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