grouper.py

"""
Provide user facing operators for doing the split part of the
split-apply-combine paradigm.
"""
from __future__ import annotations

from typing import Hashable
import warnings

import numpy as np

from pandas._typing import (
    ArrayLike,
    FrameOrSeries,
    final,
)
from pandas.errors import InvalidIndexError
from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.cast import sanitize_to_nanoseconds
from pandas.core.dtypes.common import (
    is_categorical_dtype,
    is_list_like,
    is_scalar,
)

import pandas.core.algorithms as algorithms
from pandas.core.arrays import (
    Categorical,
    ExtensionArray,
)
import pandas.core.common as com
from pandas.core.frame import DataFrame
from pandas.core.groupby import ops
from pandas.core.groupby.categorical import (
    recode_for_groupby,
    recode_from_groupby,
)
from pandas.core.indexes.api import (
    CategoricalIndex,
    Index,
    MultiIndex,
)
from pandas.core.series import Series

from pandas.io.formats.printing import pprint_thing


class Grouper:
    """
    A Grouper allows the user to specify a groupby instruction for an object.

    This specification will select a column via the key parameter, or if the
    level and/or axis parameters are given, a level of the index of the target
    object.

    If `axis` and/or `level` are passed as keywords to both `Grouper` and
    `groupby`, the values passed to `Grouper` take precedence.

    Parameters
    ----------
    key : str, defaults to None
        Groupby key, which selects the grouping column of the target.
    level : name/number, defaults to None
        The level for the target index.
    freq : str / frequency object, defaults to None
        This will groupby the specified frequency if the target selection
        (via key or level) is a datetime-like object. For full specification
        of available frequencies, please see `here
        <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`_.
    axis : str, int, defaults to 0
        Number/name of the axis.
    sort : bool, default to False
        Whether to sort the resulting labels.
    closed : {'left' or 'right'}
        Closed end of interval. Only when `freq` parameter is passed.
    label : {'left' or 'right'}
        Interval boundary to use for labeling.
        Only when `freq` parameter is passed.
    convention : {'start', 'end', 'e', 's'}
        If grouper is PeriodIndex and `freq` parameter is passed.
    base : int, default 0
        Only when `freq` parameter is passed.
        For frequencies that evenly subdivide 1 day, the "origin" of the
        aggregated intervals. For example, for '5min' frequency, base could
        range from 0 through 4. Defaults to 0.

        .. deprecated:: 1.1.0
            The new arguments that you should use are 'offset' or 'origin'.

    loffset : str, DateOffset, timedelta object
        Only when `freq` parameter is passed.

        .. deprecated:: 1.1.0
            loffset is only working for ``.resample(...)`` and not for
            Grouper (:issue:`28302`).
            However, loffset is also deprecated for ``.resample(...)``
            See: :class:`DataFrame.resample`

    origin : {{'epoch', 'start', 'start_day', 'end', 'end_day'}}, Timestamp
        or str, default 'start_day'
        The timestamp on which to adjust the grouping. The timezone of origin must
        match the timezone of the index.
        If a timestamp is not used, these values are also supported:

        - 'epoch': `origin` is 1970-01-01
        - 'start': `origin` is the first value of the timeseries
        - 'start_day': `origin` is the first day at midnight of the timeseries

        .. versionadded:: 1.1.0

        - 'end': `origin` is the last value of the timeseries
        - 'end_day': `origin` is the ceiling midnight of the last day

        .. versionadded:: 1.3.0

    offset : Timedelta or str, default is None
        An offset timedelta added to the origin.

        .. versionadded:: 1.1.0

    dropna : bool, default True
        If True, and if group keys contain NA values, NA values together with
        row/column will be dropped. If False, NA values will also be treated as
        the key in groups.

        .. versionadded:: 1.2.0

    Returns
    -------
    A specification for a groupby instruction

    Examples
    --------
    Syntactic sugar for ``df.groupby('A')``

    >>> df = pd.DataFrame(
    ...     {
    ...         "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"],
    ...         "Speed": [100, 5, 200, 300, 15],
    ...     }
    ... )
    >>> df
       Animal  Speed
    0  Falcon    100
    1  Parrot      5
    2  Falcon    200
    3  Falcon    300
    4  Parrot     15
    >>> df.groupby(pd.Grouper(key="Animal")).mean()
            Speed
    Animal
    Falcon  200.0
    Parrot   10.0

