Update type hints to python3 style (non-array files)

pandas/core/base.py

@@ -786,8 +786,7 @@ def base(self):
         return self.values.base
 
     @property
-    def array(self):
-        # type: () -> ExtensionArray
+    def array(self) -> ExtensionArray:
         """
         The ExtensionArray of the data backing this Series or Index.
 
@@ -962,8 +961,7 @@ def to_numpy(self, dtype=None, copy=False):
         return result
 
     @property
-    def _ndarray_values(self):
-        # type: () -> np.ndarray
+    def _ndarray_values(self) -> np.ndarray:
         """
         The data as an ndarray, possibly losing information.
 

pandas/core/common.py

@@ -93,8 +93,7 @@ def maybe_box_datetimelike(value):
 values_from_object = lib.values_from_object
 
 
-def is_bool_indexer(key):
-    # type: (Any) -> bool
+def is_bool_indexer(key: Any) -> bool:
     """
     Check whether `key` is a valid boolean indexer.
 

pandas/core/dtypes/base.py

@@ -1,5 +1,5 @@
 """Extend pandas with custom array types"""
-from typing import List, Optional, Type
+from typing import List, Optional
 
 import numpy as np
 
@@ -65,8 +65,7 @@ def __ne__(self, other):
         return not self.__eq__(other)
 
     @property
-    def names(self):
-        # type: () -> Optional[List[str]]
+    def names(self) -> Optional[List[str]]:
         """Ordered list of field names, or None if there are no fields.
 
         This is for compatibility with NumPy arrays, and may be removed in the
@@ -116,8 +115,7 @@ def is_dtype(cls, dtype):
             return False
 
     @property
-    def _is_numeric(self):
-        # type: () -> bool
+    def _is_numeric(self) -> bool:
         """
         Whether columns with this dtype should be considered numeric.
 
@@ -128,8 +126,7 @@ def _is_numeric(self):
         return False
 
     @property
-    def _is_boolean(self):
-        # type: () -> bool
+    def _is_boolean(self) -> bool:
         """
         Whether this dtype should be considered boolean.
 
@@ -212,8 +209,7 @@ def __str__(self):
         return self.name
 
     @property
-    def type(self):
-        # type: () -> Type
+    def type(self) -> bool:
         """
         The scalar type for the array, e.g. ``int``
 
@@ -242,8 +238,7 @@ def kind(self):
         return 'O'
 
     @property
-    def name(self):
-        # type: () -> str
+    def name(self) -> str:
         """
         A string identifying the data type.
 

pandas/core/frame.py

@@ -284,6 +284,7 @@
     Index(['value'], dtype='object')
 """
 
+
 # -----------------------------------------------------------------------
 # DataFrame class
 
@@ -6243,11 +6244,10 @@ def diff(self, periods=1, axis=0):
     # Function application
 
     def _gotitem(self,
-                 key,           # type: Union[str, List[str]]
-                 ndim,          # type: int
-                 subset=None    # type: Union[Series, DataFrame, None]
-                 ):
-        # type: (...) -> Union[Series, DataFrame]
+                 key: Union[str, List[str]],
+                 ndim: int,
+                 subset: Union[Series, 'DataFrame', None] = None,
+                 ) -> Union[Series, 'DataFrame']:
         """
         Sub-classes to define. Return a sliced object.
 

pandas/core/groupby/groupby.py

@@ -12,7 +12,7 @@ class providing the base-class of operations.
 import datetime
 from functools import partial, wraps
 import types
-from typing import Optional, Type
+from typing import Optional, Tuple, Type
 import warnings
 
 import numpy as np
@@ -1040,17 +1040,15 @@ def _bool_agg(self, val_test, skipna):
         Shared func to call any / all Cython GroupBy implementations.
         """
 
