index.py

# pylint: disable=E1101,E1103,W0232
import datetime
import warnings
from functools import partial
from pandas.compat import range, zip, lrange, lzip, u, reduce
from pandas import compat
import numpy as np

import pandas.tslib as tslib
import pandas.lib as lib
import pandas.algos as _algos
import pandas.index as _index
from pandas.lib import Timestamp, is_datetime_array
from pandas.core.base import FrozenList, FrozenNDArray, IndexOpsMixin
from pandas.util.decorators import cache_readonly, deprecate
from pandas.core.common import isnull, array_equivalent
import pandas.core.common as com
from pandas.core.common import (_values_from_object, is_float, is_integer,
                                ABCSeries)
from pandas.core.config import get_option

# simplify
default_pprint = lambda x: com.pprint_thing(x, escape_chars=('\t', '\r', '\n'),
                                            quote_strings=True)


__all__ = ['Index']


_unsortable_types = frozenset(('mixed', 'mixed-integer'))


def _try_get_item(x):
    try:
        return x.item()
    except AttributeError:
        return x


def _indexOp(opname):
    """
    Wrapper function for index comparison operations, to avoid
    code duplication.
    """

    def wrapper(self, other):
        func = getattr(self.view(np.ndarray), opname)
        result = func(other)
        try:
            return result.view(np.ndarray)
        except:  # pragma: no cover
            return result
    return wrapper


class InvalidIndexError(Exception):
    pass


_o_dtype = np.dtype(object)


def _shouldbe_timestamp(obj):
    return (tslib.is_datetime_array(obj)
            or tslib.is_datetime64_array(obj)
            or tslib.is_timestamp_array(obj))

_Identity = object


class Index(IndexOpsMixin, FrozenNDArray):

    """
    Immutable ndarray implementing an ordered, sliceable set. The basic object
    storing axis labels for all pandas objects

    Parameters
    ----------
    data : array-like (1-dimensional)
    dtype : NumPy dtype (default: object)
    copy : bool
        Make a copy of input ndarray
    name : object
        Name to be stored in the index
    tupleize_cols : bool (default: True)
        When True, attempt to create a MultiIndex if possible

    Notes
    -----
    An Index instance can **only** contain hashable objects
    """
    # To hand over control to subclasses
    _join_precedence = 1

    # Cython methods
    _groupby = _algos.groupby_object
    _arrmap = _algos.arrmap_object
    _left_indexer_unique = _algos.left_join_indexer_unique_object
    _left_indexer = _algos.left_join_indexer_object
    _inner_indexer = _algos.inner_join_indexer_object
    _outer_indexer = _algos.outer_join_indexer_object

    _box_scalars = False

    name = None
    asi8 = None
    _comparables = ['name']
    _allow_index_ops = True
    _allow_datetime_index_ops = False
    _allow_period_index_ops = False

    _engine_type = _index.ObjectEngine

    def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False,
                tupleize_cols=True, **kwargs):

        # no class inference!
        if fastpath:
            return cls._simple_new(data, name)

        from pandas.tseries.period import PeriodIndex
        if isinstance(data, (np.ndarray, ABCSeries)):
            if issubclass(data.dtype.type, np.datetime64):
                from pandas.tseries.index import DatetimeIndex
                result = DatetimeIndex(data, copy=copy, name=name, **kwargs)
                if dtype is not None and _o_dtype == dtype:
                    return Index(result.to_pydatetime(), dtype=_o_dtype)
                else:
                    return result
            elif issubclass(data.dtype.type, np.timedelta64):
                return Int64Index(data, copy=copy, name=name)

            if dtype is not None:
                try:
                    data = np.array(data, dtype=dtype, copy=copy)
                except TypeError:
                    pass
            elif isinstance(data, PeriodIndex):
                return PeriodIndex(data, copy=copy, name=name, **kwargs)

            if issubclass(data.dtype.type, np.integer):
                return Int64Index(data, copy=copy, dtype=dtype, name=name)
            if issubclass(data.dtype.type, np.floating):
                return Float64Index(data, copy=copy, dtype=dtype, name=name)

            subarr = com._asarray_tuplesafe(data, dtype=object)

            # _asarray_tuplesafe does not always copy underlying data,
            # so need to make sure that this happens
            if copy:
                subarr = subarr.copy()

        elif hasattr(data, '__array__'):
            return Index(np.asarray(data), dtype=dtype, copy=copy, name=name,
                         **kwargs)
        elif np.isscalar(data):
            cls._scalar_data_error(data)
        else:
            if tupleize_cols and isinstance(data, list) and data:
                try:
                    sorted(data)
                    has_mixed_types = False
                except (TypeError, UnicodeDecodeError):
                    has_mixed_types = True  # python3 only
                if isinstance(data[0], tuple) and not has_mixed_types:
                    try:
                        return MultiIndex.from_tuples(
                            data, names=name or kwargs.get('names'))
                    except (TypeError, KeyError):
                        pass  # python2 - MultiIndex fails on mixed types
            # other iterable of some kind
            subarr = com._asarray_tuplesafe(data, dtype=object)

        if dtype is None:
            inferred = lib.infer_dtype(subarr)
            if inferred == 'integer':
                return Int64Index(subarr.astype('i8'), copy=copy, name=name)
            elif inferred in ['floating', 'mixed-integer-float']:
                return Float64Index(subarr, copy=copy, name=name)
            elif inferred != 'string':
                if (inferred.startswith('datetime') or
                        tslib.is_timestamp_array(subarr)):
                    from pandas.tseries.index import DatetimeIndex
                    return DatetimeIndex(subarr, copy=copy, name=name, **kwargs)
                elif inferred == 'period':
                    return PeriodIndex(subarr, name=name, **kwargs)

        subarr = subarr.view(cls)
        # could also have a _set_name, but I don't think it's really necessary
        subarr._set_names([name])
        return subarr

    @classmethod
    def _simple_new(cls, values, name, **kwargs):
        result = values.view(cls)
        result.name = name
        return result

    def is_(self, other):
        """
        More flexible, faster check like ``is`` but that works through views

        Note: this is *not* the same as ``Index.identical()``, which checks
        that metadata is also the same.

