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‎.github/actions/build_pandas/action.yml

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@@ -25,8 +25,8 @@ runs:
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- name: Build Pandas
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run: |
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if [[ ${{ inputs.editable }} == "true" ]]; then
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pip install -e . --no-build-isolation -v
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pip install -e . --no-build-isolation -v --no-deps
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else
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pip install . --no-build-isolation -v
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pip install . --no-build-isolation -v --no-deps
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fi
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shell: bash -el {0}

‎.github/workflows/unit-tests.yml

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@@ -348,7 +348,7 @@ jobs:
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python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy
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python -m pip install versioneer[toml]
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python -m pip install python-dateutil pytz tzdata "cython<3.0.3" hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17
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python -m pip install -ve . --no-build-isolation --no-index
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python -m pip install -ve . --no-build-isolation --no-index --no-deps
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python -m pip list
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- name: Run Tests

‎ci/deps/actions-310.yaml

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# required dependencies
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- python-dateutil
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- numpy
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- numpy<2
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- pytz
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# optional dependencies

‎ci/deps/actions-311-downstream_compat.yaml

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# required dependencies
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- python-dateutil
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- numpy
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- numpy<2
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- pytz
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# optional dependencies

‎ci/deps/actions-311-pyarrownightly.yaml

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# required dependencies
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- python-dateutil
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- numpy
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- numpy<2
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- pytz
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- pip
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‎ci/deps/actions-311.yaml

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# required dependencies
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- python-dateutil
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- numpy
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- numpy<2
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- pytz
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# optional dependencies

‎ci/deps/actions-39-minimum_versions.yaml

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# required dependencies
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- python-dateutil=2.8.2
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- numpy=1.22.4
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- numpy=1.22.4, <2
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- pytz=2020.1
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# optional dependencies

‎ci/deps/actions-39.yaml

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# required dependencies
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- python-dateutil
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- numpy
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- numpy<2
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- pytz
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# optional dependencies

‎ci/deps/actions-pypy-39.yaml

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- hypothesis>=6.46.1
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# required
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- numpy
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- numpy<2
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- python-dateutil
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- pytz
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- pip:

‎ci/deps/circle-310-arm64.yaml

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# required dependencies
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- python-dateutil
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- numpy
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- numpy<2
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- pytz
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# optional dependencies

‎doc/source/user_guide/timeseries.rst

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@@ -896,9 +896,9 @@ into ``freq`` keyword arguments. The available date offsets and associated frequ
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:class:`~pandas.tseries.offsets.BQuarterBegin`, ``'BQS'``, "business quarter begin"
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:class:`~pandas.tseries.offsets.FY5253Quarter`, ``'REQ'``, "retail (aka 52-53 week) quarter"
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:class:`~pandas.tseries.offsets.YearEnd`, ``'Y'``, "calendar year end"
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:class:`~pandas.tseries.offsets.YearBegin`, ``'AS'`` or ``'BYS'``,"calendar year begin"
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:class:`~pandas.tseries.offsets.BYearEnd`, ``'BA'``, "business year end"
901-
:class:`~pandas.tseries.offsets.BYearBegin`, ``'BAS'``, "business year begin"
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:class:`~pandas.tseries.offsets.YearBegin`, ``'YS'`` or ``'BYS'``,"calendar year begin"
900+
:class:`~pandas.tseries.offsets.BYearEnd`, ``'BY'``, "business year end"
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:class:`~pandas.tseries.offsets.BYearBegin`, ``'BYS'``, "business year begin"
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:class:`~pandas.tseries.offsets.FY5253`, ``'RE'``, "retail (aka 52-53 week) year"
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:class:`~pandas.tseries.offsets.Easter`, None, "Easter holiday"
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:class:`~pandas.tseries.offsets.BusinessHour`, ``'bh'``, "business hour"
@@ -1259,9 +1259,9 @@ frequencies. We will refer to these aliases as *offset aliases*.
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"QS", "quarter start frequency"
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"BQS", "business quarter start frequency"
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"Y", "year end frequency"
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"BA, BY", "business year end frequency"
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"AS, YS", "year start frequency"
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"BAS, BYS", "business year start frequency"
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"BY", "business year end frequency"
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"YS", "year start frequency"
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"BYS", "business year start frequency"
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"h", "hourly frequency"
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"bh", "business hour frequency"
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"cbh", "custom business hour frequency"
@@ -1692,7 +1692,7 @@ the end of the interval.
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.. warning::
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The default values for ``label`` and ``closed`` is '**left**' for all
1695-
frequency offsets except for 'ME', 'Y', 'Q', 'BM', 'BA', 'BQ', and 'W'
1695+
frequency offsets except for 'ME', 'Y', 'Q', 'BM', 'BY', 'BQ', and 'W'
16961696
which all have a default of 'right'.
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This might unintendedly lead to looking ahead, where the value for a later

