remove references to v0.19 in docs and code (#27508)

Author: topper-123

doc/source/getting_started/basics.rst

@@ -2061,8 +2061,6 @@ Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`.
    dft
    dft.dtypes
 
-.. versionadded:: 0.19.0
-
 Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFrame.astype`.
 
 .. ipython:: python

doc/source/user_guide/categorical.rst

@@ -834,8 +834,6 @@ See also the section on :ref:`merge dtypes<merging.dtypes>` for notes about pres
 Unioning
 ~~~~~~~~
 
-.. versionadded:: 0.19.0
-
 If you want to combine categoricals that do not necessarily have the same
 categories, the :func:`~pandas.api.types.union_categoricals` function will
 combine a list-like of categoricals. The new categories will be the union of

doc/source/user_guide/computation.rst

@@ -408,9 +408,7 @@ For some windowing functions, additional parameters must be specified:
 Time-aware rolling
 ~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.19.0
-
-New in version 0.19.0 are the ability to pass an offset (or convertible) to a ``.rolling()`` method and have it produce
+It is possible to pass an offset (or convertible) to a ``.rolling()`` method and have it produce
 variable sized windows based on the passed time window. For each time point, this includes all preceding values occurring
 within the indicated time delta.
 

doc/source/user_guide/io.rst

@@ -453,8 +453,6 @@ worth trying.
 Specifying categorical dtype
 ''''''''''''''''''''''''''''
 
-.. versionadded:: 0.19.0
-
 ``Categorical`` columns can be parsed directly by specifying ``dtype='category'`` or
 ``dtype=CategoricalDtype(categories, ordered)``.
 
@@ -2192,8 +2190,6 @@ With max_level=1 the following snippet normalizes until 1st nesting level of the
 Line delimited json
 '''''''''''''''''''
 
-.. versionadded:: 0.19.0
-
 pandas is able to read and write line-delimited json files that are common in data processing pipelines
 using Hadoop or Spark.
 
@@ -2491,16 +2487,12 @@ Specify values that should be converted to NaN:
 
    dfs = pd.read_html(url, na_values=['No Acquirer'])
 
-.. versionadded:: 0.19
-
 Specify whether to keep the default set of NaN values:
 
 .. code-block:: python
 
    dfs = pd.read_html(url, keep_default_na=False)
 
-.. versionadded:: 0.19
-
 Specify converters for columns. This is useful for numerical text data that has
 leading zeros.  By default columns that are numerical are cast to numeric
 types and the leading zeros are lost. To avoid this, we can convert these
@@ -2512,8 +2504,6 @@ columns to strings.
    dfs = pd.read_html(url_mcc, match='Telekom Albania', header=0,
                       converters={'MNC': str})
 
-.. versionadded:: 0.19
-
 Use some combination of the above:
 
 .. code-block:: python

doc/source/user_guide/merging.rst

@@ -819,8 +819,6 @@ The ``indicator`` argument will also accept string arguments, in which case the
 Merge dtypes
 ~~~~~~~~~~~~
 
-.. versionadded:: 0.19.0
-
 Merging will preserve the dtype of the join keys.
 
 .. ipython:: python
@@ -1386,8 +1384,6 @@ fill/interpolate missing data:
 Merging asof
 ~~~~~~~~~~~~
 
-.. versionadded:: 0.19.0
-
 A :func:`merge_asof` is similar to an ordered left-join except that we match on
 nearest key rather than equal keys. For each row in the ``left`` ``DataFrame``,
 we select the last row in the ``right`` ``DataFrame`` whose ``on`` key is less

doc/source/user_guide/text.rst

@@ -506,8 +506,6 @@ then ``extractall(pat).xs(0, level='match')`` gives the same result as
 ``Index`` also supports ``.str.extractall``. It returns a ``DataFrame`` which has the
 same result as a ``Series.str.extractall`` with a default index (starts from 0).
 
-.. versionadded:: 0.19.0
-
 .. ipython:: python
 
    pd.Index(["a1a2", "b1", "c1"]).str.extractall(two_groups)

doc/source/user_guide/timeseries.rst

@@ -1921,8 +1921,6 @@ objects:
 Period dtypes
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.19.0
-
 ``PeriodIndex`` has a custom ``period`` dtype. This is a pandas extension
 dtype similar to the :ref:`timezone aware dtype <timeseries.timezone_series>` (``datetime64[ns, tz]``).
 

doc/source/user_guide/visualization.rst

@@ -438,10 +438,6 @@ columns:
 
 .. _visualization.box.return:
 
-.. warning::
-
-   The default changed from ``'dict'`` to ``'axes'`` in version 0.19.0.
-
 In ``boxplot``, the return type can be controlled by the ``return_type``, keyword. The valid choices are ``{"axes", "dict", "both", None}``.
 Faceting, created by ``DataFrame.boxplot`` with the ``by``
 keyword, will affect the output type as well:

pandas/core/algorithms.py

@@ -816,8 +816,6 @@ def duplicated(values, keep="first"):
     """
     Return boolean ndarray denoting duplicate values.
 
-    .. versionadded:: 0.19.0
-
     Parameters
     ----------
     values : ndarray-like

pandas/core/arrays/categorical.py

@@ -521,9 +521,6 @@ def astype(self, dtype, copy=True):
             By default, astype always returns a newly allocated object.
             If copy is set to False and dtype is categorical, the original
             object is returned.
-
-            .. versionadded:: 0.19.0
-
         """
         if is_categorical_dtype(dtype):
             # GH 10696/18593