183 行
6.0 KiB
Python
183 行
6.0 KiB
Python
from typing import Literal
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import numpy as np
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from pandas._typing import npt
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def group_median_float64(
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out: np.ndarray, # ndarray[float64_t, ndim=2]
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counts: npt.NDArray[np.int64],
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values: np.ndarray, # ndarray[float64_t, ndim=2]
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labels: npt.NDArray[np.int64],
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min_count: int = ..., # Py_ssize_t
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) -> None: ...
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def group_cumprod_float64(
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out: np.ndarray, # float64_t[:, ::1]
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values: np.ndarray, # const float64_t[:, :]
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labels: np.ndarray, # const int64_t[:]
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ngroups: int,
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is_datetimelike: bool,
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skipna: bool = ...,
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) -> None: ...
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def group_cumsum(
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out: np.ndarray, # int64float_t[:, ::1]
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values: np.ndarray, # ndarray[int64float_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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ngroups: int,
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is_datetimelike: bool,
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skipna: bool = ...,
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mask: np.ndarray | None = ...,
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result_mask: np.ndarray | None = ...,
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) -> None: ...
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def group_shift_indexer(
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out: np.ndarray, # int64_t[::1]
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labels: np.ndarray, # const int64_t[:]
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ngroups: int,
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periods: int,
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) -> None: ...
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def group_fillna_indexer(
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out: np.ndarray, # ndarray[intp_t]
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labels: np.ndarray, # ndarray[int64_t]
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sorted_labels: npt.NDArray[np.intp],
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mask: npt.NDArray[np.uint8],
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direction: Literal["ffill", "bfill"],
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limit: int, # int64_t
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dropna: bool,
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) -> None: ...
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def group_any_all(
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out: np.ndarray, # uint8_t[::1]
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values: np.ndarray, # const uint8_t[::1]
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labels: np.ndarray, # const int64_t[:]
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mask: np.ndarray, # const uint8_t[::1]
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val_test: Literal["any", "all"],
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skipna: bool,
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) -> None: ...
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def group_sum(
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out: np.ndarray, # complexfloatingintuint_t[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[complexfloatingintuint_t, ndim=2]
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labels: np.ndarray, # const intp_t[:]
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mask: np.ndarray | None,
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result_mask: np.ndarray | None = ...,
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min_count: int = ...,
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is_datetimelike: bool = ...,
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) -> None: ...
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def group_prod(
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out: np.ndarray, # int64float_t[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[int64float_t, ndim=2]
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labels: np.ndarray, # const intp_t[:]
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mask: np.ndarray | None,
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result_mask: np.ndarray | None = ...,
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min_count: int = ...,
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) -> None: ...
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def group_var(
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out: np.ndarray, # floating[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[floating, ndim=2]
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labels: np.ndarray, # const intp_t[:]
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min_count: int = ..., # Py_ssize_t
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ddof: int = ..., # int64_t
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) -> None: ...
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def group_mean(
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out: np.ndarray, # floating[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[floating, ndim=2]
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labels: np.ndarray, # const intp_t[:]
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min_count: int = ..., # Py_ssize_t
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is_datetimelike: bool = ..., # bint
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mask: np.ndarray | None = ...,
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result_mask: np.ndarray | None = ...,
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) -> None: ...
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def group_ohlc(
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out: np.ndarray, # floatingintuint_t[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[floatingintuint_t, ndim=2]
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labels: np.ndarray, # const intp_t[:]
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min_count: int = ...,
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mask: np.ndarray | None = ...,
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result_mask: np.ndarray | None = ...,
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) -> None: ...
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def group_quantile(
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out: npt.NDArray[np.float64],
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values: np.ndarray, # ndarray[numeric, ndim=1]
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labels: npt.NDArray[np.intp],
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mask: npt.NDArray[np.uint8],
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sort_indexer: npt.NDArray[np.intp], # const
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qs: npt.NDArray[np.float64], # const
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interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
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) -> None: ...
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def group_last(
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out: np.ndarray, # rank_t[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[rank_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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mask: npt.NDArray[np.bool_] | None,
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result_mask: npt.NDArray[np.bool_] | None = ...,
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min_count: int = ..., # Py_ssize_t
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is_datetimelike: bool = ...,
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) -> None: ...
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def group_nth(
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out: np.ndarray, # rank_t[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[rank_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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mask: npt.NDArray[np.bool_] | None,
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result_mask: npt.NDArray[np.bool_] | None = ...,
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min_count: int = ..., # int64_t
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rank: int = ..., # int64_t
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is_datetimelike: bool = ...,
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) -> None: ...
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def group_rank(
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out: np.ndarray, # float64_t[:, ::1]
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values: np.ndarray, # ndarray[rank_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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ngroups: int,
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is_datetimelike: bool,
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ties_method: Literal["average", "min", "max", "first", "dense"] = ...,
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ascending: bool = ...,
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pct: bool = ...,
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na_option: Literal["keep", "top", "bottom"] = ...,
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mask: npt.NDArray[np.bool_] | None = ...,
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) -> None: ...
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def group_max(
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out: np.ndarray, # groupby_t[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[groupby_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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min_count: int = ...,
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is_datetimelike: bool = ...,
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mask: np.ndarray | None = ...,
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result_mask: np.ndarray | None = ...,
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) -> None: ...
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def group_min(
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out: np.ndarray, # groupby_t[:, ::1]
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counts: np.ndarray, # int64_t[::1]
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values: np.ndarray, # ndarray[groupby_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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min_count: int = ...,
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is_datetimelike: bool = ...,
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mask: np.ndarray | None = ...,
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result_mask: np.ndarray | None = ...,
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) -> None: ...
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def group_cummin(
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out: np.ndarray, # groupby_t[:, ::1]
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values: np.ndarray, # ndarray[groupby_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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ngroups: int,
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is_datetimelike: bool,
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mask: np.ndarray | None = ...,
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result_mask: np.ndarray | None = ...,
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skipna: bool = ...,
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) -> None: ...
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def group_cummax(
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out: np.ndarray, # groupby_t[:, ::1]
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values: np.ndarray, # ndarray[groupby_t, ndim=2]
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labels: np.ndarray, # const int64_t[:]
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ngroups: int,
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is_datetimelike: bool,
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mask: np.ndarray | None = ...,
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result_mask: np.ndarray | None = ...,
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skipna: bool = ...,
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) -> None: ...
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