        Parameters
        ----------
        other : object
            other object to compare against.

        Returns
        -------
        True if both have same underlying data, False otherwise : bool
        """
        # use something other than None to be clearer
        return self._id is getattr(other, '_id', Ellipsis)

    def _reset_identity(self):
        """Initializes or resets ``_id`` attribute with new object"""
        self._id = _Identity()

    def view(self, *args, **kwargs):
        result = super(Index, self).view(*args, **kwargs)
        if isinstance(result, Index):
            result._id = self._id
        return result

    # construction helpers
    @classmethod
    def _scalar_data_error(cls, data):
        raise TypeError(
            '{0}(...) must be called with a collection of some kind, {1} was '
            'passed'.format(cls.__name__, repr(data))
        )

    @classmethod
    def _string_data_error(cls, data):
        raise TypeError('String dtype not supported, you may need '
                        'to explicitly cast to a numeric type')

    @classmethod
    def _coerce_to_ndarray(cls, data):
        """coerces data to ndarray, raises on scalar data. Converts other
        iterables to list first and then to array. Does not touch ndarrays."""

        if not isinstance(data, np.ndarray):
            if np.isscalar(data):
                cls._scalar_data_error(data)

            # other iterable of some kind
            if not isinstance(data, (ABCSeries, list, tuple)):
                data = list(data)
            data = np.asarray(data)
        return data

    def __array_finalize__(self, obj):
        self._reset_identity()
        if not isinstance(obj, type(self)):
            # Only relevant if array being created from an Index instance
            return

        self.name = getattr(obj, 'name', None)

    def _shallow_copy(self):
        return self.view()

    def copy(self, names=None, name=None, dtype=None, deep=False):
        """
        Make a copy of this object.  Name and dtype sets those attributes on
        the new object.

        Parameters
        ----------
        name : string, optional
        dtype : numpy dtype or pandas type

        Returns
        -------
        copy : Index

        Notes
        -----
        In most cases, there should be no functional difference from using
        ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
        """
        if names is not None and name is not None:
            raise TypeError("Can only provide one of `names` and `name`")
        if deep:
            from copy import deepcopy
            new_index = np.ndarray.__deepcopy__(self, {}).view(self.__class__)
            name = name or deepcopy(self.name)
        else:
            new_index = super(Index, self).copy()
        if name is not None:
            names = [name]
        if names:
            new_index = new_index.set_names(names)
        if dtype:
            new_index = new_index.astype(dtype)
        return new_index

    def to_series(self, keep_tz=False):
        """
        Create a Series with both index and values equal to the index keys
        useful with map for returning an indexer based on an index

        Parameters
        ----------
        keep_tz : optional, defaults False.
                  applies only to a DatetimeIndex

        Returns
        -------
        Series : dtype will be based on the type of the Index values.
        """

        import pandas as pd
        values = self._to_embed(keep_tz)
        return pd.Series(values, index=self, name=self.name)

    def _to_embed(self, keep_tz=False):
        """ return an array repr of this object, potentially casting to object """
        return self.values

    def astype(self, dtype):
        return Index(self.values.astype(dtype), name=self.name,
                     dtype=dtype)

    def to_datetime(self, dayfirst=False):
        """
        For an Index containing strings or datetime.datetime objects, attempt
        conversion to DatetimeIndex
        """
        from pandas.tseries.index import DatetimeIndex
        if self.inferred_type == 'string':
            from dateutil.parser import parse
            parser = lambda x: parse(x, dayfirst=dayfirst)
            parsed = lib.try_parse_dates(self.values, parser=parser)
            return DatetimeIndex(parsed)
        else:
            return DatetimeIndex(self.values)

    def _assert_can_do_setop(self, other):
        return True

    def tolist(self):
        """
        Overridden version of ndarray.tolist
        """
        return list(self.values)

    @cache_readonly
    def dtype(self):
        return self.values.dtype

    @property
    def nlevels(self):
        return 1

    # for compat with multindex code

    def _get_names(self):
        return FrozenList((self.name,))

    def _set_names(self, values):
        if len(values) != 1:
            raise ValueError('Length of new names must be 1, got %d'
                             % len(values))
        self.name = values[0]

    names = property(fset=_set_names, fget=_get_names)

    def set_names(self, names, inplace=False):
        """
        Set new names on index. Defaults to returning new index.

        Parameters
        ----------
        names : sequence
            names to set
        inplace : bool
            if True, mutates in place

        Returns
        -------
        new index (of same type and class...etc) [if inplace, returns None]
        """
        if not com.is_list_like(names):
            raise TypeError("Must pass list-like as `names`.")
        if inplace:
            idx = self
        else:
            idx = self._shallow_copy()
        idx._set_names(names)
        if not inplace:
            return idx

    def rename(self, name, inplace=False):
        """
        Set new names on index. Defaults to returning new index.

