Merge branch 'main' into plot

# Conflicts:
#	doc/source/whatsnew/v3.0.0.rst
#	pandas/plotting/_matplotlib/core.py

Author: MartinBraquet

doc/source/whatsnew/v3.0.0.rst

@@ -68,6 +68,7 @@ Other enhancements
 - :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.transform`, :meth:`.SeriesGroupBy.transform`, :meth:`.DataFrameGroupBy.agg`, :meth:`.SeriesGroupBy.agg`, :meth:`.SeriesGroupBy.apply`, :meth:`.DataFrameGroupBy.apply` now support ``kurt`` (:issue:`40139`)
 - :meth:`DataFrame.apply` supports using third-party execution engines like the Bodo.ai JIT compiler (:issue:`60668`)
+- :meth:`DataFrame.iloc` and :meth:`Series.iloc` now support boolean masks in ``__getitem__`` for more consistent indexing behavior (:issue:`60994`)
 - :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`)
 - :meth:`Series.map` can now accept kwargs to pass on to func (:issue:`59814`)
@@ -763,6 +764,7 @@ Plotting
 - Bug in :meth:`DataFrame.plot.line` raising ``ValueError`` when set both color and a ``dict`` style (:issue:`59461`)
 - Bug in :meth:`DataFrame.plot` that causes a shift to the right when the frequency multiplier is greater than one. (:issue:`57587`)
 - Bug in :meth:`Series.plot` preventing a line and bar from being aligned on the same plot (:issue:`61161`)
+- Bug in :meth:`Series.plot` preventing a line and scatter plot from being aligned (:issue:`61005`)
 - Bug in :meth:`Series.plot` with ``kind="pie"`` with :class:`ArrowDtype` (:issue:`59192`)
 
 Groupby/resample/rolling

pandas/core/algorithms.py

@@ -215,7 +215,7 @@ def _reconstruct_data(
         values = cls._from_sequence(values, dtype=dtype)  # type: ignore[assignment]
 
     else:
-        values = values.astype(dtype, copy=False)
+        values = values.astype(dtype, copy=False)  # type: ignore[assignment]
 
     return values
 

pandas/core/array_algos/quantile.py

@@ -102,7 +102,7 @@ def quantile_with_mask(
             interpolation=interpolation,
         )
 
-        result = np.asarray(result)
+        result = np.asarray(result)  # type: ignore[assignment]
         result = result.T
 
     return result
@@ -196,7 +196,7 @@ def _nanquantile(
         # Caller is responsible for ensuring mask shape match
         assert mask.shape == values.shape
         result = [
-            _nanquantile_1d(val, m, qs, na_value, interpolation=interpolation)
+            _nanquantile_1d(val, m, qs, na_value, interpolation=interpolation)  # type: ignore[arg-type]
             for (val, m) in zip(list(values), list(mask))
         ]
         if values.dtype.kind == "f":

pandas/core/arrays/_mixins.py

@@ -142,18 +142,12 @@ def view(self, dtype: Dtype | None = None) -> ArrayLike:
 
             dt64_values = arr.view(dtype)
             return DatetimeArray._simple_new(dt64_values, dtype=dtype)
-
         elif lib.is_np_dtype(dtype, "m") and is_supported_dtype(dtype):
             from pandas.core.arrays import TimedeltaArray
 
             td64_values = arr.view(dtype)
             return TimedeltaArray._simple_new(td64_values, dtype=dtype)
-
-        # error: Argument "dtype" to "view" of "_ArrayOrScalarCommon" has incompatible
-        # type "Union[ExtensionDtype, dtype[Any]]"; expected "Union[dtype[Any], None,
-        # type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any, Union[int,
-        # Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
-        return arr.view(dtype=dtype)  # type: ignore[arg-type]
+        return arr.view(dtype=dtype)
 
