304 lines
9.3 KiB
Python
304 lines
9.3 KiB
Python
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from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Iterable,
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Literal,
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cast,
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)
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import numpy as np
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from pandas._typing import PositionalIndexer
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from pandas.util._decorators import (
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cache_readonly,
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doc,
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)
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from pandas.core.dtypes.common import (
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is_integer,
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is_list_like,
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)
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if TYPE_CHECKING:
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from pandas import (
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DataFrame,
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Series,
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)
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from pandas.core.groupby import groupby
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class GroupByIndexingMixin:
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"""
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Mixin for adding ._positional_selector to GroupBy.
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"""
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@cache_readonly
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def _positional_selector(self) -> GroupByPositionalSelector:
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"""
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Return positional selection for each group.
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``groupby._positional_selector[i:j]`` is similar to
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``groupby.apply(lambda x: x.iloc[i:j])``
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but much faster and preserves the original index and order.
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``_positional_selector[]`` is compatible with and extends :meth:`~GroupBy.head`
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and :meth:`~GroupBy.tail`. For example:
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- ``head(5)``
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- ``_positional_selector[5:-5]``
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- ``tail(5)``
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together return all the rows.
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Allowed inputs for the index are:
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- An integer valued iterable, e.g. ``range(2, 4)``.
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- A comma separated list of integers and slices, e.g. ``5``, ``2, 4``, ``2:4``.
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The output format is the same as :meth:`~GroupBy.head` and
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:meth:`~GroupBy.tail`, namely
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a subset of the ``DataFrame`` or ``Series`` with the index and order preserved.
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Returns
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-------
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Series
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The filtered subset of the original Series.
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DataFrame
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The filtered subset of the original DataFrame.
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See Also
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--------
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DataFrame.iloc : Purely integer-location based indexing for selection by
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position.
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GroupBy.head : Return first n rows of each group.
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GroupBy.tail : Return last n rows of each group.
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GroupBy.nth : Take the nth row from each group if n is an int, or a
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subset of rows, if n is a list of ints.
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Notes
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-----
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- The slice step cannot be negative.
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- If the index specification results in overlaps, the item is not duplicated.
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- If the index specification changes the order of items, then
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they are returned in their original order.
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By contrast, ``DataFrame.iloc`` can change the row order.
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- ``groupby()`` parameters such as as_index and dropna are ignored.
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The differences between ``_positional_selector[]`` and :meth:`~GroupBy.nth`
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with ``as_index=False`` are:
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- Input to ``_positional_selector`` can include
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one or more slices whereas ``nth``
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just handles an integer or a list of integers.
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- ``_positional_selector`` can accept a slice relative to the
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last row of each group.
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- ``_positional_selector`` does not have an equivalent to the
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``nth()`` ``dropna`` parameter.
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Examples
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--------
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>>> df = pd.DataFrame([["a", 1], ["a", 2], ["a", 3], ["b", 4], ["b", 5]],
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... columns=["A", "B"])
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>>> df.groupby("A")._positional_selector[1:2]
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A B
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1 a 2
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4 b 5
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>>> df.groupby("A")._positional_selector[1, -1]
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A B
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1 a 2
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2 a 3
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4 b 5
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"""
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if TYPE_CHECKING:
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# pylint: disable-next=used-before-assignment
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groupby_self = cast(groupby.GroupBy, self)
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else:
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groupby_self = self
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return GroupByPositionalSelector(groupby_self)
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def _make_mask_from_positional_indexer(
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self,
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arg: PositionalIndexer | tuple,
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) -> np.ndarray:
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if is_list_like(arg):
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if all(is_integer(i) for i in cast(Iterable, arg)):
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mask = self._make_mask_from_list(cast(Iterable[int], arg))
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else:
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mask = self._make_mask_from_tuple(cast(tuple, arg))
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elif isinstance(arg, slice):
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mask = self._make_mask_from_slice(arg)
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elif is_integer(arg):
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mask = self._make_mask_from_int(cast(int, arg))
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else:
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raise TypeError(
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f"Invalid index {type(arg)}. "
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"Must be integer, list-like, slice or a tuple of "
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"integers and slices"
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)
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if isinstance(mask, bool):
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if mask:
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mask = self._ascending_count >= 0
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else:
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mask = self._ascending_count < 0
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return cast(np.ndarray, mask)
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def _make_mask_from_int(self, arg: int) -> np.ndarray:
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if arg >= 0:
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return self._ascending_count == arg
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else:
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return self._descending_count == (-arg - 1)
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def _make_mask_from_list(self, args: Iterable[int]) -> bool | np.ndarray:
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positive = [arg for arg in args if arg >= 0]
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negative = [-arg - 1 for arg in args if arg < 0]
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mask: bool | np.ndarray = False
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if positive:
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mask |= np.isin(self._ascending_count, positive)
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if negative:
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mask |= np.isin(self._descending_count, negative)
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return mask
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def _make_mask_from_tuple(self, args: tuple) -> bool | np.ndarray:
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mask: bool | np.ndarray = False
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for arg in args:
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if is_integer(arg):
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mask |= self._make_mask_from_int(cast(int, arg))
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elif isinstance(arg, slice):
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mask |= self._make_mask_from_slice(arg)
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else:
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raise ValueError(
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f"Invalid argument {type(arg)}. Should be int or slice."
