ktongue's picture
download
raw
2.78 kB
import numpy as np
from pandas._typing import npt
def inner_join(
left: np.ndarray, # const intp_t[:]
right: np.ndarray, # const intp_t[:]
max_groups: int,
sort: bool = ...,
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
def left_outer_join(
left: np.ndarray, # const intp_t[:]
right: np.ndarray, # const intp_t[:]
max_groups: int,
sort: bool = ...,
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
def full_outer_join(
left: np.ndarray, # const intp_t[:]
right: np.ndarray, # const intp_t[:]
max_groups: int,
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
def ffill_indexer(
indexer: np.ndarray, # const intp_t[:]
) -> npt.NDArray[np.intp]: ...
def left_join_indexer_unique(
left: np.ndarray, # ndarray[join_t]
right: np.ndarray, # ndarray[join_t]
) -> npt.NDArray[np.intp]: ...
def left_join_indexer(
left: np.ndarray, # ndarray[join_t]
right: np.ndarray, # ndarray[join_t]
) -> tuple[
np.ndarray, # np.ndarray[join_t]
npt.NDArray[np.intp],
npt.NDArray[np.intp],
]: ...
def inner_join_indexer(
left: np.ndarray, # ndarray[join_t]
right: np.ndarray, # ndarray[join_t]
) -> tuple[
np.ndarray, # np.ndarray[join_t]
npt.NDArray[np.intp],
npt.NDArray[np.intp],
]: ...
def outer_join_indexer(
left: np.ndarray, # ndarray[join_t]
right: np.ndarray, # ndarray[join_t]
) -> tuple[
np.ndarray, # np.ndarray[join_t]
npt.NDArray[np.intp],
npt.NDArray[np.intp],
]: ...
def asof_join_backward_on_X_by_Y(
left_values: np.ndarray, # ndarray[numeric_t]
right_values: np.ndarray, # ndarray[numeric_t]
left_by_values: np.ndarray, # const int64_t[:]
right_by_values: np.ndarray, # const int64_t[:]
allow_exact_matches: bool = ...,
tolerance: np.number | float | None = ...,
use_hashtable: bool = ...,
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
def asof_join_forward_on_X_by_Y(
left_values: np.ndarray, # ndarray[numeric_t]
right_values: np.ndarray, # ndarray[numeric_t]
left_by_values: np.ndarray, # const int64_t[:]
right_by_values: np.ndarray, # const int64_t[:]
allow_exact_matches: bool = ...,
tolerance: np.number | float | None = ...,
use_hashtable: bool = ...,
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
def asof_join_nearest_on_X_by_Y(
left_values: np.ndarray, # ndarray[numeric_t]
right_values: np.ndarray, # ndarray[numeric_t]
left_by_values: np.ndarray, # const int64_t[:]
right_by_values: np.ndarray, # const int64_t[:]
allow_exact_matches: bool = ...,
tolerance: np.number | float | None = ...,
use_hashtable: bool = ...,
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...

Xet Storage Details

Size:
2.78 kB
·
Xet hash:
94a4250ec6654149868e6b02d37f1838d5fe964c39ebfbf0b492aa4bb25bba67

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.