     def take(
         self,

pandas/core/arrays/arrow/_arrow_utils.py

@@ -44,7 +44,7 @@ def pyarrow_array_to_numpy_and_mask(
         mask = pyarrow.BooleanArray.from_buffers(
             pyarrow.bool_(), len(arr), [None, bitmask], offset=arr.offset
         )
-        mask = np.asarray(mask)
+        mask = np.asarray(mask)  # type: ignore[assignment]
     else:
         mask = np.ones(len(arr), dtype=bool)
     return data, mask

pandas/core/arrays/arrow/array.py

@@ -2540,7 +2540,7 @@ def _str_get_dummies(self, sep: str = "|", dtype: NpDtype | None = None):
             dummies_dtype = np.bool_
         dummies = np.zeros(n_rows * n_cols, dtype=dummies_dtype)
         dummies[indices] = True
-        dummies = dummies.reshape((n_rows, n_cols))
+        dummies = dummies.reshape((n_rows, n_cols))  # type: ignore[assignment]
         result = type(self)(pa.array(list(dummies)))
         return result, uniques_sorted.to_pylist()
 

pandas/core/arrays/base.py

@@ -596,7 +596,7 @@ def to_numpy(
         if copy or na_value is not lib.no_default:
             result = result.copy()
         if na_value is not lib.no_default:
-            result[self.isna()] = na_value
+            result[self.isna()] = na_value  # type: ignore[index]
         return result
 
     # ------------------------------------------------------------------------

pandas/core/arrays/categorical.py

@@ -1853,7 +1853,7 @@ def value_counts(self, dropna: bool = True) -> Series:
             count = np.bincount(obs, minlength=ncat or 0)
         else:
             count = np.bincount(np.where(mask, code, ncat))
-            ix = np.append(ix, -1)
+            ix = np.append(ix, -1)  # type: ignore[assignment]
 
         ix = coerce_indexer_dtype(ix, self.dtype.categories)
         ix_categorical = self._from_backing_data(ix)

pandas/core/arrays/datetimelike.py

@@ -2394,7 +2394,7 @@ def take(
         )
 
         indices = np.asarray(indices, dtype=np.intp)
-        maybe_slice = lib.maybe_indices_to_slice(indices, len(self))
+        maybe_slice = lib.maybe_indices_to_slice(indices, len(self))  # type: ignore[arg-type]
 
         if isinstance(maybe_slice, slice):
             freq = self._get_getitem_freq(maybe_slice)

pandas/core/arrays/datetimes.py

@@ -331,7 +331,7 @@ def _simple_new(  # type: ignore[override]
         else:
             # DatetimeTZDtype. If we have e.g. DatetimeTZDtype[us, UTC],
             #  then values.dtype should be M8[us].
-            assert dtype._creso == get_unit_from_dtype(values.dtype)
+            assert dtype._creso == get_unit_from_dtype(values.dtype)  # type: ignore[union-attr]
 
         result = super()._simple_new(values, dtype)
         result._freq = freq
@@ -542,7 +542,7 @@ def _unbox_scalar(self, value) -> np.datetime64:
             raise ValueError("'value' should be a Timestamp.")
         self._check_compatible_with(value)
         if value is NaT:
-            return np.datetime64(value._value, self.unit)
+            return np.datetime64(value._value, self.unit)  # type: ignore[call-overload]
         else:
             return value.as_unit(self.unit, round_ok=False).asm8
 
@@ -813,10 +813,7 @@ def _add_offset(self, offset: BaseOffset) -> Self:
         try:
             res_values = offset._apply_array(values._ndarray)
             if res_values.dtype.kind == "i":
-                # error: Argument 1 to "view" of "ndarray" has incompatible type
-                # "dtype[datetime64] | DatetimeTZDtype"; expected
-                # "dtype[Any] | type[Any] | _SupportsDType[dtype[Any]]"
-                res_values = res_values.view(values.dtype)  # type: ignore[arg-type]
+                res_values = res_values.view(values.dtype)
         except NotImplementedError:
             if get_option("performance_warnings"):
                 warnings.warn(