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projektAI/venv/Lib/site-packages/pandas/core/groupby/grouper.py

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2021-06-06 22:13:05 +02:00
"""
Provide user facing operators for doing the split part of the
split-apply-combine paradigm.
"""
from typing import Dict, Hashable, List, Optional, Set, Tuple
import warnings
import numpy as np
from pandas._typing import FrameOrSeries, Label, final
from pandas.errors import InvalidIndexError
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_datetime64_dtype,
is_list_like,
is_scalar,
is_timedelta64_dtype,
)
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'}, 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
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
Parrot 10
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
"""
_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
# 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 = 4 if cls is TimeGrouper else 2
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,
)
cls = TimeGrouper
return super().__new__(cls)
def __init__(
self, key=None, level=None, freq=None, axis=0, sort=False, dropna=True
):
self.key = key
self.level = level
self.freq = freq
self.axis = axis
self.sort = sort
self.grouper = None
self.obj = None
self.indexer = None
self.binner = None
self._grouper = None
self._indexer = None
self.dropna = dropna
@final
@property
def ax(self):
return self.grouper
def _get_grouper(self, obj, validate: bool = True):
"""
Parameters
----------
obj : the subject object
validate : boolean, default True
if True, validate the grouper
Returns
-------
a tuple of binner, grouper, obj (possibly sorted)
"""
self._set_grouper(obj)
# pandas\core\groupby\grouper.py:310: error: Value of type variable
# "FrameOrSeries" of "get_grouper" cannot be "Optional[Any]"
# [type-var]
self.grouper, _, self.obj = get_grouper( # type: ignore[type-var]
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:
self._grouper = self.grouper
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.grouper, "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
indexer = self.indexer = ax.argsort(kind="mergesort")
ax = ax.take(indexer)
obj = obj.take(indexer, axis=self.axis)
self.obj = obj
self.grouper = ax
return self.grouper
@final
@property
def groups(self):
# pandas\core\groupby\grouper.py:382: error: Item "None" of
# "Optional[Any]" has no attribute "groups" [union-attr]
return self.grouper.groups # type: ignore[union-attr]
@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}
"""
def __init__(
self,
index: Index,
grouper=None,
obj: Optional[FrameOrSeries] = None,
name=None,
level=None,
sort: bool = True,
observed: bool = False,
in_axis: bool = False,
dropna: bool = True,
):
self.name = name
self.level = level
self.grouper = _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
# right place for this?
if isinstance(grouper, (Series, Index)) and name is None:
self.name = grouper.name
if isinstance(grouper, MultiIndex):
self.grouper = grouper._values
# we have a single grouper which may be a myriad of things,
# some of which are dependent on the passing in level
if level is not None:
if not isinstance(level, int):
if level not in index.names:
raise AssertionError(f"Level {level} not in index")
level = index.names.index(level)
if self.name is None:
self.name = index.names[level]
(
self.grouper,
self._codes,
self._group_index,
) = index._get_grouper_for_level(self.grouper, level)
# 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.grouper, 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)
_, grouper, _ = self.grouper._get_grouper(self.obj, validate=False)
if self.name is None:
self.name = grouper.result_index.name
self.obj = self.grouper.obj
self.grouper = grouper._get_grouper()
else:
if self.grouper is None and self.name is not None and self.obj is not None:
self.grouper = self.obj[self.name]
elif isinstance(self.grouper, (list, tuple)):
self.grouper = com.asarray_tuplesafe(self.grouper)
# a passed Categorical
elif is_categorical_dtype(self.grouper):
self.grouper, self.all_grouper = recode_for_groupby(
self.grouper, self.sort, observed
)
categories = self.grouper.categories
# we make a CategoricalIndex out of the cat grouper
# preserving the categories / ordered attributes
self._codes = self.grouper.codes
if observed:
codes = algorithms.unique1d(self.grouper.codes)
codes = codes[codes != -1]
if sort or self.grouper.ordered:
codes = np.sort(codes)
else:
codes = np.arange(len(categories))
self._group_index = CategoricalIndex(
Categorical.from_codes(
codes=codes, categories=categories, ordered=self.grouper.ordered
),
name=self.name,
)
# we are done
if isinstance(self.grouper, Grouping):
self.grouper = self.grouper.grouper
# no level passed
elif not isinstance(
self.grouper, (Series, Index, ExtensionArray, np.ndarray)
):
if getattr(self.grouper, "ndim", 1) != 1:
t = self.name or str(type(self.grouper))
raise ValueError(f"Grouper for '{t}' not 1-dimensional")
self.grouper = self.index.map(self.grouper)
if not (
hasattr(self.grouper, "__len__")
and len(self.grouper) == len(self.index)
):
grper = pprint_thing(self.grouper)
errmsg = (
"Grouper result violates len(labels) == "
f"len(data)\nresult: {grper}"
)
self.grouper = None # Try for sanity
raise AssertionError(errmsg)
# if we have a date/time-like grouper, make sure that we have
# Timestamps like
if getattr(self.grouper, "dtype", None) is not None:
if is_datetime64_dtype(self.grouper):
self.grouper = self.grouper.astype("datetime64[ns]")
elif is_timedelta64_dtype(self.grouper):
self.grouper = self.grouper.astype("timedelta64[ns]")
def __repr__(self) -> str:
return f"Grouping({self.name})"
def __iter__(self):
return iter(self.indices)
_codes: Optional[np.ndarray] = None
_group_index: Optional[Index] = None
@property
def ngroups(self) -> int:
return len(self.group_index)
@cache_readonly
def indices(self):
# we have a list of groupers
if isinstance(self.grouper, ops.BaseGrouper):
return self.grouper.indices
values = Categorical(self.grouper)
return values._reverse_indexer()
@property
def codes(self) -> np.ndarray:
if self._codes is None:
self._make_codes()
return self._codes
@cache_readonly
def result_index(self) -> Index:
if self.all_grouper is not None:
group_idx = self.group_index
assert isinstance(group_idx, CategoricalIndex) # set in __init__
return recode_from_groupby(self.all_grouper, self.sort, group_idx)
return self.group_index
@property
def group_index(self) -> Index:
if self._group_index is None:
self._make_codes()
assert self._group_index is not None
return self._group_index
def _make_codes(self) -> None:
if self._codes is not None and self._group_index is not None:
return
# we have a list of groupers
if isinstance(self.grouper, ops.BaseGrouper):
codes = self.grouper.codes_info
uniques = self.grouper.result_index
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.grouper, sort=self.sort, na_sentinel=na_sentinel
)
uniques = Index(uniques, name=self.name)
self._codes = codes
self._group_index = 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", Set[Label], 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, set(), obj
else:
return grouper, {key.key}, obj
# already have a BaseGrouper, just return it
elif isinstance(key, ops.BaseGrouper):
return key, set(), 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[Label] = 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 i, (gpr, level) in enumerate(zip(keys, levels)):
if is_in_obj(gpr): # df.groupby(df['name'])
in_axis, name = True, gpr.name
exclusions.add(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]
exclusions.add(name)
elif obj._is_level_reference(gpr, axis=axis):
in_axis, name, level, gpr = False, None, 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, name = False, None
else:
in_axis, name = False, None
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,
name=name,
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, 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, (list, Series, Index, np.ndarray)):
if len(grouper) != len(axis):
raise ValueError("Grouper and axis must be same length")
return grouper
else:
return grouper
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