fix & ignore failures

Author: mroeschke

pandas/core/apply.py

@@ -246,12 +246,8 @@ def transform(self) -> DataFrame | Series:
             and not obj.empty
         ):
             raise ValueError("Transform function failed")
-        # error: Argument 1 to "__get__" of "AxisProperty" has incompatible type
-        # "Union[Series, DataFrame, GroupBy[Any], SeriesGroupBy,
-        # DataFrameGroupBy, BaseWindow, Resampler]"; expected "Union[DataFrame,
-        # Series]"
         if not isinstance(result, (ABCSeries, ABCDataFrame)) or not result.index.equals(
-            obj.index  # type: ignore[arg-type]
+            obj.index
         ):
             raise ValueError("Function did not transform")
 

pandas/core/arraylike.py

@@ -403,12 +403,12 @@ def _reconstruct(result):
             # for np.<ufunc>(..) calls
             # kwargs cannot necessarily be handled block-by-block, so only
             # take this path if there are no kwargs
-            mgr = inputs[0]._mgr
+            mgr = inputs[0]._mgr  # pyright: ignore[reportGeneralTypeIssues]
             result = mgr.apply(getattr(ufunc, method))
         else:
             # otherwise specific ufunc methods (eg np.<ufunc>.accumulate(..))
             # Those can have an axis keyword and thus can't be called block-by-block
-            result = default_array_ufunc(inputs[0], ufunc, method, *inputs, **kwargs)
+            result = default_array_ufunc(inputs[0], ufunc, method, *inputs, **kwargs)  # pyright: ignore[reportGeneralTypeIssues]
             # e.g. np.negative (only one reached), with "where" and "out" in kwargs
 
     result = reconstruct(result)

pandas/core/arrays/arrow/array.py

@@ -2426,7 +2426,7 @@ def _str_rindex(self, sub: str, start: int = 0, end: int | None = None) -> Self:
         result = self._apply_elementwise(predicate)
         return type(self)(pa.chunked_array(result))
 
-    def _str_normalize(self, form: str) -> Self:
+    def _str_normalize(self, form: Literal["NFC", "NFD", "NFKC", "NFKD"]) -> Self:
         predicate = lambda val: unicodedata.normalize(form, val)
         result = self._apply_elementwise(predicate)
         return type(self)(pa.chunked_array(result))

pandas/core/arrays/boolean.py

@@ -369,7 +369,7 @@ def _coerce_to_array(
             assert dtype == "boolean"
         return coerce_to_array(value, copy=copy)
 
-    def _logical_method(self, other, op):
+    def _logical_method(self, other, op):  # type: ignore[override]
         assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}
         other_is_scalar = lib.is_scalar(other)
         mask = None

pandas/core/computation/expr.py

@@ -168,7 +168,7 @@ def _preparse(
     the ``tokenize`` module and ``tokval`` is a string.
     """
     assert callable(f), "f must be callable"
-    return tokenize.untokenize(f(x) for x in tokenize_string(source))
+    return tokenize.untokenize(f(x) for x in tokenize_string(source))  # pyright: ignore[reportArgumentType]
 
 
 def _is_type(t):

pandas/core/frame.py

@@ -2306,7 +2306,7 @@ def maybe_reorder(
 
         if any(exclude):
             arr_exclude = (x for x in exclude if x in arr_columns)
-            to_remove = {arr_columns.get_loc(col) for col in arr_exclude}
+            to_remove = {arr_columns.get_loc(col) for col in arr_exclude}  # pyright: ignore[reportUnhashable]
             arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
 
             columns = columns.drop(exclude)

pandas/core/groupby/groupby.py

@@ -3719,7 +3719,7 @@ def blk_func(values: ArrayLike) -> ArrayLike:
             mask = isna(values)
             if values.ndim == 1:
                 indexer = np.empty(values.shape, dtype=np.intp)
-                col_func(out=indexer, mask=mask)
+                col_func(out=indexer, mask=mask)  # type: ignore[arg-type]
                 return algorithms.take_nd(values, indexer)
 
             else:
@@ -4081,7 +4081,9 @@ def _nth(
     def quantile(
         self,
         q: float | AnyArrayLike = 0.5,
-        interpolation: str = "linear",
+        interpolation: Literal[
+            "linear", "lower", "higher", "nearest", "midpoint"
+        ] = "linear",
         numeric_only: bool = False,
     ):
         """
@@ -4270,7 +4272,7 @@ def blk_func(values: ArrayLike) -> ArrayLike:
                 func(
                     out[0],
                     values=vals,
-                    mask=mask,
+                    mask=mask,  # type: ignore[arg-type]
                     result_mask=result_mask,
                     is_datetimelike=is_datetimelike,
                 )

pandas/core/indexes/interval.py

@@ -991,7 +991,7 @@ def length(self) -> Index:
     # --------------------------------------------------------------------
     # Set Operations
 
-    def _intersection(self, other, sort):
+    def _intersection(self, other, sort: bool = False):
         """
         intersection specialized to the case with matching dtypes.
         """
@@ -1006,7 +1006,7 @@ def _intersection(self, other, sort):
             # duplicates
             taken = self._intersection_non_unique(other)
 
-        if sort is None:
+        if sort:
             taken = taken.sort_values()
 
         return taken

pandas/core/resample.py

@@ -404,7 +404,7 @@ def transform(self, arg, *args, **kwargs):
             arg, *args, **kwargs
         )
 
-    def _downsample(self, f, **kwargs):
+    def _downsample(self, how, **kwargs):
         raise AbstractMethodError(self)
 
     def _upsample(self, f, limit: int | None = None, fill_value=None):

pandas/core/series.py

@@ -813,8 +813,7 @@ def _values(self):
     def _references(self) -> BlockValuesRefs:
         return self._mgr._block.refs
 
-    # error: Decorated property not supported
-    @Appender(base.IndexOpsMixin.array.__doc__)  # type: ignore[misc]
+    @Appender(base.IndexOpsMixin.array.__doc__)
     @property
     def array(self) -> ExtensionArray:
         return self._mgr.array_values()