CLN: remove versionadded:: 0.20 (#29126)

Author: simonjayhawkins

doc/source/development/contributing.rst

@@ -1197,8 +1197,6 @@ submitting a pull request.
 
 For more, see the `pytest <http://docs.pytest.org/en/latest/>`_ documentation.
 
-    .. versionadded:: 0.20.0
-
 Furthermore one can run
 
 .. code-block:: python

doc/source/getting_started/basics.rst

@@ -172,8 +172,6 @@ You are highly encouraged to install both libraries. See the section
 
 These are both enabled to be used by default, you can control this by setting the options:
 
-.. versionadded:: 0.20.0
-
 .. code-block:: python
 
    pd.set_option('compute.use_bottleneck', False)
@@ -891,8 +889,6 @@ functionality.
 Aggregation API
 ~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 The aggregation API allows one to express possibly multiple aggregation operations in a single concise way.
 This API is similar across pandas objects, see :ref:`groupby API <groupby.aggregate>`, the
 :ref:`window functions API <stats.aggregate>`, and the :ref:`resample API <timeseries.aggregate>`.
@@ -1030,8 +1026,6 @@ to the built in :ref:`describe function <basics.describe>`.
 Transform API
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 The :meth:`~DataFrame.transform` method returns an object that is indexed the same (same size)
 as the original. This API allows you to provide *multiple* operations at the same
 time rather than one-by-one. Its API is quite similar to the ``.agg`` API.

doc/source/user_guide/advanced.rst

@@ -206,8 +206,6 @@ highly performant. If you want to see only the used levels, you can use the
 To reconstruct the ``MultiIndex`` with only the used levels, the
 :meth:`~MultiIndex.remove_unused_levels` method may be used.
 
-.. versionadded:: 0.20.0
-
 .. ipython:: python
 
    new_mi = df[['foo', 'qux']].columns.remove_unused_levels()
@@ -928,8 +926,6 @@ If you need integer based selection, you should use ``iloc``:
 IntervalIndex
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 :class:`IntervalIndex` together with its own dtype, :class:`~pandas.api.types.IntervalDtype`
 as well as the :class:`Interval` scalar type,  allow first-class support in pandas
 for interval notation.

doc/source/user_guide/categorical.rst

@@ -874,8 +874,6 @@ The below raises ``TypeError`` because the categories are ordered and not identi
    Out[3]:
    TypeError: to union ordered Categoricals, all categories must be the same
 
-.. versionadded:: 0.20.0
-
 Ordered categoricals with different categories or orderings can be combined by
 using the ``ignore_ordered=True`` argument.
 

doc/source/user_guide/computation.rst

@@ -471,8 +471,6 @@ default of the index) in a DataFrame.
 Rolling window endpoints
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 The inclusion of the interval endpoints in rolling window calculations can be specified with the ``closed``
 parameter:
 

doc/source/user_guide/groupby.rst

@@ -311,8 +311,6 @@ Grouping with multiple levels is supported.
    s
    s.groupby(level=['first', 'second']).sum()
 
-.. versionadded:: 0.20
-
 Index level names may be supplied as keys.
 
 .. ipython:: python
@@ -353,8 +351,6 @@ Index levels may also be specified by name.
 
    df.groupby([pd.Grouper(level='second'), 'A']).sum()
 
-.. versionadded:: 0.20
-
 Index level names may be specified as keys directly to ``groupby``.
 
 .. ipython:: python
@@ -1274,8 +1270,6 @@ To see the order in which each row appears within its group, use the
 Enumerate groups
 ~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.2
-
 To see the ordering of the groups (as opposed to the order of rows
 within a group given by ``cumcount``) you can use
 :meth:`~pandas.core.groupby.DataFrameGroupBy.ngroup`.

doc/source/user_guide/io.rst

@@ -163,9 +163,6 @@ dtype : Type name or dict of column -> type, default ``None``
   (unsupported with ``engine='python'``). Use `str` or `object` together
   with suitable ``na_values`` settings to preserve and
   not interpret dtype.
-
-  .. versionadded:: 0.20.0 support for the Python parser.
-
 engine : {``'c'``, ``'python'``}
   Parser engine to use. The C engine is faster while the Python engine is
   currently more feature-complete.
@@ -417,10 +414,6 @@ However, if you wanted for all the data to be coerced, no matter the type, then
 using the ``converters`` argument of :func:`~pandas.read_csv` would certainly be
 worth trying.
 
-  .. versionadded:: 0.20.0 support for the Python parser.
-
-     The ``dtype`` option is supported by the 'python' engine.
-
 .. note::
    In some cases, reading in abnormal data with columns containing mixed dtypes
    will result in an inconsistent dataset. If you rely on pandas to infer the
@@ -616,8 +609,6 @@ Filtering columns (``usecols``)
 The ``usecols`` argument allows you to select any subset of the columns in a
 file, either using the column names, position numbers or a callable:
 
-.. versionadded:: 0.20.0 support for callable `usecols` arguments
-
 .. ipython:: python
 
     data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'
@@ -1447,8 +1438,6 @@ is whitespace).
    df = pd.read_fwf('bar.csv', header=None, index_col=0)
    df
 
-.. versionadded:: 0.20.0
-
 ``read_fwf`` supports the ``dtype`` parameter for specifying the types of
 parsed columns to be different from the inferred type.
 
