text: """
  Provide the groupby split-apply-combine paradigm. Define the GroupBy
  class providing the base-class of operations.
  
  The SeriesGroupBy and DataFrameGroupBy sub-class
  (defined in pandas.core.groupby.generic)
  expose these user-facing objects to provide specific functionailty.
  """
  
  import collections
  from contextlib import contextmanager
  import datetime
  from functools import partial, wraps
  import inspect
  import re
  import types
  from typing import FrozenSet, Iterable, List, Optional, Tuple, Type, Union
  
  import numpy as np
  
  from pandas._config.config import option_context
  
  from pandas._libs import Timestamp
  import pandas._libs.groupby as libgroupby
  from pandas.compat import set_function_name
  from pandas.compat.numpy import function as nv
  from pandas.errors import AbstractMethodError
  from pandas.util._decorators import Appender, Substitution, cache_readonly
  
  from pandas.core.dtypes.cast import maybe_downcast_to_dtype
  from pandas.core.dtypes.common import (
      ensure_float,
      is_datetime64_dtype,
      is_extension_array_dtype,
      is_integer_dtype,
      is_numeric_dtype,
      is_object_dtype,
      is_scalar,
  )
  from pandas.core.dtypes.missing import isna, notna
  
  from pandas.core import nanops
  import pandas.core.algorithms as algorithms
  from pandas.core.arrays import Categorical, try_cast_to_ea
  from pandas.core.base import DataError, PandasObject, SelectionMixin
  import pandas.core.common as com
  from pandas.core.frame import DataFrame
  from pandas.core.generic import NDFrame
  from pandas.core.groupby import base, ops
  from pandas.core.index import CategoricalIndex, Index, MultiIndex
  from pandas.core.series import Series
  from pandas.core.sorting import get_group_index_sorter
  
  _common_see_also = """
          See Also
          --------
          Series.%(name)s
          DataFrame.%(name)s
  """
  
  _apply_docs = dict(
      template="""
      Apply function `func`  group-wise and combine the results together.
  
      The function passed to `apply` must take a {input} as its first
      argument and return a DataFrame, Series or scalar. `apply` will
      then take care of combining the results back together into a single
      dataframe or series. `apply` is therefore a highly flexible
      grouping method.
  
      While `apply` is a very flexible method, its downside is that
      using it can be quite a bit slower than using more specific methods
      like `agg` or `transform`. Pandas offers a wide range of method that will
      be much faster than using `apply` for their specific purposes, so try to
      use them before reaching for `apply`.
  
      Parameters
      ----------
      func : callable
          A callable that takes a {input} as its first argument, and
          returns a dataframe, a series or a scalar. In addition the
          callable may take positional and keyword arguments.
      args, kwargs : tuple and dict
          Optional positional and keyword arguments to pass to `func`.
  
      Returns
      -------
      applied : Series or DataFrame
  
      See Also
      --------
      pipe : Apply function to the full GroupBy object instead of to each
          group.
      aggregate : Apply aggregate function to the GroupBy object.
      transform : Apply function column-by-column to the GroupBy object.
      Series.apply : Apply a function to a Series.
      DataFrame.apply : Apply a function to each row or column of a DataFrame.
      """,
      dataframe_examples="""
      >>> df = pd.DataFrame({'A': 'a a b'.split(),
                             'B': [1,2,3],
                             'C': [4,6, 5]})
      >>> g = df.groupby('A')
  
      Notice that ``g`` has two groups, ``a`` and ``b``.
      Calling `apply` in various ways, we can get different grouping results:
  
      Example 1: below the function passed to `apply` takes a DataFrame as
      its argument and returns a DataFrame. `apply` combines the result for
      each group together into a new DataFrame:
  
      >>> g[['B', 'C']].apply(lambda x: x / x.sum())
                B    C
      0  0.333333  0.4
      1  0.666667  0.6
      2  1.000000  1.0
  
      Example 2: The function passed to `apply` takes a DataFrame as
      its argument and returns a Series.  `apply` combines the result for
      each group together into a new DataFrame:
  
      >>> g[['B', 'C']].apply(lambda x: x.max() - x.min())
         B  C
      A
      a  1  2
      b  0  0
  
      Example 3: The function passed to `apply` takes a DataFrame as
      its argument and returns a scalar. `apply` combines the result for
      each group together into a Series, including setting the index as
      appropriate:
  
      >>> g.apply(lambda x: x.C.max() - x.B.min())
      A
      a    5
      b    2
      dtype: int64
      """,
      series_examples="""
      >>> s = pd.Series([0, 1, 2], index='a a b'.split())
      >>> g = s.groupby(s.index)
  
      From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``.
      Calling `apply` in various ways, we can get different grouping results:
  
      Example 1: The function passed to `apply` takes a Series as
      its argument and returns a Series.  `apply` combines the result for
      each group together into a new Series:
  
      >>> g.apply(lambda x:  x*2 if x.name == 'b' else x/2)
      0    0.0
      1    0.5
      2    4.0
      dtype: float64
  
      Example 2: The function passed to `apply` takes a Series as
      its argument and returns a scalar. `apply` combines the result for
      each group together into a Series, including setting the index as
      appropriate:
  
      >>> g.apply(lambda x: x.max() - x.min())
      a    1
      b    0
      dtype: int64
  
      Notes
      -----
      In the current implementation `apply` calls `func` twice on the
      first group to decide whether it can take a fast or slow code
      path. This can lead to unexpected behavior if `func` has
      side-effects, as they will take effect twice for the first
      group.
  
      Examples
      --------
      {examples}
      """,
  )
  
  _pipe_template = """
  Apply a function `func` with arguments to this %(klass)s object and return
  the function's result.
  
  %(versionadded)s
  
  Use `.pipe` when you want to improve readability by chaining together
  functions that expect Series, DataFrames, GroupBy or Resampler objects.
  Instead of writing
  
  >>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)
  
  You can write
  
  >>> (df.groupby('group')
  ...    .pipe(f)
  ...    .pipe(g, arg1=a)
  ...    .pipe(h, arg2=b, arg3=c))
  
  which is much more readable.
  
  Parameters
  ----------
  func : callable or tuple of (callable, string)
      Function to apply to this %(klass)s object or, alternatively,
      a `(callable, data_keyword)` tuple where `data_keyword` is a
      string indicating the keyword of `callable` that expects the
      %(klass)s object.
  args : iterable, optional
         Positional arguments passed into `func`.
  kwargs : dict, optional
           A dictionary of keyword arguments passed into `func`.
  
  Returns
  -------
  object : the return type of `func`.
  
  See Also
  --------
  Series.pipe : Apply a function with arguments to a series.
  DataFrame.pipe: Apply a function with arguments to a dataframe.
  apply : Apply function to each group instead of to the
      full %(klass)s object.
  
  Notes
  -----
  See more `here
  <http://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#piping-function-calls>`_
  
  Examples
  --------
  %(examples)s
  """
  
  _transform_template = """
  Call function producing a like-indexed %(klass)s on each group and
  return a %(klass)s having the same indexes as the original object
  filled with the transformed values
  
  Parameters
  ----------
  f : function
      Function to apply to each group
  
  Returns
  -------
  %(klass)s
  
  See Also
  --------
  aggregate, transform
  
  Notes
  -----
  Each group is endowed the attribute 'name' in case you need to know
  which group you are working on.
  
  The current implementation imposes three requirements on f:
  
  * f must return a value that either has the same shape as the input
    subframe or can be broadcast to the shape of the input subframe.
    For example, if `f` returns a scalar it will be broadcast to have the
    same shape as the input subframe.
  * if this is a DataFrame, f must support application column-by-column
    in the subframe. If f also supports application to the entire subframe,
    then a fast path is used starting from the second chunk.
  * f must not mutate groups. Mutation is not supported and may
    produce unexpected results.
  
