Merge branch 'main' into qcut-floating-point

Author: rob-sil

.circleci/config.yml

@@ -91,8 +91,8 @@ jobs:
           name: Build aarch64 wheels
           no_output_timeout: 30m # Sometimes the tests won't generate any output, make sure the job doesn't get killed by that
           command: |
-            pip3 install cibuildwheel==2.18.1
-            cibuildwheel --prerelease-pythons --output-dir wheelhouse
+            pip3 install cibuildwheel==2.20.0
+            cibuildwheel --output-dir wheelhouse
 
           environment:
             CIBW_BUILD: << parameters.cibw-build >>

.github/workflows/wheels.yml

@@ -158,11 +158,10 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.2
+        uses: pypa/cibuildwheel@v2.20.0
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:
-          CIBW_PRERELEASE_PYTHONS: True
           CIBW_BUILD: ${{ matrix.python[0] }}-${{ matrix.buildplat[1] }}
           CIBW_BUILD_FRONTEND: ${{ matrix.cibw_build_frontend || 'pip' }}
           CIBW_PLATFORM: ${{ matrix.buildplat[1] == 'pyodide_wasm32' && 'pyodide' || 'auto' }}

ci/code_checks.sh

@@ -70,8 +70,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         --format=actions \
         -i ES01 `# For now it is ok if docstrings are missing the extended summary` \
         -i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
-        -i "pandas.MultiIndex.get_level_values SA01" \
-        -i "pandas.MultiIndex.get_loc_level PR07" \
         -i "pandas.MultiIndex.names SA01" \
         -i "pandas.MultiIndex.reorder_levels RT03,SA01" \
         -i "pandas.MultiIndex.sortlevel PR07,SA01" \
@@ -165,7 +163,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.str.center RT03,SA01" \
         -i "pandas.Series.str.decode PR07,RT03,SA01" \
         -i "pandas.Series.str.encode PR07,RT03,SA01" \
-        -i "pandas.Series.str.fullmatch RT03" \
         -i "pandas.Series.str.index RT03" \
         -i "pandas.Series.str.ljust RT03,SA01" \
         -i "pandas.Series.str.lower RT03" \
@@ -264,9 +261,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.api.extensions.ExtensionArray.tolist RT03,SA01" \
         -i "pandas.api.extensions.ExtensionArray.unique RT03,SA01" \
         -i "pandas.api.extensions.ExtensionArray.view SA01" \
-        -i "pandas.api.indexers.VariableOffsetWindowIndexer PR01,SA01" \
         -i "pandas.api.interchange.from_dataframe RT03,SA01" \
-        -i "pandas.api.types.is_any_real_numeric_dtype SA01" \
         -i "pandas.api.types.is_bool PR01,SA01" \
         -i "pandas.api.types.is_bool_dtype SA01" \
         -i "pandas.api.types.is_categorical_dtype SA01" \
@@ -288,13 +283,9 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.api.types.is_iterator PR07,SA01" \
         -i "pandas.api.types.is_list_like SA01" \
         -i "pandas.api.types.is_named_tuple PR07,SA01" \
-        -i "pandas.api.types.is_numeric_dtype SA01" \
         -i "pandas.api.types.is_object_dtype SA01" \
-        -i "pandas.api.types.is_period_dtype SA01" \
         -i "pandas.api.types.is_re PR07,SA01" \
         -i "pandas.api.types.is_re_compilable PR07,SA01" \
-        -i "pandas.api.types.is_sparse SA01" \
-        -i "pandas.api.types.is_timedelta64_ns_dtype SA01" \
         -i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
         -i "pandas.arrays.BooleanArray SA01" \

doc/source/development/contributing.rst

@@ -74,7 +74,6 @@ If you are new to Git, you can reference some of these resources for learning Gi
 to the :ref:`contributor community <community>` for help if needed:
 
 * `Git documentation <https://git-scm.com/doc>`_.
-* `Numpy's Git resources <https://numpy.org/doc/stable/dev/gitwash/git_resources.html>`_ tutorial.
 