@@ -2221,8 +2210,6 @@ For line-delimited json files, pandas can also return an iterator which reads in
 Table schema
 ''''''''''''
 
-.. versionadded:: 0.20.0
-
 `Table Schema`_ is a spec for describing tabular datasets as a JSON
 object. The JSON includes information on the field names, types, and
 other attributes. You can use the orient ``table`` to build
@@ -3071,8 +3058,6 @@ missing data to recover integer dtype:
 Dtype specifications
 ++++++++++++++++++++
 
-.. versionadded:: 0.20
-
 As an alternative to converters, the type for an entire column can
 be specified using the `dtype` keyword, which takes a dictionary
 mapping column names to types.  To interpret data with
@@ -3345,8 +3330,6 @@ any pickled pandas object (or any other pickled object) from file:
 Compressed pickle files
 '''''''''''''''''''''''
 
-.. versionadded:: 0.20.0
-
 :func:`read_pickle`, :meth:`DataFrame.to_pickle` and :meth:`Series.to_pickle` can read
 and write compressed pickle files. The compression types of ``gzip``, ``bz2``, ``xz`` are supported for reading and writing.
 The ``zip`` file format only supports reading and must contain only one data file
@@ -4323,8 +4306,6 @@ control compression: ``complevel`` and ``complib``.
              - `bzip2 <http://bzip.org/>`_: Good compression rates.
              - `blosc <http://www.blosc.org/>`_: Fast compression and decompression.
 
-             .. versionadded:: 0.20.2
-
                 Support for alternative blosc compressors:
 
                 - `blosc:blosclz <http://www.blosc.org/>`_ This is the
@@ -4651,8 +4632,6 @@ Performance
 Feather
 -------
 
-.. versionadded:: 0.20.0
-
 Feather provides binary columnar serialization for data frames. It is designed to make reading and writing data
 frames efficient, and to make sharing data across data analysis languages easy.
 

doc/source/user_guide/merging.rst

@@ -843,8 +843,6 @@ resulting dtype will be upcast.
    pd.merge(left, right, how='outer', on='key')
    pd.merge(left, right, how='outer', on='key').dtypes
 
-.. versionadded:: 0.20.0
-
 Merging will preserve ``category`` dtypes of the mergands. See also the section on :ref:`categoricals <categorical.merge>`.
 
 The left frame.

doc/source/user_guide/options.rst

@@ -561,8 +561,6 @@ However, setting this option incorrectly for your terminal will cause these char
 Table schema display
 --------------------
 
-.. versionadded:: 0.20.0
-
 ``DataFrame`` and ``Series`` will publish a Table Schema representation
 by default. False by default, this can be enabled globally with the
 ``display.html.table_schema`` option:

doc/source/user_guide/reshaping.rst

@@ -539,8 +539,6 @@ Alternatively we can specify custom bin-edges:
    c = pd.cut(ages, bins=[0, 18, 35, 70])
    c
 
-.. versionadded:: 0.20.0
-
 If the ``bins`` keyword is an ``IntervalIndex``, then these will be
 used to bin the passed data.::
 

doc/source/user_guide/text.rst

@@ -228,8 +228,6 @@ and ``repl`` must be strings:
     dollars.str.replace(r'-\$', '-')
     dollars.str.replace('-$', '-', regex=False)
 
-.. versionadded:: 0.20.0
-
 The ``replace`` method can also take a callable as replacement. It is called
 on every ``pat`` using :func:`re.sub`. The callable should expect one
 positional argument (a regex object) and return a string.
@@ -254,8 +252,6 @@ positional argument (a regex object) and return a string.
    pd.Series(['Foo Bar Baz', np.nan],
              dtype="string").str.replace(pat, repl)
 
-.. versionadded:: 0.20.0
-
 The ``replace`` method also accepts a compiled regular expression object
 from :func:`re.compile` as a pattern. All flags should be included in the
 compiled regular expression object.

doc/source/user_guide/timedeltas.rst

@@ -327,8 +327,6 @@ similarly to the ``Series``. These are the *displayed* values of the ``Timedelta
 You can convert a ``Timedelta`` to an `ISO 8601 Duration`_ string with the
 ``.isoformat`` method
 