‎doc/source/whatsnew/v0.20.0.rst

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@@ -886,11 +886,23 @@ This would happen with a ``lexsorted``, but non-monotonic levels. (:issue:`15622
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This is *unchanged* from prior versions, but shown for illustration purposes:
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889-
.. ipython:: python
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.. code-block:: python
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891-
df = pd.DataFrame(np.arange(6), columns=['value'],
892-
index=pd.MultiIndex.from_product([list('BA'), range(3)]))
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df
891+
In [81]: df = pd.DataFrame(np.arange(6), columns=['value'],
892+
....: index=pd.MultiIndex.from_product([list('BA'), range(3)]))
893+
....:
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In [82]: df
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Out[82]:
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value
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B 0 0
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1 1
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2 2
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A 0 3
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1 4
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2 5
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[6 rows x 1 columns]
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.. code-block:: python
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‎doc/source/whatsnew/v2.2.0.rst

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@@ -76,6 +76,7 @@ Other enhancements
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- :attr:`Series.attrs` / :attr:`DataFrame.attrs` now uses a deepcopy for propagating ``attrs`` (:issue:`54134`).
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- :func:`read_csv` now supports ``on_bad_lines`` parameter with ``engine="pyarrow"``. (:issue:`54480`)
79+
- :func:`read_spss` now returns a :class:`DataFrame` that stores the metadata in :attr:`DataFrame.attrs`. (:issue:`54264`)
7980
- :meth:`ExtensionArray._explode` interface method added to allow extension type implementations of the ``explode`` method (:issue:`54833`)
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- :meth:`ExtensionArray.duplicated` added to allow extension type implementations of the ``duplicated`` method (:issue:`55255`)
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- DataFrame.apply now allows the usage of numba (via ``engine="numba"``) to JIT compile the passed function, allowing for potential speedups (:issue:`54666`)
@@ -393,6 +394,7 @@ Other
393394
^^^^^
394395
- Bug in :func:`cut` incorrectly allowing cutting of timezone-aware datetimes with timezone-naive bins (:issue:`54964`)
395396
- Bug in :meth:`DataFrame.apply` where passing ``raw=True`` ignored ``args`` passed to the applied function (:issue:`55009`)
397+
- Bug in rendering ``inf`` values inside a a :class:`DataFrame` with the ``use_inf_as_na`` option enabled (:issue:`55483`)
396398
- Bug in rendering a :class:`Series` with a :class:`MultiIndex` when one of the index level's names is 0 not having that name displayed (:issue:`55415`)
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-
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‎environment.yml

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# required dependencies
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- python-dateutil
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- numpy
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- numpy<2
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- pytz
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# optional dependencies

‎pandas/_libs/missing.pyi

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@@ -14,4 +14,3 @@ def isneginf_scalar(val: object) -> bool: ...
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def checknull(val: object, inf_as_na: bool = ...) -> bool: ...
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def isnaobj(arr: np.ndarray, inf_as_na: bool = ...) -> npt.NDArray[np.bool_]: ...
1616
def is_numeric_na(values: np.ndarray) -> npt.NDArray[np.bool_]: ...
17-
def is_float_nan(values: np.ndarray) -> npt.NDArray[np.bool_]: ...