  Examples
  --------
  
  # Same shape
  >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
  ...                           'foo', 'bar'],
  ...                    'B' : ['one', 'one', 'two', 'three',
  ...                          'two', 'two'],
  ...                    'C' : [1, 5, 5, 2, 5, 5],
  ...                    'D' : [2.0, 5., 8., 1., 2., 9.]})
  >>> grouped = df.groupby('A')
  >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
            C         D
  0 -1.154701 -0.577350
  1  0.577350  0.000000
  2  0.577350  1.154701
  3 -1.154701 -1.000000
  4  0.577350 -0.577350
  5  0.577350  1.000000
  
  # Broadcastable
  >>> grouped.transform(lambda x: x.max() - x.min())
     C    D
  0  4  6.0
  1  3  8.0
  2  4  6.0
  3  3  8.0
  4  4  6.0
  5  3  8.0
  """
  
  
  class GroupByPlot(PandasObject):
      """
      Class implementing the .plot attribute for groupby objects.
      """
  
      def __init__(self, groupby):
          self._groupby = groupby
  
      def __call__(self, *args, **kwargs):
          def f(self):
              return self.plot(*args, **kwargs)
  
          f.__name__ = "plot"
          return self._groupby.apply(f)
  
      def __getattr__(self, name):
          def attr(*args, **kwargs):
              def f(self):
                  return getattr(self.plot, name)(*args, **kwargs)
  
              return self._groupby.apply(f)
  
          return attr
  
  
  @contextmanager
  def _group_selection_context(groupby):
      """
      Set / reset the _group_selection_context.
      """
      groupby._set_group_selection()
      yield groupby
      groupby._reset_group_selection()
  
  
  class _GroupBy(PandasObject, SelectionMixin):
      _group_selection = None
      _apply_whitelist = frozenset()  # type: FrozenSet[str]
  
      def __init__(
          self,
          obj: NDFrame,
          keys=None,
          axis: int = 0,
          level=None,
          grouper: "Optional[ops.BaseGrouper]" = None,
          exclusions=None,
          selection=None,
          as_index: bool = True,
          sort: bool = True,
          group_keys: bool = True,
          squeeze: bool = False,
          observed: bool = False,
          mutated: bool = False,
      ):
  
          self._selection = selection
  
          assert isinstance(obj, NDFrame), type(obj)
          obj._consolidate_inplace()
  
          self.level = level
  
          if not as_index:
              if not isinstance(obj, DataFrame):
                  raise TypeError("as_index=False only valid with DataFrame")
              if axis != 0:
                  raise ValueError("as_index=False only valid for axis=0")
  
          self.as_index = as_index
          self.keys = keys
          self.sort = sort
          self.group_keys = group_keys
          self.squeeze = squeeze
          self.observed = observed
          self.mutated = mutated
  
          if grouper is None:
              from pandas.core.groupby.grouper import get_grouper
  
              grouper, exclusions, obj = get_grouper(
                  obj,
                  keys,
                  axis=axis,
                  level=level,
                  sort=sort,
                  observed=observed,
                  mutated=self.mutated,
              )
  
          self.obj = obj
          self.axis = obj._get_axis_number(axis)
          self.grouper = grouper
          self.exclusions = set(exclusions) if exclusions else set()
  
      def __len__(self) -> int:
          return len(self.groups)
  
      def __repr__(self) -> str:
          # TODO: Better repr for GroupBy object
          return object.__repr__(self)
  
      def _assure_grouper(self):
          """
          We create the grouper on instantiation sub-classes may have a
          different policy.
          """
          pass
  
      @property
      def groups(self):
          """
          Dict {group name -> group labels}.
          """
          self._assure_grouper()
          return self.grouper.groups
  
      @property
      def ngroups(self):
          self._assure_grouper()
          return self.grouper.ngroups
  
      @property
      def indices(self):
          """
          Dict {group name -> group indices}.
          """
          self._assure_grouper()
          return self.grouper.indices
  
      def _get_indices(self, names):
          """
          Safe get multiple indices, translate keys for
          datelike to underlying repr.
          """
  
          def get_converter(s):
              # possibly convert to the actual key types
              # in the indices, could be a Timestamp or a np.datetime64
              if isinstance(s, datetime.datetime):
                  return lambda key: Timestamp(key)
              elif isinstance(s, np.datetime64):
                  return lambda key: Timestamp(key).asm8
              else:
                  return lambda key: key
  
          if len(names) == 0:
              return []
  
          if len(self.indices) > 0:
              index_sample = next(iter(self.indices))
          else:
              index_sample = None  # Dummy sample
  
          name_sample = names[0]
          if isinstance(index_sample, tuple):
              if not isinstance(name_sample, tuple):
                  msg = "must supply a tuple to get_group with multiple grouping keys"
                  raise ValueError(msg)
              if not len(name_sample) == len(index_sample):
                  try:
                      # If the original grouper was a tuple
                      return [self.indices[name] for name in names]
                  except KeyError:
                      # turns out it wasn't a tuple
                      msg = (
                          "must supply a same-length tuple to get_group"
                          " with multiple grouping keys"
                      )
                      raise ValueError(msg)
  
              converters = [get_converter(s) for s in index_sample]
              names = (tuple(f(n) for f, n in zip(converters, name)) for name in names)
  
          else:
              converter = get_converter(index_sample)
              names = (converter(name) for name in names)
  
          return [self.indices.get(name, []) for name in names]
  
      def _get_index(self, name):
          """
          Safe get index, translate keys for datelike to underlying repr.
          """
          return self._get_indices([name])[0]
  
      @cache_readonly
      def _selected_obj(self):
          # Note: _selected_obj is always just `self.obj` for SeriesGroupBy
  
          if self._selection is None or isinstance(self.obj, Series):
              if self._group_selection is not None:
                  return self.obj[self._group_selection]
              return self.obj
          else:
              return self.obj[self._selection]
  
      def _reset_group_selection(self):
          """
          Clear group based selection.
  
          Used for methods needing to return info on each group regardless of
          whether a group selection was previously set.
          """
          if self._group_selection is not None:
              # GH12839 clear cached selection too when changing group selection
              self._group_selection = None
              self._reset_cache("_selected_obj")
  
      def _set_group_selection(self):
          """
          Create group based selection.
  
          Used when selection is not passed directly but instead via a grouper.
  
          NOTE: this should be paired with a call to _reset_group_selection
          """
          grp = self.grouper
          if not (
              self.as_index
              and getattr(grp, "groupings", None) is not None
              and self.obj.ndim > 1
              and self._group_selection is None
          ):
              return
  
          ax = self.obj._info_axis
          groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis]
  
          if len(groupers):
              # GH12839 clear selected obj cache when group selection changes
              self._group_selection = ax.difference(Index(groupers), sort=False).tolist()
              self._reset_cache("_selected_obj")
  
      def _set_result_index_ordered(self, result):
          # set the result index on the passed values object and
          # return the new object, xref 8046
  
          # the values/counts are repeated according to the group index
          # shortcut if we have an already ordered grouper
          if not self.grouper.is_monotonic:
              index = Index(np.concatenate(self._get_indices(self.grouper.result_index)))
              result.set_axis(index, axis=self.axis, inplace=True)
              result = result.sort_index(axis=self.axis)
  
          result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True)
          return result
  
      def _dir_additions(self):
          return self.obj._dir_additions() | self._apply_whitelist
  
      def __getattr__(self, attr):
          if attr in self._internal_names_set:
              return object.__getattribute__(self, attr)
          if attr in self.obj:
              return self[attr]
  
          raise AttributeError(
              "'{typ}' object has no attribute '{attr}'".format(
                  typ=type(self).__name__, attr=attr
              )
          )
  
      @Substitution(
          klass="GroupBy",
          versionadded=".. versionadded:: 0.21.0",
          examples="""\
  >>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]})
  >>> df
     A  B
  0  a  1
  1  b  2
  2  a  3
  3  b  4
  
  To get the difference between each groups maximum and minimum value in one
  pass, you can do
  
  >>> df.groupby('A').pipe(lambda x: x.max() - x.min())
     B
  A
  a  2
  b  2""",
      )
      @Appender(_pipe_template)
      def pipe(self, func, *args, **kwargs):
          return com.pipe(self, func, *args, **kwargs)
  
      plot = property(GroupByPlot)
  
      def _make_wrapper(self, name):
          assert name in self._apply_whitelist
  
          self._set_group_selection()
  
