feat: fix remaining mypy errors in db-dtypes

Author: chalmerlowe

packages/db-dtypes/db_dtypes/__init__.py

@@ -55,7 +55,8 @@ class TimeDtype(core.BaseDatetimeDtype):
     name = time_dtype_name
     type = datetime.time
 
-    def construct_array_type(self):
+    @classmethod
+    def construct_array_type(cls):
         return TimeArray
 
     @staticmethod
@@ -213,7 +214,8 @@ class DateDtype(core.BaseDatetimeDtype):
     name = date_dtype_name
     type = datetime.date
 
-    def construct_array_type(self):
+    @classmethod
+    def construct_array_type(cls):
         return DateArray
 
     @staticmethod
@@ -322,7 +324,7 @@ def __add__(self, other):
         if isinstance(other, TimeArray):
             return (other._ndarray - _NPEPOCH) + self._ndarray
 
-        return super().__add__(other)
+        return super().__add__(other)  # type: ignore[misc]
 
     def __radd__(self, other):
         return self.__add__(other)
@@ -334,7 +336,7 @@ def __sub__(self, other):
         if isinstance(other, self.__class__):
             return self._ndarray - other._ndarray
 
-        return super().__sub__(other)
+        return super().__sub__(other)  # type: ignore[misc]
 
 
 def _check_python_version():

packages/db-dtypes/db_dtypes/core.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional
+from typing import Optional, Callable, Any
 
 import numpy
 import pandas
@@ -50,6 +50,13 @@ class BaseDatetimeArray(pandas_backports.OpsMixin, _mixins.NDArrayBackedExtensio
     # https://github.com/pandas-dev/pandas/blob/main/pandas/core/arrays/_mixins.py
     _internal_fill_value = numpy.datetime64("NaT")
 
+    _box_func: Callable[[Any], Any]
+    _from_backing_data: Callable[[Any], Any]
+
+    @classmethod
+    def _datetime(cls, value: Any) -> Any:
+        raise NotImplementedError
+
     def __init__(self, values, dtype=None, copy: bool = False):
         if not (
             isinstance(values, numpy.ndarray) and values.dtype == numpy.dtype("<M8[ns]")
@@ -164,17 +171,17 @@ def min(self, *, axis: Optional[int] = None, skipna: bool = True, **kwargs):
             values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
         )
         if axis is None or self.ndim == 1:
-            return self._box_func(result)  # type: ignore[attr-defined]
-        return self._from_backing_data(result)  # type: ignore[attr-defined]
+            return self._box_func(result)
+        return self._from_backing_data(result)
 
     def max(self, *, axis: Optional[int] = None, skipna: bool = True, **kwargs):
         pandas_backports.numpy_validate_max((), kwargs)
         result = pandas_backports.nanmax(
             values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna
         )
         if axis is None or self.ndim == 1:
-            return self._box_func(result)  # type: ignore[attr-defined]
-        return self._from_backing_data(result)  # type: ignore[attr-defined]
+            return self._box_func(result)
+        return self._from_backing_data(result)
 
     def median(
         self,
@@ -191,5 +198,5 @@ def median(
         )
         result = pandas_backports.nanmedian(self._ndarray, axis=axis, skipna=skipna)
         if axis is None or self.ndim == 1:
-            return self._box_func(result)  # type: ignore[attr-defined]
-        return self._from_backing_data(result)  # type: ignore[attr-defined]
+            return self._box_func(result)
+        return self._from_backing_data(result)

packages/db-dtypes/db_dtypes/json.py

@@ -32,7 +32,7 @@ class JSONDtype(pd.api.extensions.ExtensionDtype):
     name = "dbjson"
 
     @property
-    def na_value(self) -> pd.NAType:
+    def na_value(self) -> pd.NAType:  # type: ignore[name-defined]
         """Default NA value to use for this type."""
         return pd.NA
 
@@ -203,7 +203,7 @@ def __getitem__(self, item):
                 assert item.dtype.kind == "b"
                 return type(self)(self.pa_data.filter(item))
         elif isinstance(item, tuple):
-            item = indexers.unpack_tuple_and_ellipses(item)
+            item = indexers.unpack_tuple_and_ellipses(item)  # type: ignore[attr-defined]
 
         if common.is_scalar(item) and not common.is_integer(item):
             # e.g. "foo" or 2.5