TYP: Add cast to ABC classes.

pandas/core/algorithms.py

@@ -60,7 +60,7 @@
 from pandas.core.indexers import validate_indices
 
 if TYPE_CHECKING:
-    from pandas import Categorical, DataFrame, Series
+    from pandas import Categorical, DataFrame, Index, Series
 
 _shared_docs: Dict[str, str] = {}
 
@@ -533,7 +533,7 @@ def factorize(
     sort: bool = False,
     na_sentinel: Optional[int] = -1,
     size_hint: Optional[int] = None,
-) -> Tuple[np.ndarray, Union[np.ndarray, ABCIndex]]:
+) -> Tuple[np.ndarray, Union[np.ndarray, "Index"]]:
     """
     Encode the object as an enumerated type or categorical variable.
 

pandas/core/base.py

@@ -269,12 +269,14 @@ def __getitem__(self, key):
             return self._gotitem(list(key), ndim=2)
 
         elif not getattr(self, "as_index", False):
-            if key not in self.obj.columns:
+            # error: "SelectionMixin" has no attribute "obj"  [attr-defined]
+            if key not in self.obj.columns:  # type: ignore[attr-defined]
                 raise KeyError(f"Column not found: {key}")
             return self._gotitem(key, ndim=2)
 
         else:
-            if key not in self.obj:
+            # error: "SelectionMixin" has no attribute "obj"  [attr-defined]
+            if key not in self.obj:  # type: ignore[attr-defined]
                 raise KeyError(f"Column not found: {key}")
             return self._gotitem(key, ndim=1)
 
@@ -919,10 +921,9 @@ def _map_values(self, mapper, na_action=None):
             # "astype"  [attr-defined]
             values = self.astype(object)._values  # type: ignore[attr-defined]
             if na_action == "ignore":
-
-                def map_f(values, f):
-                    return lib.map_infer_mask(values, f, isna(values).view(np.uint8))
-
+                map_f = lambda values, f: lib.map_infer_mask(
+                    values, f, isna(values).view(np.uint8)
+                )
             elif na_action is None:
                 map_f = lib.map_infer
             else:

pandas/core/common.py

@@ -469,7 +469,8 @@ def convert_to_list_like(
     inputs are returned unmodified whereas others are converted to list.
     """
     if isinstance(values, (list, np.ndarray, ABCIndex, ABCSeries, ABCExtensionArray)):
-        return values
+        # np.ndarray resolving as Any gives a false positive
+        return values  # type: ignore[return-value]
     elif isinstance(values, abc.Iterable) and not isinstance(values, str):
         return list(values)
 

pandas/core/computation/align.py

@@ -1,9 +1,10 @@
 """
 Core eval alignment algorithms.
 """
+from __future__ import annotations
 
 from functools import partial, wraps
-from typing import Dict, Optional, Sequence, Tuple, Type, Union
+from typing import TYPE_CHECKING, Dict, Optional, Sequence, Tuple, Type, Union
 import warnings
 
 import numpy as np
@@ -17,13 +18,16 @@
 import pandas.core.common as com
 from pandas.core.computation.common import result_type_many
 
+if TYPE_CHECKING:
+    from pandas.core.indexes.api import Index
+
 
 def _align_core_single_unary_op(
     term,
-) -> Tuple[Union[partial, Type[FrameOrSeries]], Optional[Dict[str, int]]]:
+) -> Tuple[Union[partial, Type[FrameOrSeries]], Optional[Dict[str, Index]]]:
 
     typ: Union[partial, Type[FrameOrSeries]]
-    axes: Optional[Dict[str, int]] = None
+    axes: Optional[Dict[str, Index]] = None
 
     if isinstance(term.value, np.ndarray):
         typ = partial(np.asanyarray, dtype=term.value.dtype)
@@ -36,8 +40,8 @@ def _align_core_single_unary_op(
 
 
 def _zip_axes_from_type(
-    typ: Type[FrameOrSeries], new_axes: Sequence[int]
-) -> Dict[str, int]:
+    typ: Type[FrameOrSeries], new_axes: Sequence[Index]
+) -> Dict[str, Index]:
     return {name: new_axes[i] for i, name in enumerate(typ._AXIS_ORDERS)}
 
 

pandas/core/computation/parsing.py

@@ -8,6 +8,8 @@
 import tokenize
 from typing import Iterator, Tuple
 
+from pandas._typing import Label
+
 # A token value Python's tokenizer probably will never use.
 BACKTICK_QUOTED_STRING = 100
 
@@ -91,7 +93,7 @@ def clean_backtick_quoted_toks(tok: Tuple[int, str]) -> Tuple[int, str]:
     return toknum, tokval
 
 
-def clean_column_name(name: str) -> str:
+def clean_column_name(name: "Label") -> "Label":
     """
     Function to emulate the cleaning of a backtick quoted name.
 
