TYP: misc typing in core\indexes\base.py (#35991)

Author: simonjayhawkins

pandas/core/frame.py

@@ -1776,13 +1776,13 @@ def from_records(
                 arrays = [data[k] for k in columns]
             else:
                 arrays = []
-                arr_columns = []
+                arr_columns_list = []
                 for k, v in data.items():
                     if k in columns:
-                        arr_columns.append(k)
+                        arr_columns_list.append(k)
                         arrays.append(v)
 
-                arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns)
+                arrays, arr_columns = reorder_arrays(arrays, arr_columns_list, columns)
 
         elif isinstance(data, (np.ndarray, DataFrame)):
             arrays, columns = to_arrays(data, columns)

pandas/core/indexes/base.py

@@ -10,6 +10,8 @@
     Hashable,
     List,
     Optional,
+    Sequence,
+    TypeVar,
     Union,
 )
 import warnings
@@ -22,7 +24,7 @@
 from pandas._libs.tslibs import OutOfBoundsDatetime, Timestamp
 from pandas._libs.tslibs.period import IncompatibleFrequency
 from pandas._libs.tslibs.timezones import tz_compare
-from pandas._typing import DtypeObj, Label
+from pandas._typing import AnyArrayLike, Dtype, DtypeObj, Label
 from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
 from pandas.errors import InvalidIndexError
@@ -98,7 +100,7 @@
 )
 
 if TYPE_CHECKING:
-    from pandas import Series
+    from pandas import RangeIndex, Series
 
 
 __all__ = ["Index"]
@@ -188,6 +190,9 @@ def _new_Index(cls, d):
     return cls.__new__(cls, **d)
 
 
+_IndexT = TypeVar("_IndexT", bound="Index")
+
+
 class Index(IndexOpsMixin, PandasObject):
     """
     Immutable ndarray implementing an ordered, sliceable set. The basic object
@@ -787,7 +792,13 @@ def repeat(self, repeats, axis=None):
     # --------------------------------------------------------------------
     # Copying Methods
 
-    def copy(self, name=None, deep=False, dtype=None, names=None):
+    def copy(
+        self: _IndexT,
+        name: Optional[Label] = None,
+        deep: bool = False,
+        dtype: Optional[Dtype] = None,
+        names: Optional[Sequence[Label]] = None,
+    ) -> _IndexT:
         """
         Make a copy of this object.
 
@@ -949,10 +960,9 @@ def _format_with_header(
             # could have nans
             mask = isna(values)
             if mask.any():
-                result = np.array(result)
-                result[mask] = na_rep
-                # error: "List[str]" has no attribute "tolist"
-                result = result.tolist()  # type: ignore[attr-defined]
+                result_arr = np.array(result)
+                result_arr[mask] = na_rep
+                result = result_arr.tolist()
         else:
             result = trim_front(format_array(values, None, justify="left"))
         return header + result
@@ -4913,7 +4923,13 @@ def _get_string_slice(self, key: str_t, use_lhs: bool = True, use_rhs: bool = Tr
         # overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
         raise NotImplementedError
 
-    def slice_indexer(self, start=None, end=None, step=None, kind=None):
+    def slice_indexer(
+        self,
+        start: Optional[Label] = None,
+        end: Optional[Label] = None,
+        step: Optional[int] = None,
+        kind: Optional[str_t] = None,
+    ) -> slice:
         """
         Compute the slice indexer for input labels and step.
 
@@ -5513,7 +5529,9 @@ def ensure_index_from_sequences(sequences, names=None):
         return MultiIndex.from_arrays(sequences, names=names)
 
 
-def ensure_index(index_like, copy: bool = False):
+def ensure_index(
+    index_like: Union[AnyArrayLike, Sequence], copy: bool = False
+) -> Index:
     """
     Ensure that we have an index from some index-like object.
 
@@ -5549,7 +5567,18 @@ def ensure_index(index_like, copy: bool = False):
             index_like = index_like.copy()
         return index_like
     if hasattr(index_like, "name"):
-        return Index(index_like, name=index_like.name, copy=copy)
+        # https://github.com/python/mypy/issues/1424
+        # error: Item "ExtensionArray" of "Union[ExtensionArray,
+        # Sequence[Any]]" has no attribute "name"  [union-attr]
+        # error: Item "Sequence[Any]" of "Union[ExtensionArray, Sequence[Any]]"
+        # has no attribute "name"  [union-attr]
+        # error: "Sequence[Any]" has no attribute "name"  [attr-defined]
+        # error: Item "Sequence[Any]" of "Union[Series, Sequence[Any]]" has no
+        # attribute "name"  [union-attr]
+        # error: Item "Sequence[Any]" of "Union[Any, Sequence[Any]]" has no
+        # attribute "name"  [union-attr]
+        name = index_like.name  # type: ignore[union-attr, attr-defined]
+        return Index(index_like, name=name, copy=copy)
 
     if is_iterator(index_like):
         index_like = list(index_like)
@@ -5604,7 +5633,7 @@ def _validate_join_method(method: str):
         raise ValueError(f"do not recognize join method {method}")
 
 
-def default_index(n):
+def default_index(n: int) -> "RangeIndex":
     from pandas.core.indexes.range import RangeIndex
 
     return RangeIndex(0, n, name=None)

pandas/core/indexes/interval.py

@@ -1,7 +1,7 @@
 """ define the IntervalIndex """
 from operator import le, lt
 import textwrap
-from typing import Any, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast
 
 import numpy as np
 
@@ -56,6 +56,9 @@
 from pandas.core.indexes.timedeltas import TimedeltaIndex, timedelta_range
 from pandas.core.ops import get_op_result_name
 
+if TYPE_CHECKING:
+    from pandas import CategoricalIndex
+
 _VALID_CLOSED = {"left", "right", "both", "neither"}
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 
@@ -786,6 +789,7 @@ def get_indexer(
             right_indexer = self.right.get_indexer(target_as_index.right)
             indexer = np.where(left_indexer == right_indexer, left_indexer, -1)
         elif is_categorical_dtype(target_as_index.dtype):
+            target_as_index = cast("CategoricalIndex", target_as_index)
             # get an indexer for unique categories then propagate to codes via take_1d
             categories_indexer = self.get_indexer(target_as_index.categories)
             indexer = take_1d(categories_indexer, target_as_index.codes, fill_value=-1)