groupby.py

import types
from functools import wraps
import numpy as np

from pandas.compat import(
    zip, builtins, range, long, lrange, lzip, OrderedDict, callable
)
from pandas import compat

from pandas.core.base import PandasObject
from pandas.core.categorical import Categorical
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
from pandas.core.index import Index, MultiIndex, _ensure_index
from pandas.core.internals import BlockManager, make_block
from pandas.core.series import Series
from pandas.core.panel import Panel
from pandas.util.decorators import cache_readonly, Appender
import pandas.core.algorithms as algos
import pandas.core.common as com
from pandas.core.common import(_possibly_downcast_to_dtype, isnull,
                               notnull, _DATELIKE_DTYPES)

import pandas.lib as lib
import pandas.algos as _algos
import pandas.hashtable as _hash

_agg_doc = """Aggregate using input function or dict of {column -> function}

Parameters
----------
arg : function or dict
    Function to use for aggregating groups. If a function, must either
    work when passed a DataFrame or when passed to DataFrame.apply. If
    passed a dict, the keys must be DataFrame column names.

Notes
-----
Numpy functions mean/median/prod/sum/std/var are special cased so the
default behavior is applying the function along axis=0
(e.g., np.mean(arr_2d, axis=0)) as opposed to
mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).

Returns
-------
aggregated : DataFrame
"""


# special case to prevent duplicate plots when catching exceptions when
# forwarding methods from NDFrames
_plotting_methods = frozenset(['plot', 'boxplot', 'hist'])

_common_apply_whitelist = frozenset([
    'last', 'first',
    'head', 'tail', 'median',
    'mean', 'sum', 'min', 'max',
    'cumsum', 'cumprod', 'cummin', 'cummax', 'cumcount',
    'resample',
    'describe',
    'rank', 'quantile', 'count',
    'fillna',
    'mad',
    'any', 'all',
    'irow', 'take',
    'shift', 'tshift',
    'ffill', 'bfill',
    'pct_change', 'skew',
    'corr', 'cov',
]) | _plotting_methods

_series_apply_whitelist = \
    (_common_apply_whitelist - set(['boxplot'])) | \
    frozenset(['dtype', 'value_counts'])

_dataframe_apply_whitelist = \
    _common_apply_whitelist | frozenset(['dtypes', 'corrwith'])


class GroupByError(Exception):
    pass


class DataError(GroupByError):
    pass


class SpecificationError(GroupByError):
    pass


def _groupby_function(name, alias, npfunc, numeric_only=True,
                      _convert=False):
    def f(self):
        try:
            return self._cython_agg_general(alias, numeric_only=numeric_only)
        except AssertionError as e:
            raise SpecificationError(str(e))
        except Exception:
            result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
            if _convert:
                result = result.convert_objects()
            return result

    f.__doc__ = "Compute %s of group values" % name
    f.__name__ = name

    return f


def _first_compat(x, axis=0):
    def _first(x):
        x = np.asarray(x)
        x = x[notnull(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 = np.asarray(x)
        x = x[notnull(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)


class GroupBy(PandasObject):

    """
    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 : string
        Most users should ignore this

    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

    Returns
    -------
    **Attributes**
    groups : dict
        {group name -> group labels}
    len(grouped) : int
        Number of groups
    """
    _apply_whitelist = _common_apply_whitelist

    def __init__(self, obj, keys=None, axis=0, level=None,
                 grouper=None, exclusions=None, selection=None, as_index=True,
                 sort=True, group_keys=True, squeeze=False):
        self._selection = selection

        if isinstance(obj, NDFrame):
            obj._consolidate_inplace()

        self.obj = obj
        self.axis = obj._get_axis_number(axis)
        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

        if grouper is None:
            grouper, exclusions = _get_grouper(obj, keys, axis=axis,
                                               level=level, sort=sort)

        self.grouper = grouper
        self.exclusions = set(exclusions) if exclusions else set()

    def __len__(self):
        return len(self.indices)

    def __unicode__(self):
        # TODO: Better unicode/repr for GroupBy object
        return object.__repr__(self)

    @property
    def groups(self):
        """ dict {group name -> group labels} """
        return self.grouper.groups

    @property
    def ngroups(self):
        return self.grouper.ngroups

    @property
    def indices(self):
        """ dict {group name -> group indices} """
        return self.grouper.indices

    @property
    def name(self):
        if self._selection is None:
            return None  # 'result'
        else:
            return self._selection

    @property
    def _selection_list(self):
        if not isinstance(self._selection, (list, tuple, Series, np.ndarray)):
            return [self._selection]
        return self._selection

    def _local_dir(self):
        return sorted(set(self.obj._local_dir() + list(self._apply_whitelist)))

    def __getattr__(self, attr):
        if attr in self.obj:
            return self[attr]

        if hasattr(self.obj, attr) and attr != '_cache':
            return self._make_wrapper(attr)

        raise AttributeError("%r object has no attribute %r" %
                             (type(self).__name__, attr))

    def __getitem__(self, key):
        raise NotImplementedError

    def _make_wrapper(self, name):
        if name not in self._apply_whitelist:
            is_callable = callable(getattr(self.obj, name, None))
            kind = ' callable ' if is_callable else ' '
            msg = ("Cannot access{0}attribute {1!r} of {2!r} objects, try "
                   "using the 'apply' method".format(kind, name,
                                                     type(self).__name__))
            raise AttributeError(msg)

        f = getattr(self.obj, name)
        if not isinstance(f, types.MethodType):
            return self.apply(lambda self: getattr(self, name))

        f = getattr(type(self.obj), name)

        def wrapper(*args, **kwargs):
            # a little trickery for aggregation functions that need an axis
            # argument
            kwargs_with_axis = kwargs.copy()
            if 'axis' not in kwargs_with_axis:
                kwargs_with_axis['axis'] = self.axis

            def curried_with_axis(x):
                return f(x, *args, **kwargs_with_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__ = curried_with_axis.__name__ = name

            # special case otherwise extra plots are created when catching the
            # exception below
            if name in _plotting_methods:
                return self.apply(curried)

            try:
                return self.apply(curried_with_axis)
            except Exception:
                return self.apply(curried)

        return wrapper

    def get_group(self, name, obj=None):
        """
        Constructs NDFrame from group with provided name

        Parameters
        ----------
        name : object
            the name of the group to get as a DataFrame
        obj : NDFrame, default None
            the NDFrame to take the DataFrame out of.  If
            it is None, the object groupby was called on will
            be used

        Returns
        -------
        group : type of obj
        """
        if obj is None:
            obj = self.obj

        inds = self.indices[name]
        return obj.take(inds, axis=self.axis, convert=False)

    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)

    def apply(self, func, *args, **kwargs):
        """
        Apply function and combine results together in an intelligent way. The
        split-apply-combine combination rules attempt to be as common sense
        based as possible. For example:

        case 1:
        group DataFrame
        apply aggregation function (f(chunk) -> Series)
        yield DataFrame, with group axis having group labels

        case 2:
        group DataFrame
        apply transform function ((f(chunk) -> DataFrame with same indexes)
        yield DataFrame with resulting chunks glued together

        case 3:
        group Series
        apply function with f(chunk) -> DataFrame
        yield DataFrame with result of chunks glued together

        Parameters
        ----------
        func : function

        Notes
        -----
        See online documentation for full exposition on how to use apply

        See also
        --------
        aggregate, transform

        Returns
        -------
        applied : type depending on grouped object and function
        """
        func = _intercept_function(func)

        @wraps(func)
        def f(g):
            return func(g, *args, **kwargs)

        return self._python_apply_general(f)

    def _python_apply_general(self, f):
        keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)

        return self._wrap_applied_output(keys, values,
                                         not_indexed_same=mutated)

    def aggregate(self, func, *args, **kwargs):
        raise NotImplementedError

    @Appender(_agg_doc)
    def agg(self, func, *args, **kwargs):
        return self.aggregate(func, *args, **kwargs)

    def _iterate_slices(self):
        yield self.name, self.obj

    def transform(self, func, *args, **kwargs):
        raise NotImplementedError

    def mean(self):
        """
        Compute mean of groups, excluding missing values

        For multiple groupings, the result index will be a MultiIndex
        """
        try:
            return self._cython_agg_general('mean')
        except GroupByError:
            raise
        except Exception:  # pragma: no cover
            f = lambda x: x.mean(axis=self.axis)
            return self._python_agg_general(f)

    def median(self):
        """
        Compute median of groups, excluding missing values

        For multiple groupings, the result index will be a MultiIndex
        """
        try:
            return self._cython_agg_general('median')
        except GroupByError:
            raise
        except Exception:  # pragma: no cover

            def f(x):
                if isinstance(x, np.ndarray):
                    x = Series(x)
                return x.median(axis=self.axis)
            return self._python_agg_general(f)

    def std(self, ddof=1):
        """
        Compute standard deviation of groups, excluding missing values

        For multiple groupings, the result index will be a MultiIndex
        """
        # todo, implement at cython level?
        if ddof == 1:
            return self._cython_agg_general('std')
        else:
            f = lambda x: x.std(ddof=ddof)
            return self._python_agg_general(f)

    def var(self, ddof=1):
        """
        Compute variance of groups, excluding missing values

        For multiple groupings, the result index will be a MultiIndex
        """
        if ddof == 1:
            return self._cython_agg_general('var')
        else:
            f = lambda x: x.var(ddof=ddof)
            return self._python_agg_general(f)

    def size(self):
        """
        Compute group sizes
        """
        return self.grouper.size()

    sum = _groupby_function('sum', 'add', np.sum)
    prod = _groupby_function('prod', 'prod', np.prod)
    min = _groupby_function('min', 'min', np.min, numeric_only=False)
    max = _groupby_function('max', 'max', np.max, numeric_only=False)
    first = _groupby_function('first', 'first', _first_compat,
                              numeric_only=False, _convert=True)
    last = _groupby_function('last', 'last', _last_compat, numeric_only=False,
                             _convert=True)

    def ohlc(self):
        """
        Compute sum of values, excluding missing values

        For multiple groupings, the result index will be a MultiIndex

        """
        return self._cython_agg_general('ohlc')

    def nth(self, n):
        def picker(arr):
            arr = arr[notnull(arr)]
            if len(arr) >= n + 1:
                return arr.iget(n)
            else:
                return np.nan
        return self.agg(picker)

    def cumcount(self, **kwargs):
        """
        Number each item in each group from 0 to the length of that group - 1.

