contain two value types: ASCII text and floating point
 values (usually 8 bytes but sometimes truncated).  For xport files,

doc/source/user_guide/merging.rst

@@ -510,12 +510,6 @@ all standard database join operations between ``DataFrame`` or named ``Series``
       dataset.
     * "many_to_many" or "m:m": allowed, but does not result in checks.
 
-.. note::
-
-   Support for specifying index levels as the ``on``, ``left_on``, and
-   ``right_on`` parameters was added in version 0.23.0.
-   Support for merging named ``Series`` objects was added in version 0.24.0.
-
 The return type will be the same as ``left``. If ``left`` is a ``DataFrame`` or named ``Series``
 and ``right`` is a subclass of ``DataFrame``, the return type will still be ``DataFrame``.
 

doc/source/user_guide/missing_data.rst

@@ -182,11 +182,6 @@ account for missing data. For example:
 Sum/prod of empties/nans
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. warning::
-
-   This behavior is now standard as of v0.22.0 and is consistent with the default in ``numpy``; previously sum/prod of all-NA or empty Series/DataFrames would return NaN.
-   See :ref:`v0.22.0 whatsnew <whatsnew_0220>` for more.
-
 The sum of an empty or all-NA Series or column of a DataFrame is 0.
 
 .. ipython:: python

doc/source/user_guide/text.rst

@@ -206,8 +206,7 @@ and replacing any remaining whitespaces with underscores:
 
 .. warning::
 
-    Before v.0.25.0, the ``.str``-accessor did only the most rudimentary type checks. Starting with
-    v.0.25.0, the type of the Series is inferred and the allowed types (i.e. strings) are enforced more rigorously.
+    The type of the Series is inferred and the allowed types (i.e. strings).
 
     Generally speaking, the ``.str`` accessor is intended to work only on strings. With very few
     exceptions, other uses are not supported, and may be disabled at a later point.
@@ -423,11 +422,6 @@ the ``join``-keyword.
    s.str.cat(u)
    s.str.cat(u, join="left")
 
-.. warning::
-
-    If the ``join`` keyword is not passed, the method :meth:`~Series.str.cat` will currently fall back to the behavior before version 0.23.0 (i.e. no alignment),
-    but a ``FutureWarning`` will be raised if any of the involved indexes differ, since this default will change to ``join='left'`` in a future version.
-
 The usual options are available for ``join`` (one of ``'left', 'outer', 'inner', 'right'``).
 In particular, alignment also means that the different lengths do not need to coincide anymore.
 
@@ -503,15 +497,6 @@ Extracting substrings
 Extract first match in each subject (extract)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. warning::
-
-   Before version 0.23, argument ``expand`` of the ``extract`` method defaulted to
-   ``False``. When ``expand=False``, ``expand`` returns a ``Series``, ``Index``, or
-   ``DataFrame``, depending on the subject and regular expression
-   pattern. When ``expand=True``, it always returns a ``DataFrame``,
-   which is more consistent and less confusing from the perspective of a user.
-   ``expand=True`` has been the default since version 0.23.0.
-
 The ``extract`` method accepts a `regular expression
 <https://docs.python.org/3/library/re.html>`__ with at least one
 capture group.

doc/source/user_guide/visualization.rst

@@ -1794,7 +1794,7 @@ when plotting a large number of points.
 Plotting backends
 -----------------
 
-Starting in version 0.25, pandas can be extended with third-party plotting backends. The
+pandas can be extended with third-party plotting backends. The
 main idea is letting users select a plotting backend different than the provided
 one based on Matplotlib.
 

pandas/core/arrays/interval.py

@@ -124,8 +124,6 @@
 ] = """
 %(summary)s
 
-.. versionadded:: %(versionadded)s
-
 Parameters
 ----------
 data : array-like (1-dimensional)
@@ -187,7 +185,6 @@
     % {
         "klass": "IntervalArray",
         "summary": "Pandas array for interval data that are closed on the same side.",
-        "versionadded": "0.24.0",
         "name": "",
         "extra_attributes": "",
         "extra_methods": "",

pandas/core/config_init.py

@@ -275,7 +275,7 @@ def use_numba_cb(key) -> None:
 pc_large_repr_doc = """
 : 'truncate'/'info'
     For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
-    show a truncated table (the default from 0.13), or switch to the view from
+    show a truncated table, or switch to the view from
     df.info() (the behaviour in earlier versions of pandas).
 """
 

pandas/core/dtypes/concat.py

@@ -240,8 +240,6 @@ def union_categoricals(
         ...
     TypeError: to union ordered Categoricals, all categories must be the same
 
-    New in version 0.20.0
-
     Ordered categoricals with different categories or orderings can be
     combined by using the `ignore_ordered=True` argument.
 

pandas/core/frame.py

@@ -379,12 +379,6 @@
 merge_asof : Merge on nearest keys.
 DataFrame.join : Similar method using indices.
 