‎pandas/_libs/missing.pyx

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@@ -255,31 +255,6 @@ cdef bint checknull_with_nat_and_na(object obj):
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return checknull_with_nat(obj) or obj is C_NA
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258-
@cython.wraparound(False)
259-
@cython.boundscheck(False)
260-
def is_float_nan(values: ndarray) -> ndarray:
261-
"""
262-
True for elements which correspond to a float nan
263-
264-
Returns
265-
-------
266-
ndarray[bool]
267-
"""
268-
cdef:
269-
ndarray[uint8_t] result
270-
Py_ssize_t i, N
271-
object val
272-
273-
N = len(values)
274-
result = np.zeros(N, dtype=np.uint8)
275-
276-
for i in range(N):
277-
val = values[i]
278-
if util.is_nan(val):
279-
result[i] = True
280-
return result.view(bool)
281-
282-
283258
@cython.wraparound(False)
284259
@cython.boundscheck(False)
285260
def is_numeric_na(values: ndarray) -> ndarray:

‎pandas/_libs/tslibs/dtypes.pyx

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@@ -192,9 +192,6 @@ OFFSET_TO_PERIOD_FREQSTR: dict = {
192192
"BQS": "Q",
193193
"QS": "Q",
194194
"BQ": "Q",
195-
"BA": "Y",
196-
"AS": "Y",
197-
"BAS": "Y",
198195
"MS": "M",
199196
"D": "D",
200197
"B": "B",
@@ -205,9 +202,9 @@ OFFSET_TO_PERIOD_FREQSTR: dict = {
205202
"ns": "ns",
206203
"h": "h",
207204
"Q": "Q",
208-
"Y": "Y",
209205
"W": "W",
210206
"ME": "M",
207+
"Y": "Y",
211208
"BY": "Y",
212209
"YS": "Y",
213210
"BYS": "Y",
@@ -244,6 +241,45 @@ DEPR_ABBREVS: dict[str, str]= {
244241
"A-SEP": "Y-SEP",
245242
"A-OCT": "Y-OCT",
246243
"A-NOV": "Y-NOV",
244+
"BA": "BY",
245+
"BA-DEC": "BY-DEC",
246+
"BA-JAN": "BY-JAN",
247+
"BA-FEB": "BY-FEB",
248+
"BA-MAR": "BY-MAR",
249+
"BA-APR": "BY-APR",
250+
"BA-MAY": "BY-MAY",
251+
"BA-JUN": "BY-JUN",
252+
"BA-JUL": "BY-JUL",
253+
"BA-AUG": "BY-AUG",
254+
"BA-SEP": "BY-SEP",
255+
"BA-OCT": "BY-OCT",
256+
"BA-NOV": "BY-NOV",
257+
"AS": "YS",
258+
"AS-DEC": "YS-DEC",
259+
"AS-JAN": "YS-JAN",
260+
"AS-FEB": "YS-FEB",
261+
"AS-MAR": "YS-MAR",
262+
"AS-APR": "YS-APR",
263+
"AS-MAY": "YS-MAY",
264+
"AS-JUN": "YS-JUN",
265+
"AS-JUL": "YS-JUL",
266+
"AS-AUG": "YS-AUG",
267+
"AS-SEP": "YS-SEP",
268+
"AS-OCT": "YS-OCT",
269+
"AS-NOV": "YS-NOV",
270+
"BAS": "BYS",
271+
"BAS-DEC": "BYS-DEC",
272+
"BAS-JAN": "BYS-JAN",
273+
"BAS-FEB": "BYS-FEB",
274+
"BAS-MAR": "BYS-MAR",
275+
"BAS-APR": "BYS-APR",
276+
"BAS-MAY": "BYS-MAY",
277+
"BAS-JUN": "BYS-JUN",
278+
"BAS-JUL": "BYS-JUL",
279+
"BAS-AUG": "BYS-AUG",
280+
"BAS-SEP": "BYS-SEP",
281+
"BAS-OCT": "BYS-OCT",
282+
"BAS-NOV": "BYS-NOV",
247283
"H": "h",
248284
"BH": "bh",
249285
"CBH": "cbh",

‎pandas/_libs/tslibs/fields.pyx

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253253
# month of year. Other offsets use month, startingMonth as ending
254254
# month of year.
255255

256-
if (freqstr[0:2] in ["MS", "QS", "AS"]) or (
257-
freqstr[1:3] in ["MS", "QS", "AS"]):
256+
if (freqstr[0:2] in ["MS", "QS", "YS"]) or (
257+
freqstr[1:3] in ["MS", "QS", "YS"]):
258258
end_month = 12 if month_kw == 1 else month_kw - 1
259259
start_month = month_kw
260260
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‎pandas/_libs/tslibs/offsets.pyx

