CLN: add typing to dtype arg in core/internals, core/reshape and core (GH38808)

pandas/core/base.py

@@ -19,7 +19,7 @@
 import numpy as np
 
 import pandas._libs.lib as lib
-from pandas._typing import DtypeObj, IndexLabel
+from pandas._typing import Dtype, DtypeObj, IndexLabel
 from pandas.compat import PYPY
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
@@ -500,7 +500,13 @@ def array(self) -> ExtensionArray:
         """
         raise AbstractMethodError(self)
 
-    def to_numpy(self, dtype=None, copy=False, na_value=lib.no_default, **kwargs):
+    def to_numpy(
+        self,
+        dtype: Optional[Dtype] = None,
+        copy: bool = False,
+        na_value=lib.no_default,
+        **kwargs,
+    ):
         """
         A NumPy ndarray representing the values in this Series or Index.
 

pandas/core/generic.py

@@ -37,13 +37,16 @@
 from pandas._typing import (
     Axis,
     CompressionOptions,
+    Dtype,
+    DtypeArg,
     FilePathOrBuffer,
     FrameOrSeries,
     IndexKeyFunc,
     IndexLabel,
     JSONSerializable,
     Label,
     Level,
+    NpDtype,
     Renamer,
     StorageOptions,
     TimedeltaConvertibleTypes,
@@ -210,7 +213,9 @@ def __init__(
         object.__setattr__(self, "_flags", Flags(self, allows_duplicate_labels=True))
 
     @classmethod
-    def _init_mgr(cls, mgr, axes, dtype=None, copy: bool = False) -> BlockManager:
+    def _init_mgr(
+        cls, mgr, axes, dtype: Optional[Dtype] = None, copy: bool = False
+    ) -> BlockManager:
         """ passed a manager and a axes dict """
         for a, axe in axes.items():
             if axe is not None:
@@ -1901,7 +1906,7 @@ def empty(self) -> bool_t:
     # GH#23114 Ensure ndarray.__op__(DataFrame) returns NotImplemented
     __array_priority__ = 1000
 
-    def __array__(self, dtype=None) -> np.ndarray:
+    def __array__(self, dtype: Optional[NpDtype] = None) -> np.ndarray:
         return np.asarray(self._values, dtype=dtype)
 
     def __array_wrap__(
@@ -2642,7 +2647,7 @@ def to_sql(
         index: bool_t = True,
         index_label=None,
         chunksize=None,
-        dtype=None,
+        dtype: Optional[DtypeArg] = None,
         method=None,
     ) -> None:
         """

pandas/core/internals/blocks.py

@@ -15,7 +15,7 @@
 )
 from pandas._libs.internals import BlockPlacement
 from pandas._libs.tslibs import conversion
-from pandas._typing import ArrayLike, DtypeObj, Scalar, Shape
+from pandas._typing import ArrayLike, Dtype, DtypeObj, Scalar, Shape
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.cast import (
@@ -241,7 +241,7 @@ def array_values(self) -> ExtensionArray:
         """
         return PandasArray(self.values)
 
-    def get_values(self, dtype=None):
+    def get_values(self, dtype: Optional[Dtype] = None):
         """
         return an internal format, currently just the ndarray
         this is often overridden to handle to_dense like operations
@@ -1669,7 +1669,7 @@ def setitem(self, indexer, value):
         self.values[indexer] = value
         return self
 
-    def get_values(self, dtype=None):
+    def get_values(self, dtype: Optional[Dtype] = None):
         # ExtensionArrays must be iterable, so this works.
         # TODO(EA2D): reshape not needed with 2D EAs
         return np.asarray(self.values).reshape(self.shape)
@@ -1990,7 +1990,7 @@ class DatetimeLikeBlockMixin(Block):
 
     _can_hold_na = True
 
-    def get_values(self, dtype=None):
+    def get_values(self, dtype: Optional[Dtype] = None):
         """
         return object dtype as boxed values, such as Timestamps/Timedelta
         """
@@ -2168,7 +2168,7 @@ def is_view(self) -> bool:
         # check the ndarray values of the DatetimeIndex values
         return self.values._data.base is not None
 
-    def get_values(self, dtype=None):
+    def get_values(self, dtype: Optional[Dtype] = None):
         """
         Returns an ndarray of values.
 
@@ -2449,7 +2449,7 @@ def replace(
 # Constructor Helpers
 
 
-def get_block_type(values, dtype=None):
+def get_block_type(values, dtype: Optional[Dtype] = None):
     """
     Find the appropriate Block subclass to use for the given values and dtype.
 
