Merge branch 'main' into Test_issue_57930

Author: MCRE-BE

.github/workflows/unit-tests.yml

@@ -22,10 +22,11 @@ defaults:
 
 jobs:
   ubuntu:
-    runs-on: ubuntu-22.04
+    runs-on: ${{ matrix.platform }}
     timeout-minutes: 90
     strategy:
       matrix:
+        platform: [ubuntu-22.04, ubuntu-24.04-arm]
         env_file: [actions-310.yaml, actions-311.yaml, actions-312.yaml]
         # Prevent the include jobs from overriding other jobs
         pattern: [""]
@@ -35,9 +36,11 @@ jobs:
             env_file: actions-311-downstream_compat.yaml
             pattern: "not slow and not network and not single_cpu"
             pytest_target: "pandas/tests/test_downstream.py"
+            platform: ubuntu-22.04
           - name: "Minimum Versions"
             env_file: actions-310-minimum_versions.yaml
             pattern: "not slow and not network and not single_cpu"
+            platform: ubuntu-22.04
           - name: "Locale: it_IT"
             env_file: actions-311.yaml
             pattern: "not slow and not network and not single_cpu"
@@ -48,6 +51,7 @@ jobs:
             # Also install it_IT (its encoding is ISO8859-1) but do not activate it.
             # It will be temporarily activated during tests with locale.setlocale
             extra_loc: "it_IT"
+            platform: ubuntu-22.04
           - name: "Locale: zh_CN"
             env_file: actions-311.yaml
             pattern: "not slow and not network and not single_cpu"
@@ -58,25 +62,32 @@ jobs:
             # Also install zh_CN (its encoding is gb2312) but do not activate it.
             # It will be temporarily activated during tests with locale.setlocale
             extra_loc: "zh_CN"
+            platform: ubuntu-22.04
           - name: "Future infer strings"
             env_file: actions-312.yaml
             pandas_future_infer_string: "1"
+            platform: ubuntu-22.04
           - name: "Future infer strings (without pyarrow)"
             env_file: actions-311.yaml
             pandas_future_infer_string: "1"
+            platform: ubuntu-22.04
           - name: "Pypy"
             env_file: actions-pypy-39.yaml
             pattern: "not slow and not network and not single_cpu"
             test_args: "--max-worker-restart 0"
+            platform: ubuntu-22.04
           - name: "Numpy Dev"
             env_file: actions-311-numpydev.yaml
             pattern: "not slow and not network and not single_cpu"
             test_args: "-W error::DeprecationWarning -W error::FutureWarning"
+            platform: ubuntu-22.04
           - name: "Pyarrow Nightly"
             env_file: actions-311-pyarrownightly.yaml
             pattern: "not slow and not network and not single_cpu"
+            pandas_future_infer_string: "1"
+            platform: ubuntu-22.04
       fail-fast: false
-    name: ${{ matrix.name || format('ubuntu-latest {0}', matrix.env_file) }}
+    name: ${{ matrix.name || format('{0} {1}', matrix.platform, matrix.env_file) }}
     env:
       PATTERN: ${{ matrix.pattern }}
       LANG: ${{ matrix.lang || 'C.UTF-8' }}
@@ -91,7 +102,7 @@ jobs:
       REMOVE_PYARROW: ${{ matrix.name == 'Future infer strings (without pyarrow)' && '1' || '0' }}
     concurrency:
       # https://github.community/t/concurrecy-not-work-for-push/183068/7
-      group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.env_file }}-${{ matrix.pattern }}-${{ matrix.extra_apt || '' }}-${{ matrix.pandas_future_infer_string }}
+      group: ${{ github.event_name == 'push' && github.run_number || github.ref }}-${{ matrix.env_file }}-${{ matrix.pattern }}-${{ matrix.extra_apt || '' }}-${{ matrix.pandas_future_infer_string }}-${{ matrix.platform }}
       cancel-in-progress: true
 
     services:

.github/workflows/wheels.yml

@@ -94,6 +94,7 @@ jobs:
         buildplat:
         - [ubuntu-22.04, manylinux_x86_64]
         - [ubuntu-22.04, musllinux_x86_64]
+        - [ubuntu-24.04-arm, manylinux_aarch64]
         - [macos-13, macosx_x86_64]
         # Note: M1 images on Github Actions start from macOS 14
         - [macos-14, macosx_arm64]

ci/code_checks.sh

@@ -72,15 +72,13 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
-        -i "pandas.RangeIndex.from_range PR01,SA01" \
         -i "pandas.Timedelta.max PR02" \
         -i "pandas.Timedelta.min PR02" \
         -i "pandas.Timedelta.resolution PR02" \
         -i "pandas.Timestamp.max PR02" \
         -i "pandas.Timestamp.min PR02" \
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
-        -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
         -i "pandas.arrays.TimedeltaArray PR07,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \

ci/deps/actions-311-pyarrownightly.yaml

@@ -23,7 +23,7 @@ dependencies:
 