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24152415
_outputName = "BusinessYearEnd"
24162416
_default_month = 12
2417-
_prefix = "BA"
2417+
_prefix = "BY"
24182418
_day_opt = "business_end"
24192419

24202420

@@ -2453,7 +2453,7 @@ cdef class BYearBegin(YearOffset):
24532453

24542454
_outputName = "BusinessYearBegin"
24552455
_default_month = 1
2456-
_prefix = "BAS"
2456+
_prefix = "BYS"
24572457
_day_opt = "business_start"
24582458

24592459

@@ -2552,7 +2552,7 @@ cdef class YearBegin(YearOffset):
25522552
"""
25532553

25542554
_default_month = 1
2555-
_prefix = "AS"
2555+
_prefix = "YS"
25562556
_day_opt = "start"
25572557

25582558

@@ -4540,10 +4540,10 @@ CDay = CustomBusinessDay
45404540
prefix_mapping = {
45414541
offset._prefix: offset
45424542
for offset in [
4543-
YearBegin, # 'AS'
4543+
YearBegin, # 'YS'
45444544
YearEnd, # 'Y'
4545-
BYearBegin, # 'BAS'
4546-
BYearEnd, # 'BA'
4545+
BYearBegin, # 'BYS'
4546+
BYearEnd, # 'BY'
45474547
BusinessDay, # 'B'
45484548
BusinessMonthBegin, # 'BMS'
45494549
BusinessMonthEnd, # 'BM'
@@ -4584,12 +4584,9 @@ _lite_rule_alias = {
45844584
"Q": "Q-DEC",
45854585

45864586
"Y": "Y-DEC", # YearEnd(month=12),
4587-
"AS": "AS-JAN", # YearBegin(month=1),
4588-
"YS": "AS-JAN",
4589-
"BA": "BA-DEC", # BYearEnd(month=12),
4590-
"BY": "BA-DEC",
4591-
"BAS": "BAS-JAN", # BYearBegin(month=1),
4592-
"BYS": "BAS-JAN",
4587+
"YS": "YS-JAN", # YearBegin(month=1),
4588+
"BY": "BY-DEC", # BYearEnd(month=12),
4589+
"BYS": "BYS-JAN", # BYearBegin(month=1),
45934590

45944591
"Min": "min",
45954592
"min": "min",

‎pandas/core/arrays/arrow/array.py

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@@ -2526,7 +2526,7 @@ def _round_temporally(
25262526
raise ValueError(f"Must specify a valid frequency: {freq}")
25272527
pa_supported_unit = {
25282528
"Y": "year",
2529-
"AS": "year",
2529+
"YS": "year",
25302530
"Q": "quarter",
25312531
"QS": "quarter",
25322532
"M": "month",

‎pandas/core/arrays/base.py

Lines changed: 10 additions & 2 deletions
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16631663
self, self._formatter(), indent_for_name=False
16641664
).rstrip(", \n")
16651665
class_name = f"<{type(self).__name__}>\n"
1666-
return f"{class_name}{data}\nLength: {len(self)}, dtype: {self.dtype}"
1666+
footer = self._get_repr_footer()
1667+
return f"{class_name}{data}\n{footer}"
1668+
1669+
def _get_repr_footer(self) -> str:
1670+
# GH#24278
1671+
if self.ndim > 1:
1672+
return f"Shape: {self.shape}, dtype: {self.dtype}"
1673+
return f"Length: {len(self)}, dtype: {self.dtype}"
16671674

16681675
def _repr_2d(self) -> str:
16691676
from pandas.io.formats.printing import format_object_summary
@@ -1679,7 +1686,8 @@ def _repr_2d(self) -> str:
16791686
]
16801687
data = ",\n".join(lines)
16811688
class_name = f"<{type(self).__name__}>"
1682-
return f"{class_name}\n[\n{data}\n]\nShape: {self.shape}, dtype: {self.dtype}"
1689+
footer = self._get_repr_footer()
1690+
return f"{class_name}\n[\n{data}\n]\n{footer}"
16831691

16841692
def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
16851693
"""

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