TYP/CLN: small cleanups from flake-pyi

.pre-commit-config.yaml

@@ -230,7 +230,7 @@ repos:
         language: python
         additional_dependencies:
         - flake8==4.0.1
-        - flake8-pyi==22.5.1
+        - flake8-pyi==22.7.0
     -   id: future-annotations
         name: import annotations from __future__
         entry: 'from __future__ import annotations'

pandas/_libs/algos.pyi

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 from typing import Any
 
 import numpy as np

pandas/_libs/interval.pyi

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 from typing import (
     Any,
     Generic,
@@ -84,7 +82,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]):
         self: Interval[_OrderableTimesT], key: _OrderableTimesT
     ) -> bool: ...
     @overload
-    def __contains__(self: Interval[_OrderableScalarT], key: int | float) -> bool: ...
+    def __contains__(self: Interval[_OrderableScalarT], key: float) -> bool: ...
     @overload
     def __add__(
         self: Interval[_OrderableTimesT], y: Timedelta
@@ -94,7 +92,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]):
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __add__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __add__(self: Interval[float], y: float) -> Interval[float]: ...
     @overload
     def __radd__(
         self: Interval[_OrderableTimesT], y: Timedelta
@@ -104,7 +102,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]):
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __radd__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __radd__(self: Interval[float], y: float) -> Interval[float]: ...
     @overload
     def __sub__(
         self: Interval[_OrderableTimesT], y: Timedelta
@@ -114,7 +112,7 @@ class Interval(IntervalMixin, Generic[_OrderableT]):
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __sub__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __sub__(self: Interval[float], y: float) -> Interval[float]: ...
     @overload
     def __rsub__(
         self: Interval[_OrderableTimesT], y: Timedelta
@@ -124,31 +122,31 @@ class Interval(IntervalMixin, Generic[_OrderableT]):
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __rsub__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __rsub__(self: Interval[float], y: float) -> Interval[float]: ...
     @overload
     def __mul__(
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __mul__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __mul__(self: Interval[float], y: float) -> Interval[float]: ...
     @overload
     def __rmul__(
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __rmul__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __rmul__(self: Interval[float], y: float) -> Interval[float]: ...
     @overload
     def __truediv__(
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __truediv__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __truediv__(self: Interval[float], y: float) -> Interval[float]: ...
     @overload
     def __floordiv__(
         self: Interval[int], y: _OrderableScalarT
     ) -> Interval[_OrderableScalarT]: ...
     @overload
-    def __floordiv__(self: Interval[float], y: int | float) -> Interval[float]: ...
+    def __floordiv__(self: Interval[float], y: float) -> Interval[float]: ...
     def overlaps(self: Interval[_OrderableT], other: Interval[_OrderableT]) -> bool: ...
 
 def intervals_to_interval_bounds(

pandas/_libs/join.pyi

@@ -55,7 +55,7 @@ def asof_join_backward_on_X_by_Y(
     left_by_values: np.ndarray,  # by_t[:]
     right_by_values: np.ndarray,  # by_t[:]
     allow_exact_matches: bool = ...,
-    tolerance: np.number | int | float | None = ...,
+    tolerance: np.number | float | None = ...,
     use_hashtable: bool = ...,
 ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
 def asof_join_forward_on_X_by_Y(
@@ -64,7 +64,7 @@ def asof_join_forward_on_X_by_Y(
     left_by_values: np.ndarray,  # by_t[:]
     right_by_values: np.ndarray,  # by_t[:]
     allow_exact_matches: bool = ...,
-    tolerance: np.number | int | float | None = ...,
+    tolerance: np.number | float | None = ...,
     use_hashtable: bool = ...,
 ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
 def asof_join_nearest_on_X_by_Y(
@@ -73,6 +73,6 @@ def asof_join_nearest_on_X_by_Y(
     left_by_values: np.ndarray,  # by_t[:]
     right_by_values: np.ndarray,  # by_t[:]
     allow_exact_matches: bool = ...,
-    tolerance: np.number | int | float | None = ...,
+    tolerance: np.number | float | None = ...,
     use_hashtable: bool = ...,
 ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...

