374 lines
10 KiB
Python
374 lines
10 KiB
Python
from __future__ import annotations
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from datetime import (
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datetime,
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timedelta,
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tzinfo,
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)
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from os import PathLike
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Dict,
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Hashable,
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Iterator,
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List,
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Literal,
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Mapping,
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Optional,
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Protocol,
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Sequence,
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Tuple,
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Type as type_t,
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TypeVar,
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Union,
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)
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import numpy as np
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# To prevent import cycles place any internal imports in the branch below
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# and use a string literal forward reference to it in subsequent types
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# https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles
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if TYPE_CHECKING:
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import numpy.typing as npt
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from pandas._libs import (
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NaTType,
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Period,
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Timedelta,
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Timestamp,
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)
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from pandas._libs.tslibs import BaseOffset
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from pandas.core.dtypes.dtypes import ExtensionDtype
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from pandas import Interval
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from pandas.arrays import (
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DatetimeArray,
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TimedeltaArray,
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)
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from pandas.core.arrays.base import ExtensionArray
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from pandas.core.frame import DataFrame
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from pandas.core.generic import NDFrame
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from pandas.core.groupby.generic import (
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DataFrameGroupBy,
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GroupBy,
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SeriesGroupBy,
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)
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from pandas.core.indexes.base import Index
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from pandas.core.internals import (
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ArrayManager,
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BlockManager,
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SingleArrayManager,
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SingleBlockManager,
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)
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from pandas.core.resample import Resampler
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from pandas.core.series import Series
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from pandas.core.window.rolling import BaseWindow
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from pandas.io.formats.format import EngFormatter
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ScalarLike_co = Union[
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int,
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float,
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complex,
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str,
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bytes,
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np.generic,
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]
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# numpy compatible types
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NumpyValueArrayLike = Union[ScalarLike_co, npt.ArrayLike]
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# Name "npt._ArrayLikeInt_co" is not defined [name-defined]
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NumpySorter = Optional[npt._ArrayLikeInt_co] # type: ignore[name-defined]
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else:
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npt: Any = None
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HashableT = TypeVar("HashableT", bound=Hashable)
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# array-like
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ArrayLike = Union["ExtensionArray", np.ndarray]
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AnyArrayLike = Union[ArrayLike, "Index", "Series"]
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TimeArrayLike = Union["DatetimeArray", "TimedeltaArray"]
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# scalars
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PythonScalar = Union[str, float, bool]
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DatetimeLikeScalar = Union["Period", "Timestamp", "Timedelta"]
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PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"]
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Scalar = Union[PythonScalar, PandasScalar, np.datetime64, np.timedelta64, datetime]
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IntStrT = TypeVar("IntStrT", int, str)
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# timestamp and timedelta convertible types
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TimestampConvertibleTypes = Union[
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"Timestamp", datetime, np.datetime64, np.int64, float, str
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]
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TimedeltaConvertibleTypes = Union[
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"Timedelta", timedelta, np.timedelta64, np.int64, float, str
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]
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Timezone = Union[str, tzinfo]
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# NDFrameT is stricter and ensures that the same subclass of NDFrame always is
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# used. E.g. `def func(a: NDFrameT) -> NDFrameT: ...` means that if a
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# Series is passed into a function, a Series is always returned and if a DataFrame is
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# passed in, a DataFrame is always returned.
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NDFrameT = TypeVar("NDFrameT", bound="NDFrame")
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NumpyIndexT = TypeVar("NumpyIndexT", np.ndarray, "Index")
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AxisInt = int
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Axis = Union[AxisInt, Literal["index", "columns", "rows"]]
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IndexLabel = Union[Hashable, Sequence[Hashable]]
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Level = Hashable
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Shape = Tuple[int, ...]