          # need to setup the selection
          # as are not passed directly but in the grouper
          f = getattr(self._selected_obj, name)
          if not isinstance(f, types.MethodType):
              return self.apply(lambda self: getattr(self, name))
  
          f = getattr(type(self._selected_obj), name)
          sig = inspect.signature(f)
  
          def wrapper(*args, **kwargs):
              # a little trickery for aggregation functions that need an axis
              # argument
              if "axis" in sig.parameters:
                  if kwargs.get("axis", None) is None:
                      kwargs["axis"] = self.axis
  
              def curried(x):
                  return f(x, *args, **kwargs)
  
              # preserve the name so we can detect it when calling plot methods,
              # to avoid duplicates
              curried.__name__ = name
  
              # special case otherwise extra plots are created when catching the
              # exception below
              if name in base.plotting_methods:
                  return self.apply(curried)
  
              try:
                  return self.apply(curried)
              except TypeError as err:
                  if not re.search(
                      "reduction operation '.*' not allowed for this dtype", str(err)
                  ):
                      # We don't have a cython implementation
                      # TODO: is the above comment accurate?
                      raise
  
              if self.obj.ndim == 1:
                  # this can be called recursively, so need to raise ValueError
                  raise ValueError
  
              # GH#3688 try to operate item-by-item
              result = self._aggregate_item_by_item(name, *args, **kwargs)
              return result
  
          wrapper.__name__ = name
          return wrapper
  
      def get_group(self, name, obj=None):
          """
          Construct DataFrame from group with provided name.
  
          Parameters
          ----------
          name : object
              The name of the group to get as a DataFrame.
          obj : DataFrame, default None
              The DataFrame to take the DataFrame out of.  If
              it is None, the object groupby was called on will
              be used.
  
          Returns
          -------
          group : same type as obj
          """
          if obj is None:
              obj = self._selected_obj
  
          inds = self._get_index(name)
          if not len(inds):
              raise KeyError(name)
  
          return obj.take(inds, axis=self.axis)
  
      def __iter__(self):
          """
          Groupby iterator.
  
          Returns
          -------
          Generator yielding sequence of (name, subsetted object)
          for each group
          """
          return self.grouper.get_iterator(self.obj, axis=self.axis)
  
      @Appender(
          _apply_docs["template"].format(
              input="dataframe", examples=_apply_docs["dataframe_examples"]
          )
      )
      def apply(self, func, *args, **kwargs):
  
          func = self._is_builtin_func(func)
  
          # this is needed so we don't try and wrap strings. If we could
          # resolve functions to their callable functions prior, this
          # wouldn't be needed
          if args or kwargs:
              if callable(func):
  
                  @wraps(func)
                  def f(g):
                      with np.errstate(all="ignore"):
                          return func(g, *args, **kwargs)
  
              elif hasattr(nanops, "nan" + func):
                  # TODO: should we wrap this in to e.g. _is_builtin_func?
                  f = getattr(nanops, "nan" + func)
  
              else:
                  raise ValueError(
                      "func must be a callable if args or kwargs are supplied"
                  )
          else:
              f = func
  
          # ignore SettingWithCopy here in case the user mutates
          with option_context("mode.chained_assignment", None):
              try:
                  result = self._python_apply_general(f)
              except TypeError:
                  # gh-20949
                  # try again, with .apply acting as a filtering
                  # operation, by excluding the grouping column
                  # This would normally not be triggered
                  # except if the udf is trying an operation that
                  # fails on *some* columns, e.g. a numeric operation
                  # on a string grouper column
  
                  with _group_selection_context(self):
                      return self._python_apply_general(f)
  
          return result
  
      def _python_apply_general(self, f):
          keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
  
          return self._wrap_applied_output(
              keys, values, not_indexed_same=mutated or self.mutated
          )
  
      def _iterate_slices(self) -> Iterable[Series]:
          raise AbstractMethodError(self)
  
      def transform(self, func, *args, **kwargs):
          raise AbstractMethodError(self)
  
      def _cumcount_array(self, ascending: bool = True):
          """
          Parameters
          ----------
          ascending : bool, default True
              If False, number in reverse, from length of group - 1 to 0.
  
          Notes
          -----
          this is currently implementing sort=False
          (though the default is sort=True) for groupby in general
          """
          ids, _, ngroups = self.grouper.group_info
          sorter = get_group_index_sorter(ids, ngroups)
          ids, count = ids[sorter], len(ids)
  
          if count == 0:
              return np.empty(0, dtype=np.int64)
  
          run = np.r_[True, ids[:-1] != ids[1:]]
          rep = np.diff(np.r_[np.nonzero(run)[0], count])
          out = (~run).cumsum()
  
          if ascending:
              out -= np.repeat(out[run], rep)
          else:
              out = np.repeat(out[np.r_[run[1:], True]], rep) - out
  
          rev = np.empty(count, dtype=np.intp)
          rev[sorter] = np.arange(count, dtype=np.intp)
          return out[rev].astype(np.int64, copy=False)
  
      def _try_cast(self, result, obj, numeric_only: bool = False):
          """
          Try to cast the result to our obj original type,
          we may have roundtripped through object in the mean-time.
  
          If numeric_only is True, then only try to cast numerics
          and not datetimelikes.
  
          """
          if obj.ndim > 1:
              dtype = obj._values.dtype
          else:
              dtype = obj.dtype
  
          if not is_scalar(result):
              if is_extension_array_dtype(dtype) and dtype.kind != "M":
                  # The function can return something of any type, so check
                  #  if the type is compatible with the calling EA.
                  # datetime64tz is handled correctly in agg_series,
                  #  so is excluded here.
  
                  # return the same type (Series) as our caller
                  cls = dtype.construct_array_type()
                  result = try_cast_to_ea(cls, result, dtype=dtype)
              elif numeric_only and is_numeric_dtype(dtype) or not numeric_only:
                  result = maybe_downcast_to_dtype(result, dtype)
  
          return result
  
      def _transform_should_cast(self, func_nm: str) -> bool:
          """
          Parameters
          ----------
          func_nm: str
              The name of the aggregation function being performed
  
          Returns
          -------
          bool
              Whether transform should attempt to cast the result of aggregation
          """
          return (self.size().fillna(0) > 0).any() and (
              func_nm not in base.cython_cast_blacklist
          )
  
      def _cython_transform(self, how: str, numeric_only: bool = True, **kwargs):
          output = collections.OrderedDict()  # type: dict
          for obj in self._iterate_slices():
              name = obj.name
              is_numeric = is_numeric_dtype(obj.dtype)
              if numeric_only and not is_numeric:
                  continue
  
              try:
                  result, names = self.grouper.transform(obj.values, how, **kwargs)
              except NotImplementedError:
                  continue
              if self._transform_should_cast(how):
                  output[name] = self._try_cast(result, obj)
              else:
                  output[name] = result
  
          if len(output) == 0:
              raise DataError("No numeric types to aggregate")
  
          return self._wrap_transformed_output(output, names)
  
      def _wrap_aggregated_output(self, output, names=None):
          raise AbstractMethodError(self)
  
      def _wrap_transformed_output(self, output, names=None):
          raise AbstractMethodError(self)
  
      def _wrap_applied_output(self, keys, values, not_indexed_same: bool = False):
          raise AbstractMethodError(self)
  
      def _cython_agg_general(
          self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
      ):
          output = {}
          for obj in self._iterate_slices():
              name = obj.name
              is_numeric = is_numeric_dtype(obj.dtype)
              if numeric_only and not is_numeric:
                  continue
  
              result, names = self.grouper.aggregate(
                  obj._values, how, min_count=min_count
              )
              output[name] = self._try_cast(result, obj)
  
          if len(output) == 0:
              raise DataError("No numeric types to aggregate")
  
          return self._wrap_aggregated_output(output, names)
  
      def _python_agg_general(self, func, *args, **kwargs):
          func = self._is_builtin_func(func)
          f = lambda x: func(x, *args, **kwargs)
  