-Notes
------
-Support for specifying index levels as the `on`, `left_on`, and
-`right_on` parameters was added in version 0.23.0
-Support for merging named Series objects was added in version 0.24.0
-
 Examples
 --------
 >>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
@@ -1501,7 +1495,7 @@ def dot(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
         This method computes the matrix product between the DataFrame and the
         values of an other Series, DataFrame or a numpy array.
 
-        It can also be called using ``self @ other`` in Python >= 3.5.
+        It can also be called using ``self @ other``.
 
         Parameters
         ----------
@@ -1619,13 +1613,13 @@ def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
 
     def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
         """
-        Matrix multiplication using binary `@` operator in Python>=3.5.
+        Matrix multiplication using binary `@` operator.
         """
         return self.dot(other)
 
     def __rmatmul__(self, other) -> DataFrame:
         """
-        Matrix multiplication using binary `@` operator in Python>=3.5.
+        Matrix multiplication using binary `@` operator.
         """
         try:
             return self.T.dot(np.transpose(other)).T
@@ -2700,8 +2694,8 @@ def to_feather(self, path: FilePath | WriteBuffer[bytes], **kwargs) -> None:
             it will be used as Root Directory path when writing a partitioned dataset.
         **kwargs :
             Additional keywords passed to :func:`pyarrow.feather.write_feather`.
-            Starting with pyarrow 0.17, this includes the `compression`,
-            `compression_level`, `chunksize` and `version` keywords.
+            This includes the `compression`, `compression_level`, `chunksize`
+            and `version` keywords.
 
             .. versionadded:: 1.1.0
 
@@ -4631,8 +4625,8 @@ def select_dtypes(self, include=None, exclude=None) -> Self:
         * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
           ``'timedelta64'``
         * To select Pandas categorical dtypes, use ``'category'``
-        * To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
-          0.20.0) or ``'datetime64[ns, tz]'``
+        * To select Pandas datetimetz dtypes, use ``'datetimetz'``
+          or ``'datetime64[ns, tz]'``
 
         Examples
         --------
@@ -9983,9 +9977,6 @@ def join(
         Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
         passing a list of `DataFrame` objects.
 
-        Support for specifying index levels as the `on` parameter was added
-        in version 0.23.0.
-
         Examples
         --------
         >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],

pandas/core/generic.py

@@ -2606,7 +2606,7 @@ def to_hdf(
             A value of 0 or None disables compression.
         complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
             Specifies the compression library to be used.
-            As of v0.20.2 these additional compressors for Blosc are supported
+            These additional compressors for Blosc are supported
             (default if no compressor specified: 'blosc:blosclz'):
             {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
             'blosc:zlib', 'blosc:zstd'}.
@@ -7537,9 +7537,7 @@ def interpolate(
               'cubicspline': Wrappers around the SciPy interpolation methods of
               similar names. See `Notes`.
             * 'from_derivatives': Refers to
-              `scipy.interpolate.BPoly.from_derivatives` which
-              replaces 'piecewise_polynomial' interpolation method in
-              scipy 0.18.
+              `scipy.interpolate.BPoly.from_derivatives`.
 
         axis : {{0 or 'index', 1 or 'columns', None}}, default None
             Axis to interpolate along. For `Series` this parameter is unused

pandas/core/indexes/interval.py

@@ -154,7 +154,6 @@ def _new_IntervalIndex(cls, d):
         "klass": "IntervalIndex",
         "summary": "Immutable index of intervals that are closed on the same side.",
         "name": _index_doc_kwargs["name"],
-        "versionadded": "0.20.0",
         "extra_attributes": "is_overlapping\nvalues\n",
         "extra_methods": "",
         "examples": textwrap.dedent(

pandas/core/resample.py

@@ -886,9 +886,7 @@ def interpolate(
               'cubicspline': Wrappers around the SciPy interpolation methods of
               similar names. See `Notes`.
             * 'from_derivatives': Refers to
-              `scipy.interpolate.BPoly.from_derivatives` which
-              replaces 'piecewise_polynomial' interpolation method in
-              scipy 0.18.
+              `scipy.interpolate.BPoly.from_derivatives`.
 