        Parameters
        ----------
        name : str or list
            name to set
        inplace : bool
            if True, mutates in place

        Returns
        -------
        new index (of same type and class...etc) [if inplace, returns None]
        """
        return self.set_names([name], inplace=inplace)

    @property
    def _has_complex_internals(self):
        # to disable groupby tricks in MultiIndex
        return False

    def summary(self, name=None):
        if len(self) > 0:
            head = self[0]
            if hasattr(head, 'format') and\
               not isinstance(head, compat.string_types):
                head = head.format()
            tail = self[-1]
            if hasattr(tail, 'format') and\
               not isinstance(tail, compat.string_types):
                tail = tail.format()
            index_summary = ', %s to %s' % (com.pprint_thing(head),
                                            com.pprint_thing(tail))
        else:
            index_summary = ''

        if name is None:
            name = type(self).__name__
        return '%s: %s entries%s' % (name, len(self), index_summary)

    def _mpl_repr(self):
        # how to represent ourselves to matplotlib
        return self.values

    @property
    def values(self):
        return np.asarray(self)

    def get_values(self):
        return self.values

    _na_value = np.nan
    """The expected NA value to use with this index."""

    @property
    def is_monotonic(self):
        return self._engine.is_monotonic

    def is_lexsorted_for_tuple(self, tup):
        return True

    @cache_readonly(allow_setting=True)
    def is_unique(self):
        return self._engine.is_unique

    def is_integer(self):
        return self.inferred_type in ['integer']

    def is_floating(self):
        return self.inferred_type in ['floating', 'mixed-integer-float']

    def is_numeric(self):
        return self.inferred_type in ['integer', 'floating']

    def is_mixed(self):
        return 'mixed' in self.inferred_type

    def holds_integer(self):
        return self.inferred_type in ['integer', 'mixed-integer']

    def _convert_scalar_indexer(self, key, typ=None):
        """ convert a scalar indexer, right now we are converting
        floats -> ints if the index supports it
        """

        def to_int():
            ikey = int(key)
            if ikey != key:
                return self._convert_indexer_error(key, 'label')
            return ikey

        if typ == 'iloc':
            if is_integer(key):
                return key
            elif is_float(key):
                key = to_int()
                warnings.warn("scalar indexers for index type {0} should be integers and not floating point".format(
                    type(self).__name__),FutureWarning)
                return key
            return self._convert_indexer_error(key, 'label')

        if is_float(key):
            if not self.is_floating():
                warnings.warn("scalar indexers for index type {0} should be integers and not floating point".format(
                    type(self).__name__),FutureWarning)
            return to_int()

        return key

    def _validate_slicer(self, key, f):
        """ validate and raise if needed on a slice indexers according to the
        passed in function """

        for c in ['start','stop','step']:
            if not f(getattr(key,c)):
                self._convert_indexer_error(key.start, 'slice {0} value'.format(c))

    def _convert_slice_indexer_getitem(self, key, is_index_slice=False):
        """ called from the getitem slicers, determine how to treat the key
            whether positional or not """
        if self.is_integer() or is_index_slice:
            return key
        return self._convert_slice_indexer(key)

    def _convert_slice_indexer(self, key, typ=None):
        """ convert a slice indexer. disallow floats in the start/stop/step """

        # if we are not a slice, then we are done
        if not isinstance(key, slice):
            return key

        # validate iloc
        if typ == 'iloc':

            # need to coerce to_int if needed
            def f(c):
                v = getattr(key,c)
                if v is None or is_integer(v):
                    return v

                # warn if its a convertible float
                if v == int(v):
                    warnings.warn("slice indexers when using iloc should be integers "
                                  "and not floating point",FutureWarning)
                    return int(v)

                self._convert_indexer_error(v, 'slice {0} value'.format(c))

            return slice(*[ f(c) for c in ['start','stop','step']])

        # validate slicers
        def validate(v):
            if v is None or is_integer(v):
                return True

            # dissallow floats
            elif is_float(v):
                return False

            return True
        self._validate_slicer(key, validate)

        # figure out if this is a positional indexer
        start, stop, step = key.start, key.stop, key.step

        def is_int(v):
            return v is None or is_integer(v)

        is_null_slice = start is None and stop is None
        is_index_slice = is_int(start) and is_int(stop)
        is_positional = is_index_slice and not self.is_integer()

        if typ == 'getitem':
            return self._convert_slice_indexer_getitem(
                key, is_index_slice=is_index_slice)

        # convert the slice to an indexer here

        # if we are mixed and have integers
        try:
            if is_positional and self.is_mixed():
                if start is not None:
                    i = self.get_loc(start)
                if stop is not None:
                    j = self.get_loc(stop)
                is_positional = False
        except KeyError:
            if self.inferred_type == 'mixed-integer-float':
                raise

        if is_null_slice:
            indexer = key
        elif is_positional:
            indexer = key
        else:
            try:
                indexer = self.slice_indexer(start, stop, step)
            except Exception:
                if is_index_slice:
                    if self.is_integer():
                        raise
                    else:
                        indexer = key
                else:
                    raise

        return indexer

    def _convert_list_indexer(self, key, typ=None):
        """ convert a list indexer. these should be locations """
        return key

    def _convert_list_indexer_for_mixed(self, keyarr, typ=None):
        """ passed a key that is tuplesafe that is integer based
            and we have a mixed index (e.g. number/labels). figure out
            the indexer. return None if we can't help
        """
        if (typ is None or typ in ['iloc','ix']) and (com.is_integer_dtype(keyarr) and not self.is_floating()):
            if self.inferred_type != 'integer':
                keyarr = np.where(keyarr < 0,
                                  len(self) + keyarr, keyarr)

            if self.inferred_type == 'mixed-integer':
                indexer = self.get_indexer(keyarr)
                if (indexer >= 0).all():
                    return indexer

                from pandas.core.indexing import _maybe_convert_indices
                return _maybe_convert_indices(indexer, len(self))

            elif not self.inferred_type == 'integer':
                return keyarr

        return None

    def _convert_indexer_error(self, key, msg=None):
        if msg is None:
            msg = 'label'
        raise TypeError("the {0} [{1}] is not a proper indexer for this index "
                        "type ({2})".format(msg, key, self.__class__.__name__))

    def get_duplicates(self):
        from collections import defaultdict
        counter = defaultdict(lambda: 0)
        for k in self.values:
            counter[k] += 1
        return sorted(k for k, v in compat.iteritems(counter) if v > 1)

