Sindbad~EG File Manager
"""
Provide classes to perform the groupby aggregate operations.
These are not exposed to the user and provide implementations of the grouping
operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
"""
from __future__ import annotations
import collections
import functools
from typing import (
TYPE_CHECKING,
Callable,
Generic,
final,
)
import numpy as np
from pandas._libs import (
NaT,
lib,
)
import pandas._libs.groupby as libgroupby
from pandas._typing import (
ArrayLike,
AxisInt,
NDFrameT,
Shape,
npt,
)
from pandas.errors import AbstractMethodError
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.cast import (
maybe_cast_pointwise_result,
maybe_downcast_to_dtype,
)
from pandas.core.dtypes.common import (
ensure_float64,
ensure_int64,
ensure_platform_int,
ensure_uint64,
is_1d_only_ea_dtype,
)
from pandas.core.dtypes.missing import (
isna,
maybe_fill,
)
from pandas.core.frame import DataFrame
from pandas.core.groupby import grouper
from pandas.core.indexes.api import (
CategoricalIndex,
Index,
MultiIndex,
ensure_index,
)
from pandas.core.series import Series
from pandas.core.sorting import (
compress_group_index,
decons_obs_group_ids,
get_flattened_list,
get_group_index,
get_group_index_sorter,
get_indexer_dict,
)
if TYPE_CHECKING:
from collections.abc import (
Hashable,
Iterator,
Sequence,
)
from pandas.core.generic import NDFrame
def check_result_array(obj, dtype) -> None:
# Our operation is supposed to be an aggregation/reduction. If
# it returns an ndarray, this likely means an invalid operation has
# been passed. See test_apply_without_aggregation, test_agg_must_agg
if isinstance(obj, np.ndarray):
if dtype != object:
# If it is object dtype, the function can be a reduction/aggregation
# and still return an ndarray e.g. test_agg_over_numpy_arrays
raise ValueError("Must produce aggregated value")
def extract_result(res):
"""
Extract the result object, it might be a 0-dim ndarray
or a len-1 0-dim, or a scalar
"""
if hasattr(res, "_values"):
# Preserve EA
res = res._values
if res.ndim == 1 and len(res) == 1:
# see test_agg_lambda_with_timezone, test_resampler_grouper.py::test_apply
res = res[0]
return res
class WrappedCythonOp:
"""
Dispatch logic for functions defined in _libs.groupby
Parameters
----------
kind: str
Whether the operation is an aggregate or transform.
how: str
Operation name, e.g. "mean".
has_dropped_na: bool
True precisely when dropna=True and the grouper contains a null value.
"""
# Functions for which we do _not_ attempt to cast the cython result
# back to the original dtype.
cast_blocklist = frozenset(
["any", "all", "rank", "count", "size", "idxmin", "idxmax"]
)
def __init__(self, kind: str, how: str, has_dropped_na: bool) -> None:
self.kind = kind
self.how = how
self.has_dropped_na = has_dropped_na
_CYTHON_FUNCTIONS: dict[str, dict] = {
"aggregate": {
"any": functools.partial(libgroupby.group_any_all, val_test="any"),
"all": functools.partial(libgroupby.group_any_all, val_test="all"),
"sum": "group_sum",
"prod": "group_prod",
"idxmin": functools.partial(libgroupby.group_idxmin_idxmax, name="idxmin"),
"idxmax": functools.partial(libgroupby.group_idxmin_idxmax, name="idxmax"),
"min": "group_min",
"max": "group_max",
"mean": "group_mean",
"median": "group_median_float64",
"var": "group_var",
"std": functools.partial(libgroupby.group_var, name="std"),
"sem": functools.partial(libgroupby.group_var, name="sem"),
"skew": "group_skew",
"first": "group_nth",
"last": "group_last",
"ohlc": "group_ohlc",
},
"transform": {
