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

pandas/core/base.py

@@ -1460,8 +1460,6 @@ def is_monotonic(self):
         Return boolean if values in the object are
         monotonic_increasing.
 
-        .. versionadded:: 0.19.0
-
         Returns
         -------
         bool
@@ -1478,8 +1476,6 @@ def is_monotonic_decreasing(self):
         Return boolean if values in the object are
         monotonic_decreasing.
 
-        .. versionadded:: 0.19.0
-
         Returns
         -------
         bool

pandas/core/computation/ops.py

@@ -306,8 +306,6 @@ def _cast_inplace(terms, acceptable_dtypes, dtype):
     acceptable_dtypes : list of acceptable numpy.dtype
         Will not cast if term's dtype in this list.
 
-        .. versionadded:: 0.19.0
-
     dtype : str or numpy.dtype
         The dtype to cast to.
     """

pandas/core/dtypes/concat.py

@@ -225,8 +225,6 @@ def union_categoricals(to_union, sort_categories=False, ignore_order=False):
     Combine list-like of Categorical-like, unioning categories. All
     categories must have the same dtype.
 
-    .. versionadded:: 0.19.0
-
     Parameters
     ----------
     to_union : list-like of Categorical, CategoricalIndex,

pandas/core/frame.py

@@ -2043,9 +2043,6 @@ def to_stata(
         variable_labels : dict
             Dictionary containing columns as keys and variable labels as
             values. Each label must be 80 characters or smaller.
-
-            .. versionadded:: 0.19.0
-
         version : {114, 117}, default 114
             Version to use in the output dta file.  Version 114 can be used
             read by Stata 10 and later.  Version 117 can be read by Stata 13
@@ -2074,8 +2071,6 @@ def to_stata(
             * Column listed in convert_dates is not in DataFrame
             * Categorical label contains more than 32,000 characters
 
-            .. versionadded:: 0.19.0
-
         See Also
         --------
         read_stata : Import Stata data files.
@@ -2265,9 +2260,6 @@ def to_html(
         border : int
             A ``border=border`` attribute is included in the opening
             `<table>` tag. Default ``pd.options.display.html.border``.
-
-            .. versionadded:: 0.19.0
-
         table_id : str, optional
             A css id is included in the opening `<table>` tag if specified.
 

pandas/core/generic.py

@@ -2328,8 +2328,6 @@ def to_json(
             throw ValueError if incorrect 'orient' since others are not list
             like.
 
-            .. versionadded:: 0.19.0
-
         compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}
 
             A string representing the compression to use in the output file,
@@ -7078,8 +7076,6 @@ def asof(self, where, subset=None):
         In case of a :class:`~pandas.DataFrame`, the last row without NaN
         considering only the subset of columns (if not `None`)
 
-        .. versionadded:: 0.19.0 For DataFrame
-
         If there is no good value, NaN is returned for a Series or
         a Series of NaN values for a DataFrame
 
@@ -8197,14 +8193,10 @@ def resample(
             For a DataFrame, column to use instead of index for resampling.
             Column must be datetime-like.
 
-            .. versionadded:: 0.19.0
-
         level : str or int, optional
             For a MultiIndex, level (name or number) to use for
             resampling. `level` must be datetime-like.
 
-            .. versionadded:: 0.19.0
-
         Returns
         -------
         Resampler object

pandas/core/indexes/base.py

@@ -788,8 +788,6 @@ def view(self, cls=None):
             satisfied, the original data is used to create a new Index
             or the original Index is returned.
 
-            .. versionadded:: 0.19.0
-
         Returns
         -------
         Index
@@ -3993,8 +3991,6 @@ def memory_usage(self, deep=False):
         entries are from self where cond is True and otherwise are from
         other.
 