    _get_duplicates = get_duplicates

    def _cleanup(self):
        self._engine.clear_mapping()

    @cache_readonly
    def _engine(self):
        # property, for now, slow to look up
        return self._engine_type(lambda: self.values, len(self))

    def _get_level_number(self, level):
        if not isinstance(level, int):
            if level != self.name:
                raise AssertionError('Level %s must be same as name (%s)'
                                     % (level, self.name))
            level = 0
        return level

    @cache_readonly
    def inferred_type(self):
        return lib.infer_dtype(self)

    def is_type_compatible(self, typ):
        return typ == self.inferred_type

    @cache_readonly
    def is_all_dates(self):
        return is_datetime_array(self.values)

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

    def __reduce__(self):
        """Necessary for making this object picklable"""
        object_state = list(np.ndarray.__reduce__(self))
        subclass_state = self.name,
        object_state[2] = (object_state[2], subclass_state)
        return tuple(object_state)

    def __setstate__(self, state):
        """Necessary for making this object picklable"""
        if len(state) == 2:
            nd_state, own_state = state
            np.ndarray.__setstate__(self, nd_state)
            self.name = own_state[0]
        else:  # pragma: no cover
            np.ndarray.__setstate__(self, state)

    def __deepcopy__(self, memo={}):
        return self.copy(deep=True)

    def __contains__(self, key):
        hash(key)
        # work around some kind of odd cython bug
        try:
            return key in self._engine
        except TypeError:
            return False

    def __hash__(self):
        raise TypeError("unhashable type: %r" % type(self).__name__)

    def __getitem__(self, key):
        """
        Override numpy.ndarray's __getitem__ method to work as desired.

        This function adds lists and Series as valid boolean indexers
        (ndarrays only supports ndarray with dtype=bool).

        If resulting ndim != 1, plain ndarray is returned instead of
        corresponding `Index` subclass.

        """
        # There's no custom logic to be implemented in __getslice__, so it's
        # not overloaded intentionally.
        __getitem__ = super(Index, self).__getitem__
        if np.isscalar(key):
            return __getitem__(key)

        if isinstance(key, slice):
            # This case is separated from the conditional above to avoid
            # pessimization of basic indexing.
            return __getitem__(key)

        if com._is_bool_indexer(key):
            return __getitem__(np.asarray(key))

        result = __getitem__(key)
        if result.ndim > 1:
            return result.view(np.ndarray)
        else:
            return result

    def append(self, other):
        """
        Append a collection of Index options together

        Parameters
        ----------
        other : Index or list/tuple of indices

        Returns
        -------
        appended : Index
        """
        name = self.name
        to_concat = [self]

        if isinstance(other, (list, tuple)):
            to_concat = to_concat + list(other)
        else:
            to_concat.append(other)

        for obj in to_concat:
            if isinstance(obj, Index) and obj.name != name:
                name = None
                break

        to_concat = self._ensure_compat_concat(to_concat)
        to_concat = [x.values if isinstance(x, Index) else x
                     for x in to_concat]

        return Index(np.concatenate(to_concat), name=name)

    @staticmethod
    def _ensure_compat_concat(indexes):
        from pandas.tseries.api import DatetimeIndex, PeriodIndex
        klasses = DatetimeIndex, PeriodIndex

        is_ts = [isinstance(idx, klasses) for idx in indexes]

        if any(is_ts) and not all(is_ts):
            return [_maybe_box(idx) for idx in indexes]

        return indexes

    def take(self, indexer, axis=0):
        """
        Analogous to ndarray.take
        """
        indexer = com._ensure_platform_int(indexer)
        taken = self.view(np.ndarray).take(indexer)
        return self._simple_new(taken, name=self.name, freq=None,
                                tz=getattr(self, 'tz', None))

    def format(self, name=False, formatter=None, **kwargs):
        """
        Render a string representation of the Index
        """
        header = []
        if name:
            header.append(com.pprint_thing(self.name,
                                           escape_chars=('\t', '\r', '\n'))
                          if self.name is not None else '')

        if formatter is not None:
            return header + list(self.map(formatter))

        return self._format_with_header(header, **kwargs)

    def _format_with_header(self, header, na_rep='NaN', **kwargs):
        values = self.values

        from pandas.core.format import format_array

        if values.dtype == np.object_:
            values = lib.maybe_convert_objects(values, safe=1)

        if values.dtype == np.object_:
            result = [com.pprint_thing(x, escape_chars=('\t', '\r', '\n'))
                      for x in values]

            # could have nans
            mask = isnull(values)
            if mask.any():
                result = np.array(result)
                result[mask] = na_rep
                result = result.tolist()

        else:
            result = _trim_front(format_array(values, None, justify='left'))
        return header + result

    def to_native_types(self, slicer=None, **kwargs):
        """ slice and dice then format """
        values = self
        if slicer is not None:
            values = values[slicer]
        return values._format_native_types(**kwargs)

    def _format_native_types(self, na_rep='', **kwargs):
        """ actually format my specific types """
        mask = isnull(self)
        values = np.array(self, dtype=object, copy=True)
        values[mask] = na_rep
        return values.tolist()

    def equals(self, other):
        """
        Determines if two Index objects contain the same elements.
        """
        if self.is_(other):
            return True

        if not isinstance(other, Index):
            return False

        if type(other) != Index:
            return other.equals(self)

        return array_equivalent(self, other)

    def identical(self, other):
        """Similar to equals, but check that other comparable attributes are
        also equal
        """
        return (self.equals(other) and
                all((getattr(self, c, None) == getattr(other, c, None)
                     for c in self._comparables)) and
                type(self) == type(other))

    def asof(self, label):
        """
        For a sorted index, return the most recent label up to and including
        the passed label. Return NaN if not found
        """
        if isinstance(label, (Index, ABCSeries, np.ndarray)):
            raise TypeError('%s' % type(label))

        if label not in self:
            loc = self.searchsorted(label, side='left')
            if loc > 0:
                return self[loc - 1]
            else:
                return np.nan

        if not isinstance(label, Timestamp):
            label = Timestamp(label)
        return label

    def asof_locs(self, where, mask):
        """
        where : array of timestamps
        mask : array of booleans where data is not NA

