Merge branch 'main' into ref-errorbars

doc/source/user_guide/10min.rst

@@ -763,14 +763,12 @@ Parquet
 Writing to a Parquet file:
 
 .. ipython:: python
-   :okwarning:
 
    df.to_parquet("foo.parquet")
 
 Reading from a Parquet file Store using :func:`read_parquet`:
 
 .. ipython:: python
-   :okwarning:
 
    pd.read_parquet("foo.parquet")
 

doc/source/user_guide/io.rst

@@ -2247,7 +2247,6 @@ For line-delimited json files, pandas can also return an iterator which reads in
 Line-limited json can also be read using the pyarrow reader by specifying ``engine="pyarrow"``.
 
 .. ipython:: python
-   :okwarning:
 
    from io import BytesIO
    df = pd.read_json(BytesIO(jsonl.encode()), lines=True, engine="pyarrow")
@@ -5372,15 +5371,13 @@ See the documentation for `pyarrow <https://arrow.apache.org/docs/python/>`__ an
 Write to a parquet file.
 
 .. ipython:: python
-   :okwarning:
 
    df.to_parquet("example_pa.parquet", engine="pyarrow")
    df.to_parquet("example_fp.parquet", engine="fastparquet")
 
 Read from a parquet file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_parquet("example_fp.parquet", engine="fastparquet")
    result = pd.read_parquet("example_pa.parquet", engine="pyarrow")
@@ -5390,7 +5387,6 @@ Read from a parquet file.
 By setting the ``dtype_backend`` argument you can control the default dtypes used for the resulting DataFrame.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_parquet("example_pa.parquet", engine="pyarrow", dtype_backend="pyarrow")
 
@@ -5404,7 +5400,6 @@ By setting the ``dtype_backend`` argument you can control the default dtypes use
 Read only certain columns of a parquet file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_parquet(
        "example_fp.parquet",
@@ -5433,7 +5428,6 @@ Serializing a ``DataFrame`` to parquet may include the implicit index as one or
 more columns in the output file. Thus, this code:
 
 .. ipython:: python
-   :okwarning:
 
     df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
     df.to_parquet("test.parquet", engine="pyarrow")
@@ -5450,7 +5444,6 @@ If you want to omit a dataframe's indexes when writing, pass ``index=False`` to
 :func:`~pandas.DataFrame.to_parquet`:
 
 .. ipython:: python
-   :okwarning:
 
     df.to_parquet("test.parquet", index=False)
 
@@ -5473,7 +5466,6 @@ Partitioning Parquet files
 Parquet supports partitioning of data based on the values of one or more columns.
 
 .. ipython:: python
-   :okwarning:
 
     df = pd.DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]})
     df.to_parquet(path="test", engine="pyarrow", partition_cols=["a"], compression=None)
@@ -5539,14 +5531,12 @@ ORC format, :func:`~pandas.read_orc` and :func:`~pandas.DataFrame.to_orc`. This
 Write to an orc file.
 
 .. ipython:: python
-   :okwarning:
 
    df.to_orc("example_pa.orc", engine="pyarrow")
 
 Read from an orc file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_orc("example_pa.orc")
 
@@ -5555,7 +5545,6 @@ Read from an orc file.
 Read only certain columns of an orc file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_orc(
        "example_pa.orc",

doc/source/user_guide/pyarrow.rst

@@ -104,7 +104,6 @@ To convert a :external+pyarrow:py:class:`pyarrow.Table` to a :class:`DataFrame`,
 :external+pyarrow:py:meth:`pyarrow.Table.to_pandas` method with ``types_mapper=pd.ArrowDtype``.
 
 .. ipython:: python
-   :okwarning:
 
    table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=["a"])
 
@@ -165,7 +164,6 @@ functions provide an ``engine`` keyword that can dispatch to PyArrow to accelera
 * :func:`read_feather`
 
 .. ipython:: python
-   :okwarning:
 
    import io
    data = io.StringIO("""a,b,c
@@ -180,7 +178,6 @@ PyArrow-backed data by specifying the parameter ``dtype_backend="pyarrow"``. A r
 ``engine="pyarrow"`` to necessarily return PyArrow-backed data.
 
 .. ipython:: python
-   :okwarning:
 
     import io
     data = io.StringIO("""a,b,c,d,e,f,g,h,i

doc/source/user_guide/scale.rst

@@ -51,7 +51,6 @@ To load the columns we want, we have two options.
 Option 1 loads in all the data and then filters to what we need.
 