    Specify a resample operation on the column 'Publish date'

    >>> df = pd.DataFrame(
    ...    {
    ...        "Publish date": [
    ...             pd.Timestamp("2000-01-02"),
    ...             pd.Timestamp("2000-01-02"),
    ...             pd.Timestamp("2000-01-09"),
    ...             pd.Timestamp("2000-01-16")
    ...         ],
    ...         "ID": [0, 1, 2, 3],
    ...         "Price": [10, 20, 30, 40]
    ...     }
    ... )
    >>> df
      Publish date  ID  Price
    0   2000-01-02   0     10
    1   2000-01-02   1     20
    2   2000-01-09   2     30
    3   2000-01-16   3     40
    >>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean()
                   ID  Price
    Publish date
    2000-01-02    0.5   15.0
    2000-01-09    2.0   30.0
    2000-01-16    3.0   40.0

    If you want to adjust the start of the bins based on a fixed timestamp:

    >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
    >>> rng = pd.date_range(start, end, freq='7min')
    >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
    >>> ts
    2000-10-01 23:30:00     0
    2000-10-01 23:37:00     3
    2000-10-01 23:44:00     6
    2000-10-01 23:51:00     9
    2000-10-01 23:58:00    12
    2000-10-02 00:05:00    15
    2000-10-02 00:12:00    18
    2000-10-02 00:19:00    21
    2000-10-02 00:26:00    24
    Freq: 7T, dtype: int64

    >>> ts.groupby(pd.Grouper(freq='17min')).sum()
    2000-10-01 23:14:00     0
    2000-10-01 23:31:00     9
    2000-10-01 23:48:00    21
    2000-10-02 00:05:00    54
    2000-10-02 00:22:00    24
    Freq: 17T, dtype: int64

    >>> ts.groupby(pd.Grouper(freq='17min', origin='epoch')).sum()
    2000-10-01 23:18:00     0
    2000-10-01 23:35:00    18
    2000-10-01 23:52:00    27
    2000-10-02 00:09:00    39
    2000-10-02 00:26:00    24
    Freq: 17T, dtype: int64

    >>> ts.groupby(pd.Grouper(freq='17min', origin='2000-01-01')).sum()
    2000-10-01 23:24:00     3
    2000-10-01 23:41:00    15
    2000-10-01 23:58:00    45
    2000-10-02 00:15:00    45
    Freq: 17T, dtype: int64

    If you want to adjust the start of the bins with an `offset` Timedelta, the two
    following lines are equivalent:

    >>> ts.groupby(pd.Grouper(freq='17min', origin='start')).sum()
    2000-10-01 23:30:00     9
    2000-10-01 23:47:00    21
    2000-10-02 00:04:00    54
    2000-10-02 00:21:00    24
    Freq: 17T, dtype: int64

    >>> ts.groupby(pd.Grouper(freq='17min', offset='23h30min')).sum()
    2000-10-01 23:30:00     9
    2000-10-01 23:47:00    21
    2000-10-02 00:04:00    54
    2000-10-02 00:21:00    24
    Freq: 17T, dtype: int64

    To replace the use of the deprecated `base` argument, you can now use `offset`,
    in this example it is equivalent to have `base=2`:

    >>> ts.groupby(pd.Grouper(freq='17min', offset='2min')).sum()
    2000-10-01 23:16:00     0
    2000-10-01 23:33:00     9
    2000-10-01 23:50:00    36
    2000-10-02 00:07:00    39
    2000-10-02 00:24:00    24
    Freq: 17T, dtype: int64
    """

    axis: int
    sort: bool
    dropna: bool
    _gpr_index: Index | None
    _grouper: Index | None

    _attributes: tuple[str, ...] = ("key", "level", "freq", "axis", "sort")

    def __new__(cls, *args, **kwargs):
        if kwargs.get("freq") is not None:
            from pandas.core.resample import TimeGrouper

            _check_deprecated_resample_kwargs(kwargs, origin=cls)
            cls = TimeGrouper
        return super().__new__(cls)

    def __init__(
        self,
        key=None,
        level=None,
        freq=None,
        axis: int = 0,
        sort: bool = False,
        dropna: bool = True,
    ):
        self.key = key
        self.level = level
        self.freq = freq
        self.axis = axis
        self.sort = sort

        self.grouper = None
        self._gpr_index = None
        self.obj = None
        self.indexer = None
        self.binner = None
        self._grouper = None
        self._indexer = None
        self.dropna = dropna