          # iterate through "columns" ex exclusions to populate output dict
          output = {}
          for obj in self._iterate_slices():
              name = obj.name
              if self.grouper.ngroups == 0:
                  # agg_series below assumes ngroups > 0
                  continue
  
              try:
                  # if this function is invalid for this dtype, we will ignore it.
                  func(obj[:0])
              except TypeError:
                  continue
              except AssertionError:
                  raise
              except Exception:
                  # Our function depends on having a non-empty argument
                  #  See test_groupby_agg_err_catching
                  pass
  
              result, counts = self.grouper.agg_series(obj, f)
              assert result is not None
              output[name] = self._try_cast(result, obj, numeric_only=True)
  
          if len(output) == 0:
              return self._python_apply_general(f)
  
          if self.grouper._filter_empty_groups:
  
              mask = counts.ravel() > 0
              for name, result in output.items():
  
                  # since we are masking, make sure that we have a float object
                  values = result
                  if is_numeric_dtype(values.dtype):
                      values = ensure_float(values)
  
                  output[name] = self._try_cast(values[mask], result)
  
          return self._wrap_aggregated_output(output)
  
      def _concat_objects(self, keys, values, not_indexed_same: bool = False):
          from pandas.core.reshape.concat import concat
  
          def reset_identity(values):
              # reset the identities of the components
              # of the values to prevent aliasing
              for v in com.not_none(*values):
                  ax = v._get_axis(self.axis)
                  ax._reset_identity()
              return values
  
          if not not_indexed_same:
              result = concat(values, axis=self.axis)
              ax = self._selected_obj._get_axis(self.axis)
  
              if isinstance(result, Series):
                  result = result.reindex(ax)
              else:
  
                  # this is a very unfortunate situation
                  # we have a multi-index that is NOT lexsorted
                  # and we have a result which is duplicated
                  # we can't reindex, so we resort to this
                  # GH 14776
                  if isinstance(ax, MultiIndex) and not ax.is_unique:
                      indexer = algorithms.unique1d(
                          result.index.get_indexer_for(ax.values)
                      )
                      result = result.take(indexer, axis=self.axis)
                  else:
                      result = result.reindex(ax, axis=self.axis)
  
          elif self.group_keys:
  
              values = reset_identity(values)
              if self.as_index:
  
                  # possible MI return case
                  group_keys = keys
                  group_levels = self.grouper.levels
                  group_names = self.grouper.names
  
                  result = concat(
                      values,
                      axis=self.axis,
                      keys=group_keys,
                      levels=group_levels,
                      names=group_names,
                      sort=False,
                  )
              else:
  
                  # GH5610, returns a MI, with the first level being a
                  # range index
                  keys = list(range(len(values)))
                  result = concat(values, axis=self.axis, keys=keys)
          else:
              values = reset_identity(values)
              result = concat(values, axis=self.axis)
  
          if isinstance(result, Series) and self._selection_name is not None:
  
              result.name = self._selection_name
  
          return result
  
      def _apply_filter(self, indices, dropna):
          if len(indices) == 0:
              indices = np.array([], dtype="int64")
          else:
              indices = np.sort(np.concatenate(indices))
          if dropna:
              filtered = self._selected_obj.take(indices, axis=self.axis)
          else:
              mask = np.empty(len(self._selected_obj.index), dtype=bool)
              mask.fill(False)
              mask[indices.astype(int)] = True
              # mask fails to broadcast when passed to where; broadcast manually.
              mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T
              filtered = self._selected_obj.where(mask)  # Fill with NaNs.
          return filtered
  
  
  class GroupBy(_GroupBy):
      """
      Class for grouping and aggregating relational data.
  
      See aggregate, transform, and apply functions on this object.
  
      It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:
  
      ::
  
          grouped = groupby(obj, ...)
  
      Parameters
      ----------
      obj : pandas object
      axis : int, default 0
      level : int, default None
          Level of MultiIndex
      groupings : list of Grouping objects
          Most users should ignore this
      exclusions : array-like, optional
          List of columns to exclude
      name : str
          Most users should ignore this
  
      Returns
      -------
      **Attributes**
      groups : dict
          {group name -> group labels}
      len(grouped) : int
          Number of groups
  
      Notes
      -----
      After grouping, see aggregate, apply, and transform functions. Here are
      some other brief notes about usage. When grouping by multiple groups, the
      result index will be a MultiIndex (hierarchical) by default.
  
      Iteration produces (key, group) tuples, i.e. chunking the data by group. So
      you can write code like:
  
      ::
  
          grouped = obj.groupby(keys, axis=axis)
          for key, group in grouped:
              # do something with the data
  
      Function calls on GroupBy, if not specially implemented, "dispatch" to the
      grouped data. So if you group a DataFrame and wish to invoke the std()
      method on each group, you can simply do:
  
      ::
  
          df.groupby(mapper).std()
  
      rather than
  
      ::
  
          df.groupby(mapper).aggregate(np.std)
  
      You can pass arguments to these "wrapped" functions, too.
  
      See the online documentation for full exposition on these topics and much
      more
      """
  
      def _bool_agg(self, val_test, skipna):
          """
          Shared func to call any / all Cython GroupBy implementations.
          """
  
          def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]:
              if is_object_dtype(vals):
                  vals = np.array([bool(x) for x in vals])
              else:
                  vals = vals.astype(np.bool)
  
              return vals.view(np.uint8), np.bool
  
          def result_to_bool(result: np.ndarray, inference: Type) -> np.ndarray:
              return result.astype(inference, copy=False)
  
          return self._get_cythonized_result(
              "group_any_all",
              aggregate=True,
              cython_dtype=np.dtype(np.uint8),
              needs_values=True,
              needs_mask=True,
              pre_processing=objs_to_bool,
              post_processing=result_to_bool,
              val_test=val_test,
              skipna=skipna,
          )
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def any(self, skipna: bool = True):
          """
          Return True if any value in the group is truthful, else False.
  
          Parameters
          ----------
          skipna : bool, default True
              Flag to ignore nan values during truth testing.
  
          Returns
          -------
          bool
          """
          return self._bool_agg("any", skipna)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def all(self, skipna: bool = True):
          """
          Return True if all values in the group are truthful, else False.
  
          Parameters
          ----------
          skipna : bool, default True
              Flag to ignore nan values during truth testing.
  
          Returns
          -------
          bool
          """
          return self._bool_agg("all", skipna)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def count(self):
          """
          Compute count of group, excluding missing values.
  
          Returns
          -------
          Series or DataFrame
              Count of values within each group.
          """
  
          # defined here for API doc
          raise NotImplementedError
  
      @Substitution(name="groupby")
      @Substitution(see_also=_common_see_also)
      def mean(self, *args, **kwargs):
          """
          Compute mean of groups, excluding missing values.
  
          Returns
          -------
          pandas.Series or pandas.DataFrame
          %(see_also)s
          Examples
          --------
          >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
          ...                    'B': [np.nan, 2, 3, 4, 5],
          ...                    'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
  
          Groupby one column and return the mean of the remaining columns in
          each group.
  
          >>> df.groupby('A').mean()
               B         C
          A
          1  3.0  1.333333
          2  4.0  1.500000
  
          Groupby two columns and return the mean of the remaining column.
  
          >>> df.groupby(['A', 'B']).mean()
                 C
          A B
          1 2.0  2
            4.0  1
          2 3.0  1
            5.0  2
  
          Groupby one column and return the mean of only particular column in
          the group.
  
          >>> df.groupby('A')['B'].mean()
          A
          1    3.0
          2    4.0
          Name: B, dtype: float64
          """
          nv.validate_groupby_func("mean", args, kwargs, ["numeric_only"])
          return self._cython_agg_general(
              "mean", alt=lambda x, axis: Series(x).mean(**kwargs), **kwargs
          )
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def median(self, **kwargs):
          """
          Compute median of groups, excluding missing values.
  
          For multiple groupings, the result index will be a MultiIndex
  
          Returns
          -------
          Series or DataFrame
              Median of values within each group.
          """
          return self._cython_agg_general(
              "median",
              alt=lambda x, axis: Series(x).median(axis=axis, **kwargs),
              **kwargs,
          )
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def std(self, ddof: int = 1, *args, **kwargs):
          """
          Compute standard deviation of groups, excluding missing values.
  
          For multiple groupings, the result index will be a MultiIndex.
  
          Parameters
          ----------
          ddof : int, default 1
              Degrees of freedom.
  