 .. ipython:: python
-   :okwarning:
 
    columns = ["id_0", "name_0", "x_0", "y_0"]
 
@@ -60,7 +59,6 @@ Option 1 loads in all the data and then filters to what we need.
 Option 2 only loads the columns we request.
 
 .. ipython:: python
-   :okwarning:
 
    pd.read_parquet("timeseries_wide.parquet", columns=columns)
 
@@ -202,7 +200,6 @@ counts up to this point. As long as each individual file fits in memory, this wi
 work for arbitrary-sized datasets.
 
 .. ipython:: python
-   :okwarning:
 
    %%time
    files = pathlib.Path("data/timeseries/").glob("ts*.parquet")

doc/source/user_guide/timeseries.rst

@@ -889,7 +889,7 @@ into ``freq`` keyword arguments. The available date offsets and associated frequ
     :class:`~pandas.tseries.offsets.BQuarterEnd`, ``'BQ``, "business quarter end"
     :class:`~pandas.tseries.offsets.BQuarterBegin`, ``'BQS'``, "business quarter begin"
     :class:`~pandas.tseries.offsets.FY5253Quarter`, ``'REQ'``, "retail (aka 52-53 week) quarter"
-    :class:`~pandas.tseries.offsets.YearEnd`, ``'Y'``, "calendar year end"
+    :class:`~pandas.tseries.offsets.YearEnd`, ``'YE'``, "calendar year end"
     :class:`~pandas.tseries.offsets.YearBegin`, ``'YS'`` or ``'BYS'``,"calendar year begin"
     :class:`~pandas.tseries.offsets.BYearEnd`, ``'BY'``, "business year end"
     :class:`~pandas.tseries.offsets.BYearBegin`, ``'BYS'``, "business year begin"
@@ -1252,7 +1252,7 @@ frequencies. We will refer to these aliases as *offset aliases*.
     "BQ", "business quarter end frequency"
     "QS", "quarter start frequency"
     "BQS", "business quarter start frequency"
-    "Y", "year end frequency"
+    "YE", "year end frequency"
     "BY", "business year end frequency"
     "YS", "year start frequency"
     "BYS", "business year start frequency"
@@ -1379,18 +1379,18 @@ For some frequencies you can specify an anchoring suffix:
     "(B)Q(E)(S)\-SEP", "quarterly frequency, year ends in September"
     "(B)Q(E)(S)\-OCT", "quarterly frequency, year ends in October"
     "(B)Q(E)(S)\-NOV", "quarterly frequency, year ends in November"
-    "(B)Y(S)\-DEC", "annual frequency, anchored end of December. Same as 'Y'"
-    "(B)Y(S)\-JAN", "annual frequency, anchored end of January"
-    "(B)Y(S)\-FEB", "annual frequency, anchored end of February"
-    "(B)Y(S)\-MAR", "annual frequency, anchored end of March"
-    "(B)Y(S)\-APR", "annual frequency, anchored end of April"
-    "(B)Y(S)\-MAY", "annual frequency, anchored end of May"
-    "(B)Y(S)\-JUN", "annual frequency, anchored end of June"
-    "(B)Y(S)\-JUL", "annual frequency, anchored end of July"
-    "(B)Y(S)\-AUG", "annual frequency, anchored end of August"
-    "(B)Y(S)\-SEP", "annual frequency, anchored end of September"
-    "(B)Y(S)\-OCT", "annual frequency, anchored end of October"
-    "(B)Y(S)\-NOV", "annual frequency, anchored end of November"
+    "(B)Y(E)(S)\-DEC", "annual frequency, anchored end of December. Same as 'YE'"
+    "(B)Y(E)(S)\-JAN", "annual frequency, anchored end of January"
+    "(B)Y(E)(S)\-FEB", "annual frequency, anchored end of February"
+    "(B)Y(E)(S)\-MAR", "annual frequency, anchored end of March"
+    "(B)Y(E)(S)\-APR", "annual frequency, anchored end of April"
+    "(B)Y(E)(S)\-MAY", "annual frequency, anchored end of May"
+    "(B)Y(E)(S)\-JUN", "annual frequency, anchored end of June"
+    "(B)Y(E)(S)\-JUL", "annual frequency, anchored end of July"
+    "(B)Y(E)(S)\-AUG", "annual frequency, anchored end of August"
+    "(B)Y(E)(S)\-SEP", "annual frequency, anchored end of September"
+    "(B)Y(E)(S)\-OCT", "annual frequency, anchored end of October"
+    "(B)Y(E)(S)\-NOV", "annual frequency, anchored end of November"
 
