Merge branch 'main' into main

Author: NavaneetthaSundararaj

ci/code_checks.sh

@@ -73,11 +73,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.NA SA01" \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
-        -i "pandas.Period.strftime PR01,SA01" \
         -i "pandas.Period.to_timestamp SA01" \
-        -i "pandas.PeriodDtype SA01" \
         -i "pandas.PeriodDtype.freq SA01" \
-        -i "pandas.RangeIndex PR07" \
         -i "pandas.RangeIndex.from_range PR01,SA01" \
         -i "pandas.RangeIndex.start SA01" \
         -i "pandas.RangeIndex.step SA01" \
@@ -135,7 +132,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.value GL08" \
         -i "pandas.Timestamp.year GL08" \
         -i "pandas.api.extensions.ExtensionArray.interpolate PR01,SA01" \
-        -i "pandas.api.interchange.from_dataframe RT03,SA01" \
         -i "pandas.api.types.is_bool PR01,SA01" \
         -i "pandas.api.types.is_categorical_dtype SA01" \
         -i "pandas.api.types.is_complex PR01,SA01" \
@@ -222,7 +218,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.var SA01" \
         -i "pandas.date_range RT03" \
-        -i "pandas.errors.AbstractMethodError PR01,SA01" \
         -i "pandas.errors.AttributeConflictWarning SA01" \
         -i "pandas.errors.CSSWarning SA01" \
         -i "pandas.errors.CategoricalConversionWarning SA01" \