"cumprod": "group_cumprod",
"cumsum": "group_cumsum",
"cummin": "group_cummin",
"cummax": "group_cummax",
"rank": "group_rank",
},
}
_cython_arity = {"ohlc": 4} # OHLC
@classmethod
def get_kind_from_how(cls, how: str) -> str:
if how in cls._CYTHON_FUNCTIONS["aggregate"]:
return "aggregate"
return "transform"
# Note: we make this a classmethod and pass kind+how so that caching
# works at the class level and not the instance level
@classmethod
@functools.cache
def _get_cython_function(
cls, kind: str, how: str, dtype: np.dtype, is_numeric: bool
):
dtype_str = dtype.name
ftype = cls._CYTHON_FUNCTIONS[kind][how]
# see if there is a fused-type version of function
# only valid for numeric
if callable(ftype):
f = ftype
else:
f = getattr(libgroupby, ftype)
if is_numeric:
return f
elif dtype == np.dtype(object):
if how in ["median", "cumprod"]:
# no fused types -> no __signatures__
raise NotImplementedError(
f"function is not implemented for this dtype: "
f"[how->{how},dtype->{dtype_str}]"
)
elif how in ["std", "sem", "idxmin", "idxmax"]:
# We have a partial object that does not have __signatures__
return f
elif how == "skew":
# _get_cython_vals will convert to float64
pass
elif "object" not in f.__signatures__:
# raise NotImplementedError here rather than TypeError later
raise NotImplementedError(
f"function is not implemented for this dtype: "
f"[how->{how},dtype->{dtype_str}]"
)
return f
else:
raise NotImplementedError(
"This should not be reached. Please report a bug at "
"github.com/pandas-dev/pandas/",
dtype,
)
def _get_cython_vals(self, values: np.ndarray) -> np.ndarray:
"""
Cast numeric dtypes to float64 for functions that only support that.
Parameters
----------
values : np.ndarray
Returns
-------
values : np.ndarray
"""
how = self.how
if how in ["median", "std", "sem", "skew"]:
# median only has a float64 implementation
# We should only get here with is_numeric, as non-numeric cases
# should raise in _get_cython_function
values = ensure_float64(values)
elif values.dtype.kind in "iu":
if how in ["var", "mean"] or (
self.kind == "transform" and self.has_dropped_na
):
# has_dropped_na check need for test_null_group_str_transformer
# result may still include NaN, so we have to cast
values = ensure_float64(values)
elif how in ["sum", "ohlc", "prod", "cumsum", "cumprod"]:
# Avoid overflow during group op
if values.dtype.kind == "i":
values = ensure_int64(values)
else:
values = ensure_uint64(values)
return values
def _get_output_shape(self, ngroups: int, values: np.ndarray) -> Shape:
how = self.how
kind = self.kind
arity = self._cython_arity.get(how, 1)
out_shape: Shape
if how == "ohlc":
out_shape = (ngroups, arity)
elif arity > 1:
raise NotImplementedError(
"arity of more than 1 is not supported for the 'how' argument"
)
elif kind == "transform":
out_shape = values.shape
else:
out_shape = (ngroups,) + values.shape[1:]
return out_shape
def _get_out_dtype(self, dtype: np.dtype) -> np.dtype:
how = self.how
if how == "rank":
out_dtype = "float64"
elif how in ["idxmin", "idxmax"]:
# The Cython implementation only produces the row number; we'll take
# from the index using this in post processing
out_dtype = "intp"
else:
if dtype.kind in "iufcb":
out_dtype = f"{dtype.kind}{dtype.itemsize}"
else:
out_dtype = "object"
return np.dtype(out_dtype)
def _get_result_dtype(self, dtype: np.dtype) -> np.dtype:
"""
Get the desired dtype of a result based on the
input dtype and how it was computed.
Parameters
----------
dtype : np.dtype
Returns
-------
np.dtype
The desired dtype of the result.
