TYP: type some un-typed decorators

pandas/core/indexes/multi.py

@@ -29,6 +29,7 @@
 from pandas._typing import (
     AnyArrayLike,
     DtypeObj,
+    F,
     Scalar,
     Shape,
     npt,
@@ -185,7 +186,7 @@ def _codes_to_ints(self, codes):
         return np.bitwise_or.reduce(codes, axis=1)
 
 
-def names_compat(meth):
+def names_compat(meth: F) -> F:
     """
     A decorator to allow either `name` or `names` keyword but not both.
 
@@ -201,7 +202,7 @@ def new_meth(self_or_cls, *args, **kwargs):
 
         return meth(self_or_cls, *args, **kwargs)
 
-    return new_meth
+    return cast(F, new_meth)
 
 
 class MultiIndex(Index):

pandas/core/nanops.py

@@ -72,7 +72,7 @@ def set_use_bottleneck(v: bool = True) -> None:
 
 
 class disallow:
-    def __init__(self, *dtypes):
+    def __init__(self, *dtypes: Dtype):
         super().__init__()
         self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)
 
@@ -449,7 +449,7 @@ def _na_for_min_count(values: np.ndarray, axis: int | None) -> Scalar | np.ndarr
         return np.full(result_shape, fill_value, dtype=values.dtype)
 
 
-def maybe_operate_rowwise(func):
+def maybe_operate_rowwise(func: F) -> F:
     """
     NumPy operations on C-contiguous ndarrays with axis=1 can be
     very slow. Operate row-by-row and concatenate the results.
@@ -475,7 +475,7 @@ def newfunc(values: np.ndarray, *, axis: int | None = None, **kwargs):
 
         return func(values, axis=axis, **kwargs)
 
-    return newfunc
+    return cast(F, newfunc)
 
 
 def nanany(

pandas/core/window/expanding.py

@@ -96,7 +96,7 @@ class Expanding(RollingAndExpandingMixin):
     4  7.0
     """
 
-    _attributes = ["min_periods", "center", "axis", "method"]
+    _attributes: list[str] = ["min_periods", "center", "axis", "method"]
 
     def __init__(
         self,

pandas/core/window/rolling.py

@@ -583,7 +583,7 @@ class BaseWindowGroupby(BaseWindow):
 
     _grouper: BaseGrouper
     _as_index: bool
-    _attributes = ["_grouper"]
+    _attributes: list[str] = ["_grouper"]
 
     def __init__(
         self,
@@ -1500,7 +1500,7 @@ def corr_func(x, y):
 
 class Rolling(RollingAndExpandingMixin):
 
-    _attributes = [
+    _attributes: list[str] = [
         "window",
         "min_periods",
         "center",

pandas/io/pytables.py

@@ -4549,10 +4549,10 @@ def read(
 
             # we could have a multi-index constructor here
             # ensure_index doesn't recognized our list-of-tuples here
-            if info.get("type") == "MultiIndex":
-                cols = MultiIndex.from_tuples(index_vals)
-            else:
+            if info.get("type") != "MultiIndex":
                 cols = Index(index_vals)
+            else:
+                cols = MultiIndex.from_tuples(index_vals)
 
             names = info.get("names")
             if names is not None:

pandas/plotting/_matplotlib/converter.py

@@ -8,7 +8,10 @@
     tzinfo,
 )
 import functools
-from typing import Any
+from typing import (
+    Any,
+    cast,
+)
 
 from dateutil.relativedelta import relativedelta
 import matplotlib.dates as dates
@@ -28,6 +31,7 @@
 )
 from pandas._libs.tslibs.dtypes import FreqGroup
 from pandas._libs.tslibs.offsets import BaseOffset
+from pandas._typing import F
 
 from pandas.core.dtypes.common import (
     is_float,
@@ -76,7 +80,7 @@ def get_pairs():
     return pairs
 
 
-def register_pandas_matplotlib_converters(func):
+def register_pandas_matplotlib_converters(func: F) -> F:
     """
     Decorator applying pandas_converters.
     """
@@ -86,7 +90,7 @@ def wrapper(*args, **kwargs):
         with pandas_converters():
             return func(*args, **kwargs)
 
-    return wrapper
+    return cast(F, wrapper)
 
 
 @contextlib.contextmanager

pandas/plotting/_matplotlib/core.py

@@ -1248,8 +1248,9 @@ def _make_plot(self):
                 left, right = get_xlim(lines)
                 ax.set_xlim(left, right)
 
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
     @classmethod
-    def _plot(
+    def _plot(  # type: ignore[override]
         cls, ax: Axes, x, y, style=None, column_num=None, stacking_id=None, **kwds
     ):
         # column_num is used to get the target column from plotf in line and
@@ -1383,16 +1384,17 @@ def __init__(self, data, **kwargs):
         if self.logy or self.loglog:
             raise ValueError("Log-y scales are not supported in area plot")
 
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
     @classmethod
-    def _plot(
+    def _plot(  # type: ignore[override]
         cls,
         ax: Axes,
         x,
         y,
         style=None,
         column_num=None,
         stacking_id=None,
-        is_errorbar=False,
+        is_errorbar: bool = False,
         **kwds,
     ):
 
@@ -1483,8 +1485,11 @@ def _args_adjust(self):
         if is_list_like(self.left):
             self.left = np.array(self.left)
 
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
     @classmethod
-    def _plot(cls, ax: Axes, x, y, w, start=0, log=False, **kwds):
+    def _plot(  # type: ignore[override]
+        cls, ax: Axes, x, y, w, start=0, log=False, **kwds
+    ):
         return ax.bar(x, y, w, bottom=start, log=log, **kwds)
 
     @property
@@ -1601,8 +1606,11 @@ class BarhPlot(BarPlot):
     def _start_base(self):
         return self.left
 
+    # error: Signature of "_plot" incompatible with supertype "MPLPlot"
     @classmethod
-    def _plot(cls, ax: Axes, x, y, w, start=0, log=False, **kwds):
+    def _plot(  # type: ignore[override]
+        cls, ax: Axes, x, y, w, start=0, log=False, **kwds
+    ):
         return ax.barh(x, y, w, left=start, log=log, **kwds)
 
     def _decorate_ticks(self, ax: Axes, name, ticklabels, start_edge, end_edge):

pandas/tests/frame/methods/test_sort_index.py

@@ -441,22 +441,22 @@ def test_sort_index_ignore_index(
                 {"M1": [1, 2], "M2": [3, 4]},
                 True,
                 False,
-                MultiIndex.from_tuples([[2, 1], [3, 4]], names=list("AB")),
+                MultiIndex.from_tuples([(2, 1), (3, 4)], names=list("AB")),
             ),
             (
                 {"M1": [1, 2], "M2": [3, 4]},
                 {"M1": [2, 1], "M2": [4, 3]},
                 False,
                 False,
-                MultiIndex.from_tuples([[3, 4], [2, 1]], names=list("AB")),
+                MultiIndex.from_tuples([(3, 4), (2, 1)], names=list("AB")),
             ),
         ],
     )
     def test_sort_index_ignore_index_multi_index(
         self, inplace, original_dict, sorted_dict, ascending, ignore_index, output_index
     ):
         # GH 30114, this is to test ignore_index on MulitIndex of index
-        mi = MultiIndex.from_tuples([[2, 1], [3, 4]], names=list("AB"))
+        mi = MultiIndex.from_tuples([(2, 1), (3, 4)], names=list("AB"))
         df = DataFrame(original_dict, index=mi)
         expected_df = DataFrame(sorted_dict, index=output_index)