    @final
    @property
    def ax(self) -> Index:
        index = self._gpr_index
        if index is None:
            raise ValueError("_set_grouper must be called before ax is accessed")
        return index

    def _get_grouper(self, obj: FrameOrSeries, validate: bool = True):
        """
        Parameters
        ----------
        obj : Series or DataFrame
        validate : bool, default True
            if True, validate the grouper

        Returns
        -------
        a tuple of binner, grouper, obj (possibly sorted)
        """
        self._set_grouper(obj)
        # error: Value of type variable "FrameOrSeries" of "get_grouper" cannot be
        # "Optional[Any]"
        # error: Incompatible types in assignment (expression has type "BaseGrouper",
        # variable has type "None")
        self.grouper, _, self.obj = get_grouper(  # type: ignore[type-var,assignment]
            self.obj,
            [self.key],
            axis=self.axis,
            level=self.level,
            sort=self.sort,
            validate=validate,
            dropna=self.dropna,
        )

        return self.binner, self.grouper, self.obj

    @final
    def _set_grouper(self, obj: FrameOrSeries, sort: bool = False):
        """
        given an object and the specifications, setup the internal grouper
        for this particular specification

        Parameters
        ----------
        obj : Series or DataFrame
        sort : bool, default False
            whether the resulting grouper should be sorted
        """
        assert obj is not None

        if self.key is not None and self.level is not None:
            raise ValueError("The Grouper cannot specify both a key and a level!")

        # Keep self.grouper value before overriding
        if self._grouper is None:
            # TODO: What are we assuming about subsequent calls?
            self._grouper = self._gpr_index
            self._indexer = self.indexer

        # the key must be a valid info item
        if self.key is not None:
            key = self.key
            # The 'on' is already defined
            if getattr(self._gpr_index, "name", None) == key and isinstance(
                obj, Series
            ):
                # Sometimes self._grouper will have been resorted while
                # obj has not. In this case there is a mismatch when we
                # call self._grouper.take(obj.index) so we need to undo the sorting
                # before we call _grouper.take.
                assert self._grouper is not None
                if self._indexer is not None:
                    reverse_indexer = self._indexer.argsort()
                    unsorted_ax = self._grouper.take(reverse_indexer)
                    ax = unsorted_ax.take(obj.index)
                else:
                    ax = self._grouper.take(obj.index)
            else:
                if key not in obj._info_axis:
                    raise KeyError(f"The grouper name {key} is not found")
                ax = Index(obj[key], name=key)

        else:
            ax = obj._get_axis(self.axis)
            if self.level is not None:
                level = self.level

                # if a level is given it must be a mi level or
                # equivalent to the axis name
                if isinstance(ax, MultiIndex):
                    level = ax._get_level_number(level)
                    ax = Index(ax._get_level_values(level), name=ax.names[level])

                else:
                    if level not in (0, ax.name):
                        raise ValueError(f"The level {level} is not valid")

        # possibly sort
        if (self.sort or sort) and not ax.is_monotonic:
            # use stable sort to support first, last, nth
            # TODO: why does putting na_position="first" fix datetimelike cases?
            indexer = self.indexer = ax.array.argsort(
                kind="mergesort", na_position="first"
            )
            ax = ax.take(indexer)
            obj = obj.take(indexer, axis=self.axis)

        # error: Incompatible types in assignment (expression has type
        # "FrameOrSeries", variable has type "None")
        self.obj = obj  # type: ignore[assignment]
        self._gpr_index = ax
        return self._gpr_index

    @final
    @property
    def groups(self):
        # error: "None" has no attribute "groups"
        return self.grouper.groups  # type: ignore[attr-defined]

    @final
    def __repr__(self) -> str:
        attrs_list = (
            f"{attr_name}={repr(getattr(self, attr_name))}"
            for attr_name in self._attributes
            if getattr(self, attr_name) is not None
        )
        attrs = ", ".join(attrs_list)
        cls_name = type(self).__name__
        return f"{cls_name}({attrs})"


@final
class Grouping:
    """
    Holds the grouping information for a single key

    Parameters
    ----------
    index : Index
    grouper :
    obj : DataFrame or Series
    name : Label
    level :
    observed : bool, default False
        If we are a Categorical, use the observed values
    in_axis : if the Grouping is a column in self.obj and hence among
        Groupby.exclusions list