-        def objs_to_bool(vals):
-            # type: (np.ndarray) -> (np.ndarray, Type)
+        def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]:
             if is_object_dtype(vals):
                 vals = np.array([bool(x) for x in vals])
             else:
                 vals = vals.astype(np.bool)
 
             return vals.view(np.uint8), np.bool
 
-        def result_to_bool(result, inference):
-            # type: (np.ndarray, Type) -> np.ndarray
+        def result_to_bool(result: np.ndarray, inference: Type) -> np.ndarray:
             return result.astype(inference, copy=False)
 
         return self._get_cythonized_result('group_any_all', self.grouper,
@@ -1738,8 +1736,8 @@ def quantile(self, q=0.5, interpolation='linear'):
         b    3.0
         """
 
-        def pre_processor(vals):
-            # type: (np.ndarray) -> (np.ndarray, Optional[Type])
+        def pre_processor(vals: np.ndarray) -> \
+                Tuple[np.ndarray, Optional[Type]]:
             if is_object_dtype(vals):
                 raise TypeError("'quantile' cannot be performed against "
                                 "'object' dtypes!")
@@ -1753,8 +1751,8 @@ def pre_processor(vals):
 
             return vals, inference
 
-        def post_processor(vals, inference):
-            # type: (np.ndarray, Optional[Type]) -> np.ndarray
+        def post_processor(vals: np.ndarray, inference: Optional[Type]) -> \
+                np.ndarray:
             if inference:
                 # Check for edge case
                 if not (is_integer_dtype(inference) and

pandas/core/indexes/base.py

@@ -3632,8 +3632,7 @@ def values(self):
         return self._data.view(np.ndarray)
 
     @property
-    def _values(self):
-        # type: () -> Union[ExtensionArray, Index, np.ndarray]
+    def _values(self) -> Union[ExtensionArray, 'Index', np.ndarray]:
         # TODO(EA): remove index types as they become extension arrays
         """
         The best array representation.

pandas/core/indexes/datetimelike.py

@@ -56,7 +56,7 @@ class DatetimeIndexOpsMixin(ExtensionOpsMixin):
     """
     common ops mixin to support a unified interface datetimelike Index
     """
-    _data = None  # type: DatetimeLikeArrayMixin
+    _data: DatetimeLikeArrayMixin = None
 
     # DatetimeLikeArrayMixin assumes subclasses are mutable, so these are
     # properties there.  They can be made into cache_readonly for Index
@@ -129,8 +129,7 @@ def _ndarray_values(self):
     # Abstract data attributes
 
     @property
-    def values(self):
-        # type: () -> np.ndarray
+    def values(self) -> np.ndarray:
         # Note: PeriodArray overrides this to return an ndarray of objects.
         return self._data._data
 

pandas/core/indexes/period.py

@@ -170,7 +170,7 @@ class PeriodIndex(DatetimeIndexOpsMixin, Int64Index, PeriodDelegateMixin):
     _is_numeric_dtype = False
     _infer_as_myclass = True
 
-    _data = None  # type: PeriodArray
+    _data: PeriodArray = None
 
     _engine_type = libindex.PeriodEngine
 

pandas/core/internals/blocks.py

@@ -3,7 +3,7 @@
 import functools
 import inspect
 import re
-from typing import Any, List
+from typing import Any, List, Optional
 import warnings
 
 import numpy as np
@@ -1828,10 +1828,9 @@ def interpolate(self, method='pad', axis=0, inplace=False, limit=None,
             placement=self.mgr_locs)
 
     def shift(self,
-              periods,                  # type: int
-              axis=0,                   # type: libinternals.BlockPlacement
-              fill_value=None):         # type: Any
-        # type: (...) -> List[ExtensionBlock]
+              periods: int,
+              axis: libinternals.BlockPlacement = 0,
+              fill_value: Optional[Any] = None) -> List['ExtensionBlock']:
         """
         Shift the block by `periods`.
 

pandas/core/internals/managers.py

@@ -1863,8 +1863,8 @@ def _shape_compat(x):
     return stacked, placement
 
 
-def _interleaved_dtype(blocks):
-    # type: (List[Block]) -> Optional[Union[np.dtype, ExtensionDtype]]
+def _interleaved_dtype(blocks: List[Block]) \
+        -> Optional[Union[np.dtype, ExtensionDtype]]:
     """Find the common dtype for `blocks`.
 
     Parameters