Merge remote-tracking branch 'upstream' into fix/issue-1918-bar-plot-dateformatter

Author: fangchenli

.github/workflows/unit-tests.yml

@@ -320,8 +320,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        # Separate out macOS 13 and 14, since macOS 14 is arm64 only
-        os: [ubuntu-24.04, macOS-13, macOS-14, windows-2025]
+        os: [ubuntu-24.04, macos-15-intel, macos-15, windows-2025]
 
     timeout-minutes: 90
 

doc/source/user_guide/io.rst

@@ -303,8 +303,6 @@ compression : {``'infer'``, ``'gzip'``, ``'bz2'``, ``'zip'``, ``'xz'``, ``'zstd'
   As an example, the following could be passed for faster compression and to
   create a reproducible gzip archive:
   ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
-
-  .. versionchanged:: 1.2.0 Previous versions forwarded dict entries for 'gzip' to ``gzip.open``.
 thousands : str, default ``None``
   Thousands separator.
 decimal : str, default ``'.'``
@@ -1472,7 +1470,7 @@ rather than reading the entire file into memory, such as the following:
    table
 
 
-By specifying a ``chunksize`` to ``read_csv``, the return
+By specifying a ``chunksize`` to :func:`read_csv` as a context manager, the return
 value will be an iterable object of type ``TextFileReader``:
 
 .. ipython:: python
@@ -1482,10 +1480,6 @@ value will be an iterable object of type ``TextFileReader``:
        for chunk in reader:
            print(chunk)
 
-.. versionchanged:: 1.2
-
-  ``read_csv/json/sas`` return a context-manager when iterating through a file.
-
 Specifying ``iterator=True`` will also return the ``TextFileReader`` object:
 
 .. ipython:: python

doc/source/user_guide/visualization.rst

@@ -326,8 +326,6 @@ The ``by`` keyword can be specified to plot grouped histograms:
 
 In addition, the ``by`` keyword can also be specified in :meth:`DataFrame.plot.hist`.
 
-.. versionchanged:: 1.4.0
-
 .. ipython:: python
 
    data = pd.DataFrame(
@@ -480,8 +478,6 @@ columns:
 
 You could also create groupings with :meth:`DataFrame.plot.box`, for instance:
 
-.. versionchanged:: 1.4.0
-
 .. ipython:: python
    :suppress:
 

doc/source/whatsnew/v3.0.0.rst

@@ -741,6 +741,7 @@ Other Deprecations
 - Deprecated allowing strings representing full dates in :meth:`DataFrame.at_time` and :meth:`Series.at_time` (:issue:`50839`)
 - Deprecated backward-compatibility behavior for :meth:`DataFrame.select_dtypes` matching "str" dtype when ``np.object_`` is specified (:issue:`61916`)
 - Deprecated option "future.no_silent_downcasting", as it is no longer used. In a future version accessing this option will raise (:issue:`59502`)
+- Deprecated passing non-Index types to :meth:`Index.join`; explicitly convert to Index first (:issue:`62897`)
 - Deprecated silent casting of non-datetime 'other' to datetime in :meth:`Series.combine_first` (:issue:`62931`)
 - Deprecated slicing on a :class:`Series` or :class:`DataFrame` with a :class:`DatetimeIndex` using a ``datetime.date`` object, explicitly cast to :class:`Timestamp` instead (:issue:`35830`)
 - Deprecated support for the Dataframe Interchange Protocol (:issue:`56732`)
@@ -1017,6 +1018,7 @@ Timedelta
 - Bug in :class:`Timedelta` constructor failing to raise when passed an invalid keyword (:issue:`53801`)
 - Bug in :meth:`DataFrame.cumsum` which was raising ``IndexError`` if dtype is ``timedelta64[ns]`` (:issue:`57956`)
 - Bug in multiplication operations with ``timedelta64`` dtype failing to raise ``TypeError`` when multiplying by ``bool`` objects or dtypes (:issue:`58054`)
+- Bug in multiplication operations with ``timedelta64`` dtype incorrectly raising when multiplying by numpy-nullable dtypes or pyarrow integer dtypes (:issue:`58054`)
 