@@ -2464,7 +2464,7 @@ def get_block_type(values, dtype=None):
     """
     # We use vtype and kind checks because they are much more performant
     #  than is_foo_dtype
-    dtype = dtype or values.dtype
+    dtype = cast(np.dtype, pandas_dtype(dtype) if dtype else values.dtype)
     vtype = dtype.type
     kind = dtype.kind
 
@@ -2500,7 +2500,7 @@ def get_block_type(values, dtype=None):
     return cls
 
 
-def make_block(values, placement, klass=None, ndim=None, dtype=None):
+def make_block(values, placement, klass=None, ndim=None, dtype: Optional[Dtype] = None):
     # Ensure that we don't allow PandasArray / PandasDtype in internals.
     # For now, blocks should be backed by ndarrays when possible.
     if isinstance(values, ABCPandasArray):

pandas/core/internals/managers.py

@@ -17,7 +17,7 @@
 import numpy as np
 
 from pandas._libs import internals as libinternals, lib
-from pandas._typing import ArrayLike, DtypeObj, Label, Shape
+from pandas._typing import ArrayLike, Dtype, DtypeObj, Label, Shape
 from pandas.errors import PerformanceWarning
 from pandas.util._validators import validate_bool_kwarg
 
@@ -816,7 +816,7 @@ def copy_func(ax):
     def as_array(
         self,
         transpose: bool = False,
-        dtype=None,
+        dtype: Optional[Dtype] = None,
         copy: bool = False,
         na_value=lib.no_default,
     ) -> np.ndarray:
@@ -872,7 +872,9 @@ def as_array(
 
         return arr.transpose() if transpose else arr
 
-    def _interleave(self, dtype=None, na_value=lib.no_default) -> np.ndarray:
+    def _interleave(
+        self, dtype: Optional[Dtype] = None, na_value=lib.no_default
+    ) -> np.ndarray:
         """
         Return ndarray from blocks with specified item order
         Items must be contained in the blocks
@@ -1842,7 +1844,7 @@ def _simple_blockify(tuples, dtype) -> List[Block]:
     return [block]
 
 
-def _multi_blockify(tuples, dtype=None):
+def _multi_blockify(tuples, dtype: Optional[Dtype] = None):
     """ return an array of blocks that potentially have different dtypes """
     # group by dtype
     grouper = itertools.groupby(tuples, lambda x: x[2].dtype)

pandas/core/reshape/reshape.py

@@ -6,6 +6,7 @@
 import pandas._libs.algos as libalgos
 import pandas._libs.reshape as libreshape
 from pandas._libs.sparse import IntIndex
+from pandas._typing import Dtype
 from pandas.util._decorators import cache_readonly
 
 from pandas.core.dtypes.cast import maybe_promote
@@ -732,11 +733,11 @@ def get_dummies(
     data,
     prefix=None,
     prefix_sep="_",
-    dummy_na=False,
+    dummy_na: bool = False,
     columns=None,
-    sparse=False,
-    drop_first=False,
-    dtype=None,
+    sparse: bool = False,
+    drop_first: bool = False,
+    dtype: Optional[Dtype] = None,
 ) -> "DataFrame":
     """
     Convert categorical variable into dummy/indicator variables.
@@ -921,7 +922,7 @@ def _get_dummies_1d(
     dummy_na=False,
     sparse=False,
     drop_first=False,
-    dtype=None,
+    dtype: Optional[Dtype] = None,
 ):
     from pandas.core.reshape.concat import concat
 

pandas/core/series.py

@@ -28,10 +28,12 @@
     AggFuncType,
     ArrayLike,
     Axis,
+    Dtype,
     DtypeObj,
     FrameOrSeriesUnion,
     IndexKeyFunc,
     Label,
+    NpDtype,
     StorageOptions,
     ValueKeyFunc,
 )
@@ -214,7 +216,13 @@ class Series(base.IndexOpsMixin, generic.NDFrame):
     # Constructors
 
     def __init__(
-        self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=False
+        self,
+        data=None,
+        index=None,
+        dtype: Optional[Dtype] = None,
+        name=None,
+        copy: bool = False,
+        fastpath: bool = False,
     ):
 
         if (
@@ -337,7 +345,7 @@ def __init__(
         self.name = name
         self._set_axis(0, index, fastpath=True)
 
-    def _init_dict(self, data, index=None, dtype=None):
+    def _init_dict(self, data, index=None, dtype: Optional[Dtype] = None):
         """
         Derive the "_mgr" and "index" attributes of a new Series from a
         dictionary input.
@@ -612,7 +620,7 @@ def __len__(self) -> int:
         """
         return len(self._mgr)
 
-    def view(self, dtype=None) -> "Series":
+    def view(self, dtype: Optional[Dtype] = None) -> "Series":
         """
         Create a new view of the Series.
 
@@ -686,7 +694,7 @@ def view(self, dtype=None) -> "Series":
     # NDArray Compat
     _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
 
-    def __array__(self, dtype=None) -> np.ndarray:
+    def __array__(self, dtype: Optional[NpDtype] = None) -> np.ndarray:
         """
         Return the values as a NumPy array.