School/.venv/lib/python3.9/site-packages/pandas/_typing.py

from datetime import (
    datetime,
    timedelta,
    tzinfo,
)
from io import (
    BufferedIOBase,
    RawIOBase,
    TextIOBase,
    TextIOWrapper,
)
from mmap import mmap
from os import PathLike
from typing import (
    IO,
    TYPE_CHECKING,
    Any,
    AnyStr,
    Callable,
    Collection,
    Dict,
    Hashable,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type as type_t,
    TypeVar,
    Union,
)

import numpy as np

# To prevent import cycles place any internal imports in the branch below
# and use a string literal forward reference to it in subsequent types
# https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
if TYPE_CHECKING:
    from typing import (
        Literal,
        TypedDict,
        final,
    )

    from pandas._libs import (
        Period,
        Timedelta,
        Timestamp,
    )

    from pandas.core.dtypes.dtypes import ExtensionDtype

    from pandas import Interval
    from pandas.core.arrays.base import ExtensionArray
    from pandas.core.frame import DataFrame
    from pandas.core.generic import NDFrame
    from pandas.core.groupby.generic import (
        DataFrameGroupBy,
        GroupBy,
        SeriesGroupBy,
    )
    from pandas.core.indexes.base import Index
    from pandas.core.internals import (
        ArrayManager,
        BlockManager,
        SingleArrayManager,
        SingleBlockManager,
    )
    from pandas.core.resample import Resampler
    from pandas.core.series import Series
    from pandas.core.window.rolling import BaseWindow

    from pandas.io.formats.format import EngFormatter
    from pandas.tseries.offsets import DateOffset
else:
    # typing.final does not exist until py38
    final = lambda x: x
    # typing.TypedDict does not exist until py38
    TypedDict = dict


# array-like

ArrayLike = Union["ExtensionArray", np.ndarray]
AnyArrayLike = Union[ArrayLike, "Index", "Series"]

# scalars

PythonScalar = Union[str, int, float, bool]
DatetimeLikeScalar = Union["Period", "Timestamp", "Timedelta"]
PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"]
Scalar = Union[PythonScalar, PandasScalar]

# timestamp and timedelta convertible types

TimestampConvertibleTypes = Union[
    "Timestamp", datetime, np.datetime64, int, np.int64, float, str
]
TimedeltaConvertibleTypes = Union[
    "Timedelta", timedelta, np.timedelta64, int, np.int64, float, str
]
Timezone = Union[str, tzinfo]

# FrameOrSeriesUnion  means either a DataFrame or a Series. E.g.
# `def func(a: FrameOrSeriesUnion) -> FrameOrSeriesUnion: ...` means that if a Series
# is passed in, either a Series or DataFrame is returned, and if a DataFrame is passed
# in, either a DataFrame or a Series is returned.
FrameOrSeriesUnion = Union["DataFrame", "Series"]

# FrameOrSeries is stricter and ensures that the same subclass of NDFrame always is
# used. E.g. `def func(a: FrameOrSeries) -> FrameOrSeries: ...` means that if a
# Series is passed into a function, a Series is always returned and if a DataFrame is
# passed in, a DataFrame is always returned.
FrameOrSeries = TypeVar("FrameOrSeries", bound="NDFrame")

Axis = Union[str, int]
IndexLabel = Union[Hashable, Sequence[Hashable]]
Level = Union[Hashable, int]
Shape = Tuple[int, ...]
Suffixes = Tuple[str, str]
Ordered = Optional[bool]
JSONSerializable = Optional[Union[PythonScalar, List, Dict]]
Frequency = Union[str, "DateOffset"]
Axes = Collection[Any]

# dtypes
NpDtype = Union[str, np.dtype]
Dtype = Union[
    "ExtensionDtype", NpDtype, type_t[Union[str, float, int, complex, bool, object]]
]
# DtypeArg specifies all allowable dtypes in a functions its dtype argument
DtypeArg = Union[Dtype, Dict[Hashable, Dtype]]
DtypeObj = Union[np.dtype, "ExtensionDtype"]

# For functions like rename that convert one label to another
Renamer = Union[Mapping[Hashable, Any], Callable[[Hashable], Hashable]]

# to maintain type information across generic functions and parametrization
T = TypeVar("T")

# used in decorators to preserve the signature of the function it decorates
# see https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
FuncType = Callable[..., Any]
F = TypeVar("F", bound=FuncType)

# types of vectorized key functions for DataFrame::sort_values and
# DataFrame::sort_index, among others
ValueKeyFunc = Optional[Callable[["Series"], Union["Series", AnyArrayLike]]]
IndexKeyFunc = Optional[Callable[["Index"], Union["Index", AnyArrayLike]]]

# types of `func` kwarg for DataFrame.aggregate and Series.aggregate
AggFuncTypeBase = Union[Callable, str]
AggFuncTypeDict = Dict[Hashable, Union[AggFuncTypeBase, List[AggFuncTypeBase]]]
AggFuncType = Union[
    AggFuncTypeBase,
    List[AggFuncTypeBase],
    AggFuncTypeDict,
]
AggObjType = Union[
    "Series",
    "DataFrame",
    "GroupBy",
    "SeriesGroupBy",
    "DataFrameGroupBy",
    "BaseWindow",
    "Resampler",
]

PythonFuncType = Callable[[Any], Any]

# filenames and file-like-objects
Buffer = Union[IO[AnyStr], RawIOBase, BufferedIOBase, TextIOBase, TextIOWrapper, mmap]
FileOrBuffer = Union[str, Buffer[AnyStr]]
FilePathOrBuffer = Union["PathLike[str]", FileOrBuffer[AnyStr]]

# for arbitrary kwargs passed during reading/writing files
StorageOptions = Optional[Dict[str, Any]]


# compression keywords and compression
CompressionDict = Dict[str, Any]
CompressionOptions = Optional[Union[str, CompressionDict]]


# types in DataFrameFormatter
FormattersType = Union[
    List[Callable], Tuple[Callable, ...], Mapping[Union[str, int], Callable]
]
ColspaceType = Mapping[Hashable, Union[str, int]]
FloatFormatType = Union[str, Callable, "EngFormatter"]
ColspaceArgType = Union[
    str, int, Sequence[Union[str, int]], Mapping[Hashable, Union[str, int]]
]

# Arguments for fillna()
if TYPE_CHECKING:
    FillnaOptions = Literal["backfill", "bfill", "ffill", "pad"]
else:
    FillnaOptions = str

# internals
Manager = Union[
    "ArrayManager", "SingleArrayManager", "BlockManager", "SingleBlockManager"
]
SingleManager = Union["SingleArrayManager", "SingleBlockManager"]
Manager2D = Union["ArrayManager", "BlockManager"]

# indexing
# PositionalIndexer -> valid 1D positional indexer, e.g. can pass
# to ndarray.__getitem__
# TODO: add Ellipsis, see
# https://github.com/python/typing/issues/684#issuecomment-548203158
# https://bugs.python.org/issue41810
PositionalIndexer = Union[int, np.integer, slice, Sequence[int], np.ndarray]
PositionalIndexer2D = Union[
    PositionalIndexer, Tuple[PositionalIndexer, PositionalIndexer]
]