    Returns
    -------
    **Attributes**:
      * indices : dict of {group -> index_list}
      * codes : ndarray, group codes
      * group_index : unique groups
      * groups : dict of {group -> label_list}
    """

    _codes: np.ndarray | None = None
    _group_index: Index | None = None
    _passed_categorical: bool
    _all_grouper: Categorical | None
    _index: Index

    def __init__(
        self,
        index: Index,
        grouper=None,
        obj: FrameOrSeries | None = None,
        level=None,
        sort: bool = True,
        observed: bool = False,
        in_axis: bool = False,
        dropna: bool = True,
    ):
        self.level = level
        self._orig_grouper = grouper
        self.grouping_vector = _convert_grouper(index, grouper)
        self._all_grouper = None
        self._index = index
        self._sort = sort
        self.obj = obj
        self._observed = observed
        self.in_axis = in_axis
        self._dropna = dropna

        self._passed_categorical = False

        # we have a single grouper which may be a myriad of things,
        # some of which are dependent on the passing in level

        ilevel = self._ilevel
        if ilevel is not None:
            mapper = self.grouping_vector
            # In extant tests, the new self.grouping_vector matches
            #  `index.get_level_values(ilevel)` whenever
            #  mapper is None and isinstance(index, MultiIndex)
            (
                self.grouping_vector,  # Index
                self._codes,
                self._group_index,
            ) = index._get_grouper_for_level(mapper, ilevel)

        # a passed Grouper like, directly get the grouper in the same way
        # as single grouper groupby, use the group_info to get codes
        elif isinstance(self.grouping_vector, Grouper):
            # get the new grouper; we already have disambiguated
            # what key/level refer to exactly, don't need to
            # check again as we have by this point converted these
            # to an actual value (rather than a pd.Grouper)
            _, newgrouper, newobj = self.grouping_vector._get_grouper(
                # error: Value of type variable "FrameOrSeries" of "_get_grouper"
                # of "Grouper" cannot be "Optional[FrameOrSeries]"
                self.obj,  # type: ignore[type-var]
                validate=False,
            )
            self.obj = newobj

            ng = newgrouper._get_grouper()
            if isinstance(newgrouper, ops.BinGrouper):
                # in this case we have `ng is newgrouper`
                self.grouping_vector = ng
            else:
                # ops.BaseGrouper
                # use Index instead of ndarray so we can recover the name
                self.grouping_vector = Index(ng, name=newgrouper.result_index.name)

        elif is_categorical_dtype(self.grouping_vector):
            # a passed Categorical
            self._passed_categorical = True

            self.grouping_vector, self._all_grouper = recode_for_groupby(
                self.grouping_vector, sort, observed
            )

        elif not isinstance(
            self.grouping_vector, (Series, Index, ExtensionArray, np.ndarray)
        ):
            # no level passed
            if getattr(self.grouping_vector, "ndim", 1) != 1:
                t = self.name or str(type(self.grouping_vector))
                raise ValueError(f"Grouper for '{t}' not 1-dimensional")

            self.grouping_vector = index.map(self.grouping_vector)

            if not (
                hasattr(self.grouping_vector, "__len__")
                and len(self.grouping_vector) == len(index)
            ):
                grper = pprint_thing(self.grouping_vector)
                errmsg = (
                    "Grouper result violates len(labels) == "
                    f"len(data)\nresult: {grper}"
                )
                self.grouping_vector = None  # Try for sanity
                raise AssertionError(errmsg)

        if isinstance(self.grouping_vector, np.ndarray):
            # if we have a date/time-like grouper, make sure that we have
            # Timestamps like
            self.grouping_vector = sanitize_to_nanoseconds(self.grouping_vector)

    def __repr__(self) -> str:
        return f"Grouping({self.name})"

    def __iter__(self):
        return iter(self.indices)

    @cache_readonly
    def name(self) -> Hashable:
        ilevel = self._ilevel
        if ilevel is not None:
            return self._index.names[ilevel]

        if isinstance(self._orig_grouper, (Index, Series)):
            return self._orig_grouper.name

        elif isinstance(self.grouping_vector, ops.BaseGrouper):
            return self.grouping_vector.result_index.name

        elif isinstance(self.grouping_vector, Index):
            return self.grouping_vector.name