 Timezones
 ^^^^^^^^^
@@ -1200,6 +1202,7 @@ Reshaping
 - Bug in :meth:`DataFrame.combine` with non-unique columns incorrectly raising (:issue:`51340`)
 - Bug in :meth:`DataFrame.explode` producing incorrect result for :class:`pyarrow.large_list` type (:issue:`61091`)
 - Bug in :meth:`DataFrame.join` inconsistently setting result index name (:issue:`55815`)
+- Bug in :meth:`DataFrame.join` not producing the correct row order when joining with a list of Series/DataFrames (:issue:`62954`)
 - Bug in :meth:`DataFrame.join` when a :class:`DataFrame` with a :class:`MultiIndex` would raise an ``AssertionError`` when :attr:`MultiIndex.names` contained ``None``. (:issue:`58721`)
 - Bug in :meth:`DataFrame.merge` where merging on a column containing only ``NaN`` values resulted in an out-of-bounds array access (:issue:`59421`)
 - Bug in :meth:`Series.combine_first` incorrectly replacing ``None`` entries with ``NaN`` (:issue:`58977`)

pandas/core/arrays/categorical.py

@@ -2545,10 +2545,6 @@ def unique(self) -> Self:
         Return the ``Categorical`` which ``categories`` and ``codes`` are
         unique.
 
-        .. versionchanged:: 1.3.0
-
-            Previously, unused categories were dropped from the new categories.
-
         Returns
         -------
         Categorical

pandas/core/arrays/masked.py

@@ -1686,8 +1686,6 @@ def any(
         missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
         is used as for logical operations.
 
-        .. versionchanged:: 1.4.0
-
         Parameters
         ----------
         skipna : bool, default True
@@ -1774,8 +1772,6 @@ def all(
         missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
         is used as for logical operations.
 
-        .. versionchanged:: 1.4.0
-
         Parameters
         ----------
         skipna : bool, default True

pandas/core/arrays/numeric.py

@@ -139,10 +139,9 @@ def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarr
         raise AbstractMethodError(cls)
 
 
-def _coerce_to_data_and_mask(
-    values, dtype, copy: bool, dtype_cls: type[NumericDtype], default_dtype: np.dtype
-):
+def _coerce_to_data_and_mask(values, dtype, copy: bool, dtype_cls: type[NumericDtype]):
     checker = dtype_cls._checker
+    default_dtype = dtype_cls._default_np_dtype
 
     mask = None
     inferred_type = None
@@ -163,7 +162,7 @@ def _coerce_to_data_and_mask(
         if copy:
             values = values.copy()
             mask = mask.copy()
-        return values, mask, dtype, inferred_type
+        return values, mask
 
     original = values
     if not copy:
@@ -174,6 +173,7 @@ def _coerce_to_data_and_mask(
     if values.dtype == object or is_string_dtype(values.dtype):
         inferred_type = lib.infer_dtype(values, skipna=True)
         if inferred_type == "boolean" and dtype is None:
+            # object dtype array of bools
             name = dtype_cls.__name__.strip("_")
             raise TypeError(f"{values.dtype} cannot be converted to {name}")
 
@@ -252,7 +252,7 @@ def _coerce_to_data_and_mask(
         values = values.astype(dtype, copy=copy)
     else:
         values = dtype_cls._safe_cast(values, dtype, copy=False)
-    return values, mask, dtype, inferred_type
+    return values, mask
 
 
 class NumericArray(BaseMaskedArray):
@@ -296,10 +296,7 @@ def _coerce_to_array(
         cls, value, *, dtype: DtypeObj, copy: bool = False
     ) -> tuple[np.ndarray, np.ndarray]:
         dtype_cls = cls._dtype_cls
-        default_dtype = dtype_cls._default_np_dtype
-        values, mask, _, _ = _coerce_to_data_and_mask(
-            value, dtype, copy, dtype_cls, default_dtype
-        )
+        values, mask = _coerce_to_data_and_mask(value, dtype, copy, dtype_cls)
         return values, mask
 
     @classmethod

pandas/core/arrays/string_.py

@@ -573,9 +573,7 @@ class StringArray(BaseStringArray, NumpyExtensionArray):  # type: ignore[misc]
            :meth:`pandas.array` with ``dtype="string"`` for a stable way of
            creating a `StringArray` from any sequence.
 