   - pip:
     - "tzdata>=2022.7"
-    - "--extra-index-url https://pypi.fury.io/arrow-nightlies/"
+    - "--extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple"
     - "--prefer-binary"
     - "--pre"
     - "pyarrow"

doc/source/reference/groupby.rst

@@ -104,6 +104,7 @@ Function application
    DataFrameGroupBy.shift
    DataFrameGroupBy.size
    DataFrameGroupBy.skew
+   DataFrameGroupBy.kurt
    DataFrameGroupBy.std
    DataFrameGroupBy.sum
    DataFrameGroupBy.var
@@ -159,6 +160,7 @@ Function application
    SeriesGroupBy.shift
    SeriesGroupBy.size
    SeriesGroupBy.skew
+   SeriesGroupBy.kurt
    SeriesGroupBy.std
    SeriesGroupBy.sum
    SeriesGroupBy.var

doc/source/reference/window.rst

@@ -30,11 +30,14 @@ Rolling window functions
    Rolling.std
    Rolling.min
    Rolling.max
+   Rolling.first
+   Rolling.last
    Rolling.corr
    Rolling.cov
    Rolling.skew
    Rolling.kurt
    Rolling.apply
+   Rolling.pipe
    Rolling.aggregate
    Rolling.quantile
    Rolling.sem
@@ -71,11 +74,14 @@ Expanding window functions
    Expanding.std
    Expanding.min
    Expanding.max
+   Expanding.first
+   Expanding.last
    Expanding.corr
    Expanding.cov
    Expanding.skew
    Expanding.kurt
    Expanding.apply
+   Expanding.pipe
    Expanding.aggregate
    Expanding.quantile
    Expanding.sem

doc/source/whatsnew/v2.3.0.rst

@@ -35,8 +35,8 @@ Other enhancements
 - The semantics for the ``copy`` keyword in ``__array__`` methods (i.e. called
   when using ``np.array()`` or ``np.asarray()`` on pandas objects) has been
   updated to work correctly with NumPy >= 2 (:issue:`57739`)
+- The :meth:`~Series.cumsum`, :meth:`~Series.cummin`, and :meth:`~Series.cummax` reductions are now implemented for ``StringDtype`` columns when backed by PyArrow (:issue:`60633`)
 - The :meth:`~Series.sum` reduction is now implemented for ``StringDtype`` columns (:issue:`59853`)
--
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_230.notable_bug_fixes:
@@ -105,6 +105,7 @@ Conversion
 
 Strings
 ^^^^^^^
+- Bug in :meth:`Series.__pos__` and :meth:`DataFrame.__pos__` did not raise for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`60710`)
 - Bug in :meth:`Series.rank` for :class:`StringDtype` with ``storage="pyarrow"`` incorrectly returning integer results in case of ``method="average"`` and raising an error if it would truncate results (:issue:`59768`)
 - Bug in :meth:`Series.replace` with :class:`StringDtype` when replacing with a non-string value was not upcasting to ``object`` dtype (:issue:`60282`)
 - Bug in :meth:`Series.str.replace` when ``n < 0`` for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`59628`)
@@ -175,7 +176,6 @@ Other
 ^^^^^
 - Fixed usage of ``inspect`` when the optional dependencies ``pyarrow`` or ``jinja2``
   are not installed (:issue:`60196`)
--
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_230.contributors:

