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from typing import (
Any,
Hashable,
Literal,
)
import numpy as np
from pandas._typing import npt
def unique_label_indices(
labels: np.ndarray, # const int64_t[:]
) -> np.ndarray: ...
class Factorizer:
count: int
uniques: Any
def __init__(self, size_hint: int) -> None: ...
def get_count(self) -> int: ...
def factorize(
self,
values: np.ndarray,
na_sentinel=...,
na_value=...,
mask=...,
) -> npt.NDArray[np.intp]: ...
class ObjectFactorizer(Factorizer):
table: PyObjectHashTable
uniques: ObjectVector
class Int64Factorizer(Factorizer):
table: Int64HashTable
uniques: Int64Vector
class UInt64Factorizer(Factorizer):
table: UInt64HashTable
uniques: UInt64Vector
class Int32Factorizer(Factorizer):
table: Int32HashTable
uniques: Int32Vector
class UInt32Factorizer(Factorizer):
table: UInt32HashTable
uniques: UInt32Vector
class Int16Factorizer(Factorizer):
table: Int16HashTable
uniques: Int16Vector
class UInt16Factorizer(Factorizer):
table: UInt16HashTable
uniques: UInt16Vector
class Int8Factorizer(Factorizer):
table: Int8HashTable
uniques: Int8Vector
class UInt8Factorizer(Factorizer):
table: UInt8HashTable
uniques: UInt8Vector
class Float64Factorizer(Factorizer):
table: Float64HashTable
uniques: Float64Vector
class Float32Factorizer(Factorizer):
table: Float32HashTable
uniques: Float32Vector
class Complex64Factorizer(Factorizer):
table: Complex64HashTable
uniques: Complex64Vector
class Complex128Factorizer(Factorizer):
table: Complex128HashTable
uniques: Complex128Vector
class Int64Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.int64]: ...
class Int32Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.int32]: ...
class Int16Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.int16]: ...
class Int8Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.int8]: ...
class UInt64Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.uint64]: ...
class UInt32Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.uint32]: ...
class UInt16Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.uint16]: ...
class UInt8Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.uint8]: ...
class Float64Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.float64]: ...
class Float32Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.float32]: ...
class Complex128Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.complex128]: ...
class Complex64Vector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.complex64]: ...
class StringVector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.object_]: ...
class ObjectVector:
def __init__(self, *args) -> None: ...
def __len__(self) -> int: ...
def to_array(self) -> npt.NDArray[np.object_]: ...
class HashTable:
# NB: The base HashTable class does _not_ actually have these methods;
# we are putting them here for the sake of mypy to avoid
# reproducing them in each subclass below.
def __init__(self, size_hint: int = ..., uses_mask: bool = ...) -> None: ...
def __len__(self) -> int: ...
def __contains__(self, key: Hashable) -> bool: ...
def sizeof(self, deep: bool = ...) -> int: ...
def get_state(self) -> dict[str, int]: ...
# TODO: `val/key` type is subclass-specific
def get_item(self, val): ... # TODO: return type?
def set_item(self, key, val) -> None: ...
def get_na(self): ... # TODO: return type?
def set_na(self, val) -> None: ...
def map_locations(
self,
values: np.ndarray, # np.ndarray[subclass-specific]
mask: npt.NDArray[np.bool_] | None = ...,
) -> None: ...
def lookup(
self,
values: np.ndarray, # np.ndarray[subclass-specific]
mask: npt.NDArray[np.bool_] | None = ...,
) -> npt.NDArray[np.intp]: ...
def get_labels(
self,
values: np.ndarray, # np.ndarray[subclass-specific]
uniques, # SubclassTypeVector
count_prior: int = ...,
na_sentinel: int = ...,
na_value: object = ...,
mask=...,
) -> npt.NDArray[np.intp]: ...
def unique(
self,
values: np.ndarray, # np.ndarray[subclass-specific]
return_inverse: bool = ...,
mask=...,
) -> (
tuple[
np.ndarray, # np.ndarray[subclass-specific]
npt.NDArray[np.intp],
]
| np.ndarray
): ... # np.ndarray[subclass-specific]
def factorize(
self,
values: np.ndarray, # np.ndarray[subclass-specific]
na_sentinel: int = ...,
na_value: object = ...,
mask=...,
ignore_na: bool = True,
) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ... # np.ndarray[subclass-specific]
class Complex128HashTable(HashTable): ...
class Complex64HashTable(HashTable): ...
class Float64HashTable(HashTable): ...
class Float32HashTable(HashTable): ...
class Int64HashTable(HashTable):
# Only Int64HashTable has get_labels_groupby, map_keys_to_values
def get_labels_groupby(
self,
values: npt.NDArray[np.int64], # const int64_t[:]
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]: ...
def map_keys_to_values(
self,
keys: npt.NDArray[np.int64],
values: npt.NDArray[np.int64], # const int64_t[:]
) -> None: ...
class Int32HashTable(HashTable): ...
class Int16HashTable(HashTable): ...
class Int8HashTable(HashTable): ...
class UInt64HashTable(HashTable): ...
class UInt32HashTable(HashTable): ...
class UInt16HashTable(HashTable): ...
class UInt8HashTable(HashTable): ...
class StringHashTable(HashTable): ...
class PyObjectHashTable(HashTable): ...
class IntpHashTable(HashTable): ...
def duplicated(
values: np.ndarray,
keep: Literal["last", "first", False] = ...,
mask: npt.NDArray[np.bool_] | None = ...,
) -> npt.NDArray[np.bool_]: ...
def mode(
values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = ...
) -> np.ndarray: ...
def value_count(
values: np.ndarray,
dropna: bool,
mask: npt.NDArray[np.bool_] | None = ...,
) -> tuple[np.ndarray, npt.NDArray[np.int64], int]: ... # np.ndarray[same-as-values]
# arr and values should have same dtype
def ismember(
arr: np.ndarray,
values: np.ndarray,
) -> npt.NDArray[np.bool_]: ...
def object_hash(obj) -> int: ...
def objects_are_equal(a, b) -> bool: ...
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