reduce np.double

Author: cmp0xff

pandas-stubs/_typing.pyi

@@ -859,6 +859,7 @@ np_ndarray: TypeAlias = np.ndarray[ShapeT, np.dtype[GenericT]]
 # Numpy arrays with known shape (Do not use as argument types, only as return types)
 np_1darray: TypeAlias = np.ndarray[tuple[int], np.dtype[GenericT]]
 np_1darray_int64: TypeAlias = np_1darray[np.int64]
+np_1darray_float: TypeAlias = np_1darray[np.floating]
 
 np_2darray: TypeAlias = np.ndarray[tuple[int, int], np.dtype[GenericT]]
 

pandas-stubs/core/resample.pyi

@@ -12,7 +12,6 @@ from typing import (
 )
 
 import numpy as np
-from numpy import typing as npt
 from pandas.core.frame import DataFrame
 from pandas.core.groupby.generic import SeriesGroupBy
 from pandas.core.groupby.groupby import BaseGroupBy
@@ -29,6 +28,7 @@ from pandas._typing import (
     Scalar,
     TimeGrouperOrigin,
     TimestampConvention,
+    np_ndarray_float,
 )
 
 _FrameGroupByFunc: TypeAlias = (
@@ -165,7 +165,7 @@ class Resampler(BaseGroupBy[NDFrameT]):
     @final
     def quantile(
         self,
-        q: float | list[float] | npt.NDArray[np.double] | Series[float] = 0.5,
+        q: float | list[float] | np_ndarray_float | Series[float] = 0.5,
         **kwargs: Any,
     ) -> NDFrameT: ...
 

pandas-stubs/core/reshape/tile.pyi

@@ -6,6 +6,7 @@ from typing import (
 )
 
 import numpy as np
+from numpy import typing as npt
 from pandas import (
     Categorical,
     CategoricalDtype,
@@ -21,9 +22,9 @@ from pandas._typing import (
     IntervalT,
     Label,
     np_1darray,
+    np_1darray_float,
     np_ndarray_anyint,
     np_ndarray_float,
-    npt,
 )
 
 @overload
@@ -38,7 +39,7 @@ def cut(
     include_lowest: bool = ...,
     duplicates: Literal["raise", "drop"] = ...,
     ordered: bool = ...,
-) -> tuple[np_1darray[np.intp], np_1darray[np.double]]: ...
+) -> tuple[np_1darray[np.intp], np_1darray_float]: ...
 @overload
 def cut(
     x: Index | npt.NDArray[Any] | Sequence[int] | Sequence[float],
@@ -96,7 +97,7 @@ def cut(
     include_lowest: bool = ...,
     duplicates: Literal["raise", "drop"] = ...,
     ordered: bool = ...,
-) -> tuple[Series, np_1darray[np.double]]: ...
+) -> tuple[Series, np_1darray_float]: ...
 @overload
 def cut(
     x: Series,
@@ -122,7 +123,7 @@ def cut(
     include_lowest: bool = ...,
     duplicates: Literal["raise", "drop"] = ...,
     ordered: bool = ...,
-) -> tuple[Categorical, np_1darray[np.double]]: ...
+) -> tuple[Categorical, np_1darray_float]: ...
 @overload
 def cut(
     x: Index | npt.NDArray[Any] | Sequence[int] | Sequence[float],
@@ -258,7 +259,7 @@ def qcut(
     precision: int = 3,
     duplicates: Literal["raise", "drop"] = "raise",
     # double when there are nan's
-) -> tuple[np_1darray[np.intp | np.double], np_1darray[np.double]]: ...
+) -> tuple[np_1darray[np.intp | np.double], np_1darray_float]: ...
 @overload
 def qcut(
     x: Series[int] | Series[float],
@@ -268,7 +269,7 @@ def qcut(
     retbins: Literal[True],
     precision: int = 3,
     duplicates: Literal["raise", "drop"] = "raise",
-) -> tuple[Series, np_1darray[np.double]]: ...
+) -> tuple[Series, np_1darray_float]: ...
 @overload
 def qcut(
     x: (
@@ -284,4 +285,4 @@ def qcut(
     retbins: Literal[True],
     precision: int = 3,
     duplicates: Literal["raise", "drop"] = "raise",
-) -> tuple[Categorical, np_1darray[np.double]]: ...
+) -> tuple[Categorical, np_1darray_float]: ...

pandas-stubs/plotting/_core.pyi

@@ -27,6 +27,7 @@ from pandas._typing import (
     HashableT1,
     HashableT2,
     HashableT3,
+    np_ndarray_float,
     npt,
 )
 