        """
        locs = self.values[mask].searchsorted(where.values, side='right')

        locs = np.where(locs > 0, locs - 1, 0)
        result = np.arange(len(self))[mask].take(locs)

        first = mask.argmax()
        result[(locs == 0) & (where < self.values[first])] = -1

        return result

    def order(self, return_indexer=False, ascending=True):
        """
        Return sorted copy of Index
        """
        _as = self.argsort()
        if not ascending:
            _as = _as[::-1]

        sorted_index = self.take(_as)

        if return_indexer:
            return sorted_index, _as
        else:
            return sorted_index

    def sort(self, *args, **kwargs):
        raise TypeError('Cannot sort an %r object' % self.__class__.__name__)

    def shift(self, periods=1, freq=None):
        """
        Shift Index containing datetime objects by input number of periods and
        DateOffset

        Returns
        -------
        shifted : Index
        """
        if periods == 0:
            # OK because immutable
            return self

        offset = periods * freq
        return Index([idx + offset for idx in self], name=self.name)

    def argsort(self, *args, **kwargs):
        """
        See docstring for ndarray.argsort
        """
        result = self.asi8
        if result is None:
            result = self.view(np.ndarray)
        return result.argsort(*args, **kwargs)

    def __add__(self, other):
        if isinstance(other, Index):
            return self.union(other)
        else:
            return Index(self.view(np.ndarray) + other)

    __iadd__ = __add__
    __eq__ = _indexOp('__eq__')
    __ne__ = _indexOp('__ne__')
    __lt__ = _indexOp('__lt__')
    __gt__ = _indexOp('__gt__')
    __le__ = _indexOp('__le__')
    __ge__ = _indexOp('__ge__')

    def __sub__(self, other):
        return self.diff(other)

    def __and__(self, other):
        return self.intersection(other)

    def __or__(self, other):
        return self.union(other)

    def __xor__(self, other):
        return self.sym_diff(other)

    def union(self, other):
        """
        Form the union of two Index objects and sorts if possible

        Parameters
        ----------
        other : Index or array-like

        Returns
        -------
        union : Index
        """
        if not hasattr(other, '__iter__'):
            raise TypeError('Input must be iterable.')

        if len(other) == 0 or self.equals(other):
            return self

        if len(self) == 0:
            return _ensure_index(other)

        self._assert_can_do_setop(other)

        if self.dtype != other.dtype:
            this = self.astype('O')
            other = other.astype('O')
            return this.union(other)

        if self.is_monotonic and other.is_monotonic:
            try:
                result = self._outer_indexer(self, other.values)[0]
            except TypeError:
                # incomparable objects
                result = list(self.values)

                # worth making this faster? a very unusual case
                value_set = set(self.values)
                result.extend([x for x in other.values if x not in value_set])
        else:
            indexer = self.get_indexer(other)
            indexer, = (indexer == -1).nonzero()

            if len(indexer) > 0:
                other_diff = com.take_nd(other.values, indexer,
                                         allow_fill=False)
                result = com._concat_compat((self.values, other_diff))

                try:
                    self.values[0] < other_diff[0]
                except TypeError as e:
                    warnings.warn("%s, sort order is undefined for "
                                  "incomparable objects" % e, RuntimeWarning)
                else:
                    types = frozenset((self.inferred_type,
                                       other.inferred_type))
                    if not types & _unsortable_types:
                        result.sort()

            else:
                result = self.values

                try:
                    result = np.sort(result)
                except TypeError as e:
                    warnings.warn("%s, sort order is undefined for "
                                  "incomparable objects" % e, RuntimeWarning)

        # for subclasses
        return self._wrap_union_result(other, result)

    def _wrap_union_result(self, other, result):
        name = self.name if self.name == other.name else None
        return self.__class__(data=result, name=name)

    def intersection(self, other):
        """
        Form the intersection of two Index objects. Sortedness of the result is
        not guaranteed

        Parameters
        ----------
        other : Index or array-like

        Returns
        -------
        intersection : Index
        """
        if not hasattr(other, '__iter__'):
            raise TypeError('Input must be iterable!')

        self._assert_can_do_setop(other)

        other = _ensure_index(other)

        if self.equals(other):
            return self

        if self.dtype != other.dtype:
            this = self.astype('O')
            other = other.astype('O')
            return this.intersection(other)

        if self.is_monotonic and other.is_monotonic:
            try:
                result = self._inner_indexer(self, other.values)[0]
                return self._wrap_union_result(other, result)
            except TypeError:
                pass

        try:
            indexer = self.get_indexer(other.values)
            indexer = indexer.take((indexer != -1).nonzero()[0])
        except:
            # duplicates
            indexer = self.get_indexer_non_unique(other.values)[0].unique()

        taken = self.take(indexer)
        if self.name != other.name:
            taken.name = None
        return taken

    def diff(self, other):
        """
        Compute sorted set difference of two Index objects

        Parameters
        ----------
        other : Index or array-like

        Returns
        -------
        diff : Index

        Notes
        -----
        One can do either of these and achieve the same result

        >>> index - index2
        >>> index.diff(index2)
        """

        if not hasattr(other, '__iter__'):
            raise TypeError('Input must be iterable!')

        if self.equals(other):
            return Index([], name=self.name)

        if not isinstance(other, Index):
            other = np.asarray(other)
            result_name = self.name
        else:
            result_name = self.name if self.name == other.name else None

        theDiff = sorted(set(self) - set(other))
        return Index(theDiff, name=result_name)

    def sym_diff(self, other, result_name=None):
        """
        Compute the sorted symmetric difference of two Index objects.