"""
how = self.how
if how in ["sum", "cumsum", "sum", "prod", "cumprod"]:
if dtype == np.dtype(bool):
return np.dtype(np.int64)
elif how in ["mean", "median", "var", "std", "sem"]:
if dtype.kind in "fc":
return dtype
elif dtype.kind in "iub":
return np.dtype(np.float64)
return dtype
@final
def _cython_op_ndim_compat(
self,
values: np.ndarray,
*,
min_count: int,
ngroups: int,
comp_ids: np.ndarray,
mask: npt.NDArray[np.bool_] | None = None,
result_mask: npt.NDArray[np.bool_] | None = None,
**kwargs,
) -> np.ndarray:
if values.ndim == 1:
# expand to 2d, dispatch, then squeeze if appropriate
values2d = values[None, :]
if mask is not None:
mask = mask[None, :]
if result_mask is not None:
result_mask = result_mask[None, :]
res = self._call_cython_op(
values2d,
min_count=min_count,
ngroups=ngroups,
comp_ids=comp_ids,
mask=mask,
result_mask=result_mask,
**kwargs,
)
if res.shape[0] == 1:
return res[0]
# otherwise we have OHLC
return res.T
return self._call_cython_op(
values,
min_count=min_count,
ngroups=ngroups,
comp_ids=comp_ids,
mask=mask,
result_mask=result_mask,
**kwargs,
)
@final
def _call_cython_op(
self,
values: np.ndarray, # np.ndarray[ndim=2]
*,
min_count: int,
ngroups: int,
comp_ids: np.ndarray,
mask: npt.NDArray[np.bool_] | None,
result_mask: npt.NDArray[np.bool_] | None,
**kwargs,
) -> np.ndarray: # np.ndarray[ndim=2]
orig_values = values
dtype = values.dtype
is_numeric = dtype.kind in "iufcb"
is_datetimelike = dtype.kind in "mM"
if is_datetimelike:
values = values.view("int64")
is_numeric = True
elif dtype.kind == "b":
values = values.view("uint8")
if values.dtype == "float16":
values = values.astype(np.float32)
if self.how in ["any", "all"]:
if mask is None:
mask = isna(values)
if dtype == object:
if kwargs["skipna"]:
# GH#37501: don't raise on pd.NA when skipna=True
if mask.any():
# mask on original values computed separately
values = values.copy()
values[mask] = True
values = values.astype(bool, copy=False).view(np.int8)
is_numeric = True
values = values.T
if mask is not None:
mask = mask.T
if result_mask is not None:
result_mask = result_mask.T
out_shape = self._get_output_shape(ngroups, values)
func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric)
values = self._get_cython_vals(values)
out_dtype = self._get_out_dtype(values.dtype)
result = maybe_fill(np.empty(out_shape, dtype=out_dtype))
if self.kind == "aggregate":
counts = np.zeros(ngroups, dtype=np.int64)
if self.how in [
"idxmin",
"idxmax",
"min",
"max",
"mean",
"last",
"first",
"sum",
]:
func(
out=result,
counts=counts,
values=values,
labels=comp_ids,
min_count=min_count,
mask=mask,
result_mask=result_mask,
is_datetimelike=is_datetimelike,
**kwargs,
)
elif self.how in ["sem", "std", "var", "ohlc", "prod", "median"]:
if self.how in ["std", "sem"]:
kwargs["is_datetimelike"] = is_datetimelike
func(
result,
counts,
values,
comp_ids,
min_count=min_count,
mask=mask,
result_mask=result_mask,
**kwargs,
)
elif self.how in ["any", "all"]:
func(
out=result,
values=values,
labels=comp_ids,
mask=mask,
result_mask=result_mask,
**kwargs,
)
result = result.astype(bool, copy=False)
elif self.how in ["skew"]:
func(
out=result,
counts=counts,
values=values,
labels=comp_ids,
mask=mask,
result_mask=result_mask,
**kwargs,
)
if dtype == object:
result = result.astype(object)
else:
raise NotImplementedError(f"{self.how} is not implemented")
else:
# TODO: min_count
if self.how != "rank":