        Essentially this is equivalent to

        >>> self.apply(lambda x: Series(np.arange(len(x)), x.index))

        Parameters
        ----------
        ascending : bool, default True
            If False, number in reverse, from length of group - 1 to 0.

        Example
        -------

        >>> 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

        """
        ascending = kwargs.pop('ascending', True)

        index = self.obj.index
        rng = np.arange(self.grouper._max_groupsize, dtype='int64')
        cumcounts = self._cumcount_array(rng, ascending=ascending)
        return Series(cumcounts, index)

    def head(self, n=5):
        """
        Returns first n rows of each group.

        Essentially equivalent to ``.apply(lambda x: x.head(n))``

        Example
        -------

        >>> df = DataFrame([[1, 2], [1, 4], [5, 6]],
                            columns=['A', 'B'])
        >>> df.groupby('A', as_index=False).head(1)
           A  B
        0  1  2
        2  5  6
        >>> df.groupby('A').head(1)
             A  B
        A
        1 0  1  2
        5 2  5  6

        """
        rng = np.arange(self.grouper._max_groupsize, dtype='int64')
        in_head = self._cumcount_array(rng) < n
        head = self.obj[in_head]
        if self.as_index:
            head.index = self._index_with_as_index(in_head)
        return head

    def tail(self, n=5):
        """
        Returns last n rows of each group

        Essentially equivalent to ``.apply(lambda x: x.tail(n))``

        Example
        -------

        >>> df = DataFrame([[1, 2], [1, 4], [5, 6]],
                            columns=['A', 'B'])
        >>> df.groupby('A', as_index=False).tail(1)
           A  B
        0  1  2
        2  5  6
        >>> df.groupby('A').head(1)
             A  B
        A
        1 0  1  2
        5 2  5  6

        """
        rng = np.arange(0, -self.grouper._max_groupsize, -1, dtype='int64')
        in_tail = self._cumcount_array(rng, ascending=False) > -n
        tail = self.obj[in_tail]
        if self.as_index:
            tail.index = self._index_with_as_index(in_tail)
        return tail

    def _cumcount_array(self, arr, **kwargs):
        ascending = kwargs.pop('ascending', True)

        len_index = len(self.obj.index)
        cumcounts = np.zeros(len_index, dtype='int64')
        if ascending:
            for v in self.indices.values():
                cumcounts[v] = arr[:len(v)]
        else:
            for v in self.indices.values():
                cumcounts[v] = arr[len(v)-1::-1]
        return cumcounts

    def _index_with_as_index(self, b):
        """
        Take boolean mask of index to be returned from apply, if as_index=True

        """
        # TODO perf, it feels like this should already be somewhere...
        from itertools import chain
        original = self.obj.index
        gp = self.grouper
        levels = chain((gp.levels[i][gp.labels[i][b]]
                        for i in range(len(gp.groupings))),
                       (original.get_level_values(i)[b]
                        for i in range(original.nlevels)))
        new = MultiIndex.from_arrays(list(levels))
        new.names = gp.names + original.names
        return new

    def _try_cast(self, result, obj):
        """
        try to cast the result to our obj original type,
        we may have roundtripped thru object in the mean-time

        """
        if obj.ndim > 1:
            dtype = obj.values.dtype
        else:
            dtype = obj.dtype

        if not np.isscalar(result):
            result = _possibly_downcast_to_dtype(result, dtype)

        return result

    def _cython_agg_general(self, how, numeric_only=True):
        output = {}
        for name, obj in self._iterate_slices():
            is_numeric = _is_numeric_dtype(obj.dtype)
            if numeric_only and not is_numeric:
                continue

            try:
                result, names = self.grouper.aggregate(obj.values, how)
            except AssertionError as e:
                raise GroupByError(str(e))
            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 = _intercept_function(func)
        f = lambda x: func(x, *args, **kwargs)

        # iterate through "columns" ex exclusions to populate output dict
        output = {}
        for name, obj in self._iterate_slices():
            try:
                result, counts = self.grouper.agg_series(obj, f)
                output[name] = self._try_cast(result, obj)
            except TypeError:
                continue

        if len(output) == 0:
            return self._python_apply_general(f)

        if self.grouper._filter_empty_groups:

            mask = counts.ravel() > 0
            for name, result in compat.iteritems(output):

                # since we are masking, make sure that we have a float object
                values = result
                if _is_numeric_dtype(values.dtype):
                    values = com.ensure_float(values)

                output[name] = self._try_cast(values[mask], result)

        return self._wrap_aggregated_output(output)

    def _wrap_applied_output(self, *args, **kwargs):
        raise NotImplementedError

    def _concat_objects(self, keys, values, not_indexed_same=False):
        from pandas.tools.merge import concat

        if not not_indexed_same:
            result = concat(values, axis=self.axis)
            ax = self.obj._get_axis(self.axis)

            if isinstance(result, Series):
                result = result.reindex(ax)
            else:
                result = result.reindex_axis(ax, axis=self.axis)
        elif self.group_keys and self.as_index:
            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)
        else:
            result = concat(values, axis=self.axis)

        return result

    def _apply_filter(self, indices, dropna):
        if len(indices) == 0:
            indices = []
        else:
            indices = np.sort(np.concatenate(indices))
        if dropna:
            filtered = self.obj.take(indices)
        else:
            mask = np.empty(len(self.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.obj.shape[1:]) + [1]).T
            filtered = self.obj.where(mask)  # Fill with NaNs.
        return filtered


@Appender(GroupBy.__doc__)
def groupby(obj, by, **kwds):
    if isinstance(obj, Series):
        klass = SeriesGroupBy
    elif isinstance(obj, DataFrame):
        klass = DataFrameGroupBy
    else:  # pragma: no cover
        raise TypeError('invalid type: %s' % type(obj))

    return klass(obj, by, **kwds)


def _get_axes(group):
    if isinstance(group, Series):
        return [group.index]
    else:
        return group.axes


def _is_indexed_like(obj, axes):
    if isinstance(obj, Series):
        if len(axes) > 1:
            return False
        return obj.index.equals(axes[0])
    elif isinstance(obj, DataFrame):
        return obj.index.equals(axes[0])

    return False


class Grouper(object):

    """

    """

    def __init__(self, axis, groupings, sort=True, group_keys=True):
        self.axis = axis
        self.groupings = groupings
        self.sort = sort
        self.group_keys = group_keys
        self.compressed = True

    @property
    def shape(self):
        return tuple(ping.ngroups for ping in self.groupings)

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

    @property
    def nkeys(self):
        return len(self.groupings)

    def get_iterator(self, data, axis=0):
        """
        Groupby iterator

        Returns
        -------
        Generator yielding sequence of (name, subsetted object)
        for each group
        """
        splitter = self._get_splitter(data, axis=axis)
        keys = self._get_group_keys()
        for key, (i, group) in zip(keys, splitter):
            yield key, group

    def _get_splitter(self, data, axis=0):
        comp_ids, _, ngroups = self.group_info
        return get_splitter(data, comp_ids, ngroups, axis=axis)

    def _get_group_keys(self):
        if len(self.groupings) == 1:
            return self.levels[0]
        else:
            comp_ids, _, ngroups = self.group_info
            # provide "flattened" iterator for multi-group setting
            mapper = _KeyMapper(comp_ids, ngroups, self.labels, self.levels)
            return [mapper.get_key(i) for i in range(ngroups)]

    def apply(self, f, data, axis=0):
        mutated = False
        splitter = self._get_splitter(data, axis=axis)
        group_keys = self._get_group_keys()

        # oh boy
        if (f.__name__ not in _plotting_methods and
                hasattr(splitter, 'fast_apply') and axis == 0):
            try:
                values, mutated = splitter.fast_apply(f, group_keys)
                return group_keys, values, mutated
            except Exception:
                # we detect a mutation of some kind
                # so take slow path
                pass

        result_values = []
        for key, (i, group) in zip(group_keys, splitter):
            object.__setattr__(group, 'name', key)

            # group might be modified
            group_axes = _get_axes(group)
            res = f(group)
            if not _is_indexed_like(res, group_axes):
                mutated = True
            result_values.append(res)

        return group_keys, result_values, mutated

    @cache_readonly
    def indices(self):
        """ dict {group name -> group indices} """
        if len(self.groupings) == 1:
            return self.groupings[0].indices
        else:
            label_list = [ping.labels for ping in self.groupings]
            keys = [ping.group_index for ping in self.groupings]
            return _get_indices_dict(label_list, keys)

