done

lib/python3.11/site-packages/pandas-stubs/core/base.pyi

@@ -0,0 +1,142 @@
+from collections.abc import (
+    Hashable,
+    Iterator,
+)
+from typing import (
+    Any,
+    Generic,
+    Literal,
+    final,
+    overload,
+)
+
+import numpy as np
+from pandas import (
+    Index,
+    Series,
+)
+from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays import ExtensionArray
+from pandas.core.arrays.categorical import Categorical
+from typing_extensions import Self
+
+from pandas._typing import (
+    S1,
+    AxisIndex,
+    DropKeep,
+    DTypeLike,
+    GenericT,
+    GenericT_co,
+    NDFrameT,
+    Scalar,
+    SupportsDType,
+    np_1darray,
+)
+from pandas.util._decorators import cache_readonly
+
+class NoNewAttributesMixin:
+    def __setattr__(self, key: str, value: Any) -> None: ...
+
+class SelectionMixin(Generic[NDFrameT]):
+    obj: NDFrameT
+    exclusions: frozenset[Hashable]
+    @final
+    @cache_readonly
+    def ndim(self) -> int: ...
+    def __getitem__(self, key): ...
+    def aggregate(self, func, *args, **kwargs): ...
+
+class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
+    __array_priority__: int = ...
+    @property
+    def T(self) -> Self: ...
+    @property
+    def shape(self) -> tuple: ...
+    @property
+    def ndim(self) -> int: ...
+    def item(self) -> S1: ...
+    @property
+    def nbytes(self) -> int: ...
+    @property
+    def size(self) -> int: ...
+    @property
+    def array(self) -> ExtensionArray: ...
+    @overload
+    def to_numpy(
+        self,
+        dtype: None = None,
+        copy: bool = False,
+        na_value: Scalar = ...,
+        **kwargs,
+    ) -> np_1darray[GenericT_co]: ...
+    @overload
+    def to_numpy(
+        self,
+        dtype: np.dtype[GenericT] | SupportsDType[GenericT] | type[GenericT],
+        copy: bool = False,
+        na_value: Scalar = ...,
+        **kwargs,
+    ) -> np_1darray[GenericT]: ...
+    @overload
+    def to_numpy(
+        self,
+        dtype: DTypeLike,
+        copy: bool = False,
+        na_value: Scalar = ...,
+        **kwargs,
+    ) -> np_1darray: ...
+    @property
+    def empty(self) -> bool: ...
+    def max(self, axis=..., skipna: bool = ..., **kwargs): ...
+    def min(self, axis=..., skipna: bool = ..., **kwargs): ...
+    def argmax(
+        self,
+        axis: AxisIndex | None = ...,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ) -> np.int64: ...
+    def argmin(
+        self,
+        axis: AxisIndex | None = ...,
+        skipna: bool = True,
+        *args,
+        **kwargs,
+    ) -> np.int64: ...
+    def tolist(self) -> list[S1]: ...
+    def to_list(self) -> list[S1]: ...
+    def __iter__(self) -> Iterator[S1]: ...
+    @property
+    def hasnans(self) -> bool: ...
+    @overload
+    def value_counts(
+        self,
+        normalize: Literal[False] = ...,
+        sort: bool = ...,
+        ascending: bool = ...,
+        bins=...,
+        dropna: bool = ...,
+    ) -> Series[int]: ...
+    @overload
+    def value_counts(
+        self,
+        normalize: Literal[True],
+        sort: bool = ...,
+        ascending: bool = ...,
+        bins=...,
+        dropna: bool = ...,
+    ) -> Series[float]: ...
+    def nunique(self, dropna: bool = True) -> int: ...
+    @property
+    def is_unique(self) -> bool: ...
+    @property
+    def is_monotonic_decreasing(self) -> bool: ...
+    @property
+    def is_monotonic_increasing(self) -> bool: ...
+    def factorize(
+        self, sort: bool = False, use_na_sentinel: bool = True
+    ) -> tuple[np_1darray, np_1darray | Index | Categorical]: ...
+    def searchsorted(
+        self, value, side: Literal["left", "right"] = ..., sorter=...
+    ) -> int | list[int]: ...
+    def drop_duplicates(self, *, keep: DropKeep = ...) -> Self: ...