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groupby.pyi
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from typing import Literal
import numpy as np
from pandas._typing import npt
def group_median_float64(
out: np.ndarray, # ndarray[float64_t, ndim=2]
counts: npt.NDArray[np.int64],
values: np.ndarray, # ndarray[float64_t, ndim=2]
labels: npt.NDArray[np.int64],
min_count: int = ..., # Py_ssize_t
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_cumprod(
out: np.ndarray, # float64_t[:, ::1]
values: np.ndarray, # const float64_t[:, :]
labels: np.ndarray, # const int64_t[:]
ngroups: int,
is_datetimelike: bool,
skipna: bool = ...,
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_cumsum(
out: np.ndarray, # int64float_t[:, ::1]
values: np.ndarray, # ndarray[int64float_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
ngroups: int,
is_datetimelike: bool,
skipna: bool = ...,
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_shift_indexer(
out: np.ndarray, # int64_t[::1]
labels: np.ndarray, # const int64_t[:]
ngroups: int,
periods: int,
) -> None: ...
def group_fillna_indexer(
out: np.ndarray, # ndarray[intp_t]
labels: np.ndarray, # ndarray[int64_t]
sorted_labels: npt.NDArray[np.intp],
mask: npt.NDArray[np.uint8],
direction: Literal["ffill", "bfill"],
limit: int, # int64_t
dropna: bool,
) -> None: ...
def group_any_all(
out: np.ndarray, # uint8_t[::1]
values: np.ndarray, # const uint8_t[::1]
labels: np.ndarray, # const int64_t[:]
mask: np.ndarray, # const uint8_t[::1]
val_test: Literal["any", "all"],
skipna: bool,
) -> None: ...
def group_sum(
out: np.ndarray, # complexfloatingintuint_t[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[complexfloatingintuint_t, ndim=2]
labels: np.ndarray, # const intp_t[:]
mask: np.ndarray | None,
result_mask: np.ndarray | None = ...,
min_count: int = ...,
is_datetimelike: bool = ...,
) -> None: ...
def group_prod(
out: np.ndarray, # int64float_t[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[int64float_t, ndim=2]
labels: np.ndarray, # const intp_t[:]
mask: np.ndarray | None,
result_mask: np.ndarray | None = ...,
min_count: int = ...,
) -> None: ...
def group_var(
out: np.ndarray, # floating[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[floating, ndim=2]
labels: np.ndarray, # const intp_t[:]
min_count: int = ..., # Py_ssize_t
ddof: int = ..., # int64_t
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_mean(
out: np.ndarray, # floating[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[floating, ndim=2]
labels: np.ndarray, # const intp_t[:]
min_count: int = ..., # Py_ssize_t
is_datetimelike: bool = ..., # bint
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_ohlc(
out: np.ndarray, # floatingintuint_t[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[floatingintuint_t, ndim=2]
labels: np.ndarray, # const intp_t[:]
min_count: int = ...,
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_quantile(
out: npt.NDArray[np.float64],
values: np.ndarray, # ndarray[numeric, ndim=1]
labels: npt.NDArray[np.intp],
mask: npt.NDArray[np.uint8],
sort_indexer: npt.NDArray[np.intp], # const
qs: npt.NDArray[np.float64], # const
interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_last(
out: np.ndarray, # rank_t[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[rank_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
mask: npt.NDArray[np.bool_] | None,
result_mask: npt.NDArray[np.bool_] | None = ...,
min_count: int = ..., # Py_ssize_t
is_datetimelike: bool = ...,
) -> None: ...
def group_nth(
out: np.ndarray, # rank_t[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[rank_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
mask: npt.NDArray[np.bool_] | None,
result_mask: npt.NDArray[np.bool_] | None = ...,
min_count: int = ..., # int64_t
rank: int = ..., # int64_t
is_datetimelike: bool = ...,
) -> None: ...
def group_rank(
out: np.ndarray, # float64_t[:, ::1]
values: np.ndarray, # ndarray[rank_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
ngroups: int,
is_datetimelike: bool,
ties_method: Literal["average", "min", "max", "first", "dense"] = ...,
ascending: bool = ...,
pct: bool = ...,
na_option: Literal["keep", "top", "bottom"] = ...,
mask: npt.NDArray[np.bool_] | None = ...,
) -> None: ...
def group_max(
out: np.ndarray, # groupby_t[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[groupby_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
min_count: int = ...,
is_datetimelike: bool = ...,
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_min(
out: np.ndarray, # groupby_t[:, ::1]
counts: np.ndarray, # int64_t[::1]
values: np.ndarray, # ndarray[groupby_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
min_count: int = ...,
is_datetimelike: bool = ...,
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
) -> None: ...
def group_cummin(
out: np.ndarray, # groupby_t[:, ::1]
values: np.ndarray, # ndarray[groupby_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
ngroups: int,
is_datetimelike: bool,
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
skipna: bool = ...,
) -> None: ...
def group_cummax(
out: np.ndarray, # groupby_t[:, ::1]
values: np.ndarray, # ndarray[groupby_t, ndim=2]
labels: np.ndarray, # const int64_t[:]
ngroups: int,
is_datetimelike: bool,
mask: np.ndarray | None = ...,
result_mask: np.ndarray | None = ...,
skipna: bool = ...,
) -> None: ...