          Returns
          -------
          Series or DataFrame
              Standard deviation of values within each group.
          """
  
          # TODO: implement at Cython level?
          nv.validate_groupby_func("std", args, kwargs)
          return np.sqrt(self.var(ddof=ddof, **kwargs))
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def var(self, ddof: int = 1, *args, **kwargs):
          """
          Compute variance of groups, excluding missing values.
  
          For multiple groupings, the result index will be a MultiIndex.
  
          Parameters
          ----------
          ddof : int, default 1
              Degrees of freedom.
  
          Returns
          -------
          Series or DataFrame
              Variance of values within each group.
          """
          nv.validate_groupby_func("var", args, kwargs)
          if ddof == 1:
              return self._cython_agg_general(
                  "var", alt=lambda x, axis: Series(x).var(ddof=ddof, **kwargs), **kwargs
              )
          else:
              f = lambda x: x.var(ddof=ddof, **kwargs)
              with _group_selection_context(self):
                  return self._python_agg_general(f)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def sem(self, ddof: int = 1):
          """
          Compute standard error of the mean of groups, excluding missing values.
  
          For multiple groupings, the result index will be a MultiIndex.
  
          Parameters
          ----------
          ddof : int, default 1
              Degrees of freedom.
  
          Returns
          -------
          Series or DataFrame
              Standard error of the mean of values within each group.
          """
          return self.std(ddof=ddof) / np.sqrt(self.count())
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def size(self):
          """
          Compute group sizes.
  
          Returns
          -------
          Series
              Number of rows in each group.
          """
          result = self.grouper.size()
  
          if isinstance(self.obj, Series):
              result.name = self.obj.name
          return result
  
      @classmethod
      def _add_numeric_operations(cls):
          """
          Add numeric operations to the GroupBy generically.
          """
  
          def groupby_function(
              name: str,
              alias: str,
              npfunc,
              numeric_only: bool = True,
              min_count: int = -1,
          ):
  
              _local_template = """
              Compute %(f)s of group values.
  
              Returns
              -------
              Series or DataFrame
                  Computed %(f)s of values within each group.
              """
  
              @Substitution(name="groupby", f=name)
              @Appender(_common_see_also)
              @Appender(_local_template)
              def f(self, **kwargs):
                  if "numeric_only" not in kwargs:
                      kwargs["numeric_only"] = numeric_only
                  if "min_count" not in kwargs:
                      kwargs["min_count"] = min_count
  
                  self._set_group_selection()
  
                  # try a cython aggregation if we can
                  try:
                      return self._cython_agg_general(alias, alt=npfunc, **kwargs)
                  except DataError:
                      pass
                  except NotImplementedError as err:
                      if "function is not implemented for this dtype" in str(err):
                          # raised in _get_cython_function, in some cases can
                          #  be trimmed by implementing cython funcs for more dtypes
                          pass
                      else:
                          raise
  
                  # apply a non-cython aggregation
                  result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
                  return result
  
              set_function_name(f, name, cls)
  
              return f
  
          def first_compat(x, axis=0):
              def first(x):
                  x = x.to_numpy()
  
                  x = x[notna(x)]
                  if len(x) == 0:
                      return np.nan
                  return x[0]
  
              if isinstance(x, DataFrame):
                  return x.apply(first, axis=axis)
              else:
                  return first(x)
  
          def last_compat(x, axis=0):
              def last(x):
                  x = x.to_numpy()
                  x = x[notna(x)]
                  if len(x) == 0:
                      return np.nan
                  return x[-1]
  
              if isinstance(x, DataFrame):
                  return x.apply(last, axis=axis)
              else:
                  return last(x)
  
          cls.sum = groupby_function("sum", "add", np.sum, min_count=0)
          cls.prod = groupby_function("prod", "prod", np.prod, min_count=0)
          cls.min = groupby_function("min", "min", np.min, numeric_only=False)
          cls.max = groupby_function("max", "max", np.max, numeric_only=False)
          cls.first = groupby_function("first", "first", first_compat, numeric_only=False)
          cls.last = groupby_function("last", "last", last_compat, numeric_only=False)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def ohlc(self) -> DataFrame:
          """
          Compute sum of values, excluding missing values.
  
          For multiple groupings, the result index will be a MultiIndex
  
          Returns
          -------
          DataFrame
              Open, high, low and close values within each group.
          """
  
          return self._apply_to_column_groupbys(lambda x: x._cython_agg_general("ohlc"))
  
      @Appender(DataFrame.describe.__doc__)
      def describe(self, **kwargs):
          with _group_selection_context(self):
              result = self.apply(lambda x: x.describe(**kwargs))
              if self.axis == 1:
                  return result.T
              return result.unstack()
  
      def resample(self, rule, *args, **kwargs):
          """
          Provide resampling when using a TimeGrouper.
  
          Given a grouper, the function resamples it according to a string
          "string" -> "frequency".
  
          See the :ref:`frequency aliases <timeseries.offset_aliases>`
          documentation for more details.
  
          Parameters
          ----------
          rule : str or DateOffset
              The offset string or object representing target grouper conversion.
          *args, **kwargs
              Possible arguments are `how`, `fill_method`, `limit`, `kind` and
              `on`, and other arguments of `TimeGrouper`.
  
          Returns
          -------
          Grouper
              Return a new grouper with our resampler appended.
  
          See Also
          --------
          Grouper : Specify a frequency to resample with when
              grouping by a key.
          DatetimeIndex.resample : Frequency conversion and resampling of
              time series.
  
          Examples
          --------
          >>> idx = pd.date_range('1/1/2000', periods=4, freq='T')
          >>> df = pd.DataFrame(data=4 * [range(2)],
          ...                   index=idx,
          ...                   columns=['a', 'b'])
          >>> df.iloc[2, 0] = 5
          >>> df
                              a  b
          2000-01-01 00:00:00  0  1
          2000-01-01 00:01:00  0  1
          2000-01-01 00:02:00  5  1
          2000-01-01 00:03:00  0  1
  
          Downsample the DataFrame into 3 minute bins and sum the values of
          the timestamps falling into a bin.
  
          >>> df.groupby('a').resample('3T').sum()
                                   a  b
          a
          0   2000-01-01 00:00:00  0  2
              2000-01-01 00:03:00  0  1
          5   2000-01-01 00:00:00  5  1
  
          Upsample the series into 30 second bins.
  
          >>> df.groupby('a').resample('30S').sum()
                              a  b
          a
          0   2000-01-01 00:00:00  0  1
              2000-01-01 00:00:30  0  0
              2000-01-01 00:01:00  0  1
              2000-01-01 00:01:30  0  0
              2000-01-01 00:02:00  0  0
              2000-01-01 00:02:30  0  0
              2000-01-01 00:03:00  0  1
          5   2000-01-01 00:02:00  5  1
  
          Resample by month. Values are assigned to the month of the period.
  
          >>> df.groupby('a').resample('M').sum()
                      a  b
          a
          0   2000-01-31  0  3
          5   2000-01-31  5  1
  
          Downsample the series into 3 minute bins as above, but close the right
          side of the bin interval.
  
          >>> df.groupby('a').resample('3T', closed='right').sum()
                                   a  b
          a
          0   1999-12-31 23:57:00  0  1
              2000-01-01 00:00:00  0  2
          5   2000-01-01 00:00:00  5  1
  
          Downsample the series into 3 minute bins and close the right side of
          the bin interval, but label each bin using the right edge instead of
          the left.
  
          >>> df.groupby('a').resample('3T', closed='right', label='right').sum()
                                   a  b
          a
          0   2000-01-01 00:00:00  0  1
              2000-01-01 00:03:00  0  2
          5   2000-01-01 00:03:00  5  1
  
          Add an offset of twenty seconds.
  
          >>> df.groupby('a').resample('3T', loffset='20s').sum()
                                 a  b
          a
          0   2000-01-01 00:00:20  0  2
              2000-01-01 00:03:20  0  1
          5   2000-01-01 00:00:20  5  1
          """
          from pandas.core.resample import get_resampler_for_grouping
  
          return get_resampler_for_grouping(self, rule, *args, **kwargs)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def rolling(self, *args, **kwargs):
          """
          Return a rolling grouper, providing rolling functionality per group.
          """
          from pandas.core.window import RollingGroupby
  
          return RollingGroupby(self, *args, **kwargs)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def expanding(self, *args, **kwargs):
          """
          Return an expanding grouper, providing expanding
          functionality per group.
          """
          from pandas.core.window import ExpandingGroupby
  
          return ExpandingGroupby(self, *args, **kwargs)
  
      def _fill(self, direction, limit=None):
          """
          Shared function for `pad` and `backfill` to call Cython method.
  