-.. versionadded:: 0.20.0
-
 .. ipython:: python
 
     pd.Timedelta(days=6, minutes=50, seconds=3,

doc/source/user_guide/timeseries.rst

@@ -376,8 +376,6 @@ We subtract the epoch (midnight at January 1, 1970 UTC) and then floor divide by
 Using the ``origin`` Parameter
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 Using the ``origin`` parameter, one can specify an alternative starting point for creation
 of a ``DatetimeIndex``. For example, to use 1960-01-01 as the starting date:
 

doc/source/user_guide/visualization.rst

@@ -1247,8 +1247,6 @@ in ``pandas.plotting.plot_params`` can be used in a `with statement`:
 Automatic date tick adjustment
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.20.0
-
 ``TimedeltaIndex`` now uses the native matplotlib
 tick locator methods, it is useful to call the automatic
 date tick adjustment from matplotlib for figures whose ticklabels overlap.

pandas/_libs/interval.pyx

@@ -191,8 +191,6 @@ cdef class Interval(IntervalMixin):
     """
     Immutable object implementing an Interval, a bounded slice-like interval.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     left : orderable scalar

pandas/_libs/tslibs/timedeltas.pyx

@@ -1157,8 +1157,6 @@ cdef class _Timedelta(timedelta):
         ``P[n]Y[n]M[n]DT[n]H[n]M[n]S``, where the ``[n]`` s are replaced by the
         values. See https://en.wikipedia.org/wiki/ISO_8601#Durations.
 
-        .. versionadded:: 0.20.0
-
         Returns
         -------
         formatted : str

pandas/core/dtypes/concat.py

@@ -199,8 +199,6 @@ def union_categoricals(to_union, sort_categories=False, ignore_order=False):
         If true, the ordered attribute of the Categoricals will be ignored.
         Results in an unordered categorical.
 
-        .. versionadded:: 0.20.0
-
     Returns
     -------
     result : Categorical

pandas/core/dtypes/inference.py

@@ -162,8 +162,6 @@ def is_file_like(obj):
     Note: file-like objects must be iterable, but
     iterable objects need not be file-like.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     obj : The object to check
@@ -281,8 +279,6 @@ def is_nested_list_like(obj):
     Check if the object is list-like, and that all of its elements
     are also list-like.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     obj : The object to check

pandas/core/frame.py

@@ -2082,8 +2082,6 @@ def to_feather(self, fname):
         """
         Write out the binary feather-format for DataFrames.
 
-        .. versionadded:: 0.20.0
-
         Parameters
         ----------
         fname : str
@@ -7868,8 +7866,6 @@ def nunique(self, axis=0, dropna=True):
         Return Series with number of distinct observations. Can ignore NaN
         values.
 
-        .. versionadded:: 0.20.0
-
         Parameters
         ----------
         axis : {0 or 'index', 1 or 'columns'}, default 0

pandas/core/generic.py

@@ -897,8 +897,6 @@ def squeeze(self, axis=None):
             A specific axis to squeeze. By default, all length-1 axes are
             squeezed.
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         DataFrame, Series, or scalar
@@ -2163,8 +2161,6 @@ def _repr_data_resource_(self):
         Specifies the one-based bottommost row and rightmost column that
         is to be frozen.
 
-        .. versionadded:: 0.20.0.
-
     See Also
     --------
     to_csv : Write DataFrame to a comma-separated values (csv) file.
@@ -2756,8 +2752,6 @@ def to_pickle(self, path, compression="infer", protocol=pickle.HIGHEST_PROTOCOL)
         default 'infer'
             A string representing the compression to use in the output file. By
             default, infers from the file extension in specified path.
-
-            .. versionadded:: 0.20.0
         protocol : int
             Int which indicates which protocol should be used by the pickler,
             default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
@@ -3032,22 +3026,15 @@ def to_latex(
         multicolumn : bool, default True
             Use \multicolumn to enhance MultiIndex columns.
             The default will be read from the config module.
-
-            .. versionadded:: 0.20.0
         multicolumn_format : str, default 'l'
             The alignment for multicolumns, similar to `column_format`
             The default will be read from the config module.
-
-            .. versionadded:: 0.20.0
         multirow : bool, default False
             Use \multirow to enhance MultiIndex rows. Requires adding a
             \usepackage{multirow} to your LaTeX preamble. Will print
             centered labels (instead of top-aligned) across the contained
             rows, separating groups via clines. The default will be read
             from the pandas config module.
-
-            .. versionadded:: 0.20.0
-
         caption : str, optional
             The LaTeX caption to be placed inside ``\caption{}`` in the output.
 
@@ -5133,8 +5120,6 @@ def pipe(self, func, *args, **kwargs):
     Call ``func`` on self producing a %(klass)s with transformed values
     and that has the same axis length as self.
 
-    .. versionadded:: 0.20.0
-
     Parameters
     ----------
     func : function, str, list or dict
@@ -5805,8 +5790,6 @@ def astype(self, dtype, copy=True, errors="raise"):
             - ``raise`` : allow exceptions to be raised
             - ``ignore`` : suppress exceptions. On error return original object.
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         casted : same type as caller
@@ -7946,8 +7929,6 @@ def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
             Value to use for missing values, applied during upsampling (note
             this does not fill NaNs that already were present).
 
-            .. versionadded:: 0.20.0
-
         Returns
         -------
         converted : same type as caller