@@ -102,12 +104,12 @@ def clean_column_name(name: str) -> str:
 
     Parameters
     ----------
-    name : str
+    name : hashable
         Name to be cleaned.
 
     Returns
     -------
-    name : str
+    name : hashable
         Returns the name after tokenizing and cleaning.
 
     Notes

pandas/core/construction.py

@@ -351,7 +351,7 @@ def array(
     return result
 
 
-def extract_array(obj: AnyArrayLike, extract_numpy: bool = False) -> ArrayLike:
+def extract_array(obj: object, extract_numpy: bool = False) -> Union[Any, ArrayLike]:
     """
     Extract the ndarray or ExtensionArray from a Series or Index.
 
@@ -399,9 +399,7 @@ def extract_array(obj: AnyArrayLike, extract_numpy: bool = False) -> ArrayLike:
     if extract_numpy and isinstance(obj, ABCPandasArray):
         obj = obj.to_numpy()
 
-    # error: Incompatible return value type (got "Index", expected "ExtensionArray")
-    # error: Incompatible return value type (got "Series", expected "ExtensionArray")
-    return obj  # type: ignore[return-value]
+    return obj
 
 
 def sanitize_array(

pandas/core/dtypes/generic.py

@@ -1,4 +1,11 @@
 """ define generic base classes for pandas objects """
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Type, cast
+
+if TYPE_CHECKING:
+    from pandas import DataFrame, Series
+    from pandas.core.generic import NDFrame
 
 
 # define abstract base classes to enable isinstance type checking on our
@@ -53,9 +60,17 @@ def _check(cls, inst) -> bool:
     },
 )
 
-ABCNDFrame = create_pandas_abc_type("ABCNDFrame", "_typ", ("series", "dataframe"))
-ABCSeries = create_pandas_abc_type("ABCSeries", "_typ", ("series",))
-ABCDataFrame = create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe",))
+ABCNDFrame = cast(
+    "Type[NDFrame]",
+    create_pandas_abc_type("ABCNDFrame", "_typ", ("series", "dataframe")),
+)
+ABCSeries = cast(
+    "Type[Series]",
+    create_pandas_abc_type("ABCSeries", "_typ", ("series",)),
+)
+ABCDataFrame = cast(
+    "Type[DataFrame]", create_pandas_abc_type("ABCDataFrame", "_typ", ("dataframe",))
+)
 
 ABCCategorical = create_pandas_abc_type("ABCCategorical", "_typ", ("categorical"))
 ABCDatetimeArray = create_pandas_abc_type("ABCDatetimeArray", "_typ", ("datetimearray"))

pandas/core/generic.py

@@ -511,7 +511,7 @@ def _get_axis_resolvers(self, axis: str) -> Dict[str, Union[Series, MultiIndex]]
         return d
 
     @final
-    def _get_index_resolvers(self) -> Dict[str, Union[Series, MultiIndex]]:
+    def _get_index_resolvers(self) -> Dict[Label, Union[Series, MultiIndex]]:
         from pandas.core.computation.parsing import clean_column_name
 
         d: Dict[str, Union[Series, MultiIndex]] = {}
@@ -521,7 +521,7 @@ def _get_index_resolvers(self) -> Dict[str, Union[Series, MultiIndex]]:
         return {clean_column_name(k): v for k, v in d.items() if not isinstance(k, int)}
 
     @final
-    def _get_cleaned_column_resolvers(self) -> Dict[str, ABCSeries]:
+    def _get_cleaned_column_resolvers(self) -> Dict[Label, Series]:
         """
         Return the special character free column resolvers of a dataframe.
 