# TODO: should rank take result_mask?
kwargs["result_mask"] = result_mask
func(
out=result,
values=values,
labels=comp_ids,
ngroups=ngroups,
is_datetimelike=is_datetimelike,
mask=mask,
**kwargs,
)
if self.kind == "aggregate" and self.how not in ["idxmin", "idxmax"]:
# i.e. counts is defined. Locations where count<min_count
# need to have the result set to np.nan, which may require casting,
# see GH#40767. For idxmin/idxmax is handled specially via post-processing
if result.dtype.kind in "iu" and not is_datetimelike:
# if the op keeps the int dtypes, we have to use 0
cutoff = max(0 if self.how in ["sum", "prod"] else 1, min_count)
empty_groups = counts < cutoff
if empty_groups.any():
if result_mask is not None:
assert result_mask[empty_groups].all()
else:
# Note: this conversion could be lossy, see GH#40767
result = result.astype("float64")
result[empty_groups] = np.nan
result = result.T
if self.how not in self.cast_blocklist:
# e.g. if we are int64 and need to restore to datetime64/timedelta64
# "rank" is the only member of cast_blocklist we get here
# Casting only needed for float16, bool, datetimelike,
# and self.how in ["sum", "prod", "ohlc", "cumprod"]
res_dtype = self._get_result_dtype(orig_values.dtype)
op_result = maybe_downcast_to_dtype(result, res_dtype)
else:
op_result = result
return op_result
@final
def _validate_axis(self, axis: AxisInt, values: ArrayLike) -> None:
if values.ndim > 2:
raise NotImplementedError("number of dimensions is currently limited to 2")
if values.ndim == 2:
assert axis == 1, axis
elif not is_1d_only_ea_dtype(values.dtype):
# Note: it is *not* the case that axis is always 0 for 1-dim values,
# as we can have 1D ExtensionArrays that we need to treat as 2D
assert axis == 0
@final
def cython_operation(
self,
*,
values: ArrayLike,
axis: AxisInt,
min_count: int = -1,
comp_ids: np.ndarray,
ngroups: int,
**kwargs,
) -> ArrayLike:
"""
Call our cython function, with appropriate pre- and post- processing.
"""
self._validate_axis(axis, values)
if not isinstance(values, np.ndarray):
# i.e. ExtensionArray
return values._groupby_op(
how=self.how,
has_dropped_na=self.has_dropped_na,
min_count=min_count,
ngroups=ngroups,
ids=comp_ids,
**kwargs,
)
return self._cython_op_ndim_compat(
values,
min_count=min_count,
ngroups=ngroups,
comp_ids=comp_ids,
mask=None,
**kwargs,
)
class BaseGrouper:
"""
This is an internal Grouper class, which actually holds
the generated groups
Parameters
----------
axis : Index
groupings : Sequence[Grouping]
all the grouping instances to handle in this grouper
for example for grouper list to groupby, need to pass the list
sort : bool, default True
whether this grouper will give sorted result or not
"""
axis: Index
def __init__(
self,
axis: Index,
groupings: Sequence[grouper.Grouping],
sort: bool = True,
dropna: bool = True,
) -> None:
assert isinstance(axis, Index), axis
self.axis = axis
self._groupings: list[grouper.Grouping] = list(groupings)
self._sort = sort
self.dropna = dropna
@property
def groupings(self) -> list[grouper.Grouping]:
return self._groupings
@property
def shape(self) -> Shape:
return tuple(ping.ngroups for ping in self.groupings)
def __iter__(self) -> Iterator[Hashable]:
return iter(self.indices)
@property
def nkeys(self) -> int:
return len(self.groupings)
def get_iterator(
self, data: NDFrameT, axis: AxisInt = 0
) -> Iterator[tuple[Hashable, NDFrameT]]:
"""
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group
"""
splitter = self._get_splitter(data, axis=axis)
keys = self.group_keys_seq
yield from zip(keys, splitter)
@final
def _get_splitter(self, data: NDFrame, axis: AxisInt = 0) -> DataSplitter:
"""
Returns
-------
Generator yielding subsetted objects
"""
ids, _, ngroups = self.group_info
return _get_splitter(
data,
ids,
ngroups,
sorted_ids=self._sorted_ids,
sort_idx=self._sort_idx,
axis=axis,
)
@final
@cache_readonly
def group_keys_seq(self):
if len(self.groupings) == 1:
return self.levels[0]
else:
ids, _, ngroups = self.group_info
# provide "flattened" iterator for multi-group setting
return get_flattened_list(ids, ngroups, self.levels, self.codes)
@cache_readonly
def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
"""dict {group name -> group indices}"""
if len(self.groupings) == 1 and isinstance(self.result_index, CategoricalIndex):
# This shows unused categories in indices GH#38642
return self.groupings[0].indices
codes_list = [ping.codes for ping in self.groupings]
keys = [ping._group_index for ping in self.groupings]
return get_indexer_dict(codes_list, keys)
@final
def result_ilocs(self) -> npt.NDArray[np.intp]:
"""
Get the original integer locations of result_index in the input.