          Parameters
          ----------
          direction : {'ffill', 'bfill'}
              Direction passed to underlying Cython function. `bfill` will cause
              values to be filled backwards. `ffill` and any other values will
              default to a forward fill
          limit : int, default None
              Maximum number of consecutive values to fill. If `None`, this
              method will convert to -1 prior to passing to Cython
  
          Returns
          -------
          `Series` or `DataFrame` with filled values
  
          See Also
          --------
          pad
          backfill
          """
          # Need int value for Cython
          if limit is None:
              limit = -1
  
          return self._get_cythonized_result(
              "group_fillna_indexer",
              needs_mask=True,
              cython_dtype=np.dtype(np.int64),
              result_is_index=True,
              direction=direction,
              limit=limit,
          )
  
      @Substitution(name="groupby")
      def pad(self, limit=None):
          """
          Forward fill the values.
  
          Parameters
          ----------
          limit : int, optional
              Limit of how many values to fill.
  
          Returns
          -------
          Series or DataFrame
              Object with missing values filled.
  
          See Also
          --------
          Series.pad
          DataFrame.pad
          Series.fillna
          DataFrame.fillna
          """
          return self._fill("ffill", limit=limit)
  
      ffill = pad
  
      @Substitution(name="groupby")
      def backfill(self, limit=None):
          """
          Backward fill the values.
  
          Parameters
          ----------
          limit : int, optional
              Limit of how many values to fill.
  
          Returns
          -------
          Series or DataFrame
              Object with missing values filled.
  
          See Also
          --------
          Series.backfill
          DataFrame.backfill
          Series.fillna
          DataFrame.fillna
          """
          return self._fill("bfill", limit=limit)
  
      bfill = backfill
  
      @Substitution(name="groupby")
      @Substitution(see_also=_common_see_also)
      def nth(self, n: Union[int, List[int]], dropna: Optional[str] = None) -> DataFrame:
          """
          Take the nth row from each group if n is an int, or a subset of rows
          if n is a list of ints.
  
          If dropna, will take the nth non-null row, dropna is either
          'all' or 'any'; this is equivalent to calling dropna(how=dropna)
          before the groupby.
  
          Parameters
          ----------
          n : int or list of ints
              A single nth value for the row or a list of nth values.
          dropna : None or str, optional
              Apply the specified dropna operation before counting which row is
              the nth row. Needs to be None, 'any' or 'all'.
  
          Returns
          -------
          Series or DataFrame
              N-th value within each group.
          %(see_also)s
          Examples
          --------
  
          >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
          ...                    'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
          >>> g = df.groupby('A')
          >>> g.nth(0)
               B
          A
          1  NaN
          2  3.0
          >>> g.nth(1)
               B
          A
          1  2.0
          2  5.0
          >>> g.nth(-1)
               B
          A
          1  4.0
          2  5.0
          >>> g.nth([0, 1])
               B
          A
          1  NaN
          1  2.0
          2  3.0
          2  5.0
  
          Specifying `dropna` allows count ignoring ``NaN``
  
          >>> g.nth(0, dropna='any')
               B
          A
          1  2.0
          2  3.0
  
          NaNs denote group exhausted when using dropna
  
          >>> g.nth(3, dropna='any')
              B
          A
          1 NaN
          2 NaN
  
          Specifying `as_index=False` in `groupby` keeps the original index.
  
          >>> df.groupby('A', as_index=False).nth(1)
             A    B
          1  1  2.0
          4  2  5.0
          """
  
          valid_containers = (set, list, tuple)
          if not isinstance(n, (valid_containers, int)):
              raise TypeError("n needs to be an int or a list/set/tuple of ints")
  
          if not dropna:
  
              if isinstance(n, int):
                  nth_values = [n]
              elif isinstance(n, valid_containers):
                  nth_values = list(set(n))
  
              nth_array = np.array(nth_values, dtype=np.intp)
              self._set_group_selection()
  
              mask_left = np.in1d(self._cumcount_array(), nth_array)
              mask_right = np.in1d(self._cumcount_array(ascending=False) + 1, -nth_array)
              mask = mask_left | mask_right
  
              ids, _, _ = self.grouper.group_info
  
              # Drop NA values in grouping
              mask = mask & (ids != -1)
  
              out = self._selected_obj[mask]
              if not self.as_index:
                  return out
  
              result_index = self.grouper.result_index
              out.index = result_index[ids[mask]]
  
              if not self.observed and isinstance(result_index, CategoricalIndex):
                  out = out.reindex(result_index)
  
              return out.sort_index() if self.sort else out
  
          # dropna is truthy
          if isinstance(n, valid_containers):
              raise ValueError("dropna option with a list of nth values is not supported")
  
          if dropna not in ["any", "all"]:
              # Note: when agg-ing picker doesn't raise this, just returns NaN
              raise ValueError(
                  "For a DataFrame groupby, dropna must be "
                  "either None, 'any' or 'all', "
                  "(was passed {dropna}).".format(dropna=dropna)
              )
  
          # old behaviour, but with all and any support for DataFrames.
          # modified in GH 7559 to have better perf
          max_len = n if n >= 0 else -1 - n
          dropped = self.obj.dropna(how=dropna, axis=self.axis)
  
          # get a new grouper for our dropped obj
          if self.keys is None and self.level is None:
  
              # we don't have the grouper info available
              # (e.g. we have selected out
              # a column that is not in the current object)
              axis = self.grouper.axis
              grouper = axis[axis.isin(dropped.index)]
  
          else:
  
              # create a grouper with the original parameters, but on dropped
              # object
              from pandas.core.groupby.grouper import get_grouper
  
              grouper, _, _ = get_grouper(
                  dropped,
                  key=self.keys,
                  axis=self.axis,
                  level=self.level,
                  sort=self.sort,
                  mutated=self.mutated,
              )
  
          grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
          sizes, result = grb.size(), grb.nth(n)
          mask = (sizes < max_len).values
  
          # set the results which don't meet the criteria
          if len(result) and mask.any():
              result.loc[mask] = np.nan
  
          # reset/reindex to the original groups
          if len(self.obj) == len(dropped) or len(result) == len(
              self.grouper.result_index
          ):
              result.index = self.grouper.result_index
          else:
              result = result.reindex(self.grouper.result_index)
  
          return result
  
      def quantile(self, q=0.5, interpolation: str = "linear"):
          """
          Return group values at the given quantile, a la numpy.percentile.
  
          Parameters
          ----------
          q : float or array-like, default 0.5 (50% quantile)
              Value(s) between 0 and 1 providing the quantile(s) to compute.
          interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
              Method to use when the desired quantile falls between two points.
  
          Returns
          -------
          Series or DataFrame
              Return type determined by caller of GroupBy object.
  
          See Also
          --------
          Series.quantile : Similar method for Series.
          DataFrame.quantile : Similar method for DataFrame.
          numpy.percentile : NumPy method to compute qth percentile.
  