    @property
    def labels(self):
        return [ping.labels for ping in self.groupings]

    @property
    def levels(self):
        return [ping.group_index for ping in self.groupings]

    @property
    def names(self):
        return [ping.name for ping in self.groupings]

    def size(self):
        """
        Compute group sizes

        """
        # TODO: better impl
        labels, _, ngroups = self.group_info
        bin_counts = algos.value_counts(labels, sort=False)
        bin_counts = bin_counts.reindex(np.arange(ngroups))
        bin_counts.index = self.result_index
        return bin_counts

    @cache_readonly
    def _max_groupsize(self):
        '''
        Compute size of largest group

        '''
        # For many items in each group this is much faster than
        # self.size().max(), in worst case marginally slower
        if self.indices:
            return max(len(v) for v in self.indices.values())
        else:
            return 0

    @cache_readonly
    def groups(self):
        """ dict {group name -> group labels} """
        if len(self.groupings) == 1:
            return self.groupings[0].groups
        else:
            to_groupby = lzip(*(ping.grouper for ping in self.groupings))
            to_groupby = Index(to_groupby)

            return self.axis.groupby(to_groupby)

    @cache_readonly
    def group_info(self):
        comp_ids, obs_group_ids = self._get_compressed_labels()

        ngroups = len(obs_group_ids)
        comp_ids = com._ensure_int64(comp_ids)
        return comp_ids, obs_group_ids, ngroups

    def _get_compressed_labels(self):
        all_labels = [ping.labels for ping in self.groupings]
        if self._overflow_possible:
            tups = lib.fast_zip(all_labels)
            labs, uniques = algos.factorize(tups)

            if self.sort:
                uniques, labs = _reorder_by_uniques(uniques, labs)

            return labs, uniques
        else:
            if len(all_labels) > 1:
                group_index = get_group_index(all_labels, self.shape)
                comp_ids, obs_group_ids = _compress_group_index(group_index)
            else:
                ping = self.groupings[0]
                comp_ids = ping.labels
                obs_group_ids = np.arange(len(ping.group_index))
                self.compressed = False
                self._filter_empty_groups = False

            return comp_ids, obs_group_ids

    @cache_readonly
    def _overflow_possible(self):
        return _int64_overflow_possible(self.shape)

    @cache_readonly
    def ngroups(self):
        return len(self.result_index)

    @cache_readonly
    def result_index(self):
        recons = self.get_group_levels()
        return MultiIndex.from_arrays(recons, names=self.names)

    def get_group_levels(self):
        obs_ids = self.group_info[1]

        if not self.compressed and len(self.groupings) == 1:
            return [self.groupings[0].group_index]

        if self._overflow_possible:
            recons_labels = [np.array(x) for x in zip(*obs_ids)]
        else:
            recons_labels = decons_group_index(obs_ids, self.shape)

        name_list = []
        for ping, labels in zip(self.groupings, recons_labels):
            labels = com._ensure_platform_int(labels)
            name_list.append(ping.group_index.take(labels))

        return name_list

    #------------------------------------------------------------
    # Aggregation functions

    _cython_functions = {
        'add': 'group_add',
        'prod': 'group_prod',
        'min': 'group_min',
        'max': 'group_max',
        'mean': 'group_mean',
        'median': {
            'name': 'group_median'
        },
        'var': 'group_var',
        'std': 'group_var',
        'first': {
            'name': 'group_nth',
            'f': lambda func, a, b, c, d: func(a, b, c, d, 1)
        },
        'last': 'group_last',
    }

    _cython_transforms = {
        'std': np.sqrt
    }

    _cython_arity = {
        'ohlc': 4,  # OHLC
    }

    _name_functions = {}

    _filter_empty_groups = True

    def _get_aggregate_function(self, how, values):

        dtype_str = values.dtype.name

        def get_func(fname):
            # find the function, or use the object function, or return a
            # generic
            for dt in [dtype_str, 'object']:
                f = getattr(_algos, "%s_%s" % (fname, dtype_str), None)
                if f is not None:
                    return f
            return getattr(_algos, fname, None)

        ftype = self._cython_functions[how]

        if isinstance(ftype, dict):
            func = afunc = get_func(ftype['name'])

            # a sub-function
            f = ftype.get('f')
            if f is not None:

                def wrapper(*args, **kwargs):
                    return f(afunc, *args, **kwargs)

                # need to curry our sub-function
                func = wrapper

        else:
            func = get_func(ftype)

        if func is None:
            raise NotImplementedError("function is not implemented for this"
                                      "dtype: [how->%s,dtype->%s]" %
                                      (how, dtype_str))
        return func, dtype_str

    def aggregate(self, values, how, axis=0):

        arity = self._cython_arity.get(how, 1)

        vdim = values.ndim
        swapped = False
        if vdim == 1:
            values = values[:, None]
            out_shape = (self.ngroups, arity)
        else:
            if axis > 0:
                swapped = True
                values = values.swapaxes(0, axis)
            if arity > 1:
                raise NotImplementedError
            out_shape = (self.ngroups,) + values.shape[1:]

        if _is_numeric_dtype(values.dtype):
            values = com.ensure_float(values)
            is_numeric = True
        else:
            if issubclass(values.dtype.type, np.datetime64):
                raise Exception('Cython not able to handle this case')

            values = values.astype(object)
            is_numeric = False

        # will be filled in Cython function
        result = np.empty(out_shape, dtype=values.dtype)
        counts = np.zeros(self.ngroups, dtype=np.int64)

        result = self._aggregate(result, counts, values, how, is_numeric)

        if self._filter_empty_groups:
            if result.ndim == 2:
                if is_numeric:
                    result = lib.row_bool_subset(
                        result, (counts > 0).view(np.uint8))
                else:
                    result = lib.row_bool_subset_object(
                        result, (counts > 0).view(np.uint8))
            else:
                result = result[counts > 0]

        if vdim == 1 and arity == 1:
            result = result[:, 0]

        if how in self._name_functions:
            # TODO
            names = self._name_functions[how]()
        else:
            names = None

        if swapped:
            result = result.swapaxes(0, axis)

        return result, names

    def _aggregate(self, result, counts, values, how, is_numeric):
        agg_func, dtype = self._get_aggregate_function(how, values)
        trans_func = self._cython_transforms.get(how, lambda x: x)

        comp_ids, _, ngroups = self.group_info
        if values.ndim > 3:
            # punting for now
            raise NotImplementedError
        elif values.ndim > 2:
            for i, chunk in enumerate(values.transpose(2, 0, 1)):

                chunk = chunk.squeeze()
                agg_func(result[:, :, i], counts, chunk, comp_ids)
        else:
            agg_func(result, counts, values, comp_ids)

        return trans_func(result)

    def agg_series(self, obj, func):
        try:
            return self._aggregate_series_fast(obj, func)
        except Exception:
            return self._aggregate_series_pure_python(obj, func)

    def _aggregate_series_fast(self, obj, func):
        func = _intercept_function(func)

        if obj.index._has_complex_internals:
            raise TypeError('Incompatible index for Cython grouper')

        group_index, _, ngroups = self.group_info

        # avoids object / Series creation overhead
        dummy = obj._get_values(slice(None, 0)).to_dense()
        indexer = _algos.groupsort_indexer(group_index, ngroups)[0]
        obj = obj.take(indexer, convert=False)
        group_index = com.take_nd(group_index, indexer, allow_fill=False)
        grouper = lib.SeriesGrouper(obj, func, group_index, ngroups,
                                    dummy)
        result, counts = grouper.get_result()
        return result, counts

    def _aggregate_series_pure_python(self, obj, func):

        group_index, _, ngroups = self.group_info

        counts = np.zeros(ngroups, dtype=int)
        result = None

        splitter = get_splitter(obj, group_index, ngroups, axis=self.axis)

        for label, group in splitter:
            res = func(group)
            if result is None:
                if (isinstance(res, (Series, np.ndarray)) or
                        isinstance(res, list)):
                    raise ValueError('Function does not reduce')
                result = np.empty(ngroups, dtype='O')

            counts[label] = group.shape[0]
            result[label] = res

        result = lib.maybe_convert_objects(result, try_float=0)
        return result, counts


def generate_bins_generic(values, binner, closed):
    """
    Generate bin edge offsets and bin labels for one array using another array
    which has bin edge values. Both arrays must be sorted.