"""
# Original indices are where group_index would go via sorting.
# But when dropna is true, we need to remove null values while accounting for
# any gaps that then occur because of them.
group_index = get_group_index(
self.codes, self.shape, sort=self._sort, xnull=True
)
group_index, _ = compress_group_index(group_index, sort=self._sort)
if self.has_dropped_na:
mask = np.where(group_index >= 0)
# Count how many gaps are caused by previous null values for each position
null_gaps = np.cumsum(group_index == -1)[mask]
group_index = group_index[mask]
result = get_group_index_sorter(group_index, self.ngroups)
if self.has_dropped_na:
# Shift by the number of prior null gaps
result += np.take(null_gaps, result)
return result
@final
@property
def codes(self) -> list[npt.NDArray[np.signedinteger]]:
return [ping.codes for ping in self.groupings]
@property
def levels(self) -> list[Index]:
return [ping._group_index for ping in self.groupings]
@property
def names(self) -> list[Hashable]:
return [ping.name for ping in self.groupings]
@final
def size(self) -> Series:
"""
Compute group sizes.
"""
ids, _, ngroups = self.group_info
out: np.ndarray | list
if ngroups:
out = np.bincount(ids[ids != -1], minlength=ngroups)
else:
out = []
return Series(out, index=self.result_index, dtype="int64", copy=False)
@cache_readonly
def groups(self) -> dict[Hashable, np.ndarray]:
"""dict {group name -> group labels}"""
if len(self.groupings) == 1:
return self.groupings[0].groups
else:
to_groupby = []
for ping in self.groupings:
gv = ping.grouping_vector
if not isinstance(gv, BaseGrouper):
to_groupby.append(gv)
else:
to_groupby.append(gv.groupings[0].grouping_vector)
index = MultiIndex.from_arrays(to_groupby)
return self.axis.groupby(index)
@final
@cache_readonly
def is_monotonic(self) -> bool:
# return if my group orderings are monotonic
return Index(self.group_info[0]).is_monotonic_increasing
@final
@cache_readonly
def has_dropped_na(self) -> bool:
"""
Whether grouper has null value(s) that are dropped.