 Also, the project follows a forking workflow further described on this page whereby
 contributors fork the repository, make changes and then create a pull request.

doc/source/whatsnew/v3.0.0.rst

@@ -50,6 +50,7 @@ Other enhancements
 - :meth:`DataFrame.pivot_table` and :func:`pivot_table` now allow the passing of keyword arguments to ``aggfunc`` through ``**kwargs`` (:issue:`57884`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
+- Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)
 - Support reading Stata 102-format (Stata 1) dta files (:issue:`58978`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
@@ -547,6 +548,7 @@ Strings
 
 Interval
 ^^^^^^^^
+- :meth:`Index.is_monotonic_decreasing`, :meth:`Index.is_monotonic_increasing`, and :meth:`Index.is_unique` could incorrectly be ``False`` for an ``Index`` created from a slice of another ``Index``. (:issue:`57911`)
 - Bug in :func:`interval_range` where start and end numeric types were always cast to 64 bit (:issue:`57268`)
 -
 
@@ -609,6 +611,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupBy.agg` that raises ``AttributeError`` when there is dictionary input and duplicated columns, instead of returning a DataFrame with the aggregation of all duplicate columns. (:issue:`55041`)
 - Bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`)
 - Bug in :meth:`DataFrameGroupBy.apply` with ``as_index=False`` that was returning :class:`MultiIndex` instead of returning :class:`Index`. (:issue:`58291`)
+- Bug in :meth:`DataFrameGroupBy.cumsum` and :meth:`DataFrameGroupBy.cumprod` where ``numeric_only`` parameter was passed indirectly through kwargs instead of passing directly. (:issue:`58811`)
 - Bug in :meth:`DataFrameGroupBy.cumsum` where it did not return the correct dtype when the label contained ``None``. (:issue:`58811`)
 - Bug in :meth:`DataFrameGroupby.transform` and :meth:`SeriesGroupby.transform` with a reducer and ``observed=False`` that coerces dtype to float when there are unobserved categories. (:issue:`55326`)
 - Bug in :meth:`Rolling.apply` where the applied function could be called on fewer than ``min_period`` periods if ``method="table"``. (:issue:`58868`)

pandas/_libs/index.pyx

@@ -252,14 +252,24 @@ cdef class IndexEngine:
         return self.sizeof()
 
     cpdef _update_from_sliced(self, IndexEngine other, reverse: bool):
-        self.unique = other.unique
-        self.need_unique_check = other.need_unique_check
+        if other.unique:
+            self.unique = other.unique
+            self.need_unique_check = other.need_unique_check
+
         if not other.need_monotonic_check and (
                 other.is_monotonic_increasing or other.is_monotonic_decreasing):
-            self.need_monotonic_check = other.need_monotonic_check
-            # reverse=True means the index has been reversed
-            self.monotonic_inc = other.monotonic_dec if reverse else other.monotonic_inc
-            self.monotonic_dec = other.monotonic_inc if reverse else other.monotonic_dec
+            self.need_monotonic_check = 0
+            if len(self.values) > 0 and self.values[0] != self.values[-1]:
+                # reverse=True means the index has been reversed
+                if reverse:
+                    self.monotonic_inc = other.monotonic_dec
+                    self.monotonic_dec = other.monotonic_inc
+                else:
+                    self.monotonic_inc = other.monotonic_inc
+                    self.monotonic_dec = other.monotonic_dec
+            else:
+                self.monotonic_inc = 1
+                self.monotonic_dec = 1
 
     @property
     def is_unique(self) -> bool:
@@ -882,14 +892,24 @@ cdef class SharedEngine:
         pass
 
     cpdef _update_from_sliced(self, ExtensionEngine other, reverse: bool):
-        self.unique = other.unique
-        self.need_unique_check = other.need_unique_check
+        if other.unique:
+            self.unique = other.unique
+            self.need_unique_check = other.need_unique_check
+
         if not other.need_monotonic_check and (
                 other.is_monotonic_increasing or other.is_monotonic_decreasing):
-            self.need_monotonic_check = other.need_monotonic_check
-            # reverse=True means the index has been reversed
-            self.monotonic_inc = other.monotonic_dec if reverse else other.monotonic_inc
-            self.monotonic_dec = other.monotonic_inc if reverse else other.monotonic_dec
+            self.need_monotonic_check = 0
+            if len(self.values) > 0 and self.values[0] != self.values[-1]:
+                # reverse=True means the index has been reversed
+                if reverse:
+                    self.monotonic_inc = other.monotonic_dec
+                    self.monotonic_dec = other.monotonic_inc
+                else:
+                    self.monotonic_inc = other.monotonic_inc
+                    self.monotonic_dec = other.monotonic_dec
+            else:
+                self.monotonic_inc = 1
+                self.monotonic_dec = 1
 