    Parameters
    ----------
    values : array of values
    binner : a comparable array of values representing bins into which to bin
        the first array. Note, 'values' end-points must fall within 'binner'
        end-points.
    closed : which end of bin is closed; left (default), right

    Returns
    -------
    bins : array of offsets (into 'values' argument) of bins.
        Zero and last edge are excluded in result, so for instance the first
        bin is values[0:bin[0]] and the last is values[bin[-1]:]
    """
    lenidx = len(values)
    lenbin = len(binner)

    if lenidx <= 0 or lenbin <= 0:
        raise ValueError("Invalid length for values or for binner")

    # check binner fits data
    if values[0] < binner[0]:
        raise ValueError("Values falls before first bin")

    if values[lenidx - 1] > binner[lenbin - 1]:
        raise ValueError("Values falls after last bin")

    bins = np.empty(lenbin - 1, dtype=np.int64)

    j = 0  # index into values
    bc = 0  # bin count

    # linear scan, presume nothing about values/binner except that it fits ok
    for i in range(0, lenbin - 1):
        r_bin = binner[i + 1]

        # count values in current bin, advance to next bin
        while j < lenidx and (values[j] < r_bin or
                              (closed == 'right' and values[j] == r_bin)):
            j += 1

        bins[bc] = j
        bc += 1

    return bins


class CustomGrouper(object):

    def get_grouper(self, obj):
        raise NotImplementedError


class BinGrouper(Grouper):

    def __init__(self, bins, binlabels, filter_empty=False):
        self.bins = com._ensure_int64(bins)
        self.binlabels = _ensure_index(binlabels)
        self._filter_empty_groups = filter_empty

    @property
    def nkeys(self):
        return 1

    def get_iterator(self, data, axis=0):
        """
        Groupby iterator

        Returns
        -------
        Generator yielding sequence of (name, subsetted object)
        for each group
        """
        if axis == 0:
            start = 0
            for edge, label in zip(self.bins, self.binlabels):
                yield label, data[start:edge]
                start = edge

            if start < len(data):
                yield self.binlabels[-1], data[start:]
        else:
            start = 0
            for edge, label in zip(self.bins, self.binlabels):
                inds = lrange(start, edge)
                yield label, data.take(inds, axis=axis)
                start = edge

            n = len(data.axes[axis])
            if start < n:
                inds = lrange(start, n)
                yield self.binlabels[-1], data.take(inds, axis=axis)

    def apply(self, f, data, axis=0):
        result_keys = []
        result_values = []
        mutated = False
        for key, group in self.get_iterator(data, axis=axis):
            object.__setattr__(group, 'name', key)

            # group might be modified
            group_axes = _get_axes(group)
            res = f(group)

            if not _is_indexed_like(res, group_axes):
                mutated = True

            result_keys.append(key)
            result_values.append(res)

        return result_keys, result_values, mutated

    @cache_readonly
    def ngroups(self):
        return len(self.binlabels)

    @cache_readonly
    def result_index(self):
        return self.binlabels

    @property
    def levels(self):
        return [self.binlabels]

    @property
    def names(self):
        return [self.binlabels.name]

    #----------------------------------------------------------------------
    # cython aggregation

    _cython_functions = {
        'add': 'group_add_bin',
        'prod': 'group_prod_bin',
        'mean': 'group_mean_bin',
        'min': 'group_min_bin',
        'max': 'group_max_bin',
        'var': 'group_var_bin',
        'std': 'group_var_bin',
        'ohlc': 'group_ohlc',
        'first': {
            'name': 'group_nth_bin',
            'f': lambda func, a, b, c, d: func(a, b, c, d, 1)
        },
        'last': 'group_last_bin',
    }

    _name_functions = {
        'ohlc': lambda *args: ['open', 'high', 'low', 'close']
    }

    _filter_empty_groups = True

    def _aggregate(self, result, counts, values, how, is_numeric=True):

        agg_func, dtype = self._get_aggregate_function(how, values)
        trans_func = self._cython_transforms.get(how, lambda x: x)

        if values.ndim > 3:
            # punting for now
            raise NotImplementedError
        elif values.ndim > 2:
            for i, chunk in enumerate(values.transpose(2, 0, 1)):
                agg_func(result[:, :, i], counts, chunk, self.bins)
        else:
            agg_func(result, counts, values, self.bins)

        return trans_func(result)

    def agg_series(self, obj, func):
        dummy = obj[:0]
        grouper = lib.SeriesBinGrouper(obj, func, self.bins, dummy)
        return grouper.get_result()


class Grouping(object):

    """
    Holds the grouping information for a single key

    Parameters
    ----------
    index : Index
    grouper :
    name :
    level :

    Returns
    -------
    **Attributes**:
      * indices : dict of {group -> index_list}
      * labels : ndarray, group labels
      * ids : mapping of label -> group
      * counts : array of group counts
      * group_index : unique groups
      * groups : dict of {group -> label_list}
    """

    def __init__(self, index, grouper=None, name=None, level=None,
                 sort=True):

        self.name = name
        self.level = level
        self.grouper = _convert_grouper(index, grouper)
        self.index = index
        self.sort = sort

        # 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

        # pre-computed
        self._was_factor = False
        self._should_compress = True

        if level is not None:
            if not isinstance(level, int):
                if level not in index.names:
                    raise AssertionError('Level %s not in index' % str(level))
                level = index.names.index(level)

            inds = index.labels[level]
            level_index = index.levels[level]

            if self.name is None:
                self.name = index.names[level]

            # XXX complete hack

            if grouper is not None:
                level_values = index.levels[level].take(inds)
                self.grouper = level_values.map(self.grouper)
            else:
                self._was_factor = True

                # all levels may not be observed
                labels, uniques = algos.factorize(inds, sort=True)

                if len(uniques) > 0 and uniques[0] == -1:
                    # handle NAs
                    mask = inds != -1
                    ok_labels, uniques = algos.factorize(inds[mask], sort=True)

                    labels = np.empty(len(inds), dtype=inds.dtype)
                    labels[mask] = ok_labels
                    labels[-mask] = -1

                if len(uniques) < len(level_index):
                    level_index = level_index.take(uniques)

                self._labels = labels
                self._group_index = level_index
                self.grouper = level_index.take(labels)
        else:
            if isinstance(self.grouper, (list, tuple)):
                self.grouper = com._asarray_tuplesafe(self.grouper)
            elif isinstance(self.grouper, Categorical):
                factor = self.grouper
                self._was_factor = True

                # Is there any way to avoid this?
                self.grouper = np.asarray(factor)

                self._labels = factor.labels
                self._group_index = factor.levels
                if self.name is None:
                    self.name = factor.name

            # no level passed
            if not isinstance(self.grouper, (Series, np.ndarray)):
                self.grouper = self.index.map(self.grouper)
                if not (hasattr(self.grouper, "__len__") and
                        len(self.grouper) == len(self.index)):
                    errmsg = ('Grouper result violates len(labels) == '
                              'len(data)\nresult: %s' %
                              com.pprint_thing(self.grouper))
                    self.grouper = None  # Try for sanity
                    raise AssertionError(errmsg)

    def __repr__(self):
        return 'Grouping(%s)' % self.name

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

    _labels = None
    _group_index = None

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

    @cache_readonly
    def indices(self):
        return _groupby_indices(self.grouper)

    @property
    def labels(self):
        if self._labels is None:
            self._make_labels()
        return self._labels

    @property
    def group_index(self):
        if self._group_index is None:
            self._make_labels()
        return self._group_index

    def _make_labels(self):
        if self._was_factor:  # pragma: no cover
            raise Exception('Should not call this method grouping by level')
        else:
            labs, uniques = algos.factorize(self.grouper, sort=self.sort)
            uniques = Index(uniques, name=self.name)
            self._labels = labs
            self._group_index = uniques

    _groups = None

    @property
    def groups(self):
        if self._groups is None:
            self._groups = self.index.groupby(self.grouper)
        return self._groups


def _get_grouper(obj, key=None, axis=0, level=None, sort=True):
    group_axis = obj._get_axis(axis)

    if level is not None:
        if not isinstance(group_axis, MultiIndex):
            if isinstance(level, compat.string_types):
                if obj.index.name != level:
                    raise ValueError('level name %s is not the name of the '
                                     'index' % level)
            elif level > 0:
                raise ValueError('level > 0 only valid with MultiIndex')

            level = None
            key = group_axis

    if isinstance(key, CustomGrouper):
        gpr = key.get_grouper(obj)
        return gpr, []
    elif isinstance(key, Grouper):
        return key, []

    if not isinstance(key, (tuple, list)):
        keys = [key]
    else:
        keys = key

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

    try:
        if isinstance(obj, DataFrame):
            all_in_columns = all(g in obj.columns for g in keys)
        else:
            all_in_columns = False
    except Exception:
        all_in_columns = False

    if (not any_callable and not all_in_columns
        and not any_arraylike and match_axis_length
            and level is None):
        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 = []
    exclusions = []
    for i, (gpr, level) in enumerate(zip(keys, levels)):
        name = None
        try:
            obj._data.items.get_loc(gpr)
            in_axis = True
        except Exception:
            in_axis = False

        if _is_label_like(gpr) or in_axis:
            exclusions.append(gpr)
            name = gpr
            gpr = obj[gpr]

        if isinstance(gpr, Categorical) and len(gpr) != len(obj):
            errmsg = "Categorical grouper must have len(grouper) == len(data)"
            raise AssertionError(errmsg)

        ping = Grouping(group_axis, gpr, name=name, level=level, sort=sort)
        groupings.append(ping)

    if len(groupings) == 0:
        raise ValueError('No group keys passed!')

    grouper = Grouper(group_axis, groupings, sort=sort)

    return grouper, exclusions


def _is_label_like(val):
    return isinstance(val, compat.string_types) or np.isscalar(val)


def _convert_grouper(axis, 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, np.ndarray)):
        if len(grouper) != len(axis):
            raise AssertionError('Grouper and axis must be same length')
        return grouper
    else:
        return grouper


class SeriesGroupBy(GroupBy):
    _apply_whitelist = _series_apply_whitelist

    def aggregate(self, func_or_funcs, *args, **kwargs):
        """
        Apply aggregation function or functions to groups, yielding most likely
        Series but in some cases DataFrame depending on the output of the
        aggregation function

        Parameters
        ----------
        func_or_funcs : function or list / dict of functions
            List/dict of functions will produce DataFrame with column names
            determined by the function names themselves (list) or the keys in
            the dict

        Notes
        -----
        agg is an alias for aggregate. Use it.