doc/source/whatsnew/v2.3.0.rst

@@ -53,7 +53,7 @@ notable_bug_fix1
 
 Deprecations
 ~~~~~~~~~~~~
--
+- Deprecated allowing non-``bool`` values for ``na`` in :meth:`.str.contains`, :meth:`.str.startswith`, and :meth:`.str.endswith` for dtypes that do not already disallow these (:issue:`59615`)
 -
 
 .. ---------------------------------------------------------------------------
@@ -103,7 +103,8 @@ Conversion
 Strings
 ^^^^^^^
 - Bug in :meth:`Series.str.replace` when ``n < 0`` for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`59628`)
--
+- Bug in the ``center`` method on :class:`Series` and :class:`Index` object ``str`` accessors with pyarrow-backed dtype not matching the python behavior in corner cases with an odd number of fill characters (:issue:`54792`)
+
 
 Interval
 ^^^^^^^^

doc/source/whatsnew/v3.0.0.rst

@@ -503,6 +503,7 @@ Performance improvements
 - :meth:`Series.str.extract` returns a :class:`RangeIndex` columns instead of an :class:`Index` column when possible (:issue:`57542`)
 - :meth:`Series.str.partition` with :class:`ArrowDtype` returns a :class:`RangeIndex` columns instead of an :class:`Index` column when possible (:issue:`57768`)
 - Performance improvement in :class:`DataFrame` when ``data`` is a ``dict`` and ``columns`` is specified (:issue:`24368`)
+- Performance improvement in :class:`MultiIndex` when setting :attr:`MultiIndex.names` doesn't invalidate all cached operations (:issue:`59578`)
 - Performance improvement in :meth:`DataFrame.join` for sorted but non-unique indexes (:issue:`56941`)
 - Performance improvement in :meth:`DataFrame.join` when left and/or right are non-unique and ``how`` is ``"left"``, ``"right"``, or ``"inner"`` (:issue:`56817`)
 - Performance improvement in :meth:`DataFrame.join` with ``how="left"`` or ``how="right"`` and ``sort=True`` (:issue:`56919`)

pandas/_libs/tslibs/period.pyx

@@ -2755,6 +2755,27 @@ cdef class _Period(PeriodMixin):
         | ``%%``    | A literal ``'%'`` character.   |       |
         +-----------+--------------------------------+-------+
 
+        The `strftime` method provides a way to represent a :class:`Period`
+        object as a string in a specified format. This is particularly useful
+        when displaying date and time data in different locales or customized
+        formats, suitable for reports or user interfaces. It extends the standard
+        Python string formatting capabilities with additional directives specific
+        to `pandas`, accommodating features like fiscal years and precise
+        sub-second components.
+
+        Parameters
+        ----------
+        fmt : str or None
+            String containing the desired format directives. If ``None``, the
+            format is determined based on the Period's frequency.
+
+        See Also
+        --------
+        Timestamp.strftime : Return a formatted string of the Timestamp.
+        to_datetime : Convert argument to datetime.
+        time.strftime : Format a time object as a string according to a
+            specified format string in the standard Python library.
+
         Notes
         -----
 

pandas/core/array_algos/quantile.py

@@ -91,7 +91,7 @@ def quantile_with_mask(
     if is_empty:
         # create the array of na_values
         # 2d len(values) * len(qs)
-        flat = np.array([fill_value] * len(qs))
+        flat = np.full(len(qs), fill_value)
         result = np.repeat(flat, len(values)).reshape(len(values), len(qs))
     else:
         result = _nanquantile(

pandas/core/arrays/_arrow_string_mixins.py

@@ -1,13 +1,17 @@
 from __future__ import annotations
 
+from functools import partial
 from typing import (
     TYPE_CHECKING,
     Literal,
 )
 
 import numpy as np
 
-from pandas.compat import pa_version_under10p1
+from pandas.compat import (
+    pa_version_under10p1,
+    pa_version_under17p0,
+)
 
 from pandas.core.dtypes.missing import isna
 
@@ -49,7 +53,19 @@ def _str_pad(
         elif side == "right":
             pa_pad = pc.utf8_rpad
         elif side == "both":
-            pa_pad = pc.utf8_center
+            if pa_version_under17p0:
+                # GH#59624 fall back to object dtype
+                from pandas import array
+
+                obj_arr = self.astype(object, copy=False)  # type: ignore[attr-defined]
+                obj = array(obj_arr, dtype=object)
+                result = obj._str_pad(width, side, fillchar)  # type: ignore[attr-defined]
+                return type(self)._from_sequence(result, dtype=self.dtype)  # type: ignore[attr-defined]
+            else:
+                # GH#54792
+                # https://github.com/apache/arrow/issues/15053#issuecomment-2317032347
+                lean_left = (width % 2) == 0
+                pa_pad = partial(pc.utf8_center, lean_left_on_odd_padding=lean_left)
         else:
             raise ValueError(
                 f"Invalid side: {side}. Side must be one of 'left', 'right', 'both'"

pandas/core/arrays/_ranges.py

@@ -18,6 +18,8 @@
     iNaT,
 )
 
+from pandas.core.construction import range_to_ndarray
+
 if TYPE_CHECKING:
     from pandas._typing import npt
 
@@ -82,17 +84,7 @@ def generate_regular_range(
             "at least 'start' or 'end' should be specified if a 'period' is given."
         )
 
-    with np.errstate(over="raise"):
-        # If the range is sufficiently large, np.arange may overflow
-        #  and incorrectly return an empty array if not caught.
-        try:
-            values = np.arange(b, e, stride, dtype=np.int64)
-        except FloatingPointError:
-            xdr = [b]
-            while xdr[-1] != e:
-                xdr.append(xdr[-1] + stride)
-            values = np.array(xdr[:-1], dtype=np.int64)
-    return values
+    return range_to_ndarray(range(b, e, stride))
 
 
 def _generate_range_overflow_safe(

pandas/core/arrays/string_arrow.py

@@ -6,6 +6,7 @@
     TYPE_CHECKING,
     Union,
 )
+import warnings
 
 import numpy as np
 
@@ -19,6 +20,7 @@
     pa_version_under10p1,
     pa_version_under13p0,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     is_scalar,
@@ -282,6 +284,7 @@ def astype(self, dtype, copy: bool = True):
     _str_map = BaseStringArray._str_map
     _str_startswith = ArrowStringArrayMixin._str_startswith
     _str_endswith = ArrowStringArrayMixin._str_endswith
+    _str_pad = ArrowStringArrayMixin._str_pad
 
     def _str_contains(
         self, pat, case: bool = True, flags: int = 0, na=np.nan, regex: bool = True
@@ -297,6 +300,14 @@ def _str_contains(
             result = pc.match_substring(self._pa_array, pat, ignore_case=not case)
         result = self._convert_bool_result(result, na=na)
         if not isna(na):
+            if not isinstance(na, bool):
+                # GH#59561
+                warnings.warn(
+                    "Allowing a non-bool 'na' in obj.str.contains is deprecated "
+                    "and will raise in a future version.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
             result[isna(result)] = bool(na)
         return result
 
@@ -536,7 +547,6 @@ class ArrowStringArrayNumpySemantics(ArrowStringArray):
     _str_get = ArrowStringArrayMixin._str_get
     _str_removesuffix = ArrowStringArrayMixin._str_removesuffix
     _str_capitalize = ArrowStringArrayMixin._str_capitalize
-    _str_pad = ArrowStringArrayMixin._str_pad
     _str_title = ArrowStringArrayMixin._str_title
     _str_swapcase = ArrowStringArrayMixin._str_swapcase
     _str_slice_replace = ArrowStringArrayMixin._str_slice_replace

pandas/core/dtypes/dtypes.py

@@ -513,7 +513,7 @@ def _hash_categories(self) -> int:
                 [cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)]
             )
         else:
-            cat_array = np.array([cat_array])
+            cat_array = cat_array.reshape(1, len(cat_array))
         combined_hashed = combine_hash_arrays(iter(cat_array), num_items=len(cat_array))
         return np.bitwise_xor.reduce(combined_hashed)
 
@@ -986,6 +986,14 @@ class PeriodDtype(PeriodDtypeBase, PandasExtensionDtype):
     -------
     None
 
+    See Also
+    --------
+    Period : Represents a single time period.
+    PeriodIndex : Immutable index for period data.
+    date_range : Return a fixed frequency DatetimeIndex.
+    Series : One-dimensional array with axis labels.
+    DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data.
+
     Examples
     --------
     >>> pd.PeriodDtype(freq="D")

pandas/core/groupby/groupby.py

@@ -4370,11 +4370,12 @@ def post_processor(
 
             return vals
 
-        qs = np.array(q, dtype=np.float64)
-        pass_qs: np.ndarray | None = qs
         if is_scalar(q):
             qs = np.array([q], dtype=np.float64)
-            pass_qs = None
+            pass_qs: None | np.ndarray = None
+        else:
+            qs = np.asarray(q, dtype=np.float64)
+            pass_qs = qs
 
         ids = self._grouper.ids
         ngroups = self._grouper.ngroups