     @property
     def is_unique(self) -> bool:

pandas/_libs/lib.pyx

@@ -2702,7 +2702,7 @@ def maybe_convert_objects(ndarray[object] objects,
         if using_string_dtype() and is_string_array(objects, skipna=True):
             from pandas.core.arrays.string_ import StringDtype
 
-            dtype = StringDtype(storage="pyarrow", na_value=np.nan)
+            dtype = StringDtype(na_value=np.nan)
             return dtype.construct_array_type()._from_sequence(objects, dtype=dtype)
 
         elif convert_to_nullable_dtype and is_string_array(objects, skipna=True):

pandas/_libs/tslibs/offsets.pyx

@@ -491,6 +491,12 @@ cdef class BaseOffset:
         elif is_integer_object(other):
             return type(self)(n=other * self.n, normalize=self.normalize,
                               **self.kwds)
+        elif isinstance(other, BaseOffset):
+            # Otherwise raises RecurrsionError due to __rmul__
+            raise TypeError(
+                f"Cannot multiply {type(self).__name__} with "
+                f"{type(other).__name__}."
+            )
         return NotImplemented
 
     def __rmul__(self, other):

pandas/_testing/__init__.py

@@ -12,6 +12,7 @@
 
 import numpy as np
 
+from pandas._config import using_string_dtype
 from pandas._config.localization import (
     can_set_locale,
     get_locales,
@@ -106,7 +107,10 @@
 ALL_FLOAT_DTYPES: list[Dtype] = [*FLOAT_NUMPY_DTYPES, *FLOAT_EA_DTYPES]
 
 COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
-STRING_DTYPES: list[Dtype] = [str, "str", "U"]
+if using_string_dtype():
+    STRING_DTYPES: list[Dtype] = [str, "U"]
+else:
+    STRING_DTYPES: list[Dtype] = [str, "str", "U"]  # type: ignore[no-redef]
 COMPLEX_FLOAT_DTYPES: list[Dtype] = [*COMPLEX_DTYPES, *FLOAT_NUMPY_DTYPES]
 
 DATETIME64_DTYPES: list[Dtype] = ["datetime64[ns]", "M8[ns]"]

pandas/_testing/asserters.py

@@ -796,6 +796,24 @@ def assert_extension_array_equal(
         left_na, right_na, obj=f"{obj} NA mask", index_values=index_values
     )
 
+    # Specifically for StringArrayNumpySemantics, validate here we have a valid array
+    if (
+        isinstance(left.dtype, StringDtype)
+        and left.dtype.storage == "python"
+        and left.dtype.na_value is np.nan
+    ):
+        assert np.all(
+            [np.isnan(val) for val in left._ndarray[left_na]]  # type: ignore[attr-defined]
+        ), "wrong missing value sentinels"
+    if (
+        isinstance(right.dtype, StringDtype)
+        and right.dtype.storage == "python"
+        and right.dtype.na_value is np.nan
+    ):
+        assert np.all(
+            [np.isnan(val) for val in right._ndarray[right_na]]  # type: ignore[attr-defined]
+        ), "wrong missing value sentinels"
+
     left_valid = left[~left_na].to_numpy(dtype=object)
     right_valid = right[~right_na].to_numpy(dtype=object)
     if check_exact:
@@ -1158,7 +1176,10 @@ def assert_frame_equal(
         Specify how to compare internal data. If False, compare by columns.
         If True, compare by blocks.
     check_exact : bool, default False
-        Whether to compare number exactly.
+        Whether to compare number exactly. If False, the comparison uses the
+        relative tolerance (``rtol``) and absolute tolerance (``atol``)
+        parameters to determine if two values are considered close,
+        according to the formula: ``|a - b| <= (atol + rtol * |b|)``.
 
         .. versionchanged:: 2.2.0