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Suffixes = Tuple[Optional[str], Optional[str]]
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Ordered = Optional[bool]
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JSONSerializable = Optional[Union[PythonScalar, List, Dict]]
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Frequency = Union[str, "BaseOffset"]
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Axes = Union[AnyArrayLike, List, range]
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RandomState = Union[
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int,
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ArrayLike,
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np.random.Generator,
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np.random.BitGenerator,
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np.random.RandomState,
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]
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# dtypes
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NpDtype = Union[str, np.dtype, type_t[Union[str, complex, bool, object]]]
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Dtype = Union["ExtensionDtype", NpDtype]
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AstypeArg = Union["ExtensionDtype", "npt.DTypeLike"]
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# DtypeArg specifies all allowable dtypes in a functions its dtype argument
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DtypeArg = Union[Dtype, Dict[Hashable, Dtype]]
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DtypeObj = Union[np.dtype, "ExtensionDtype"]
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# converters
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ConvertersArg = Dict[Hashable, Callable[[Dtype], Dtype]]
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# parse_dates
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ParseDatesArg = Union[
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bool, List[Hashable], List[List[Hashable]], Dict[Hashable, List[Hashable]]
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]
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# For functions like rename that convert one label to another
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Renamer = Union[Mapping[Any, Hashable], Callable[[Any], Hashable]]
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# to maintain type information across generic functions and parametrization
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T = TypeVar("T")
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# used in decorators to preserve the signature of the function it decorates
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# see https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators
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FuncType = Callable[..., Any]
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F = TypeVar("F", bound=FuncType)
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# types of vectorized key functions for DataFrame::sort_values and
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# DataFrame::sort_index, among others
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ValueKeyFunc = Optional[Callable[["Series"], Union["Series", AnyArrayLike]]]
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IndexKeyFunc = Optional[Callable[["Index"], Union["Index", AnyArrayLike]]]
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# types of `func` kwarg for DataFrame.aggregate and Series.aggregate
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AggFuncTypeBase = Union[Callable, str]
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AggFuncTypeDict = Dict[Hashable, Union[AggFuncTypeBase, List[AggFuncTypeBase]]]
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AggFuncType = Union[
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AggFuncTypeBase,
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List[AggFuncTypeBase],
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AggFuncTypeDict,
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]
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AggObjType = Union[
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"Series",
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"DataFrame",
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"GroupBy",
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"SeriesGroupBy",
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"DataFrameGroupBy",
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"BaseWindow",
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"Resampler",
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]
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PythonFuncType = Callable[[Any], Any]
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# filenames and file-like-objects
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AnyStr_co = TypeVar("AnyStr_co", str, bytes, covariant=True)
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AnyStr_contra = TypeVar("AnyStr_contra", str, bytes, contravariant=True)
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class BaseBuffer(Protocol):
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@property
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def mode(self) -> str:
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# for _get_filepath_or_buffer
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...
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def seek(self, __offset: int, __whence: int = ...) -> int:
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# with one argument: gzip.GzipFile, bz2.BZ2File
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# with two arguments: zip.ZipFile, read_sas
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...
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def seekable(self) -> bool:
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# for bz2.BZ2File
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...
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def tell(self) -> int:
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# for zip.ZipFile, read_stata, to_stata
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...
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class ReadBuffer(BaseBuffer, Protocol[AnyStr_co]):
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def read(self, __n: int = ...) -> AnyStr_co:
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# for BytesIOWrapper, gzip.GzipFile, bz2.BZ2File
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...
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class WriteBuffer(BaseBuffer, Protocol[AnyStr_contra]):
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def write(self, __b: AnyStr_contra) -> Any:
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# for gzip.GzipFile, bz2.BZ2File
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...
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def flush(self) -> Any:
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# for gzip.GzipFile, bz2.BZ2File
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...
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class ReadPickleBuffer(ReadBuffer[bytes], Protocol):
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def readline(self) -> bytes:
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...
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class WriteExcelBuffer(WriteBuffer[bytes], Protocol):
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def truncate(self, size: int | None = ...) -> int:
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...
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class ReadCsvBuffer(ReadBuffer[AnyStr_co], Protocol):
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def __iter__(self) -> Iterator[AnyStr_co]:
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# for engine=python
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...
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def fileno(self) -> int:
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# for _MMapWrapper
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...
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def readline(self) -> AnyStr_co:
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# for engine=python
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...
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@property
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def closed(self) -> bool:
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# for enine=pyarrow
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...
