# encoding: utf-8 # module pandas._libs.groupby # from D:\project\A_Board\.venv\Lib\site-packages\pandas\_libs\groupby.cp313-win_amd64.pyd # by generator 1.147 # no doc # imports import builtins as __builtins__ # import numpy as np # D:\project\A_Board\.venv\Lib\site-packages\numpy\__init__.py from pandas._libs.algos import (groupsort_indexer, rank_1d, take_2d_axis1_bool_bool, take_2d_axis1_float64_float64) # functions def group_any_all(*args, **kwargs): # real signature unknown """ Aggregated boolean values to show truthfulness of group elements. If the input is a nullable type (result_mask is not None), the result will be computed using Kleene logic. Parameters ---------- out : np.ndarray[np.int8] Values into which this method will write its results. labels : np.ndarray[np.intp] Array containing unique label for each group, with its ordering matching up to the corresponding record in `values` values : np.ndarray[np.int8] Containing the truth value of each element. mask : np.ndarray[np.uint8] Indicating whether a value is na or not. val_test : {'any', 'all'} String object dictating whether to use any or all truth testing skipna : bool Flag to ignore nan values during truth testing result_mask : ndarray[bool, ndim=2], optional If not None, these specify locations in the output that are NA. Modified in-place. Notes ----- This method modifies the `out` parameter rather than returning an object. The returned values will either be 0, 1 (False or True, respectively), or -1 to signify a masked position in the case of a nullable input. """ pass def group_cummax(*args, **kwargs): # real signature unknown """ See group_cummin_max.__doc__ """ pass def group_cummin(*args, **kwargs): # real signature unknown """ See group_cummin_max.__doc__ """ pass def group_cumprod(*args, **kwargs): # real signature unknown """ Cumulative product of columns of `values`, in row groups `labels`. Parameters ---------- out : np.ndarray[np.float64, ndim=2] Array to store cumprod in. values : np.ndarray[np.float64, ndim=2] Values to take cumprod of. labels : np.ndarray[np.intp] Labels to group by. ngroups : int Number of groups, larger than all entries of `labels`. is_datetimelike : bool Always false, `values` is never datetime-like. skipna : bool If true, ignore nans in `values`. mask : np.ndarray[uint8], optional Mask of values result_mask : np.ndarray[int8], optional Mask of out array Notes ----- This method modifies the `out` parameter, rather than returning an object. """ pass def group_cumsum(*args, **kwargs): # real signature unknown """ Cumulative sum of columns of `values`, in row groups `labels`. Parameters ---------- out : np.ndarray[ndim=2] Array to store cumsum in. values : np.ndarray[ndim=2] Values to take cumsum of. labels : np.ndarray[np.intp] Labels to group by. ngroups : int Number of groups, larger than all entries of `labels`. is_datetimelike : bool True if `values` contains datetime-like entries. skipna : bool If true, ignore nans in `values`. mask : np.ndarray[uint8], optional Mask of values result_mask : np.ndarray[int8], optional Mask of out array Notes ----- This method modifies the `out` parameter, rather than returning an object. """ pass def group_fillna_indexer(*args, **kwargs): # real signature unknown """ Indexes how to fill values forwards or backwards within a group. Parameters ---------- out : np.ndarray[np.intp] Values into which this method will write its results. labels : np.ndarray[np.intp] Array containing unique label for each group, with its ordering matching up to the corresponding record in `values`. sorted_labels : np.ndarray[np.intp] obtained by `np.argsort(labels, kind="mergesort")` values : np.ndarray[np.uint8] Containing the truth value of each element. mask : np.ndarray[np.uint8] Indicating whether a value is na or not. limit : Consecutive values to fill before stopping, or -1 for no limit dropna : Flag to indicate if NaN groups should return all NaN values Notes ----- This method modifies the `out` parameter rather than returning an object """ pass def group_idxmin_idxmax(*args, **kwargs): # real signature unknown """ Compute index of minimum/maximum of columns of `values`, in row groups `labels`. This function only computes the row number where the minimum/maximum occurs, we'll take the corresponding index value after this function. Parameters ---------- out : np.ndarray[intp, ndim=2] Array to store result in. counts : np.ndarray[int64] Input as a zeroed array, populated by group sizes during algorithm values : np.ndarray[numeric_object_t, ndim=2] Values to find column-wise min/max of. labels : np.ndarray[np.intp] Labels to group by. min_count : Py_ssize_t, default -1 The minimum number of non-NA group elements, NA result if threshold is not met. is_datetimelike : bool True if `values` contains datetime-like entries. name : {"idxmin", "idxmax"}, default "idxmin" Whether to compute idxmin or idxmax. mask : ndarray[bool, ndim=2], optional If not None, indices represent missing values, otherwise the mask will not be used skipna : bool, default True Flag to ignore nan values during truth testing result_mask : ndarray[bool, ndim=2], optional If not None, these specify locations in the output that are NA. Modified in-place. Notes ----- This method modifies the `out` parameter, rather than returning an object. `counts` is modified to hold group sizes """ pass def group_last(*args, **kwargs): # real signature unknown """ Only aggregates on axis=0 """ pass def group_max(*args, **kwargs): # real signature unknown """ See group_min_max.