 These can be used as arguments to ``date_range``, ``bdate_range``, constructors
 for ``DatetimeIndex``, as well as various other timeseries-related functions
@@ -1686,7 +1686,7 @@ the end of the interval.
 .. warning::
 
     The default values for ``label`` and ``closed`` is '**left**' for all
-    frequency offsets except for 'ME', 'Y', 'QE', 'BME', 'BY', 'BQ', and 'W'
+    frequency offsets except for 'ME', 'YE', 'QE', 'BME', 'BY', 'BQ', and 'W'
     which all have a default of 'right'.
 
     This might unintendedly lead to looking ahead, where the value for a later

doc/source/whatsnew/v2.0.0.rst

@@ -152,7 +152,6 @@ When this keyword is set to ``"pyarrow"``, then these functions will return pyar
 * :meth:`Series.convert_dtypes`
 
 .. ipython:: python
-    :okwarning:
 
     import io
     data = io.StringIO("""a,b,c,d,e,f,g,h,i

doc/source/whatsnew/v2.1.3.rst

@@ -13,7 +13,7 @@ including other versions of pandas.
 
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
--
+- Fixed infinite recursion from operations that return a new object on some DataFrame subclasses (:issue:`55763`)
 -
 
 .. ---------------------------------------------------------------------------

doc/source/whatsnew/v2.2.0.rst

@@ -232,29 +232,14 @@ Other API changes
 Deprecations
 ~~~~~~~~~~~~
 
-Deprecate aliases ``M`` and ``Q`` in favour of ``ME`` and ``QE`` for offsets
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Deprecate aliases ``M``, ``Q``, and ``Y`` in favour of ``ME``, ``QE``, and ``YE`` for offsets
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The alias ``M`` is deprecated in favour of ``ME`` for offsets, please use ``ME`` for "month end" instead of ``M`` (:issue:`9586`)
+Deprecated the following frequency aliases (:issue:`9586`):
 
-For example:
-
-*Previous behavior*:
-
-.. code-block:: ipython
-
-    In [7]: pd.date_range('2020-01-01', periods=3, freq='M')
-    Out [7]:
-    DatetimeIndex(['2020-01-31', '2020-02-29', '2020-03-31'],
-                  dtype='datetime64[ns]', freq='M')
-
-*Future behavior*:
-
-.. ipython:: python
-
-    pd.date_range('2020-01-01', periods=3, freq='ME')
-
-The alias ``Q`` is deprecated in favour of ``QE`` for offsets, please use ``QE`` for "quarter end" instead of ``Q`` (:issue:`9586`)
+- ``M`` (month end) has been renamed ``ME`` for offsets
+- ``Q`` (quarter end) has been renamed ``QE`` for offsets
+- ``Y`` (year end) has been renamed ``YE`` for offsets
 
 For example:
 

pandas/_libs/tslibs/dtypes.pyx

@@ -214,6 +214,19 @@ OFFSET_TO_PERIOD_FREQSTR: dict = {
     "QE-SEP": "Q-SEP",
     "QE-OCT": "Q-OCT",
     "QE-NOV": "Q-NOV",
+    "YE": "Y",
+    "YE-DEC": "Y-DEC",
+    "YE-JAN": "Y-JAN",
+    "YE-FEB": "Y-FEB",
+    "YE-MAR": "Y-MAR",
+    "YE-APR": "Y-APR",
+    "YE-MAY": "Y-MAY",
+    "YE-JUN": "Y-JUN",
+    "YE-JUL": "Y-JUL",
+    "YE-AUG": "Y-AUG",
+    "YE-SEP": "Y-SEP",
+    "YE-OCT": "Y-OCT",
+    "YE-NOV": "Y-NOV",
     "W": "W",
     "ME": "M",
     "Y": "Y",
@@ -236,6 +249,32 @@ OFFSET_DEPR_FREQSTR: dict[str, str]= {
     "Q-SEP": "QE-SEP",
     "Q-OCT": "QE-OCT",
     "Q-NOV": "QE-NOV",
+    "Y": "YE",
+    "Y-DEC": "YE-DEC",
+    "Y-JAN": "YE-JAN",
+    "Y-FEB": "YE-FEB",
+    "Y-MAR": "YE-MAR",
+    "Y-APR": "YE-APR",