@@ -532,7 +532,6 @@ def _get_cleaned_column_resolvers(self) -> Dict[str, ABCSeries]:
         from pandas.core.computation.parsing import clean_column_name
 
         if isinstance(self, ABCSeries):
-            self = cast("Series", self)
             return {clean_column_name(self.name): self}
 
         return {

pandas/core/indexing.py

@@ -2016,7 +2016,7 @@ def ravel(i):
 
         raise ValueError("Incompatible indexer with Series")
 
-    def _align_frame(self, indexer, df: ABCDataFrame):
+    def _align_frame(self, indexer, df: "DataFrame"):
         is_frame = self.ndim == 2
 
         if isinstance(indexer, tuple):

pandas/core/internals/construction.py

@@ -369,7 +369,7 @@ def extract_index(data) -> Index:
         index = Index([])
     elif len(data) > 0:
         raw_lengths = []
-        indexes = []
+        indexes: List[Union[List[Label], Index]] = []
 
         have_raw_arrays = False
         have_series = False

pandas/core/reshape/concat.py

@@ -3,11 +3,21 @@
 """
 
 from collections import abc
-from typing import TYPE_CHECKING, Iterable, List, Mapping, Type, Union, cast, overload
+from typing import (
+    TYPE_CHECKING,
+    Iterable,
+    List,
+    Mapping,
+    Optional,
+    Type,
+    Union,
+    cast,
+    overload,
+)
 
 import numpy as np
 
-from pandas._typing import FrameOrSeries, FrameOrSeriesUnion, Label
+from pandas._typing import FrameOrSeriesUnion, Label
 
 from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
@@ -295,7 +305,7 @@ class _Concatenator:
 
     def __init__(
         self,
-        objs: Union[Iterable[FrameOrSeries], Mapping[Label, FrameOrSeries]],
+        objs: Union[Iterable["NDFrame"], Mapping[Label, "NDFrame"]],
         axis=0,
         join: str = "outer",
         keys=None,
@@ -366,7 +376,7 @@ def __init__(
         # get the sample
         # want the highest ndim that we have, and must be non-empty
         # unless all objs are empty
-        sample = None
+        sample: Optional["NDFrame"] = None
         if len(ndims) > 1:
             max_ndim = max(ndims)
             for obj in objs:
@@ -436,6 +446,8 @@ def __init__(
                     # to line up
                     if self._is_frame and axis == 1:
                         name = 0
+                    # mypy needs to know sample is not an NDFrame
+                    sample = cast("FrameOrSeriesUnion", sample)
                     obj = sample._constructor({name: obj})
 
                 self.objs.append(obj)

pandas/core/strings/accessor.py

@@ -157,11 +157,10 @@ def __init__(self, data):
         array = data.array
         self._array = array
 
+        self._index = self._name = None
         if isinstance(data, ABCSeries):
             self._index = data.index
             self._name = data.name
-        else:
-            self._index = self._name = None
 
         # ._values.categories works for both Series/Index
         self._parent = data._values.categories if self._is_categorical else data

pandas/core/window/common.py

@@ -1,5 +1,6 @@
 """Common utility functions for rolling operations"""
 from collections import defaultdict
+from typing import cast
 import warnings
 
 import numpy as np
@@ -109,6 +110,9 @@ def dataframe_from_int_dict(data, frame_template):
 
                     # set the index and reorder
                     if arg2.columns.nlevels > 1:
+                        # mypy needs to know columns is a MultiIndex, Index doesn't
+                        # have levels attribute
+                        arg2.columns = cast(MultiIndex, arg2.columns)
                         result.index = MultiIndex.from_product(
                             arg2.columns.levels + [result_index]
                         )

pandas/plotting/_matplotlib/tools.py

@@ -7,7 +7,7 @@
 import matplotlib.ticker as ticker
 import numpy as np
 
-from pandas._typing import FrameOrSeries
+from pandas._typing import FrameOrSeriesUnion
 
 from pandas.core.dtypes.common import is_list_like
 from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
@@ -30,7 +30,9 @@ def format_date_labels(ax: "Axes", rot):
     fig.subplots_adjust(bottom=0.2)
 
 
-def table(ax, data: FrameOrSeries, rowLabels=None, colLabels=None, **kwargs) -> "Table":
+def table(
+    ax, data: FrameOrSeriesUnion, rowLabels=None, colLabels=None, **kwargs
+) -> "Table":
     if isinstance(data, ABCSeries):
         data = data.to_frame()
     elif isinstance(data, ABCDataFrame):