        # otherwise we have ndarray or ExtensionArray -> no name
        return None

    @cache_readonly
    def _ilevel(self) -> int | None:
        """
        If necessary, converted index level name to index level position.
        """
        level = self.level
        if level is None:
            return None
        if not isinstance(level, int):
            index = self._index
            if level not in index.names:
                raise AssertionError(f"Level {level} not in index")
            return index.names.index(level)
        return level

    @property
    def ngroups(self) -> int:
        return len(self.group_index)

    @cache_readonly
    def indices(self):
        # we have a list of groupers
        if isinstance(self.grouping_vector, ops.BaseGrouper):
            return self.grouping_vector.indices

        values = Categorical(self.grouping_vector)
        return values._reverse_indexer()

    @property
    def codes(self) -> np.ndarray:
        if self._codes is not None:
            # _codes is set in __init__ for MultiIndex cases
            return self._codes

        return self._codes_and_uniques[0]

    @cache_readonly
    def group_arraylike(self) -> ArrayLike:
        """
        Analogous to result_index, but holding an ArrayLike to ensure
        we can can retain ExtensionDtypes.
        """
        if self._group_index is not None:
            # _group_index is set in __init__ for MultiIndex cases
            return self._group_index._values

        elif self._all_grouper is not None:
            # retain dtype for categories, including unobserved ones
            return self.result_index._values

        return self._codes_and_uniques[1]

    @cache_readonly
    def result_index(self) -> Index:
        # result_index retains dtype for categories, including unobserved ones,
        #  which group_index does not
        if self._all_grouper is not None:
            group_idx = self.group_index
            assert isinstance(group_idx, CategoricalIndex)
            return recode_from_groupby(self._all_grouper, self._sort, group_idx)
        return self.group_index

    @cache_readonly
    def group_index(self) -> Index:
        if self._group_index is not None:
            # _group_index is set in __init__ for MultiIndex cases
            return self._group_index

        uniques = self._codes_and_uniques[1]
        return Index(uniques, name=self.name)

    @cache_readonly
    def _codes_and_uniques(self) -> tuple[np.ndarray, ArrayLike]:
        if self._passed_categorical:
            # we make a CategoricalIndex out of the cat grouper
            # preserving the categories / ordered attributes
            cat = self.grouping_vector
            categories = cat.categories

            if self._observed:
                ucodes = algorithms.unique1d(cat.codes)
                ucodes = ucodes[ucodes != -1]
                if self._sort or cat.ordered:
                    ucodes = np.sort(ucodes)
            else:
                ucodes = np.arange(len(categories))

            uniques = Categorical.from_codes(
                codes=ucodes, categories=categories, ordered=cat.ordered
            )
            return cat.codes, uniques

        elif isinstance(self.grouping_vector, ops.BaseGrouper):
            # we have a list of groupers
            codes = self.grouping_vector.codes_info
            uniques = self.grouping_vector.result_arraylike
        else:
            # GH35667, replace dropna=False with na_sentinel=None
            if not self._dropna:
                na_sentinel = None
            else:
                na_sentinel = -1
            codes, uniques = algorithms.factorize(
                self.grouping_vector, sort=self._sort, na_sentinel=na_sentinel
            )
        return codes, uniques

    @cache_readonly
    def groups(self) -> dict[Hashable, np.ndarray]:
        return self._index.groupby(Categorical.from_codes(self.codes, self.group_index))


def get_grouper(
    obj: FrameOrSeries,
    key=None,
    axis: int = 0,
    level=None,
    sort: bool = True,
    observed: bool = False,
    mutated: bool = False,
    validate: bool = True,
    dropna: bool = True,
) -> tuple[ops.BaseGrouper, frozenset[Hashable], FrameOrSeries]:
    """
    Create and return a BaseGrouper, which is an internal
    mapping of how to create the grouper indexers.
    This may be composed of multiple Grouping objects, indicating
    multiple groupers

    Groupers are ultimately index mappings. They can originate as:
    index mappings, keys to columns, functions, or Groupers

    Groupers enable local references to axis,level,sort, while
    the passed in axis, level, and sort are 'global'.

    This routine tries to figure out what the passing in references
    are and then creates a Grouping for each one, combined into
    a BaseGrouper.

    If observed & we have a categorical grouper, only show the observed
    values.

    If validate, then check for key/level overlaps.