        Examples
        --------
        >>> series
        bar    1.0
        baz    2.0
        qot    3.0
        qux    4.0

        >>> mapper = lambda x: x[0] # first letter
        >>> grouped = series.groupby(mapper)

        >>> grouped.aggregate(np.sum)
        b    3.0
        q    7.0

        >>> grouped.aggregate([np.sum, np.mean, np.std])
           mean  std  sum
        b  1.5   0.5  3
        q  3.5   0.5  7

        >>> grouped.agg({'result' : lambda x: x.mean() / x.std(),
        ...              'total' : np.sum})
           result  total
        b  2.121   3
        q  4.95    7

        See also
        --------
        apply, transform

        Returns
        -------
        Series or DataFrame
        """
        if isinstance(func_or_funcs, compat.string_types):
            return getattr(self, func_or_funcs)(*args, **kwargs)

        if hasattr(func_or_funcs, '__iter__'):
            ret = self._aggregate_multiple_funcs(func_or_funcs)
        else:
            cyfunc = _intercept_cython(func_or_funcs)
            if cyfunc and not args and not kwargs:
                return getattr(self, cyfunc)()

            if self.grouper.nkeys > 1:
                return self._python_agg_general(func_or_funcs, *args, **kwargs)

            try:
                return self._python_agg_general(func_or_funcs, *args, **kwargs)
            except Exception:
                result = self._aggregate_named(func_or_funcs, *args, **kwargs)

            index = Index(sorted(result), name=self.grouper.names[0])
            ret = Series(result, index=index)

        if not self.as_index:  # pragma: no cover
            print('Warning, ignoring as_index=True')

        return ret

    def _aggregate_multiple_funcs(self, arg):
        if isinstance(arg, dict):
            columns = list(arg.keys())
            arg = list(arg.items())
        elif any(isinstance(x, (tuple, list)) for x in arg):
            arg = [(x, x) if not isinstance(x, (tuple, list)) else x
                   for x in arg]

            # indicated column order
            columns = lzip(*arg)[0]
        else:
            # list of functions / function names
            columns = []
            for f in arg:
                if isinstance(f, compat.string_types):
                    columns.append(f)
                else:
                    columns.append(f.__name__)
            arg = lzip(columns, arg)

        results = {}

        for name, func in arg:
            if name in results:
                raise SpecificationError('Function names must be unique, '
                                         'found multiple named %s' % name)

            results[name] = self.aggregate(func)

        return DataFrame(results, columns=columns)

    def _wrap_aggregated_output(self, output, names=None):
        # sort of a kludge
        output = output[self.name]
        index = self.grouper.result_index

        if names is not None:
            return DataFrame(output, index=index, columns=names)
        else:
            return Series(output, index=index, name=self.name)

    def _wrap_applied_output(self, keys, values, not_indexed_same=False):
        if len(keys) == 0:
            return Series([])

        def _get_index():
            if self.grouper.nkeys > 1:
                index = MultiIndex.from_tuples(keys, names=self.grouper.names)
            else:
                index = Index(keys, name=self.grouper.names[0])
            return index

        if isinstance(values[0], dict):
            # GH #823
            index = _get_index()
            return DataFrame(values, index=index).stack()

        if isinstance(values[0], (Series, dict)):
            return self._concat_objects(keys, values,
                                        not_indexed_same=not_indexed_same)
        elif isinstance(values[0], DataFrame):
            # possible that Series -> DataFrame by applied function
            return self._concat_objects(keys, values,
                                        not_indexed_same=not_indexed_same)
        else:
            return Series(values, index=_get_index())

    def _aggregate_named(self, func, *args, **kwargs):
        result = {}

        for name, group in self:
            group.name = name
            output = func(group, *args, **kwargs)
            if isinstance(output, (Series, np.ndarray)):
                raise Exception('Must produce aggregated value')
            result[name] = self._try_cast(output, group)

        return result

    def transform(self, func, *args, **kwargs):
        """
        Call function producing a like-indexed Series on each group and return
        a Series with the transformed values

        Parameters
        ----------
        func : function
            To apply to each group. Should return a Series with the same index

        Examples
        --------
        >>> grouped.transform(lambda x: (x - x.mean()) / x.std())

        Returns
        -------
        transformed : Series
        """
        result = self.obj.copy()
        if hasattr(result, 'values'):
            result = result.values
        dtype = result.dtype

        if isinstance(func, compat.string_types):
            wrapper = lambda x: getattr(x, func)(*args, **kwargs)
        else:
            wrapper = lambda x: func(x, *args, **kwargs)

        for name, group in self:

            group = com.ensure_float(group)
            object.__setattr__(group, 'name', name)
            res = wrapper(group)
            if hasattr(res, 'values'):
                res = res.values

            # need to do a safe put here, as the dtype may be different
            # this needs to be an ndarray
            result = Series(result)
            result.iloc[self.indices[name]] = res
            result = result.values

        # downcast if we can (and need)
        result = _possibly_downcast_to_dtype(result, dtype)
        return self.obj.__class__(result, index=self.obj.index,
                                  name=self.obj.name)

    def filter(self, func, dropna=True, *args, **kwargs):
        """
        Return a copy of a Series excluding elements from groups that
        do not satisfy the boolean criterion specified by func.

        Parameters
        ----------
        func : function
            To apply to each group. Should return True or False.
        dropna : Drop groups that do not pass the filter. True by default;
            if False, groups that evaluate False are filled with NaNs.

        Example
        -------
        >>> grouped.filter(lambda x: x.mean() > 0)

        Returns
        -------
        filtered : Series
        """
        if isinstance(func, compat.string_types):
            wrapper = lambda x: getattr(x, func)(*args, **kwargs)
        else:
            wrapper = lambda x: func(x, *args, **kwargs)

        # Interpret np.nan as False.
        def true_and_notnull(x, *args, **kwargs):
            b = wrapper(x, *args, **kwargs)
            return b and notnull(b)

        try:
            indices = [self.indices[name] if true_and_notnull(group) else []
                       for name, group in self]
        except ValueError:
            raise TypeError("the filter must return a boolean result")
        except TypeError:
            raise TypeError("the filter must return a boolean result")

        filtered = self._apply_filter(indices, dropna)
        return filtered


class NDFrameGroupBy(GroupBy):

    def _iterate_slices(self):
        if self.axis == 0:
            # kludge
            if self._selection is None:
                slice_axis = self.obj.columns
            else:
                slice_axis = self._selection_list
            slicer = lambda x: self.obj[x]
        else:
            slice_axis = self.obj.index
            slicer = self.obj.xs

        for val in slice_axis:
            if val in self.exclusions:
                continue
            yield val, slicer(val)

    def _cython_agg_general(self, how, numeric_only=True):
        new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
        return self._wrap_agged_blocks(new_blocks)

    def _wrap_agged_blocks(self, blocks):
        obj = self._obj_with_exclusions

        new_axes = list(obj._data.axes)

        # more kludge
        if self.axis == 0:
            new_axes[0], new_axes[1] = new_axes[1], self.grouper.result_index
        else:
            new_axes[self.axis] = self.grouper.result_index

        mgr = BlockManager(blocks, new_axes)

        new_obj = type(obj)(mgr)

        return self._post_process_cython_aggregate(new_obj)

    _block_agg_axis = 0

    def _cython_agg_blocks(self, how, numeric_only=True):
        data, agg_axis = self._get_data_to_aggregate()

        new_blocks = []

        for block in data.blocks:
            values = block.values

            is_numeric = _is_numeric_dtype(values.dtype)

            if numeric_only and not is_numeric:
                continue

            if is_numeric:
                values = com.ensure_float(values)

            result, _ = self.grouper.aggregate(values, how, axis=agg_axis)

            # see if we can cast the block back to the original dtype
            result = block._try_cast_result(result)

            newb = make_block(result, block.items, block.ref_items)
            new_blocks.append(newb)

        if len(new_blocks) == 0:
            raise DataError('No numeric types to aggregate')

        return new_blocks

    def _get_data_to_aggregate(self):
        obj = self._obj_with_exclusions
        if self.axis == 0:
            return obj.swapaxes(0, 1)._data, 1
        else:
            return obj._data, self.axis

    def _post_process_cython_aggregate(self, obj):
        # undoing kludge from below
        if self.axis == 0:
            obj = obj.swapaxes(0, 1)
        return obj

    @cache_readonly
    def _obj_with_exclusions(self):
        if self._selection is not None:
            return self.obj.reindex(columns=self._selection_list)

        if len(self.exclusions) > 0:
            return self.obj.drop(self.exclusions, axis=1)
        else:
            return self.obj