doc/source/whatsnew/v3.0.0.rst

@@ -30,6 +30,8 @@ Other enhancements
 ^^^^^^^^^^^^^^^^^^
 - :class:`pandas.api.typing.FrozenList` is available for typing the outputs of :attr:`MultiIndex.names`, :attr:`MultiIndex.codes` and :attr:`MultiIndex.levels` (:issue:`58237`)
 - :class:`pandas.api.typing.SASReader` is available for typing the output of :func:`read_sas` (:issue:`55689`)
+- :meth:`pandas.api.interchange.from_dataframe` now uses the `PyCapsule Interface <https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html>`_ if available, only falling back to the Dataframe Interchange Protocol if that fails (:issue:`60739`)
+- :class:`pandas.api.typing.NoDefault` is available for typing ``no_default``
 - :func:`DataFrame.to_excel` now raises an ``UserWarning`` when the character count in a cell exceeds Excel's limitation of 32767 characters (:issue:`56954`)
 - :func:`pandas.merge` now validates the ``how`` parameter input (merge type) (:issue:`59435`)
 - :func:`read_spss` now supports kwargs to be passed to pyreadstat (:issue:`56356`)
@@ -44,17 +46,21 @@ Other enhancements
 - Users can globally disable any ``PerformanceWarning`` by setting the option ``mode.performance_warnings`` to ``False`` (:issue:`56920`)
 - :meth:`Styler.format_index_names` can now be used to format the index and column names (:issue:`48936` and :issue:`47489`)
 - :class:`.errors.DtypeWarning` improved to include column names when mixed data types are detected (:issue:`58174`)
+- :class:`Rolling` and :class:`Expanding` now support ``pipe`` method (:issue:`57076`)
 - :class:`Series` now supports the Arrow PyCapsule Interface for export (:issue:`59518`)
 - :func:`DataFrame.to_excel` argument ``merge_cells`` now accepts a value of ``"columns"`` to only merge :class:`MultiIndex` column header header cells (:issue:`35384`)
 - :meth:`DataFrame.corrwith` now accepts ``min_periods`` as optional arguments, as in :meth:`DataFrame.corr` and :meth:`Series.corr` (:issue:`9490`)
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.ewm` now allows ``adjust=False`` when ``times`` is provided (:issue:`54328`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`DataFrame.pivot_table` and :func:`pivot_table` now allow the passing of keyword arguments to ``aggfunc`` through ``**kwargs`` (:issue:`57884`)
+- :meth:`DataFrame.to_json` now encodes ``Decimal`` as strings instead of floats (:issue:`60698`)
 - :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`)
 - :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
+- :class:`Rolling` and :class:`Expanding` now support aggregations ``first`` and ``last`` (:issue:`33155`)
 - :func:`read_parquet` accepts ``to_pandas_kwargs`` which are forwarded to :meth:`pyarrow.Table.to_pandas` which enables passing additional keywords to customize the conversion to pandas, such as ``maps_as_pydicts`` to read the Parquet map data type as python dictionaries (:issue:`56842`)
+- :meth:`.DataFrameGroupBy.mean`, :meth:`.DataFrameGroupBy.sum`, :meth:`.SeriesGroupBy.mean` and :meth:`.SeriesGroupBy.sum` now accept ``skipna`` parameter (:issue:`15675`)
 - :meth:`.DataFrameGroupBy.transform`, :meth:`.SeriesGroupBy.transform`, :meth:`.DataFrameGroupBy.agg`, :meth:`.SeriesGroupBy.agg`, :meth:`.SeriesGroupBy.apply`, :meth:`.DataFrameGroupBy.apply` now support ``kurt`` (:issue:`40139`)
 - :meth:`DataFrameGroupBy.transform`, :meth:`SeriesGroupBy.transform`, :meth:`DataFrameGroupBy.agg`, :meth:`SeriesGroupBy.agg`, :meth:`RollingGroupby.apply`, :meth:`ExpandingGroupby.apply`, :meth:`Rolling.apply`, :meth:`Expanding.apply`, :meth:`DataFrame.apply` with ``engine="numba"`` now supports positional arguments passed as kwargs (:issue:`58995`)
 - :meth:`Rolling.agg`, :meth:`Expanding.agg` and :meth:`ExponentialMovingWindow.agg` now accept :class:`NamedAgg` aggregations through ``**kwargs`` (:issue:`28333`)
@@ -686,6 +692,7 @@ MultiIndex
 - :meth:`DataFrame.melt` would not accept multiple names in ``var_name`` when the columns were a :class:`MultiIndex` (:issue:`58033`)
 - :meth:`MultiIndex.insert` would not insert NA value correctly at unified location of index -1 (:issue:`59003`)
 - :func:`MultiIndex.get_level_values` accessing a :class:`DatetimeIndex` does not carry the frequency attribute along (:issue:`58327`, :issue:`57949`)
+- Bug in :class:`DataFrame` arithmetic operations in case of unaligned MultiIndex columns (:issue:`60498`)
 -
 
 I/O
@@ -807,6 +814,7 @@ Other
 - Bug in ``Series.list`` methods not preserving the original name. (:issue:`60522`)
 - Bug in printing a :class:`DataFrame` with a :class:`DataFrame` stored in :attr:`DataFrame.attrs` raised a ``ValueError`` (:issue:`60455`)
 - Bug in printing a :class:`Series` with a :class:`DataFrame` stored in :attr:`Series.attrs` raised a ``ValueError`` (:issue:`60568`)
+- Fixed regression in :meth:`DataFrame.from_records` not initializing subclasses properly (:issue:`57008`)
 