"""
return bool((self.group_info[0] < 0).any())
@cache_readonly
def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
comp_ids, obs_group_ids = self._get_compressed_codes()
ngroups = len(obs_group_ids)
comp_ids = ensure_platform_int(comp_ids)
return comp_ids, obs_group_ids, ngroups
@cache_readonly
def codes_info(self) -> npt.NDArray[np.intp]:
# return the codes of items in original grouped axis
ids, _, _ = self.group_info
return ids
@final
def _get_compressed_codes(
self,
) -> tuple[npt.NDArray[np.signedinteger], npt.NDArray[np.intp]]:
# The first returned ndarray may have any signed integer dtype
if len(self.groupings) > 1:
group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)
return compress_group_index(group_index, sort=self._sort)
# FIXME: compress_group_index's second return value is int64, not intp
ping = self.groupings[0]
return ping.codes, np.arange(len(ping._group_index), dtype=np.intp)
@final
@cache_readonly
def ngroups(self) -> int:
return len(self.result_index)
@property
def reconstructed_codes(self) -> list[npt.NDArray[np.intp]]:
codes = self.codes
ids, obs_ids, _ = self.group_info
return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True)
@cache_readonly
def result_index(self) -> Index:
if len(self.groupings) == 1:
return self.groupings[0]._result_index.rename(self.names[0])
codes = self.reconstructed_codes
levels = [ping._result_index for ping in self.groupings]
return MultiIndex(
levels=levels, codes=codes, verify_integrity=False, names=self.names
)
@final
def get_group_levels(self) -> list[ArrayLike]:
# Note: only called from _insert_inaxis_grouper, which
# is only called for BaseGrouper, never for BinGrouper
if len(self.groupings) == 1:
return [self.groupings[0]._group_arraylike]
name_list = []
for ping, codes in zip(self.groupings, self.reconstructed_codes):
codes = ensure_platform_int(codes)
levels = ping._group_arraylike.take(codes)
name_list.append(levels)
return name_list
# ------------------------------------------------------------
# Aggregation functions
@final
def _cython_operation(
self,
kind: str,
values,
how: str,
axis: AxisInt,
min_count: int = -1,
**kwargs,
) -> ArrayLike:
"""
Returns the values of a cython operation.
"""
assert kind in ["transform", "aggregate"]
cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na)
ids, _, _ = self.group_info
ngroups = self.ngroups
return cy_op.cython_operation(
values=values,
axis=axis,
min_count=min_count,
comp_ids=ids,
ngroups=ngroups,
**kwargs,
)
@final
def agg_series(
self, obj: Series, func: Callable, preserve_dtype: bool = False
) -> ArrayLike:
"""
Parameters
----------
obj : Series
func : function taking a Series and returning a scalar-like
preserve_dtype : bool
Whether the aggregation is known to be dtype-preserving.
Returns
-------
np.ndarray or ExtensionArray
"""
if not isinstance(obj._values, np.ndarray):
# we can preserve a little bit more aggressively with EA dtype
# because maybe_cast_pointwise_result will do a try/except
# with _from_sequence. NB we are assuming here that _from_sequence
# is sufficiently strict that it casts appropriately.
preserve_dtype = True
result = self._aggregate_series_pure_python(obj, func)
npvalues = lib.maybe_convert_objects(result, try_float=False)
if preserve_dtype:
out = maybe_cast_pointwise_result(npvalues, obj.dtype, numeric_only=True)
else:
out = npvalues
return out
@final
def _aggregate_series_pure_python(
self, obj: Series, func: Callable
) -> npt.NDArray[np.object_]:
_, _, ngroups = self.group_info
result = np.empty(ngroups, dtype="O")
initialized = False
splitter = self._get_splitter(obj, axis=0)
for i, group in enumerate(splitter):
res = func(group)
res = extract_result(res)
if not initialized:
# We only do this validation on the first iteration
check_result_array(res, group.dtype)
initialized = True
result[i] = res
return result
@final
def apply_groupwise(
self, f: Callable, data: DataFrame | Series, axis: AxisInt = 0
) -> tuple[list, bool]:
mutated = False
splitter = self._get_splitter(data, axis=axis)
group_keys = self.group_keys_seq
result_values = []
# This calls DataSplitter.__iter__
zipped = zip(group_keys, splitter)
for key, group in zipped:
# Pinning name is needed for
# test_group_apply_once_per_group,
# test_inconsistent_return_type, test_set_group_name,
# test_group_name_available_in_inference_pass,
# test_groupby_multi_timezone
object.__setattr__(group, "name", key)
# group might be modified
group_axes = group.axes
res = f(group)
if not mutated and not _is_indexed_like(res, group_axes, axis):
mutated = True
result_values.append(res)
# getattr pattern for __name__ is needed for functools.partial objects
if len(group_keys) == 0 and getattr(f, "__name__", None) in [
"skew",
"sum",
"prod",
]:
