TYP: Annotations

pandas/core/algorithms.py

@@ -10,7 +10,7 @@
 import numpy as np
 
 from pandas._libs import Timestamp, algos, hashtable as htable, iNaT, lib
-from pandas._typing import AnyArrayLike, ArrayLike, DtypeObj
+from pandas._typing import AnyArrayLike, ArrayLike, DtypeObj, FrameOrSeriesUnion
 from pandas.util._decorators import doc
 
 from pandas.core.dtypes.cast import (
@@ -58,7 +58,7 @@
 from pandas.core.indexers import validate_indices
 
 if TYPE_CHECKING:
-    from pandas import Series
+    from pandas import DataFrame, Series
 
 _shared_docs: Dict[str, str] = {}
 
@@ -1101,6 +1101,9 @@ def __init__(self, obj, n: int, keep: str):
         if self.keep not in ("first", "last", "all"):
             raise ValueError('keep must be either "first", "last" or "all"')
 
+    def compute(self, method: str) -> FrameOrSeriesUnion:
+        raise NotImplementedError
+
     def nlargest(self):
         return self.compute("nlargest")
 
@@ -1133,7 +1136,7 @@ class SelectNSeries(SelectN):
     nordered : Series
     """
 
-    def compute(self, method):
+    def compute(self, method: str) -> "Series":
 
         n = self.n
         dtype = self.obj.dtype
@@ -1207,7 +1210,7 @@ def __init__(self, obj, n: int, keep: str, columns):
         columns = list(columns)
         self.columns = columns
 
-    def compute(self, method):
+    def compute(self, method: str) -> "DataFrame":
 
         from pandas import Int64Index
 

pandas/core/arrays/base.py

@@ -1167,6 +1167,10 @@ class ExtensionOpsMixin:
        with NumPy arrays.
     """
 
+    @classmethod
+    def _create_arithmetic_method(cls, op):
+        raise AbstractMethodError(cls)
+
     @classmethod
     def _add_arithmetic_ops(cls):
         cls.__add__ = cls._create_arithmetic_method(operator.add)
@@ -1186,6 +1190,10 @@ def _add_arithmetic_ops(cls):
         cls.__divmod__ = cls._create_arithmetic_method(divmod)
         cls.__rdivmod__ = cls._create_arithmetic_method(ops.rdivmod)
 
+    @classmethod
+    def _create_comparison_method(cls, op):
+        raise AbstractMethodError(cls)
+
     @classmethod
     def _add_comparison_ops(cls):
         cls.__eq__ = cls._create_comparison_method(operator.eq)
@@ -1195,6 +1203,10 @@ def _add_comparison_ops(cls):
         cls.__le__ = cls._create_comparison_method(operator.le)
         cls.__ge__ = cls._create_comparison_method(operator.ge)
 
+    @classmethod
+    def _create_logical_method(cls, op):
+        raise AbstractMethodError(cls)
+
     @classmethod
     def _add_logical_ops(cls):
         cls.__and__ = cls._create_logical_method(operator.and_)

pandas/core/groupby/base.py

@@ -4,25 +4,30 @@
 SeriesGroupBy and the DataFrameGroupBy objects.
 """
 import collections
+from typing import List
 
 from pandas.core.dtypes.common import is_list_like, is_scalar
 
+from pandas.core.base import PandasObject
+
 OutputKey = collections.namedtuple("OutputKey", ["label", "position"])
 
 
-class GroupByMixin:
+class GroupByMixin(PandasObject):
     """
     Provide the groupby facilities to the mixed object.
     """
 
+    _attributes: List[str]
+
     def _gotitem(self, key, ndim, subset=None):
         """
         Sub-classes to define. Return a sliced object.
 
         Parameters
         ----------
         key : string / list of selections
-        ndim : 1,2
+        ndim : {1, 2}
             requested ndim of result
         subset : object, default None
             subset to act on

pandas/core/indexes/base.py

@@ -3541,10 +3541,7 @@ def _join_multi(self, other, how, return_indexers=True):
         if not overlap:
             raise ValueError("cannot join with no overlapping index names")
 
-        self_is_mi = isinstance(self, ABCMultiIndex)
-        other_is_mi = isinstance(other, ABCMultiIndex)
-
-        if self_is_mi and other_is_mi:
+        if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
 
             # Drop the non-matching levels from left and right respectively
             ldrop_names = list(self_names - overlap)
@@ -3590,7 +3587,7 @@ def _join_multi(self, other, how, return_indexers=True):
         # Case where only one index is multi
         # make the indices into mi's that match
         flip_order = False
-        if self_is_mi:
+        if isinstance(self, MultiIndex):
             self, other = other, self
             flip_order = True
             # flip if join method is right or left

pandas/core/indexes/datetimelike.py

@@ -1,7 +1,7 @@
 """
 Base and utility classes for tseries type pandas objects.
 """
-from datetime import datetime
+from datetime import datetime, tzinfo
 from typing import Any, List, Optional, TypeVar, Union, cast
 
 import numpy as np
@@ -632,6 +632,8 @@ class DatetimeTimedeltaMixin(DatetimeIndexOpsMixin, Int64Index):
     but not PeriodIndex
     """
 
+    tz: Optional[tzinfo]
+
     # Compat for frequency inference, see GH#23789
     _is_monotonic_increasing = Index.is_monotonic_increasing
     _is_monotonic_decreasing = Index.is_monotonic_decreasing

pandas/core/indexes/numeric.py

@@ -45,6 +45,8 @@ class NumericIndex(Index):
     This is an abstract class.
     """
 
+    _default_dtype: np.dtype
+
     _is_numeric_dtype = True
 
     def __new__(cls, data=None, dtype=None, copy=False, name=None):

pandas/core/internals/blocks.py

@@ -1382,7 +1382,7 @@ def where_func(cond, values, other):
         cond = cond.swapaxes(axis, 0)
         mask = np.array([cond[i].all() for i in range(cond.shape[0])], dtype=bool)
 
-        result_blocks = []
+        result_blocks: List["Block"] = []
         for m in [mask, ~mask]:
             if m.any():
                 taken = result.take(m.nonzero()[0], axis=axis)

pandas/core/internals/managers.py

@@ -334,7 +334,7 @@ def reduce(self: T, func) -> T:
         # If 2D, we assume that we're operating column-wise
         assert self.ndim == 2
 
-        res_blocks = []
+        res_blocks: List[Block] = []
         for blk in self.blocks:
             nbs = blk.reduce(func)
             res_blocks.extend(nbs)
@@ -730,7 +730,7 @@ def _combine(self, blocks: List[Block], copy: bool = True) -> "BlockManager":
         indexer = np.sort(np.concatenate([b.mgr_locs.as_array for b in blocks]))
         inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0])
 
-        new_blocks = []
+        new_blocks: List[Block] = []
         for b in blocks:
             b = b.copy(deep=copy)
             b.mgr_locs = inv_indexer[b.mgr_locs.indexer]