    @Appender(_agg_doc)
    def aggregate(self, arg, *args, **kwargs):
        if isinstance(arg, compat.string_types):
            return getattr(self, arg)(*args, **kwargs)

        result = OrderedDict()
        if isinstance(arg, dict):
            if self.axis != 0:  # pragma: no cover
                raise ValueError('Can only pass dict with axis=0')

            obj = self.obj

            if any(isinstance(x, (list, tuple, dict)) for x in arg.values()):
                new_arg = OrderedDict()
                for k, v in compat.iteritems(arg):
                    if not isinstance(v, (tuple, list, dict)):
                        new_arg[k] = [v]
                    else:
                        new_arg[k] = v
                arg = new_arg

            keys = []
            if self._selection is not None:
                subset = obj[self._selection]
                if isinstance(subset, DataFrame):
                    raise NotImplementedError

                for fname, agg_how in compat.iteritems(arg):
                    colg = SeriesGroupBy(subset, selection=self._selection,
                                         grouper=self.grouper)
                    result[fname] = colg.aggregate(agg_how)
                    keys.append(fname)
            else:
                for col, agg_how in compat.iteritems(arg):
                    colg = SeriesGroupBy(obj[col], selection=col,
                                         grouper=self.grouper)
                    result[col] = colg.aggregate(agg_how)
                    keys.append(col)

            if isinstance(list(result.values())[0], DataFrame):
                from pandas.tools.merge import concat
                result = concat([result[k] for k in keys], keys=keys, axis=1)
            else:
                result = DataFrame(result)
        elif isinstance(arg, list):
            return self._aggregate_multiple_funcs(arg)
        else:
            cyfunc = _intercept_cython(arg)
            if cyfunc and not args and not kwargs:
                return getattr(self, cyfunc)()

            if self.grouper.nkeys > 1:
                return self._python_agg_general(arg, *args, **kwargs)
            else:
                result = self._aggregate_generic(arg, *args, **kwargs)

        if not self.as_index:
            if isinstance(result.index, MultiIndex):
                zipped = zip(result.index.levels, result.index.labels,
                             result.index.names)
                for i, (lev, lab, name) in enumerate(zipped):
                    result.insert(i, name,
                                  com.take_nd(lev.values, lab,
                                              allow_fill=False))
                result = result.consolidate()
            else:
                values = result.index.values
                name = self.grouper.groupings[0].name
                result.insert(0, name, values)
            result.index = np.arange(len(result))

        return result.convert_objects()

    def _aggregate_multiple_funcs(self, arg):
        from pandas.tools.merge import concat

        if self.axis != 0:
            raise NotImplementedError

        obj = self._obj_with_exclusions

        results = []
        keys = []
        for col in obj:
            try:
                colg = SeriesGroupBy(obj[col], selection=col,
                                     grouper=self.grouper)
                results.append(colg.aggregate(arg))
                keys.append(col)
            except (TypeError, DataError):
                pass
            except SpecificationError:
                raise
        result = concat(results, keys=keys, axis=1)

        return result

    def _aggregate_generic(self, func, *args, **kwargs):
        if self.grouper.nkeys != 1:
            raise AssertionError('Number of keys must be 1')

        axis = self.axis
        obj = self._obj_with_exclusions

        result = {}
        if axis != obj._info_axis_number:
            try:
                for name, data in self:
                    # for name in self.indices:
                    #     data = self.get_group(name, obj=obj)
                    result[name] = self._try_cast(func(data, *args, **kwargs),
                                                  data)
            except Exception:
                return self._aggregate_item_by_item(func, *args, **kwargs)
        else:
            for name in self.indices:
                try:
                    data = self.get_group(name, obj=obj)
                    result[name] = self._try_cast(func(data, *args, **kwargs),
                                                  data)
                except Exception:
                    wrapper = lambda x: func(x, *args, **kwargs)
                    result[name] = data.apply(wrapper, axis=axis)

        return self._wrap_generic_output(result, obj)

    def _wrap_aggregated_output(self, output, names=None):
        raise NotImplementedError

    def _aggregate_item_by_item(self, func, *args, **kwargs):
        # only for axis==0

        obj = self._obj_with_exclusions
        result = {}
        cannot_agg = []
        for item in obj:
            try:
                data = obj[item]
                colg = SeriesGroupBy(data, selection=item,
                                     grouper=self.grouper)
                result[item] = self._try_cast(
                    colg.aggregate(func, *args, **kwargs), data)
            except ValueError:
                cannot_agg.append(item)
                continue

        result_columns = obj.columns
        if cannot_agg:
            result_columns = result_columns.drop(cannot_agg)

        return DataFrame(result, columns=result_columns)

    def _decide_output_index(self, output, labels):
        if len(output) == len(labels):
            output_keys = labels
        else:
            output_keys = sorted(output)
            try:
                output_keys.sort()
            except Exception:  # pragma: no cover
                pass

            if isinstance(labels, MultiIndex):
                output_keys = MultiIndex.from_tuples(output_keys,
                                                     names=labels.names)

        return output_keys

    def _wrap_applied_output(self, keys, values, not_indexed_same=False):
        from pandas.core.index import _all_indexes_same

        if len(keys) == 0:
            # XXX
            return DataFrame({})

        key_names = self.grouper.names

        if isinstance(values[0], DataFrame):
            return self._concat_objects(keys, values,
                                        not_indexed_same=not_indexed_same)
        elif hasattr(self.grouper, 'groupings'):
            if len(self.grouper.groupings) > 1:
                key_index = MultiIndex.from_tuples(keys, names=key_names)
            else:
                ping = self.grouper.groupings[0]
                if len(keys) == ping.ngroups:
                    key_index = ping.group_index
                    key_index.name = key_names[0]

                    key_lookup = Index(keys)
                    indexer = key_lookup.get_indexer(key_index)

                    # reorder the values
                    values = [values[i] for i in indexer]
                else:
                    key_index = Index(keys, name=key_names[0])

            # make Nones an empty object
            if com._count_not_none(*values) != len(values):
                v = None
                for v in values:
                    if v is not None:
                        break
                if v is None:
                    return DataFrame()
                elif isinstance(v, NDFrame):
                    values = [
                        x if x is not None else
                        v._constructor(**v._construct_axes_dict())
                        for x in values
                        ]

            v = values[0]

            if isinstance(v, (np.ndarray, Series)):
                if isinstance(v, Series):
                    applied_index = self.obj._get_axis(self.axis)
                    all_indexed_same = _all_indexes_same([
                        x.index for x in values
                    ])
                    singular_series = (len(values) == 1 and
                                       applied_index.nlevels == 1)

                    # GH3596
                    # provide a reduction (Frame -> Series) if groups are
                    # unique
                    if self.squeeze:

                        # assign the name to this series
                        if singular_series:
                            values[0].name = keys[0]

                            # GH2893
                            # we have series in the values array, we want to
                            # produce a series:
                            # if any of the sub-series are not indexed the same
                            # OR we don't have a multi-index and we have only a
                            # single values
                            return self._concat_objects(
                                keys, values, not_indexed_same=not_indexed_same
                            )

                        # still a series
                        # path added as of GH 5545
                        elif all_indexed_same:
                            from pandas.tools.merge import concat
                            return concat(values)

                    if not all_indexed_same:
                        return self._concat_objects(
                            keys, values, not_indexed_same=not_indexed_same
                        )

                try:
                    if self.axis == 0:

                        stacked_values = np.vstack([np.asarray(x)
                                                    for x in values])
                        columns = v.index
                        index = key_index
                    else:
                        stacked_values = np.vstack([np.asarray(x)
                                                    for x in values]).T

                        index = v.index
                        columns = key_index

                except (ValueError, AttributeError):
                    # GH1738: values is list of arrays of unequal lengths fall
                    # through to the outer else caluse
                    return Series(values, index=key_index)

                # if we have date/time like in the original, then coerce dates
                # as we are stacking can easily have object dtypes here
                cd = True
                if self.obj.ndim == 2 and self.obj.dtypes.isin(_DATELIKE_DTYPES).any():
                    cd = 'coerce'
                return DataFrame(stacked_values, index=index,
                                 columns=columns).convert_objects(convert_dates=cd, convert_numeric=True)

            else:
                return Series(values, index=key_index).convert_objects(
                    convert_dates='coerce',convert_numeric=True)
        else:
            # Handle cases like BinGrouper
            return self._concat_objects(keys, values,
                                        not_indexed_same=not_indexed_same)

    def transform(self, func, *args, **kwargs):
        """
        Call function producing a like-indexed DataFrame on each group and
        return a DataFrame having the same indexes as the original object
        filled with the transformed values

        Parameters
        ----------
        f : function
            Function to apply to each subframe

        Notes
        -----
        Each subframe is endowed the attribute 'name' in case you need to know
        which group you are working on.