# If group_keys is empty, then no function calls have been made,
# so we will not have raised even if this is an invalid dtype.
# So do one dummy call here to raise appropriate TypeError.
f(data.iloc[:0])
return result_values, mutated
# ------------------------------------------------------------
# Methods for sorting subsets of our GroupBy's object
@final
@cache_readonly
def _sort_idx(self) -> npt.NDArray[np.intp]:
# Counting sort indexer
ids, _, ngroups = self.group_info
return get_group_index_sorter(ids, ngroups)
@final
@cache_readonly
def _sorted_ids(self) -> npt.NDArray[np.intp]:
ids, _, _ = self.group_info
return ids.take(self._sort_idx)
class BinGrouper(BaseGrouper):
"""
This is an internal Grouper class
Parameters
----------
bins : the split index of binlabels to group the item of axis
binlabels : the label list
indexer : np.ndarray[np.intp], optional
the indexer created by Grouper
some groupers (TimeGrouper) will sort its axis and its
group_info is also sorted, so need the indexer to reorder
Examples
--------
bins: [2, 4, 6, 8, 10]
binlabels: DatetimeIndex(['2005-01-01', '2005-01-03',
'2005-01-05', '2005-01-07', '2005-01-09'],
dtype='datetime64[ns]', freq='2D')
the group_info, which contains the label of each item in grouped
axis, the index of label in label list, group number, is
(array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]), array([0, 1, 2, 3, 4]), 5)
means that, the grouped axis has 10 items, can be grouped into 5
labels, the first and second items belong to the first label, the
third and forth items belong to the second label, and so on
"""
bins: npt.NDArray[np.int64]
binlabels: Index
def __init__(
self,
bins,
binlabels,
indexer=None,
) -> None:
self.bins = ensure_int64(bins)
self.binlabels = ensure_index(binlabels)
self.indexer = indexer
# These lengths must match, otherwise we could call agg_series
# with empty self.bins, which would raise later.
assert len(self.binlabels) == len(self.bins)
@cache_readonly
def groups(self):
"""dict {group name -> group labels}"""
# this is mainly for compat
# GH 3881
result = {
key: value
for key, value in zip(self.binlabels, self.bins)
if key is not NaT
}
return result
@property
def nkeys(self) -> int:
# still matches len(self.groupings), but we can hard-code
return 1
@cache_readonly
def codes_info(self) -> npt.NDArray[np.intp]:
# return the codes of items in original grouped axis
ids, _, _ = self.group_info
if self.indexer is not None:
sorter = np.lexsort((ids, self.indexer))
ids = ids[sorter]
return ids
def get_iterator(self, data: NDFrame, axis: AxisInt = 0):
"""
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group
"""
if axis == 0:
slicer = lambda start, edge: data.iloc[start:edge]
else:
slicer = lambda start, edge: data.iloc[:, start:edge]
length = len(data.axes[axis])
start = 0
for edge, label in zip(self.bins, self.binlabels):
if label is not NaT:
yield label, slicer(start, edge)
start = edge
if start < length:
yield self.binlabels[-1], slicer(start, None)
@cache_readonly
def indices(self):
indices = collections.defaultdict(list)
i = 0
for label, bin in zip(self.binlabels, self.bins):
if i < bin:
if label is not NaT:
indices[label] = list(range(i, bin))
i = bin
return indices
@cache_readonly
def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
ngroups = self.ngroups
obs_group_ids = np.arange(ngroups, dtype=np.intp)
rep = np.diff(np.r_[0, self.bins])
