234 行
7.5 KiB
Python
234 行
7.5 KiB
Python
# TODO(npdtypes): Many types specified here can be made more specific/accurate;
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# the more specific versions are specified in comments
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from typing import (
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Any,
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Callable,
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Final,
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Generator,
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Hashable,
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Literal,
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overload,
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)
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import numpy as np
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from pandas._typing import (
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ArrayLike,
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DtypeObj,
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npt,
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)
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# placeholder until we can specify np.ndarray[object, ndim=2]
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ndarray_obj_2d = np.ndarray
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from enum import Enum
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class _NoDefault(Enum):
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no_default = ...
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no_default: Final = _NoDefault.no_default
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NoDefault = Literal[_NoDefault.no_default]
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i8max: int
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u8max: int
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def item_from_zerodim(val: object) -> object: ...
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def infer_dtype(value: object, skipna: bool = ...) -> str: ...
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def is_iterator(obj: object) -> bool: ...
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def is_scalar(val: object) -> bool: ...
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def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
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def is_period(val: object) -> bool: ...
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def is_interval(val: object) -> bool: ...
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def is_decimal(val: object) -> bool: ...
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def is_complex(val: object) -> bool: ...
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def is_bool(val: object) -> bool: ...
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def is_integer(val: object) -> bool: ...
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def is_float(val: object) -> bool: ...
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def is_interval_array(values: np.ndarray) -> bool: ...
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def is_datetime64_array(values: np.ndarray) -> bool: ...
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def is_timedelta_or_timedelta64_array(values: np.ndarray) -> bool: ...
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def is_datetime_with_singletz_array(values: np.ndarray) -> bool: ...
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def is_time_array(values: np.ndarray, skipna: bool = ...): ...
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def is_date_array(values: np.ndarray, skipna: bool = ...): ...
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def is_datetime_array(values: np.ndarray, skipna: bool = ...): ...
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def is_string_array(values: np.ndarray, skipna: bool = ...): ...
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def is_float_array(values: np.ndarray, skipna: bool = ...): ...
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def is_integer_array(values: np.ndarray, skipna: bool = ...): ...
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def is_bool_array(values: np.ndarray, skipna: bool = ...): ...
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def fast_multiget(mapping: dict, keys: np.ndarray, default=...) -> np.ndarray: ...
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def fast_unique_multiple_list_gen(gen: Generator, sort: bool = ...) -> list: ...
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def fast_unique_multiple_list(lists: list, sort: bool | None = ...) -> list: ...
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def fast_unique_multiple(arrays: list, sort: bool = ...) -> list: ...
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def map_infer(
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arr: np.ndarray,
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f: Callable[[Any], Any],
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convert: bool = ...,
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ignore_na: bool = ...,
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) -> np.ndarray: ...
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@overload # both convert_datetime and convert_to_nullable_integer False -> np.ndarray
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def maybe_convert_objects(
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objects: npt.NDArray[np.object_],
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*,
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try_float: bool = ...,
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safe: bool = ...,
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convert_datetime: Literal[False] = ...,
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convert_timedelta: bool = ...,
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convert_period: Literal[False] = ...,
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convert_interval: Literal[False] = ...,
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convert_to_nullable_integer: Literal[False] = ...,
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dtype_if_all_nat: DtypeObj | None = ...,
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) -> np.ndarray: ...
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@overload
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def maybe_convert_objects(
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objects: npt.NDArray[np.object_],
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*,
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try_float: bool = ...,
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safe: bool = ...,
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convert_datetime: bool = ...,
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convert_timedelta: bool = ...,
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convert_period: bool = ...,
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convert_interval: bool = ...,
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convert_to_nullable_integer: Literal[True] = ...,
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dtype_if_all_nat: DtypeObj | None = ...,
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) -> ArrayLike: ...
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@overload
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def maybe_convert_objects(
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objects: npt.NDArray[np.object_],
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*,
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try_float: bool = ...,
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safe: bool = ...,
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convert_datetime: Literal[True] = ...,
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convert_timedelta: bool = ...,
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convert_period: bool = ...,
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convert_interval: bool = ...,
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convert_to_nullable_integer: bool = ...,
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dtype_if_all_nat: DtypeObj | None = ...,
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) -> ArrayLike: ...
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@overload
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def maybe_convert_objects(
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objects: npt.NDArray[np.object_],
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*,
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try_float: bool = ...,
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safe: bool = ...,
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convert_datetime: bool = ...,
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convert_timedelta: bool = ...,
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convert_period: Literal[True] = ...,
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convert_interval: bool = ...,
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convert_to_nullable_integer: bool = ...,
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dtype_if_all_nat: DtypeObj | None = ...,
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) -> ArrayLike: ...