pandas/_libs/json.pyi

@@ -12,7 +12,7 @@ def dumps(
     date_unit: str = ...,
     iso_dates: bool = ...,
     default_handler: None
-    | Callable[[Any], str | int | float | bool | list | dict | None] = ...,
+    | Callable[[Any], str | float | bool | list | dict | None] = ...,
 ) -> str: ...
 def loads(
     s: str,

pandas/_libs/tslibs/offsets.pyi

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 from datetime import (
     datetime,
     timedelta,

pandas/_libs/tslibs/timedeltas.pyi

@@ -86,7 +86,7 @@ class Timedelta(timedelta):
         cls: type[_S],
         value=...,
         unit: str = ...,
-        **kwargs: int | float | np.integer | np.floating,
+        **kwargs: float | np.integer | np.floating,
     ) -> _S: ...
     # GH 46171
     # While Timedelta can return pd.NaT, having the constructor return
@@ -123,7 +123,7 @@ class Timedelta(timedelta):
     @overload  # type: ignore[override]
     def __floordiv__(self, other: timedelta) -> int: ...
     @overload
-    def __floordiv__(self, other: int | float) -> Timedelta: ...
+    def __floordiv__(self, other: float) -> Timedelta: ...
     @overload
     def __floordiv__(
         self, other: npt.NDArray[np.timedelta64]

pandas/_libs/tslibs/timestamps.pyi

@@ -33,13 +33,7 @@ class Timestamp(datetime):
     value: int  # np.int64
     def __new__(
         cls: type[_DatetimeT],
-        ts_input: int
-        | np.integer
-        | float
-        | str
-        | _date
-        | datetime
-        | np.datetime64 = ...,
+        ts_input: np.integer | float | str | _date | datetime | np.datetime64 = ...,
         freq: int | None | str | BaseOffset = ...,
         tz: str | _tzinfo | None | int = ...,
         unit: str | int | None = ...,

pandas/_libs/writers.pyi

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import numpy as np
 
 from pandas._typing import ArrayLike

pandas/_typing.py

@@ -82,7 +82,7 @@
 
 # scalars
 
-PythonScalar = Union[str, int, float, bool]
+PythonScalar = Union[str, float, bool]
 DatetimeLikeScalar = Union["Period", "Timestamp", "Timedelta"]
 PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"]
 Scalar = Union[PythonScalar, PandasScalar, np.datetime64, np.timedelta64, datetime]
@@ -92,10 +92,10 @@
 # timestamp and timedelta convertible types
 
 TimestampConvertibleTypes = Union[
-    "Timestamp", datetime, np.datetime64, int, np.int64, float, str
+    "Timestamp", datetime, np.datetime64, np.int64, float, str
 ]
 TimedeltaConvertibleTypes = Union[
-    "Timedelta", timedelta, np.timedelta64, int, np.int64, float, str
+    "Timedelta", timedelta, np.timedelta64, np.int64, float, str
 ]
 Timezone = Union[str, tzinfo]
 
@@ -126,7 +126,7 @@
 ]
 
 # dtypes
-NpDtype = Union[str, np.dtype, type_t[Union[str, float, int, complex, bool, object]]]
+NpDtype = Union[str, np.dtype, type_t[Union[str, complex, bool, object]]]
 Dtype = Union["ExtensionDtype", NpDtype]
 AstypeArg = Union["ExtensionDtype", "npt.DTypeLike"]
 # DtypeArg specifies all allowable dtypes in a functions its dtype argument

pandas/core/arrays/datetimelike.py

@@ -2170,11 +2170,11 @@ def validate_periods(periods: None) -> None:
 
 
 @overload
-def validate_periods(periods: int | float) -> int:
+def validate_periods(periods: float) -> int:
     ...
 
 
-def validate_periods(periods: int | float | None) -> int | None:
+def validate_periods(periods: float | None) -> int | None:
     """
     If a `periods` argument is passed to the Datetime/Timedelta Array/Index
     constructor, cast it to an integer.

pandas/core/frame.py

@@ -2563,7 +2563,7 @@ def to_stata(
         compression: CompressionOptions = "infer",
         storage_options: StorageOptions = None,
         *,
-        value_labels: dict[Hashable, dict[float | int, str]] | None = None,
+        value_labels: dict[Hashable, dict[float, str]] | None = None,
     ) -> None:
         """
         Export DataFrame object to Stata dta format.

pandas/core/nanops.py

@@ -827,7 +827,7 @@ def _get_counts_nanvar(
     axis: int | None,
     ddof: int,
     dtype: np.dtype = np.dtype(np.float64),
-) -> tuple[int | float | np.ndarray, int | float | np.ndarray]:
+) -> tuple[float | np.ndarray, float | np.ndarray]:
     """
     Get the count of non-null values along an axis, accounting
     for degrees of freedom.
@@ -1414,7 +1414,7 @@ def _get_counts(
     mask: npt.NDArray[np.bool_] | None,
     axis: int | None,
     dtype: np.dtype = np.dtype(np.float64),
-) -> int | float | np.ndarray:
+) -> float | np.ndarray:
     """
     Get the count of non-null values along an axis
 

pandas/core/reshape/encoding.py

@@ -272,7 +272,7 @@ def get_empty_frame(data) -> DataFrame:
 
     if sparse:
 