+    "Y-MAY": "YE-MAY",
+    "Y-JUN": "YE-JUN",
+    "Y-JUL": "YE-JUL",
+    "Y-AUG": "YE-AUG",
+    "Y-SEP": "YE-SEP",
+    "Y-OCT": "YE-OCT",
+    "Y-NOV": "YE-NOV",
+    "A": "YE",
+    "A-DEC": "YE-DEC",
+    "A-JAN": "YE-JAN",
+    "A-FEB": "YE-FEB",
+    "A-MAR": "YE-MAR",
+    "A-APR": "YE-APR",
+    "A-MAY": "YE-MAY",
+    "A-JUN": "YE-JUN",
+    "A-JUL": "YE-JUL",
+    "A-AUG": "YE-AUG",
+    "A-SEP": "YE-SEP",
+    "A-OCT": "YE-OCT",
+    "A-NOV": "YE-NOV",
 }
 cdef dict c_OFFSET_TO_PERIOD_FREQSTR = OFFSET_TO_PERIOD_FREQSTR
 cdef dict c_OFFSET_DEPR_FREQSTR = OFFSET_DEPR_FREQSTR

pandas/_libs/tslibs/offsets.pyx

@@ -2518,7 +2518,7 @@ cdef class YearEnd(YearOffset):
     """
 
     _default_month = 12
-    _prefix = "Y"
+    _prefix = "YE"
     _day_opt = "end"
 
     cdef readonly:
@@ -4562,7 +4562,7 @@ prefix_mapping = {
     offset._prefix: offset
     for offset in [
         YearBegin,  # 'YS'
-        YearEnd,  # 'Y'
+        YearEnd,  # 'YE'
         BYearBegin,  # 'BYS'
         BYearEnd,  # 'BY'
         BusinessDay,  # 'B'
@@ -4604,7 +4604,7 @@ _lite_rule_alias = {
     "W": "W-SUN",
     "QE": "QE-DEC",
 
-    "Y": "Y-DEC",      # YearEnd(month=12),
+    "YE": "YE-DEC",      # YearEnd(month=12),
     "YS": "YS-JAN",    # YearBegin(month=1),
     "BY": "BY-DEC",    # BYearEnd(month=12),
     "BYS": "BYS-JAN",  # BYearBegin(month=1),
@@ -4637,6 +4637,7 @@ _dont_uppercase = {
     "qe-sep",
     "qe-oct",
     "qe-nov",
+    "ye",
 }
 
 
@@ -4762,6 +4763,14 @@ cpdef to_offset(freq, bint is_period=False):
                         f"instead of \'{name}\'"
                     )
                 elif is_period is True and name in c_OFFSET_DEPR_FREQSTR:
+                    if name.startswith("A"):
+                        warnings.warn(
+                            f"\'{name}\' is deprecated and will be removed in a future "
+                            f"version, please use \'{c_DEPR_ABBREVS.get(name)}\' "
+                            f"instead.",
+                            FutureWarning,
+                            stacklevel=find_stack_level(),
+                        )
                     name = c_OFFSET_DEPR_FREQSTR.get(name)
 
                 if sep != "" and not sep.isspace():

pandas/core/arrays/datetimes.py

@@ -1520,7 +1520,7 @@ def isocalendar(self) -> DataFrame:
         Examples
         --------
         >>> datetime_series = pd.Series(
-        ...     pd.date_range("2000-01-01", periods=3, freq="Y")
+        ...     pd.date_range("2000-01-01", periods=3, freq="YE")
         ... )
         >>> datetime_series
         0   2000-12-31
@@ -2058,10 +2058,10 @@ def isocalendar(self) -> DataFrame:
         This method is available on Series with datetime values under
         the ``.dt`` accessor, and directly on DatetimeIndex.
 
-        >>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="Y")
+        >>> idx = pd.date_range("2012-01-01", "2015-01-01", freq="YE")
         >>> idx
         DatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],
-                      dtype='datetime64[ns]', freq='Y-DEC')
+                      dtype='datetime64[ns]', freq='YE-DEC')
         >>> idx.is_leap_year
         array([ True, False, False])