__doc__ """ pass def group_mean(*args, **kwargs): # real signature unknown """ Compute the mean per label given a label assignment for each value. NaN values are ignored. Parameters ---------- out : np.ndarray[floating] Values into which this method will write its results. counts : np.ndarray[int64] A zeroed array of the same shape as labels, populated by group sizes during algorithm. values : np.ndarray[floating] 2-d array of the values to find the mean of. labels : np.ndarray[np.intp] Array containing unique label for each group, with its ordering matching up to the corresponding record in `values`. min_count : Py_ssize_t Only used in sum and prod. Always -1. is_datetimelike : bool True if `values` contains datetime-like entries. mask : ndarray[bool, ndim=2], optional Mask of the input values. result_mask : ndarray[bool, ndim=2], optional Mask of the out array Notes ----- This method modifies the `out` parameter rather than returning an object. `counts` is modified to hold group sizes """ pass def group_median_float64(*args, **kwargs): # real signature unknown """ Only aggregates on axis=0 """ pass def group_min(*args, **kwargs): # real signature unknown """ See group_min_max.__doc__ """ pass def group_nth(*args, **kwargs): # real signature unknown """ Only aggregates on axis=0 """ pass def group_ohlc(*args, **kwargs): # real signature unknown """ Only aggregates on axis=0 """ pass def group_prod(*args, **kwargs): # real signature unknown """ Only aggregates on axis=0 """ pass def group_quantile(*args, **kwargs): # real signature unknown """ Calculate the quantile per group. Parameters ---------- out : np.ndarray[np.float64, ndim=2] Array of aggregated values that will be written to. values : np.ndarray Array containing the values to apply the function against. labels : ndarray[np.intp] Array containing the unique group labels. qs : ndarray[float64_t] The quantile values to search for. starts : ndarray[int64] Positions at which each group begins. ends : ndarray[int64] Positions at which each group ends. interpolation : {'linear', 'lower', 'highest', 'nearest', 'midpoint'} result_mask : ndarray[bool, ndim=2] or None is_datetimelike : bool Whether int64 values represent datetime64-like values. Notes ----- Rather than explicitly returning a value, this function modifies the provided `out` parameter. """ pass def group_rank(*args, **kwargs): # real signature unknown """ Provides the rank of values within each group. Parameters ---------- out : np.ndarray[np.float64, ndim=2] Values to which this method will write its results. values : np.ndarray of numeric_object_t values to be ranked labels : np.ndarray[np.intp] Array containing unique label for each group, with its ordering matching up to the corresponding record in `values` ngroups : int This parameter is not used, is needed to match signatures of other groupby functions. is_datetimelike : bool True if `values` contains datetime-like entries. ties_method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank of group * min: lowest rank in group * max: highest rank in group * first: ranks assigned in order they appear in the array * dense: like 'min', but rank always increases by 1 between groups ascending : bool, default True False for ranks by high (1) to low (N) na_option : {'keep', 'top', 'bottom'}, default 'keep' pct : bool, default False Compute percentage rank of data within each group na_option : {'keep', 'top', 'bottom'}, default 'keep' * keep: leave NA values where they are * top: smallest rank if ascending * bottom: smallest rank if descending mask : np.ndarray[bool] or None, default None Notes ----- This method modifies the `out` parameter rather than returning an object """ pass def group_shift_indexer(*args, **kwargs): # real signature unknown pass def group_skew(*args, **kwargs): # real signature unknown pass def group_sum(*args, **kwargs): # real signature unknown """ Only aggregates on axis=0 using Kahan summation """ pass def group_var(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_Enum(*args, **kwargs): # real signature unknown pass # no classes # variables with complex values __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x00000284928BBA10>' __spec__ = None # (!) real value is "ModuleSpec(name='pandas._libs.groupby', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x00000284928BBA10>, origin='D:\\\\project\\\\A_Board\\\\.venv\\\\Lib\\\\site-packages\\\\pandas\\\\_libs\\\\groupby.cp313-win_amd64.pyd')" __test__ = {}