-        .. versionadded:: 0.19.0
-
         Parameters
         ----------
         cond : boolean array-like with the same length as self

pandas/core/reshape/merge.py

@@ -217,8 +217,6 @@ def merge_ordered(
         * outer: use union of keys from both frames (SQL: full outer join)
         * inner: use intersection of keys from both frames (SQL: inner join)
 
-        .. versionadded:: 0.19.0
-
     Returns
     -------
     merged : DataFrame
@@ -328,8 +326,6 @@ def merge_asof(
 
     Optionally match on equivalent keys with 'by' before searching with 'on'.
 
-    .. versionadded:: 0.19.0
-
     Parameters
     ----------
     left : DataFrame
@@ -345,26 +341,14 @@ def merge_asof(
         Field name to join on in right DataFrame.
     left_index : boolean
         Use the index of the left DataFrame as the join key.
-
-        .. versionadded:: 0.19.2
-
     right_index : boolean
         Use the index of the right DataFrame as the join key.
-
-        .. versionadded:: 0.19.2
-
     by : column name or list of column names
         Match on these columns before performing merge operation.
     left_by : column name
         Field names to match on in the left DataFrame.
-
-        .. versionadded:: 0.19.2
-
     right_by : column name
         Field names to match on in the right DataFrame.
-
-        .. versionadded:: 0.19.2
-
     suffixes : 2-length sequence (tuple, list, ...)
         Suffix to apply to overlapping column names in the left and right
         side, respectively.

pandas/core/series.py

@@ -2700,9 +2700,6 @@ def append(self, to_append, ignore_index=False, verify_integrity=False):
             Series to append with self.
         ignore_index : bool, default False
             If True, do not use the index labels.
-
-            .. versionadded:: 0.19.0
-
         verify_integrity : bool, default False
             If True, raise Exception on creating index with duplicates.
 

pandas/core/sorting.py

@@ -399,8 +399,6 @@ def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False, verify=T
     ``values`` should be unique if ``labels`` is not None.
     Safe for use with mixed types (int, str), orders ints before strs.
 
-    .. versionadded:: 0.19.0
-
     Parameters
     ----------
     values : list-like

pandas/core/tools/numeric.py

@@ -59,8 +59,6 @@ def to_numeric(arg, errors="raise", downcast=None):
         checked satisfy that specification, no downcasting will be
         performed on the data.
 
-        .. versionadded:: 0.19.0
-
     Returns
     -------
     ret : numeric if parsing succeeded.

pandas/core/util/hashing.py

@@ -60,8 +60,6 @@ def hash_pandas_object(
     """
     Return a data hash of the Index/Series/DataFrame
 
-    .. versionadded:: 0.19.2
-
     Parameters
     ----------
     index : boolean, default True
@@ -245,8 +243,6 @@ def hash_array(vals, encoding="utf8", hash_key=None, categorize=True):
     """
     Given a 1d array, return an array of deterministic integers.
 
-    .. versionadded:: 0.19.2
-
     Parameters
     ----------
     vals : ndarray, Categorical

pandas/core/window.py

@@ -485,8 +485,7 @@ class Window(_Window):
 
         If its an offset then this will be the time period of each window. Each
         window will be a variable sized based on the observations included in
-        the time-period. This is only valid for datetimelike indexes. This is
-        new in 0.19.0
+        the time-period. This is only valid for datetimelike indexes.
     min_periods : int, default None
         Minimum number of observations in window required to have a value
         (otherwise result is NA). For a window that is specified by an offset,

pandas/io/excel/_base.py

@@ -120,14 +120,8 @@
     content.
 true_values : list, default None
     Values to consider as True.
-
-    .. versionadded:: 0.19.0
-
 false_values : list, default None
     Values to consider as False.
-
-    .. versionadded:: 0.19.0
-
 skiprows : list-like
     Rows to skip at the beginning (0-indexed).
 nrows : int, default None

pandas/io/formats/format.py

@@ -846,8 +846,6 @@ def to_html(
         border : int
             A ``border=border`` attribute is included in the opening
             ``<table>`` tag. Default ``pd.options.display.html.border``.
-
-            .. versionadded:: 0.19.0
          """
         from pandas.io.formats.html import HTMLFormatter, NotebookFormatter