rep = ensure_platform_int(rep)
if ngroups == len(self.bins):
comp_ids = np.repeat(np.arange(ngroups), rep)
else:
comp_ids = np.repeat(np.r_[-1, np.arange(ngroups)], rep)
return (
ensure_platform_int(comp_ids),
obs_group_ids,
ngroups,
)
@cache_readonly
def reconstructed_codes(self) -> list[np.ndarray]:
# get unique result indices, and prepend 0 as groupby starts from the first
return [np.r_[0, np.flatnonzero(self.bins[1:] != self.bins[:-1]) + 1]]
@cache_readonly
def result_index(self) -> Index:
if len(self.binlabels) != 0 and isna(self.binlabels[0]):
return self.binlabels[1:]
return self.binlabels
@property
def levels(self) -> list[Index]:
return [self.binlabels]
@property
def names(self) -> list[Hashable]:
return [self.binlabels.name]
@property
def groupings(self) -> list[grouper.Grouping]:
lev = self.binlabels
codes = self.group_info[0]
labels = lev.take(codes)
ping = grouper.Grouping(
labels, labels, in_axis=False, level=None, uniques=lev._values
)
return [ping]
def _is_indexed_like(obj, axes, axis: AxisInt) -> bool:
if isinstance(obj, Series):
if len(axes) > 1:
return False
return obj.axes[axis].equals(axes[axis])
elif isinstance(obj, DataFrame):
return obj.axes[axis].equals(axes[axis])
return False
# ----------------------------------------------------------------------
# Splitting / application
class DataSplitter(Generic[NDFrameT]):
def __init__(
self,
data: NDFrameT,
labels: npt.NDArray[np.intp],
ngroups: int,
*,
sort_idx: npt.NDArray[np.intp],
sorted_ids: npt.NDArray[np.intp],
axis: AxisInt = 0,
) -> None:
self.data = data
self.labels = ensure_platform_int(labels) # _should_ already be np.intp
self.ngroups = ngroups
self._slabels = sorted_ids
self._sort_idx = sort_idx
self.axis = axis
assert isinstance(axis, int), axis
def __iter__(self) -> Iterator:
sdata = self._sorted_data
if self.ngroups == 0:
# we are inside a generator, rather than raise StopIteration
# we merely return signal the end
return
starts, ends = lib.generate_slices(self._slabels, self.ngroups)
for start, end in zip(starts, ends):
yield self._chop(sdata, slice(start, end))
@cache_readonly
def _sorted_data(self) -> NDFrameT:
return self.data.take(self._sort_idx, axis=self.axis)
def _chop(self, sdata, slice_obj: slice) -> NDFrame:
raise AbstractMethodError(self)
class SeriesSplitter(DataSplitter):
def _chop(self, sdata: Series, slice_obj: slice) -> Series:
# fastpath equivalent to `sdata.iloc[slice_obj]`
mgr = sdata._mgr.get_slice(slice_obj)
ser = sdata._constructor_from_mgr(mgr, axes=mgr.axes)
ser._name = sdata.name
return ser.__finalize__(sdata, method="groupby")
class FrameSplitter(DataSplitter):
def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
# Fastpath equivalent to:
# if self.axis == 0:
# return sdata.iloc[slice_obj]
# else:
# return sdata.iloc[:, slice_obj]
mgr = sdata._mgr.get_slice(slice_obj, axis=1 - self.axis)
df = sdata._constructor_from_mgr(mgr, axes=mgr.axes)
return df.__finalize__(sdata, method="groupby")
def _get_splitter(
data: NDFrame,
labels: npt.NDArray[np.intp],
ngroups: int,
*,
sort_idx: npt.NDArray[np.intp],
sorted_ids: npt.NDArray[np.intp],
axis: AxisInt = 0,
) -> DataSplitter:
if isinstance(data, Series):
klass: type[DataSplitter] = SeriesSplitter
else:
# i.e. DataFrame
klass = FrameSplitter
return klass(
data, labels, ngroups, sort_idx=sort_idx, sorted_ids=sorted_ids, axis=axis
)
Sindbad File Manager Version 1.0, Coded By Sindbad EG ~ The Terrorists