    """
    group_axis = obj._get_axis(axis)

    # validate that the passed single level is compatible with the passed
    # axis of the object
    if level is not None:
        # TODO: These if-block and else-block are almost same.
        # MultiIndex instance check is removable, but it seems that there are
        # some processes only for non-MultiIndex in else-block,
        # eg. `obj.index.name != level`. We have to consider carefully whether
        # these are applicable for MultiIndex. Even if these are applicable,
        # we need to check if it makes no side effect to subsequent processes
        # on the outside of this condition.
        # (GH 17621)
        if isinstance(group_axis, MultiIndex):
            if is_list_like(level) and len(level) == 1:
                level = level[0]

            if key is None and is_scalar(level):
                # Get the level values from group_axis
                key = group_axis.get_level_values(level)
                level = None

        else:
            # allow level to be a length-one list-like object
            # (e.g., level=[0])
            # GH 13901
            if is_list_like(level):
                nlevels = len(level)
                if nlevels == 1:
                    level = level[0]
                elif nlevels == 0:
                    raise ValueError("No group keys passed!")
                else:
                    raise ValueError("multiple levels only valid with MultiIndex")

            if isinstance(level, str):
                if obj._get_axis(axis).name != level:
                    raise ValueError(
                        f"level name {level} is not the name "
                        f"of the {obj._get_axis_name(axis)}"
                    )
            elif level > 0 or level < -1:
                raise ValueError("level > 0 or level < -1 only valid with MultiIndex")

            # NOTE: `group_axis` and `group_axis.get_level_values(level)`
            # are same in this section.
            level = None
            key = group_axis

    # a passed-in Grouper, directly convert
    if isinstance(key, Grouper):
        binner, grouper, obj = key._get_grouper(obj, validate=False)
        if key.key is None:
            return grouper, frozenset(), obj
        else:
            return grouper, frozenset({key.key}), obj

    # already have a BaseGrouper, just return it
    elif isinstance(key, ops.BaseGrouper):
        return key, frozenset(), obj

    if not isinstance(key, list):
        keys = [key]
        match_axis_length = False
    else:
        keys = key
        match_axis_length = len(keys) == len(group_axis)

    # what are we after, exactly?
    any_callable = any(callable(g) or isinstance(g, dict) for g in keys)
    any_groupers = any(isinstance(g, Grouper) for g in keys)
    any_arraylike = any(
        isinstance(g, (list, tuple, Series, Index, np.ndarray)) for g in keys
    )

    # is this an index replacement?
    if (
        not any_callable
        and not any_arraylike
        and not any_groupers
        and match_axis_length
        and level is None
    ):
        if isinstance(obj, DataFrame):
            all_in_columns_index = all(
                g in obj.columns or g in obj.index.names for g in keys
            )
        else:
            assert isinstance(obj, Series)
            all_in_columns_index = all(g in obj.index.names for g in keys)

        if not all_in_columns_index:
            keys = [com.asarray_tuplesafe(keys)]

    if isinstance(level, (tuple, list)):
        if key is None:
            keys = [None] * len(level)
        levels = level
    else:
        levels = [level] * len(keys)

    groupings: list[Grouping] = []
    exclusions: set[Hashable] = set()

    # if the actual grouper should be obj[key]
    def is_in_axis(key) -> bool:
        if not _is_label_like(key):
            # items -> .columns for DataFrame, .index for Series
            items = obj.axes[-1]
            try:
                items.get_loc(key)
            except (KeyError, TypeError, InvalidIndexError):
                # TypeError shows up here if we pass e.g. Int64Index
                return False

        return True

    # if the grouper is obj[name]
    def is_in_obj(gpr) -> bool:
        if not hasattr(gpr, "name"):
            return False
        try:
            return gpr is obj[gpr.name]
        except (KeyError, IndexError):
            # IndexError reached in e.g. test_skip_group_keys when we pass
            #  lambda here
            return False

    for gpr, level in zip(keys, levels):

        if is_in_obj(gpr):  # df.groupby(df['name'])
            in_axis = True
            exclusions.add(gpr.name)

        elif is_in_axis(gpr):  # df.groupby('name')
            if gpr in obj:
                if validate:
                    obj._check_label_or_level_ambiguity(gpr, axis=axis)
                in_axis, name, gpr = True, gpr, obj[gpr]
                if gpr.ndim != 1:
                    # non-unique columns; raise here to get the name in the
                    # exception message
                    raise ValueError(f"Grouper for '{name}' not 1-dimensional")
                exclusions.add(name)
            elif obj._is_level_reference(gpr, axis=axis):
                in_axis, level, gpr = False, gpr, None
            else:
                raise KeyError(gpr)
        elif isinstance(gpr, Grouper) and gpr.key is not None:
            # Add key to exclusions
            exclusions.add(gpr.key)
            in_axis = False
        else:
            in_axis = False

        if is_categorical_dtype(gpr) and len(gpr) != obj.shape[axis]:
            raise ValueError(
                f"Length of grouper ({len(gpr)}) and axis ({obj.shape[axis]}) "
                "must be same length"
            )