        Parameters
        ----------

        other : array-like
        result_name : str

        Returns
        -------
        sym_diff : Index

        Notes
        -----
        ``sym_diff`` contains elements that appear in either ``idx1`` or
        ``idx2`` but not both. Equivalent to the Index created by
        ``(idx1 - idx2) + (idx2 - idx1)`` with duplicates dropped.

        The sorting of a result containing ``NaN`` values is not guaranteed
        across Python versions. See GitHub issue #6444.

        Examples
        --------
        >>> idx1 = Index([1, 2, 3, 4])
        >>> idx2 = Index([2, 3, 4, 5])
        >>> idx1.sym_diff(idx2)
        Int64Index([1, 5], dtype='int64')

        You can also use the ``^`` operator:

        >>> idx1 ^ idx2
        Int64Index([1, 5], dtype='int64')
        """
        if not hasattr(other, '__iter__'):
            raise TypeError('Input must be iterable!')

        if not isinstance(other, Index):
            other = Index(other)
            result_name = result_name or self.name

        the_diff = sorted(set((self - other) + (other - self)))
        return Index(the_diff, name=result_name)

    def get_loc(self, key):
        """
        Get integer location for requested label

        Returns
        -------
        loc : int if unique index, possibly slice or mask if not
        """
        return self._engine.get_loc(_values_from_object(key))

    def get_value(self, series, key):
        """
        Fast lookup of value from 1-dimensional ndarray. Only use this if you
        know what you're doing
        """
        s = _values_from_object(series)
        k = _values_from_object(key)

        # prevent integer truncation bug in indexing
        if is_float(k) and not self.is_floating():
            raise KeyError

        try:
            return self._engine.get_value(s, k)
        except KeyError as e1:
            if len(self) > 0 and self.inferred_type in ['integer','boolean']:
                raise

            try:
                return tslib.get_value_box(s, key)
            except IndexError:
                raise
            except TypeError:
                # generator/iterator-like
                if com.is_iterator(key):
                    raise InvalidIndexError(key)
                else:
                    raise e1
            except Exception:  # pragma: no cover
                raise e1
        except TypeError:
            # python 3
            if np.isscalar(key):  # pragma: no cover
                raise IndexError(key)
            raise InvalidIndexError(key)

    def set_value(self, arr, key, value):
        """
        Fast lookup of value from 1-dimensional ndarray. Only use this if you
        know what you're doing
        """
        self._engine.set_value(
            _values_from_object(arr), _values_from_object(key), value)

    def get_level_values(self, level):
        """
        Return vector of label values for requested level, equal to the length
        of the index

        Parameters
        ----------
        level : int

        Returns
        -------
        values : ndarray
        """
        # checks that level number is actually just 1
        self._get_level_number(level)
        return self

    def get_indexer(self, target, method=None, limit=None):
        """
        Compute indexer and mask for new index given the current index. The
        indexer should be then used as an input to ndarray.take to align the
        current data to the new index. The mask determines whether labels are
        found or not in the current index

        Parameters
        ----------
        target : Index
        method : {'pad', 'ffill', 'backfill', 'bfill'}
            pad / ffill: propagate LAST valid observation forward to next valid
            backfill / bfill: use NEXT valid observation to fill gap

        Notes
        -----
        This is a low-level method and probably should be used at your own risk

        Examples
        --------
        >>> indexer = index.get_indexer(new_index)
        >>> new_values = cur_values.take(indexer)

        Returns
        -------
        indexer : ndarray
        """
        method = self._get_method(method)
        target = _ensure_index(target)

        pself, ptarget = self._possibly_promote(target)
        if pself is not self or ptarget is not target:
            return pself.get_indexer(ptarget, method=method, limit=limit)

        if self.dtype != target.dtype:
            this = self.astype(object)
            target = target.astype(object)
            return this.get_indexer(target, method=method, limit=limit)

        if not self.is_unique:
            raise InvalidIndexError('Reindexing only valid with uniquely'
                                    ' valued Index objects')

        if method == 'pad':
            if not self.is_monotonic or not target.is_monotonic:
                raise ValueError('Must be monotonic for forward fill')
            indexer = self._engine.get_pad_indexer(target.values, limit)
        elif method == 'backfill':
            if not self.is_monotonic or not target.is_monotonic:
                raise ValueError('Must be monotonic for backward fill')
            indexer = self._engine.get_backfill_indexer(target.values, limit)
        elif method is None:
            indexer = self._engine.get_indexer(target.values)
        else:
            raise ValueError('unrecognized method: %s' % method)

        return com._ensure_platform_int(indexer)

    def get_indexer_non_unique(self, target, **kwargs):
        """ return an indexer suitable for taking from a non unique index
            return the labels in the same order as the target, and
            return a missing indexer into the target (missing are marked as -1
            in the indexer); target must be an iterable """
        target = _ensure_index(target)
        pself, ptarget = self._possibly_promote(target)
        if pself is not self or ptarget is not target:
            return pself.get_indexer_non_unique(ptarget)