          Examples
          --------
          >>> df = pd.DataFrame([
          ...     ['a', 1], ['a', 2], ['a', 3],
          ...     ['b', 1], ['b', 3], ['b', 5]
          ... ], columns=['key', 'val'])
          >>> df.groupby('key').quantile()
              val
          key
          a    2.0
          b    3.0
          """
          from pandas import concat
  
          def pre_processor(vals: np.ndarray) -> Tuple[np.ndarray, Optional[Type]]:
              if is_object_dtype(vals):
                  raise TypeError(
                      "'quantile' cannot be performed against 'object' dtypes!"
                  )
  
              inference = None
              if is_integer_dtype(vals):
                  inference = np.int64
              elif is_datetime64_dtype(vals):
                  inference = "datetime64[ns]"
                  vals = vals.astype(np.float)
  
              return vals, inference
  
          def post_processor(vals: np.ndarray, inference: Optional[Type]) -> np.ndarray:
              if inference:
                  # Check for edge case
                  if not (
                      is_integer_dtype(inference)
                      and interpolation in {"linear", "midpoint"}
                  ):
                      vals = vals.astype(inference)
  
              return vals
  
          if is_scalar(q):
              return self._get_cythonized_result(
                  "group_quantile",
                  aggregate=True,
                  needs_values=True,
                  needs_mask=True,
                  cython_dtype=np.dtype(np.float64),
                  pre_processing=pre_processor,
                  post_processing=post_processor,
                  q=q,
                  interpolation=interpolation,
              )
          else:
              results = [
                  self._get_cythonized_result(
                      "group_quantile",
                      aggregate=True,
                      needs_values=True,
                      needs_mask=True,
                      cython_dtype=np.dtype(np.float64),
                      pre_processing=pre_processor,
                      post_processing=post_processor,
                      q=qi,
                      interpolation=interpolation,
                  )
                  for qi in q
              ]
              result = concat(results, axis=0, keys=q)
              # fix levels to place quantiles on the inside
              # TODO(GH-10710): Ideally, we could write this as
              #  >>> result.stack(0).loc[pd.IndexSlice[:, ..., q], :]
              #  but this hits https://github.com/pandas-dev/pandas/issues/10710
              #  which doesn't reorder the list-like `q` on the inner level.
              order = np.roll(list(range(result.index.nlevels)), -1)
              result = result.reorder_levels(order)
              result = result.reindex(q, level=-1)
  
              # fix order.
              hi = len(q) * self.ngroups
              arr = np.arange(0, hi, self.ngroups)
              arrays = []
  
              for i in range(self.ngroups):
                  arr2 = arr + i
                  arrays.append(arr2)
  
              indices = np.concatenate(arrays)
              assert len(indices) == len(result)
              return result.take(indices)
  
      @Substitution(name="groupby")
      def ngroup(self, ascending: bool = True):
          """
          Number each group from 0 to the number of groups - 1.
  
          This is the enumerative complement of cumcount.  Note that the
          numbers given to the groups match the order in which the groups
          would be seen when iterating over the groupby object, not the
          order they are first observed.
  
          Parameters
          ----------
          ascending : bool, default True
              If False, number in reverse, from number of group - 1 to 0.
  
          Returns
          -------
          Series
              Unique numbers for each group.
  
          See Also
          --------
          .cumcount : Number the rows in each group.
  
          Examples
          --------
  
          >>> df = pd.DataFrame({"A": list("aaabba")})
          >>> df
             A
          0  a
          1  a
          2  a
          3  b
          4  b
          5  a
          >>> df.groupby('A').ngroup()
          0    0
          1    0
          2    0
          3    1
          4    1
          5    0
          dtype: int64
          >>> df.groupby('A').ngroup(ascending=False)
          0    1
          1    1
          2    1
          3    0
          4    0
          5    1
          dtype: int64
          >>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
          0    0
          1    0
          2    1
          3    3
          4    2
          5    0
          dtype: int64
          """
  
          with _group_selection_context(self):
              index = self._selected_obj.index
              result = Series(self.grouper.group_info[0], index)
              if not ascending:
                  result = self.ngroups - 1 - result
              return result
  
      @Substitution(name="groupby")
      def cumcount(self, ascending: bool = True):
          """
          Number each item in each group from 0 to the length of that group - 1.
  
          Essentially this is equivalent to
  
          >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
  
          Parameters
          ----------
          ascending : bool, default True
              If False, number in reverse, from length of group - 1 to 0.
  
          Returns
          -------
          Series
              Sequence number of each element within each group.
  
          See Also
          --------
          .ngroup : Number the groups themselves.
  
          Examples
          --------
  
          >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
          ...                   columns=['A'])
          >>> df
             A
          0  a
          1  a
          2  a
          3  b
          4  b
          5  a
          >>> df.groupby('A').cumcount()
          0    0
          1    1
          2    2
          3    0
          4    1
          5    3
          dtype: int64
          >>> df.groupby('A').cumcount(ascending=False)
          0    3
          1    2
          2    1
          3    1
          4    0
          5    0
          dtype: int64
          """
  
          with _group_selection_context(self):
              index = self._selected_obj.index
              cumcounts = self._cumcount_array(ascending=ascending)
              return Series(cumcounts, index)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def rank(
          self,
          method: str = "average",
          ascending: bool = True,
          na_option: str = "keep",
          pct: bool = False,
          axis: int = 0,
      ):
          """
          Provide the rank of values within each group.
  
          Parameters
          ----------
          method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
              * average: average rank of group
              * min: lowest rank in group
              * max: highest rank in group
              * first: ranks assigned in order they appear in the array
              * dense: like 'min', but rank always increases by 1 between groups
          ascending : bool, default True
              False for ranks by high (1) to low (N).
          na_option : {'keep', 'top', 'bottom'}, default 'keep'
              * keep: leave NA values where they are
              * top: smallest rank if ascending
              * bottom: smallest rank if descending
          pct : bool, default False
              Compute percentage rank of data within each group.
          axis : int, default 0
              The axis of the object over which to compute the rank.
  
          Returns
          -------
          DataFrame with ranking of values within each group
          """
          if na_option not in {"keep", "top", "bottom"}:
              msg = "na_option must be one of 'keep', 'top', or 'bottom'"
              raise ValueError(msg)
          return self._cython_transform(
              "rank",
              numeric_only=False,
              ties_method=method,
              ascending=ascending,
              na_option=na_option,
              pct=pct,
              axis=axis,
          )
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def cumprod(self, axis=0, *args, **kwargs):
          """
          Cumulative product for each group.
  
          Returns
          -------
          Series or DataFrame
          """
          nv.validate_groupby_func("cumprod", args, kwargs, ["numeric_only", "skipna"])
          if axis != 0:
              return self.apply(lambda x: x.cumprod(axis=axis, **kwargs))
  
          return self._cython_transform("cumprod", **kwargs)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def cumsum(self, axis=0, *args, **kwargs):
          """
          Cumulative sum for each group.
  
          Returns
          -------
          Series or DataFrame
          """
          nv.validate_groupby_func("cumsum", args, kwargs, ["numeric_only", "skipna"])
          if axis != 0:
              return self.apply(lambda x: x.cumsum(axis=axis, **kwargs))
  
          return self._cython_transform("cumsum", **kwargs)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def cummin(self, axis=0, **kwargs):
          """
          Cumulative min for each group.
  
          Returns
          -------
          Series or DataFrame
          """
          if axis != 0:
              return self.apply(lambda x: np.minimum.accumulate(x, axis))
  
          return self._cython_transform("cummin", numeric_only=False)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def cummax(self, axis=0, **kwargs):
          """
          Cumulative max for each group.
  
          Returns
          -------
          Series or DataFrame
          """
          if axis != 0:
              return self.apply(lambda x: np.maximum.accumulate(x, axis))
  
          return self._cython_transform("cummax", numeric_only=False)
  
      def _get_cythonized_result(
          self,
          how: str,
          cython_dtype: np.dtype,
          aggregate: bool = False,
          needs_values: bool = False,
          needs_mask: bool = False,
          needs_ngroups: bool = False,
          result_is_index: bool = False,
          pre_processing=None,
          post_processing=None,
          **kwargs,
      ):
          """
          Get result for Cythonized functions.
  