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)
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return mask
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def _make_mask_from_slice(self, arg: slice) -> bool | np.ndarray:
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start = arg.start
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stop = arg.stop
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step = arg.step
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if step is not None and step < 0:
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raise ValueError(f"Invalid step {step}. Must be non-negative")
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mask: bool | np.ndarray = True
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if step is None:
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step = 1
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if start is None:
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if step > 1:
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mask &= self._ascending_count % step == 0
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elif start >= 0:
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mask &= self._ascending_count >= start
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if step > 1:
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mask &= (self._ascending_count - start) % step == 0
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else:
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mask &= self._descending_count < -start
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offset_array = self._descending_count + start + 1
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limit_array = (
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self._ascending_count + self._descending_count + (start + 1)
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) < 0
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offset_array = np.where(limit_array, self._ascending_count, offset_array)
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mask &= offset_array % step == 0
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if stop is not None:
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if stop >= 0:
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mask &= self._ascending_count < stop
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else:
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mask &= self._descending_count >= -stop
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return mask
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@cache_readonly
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def _ascending_count(self) -> np.ndarray:
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if TYPE_CHECKING:
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groupby_self = cast(groupby.GroupBy, self)
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else:
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groupby_self = self
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return groupby_self._cumcount_array()
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@cache_readonly
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def _descending_count(self) -> np.ndarray:
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if TYPE_CHECKING:
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groupby_self = cast(groupby.GroupBy, self)
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else:
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groupby_self = self
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return groupby_self._cumcount_array(ascending=False)
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@doc(GroupByIndexingMixin._positional_selector)
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class GroupByPositionalSelector:
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def __init__(self, groupby_object: groupby.GroupBy) -> None:
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self.groupby_object = groupby_object
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def __getitem__(self, arg: PositionalIndexer | tuple) -> DataFrame | Series:
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"""
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Select by positional index per group.
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Implements GroupBy._positional_selector
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Parameters
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----------
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arg : PositionalIndexer | tuple
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Allowed values are:
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- int
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- int valued iterable such as list or range
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- slice with step either None or positive
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- tuple of integers and slices
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Returns
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-------
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Series
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The filtered subset of the original groupby Series.
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DataFrame
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The filtered subset of the original groupby DataFrame.
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See Also
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--------
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DataFrame.iloc : Integer-location based indexing for selection by position.
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GroupBy.head : Return first n rows of each group.
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GroupBy.tail : Return last n rows of each group.
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GroupBy._positional_selector : Return positional selection for each group.
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GroupBy.nth : Take the nth row from each group if n is an int, or a
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subset of rows, if n is a list of ints.
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"""
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mask = self.groupby_object._make_mask_from_positional_indexer(arg)
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return self.groupby_object._mask_selected_obj(mask)
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class GroupByNthSelector:
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"""
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Dynamically substituted for GroupBy.nth to enable both call and index
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"""
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def __init__(self, groupby_object: groupby.GroupBy) -> None:
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self.groupby_object = groupby_object
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def __call__(
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self,
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n: PositionalIndexer | tuple,
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dropna: Literal["any", "all", None] = None,
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) -> DataFrame | Series:
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return self.groupby_object._nth(n, dropna)
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def __getitem__(self, n: PositionalIndexer | tuple) -> DataFrame | Series:
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return self.groupby_object._nth(n)
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