        if self.is_all_dates:
            self = Index(self.asi8)
            tgt_values = target.asi8
        else:
            tgt_values = target.values

        indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
        return Index(indexer), missing

    def get_indexer_for(self, target, **kwargs):
        """ guaranteed return of an indexer even when non-unique """
        if self.is_unique:
            return self.get_indexer(target, **kwargs)
        return self.get_indexer_non_unique(target, **kwargs)[0]

    def _possibly_promote(self, other):
        # A hack, but it works
        from pandas.tseries.index import DatetimeIndex
        if self.inferred_type == 'date' and isinstance(other, DatetimeIndex):
            return DatetimeIndex(self), other
        return self, other

    def groupby(self, to_groupby):
        return self._groupby(self.values, to_groupby)

    def map(self, mapper):
        return self._arrmap(self.values, mapper)

    def isin(self, values):
        """
        Compute boolean array of whether each index value is found in the
        passed set of values

        Parameters
        ----------
        values : set or sequence of values

        Returns
        -------
        is_contained : ndarray (boolean dtype)
        """
        value_set = set(values)
        return lib.ismember(self._array_values(), value_set)

    def _array_values(self):
        return self

    def _get_method(self, method):
        if method:
            method = method.lower()

        aliases = {
            'ffill': 'pad',
            'bfill': 'backfill'
        }
        return aliases.get(method, method)

    def reindex(self, target, method=None, level=None, limit=None,
                copy_if_needed=False):
        """
        For Index, simply returns the new index and the results of
        get_indexer. Provided here to enable an interface that is amenable for
        subclasses of Index whose internals are different (like MultiIndex)

        Returns
        -------
        (new_index, indexer, mask) : tuple
        """
        target = _ensure_index(target)
        if level is not None:
            if method is not None:
                raise TypeError('Fill method not supported if level passed')
            _, indexer, _ = self._join_level(target, level, how='right',
                                             return_indexers=True)
        else:

            if self.equals(target):
                indexer = None

                # to avoid aliasing an existing index
                if (copy_if_needed and target.name != self.name and
                        self.name is not None):
                    if target.name is None:
                        target = self.copy()

            else:
                if self.is_unique:
                    indexer = self.get_indexer(target, method=method,
                                               limit=limit)
                else:
                    if method is not None or limit is not None:
                        raise ValueError("cannot reindex a non-unique index "
                                         "with a method or limit")
                    indexer, missing = self.get_indexer_non_unique(target)

        return target, indexer

    def join(self, other, how='left', level=None, return_indexers=False):
        """
        Internal API method. Compute join_index and indexers to conform data
        structures to the new index.

        Parameters
        ----------
        other : Index
        how : {'left', 'right', 'inner', 'outer'}
        level : int or level name, default None
        return_indexers : boolean, default False

        Returns
        -------
        join_index, (left_indexer, right_indexer)
        """
        self_is_mi = isinstance(self, MultiIndex)
        other_is_mi = isinstance(other, MultiIndex)

        # try to figure out the join level
        # GH3662
        if (level is None and (self_is_mi or other_is_mi)):

            # have the same levels/names so a simple join
            if self.names == other.names:
                pass
            else:
                return self._join_multi(other, how=how, return_indexers=return_indexers)

        # join on the level
        if (level is not None and (self_is_mi or other_is_mi)):
            return self._join_level(other, level, how=how,
                                    return_indexers=return_indexers)

        other = _ensure_index(other)

        if len(other) == 0 and how in ('left', 'outer'):
            join_index = self._shallow_copy()
            if return_indexers:
                rindexer = np.repeat(-1, len(join_index))
                return join_index, None, rindexer
            else:
                return join_index

        if len(self) == 0 and how in ('right', 'outer'):
            join_index = other._shallow_copy()
            if return_indexers:
                lindexer = np.repeat(-1, len(join_index))
                return join_index, lindexer, None
            else:
                return join_index

        if self._join_precedence < other._join_precedence:
            how = {'right': 'left', 'left': 'right'}.get(how, how)
            result = other.join(self, how=how, level=level,
                                return_indexers=return_indexers)
            if return_indexers:
                x, y, z = result
                result = x, z, y
            return result

        if self.dtype != other.dtype:
            this = self.astype('O')
            other = other.astype('O')
            return this.join(other, how=how,
                             return_indexers=return_indexers)

        _validate_join_method(how)

        if not self.is_unique and not other.is_unique:
            return self._join_non_unique(other, how=how,
                                         return_indexers=return_indexers)
        elif not self.is_unique or not other.is_unique:
            if self.is_monotonic and other.is_monotonic:
                return self._join_monotonic(other, how=how,
                                            return_indexers=return_indexers)
            else:
                return self._join_non_unique(other, how=how,
                                             return_indexers=return_indexers)
        elif self.is_monotonic and other.is_monotonic:
            try:
                return self._join_monotonic(other, how=how,
                                            return_indexers=return_indexers)
            except TypeError:
                pass

        if how == 'left':
            join_index = self
        elif how == 'right':
            join_index = other
        elif how == 'inner':
            join_index = self.intersection(other)
        elif how == 'outer':
            join_index = self.union(other)

        if return_indexers:
            if join_index is self:
                lindexer = None
            else:
                lindexer = self.get_indexer(join_index)
            if join_index is other:
                rindexer = None
            else:
                rindexer = other.get_indexer(join_index)
            return join_index, lindexer, rindexer
        else:
            return join_index

    def _join_multi(self, other, how, return_indexers=True):

        self_is_mi = isinstance(self, MultiIndex)
        other_is_mi = isinstance(other, MultiIndex)

        # figure out join names
        self_names = [ n for n in self.names if n is not None ]
        other_names = [ n for n in other.names if n is not None ]
        overlap = list(set(self_names) & set(other_names))