          Parameters
          ----------
          how : str, Cythonized function name to be called
          cython_dtype : np.dtype
              Type of the array that will be modified by the Cython call.
          aggregate : bool, default False
              Whether the result should be aggregated to match the number of
              groups
          needs_values : bool, default False
              Whether the values should be a part of the Cython call
              signature
          needs_mask : bool, default False
              Whether boolean mask needs to be part of the Cython call
              signature
          needs_ngroups : bool, default False
              Whether number of groups is part of the Cython call signature
          result_is_index : bool, default False
              Whether the result of the Cython operation is an index of
              values to be retrieved, instead of the actual values themselves
          pre_processing : function, default None
              Function to be applied to `values` prior to passing to Cython.
              Function should return a tuple where the first element is the
              values to be passed to Cython and the second element is an optional
              type which the values should be converted to after being returned
              by the Cython operation. Raises if `needs_values` is False.
          post_processing : function, default None
              Function to be applied to result of Cython function. Should accept
              an array of values as the first argument and type inferences as its
              second argument, i.e. the signature should be
              (ndarray, Type).
          **kwargs : dict
              Extra arguments to be passed back to Cython funcs
  
          Returns
          -------
          `Series` or `DataFrame`  with filled values
          """
          if result_is_index and aggregate:
              raise ValueError("'result_is_index' and 'aggregate' cannot both be True!")
          if post_processing:
              if not callable(pre_processing):
                  raise ValueError("'post_processing' must be a callable!")
          if pre_processing:
              if not callable(pre_processing):
                  raise ValueError("'pre_processing' must be a callable!")
              if not needs_values:
                  raise ValueError(
                      "Cannot use 'pre_processing' without specifying 'needs_values'!"
                  )
  
          grouper = self.grouper
  
          labels, _, ngroups = grouper.group_info
          output = collections.OrderedDict()  # type: dict
          base_func = getattr(libgroupby, how)
  
          for obj in self._iterate_slices():
              name = obj.name
              values = obj._data._values
  
              if aggregate:
                  result_sz = ngroups
              else:
                  result_sz = len(values)
  
              result = np.zeros(result_sz, dtype=cython_dtype)
              func = partial(base_func, result, labels)
              inferences = None
  
              if needs_values:
                  vals = values
                  if pre_processing:
                      vals, inferences = pre_processing(vals)
                  func = partial(func, vals)
  
              if needs_mask:
                  mask = isna(values).view(np.uint8)
                  func = partial(func, mask)
  
              if needs_ngroups:
                  func = partial(func, ngroups)
  
              func(**kwargs)  # Call func to modify indexer values in place
  
              if result_is_index:
                  result = algorithms.take_nd(values, result)
  
              if post_processing:
                  result = post_processing(result, inferences)
  
              output[name] = result
  
          if aggregate:
              return self._wrap_aggregated_output(output)
          else:
              return self._wrap_transformed_output(output)
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def shift(self, periods=1, freq=None, axis=0, fill_value=None):
          """
          Shift each group by periods observations.
  
          Parameters
          ----------
          periods : int, default 1
              Number of periods to shift.
          freq : frequency string
          axis : axis to shift, default 0
          fill_value : optional
  
              .. versionadded:: 0.24.0
  
          Returns
          -------
          Series or DataFrame
              Object shifted within each group.
          """
  
          if freq is not None or axis != 0 or not isna(fill_value):
              return self.apply(lambda x: x.shift(periods, freq, axis, fill_value))
  
          return self._get_cythonized_result(
              "group_shift_indexer",
              cython_dtype=np.dtype(np.int64),
              needs_ngroups=True,
              result_is_index=True,
              periods=periods,
          )
  
      @Substitution(name="groupby")
      @Appender(_common_see_also)
      def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None, axis=0):
          """
          Calculate pct_change of each value to previous entry in group.
  
          Returns
          -------
          Series or DataFrame
              Percentage changes within each group.
          """
          if freq is not None or axis != 0:
              return self.apply(
                  lambda x: x.pct_change(
                      periods=periods,
                      fill_method=fill_method,
                      limit=limit,
                      freq=freq,
                      axis=axis,
                  )
              )
          filled = getattr(self, fill_method)(limit=limit)
          fill_grp = filled.groupby(self.grouper.codes)
          shifted = fill_grp.shift(periods=periods, freq=freq)
          return (filled / shifted) - 1
  
      @Substitution(name="groupby")
      @Substitution(see_also=_common_see_also)
      def head(self, n=5):
          """
          Return first n rows of each group.
  
          Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows
          from the original DataFrame with original index and order preserved
          (``as_index`` flag is ignored).
  
          Returns
          -------
          Series or DataFrame
          %(see_also)s
          Examples
          --------
  
          >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
          ...                   columns=['A', 'B'])
          >>> df.groupby('A').head(1)
             A  B
          0  1  2
          2  5  6
          """
          self._reset_group_selection()
          mask = self._cumcount_array() < n
          return self._selected_obj[mask]
  
      @Substitution(name="groupby")
      @Substitution(see_also=_common_see_also)
      def tail(self, n=5):
          """
          Return last n rows of each group.
  
          Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows
          from the original DataFrame with original index and order preserved
          (``as_index`` flag is ignored).
  
          Returns
          -------
          Series or DataFrame
          %(see_also)s
          Examples
          --------
  
          >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
          ...                   columns=['A', 'B'])
          >>> df.groupby('A').tail(1)
             A  B
          1  a  2
          3  b  2
          """
          self._reset_group_selection()
          mask = self._cumcount_array(ascending=False) < n
          return self._selected_obj[mask]
  
      def _reindex_output(self, output):
          """
          If we have categorical groupers, then we might want to make sure that
          we have a fully re-indexed output to the levels. This means expanding
          the output space to accommodate all values in the cartesian product of
          our groups, regardless of whether they were observed in the data or
          not. This will expand the output space if there are missing groups.
  
          The method returns early without modifying the input if the number of
          groupings is less than 2, self.observed == True or none of the groupers
          are categorical.
  
          Parameters
          ----------
          output: Series or DataFrame
              Object resulting from grouping and applying an operation.
  
          Returns
          -------
          Series or DataFrame
              Object (potentially) re-indexed to include all possible groups.
          """
          groupings = self.grouper.groupings
          if groupings is None:
              return output
          elif len(groupings) == 1:
              return output
  
          # if we only care about the observed values
          # we are done
          elif self.observed:
              return output
  
          # reindexing only applies to a Categorical grouper
          elif not any(
              isinstance(ping.grouper, (Categorical, CategoricalIndex))
              for ping in groupings
          ):
              return output
  
          levels_list = [ping.group_index for ping in groupings]
          index, _ = MultiIndex.from_product(
              levels_list, names=self.grouper.names
          ).sortlevel()
  
          if self.as_index:
              d = {self.obj._get_axis_name(self.axis): index, "copy": False}
              return output.reindex(**d)
  
          # GH 13204
          # Here, the categorical in-axis groupers, which need to be fully
          # expanded, are columns in `output`. An idea is to do:
          # output = output.set_index(self.grouper.names)
          #                .reindex(index).reset_index()
          # but special care has to be taken because of possible not-in-axis
          # groupers.
          # So, we manually select and drop the in-axis grouper columns,
          # reindex `output`, and then reset the in-axis grouper columns.
  
          # Select in-axis groupers
          in_axis_grps = (
              (i, ping.name) for (i, ping) in enumerate(groupings) if ping.in_axis
          )
          g_nums, g_names = zip(*in_axis_grps)
  
          output = output.drop(labels=list(g_names), axis=1)
  
          # Set a temp index and reindex (possibly expanding)
          output = output.set_index(self.grouper.result_index).reindex(index, copy=False)
  
          # Reset in-axis grouper columns
          # (using level numbers `g_nums` because level names may not be unique)
          output = output.reset_index(level=g_nums)
  
          return output.reset_index(drop=True)
  
  
  GroupBy._add_numeric_operations()
  
  
  @Appender(GroupBy.__doc__)
  def get_groupby(
      obj: NDFrame,
      by=None,
      axis: int = 0,
      level=None,
      grouper: "Optional[ops.BaseGrouper]" = None,
      exclusions=None,
      selection=None,
      as_index: bool = True,
      sort: bool = True,
      group_keys: bool = True,
      squeeze: bool = False,
      observed: bool = False,
      mutated: bool = False,
  ):
  
      if isinstance(obj, Series):
          from pandas.core.groupby.generic import SeriesGroupBy
  
          klass = (
              SeriesGroupBy
          )  # type: Union[Type["SeriesGroupBy"], Type["DataFrameGroupBy"]]
      elif isinstance(obj, DataFrame):
          from pandas.core.groupby.generic import DataFrameGroupBy
  
          klass = DataFrameGroupBy
      else:
          raise TypeError("invalid type: {obj}".format(obj=obj))
  
      return klass(
          obj=obj,
          keys=by,
          axis=axis,
          level=level,
          grouper=grouper,
          exclusions=exclusions,
          selection=selection,
          as_index=as_index,
          sort=sort,
          group_keys=group_keys,
          squeeze=squeeze,
          observed=observed,
          mutated=mutated,
      )
  