        # create the Grouping
        # allow us to passing the actual Grouping as the gpr
        ping = (
            Grouping(
                group_axis,
                gpr,
                obj=obj,
                level=level,
                sort=sort,
                observed=observed,
                in_axis=in_axis,
                dropna=dropna,
            )
            if not isinstance(gpr, Grouping)
            else gpr
        )

        groupings.append(ping)

    if len(groupings) == 0 and len(obj):
        raise ValueError("No group keys passed!")
    elif len(groupings) == 0:
        groupings.append(Grouping(Index([], dtype="int"), np.array([], dtype=np.intp)))

    # create the internals grouper
    grouper = ops.BaseGrouper(
        group_axis, groupings, sort=sort, mutated=mutated, dropna=dropna
    )
    return grouper, frozenset(exclusions), obj


def _is_label_like(val) -> bool:
    return isinstance(val, (str, tuple)) or (val is not None and is_scalar(val))


def _convert_grouper(axis: Index, grouper):
    if isinstance(grouper, dict):
        return grouper.get
    elif isinstance(grouper, Series):
        if grouper.index.equals(axis):
            return grouper._values
        else:
            return grouper.reindex(axis)._values
    elif isinstance(grouper, MultiIndex):
        return grouper._values
    elif isinstance(grouper, (list, tuple, Series, Index, np.ndarray)):
        if len(grouper) != len(axis):
            raise ValueError("Grouper and axis must be same length")

        if isinstance(grouper, (list, tuple)):
            grouper = com.asarray_tuplesafe(grouper)
        return grouper
    else:
        return grouper


def _check_deprecated_resample_kwargs(kwargs, origin):
    """
    Check for use of deprecated parameters in ``resample`` and related functions.

    Raises the appropriate warnings if these parameters are detected.
    Only sets an approximate ``stacklevel`` for the warnings (see #37603, #36629).

    Parameters
    ----------
    kwargs : dict
        Dictionary of keyword arguments to check for deprecated parameters.
    origin : object
        From where this function is being called; either Grouper or TimeGrouper. Used
        to determine an approximate stacklevel.
    """
    from pandas.core.resample import TimeGrouper

    # Deprecation warning of `base` and `loffset` since v1.1.0:
    # we are raising the warning here to be able to set the `stacklevel`
    # properly since we need to raise the `base` and `loffset` deprecation
    # warning from three different cases:
    #   core/generic.py::NDFrame.resample
    #   core/groupby/groupby.py::GroupBy.resample
    #   core/groupby/grouper.py::Grouper
    # raising these warnings from TimeGrouper directly would fail the test:
    #   tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base
    # hacky way to set the stacklevel: if cls is TimeGrouper it means
    # that the call comes from a pandas internal call of resample,
    # otherwise it comes from pd.Grouper
    stacklevel = (5 if origin is TimeGrouper else 2) + 1
    # the + 1 is for this helper function, check_deprecated_resample_kwargs

    if kwargs.get("base", None) is not None:
        warnings.warn(
            "'base' in .resample() and in Grouper() is deprecated.\n"
            "The new arguments that you should use are 'offset' or 'origin'.\n"
            '\n>>> df.resample(freq="3s", base=2)\n'
            "\nbecomes:\n"
            '\n>>> df.resample(freq="3s", offset="2s")\n',
            FutureWarning,
            stacklevel=stacklevel,
        )
    if kwargs.get("loffset", None) is not None:
        warnings.warn(
            "'loffset' in .resample() and in Grouper() is deprecated.\n"
            '\n>>> df.resample(freq="3s", loffset="8H")\n'
            "\nbecomes:\n"
            "\n>>> from pandas.tseries.frequencies import to_offset"
            '\n>>> df = df.resample(freq="3s").mean()'
            '\n>>> df.index = df.index.to_timestamp() + to_offset("8H")\n',
            FutureWarning,
            stacklevel=stacklevel,
        )