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FilePath = Union[str, "PathLike[str]"]
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# for arbitrary kwargs passed during reading/writing files
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StorageOptions = Optional[Dict[str, Any]]
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# compression keywords and compression
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CompressionDict = Dict[str, Any]
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CompressionOptions = Optional[
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Union[Literal["infer", "gzip", "bz2", "zip", "xz", "zstd", "tar"], CompressionDict]
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]
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# types in DataFrameFormatter
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FormattersType = Union[
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List[Callable], Tuple[Callable, ...], Mapping[Union[str, int], Callable]
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]
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ColspaceType = Mapping[Hashable, Union[str, int]]
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FloatFormatType = Union[str, Callable, "EngFormatter"]
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ColspaceArgType = Union[
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str, int, Sequence[Union[str, int]], Mapping[Hashable, Union[str, int]]
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]
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# Arguments for fillna()
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FillnaOptions = Literal["backfill", "bfill", "ffill", "pad"]
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# internals
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Manager = Union[
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"ArrayManager", "SingleArrayManager", "BlockManager", "SingleBlockManager"
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]
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SingleManager = Union["SingleArrayManager", "SingleBlockManager"]
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Manager2D = Union["ArrayManager", "BlockManager"]
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# indexing
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# PositionalIndexer -> valid 1D positional indexer, e.g. can pass
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# to ndarray.__getitem__
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# ScalarIndexer is for a single value as the index
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# SequenceIndexer is for list like or slices (but not tuples)
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# PositionalIndexerTuple is extends the PositionalIndexer for 2D arrays
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# These are used in various __getitem__ overloads
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# TODO(typing#684): add Ellipsis, see
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# https://github.com/python/typing/issues/684#issuecomment-548203158
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# https://bugs.python.org/issue41810
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# Using List[int] here rather than Sequence[int] to disallow tuples.
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ScalarIndexer = Union[int, np.integer]
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SequenceIndexer = Union[slice, List[int], np.ndarray]
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PositionalIndexer = Union[ScalarIndexer, SequenceIndexer]
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PositionalIndexerTuple = Tuple[PositionalIndexer, PositionalIndexer]
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PositionalIndexer2D = Union[PositionalIndexer, PositionalIndexerTuple]
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if TYPE_CHECKING:
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TakeIndexer = Union[Sequence[int], Sequence[np.integer], npt.NDArray[np.integer]]
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else:
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TakeIndexer = Any
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# Shared by functions such as drop and astype
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IgnoreRaise = Literal["ignore", "raise"]
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# Windowing rank methods
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WindowingRankType = Literal["average", "min", "max"]
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# read_csv engines
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CSVEngine = Literal["c", "python", "pyarrow", "python-fwf"]
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# read_json engines
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JSONEngine = Literal["ujson", "pyarrow"]
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# read_xml parsers
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XMLParsers = Literal["lxml", "etree"]
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# Interval closed type
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IntervalLeftRight = Literal["left", "right"]
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IntervalClosedType = Union[IntervalLeftRight, Literal["both", "neither"]]
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# datetime and NaTType
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DatetimeNaTType = Union[datetime, "NaTType"]
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DateTimeErrorChoices = Union[IgnoreRaise, Literal["coerce"]]
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# sort_index
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SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
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NaPosition = Literal["first", "last"]
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# quantile interpolation
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QuantileInterpolation = Literal["linear", "lower", "higher", "midpoint", "nearest"]
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# plotting
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PlottingOrientation = Literal["horizontal", "vertical"]
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# dropna
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AnyAll = Literal["any", "all"]
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# merge
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MergeHow = Literal["left", "right", "inner", "outer", "cross"]
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# join
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JoinHow = Literal["left", "right", "inner", "outer"]
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MatplotlibColor = Union[str, Sequence[float]]
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TimeGrouperOrigin = Union[
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"Timestamp", Literal["epoch", "start", "start_day", "end", "end_day"]
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]
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TimeAmbiguous = Union[Literal["infer", "NaT", "raise"], "npt.NDArray[np.bool_]"]
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TimeNonexistent = Union[
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Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta
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]
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DropKeep = Literal["first", "last", False]
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CorrelationMethod = Union[
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Literal["pearson", "kendall", "spearman"], Callable[[np.ndarray, np.ndarray], float]
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]
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AlignJoin = Literal["outer", "inner", "left", "right"]
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DtypeBackend = Literal["pyarrow", "numpy_nullable"]
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