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@overload
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def maybe_convert_objects(
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objects: npt.NDArray[np.object_],
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*,
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try_float: bool = ...,
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safe: bool = ...,
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convert_datetime: bool = ...,
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convert_timedelta: bool = ...,
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convert_period: bool = ...,
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convert_interval: bool = ...,
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convert_to_nullable_integer: bool = ...,
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dtype_if_all_nat: DtypeObj | None = ...,
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) -> ArrayLike: ...
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@overload
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def maybe_convert_numeric(
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values: npt.NDArray[np.object_],
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na_values: set,
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convert_empty: bool = ...,
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coerce_numeric: bool = ...,
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convert_to_masked_nullable: Literal[False] = ...,
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) -> tuple[np.ndarray, None]: ...
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@overload
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def maybe_convert_numeric(
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values: npt.NDArray[np.object_],
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na_values: set,
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convert_empty: bool = ...,
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coerce_numeric: bool = ...,
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*,
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convert_to_masked_nullable: Literal[True],
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) -> tuple[np.ndarray, np.ndarray]: ...
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# TODO: restrict `arr`?
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def ensure_string_array(
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arr,
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na_value: object = ...,
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convert_na_value: bool = ...,
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copy: bool = ...,
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skipna: bool = ...,
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) -> npt.NDArray[np.object_]: ...
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def infer_datetimelike_array(
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arr: npt.NDArray[np.object_],
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) -> tuple[str, bool]: ...
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def convert_nans_to_NA(
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arr: npt.NDArray[np.object_],
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) -> npt.NDArray[np.object_]: ...
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def fast_zip(ndarrays: list) -> npt.NDArray[np.object_]: ...
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# TODO: can we be more specific about rows?
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def to_object_array_tuples(rows: object) -> ndarray_obj_2d: ...
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def tuples_to_object_array(
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tuples: npt.NDArray[np.object_],
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) -> ndarray_obj_2d: ...
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# TODO: can we be more specific about rows?
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def to_object_array(rows: object, min_width: int = ...) -> ndarray_obj_2d: ...
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def dicts_to_array(dicts: list, columns: list) -> ndarray_obj_2d: ...
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def maybe_booleans_to_slice(
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mask: npt.NDArray[np.uint8],
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) -> slice | npt.NDArray[np.uint8]: ...
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def maybe_indices_to_slice(
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indices: npt.NDArray[np.intp],
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max_len: int,
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) -> slice | npt.NDArray[np.intp]: ...
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def is_all_arraylike(obj: list) -> bool: ...
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# -----------------------------------------------------------------
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# Functions which in reality take memoryviews
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def memory_usage_of_objects(arr: np.ndarray) -> int: ... # object[:] # np.int64
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def map_infer_mask(
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arr: np.ndarray,
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f: Callable[[Any], Any],
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mask: np.ndarray, # const uint8_t[:]
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convert: bool = ...,
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na_value: Any = ...,
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dtype: np.dtype = ...,
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) -> np.ndarray: ...
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def indices_fast(
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index: npt.NDArray[np.intp],
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labels: np.ndarray, # const int64_t[:]
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keys: list,
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sorted_labels: list[npt.NDArray[np.int64]],
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) -> dict[Hashable, npt.NDArray[np.intp]]: ...
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def generate_slices(
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labels: np.ndarray, ngroups: int # const intp_t[:]
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) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: ...
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def count_level_2d(
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mask: np.ndarray, # ndarray[uint8_t, ndim=2, cast=True],
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labels: np.ndarray, # const intp_t[:]
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max_bin: int,
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axis: int,
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) -> np.ndarray: ... # np.ndarray[np.int64, ndim=2]
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def get_level_sorter(
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label: np.ndarray, # const int64_t[:]
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starts: np.ndarray, # const intp_t[:]
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) -> np.ndarray: ... # np.ndarray[np.intp, ndim=1]
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def generate_bins_dt64(
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values: npt.NDArray[np.int64],
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binner: np.ndarray, # const int64_t[:]
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closed: object = ...,
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hasnans: bool = ...,
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) -> np.ndarray: ... # np.ndarray[np.int64, ndim=1]
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def array_equivalent_object(
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left: np.ndarray, # object[:]
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right: np.ndarray, # object[:]
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) -> bool: ...
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def has_infs(arr: np.ndarray) -> bool: ... # const floating[:]
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def get_reverse_indexer(
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indexer: np.ndarray, # const intp_t[:]
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length: int,
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) -> npt.NDArray[np.intp]: ...
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def is_bool_list(obj: list) -> bool: ...
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def dtypes_all_equal(types: list[DtypeObj]) -> bool: ...
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