-        fill_value: bool | float | int
+        fill_value: bool | float
         if is_integer_dtype(dtype):
             fill_value = 0
         elif dtype == np.dtype(bool):

pandas/core/tools/datetimes.py

@@ -91,7 +91,7 @@
 # types used in annotations
 
 ArrayConvertible = Union[List, Tuple, AnyArrayLike]
-Scalar = Union[int, float, str]
+Scalar = Union[float, str]
 DatetimeScalar = Union[Scalar, datetime]
 
 DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]

pandas/core/tools/timedeltas.py

@@ -45,7 +45,7 @@
 
 @overload
 def to_timedelta(
-    arg: str | int | float | timedelta,
+    arg: str | float | timedelta,
     unit: UnitChoices | None = ...,
     errors: DateTimeErrorChoices = ...,
 ) -> Timedelta:

pandas/io/formats/excel.py

@@ -331,7 +331,7 @@ def build_number_format(self, props: Mapping[str, str]) -> dict[str, str | None]
 
     def build_font(
         self, props: Mapping[str, str]
-    ) -> dict[str, bool | int | float | str | None]:
+    ) -> dict[str, bool | float | str | None]:
         font_names = self._get_font_names(props)
         decoration = self._get_decoration(props)
         return {

pandas/io/formats/format.py

@@ -1447,7 +1447,7 @@ def __init__(self, *args, **kwargs) -> None:
     def _value_formatter(
         self,
         float_format: FloatFormatType | None = None,
-        threshold: float | int | None = None,
+        threshold: float | None = None,
     ) -> Callable:
         """Returns a function to be applied on each value to format it"""
         # the float_format parameter supersedes self.float_format
@@ -2047,7 +2047,7 @@ def __init__(
         self.accuracy = accuracy
         self.use_eng_prefix = use_eng_prefix
 
-    def __call__(self, num: int | float) -> str:
+    def __call__(self, num: float) -> str:
         """
         Formats a number in engineering notation, appending a letter
         representing the power of 1000 of the original number. Some examples:

pandas/io/formats/info.py

@@ -325,7 +325,7 @@ def _put_str(s: str | Dtype, space: int) -> str:
     return str(s)[:space].ljust(space)
 
 
-def _sizeof_fmt(num: int | float, size_qualifier: str) -> str:
+def _sizeof_fmt(num: float, size_qualifier: str) -> str:
     """
     Return size in human readable format.
 

pandas/io/formats/style.py

@@ -3213,7 +3213,7 @@ def bar(
         cmap: Any | None = None,
         width: float = 100,
         height: float = 100,
-        align: str | float | int | Callable = "mid",
+        align: str | float | Callable = "mid",
         vmin: float | None = None,
         vmax: float | None = None,
         props: str = "width: 10em;",
@@ -4039,7 +4039,7 @@ def _highlight_value(data: DataFrame | Series, op: str, props: str) -> np.ndarra
 
 def _bar(
     data: NDFrame,
-    align: str | float | int | Callable,
+    align: str | float | Callable,
     colors: str | list | tuple,
     cmap: Any,
     width: float,

pandas/io/formats/style_render.py

@@ -48,7 +48,7 @@
 
 BaseFormatter = Union[str, Callable]
 ExtFormatter = Union[BaseFormatter, Dict[Any, Optional[BaseFormatter]]]
-CSSPair = Tuple[str, Union[str, int, float]]
+CSSPair = Tuple[str, Union[str, float]]
 CSSList = List[CSSPair]
 CSSProperties = Union[str, CSSList]
 
@@ -2055,7 +2055,7 @@ def _parse_latex_header_span(
         return display_val
 
 
-def _parse_latex_options_strip(value: str | int | float, arg: str) -> str:
+def _parse_latex_options_strip(value: str | float, arg: str) -> str:
     """
     Strip a css_value which may have latex wrapping arguments, css comment identifiers,
     and whitespaces, to a valid string for latex options parsing.

pandas/io/parsers/readers.py

@@ -499,7 +499,7 @@ def validate_integer(name, val: None, min_val=...) -> None:
 
 
 @overload
-def validate_integer(name, val: int | float, min_val=...) -> int:
+def validate_integer(name, val: float, min_val=...) -> int:
     ...
 
 
@@ -1910,7 +1910,7 @@ def _floatify_na_values(na_values):
 
 def _stringify_na_values(na_values):
     """return a stringified and numeric for these values"""
-    result: list[int | str | float] = []
+    result: list[str | float] = []
     for x in na_values:
         result.append(str(x))
         result.append(x)