         axis : {{0 or 'index', 1 or 'columns', None}}, default None
             Axis to interpolate along. For `Series` this parameter is unused

pandas/core/reshape/merge.py

@@ -389,10 +389,6 @@ def merge_asof(
       - A "nearest" search selects the row in the right DataFrame whose 'on'
         key is closest in absolute distance to the left's key.
 
-    The default is "backward" and is compatible in versions below 0.20.0.
-    The direction parameter was added in version 0.20.0 and introduces
-    "forward" and "nearest".
-
     Optionally match on equivalent keys with 'by' before searching with 'on'.
 
     Parameters

pandas/core/series.py

@@ -2891,7 +2891,7 @@ def dot(self, other: AnyArrayLike) -> Series | np.ndarray:
         one, or the Series and each columns of a DataFrame, or the Series and
         each columns of an array.
 
-        It can also be called using `self @ other` in Python >= 3.5.
+        It can also be called using `self @ other`.
 
         Parameters
         ----------
@@ -2963,13 +2963,13 @@ def dot(self, other: AnyArrayLike) -> Series | np.ndarray:
 
     def __matmul__(self, other):
         """
-        Matrix multiplication using binary `@` operator in Python>=3.5.
+        Matrix multiplication using binary `@` operator.
         """
         return self.dot(other)
 
     def __rmatmul__(self, other):
         """
-        Matrix multiplication using binary `@` operator in Python>=3.5.
+        Matrix multiplication using binary `@` operator.
         """
         return self.dot(np.transpose(other))
 

pandas/io/common.py

@@ -243,7 +243,7 @@ def stringify_path(
 
     Notes
     -----
-    Objects supporting the fspath protocol (python 3.6+) are coerced
+    Objects supporting the fspath protocol are coerced
     according to its __fspath__ method.
 
     Any other object is passed through unchanged, which includes bytes,

pandas/io/gbq.py

@@ -134,8 +134,6 @@ def read_gbq(
         If set, limit the maximum number of rows to fetch from the query
         results.
 
-        *New in version 0.12.0 of pandas-gbq*.
-
         .. versionadded:: 1.1.0
     progress_bar_type : Optional, str
         If set, use the `tqdm <https://tqdm.github.io/>`__ library to
@@ -156,10 +154,6 @@ def read_gbq(
             Use the :func:`tqdm.tqdm_gui` function to display a
             progress bar as a graphical dialog box.
 
-        Note that this feature requires version 0.12.0 or later of the
-        ``pandas-gbq`` package. And it requires the ``tqdm`` package. Slightly
-        different than ``pandas-gbq``, here the default is ``None``.
-
     Returns
     -------
     df: DataFrame

pandas/io/pytables.py

@@ -515,7 +515,7 @@ class HDFStore:
         A value of 0 or None disables compression.
     complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
         Specifies the compression library to be used.
-        As of v0.20.2 these additional compressors for Blosc are supported
+        These additional compressors for Blosc are supported
         (default if no compressor specified: 'blosc:blosclz'):
         {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
          'blosc:zlib', 'blosc:zstd'}.

pandas/tests/io/formats/test_to_csv.py

@@ -508,7 +508,7 @@ def test_to_csv_stdout_file(self, capsys):
         reason=(
             "Especially in Windows, file stream should not be passed"
             "to csv writer without newline='' option."
-            "(https://docs.python.org/3.6/library/csv.html#csv.writer)"
+            "(https://docs.python.org/3/library/csv.html#csv.writer)"
         ),
     )
     def test_to_csv_write_to_open_file(self):

pandas/tests/series/test_constructors.py

@@ -1346,8 +1346,7 @@ def test_constructor_dict_list_value_explicit_dtype(self):
 
     def test_constructor_dict_order(self):
         # GH19018
-        # initialization ordering: by insertion order if python>= 3.6, else
-        # order by value
+        # initialization ordering: by insertion order
         d = {"b": 1, "a": 0, "c": 2}
         result = Series(d)
         expected = Series([1, 0, 2], index=list("bac"))