TYP: Implicit generic "Any" for builtins

pandas/_typing.py

@@ -23,6 +23,7 @@
     from pandas.core.indexes.base import Index  # noqa: F401
     from pandas.core.series import Series  # noqa: F401
     from pandas.core.generic import NDFrame  # noqa: F401
+    from pandas import Interval  # noqa: F401
 
 
 AnyArrayLike = TypeVar("AnyArrayLike", "ExtensionArray", "Index", "Series", np.ndarray)
@@ -32,10 +33,12 @@
 FilePathOrBuffer = Union[str, Path, IO[AnyStr]]
 
 FrameOrSeries = TypeVar("FrameOrSeries", bound="NDFrame")
-Scalar = Union[str, int, float, bool]
+PythonScalar = Union[str, int, float, bool]
+PandasScalar = Union[DatetimeLikeScalar, "Interval"]
+Scalar = Union[PythonScalar, PandasScalar]
 Axis = Union[str, int]
 Ordered = Optional[bool]
-JSONSerializable = Union[Scalar, List, Dict]
+JSONSerializable = Union[PythonScalar, List, Dict]
 
 Axes = Collection
 

pandas/core/arrays/categorical.py

@@ -1,14 +1,14 @@
 import operator
 from shutil import get_terminal_size
-from typing import Type, Union, cast
+from typing import Dict, Hashable, List, Type, Union, cast
 from warnings import warn
 
 import numpy as np
 
 from pandas._config import get_option
 
 from pandas._libs import algos as libalgos, hashtable as htable
-from pandas._typing import ArrayLike, Dtype, Ordered
+from pandas._typing import ArrayLike, Dtype, Ordered, Scalar
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import (
     Appender,
@@ -511,7 +511,7 @@ def itemsize(self) -> int:
         """
         return self.categories.itemsize
 
-    def tolist(self) -> list:
+    def tolist(self) -> List[Scalar]:
         """
         Return a list of the values.
 
@@ -2067,7 +2067,7 @@ def __setitem__(self, key, value):
         lindexer = self._maybe_coerce_indexer(lindexer)
         self._codes[key] = lindexer
 
-    def _reverse_indexer(self):
+    def _reverse_indexer(self) -> Dict[Hashable, np.ndarray]:
         """
         Compute the inverse of a categorical, returning
         a dict of categories -> indexers.
@@ -2097,8 +2097,8 @@ def _reverse_indexer(self):
             self.codes.astype("int64"), categories.size
         )
         counts = counts.cumsum()
-        result = (r[start:end] for start, end in zip(counts, counts[1:]))
-        result = dict(zip(categories, result))
+        _result = (r[start:end] for start, end in zip(counts, counts[1:]))
+        result = dict(zip(categories, _result))
         return result
 
     # reduction ops #

pandas/core/common.py

@@ -9,7 +9,7 @@
 from datetime import datetime, timedelta
 from functools import partial
 import inspect
-from typing import Any, Iterable, Union
+from typing import Any, Collection, Iterable, TypeVar, Union
 
 import numpy as np
 
@@ -270,7 +270,10 @@ def maybe_make_list(obj):
     return obj
 
 
-def maybe_iterable_to_list(obj: Union[Iterable, Any]) -> Union[list, Any]:
+_T = TypeVar("_T")
+
+
+def maybe_iterable_to_list(obj: Union[Iterable[_T], _T]) -> Union[Collection[_T], _T]:
     """
     If obj is Iterable but not list-like, consume into list.
     """

pandas/core/groupby/grouper.py

@@ -3,7 +3,7 @@
 split-apply-combine paradigm.
 """
 
-from typing import Hashable, List, Optional, Tuple
+from typing import Dict, Hashable, List, Optional, Tuple
 
 import numpy as np
 
@@ -419,7 +419,7 @@ def _make_codes(self) -> None:
             self._group_index = uniques
 
     @cache_readonly
-    def groups(self) -> dict:
+    def groups(self) -> Dict[Hashable, np.ndarray]:
         return self.index.groupby(Categorical.from_codes(self.codes, self.group_index))
 
 

pandas/core/indexes/base.py

@@ -1,7 +1,7 @@
 from datetime import datetime
 import operator
 from textwrap import dedent
-from typing import FrozenSet, Hashable, Optional, Union
+from typing import Dict, FrozenSet, Hashable, Optional, Union
 import warnings
 
 import numpy as np
@@ -4594,7 +4594,7 @@ def _maybe_promote(self, other):
                 return self.astype("object"), other.astype("object")
         return self, other
 
-    def groupby(self, values):
+    def groupby(self, values) -> Dict[Hashable, np.ndarray]:
         """
         Group the index labels by a given array of values.
 
@@ -4605,7 +4605,7 @@ def groupby(self, values):
 
         Returns
         -------
-        groups : dict
+        dict
             {group name -> group labels}
         """
 

pandas/core/indexing.py

@@ -1,4 +1,4 @@
-from typing import Tuple
+from typing import Hashable, List, Tuple, Union
 
 import numpy as np
 
@@ -2224,7 +2224,7 @@ def _convert_key(self, key, is_setter: bool = False):
         return key
 
 
-def _tuplify(ndim: int, loc) -> tuple:
+def _tuplify(ndim: int, loc: Hashable) -> Tuple[Union[Hashable, slice], ...]:
     """
     Given an indexer for the first dimension, create an equivalent tuple
     for indexing over all dimensions.
@@ -2238,9 +2238,10 @@ def _tuplify(ndim: int, loc) -> tuple:
     -------
     tuple
     """
-    tup = [slice(None, None) for _ in range(ndim)]
-    tup[0] = loc
-    return tuple(tup)
+    _tup: List[Union[Hashable, slice]]
+    _tup = [slice(None, None) for _ in range(ndim)]
+    _tup[0] = loc
+    return tuple(_tup)
 
 
 def convert_to_index_sliceable(obj, key):

pandas/io/pytables.py

@@ -1459,7 +1459,7 @@ def copy(
                 data = self.select(k)
                 if isinstance(s, Table):
 
-                    index: Union[bool, list] = False
+                    index: Union[bool, List[str]] = False
                     if propindexes:
                         index = [a.name for a in s.axes if a.is_indexed]
                     new_store.append(

pandas/tests/frame/methods/test_replace.py

@@ -1,7 +1,7 @@
 from datetime import datetime
 from io import StringIO
 import re
-from typing import Dict
+from typing import Dict, List, Union
 
 import numpy as np
 import pytest
@@ -12,12 +12,12 @@
 
 
 @pytest.fixture
-def mix_ab() -> Dict[str, list]:
+def mix_ab() -> Dict[str, List[Union[int, str]]]:
     return {"a": list(range(4)), "b": list("ab..")}
 
 
 @pytest.fixture
-def mix_abc() -> Dict[str, list]:
+def mix_abc() -> Dict[str, List[Union[float, str]]]:
     return {"a": list(range(4)), "b": list("ab.."), "c": ["a", "b", np.nan, "d"]}