        Examples
        --------
        >>> grouped = df.groupby(lambda x: mapping[x])
        >>> grouped.transform(lambda x: (x - x.mean()) / x.std())
        """
        from pandas.tools.merge import concat

        applied = []

        obj = self._obj_with_exclusions
        gen = self.grouper.get_iterator(obj, axis=self.axis)
        fast_path, slow_path = self._define_paths(func, *args, **kwargs)

        path = None
        for name, group in gen:
            object.__setattr__(group, 'name', name)

            if path is None:
                # Try slow path and fast path.
                try:
                    path, res = self._choose_path(fast_path, slow_path, group)
                except TypeError:
                    return self._transform_item_by_item(obj, fast_path)
                except Exception:  # pragma: no cover
                    res = fast_path(group)
                    path = fast_path
            else:
                res = path(group)

            # broadcasting
            if isinstance(res, Series):
                if res.index.is_(obj.index):
                    group.T.values[:] = res
                else:
                    group.values[:] = res

                applied.append(group)
            else:
                applied.append(res)

        concat_index = obj.columns if self.axis == 0 else obj.index
        concatenated = concat(applied, join_axes=[concat_index],
                              axis=self.axis, verify_integrity=False)
        concatenated.sort_index(inplace=True)
        return concatenated

    def _define_paths(self, func, *args, **kwargs):
        if isinstance(func, compat.string_types):
            fast_path = lambda group: getattr(group, func)(*args, **kwargs)
            slow_path = lambda group: group.apply(
                lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis)
        else:
            fast_path = lambda group: func(group, *args, **kwargs)
            slow_path = lambda group: group.apply(
                lambda x: func(x, *args, **kwargs), axis=self.axis)
        return fast_path, slow_path

    def _choose_path(self, fast_path, slow_path, group):
        path = slow_path
        res = slow_path(group)

        # if we make it here, test if we can use the fast path
        try:
            res_fast = fast_path(group)

            # compare that we get the same results
            if res.shape == res_fast.shape:
                res_r = res.values.ravel()
                res_fast_r = res_fast.values.ravel()
                mask = notnull(res_r)
            if (res_r[mask] == res_fast_r[mask]).all():
                path = fast_path

        except:
            pass
        return path, res

    def _transform_item_by_item(self, obj, wrapper):
        # iterate through columns
        output = {}
        inds = []
        for i, col in enumerate(obj):
            try:
                output[col] = self[col].transform(wrapper)
                inds.append(i)
            except Exception:
                pass

        if len(output) == 0:  # pragma: no cover
            raise TypeError('Transform function invalid for data types')

        columns = obj.columns
        if len(output) < len(obj.columns):
            columns = columns.take(inds)

        return DataFrame(output, index=obj.index, columns=columns)

    def filter(self, func, dropna=True, *args, **kwargs):
        """
        Return a copy of a DataFrame excluding elements from groups that
        do not satisfy the boolean criterion specified by func.

        Parameters
        ----------
        f : function
            Function to apply to each subframe. Should return True or False.
        dropna : Drop groups that do not pass the filter. True by default;
            if False, groups that evaluate False are filled with NaNs.

        Notes
        -----
        Each subframe is endowed the attribute 'name' in case you need to know
        which group you are working on.

        Example
        --------
        >>> grouped = df.groupby(lambda x: mapping[x])
        >>> grouped.filter(lambda x: x['A'].sum() + x['B'].sum() > 0)
        """
        from pandas.tools.merge import concat

        indices = []

        obj = self._obj_with_exclusions
        gen = self.grouper.get_iterator(obj, axis=self.axis)

        fast_path, slow_path = self._define_paths(func, *args, **kwargs)

        path = None
        for name, group in gen:
            object.__setattr__(group, 'name', name)

            if path is None:
                # Try slow path and fast path.
                try:
                    path, res = self._choose_path(fast_path, slow_path, group)
                except Exception:  # pragma: no cover
                    res = fast_path(group)
                    path = fast_path
            else:
                res = path(group)

            def add_indices():
                indices.append(self.indices[name])

            # interpret the result of the filter
            if isinstance(res, (bool, np.bool_)):
                if res:
                    add_indices()
            else:
                if getattr(res, 'ndim', None) == 1:
                    val = res.ravel()[0]
                    if val and notnull(val):
                        add_indices()
                else:

                    # in theory you could do .all() on the boolean result ?
                    raise TypeError("the filter must return a boolean result")

        filtered = self._apply_filter(indices, dropna)
        return filtered


class DataFrameGroupBy(NDFrameGroupBy):
    _apply_whitelist = _dataframe_apply_whitelist

    _block_agg_axis = 1

    def __getitem__(self, key):
        if self._selection is not None:
            raise Exception('Column(s) %s already selected' % self._selection)

        if (isinstance(key, (list, tuple, Series, np.ndarray)) or
                not self.as_index):
            return DataFrameGroupBy(self.obj, self.grouper, selection=key,
                                    grouper=self.grouper,
                                    exclusions=self.exclusions,
                                    as_index=self.as_index)
        else:
            if key not in self.obj:  # pragma: no cover
                raise KeyError(str(key))
            # kind of a kludge
            return SeriesGroupBy(self.obj[key], selection=key,
                                 grouper=self.grouper,
                                 exclusions=self.exclusions)

    def _wrap_generic_output(self, result, obj):
        result_index = self.grouper.levels[0]

        if result:
            if self.axis == 0:
                result = DataFrame(result, index=obj.columns,
                                   columns=result_index).T
            else:
                result = DataFrame(result, index=obj.index,
                                   columns=result_index)
        else:
            result = DataFrame(result)

        return result

    def _get_data_to_aggregate(self):
        obj = self._obj_with_exclusions
        if self.axis == 1:
            return obj.T._data, 1
        else:
            return obj._data, 1

    def _wrap_aggregated_output(self, output, names=None):
        agg_axis = 0 if self.axis == 1 else 1
        agg_labels = self._obj_with_exclusions._get_axis(agg_axis)

        output_keys = self._decide_output_index(output, agg_labels)

        if not self.as_index:
            result = DataFrame(output, columns=output_keys)
            group_levels = self.grouper.get_group_levels()
            zipped = zip(self.grouper.names, group_levels)

            for i, (name, labels) in enumerate(zipped):
                result.insert(i, name, labels)
            result = result.consolidate()
        else:
            index = self.grouper.result_index
            result = DataFrame(output, index=index, columns=output_keys)

        if self.axis == 1:
            result = result.T

        return result.convert_objects()

    def _wrap_agged_blocks(self, blocks):
        obj = self._obj_with_exclusions

        if self.axis == 0:
            agg_labels = obj.columns
        else:
            agg_labels = obj.index

        if sum(len(x.items) for x in blocks) == len(agg_labels):
            output_keys = agg_labels
        else:
            all_items = []
            for b in blocks:
                all_items.extend(b.items)
            output_keys = agg_labels[agg_labels.isin(all_items)]

            for blk in blocks:
                blk.set_ref_items(output_keys, maybe_rename=False)

        if not self.as_index:
            index = np.arange(blocks[0].values.shape[1])
            mgr = BlockManager(blocks, [output_keys, index])
            result = DataFrame(mgr)

            group_levels = self.grouper.get_group_levels()
            zipped = zip(self.grouper.names, group_levels)

            for i, (name, labels) in enumerate(zipped):
                result.insert(i, name, labels)
            result = result.consolidate()
        else:
            index = self.grouper.result_index
            mgr = BlockManager(blocks, [output_keys, index])
            result = DataFrame(mgr)

        if self.axis == 1:
            result = result.T

        return result.convert_objects()

    def _iterate_column_groupbys(self):
        for i, colname in enumerate(self.obj.columns):
            yield colname, SeriesGroupBy(self.obj.iloc[:, i],
                                         selection=colname,
                                         grouper=self.grouper,
                                         exclusions=self.exclusions)

    def _apply_to_column_groupbys(self, func):
        from pandas.tools.merge import concat
        return concat(
            (func(col_groupby) for _, col_groupby
             in self._iterate_column_groupbys()),
            keys=self.obj.columns, axis=1)

    def ohlc(self):
        """
        Compute sum of values, excluding missing values

        For multiple groupings, the result index will be a MultiIndex
        """
        return self._apply_to_column_groupbys(
            lambda x: x._cython_agg_general('ohlc'))


from pandas.tools.plotting import boxplot_frame_groupby
DataFrameGroupBy.boxplot = boxplot_frame_groupby


class PanelGroupBy(NDFrameGroupBy):

    def _iterate_slices(self):
        if self.axis == 0:
            # kludge
            if self._selection is None:
                slice_axis = self.obj.items
            else:
                slice_axis = self._selection_list
            slicer = lambda x: self.obj[x]
        else:
            raise NotImplementedError

        for val in slice_axis:
            if val in self.exclusions:
                continue

            yield val, slicer(val)

    def aggregate(self, arg, *args, **kwargs):
        """
        Aggregate using input function or dict of {column -> function}

        Parameters
        ----------
        arg : function or dict
            Function to use for aggregating groups. If a function, must either
            work when passed a Panel or when passed to Panel.apply. If
            pass a dict, the keys must be DataFrame column names

        Returns
        -------
        aggregated : Panel
        """
        if isinstance(arg, compat.string_types):
            return getattr(self, arg)(*args, **kwargs)

        return self._aggregate_generic(arg, *args, **kwargs)

    def _wrap_generic_output(self, result, obj):
        if self.axis == 0:
            new_axes = list(obj.axes)
            new_axes[0] = self.grouper.result_index
        elif self.axis == 1:
            x, y, z = obj.axes
            new_axes = [self.grouper.result_index, z, x]
        else:
            x, y, z = obj.axes
            new_axes = [self.grouper.result_index, y, x]

        result = Panel._from_axes(result, new_axes)

        if self.axis == 1:
            result = result.swapaxes(0, 1).swapaxes(0, 2)
        elif self.axis == 2:
            result = result.swapaxes(0, 2)

        return result

    def _aggregate_item_by_item(self, func, *args, **kwargs):
        obj = self._obj_with_exclusions
        result = {}

        if self.axis > 0:
            for item in obj:
                try:
                    itemg = DataFrameGroupBy(obj[item],
                                             axis=self.axis - 1,
                                             grouper=self.grouper)
                    result[item] = itemg.aggregate(func, *args, **kwargs)
                except (ValueError, TypeError):
                    raise
            new_axes = list(obj.axes)
            new_axes[self.axis] = self.grouper.result_index
            return Panel._from_axes(result, new_axes)
        else:
            raise NotImplementedError

    def _wrap_aggregated_output(self, output, names=None):
        raise NotImplementedError


class NDArrayGroupBy(GroupBy):
    pass


#----------------------------------------------------------------------
# Splitting / application


class DataSplitter(object):

    def __init__(self, data, labels, ngroups, axis=0):
        self.data = data
        self.labels = com._ensure_int64(labels)
        self.ngroups = ngroups

        self.axis = axis

    @cache_readonly
    def slabels(self):
        # Sorted labels
        return com.take_nd(self.labels, self.sort_idx, allow_fill=False)

