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