@@ -340,7 +341,7 @@ class PlotAccessor:
             | Callable[[gaussian_kde], float]
             | None
         ) = ...,
-        ind: npt.NDArray[np.double] | int | None = ...,
+        ind: np_ndarray_float | int | None = ...,
         *,
         subplots: Literal[False] | None = ...,
         **kwargs: Any,
@@ -354,7 +355,7 @@ class PlotAccessor:
             | Callable[[gaussian_kde], float]
             | None
         ) = ...,
-        ind: npt.NDArray[np.double] | int | None = ...,
+        ind: np_ndarray_float | int | None = ...,
         *,
         subplots: Literal[True],
         **kwargs: Any,

tests/__init__.py

@@ -45,6 +45,7 @@
         UIntDtypeArg as UIntDtypeArg,
         VoidDtypeArg as VoidDtypeArg,
         np_1darray as np_1darray,
+        np_1darray_float as np_1darray_float,
         np_1darray_int64 as np_1darray_int64,
         np_2darray as np_2darray,
         np_ndarray as np_ndarray,
@@ -57,6 +58,7 @@
     # Separately define here so pytest works
     np_1darray: TypeAlias = np.ndarray[tuple[int], np.dtype[_G]]
     np_1darray_int64: TypeAlias = np_1darray[np.int64]
+    np_1darray_float: TypeAlias = np_1darray[np.floating]
     np_2darray: TypeAlias = np.ndarray[tuple[int, int], np.dtype[_G]]
     np_ndarray: TypeAlias = np.ndarray[_S, np.dtype[_G]]
     np_ndarray_bool: TypeAlias = npt.NDArray[np.bool_]

tests/test_pandas.py

@@ -33,6 +33,7 @@
     TYPE_CHECKING_INVALID_USAGE,
     check,
     np_1darray,
+    np_1darray_float,
     np_2darray,
     np_ndarray_bool,
     pytest_warns_bounded,
@@ -1030,14 +1031,14 @@ def test_cut() -> None:
         [1, 2, 3, 4, 5, 6, 7, 8], 4, labels=["1", "2", "3", "4"], retbins=True
     )
     check(assert_type(d0, pd.Categorical), pd.Categorical)
-    check(assert_type(d1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(d1, np_1darray_float), np_1darray[Any], np.floating)
     check(
         assert_type(e0, np_1darray[np.intp]),
         np_1darray[np.intp],
     )
-    check(assert_type(e1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(e1, np_1darray_float), np_1darray[Any], np.floating)
     check(assert_type(f0, pd.Categorical), pd.Categorical)
-    check(assert_type(f1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(f1, np_1darray_float), np_1darray[Any], np.floating)
 
     g = pd.cut(pd.Series([1, 2, 3, 4, 5, 6, 7, 8]), 4, precision=1, duplicates="drop")
     h = pd.cut(pd.Series([1, 2, 3, 4, 5, 6, 7, 8]), 4, labels=False, duplicates="raise")
@@ -1072,11 +1073,11 @@ def test_cut() -> None:
         retbins=True,
     )
     check(assert_type(j0, pd.Series), pd.Series)
-    check(assert_type(j1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(j1, np_1darray_float), np_1darray[Any], np.floating)
     check(assert_type(k0, pd.Series), pd.Series)
-    check(assert_type(k1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(k1, np_1darray_float), np_1darray[Any], np.floating)
     check(assert_type(l0, pd.Series), pd.Series)
-    check(assert_type(l1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(l1, np_1darray_float), np_1darray[Any], np.floating)
     check(assert_type(m0, pd.Series), pd.Series)
     check(assert_type(m1, pd.IntervalIndex), pd.IntervalIndex)
 
@@ -1149,10 +1150,10 @@ def test_qcut() -> None:
     check(assert_type(c0, pd.Categorical), pd.Categorical)
     check(assert_type(d0, pd.Series), pd.Series)
 
-    check(assert_type(a1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(b1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(c1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(d1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(a1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(b1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(c1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(d1, np_1darray_float), np_1darray[Any], np.floating)
 
     e0, e1 = pd.qcut(val_list, [0.25, 0.5, 0.75], retbins=True)
     f0, f1 = pd.qcut(val_arr, np.array([0.25, 0.5, 0.75]), retbins=True)
@@ -1170,16 +1171,19 @@ def test_qcut() -> None:
     check(assert_type(h0, pd.Series), pd.Series)
     check(
         assert_type(i0, np_1darray[np.intp | np.double]),
-        np_1darray[np.double],  # because of nans
+        np_1darray[Any],  # because of nans
+        np.floating,
+    )
+    check(
+        assert_type(j0, np_1darray[np.intp | np.double]), np_1darray[Any], np.floating
     )
-    check(assert_type(j0, np_1darray[np.intp | np.double]), np_1darray[np.double])
 
-    check(assert_type(e1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(f1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(g1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(h1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(i1, np_1darray[np.double]), np_1darray[np.double])
-    check(assert_type(j1, np_1darray[np.double]), np_1darray[np.double])
+    check(assert_type(e1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(f1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(g1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(h1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(i1, np_1darray_float), np_1darray[Any], np.floating)
+    check(assert_type(j1, np_1darray_float), np_1darray[Any], np.floating)
 
 
 @pytest.mark.xfail(