    @cache_readonly
    def sort_idx(self):
        # Counting sort indexer
        return _algos.groupsort_indexer(self.labels, self.ngroups)[0]

    def __iter__(self):
        sdata = self._get_sorted_data()

        if self.ngroups == 0:
            raise StopIteration

        starts, ends = lib.generate_slices(self.slabels, self.ngroups)

        for i, (start, end) in enumerate(zip(starts, ends)):
            # Since I'm now compressing the group ids, it's now not "possible"
            # to produce empty slices because such groups would not be observed
            # in the data
            # if start >= end:
            #     raise AssertionError('Start %s must be less than end %s'
            #                          % (str(start), str(end)))
            yield i, self._chop(sdata, slice(start, end))

    def _get_sorted_data(self):
        return self.data.take(self.sort_idx, axis=self.axis, convert=False)

    def _chop(self, sdata, slice_obj):
        return sdata.iloc[slice_obj]

    def apply(self, f):
        raise NotImplementedError


class ArraySplitter(DataSplitter):
    pass


class SeriesSplitter(DataSplitter):

    def _chop(self, sdata, slice_obj):
        return sdata._get_values(slice_obj).to_dense()


class FrameSplitter(DataSplitter):

    def __init__(self, data, labels, ngroups, axis=0):
        super(FrameSplitter, self).__init__(data, labels, ngroups, axis=axis)

    def fast_apply(self, f, names):
        # must return keys::list, values::list, mutated::bool
        try:
            starts, ends = lib.generate_slices(self.slabels, self.ngroups)
        except:
            # fails when all -1
            return [], True

        sdata = self._get_sorted_data()
        results, mutated = lib.apply_frame_axis0(sdata, f, names, starts, ends)

        return results, mutated

    def _chop(self, sdata, slice_obj):
        if self.axis == 0:
            return sdata.iloc[slice_obj]
        else:
            return sdata._slice(slice_obj, axis=1)  # ix[:, slice_obj]


class NDFrameSplitter(DataSplitter):

    def __init__(self, data, labels, ngroups, axis=0):
        super(NDFrameSplitter, self).__init__(data, labels, ngroups, axis=axis)

        self.factory = data._constructor

    def _get_sorted_data(self):
        # this is the BlockManager
        data = self.data._data

        # this is sort of wasteful but...
        sorted_axis = data.axes[self.axis].take(self.sort_idx)
        sorted_data = data.reindex_axis(sorted_axis, axis=self.axis)

        return sorted_data

    def _chop(self, sdata, slice_obj):
        return self.factory(sdata.get_slice(slice_obj, axis=self.axis))


def get_splitter(data, *args, **kwargs):
    if isinstance(data, Series):
        klass = SeriesSplitter
    elif isinstance(data, DataFrame):
        klass = FrameSplitter
    else:
        klass = NDFrameSplitter

    return klass(data, *args, **kwargs)


#----------------------------------------------------------------------
# Misc utilities


def get_group_index(label_list, shape):
    """
    For the particular label_list, gets the offsets into the hypothetical list
    representing the totally ordered cartesian product of all possible label
    combinations.
    """
    if len(label_list) == 1:
        return label_list[0]

    n = len(label_list[0])
    group_index = np.zeros(n, dtype=np.int64)
    mask = np.zeros(n, dtype=bool)
    for i in range(len(shape)):
        stride = np.prod([x for x in shape[i + 1:]], dtype=np.int64)
        group_index += com._ensure_int64(label_list[i]) * stride
        mask |= label_list[i] < 0

    np.putmask(group_index, mask, -1)
    return group_index

_INT64_MAX = np.iinfo(np.int64).max


def _int64_overflow_possible(shape):
    the_prod = long(1)
    for x in shape:
        the_prod *= long(x)

    return the_prod >= _INT64_MAX


def decons_group_index(comp_labels, shape):
    # reconstruct labels
    label_list = []
    factor = 1
    y = 0
    x = comp_labels
    for i in reversed(range(len(shape))):
        labels = (x - y) % (factor * shape[i]) // factor
        np.putmask(labels, comp_labels < 0, -1)
        label_list.append(labels)
        y = labels * factor
        factor *= shape[i]
    return label_list[::-1]


def _indexer_from_factorized(labels, shape, compress=True):
    if _int64_overflow_possible(shape):
        indexer = np.lexsort(np.array(labels[::-1]))
        return indexer

    group_index = get_group_index(labels, shape)

    if compress:
        comp_ids, obs_ids = _compress_group_index(group_index)
        max_group = len(obs_ids)
    else:
        comp_ids = group_index
        max_group = com._long_prod(shape)

    if max_group > 1e6:
        # Use mergesort to avoid memory errors in counting sort
        indexer = comp_ids.argsort(kind='mergesort')
    else:
        indexer, _ = _algos.groupsort_indexer(comp_ids.astype(np.int64),
                                              max_group)

    return indexer


def _lexsort_indexer(keys, orders=None):
    labels = []
    shape = []

    if isinstance(orders, bool):
        orders = [orders] * len(keys)
    elif orders is None:
        orders = [True] * len(keys)

    for key, order in zip(keys, orders):
        rizer = _hash.Factorizer(len(key))

        if not key.dtype == np.object_:
            key = key.astype('O')

        ids = rizer.factorize(key, sort=True)

        n = len(rizer.uniques)
        shape.append(n)
        if not order:
            mask = ids == -1
            ids = np.where(mask, -1, n - ids)

        labels.append(ids)

    return _indexer_from_factorized(labels, shape)


class _KeyMapper(object):

    """
    Ease my suffering. Map compressed group id -> key tuple
    """

    def __init__(self, comp_ids, ngroups, labels, levels):
        self.levels = levels
        self.labels = labels
        self.comp_ids = comp_ids.astype(np.int64)

        self.k = len(labels)
        self.tables = [_hash.Int64HashTable(ngroups) for _ in range(self.k)]

        self._populate_tables()

    def _populate_tables(self):
        for labs, table in zip(self.labels, self.tables):
            table.map(self.comp_ids, labs.astype(np.int64))

    def get_key(self, comp_id):
        return tuple(level[table.get_item(comp_id)]
                     for table, level in zip(self.tables, self.levels))


def _get_indices_dict(label_list, keys):
    shape = [len(x) for x in keys]
    group_index = get_group_index(label_list, shape)

    sorter, _ = _algos.groupsort_indexer(com._ensure_int64(group_index),
                                         np.prod(shape))

    sorter_int = com._ensure_platform_int(sorter)

    sorted_labels = [lab.take(sorter_int) for lab in label_list]
    group_index = group_index.take(sorter_int)

    return lib.indices_fast(sorter, group_index, keys, sorted_labels)


#----------------------------------------------------------------------
# sorting levels...cleverly?


def _compress_group_index(group_index, sort=True):
    """
    Group_index is offsets into cartesian product of all possible labels. This
    space can be huge, so this function compresses it, by computing offsets
    (comp_ids) into the list of unique labels (obs_group_ids).
    """

    table = _hash.Int64HashTable(min(1000000, len(group_index)))

    group_index = com._ensure_int64(group_index)

    # note, group labels come out ascending (ie, 1,2,3 etc)
    comp_ids, obs_group_ids = table.get_labels_groupby(group_index)

    if sort and len(obs_group_ids) > 0:
        obs_group_ids, comp_ids = _reorder_by_uniques(obs_group_ids, comp_ids)

    return comp_ids, obs_group_ids


def _reorder_by_uniques(uniques, labels):
    # sorter is index where elements ought to go
    sorter = uniques.argsort()

    # reverse_indexer is where elements came from
    reverse_indexer = np.empty(len(sorter), dtype=np.int64)
    reverse_indexer.put(sorter, np.arange(len(sorter)))

    mask = labels < 0

    # move labels to right locations (ie, unsort ascending labels)
    labels = com.take_nd(reverse_indexer, labels, allow_fill=False)
    np.putmask(labels, mask, -1)

    # sort observed ids
    uniques = com.take_nd(uniques, sorter, allow_fill=False)

    return uniques, labels


_func_table = {
    builtins.sum: np.sum
}


_cython_table = {
    builtins.sum: 'sum',
    np.sum: 'sum',
    np.mean: 'mean',
    np.prod: 'prod',
    np.std: 'std',
    np.var: 'var',
    np.median: 'median'
}


def _is_numeric_dtype(dt):
    typ = dt.type
    return (issubclass(typ, (np.number, np.bool_))
            and not issubclass(typ, (np.datetime64, np.timedelta64)))


def _intercept_function(func):
    return _func_table.get(func, func)


def _intercept_cython(func):
    return _cython_table.get(func)


def _groupby_indices(values):
    return _algos.groupby_indices(com._ensure_object(values))


def numpy_groupby(data, labels, axis=0):
    s = np.argsort(labels)
    keys, inv = np.unique(labels, return_inverse=True)
    i = inv.take(s)
    groups_at = np.where(i != np.concatenate(([-1], i[:-1])))[0]
    ordered_data = data.take(s, axis=axis)
    group_sums = np.add.reduceat(ordered_data, groups_at, axis=axis)

    return group_sums