-        .. versionchanged:: 1.5.0
-
-           StringArray now accepts array-likes containing
+           StringArray accepts array-likes containing
            nan-likes(``None``, ``np.nan``) for the ``values`` parameter
            in addition to strings and :attr:`pandas.NA`
 

pandas/core/arrays/timedeltas.py

@@ -51,7 +51,11 @@
     is_string_dtype,
     pandas_dtype,
 )
-from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    BaseMaskedDtype,
+    ExtensionDtype,
+)
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import (
@@ -501,6 +505,10 @@ def __mul__(self, other) -> Self:
                 f"Cannot multiply '{self.dtype}' by bool, explicitly cast to "
                 "integers instead"
             )
+        if isinstance(other.dtype, (ArrowDtype, BaseMaskedDtype)):
+            # GH#58054
+            return NotImplemented
+
         if len(other) != len(self) and not lib.is_np_dtype(other.dtype, "m"):
             # Exclude timedelta64 here so we correctly raise TypeError
             #  for that instead of ValueError

pandas/core/frame.py

@@ -554,8 +554,6 @@ class DataFrame(NDFrame, OpsMixin):
         If data is a dict containing one or more Series (possibly of different dtypes),
         ``copy=False`` will ensure that these inputs are not copied.
 
-        .. versionchanged:: 1.3.0
-
     See Also
     --------
     DataFrame.from_records : Constructor from tuples, also record arrays.
@@ -2686,17 +2684,13 @@ def to_stata(
             8 characters and values are repeated.
         {compression_options}
 
-            .. versionchanged:: 1.4.0 Zstandard support.
-
         {storage_options}
 
         value_labels : dict of dicts
             Dictionary containing columns as keys and dictionaries of column value
             to labels as values. Labels for a single variable must be 32,000
             characters or smaller.
 
-            .. versionadded:: 1.4.0
-
         Raises
         ------
         NotImplementedError
@@ -3534,8 +3528,6 @@ def to_xml(
             scripts and not later versions is currently supported.
         {compression_options}
 
-            .. versionchanged:: 1.4.0 Zstandard support.
-
         {storage_options}
 
         Returns
@@ -9487,13 +9479,6 @@ def groupby(
             when the result's index (and column) labels match the inputs, and
             are included otherwise.
 
-            .. versionchanged:: 1.5.0
-
-               Warns that ``group_keys`` will no longer be ignored when the
-               result from ``apply`` is a like-indexed Series or DataFrame.
-               Specify ``group_keys`` explicitly to include the group keys or
-               not.
-
             .. versionchanged:: 2.0.0
 
                ``group_keys`` now defaults to ``True``.
@@ -11408,12 +11393,18 @@ def join(
 
             # join indexes only using concat
             if can_concat:
-                if how == "left":
+                if how == "left" or how == "right":
                     res = concat(
                         frames, axis=1, join="outer", verify_integrity=True, sort=sort
                     )
-                    return res.reindex(self.index)
+                    index = self.index if how == "left" else frames[-1].index
+                    if sort:
+                        index = index.sort_values()
+                    result = res.reindex(index)
+                    return result
                 else:
+                    if how == "outer":
+                        sort = True
                     return concat(
                         frames, axis=1, join=how, verify_integrity=True, sort=sort
                     )
@@ -11424,6 +11415,7 @@ def join(
                 joined = merge(
                     joined,
                     frame,
+                    sort=sort,
                     how=how,
                     left_index=True,
                     right_index=True,