 .. ***DO NOT USE THIS SECTION***
 

pandas/_libs/groupby.pyi

@@ -66,6 +66,7 @@ def group_sum(
     result_mask: np.ndarray | None = ...,
     min_count: int = ...,
     is_datetimelike: bool = ...,
+    skipna: bool = ...,
 ) -> None: ...
 def group_prod(
     out: np.ndarray,  # int64float_t[:, ::1]
@@ -115,6 +116,7 @@ def group_mean(
     is_datetimelike: bool = ...,  # bint
     mask: np.ndarray | None = ...,
     result_mask: np.ndarray | None = ...,
+    skipna: bool = ...,
 ) -> None: ...
 def group_ohlc(
     out: np.ndarray,  # floatingintuint_t[:, ::1]

pandas/_libs/groupby.pyx

@@ -700,18 +700,19 @@ def group_sum(
     uint8_t[:, ::1] result_mask=None,
     Py_ssize_t min_count=0,
     bint is_datetimelike=False,
+    bint skipna=True,
 ) -> None:
     """
     Only aggregates on axis=0 using Kahan summation
     """
     cdef:
         Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
-        sum_t val, t, y
+        sum_t val, t, y, nan_val
         sum_t[:, ::1] sumx, compensation
         int64_t[:, ::1] nobs
         Py_ssize_t len_values = len(values), len_labels = len(labels)
         bint uses_mask = mask is not None
-        bint isna_entry
+        bint isna_entry, isna_result
 
     if len_values != len_labels:
         raise ValueError("len(index) != len(labels)")
@@ -722,6 +723,15 @@ def group_sum(
     compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
 
     N, K = (<object>values).shape
+    if uses_mask:
+        nan_val = 0
+    elif is_datetimelike:
+        nan_val = NPY_NAT
+    elif sum_t is int64_t or sum_t is uint64_t:
+        # This has no effect as int64 can't be nan. Setting to 0 to avoid type error
+        nan_val = 0
+    else:
+        nan_val = NAN
 
     with nogil(sum_t is not object):
         for i in range(N):
@@ -736,8 +746,16 @@ def group_sum(
 
                 if uses_mask:
                     isna_entry = mask[i, j]
+                    isna_result = result_mask[lab, j]
                 else:
                     isna_entry = _treat_as_na(val, is_datetimelike)
+                    isna_result = _treat_as_na(sumx[lab, j], is_datetimelike)
+
+                if not skipna and isna_result:
+                    # If sum is already NA, don't add to it. This is important for
+                    # datetimelikebecause adding a value to NPY_NAT may not result
+                    # in a NPY_NAT
+                    continue
 
                 if not isna_entry:
                     nobs[lab, j] += 1
@@ -765,6 +783,11 @@ def group_sum(
                             # because of no gil
                             compensation[lab, j] = 0
                         sumx[lab, j] = t
+                elif not skipna:
+                    if uses_mask:
+                        result_mask[lab, j] = True
+                    else:
+                        sumx[lab, j] = nan_val
 
     _check_below_mincount(
         out, uses_mask, result_mask, ncounts, K, nobs, min_count, sumx
@@ -1100,6 +1123,7 @@ def group_mean(
     bint is_datetimelike=False,
     const uint8_t[:, ::1] mask=None,
     uint8_t[:, ::1] result_mask=None,
+    bint skipna=True,
 ) -> None:
     """
     Compute the mean per label given a label assignment for each value.
@@ -1125,6 +1149,8 @@ def group_mean(
         Mask of the input values.
     result_mask : ndarray[bool, ndim=2], optional
         Mask of the out array
+    skipna : bool, optional
+        If True, ignore nans in `values`.
 
     Notes
     -----
@@ -1168,6 +1194,16 @@ def group_mean(
             for j in range(K):
                 val = values[i, j]
 
+                if not skipna and (
+                    (uses_mask and result_mask[lab, j]) or
+                    (is_datetimelike and sumx[lab, j] == NPY_NAT) or
+                    _treat_as_na(sumx[lab, j], False)
+                ):
+                    # If sum is already NA, don't add to it. This is important for
+                    # datetimelike because adding a value to NPY_NAT may not result
+                    # in NPY_NAT
+                    continue
+
                 if uses_mask:
                     isna_entry = mask[i, j]
                 elif is_datetimelike:
@@ -1191,6 +1227,14 @@ def group_mean(
                         # because of no gil
                         compensation[lab, j] = 0.
                     sumx[lab, j] = t
+                elif not skipna:
+                    # Set the nobs to 0 so that in case of datetimelike,
+                    # dividing NPY_NAT by nobs may not result in a NPY_NAT
+                    nobs[lab, j] = 0
+                    if uses_mask:
+                        result_mask[lab, j] = True
+                    else:
+                        sumx[lab, j] = nan_val
 
         for i in range(ncounts):
             for j in range(K):