        # need at least 1 in common, but not more than 1
        if not len(overlap):
            raise ValueError("cannot join with no level specified and no overlapping names")
        if len(overlap) > 1:
            raise NotImplementedError("merging with more than one level overlap on a multi-index is not implemented")
        jl = overlap[0]

        # make the indices into mi's that match
        if not (self_is_mi and other_is_mi):

            flip_order = False
            if self_is_mi:
                self, other = other, self
                flip_order = True

            level = other.names.index(jl)
            result = self._join_level(other, level, how=how,
                                      return_indexers=return_indexers)

            if flip_order:
                if isinstance(result, tuple):
                    return result[0], result[2], result[1]
            return result

        # 2 multi-indexes
        raise NotImplementedError("merging with both multi-indexes is not implemented")

    def _join_non_unique(self, other, how='left', return_indexers=False):
        from pandas.tools.merge import _get_join_indexers

        left_idx, right_idx = _get_join_indexers([self.values], [other.values],
                                                 how=how, sort=True)

        left_idx = com._ensure_platform_int(left_idx)
        right_idx = com._ensure_platform_int(right_idx)

        join_index = self.values.take(left_idx)
        mask = left_idx == -1
        np.putmask(join_index, mask, other.values.take(right_idx))

        join_index = self._wrap_joined_index(join_index, other)

        if return_indexers:
            return join_index, left_idx, right_idx
        else:
            return join_index

    def _join_level(self, other, level, how='left', return_indexers=False):
        """
        The join method *only* affects the level of the resulting
        MultiIndex. Otherwise it just exactly aligns the Index data to the
        labels of the level in the MultiIndex. The order of the data indexed by
        the MultiIndex will not be changed (currently)
        """
        if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
            raise TypeError('Join on level between two MultiIndex objects '
                            'is ambiguous')

        left, right = self, other

        flip_order = not isinstance(self, MultiIndex)
        if flip_order:
            left, right = right, left
            how = {'right': 'left', 'left': 'right'}.get(how, how)

        level = left._get_level_number(level)
        old_level = left.levels[level]

        new_level, left_lev_indexer, right_lev_indexer = \
            old_level.join(right, how=how, return_indexers=True)

        if left_lev_indexer is not None:
            left_lev_indexer = com._ensure_int64(left_lev_indexer)
            rev_indexer = lib.get_reverse_indexer(left_lev_indexer,
                                                  len(old_level))

            new_lev_labels = com.take_nd(rev_indexer, left.labels[level],
                                         allow_fill=False)
            omit_mask = new_lev_labels != -1

            new_labels = list(left.labels)
            new_labels[level] = new_lev_labels

            if not omit_mask.all():
                new_labels = [lab[omit_mask] for lab in new_labels]

            new_levels = list(left.levels)
            new_levels[level] = new_level

            join_index = MultiIndex(levels=new_levels, labels=new_labels,
                                    names=left.names, verify_integrity=False)
            left_indexer = np.arange(len(left))[new_lev_labels != -1]
        else:
            join_index = left
            left_indexer = None

        if right_lev_indexer is not None:
            right_indexer = com.take_nd(right_lev_indexer,
                                        join_index.labels[level],
                                        allow_fill=False)
        else:
            right_indexer = join_index.labels[level]

        if flip_order:
            left_indexer, right_indexer = right_indexer, left_indexer

        if return_indexers:
            return join_index, left_indexer, right_indexer
        else:
            return join_index

    def _join_monotonic(self, other, how='left', return_indexers=False):
        if self.equals(other):
            ret_index = other if how == 'right' else self
            if return_indexers:
                return ret_index, None, None
            else:
                return ret_index

        sv = self.values
        ov = other.values

        if self.is_unique and other.is_unique:
            # We can perform much better than the general case
            if how == 'left':
                join_index = self
                lidx = None
                ridx = self._left_indexer_unique(sv, ov)
            elif how == 'right':
                join_index = other
                lidx = self._left_indexer_unique(ov, sv)
                ridx = None
            elif how == 'inner':
                join_index, lidx, ridx = self._inner_indexer(sv, ov)
                join_index = self._wrap_joined_index(join_index, other)
            elif how == 'outer':
                join_index, lidx, ridx = self._outer_indexer(sv, ov)
                join_index = self._wrap_joined_index(join_index, other)
        else:
            if how == 'left':
                join_index, lidx, ridx = self._left_indexer(sv, ov)
            elif how == 'right':
                join_index, ridx, lidx = self._left_indexer(other, self)
            elif how == 'inner':
                join_index, lidx, ridx = self._inner_indexer(sv, ov)
            elif how == 'outer':
                join_index, lidx, ridx = self._outer_indexer(sv, ov)
            join_index = self._wrap_joined_index(join_index, other)

        if return_indexers:
            return join_index, lidx, ridx
        else:
            return join_index

    def _wrap_joined_index(self, joined, other):
        name = self.name if self.name == other.name else None
        return Index(joined, name=name)

    def slice_indexer(self, start=None, end=None, step=None):
        """
        For an ordered Index, compute the slice indexer for input labels and
        step

        Parameters
        ----------
        start : label, default None
            If None, defaults to the beginning
        end : label, default None
            If None, defaults to the end
        step : int, default None

        Returns
        -------
        indexer : ndarray or slice

        Notes
        -----
        This function assumes that the data is sorted, so use at your own peril
        """
        start_slice, end_slice = self.slice_locs(start, end)

        # return a slice
        if np.isscalar(start_slice) and np.isscalar(end_slice):

            # degenerate cases
            if start is None and end is None:
                return slice(None, None, step)

            return slice(start_slice, end_slice, step)

        # loc indexers
        return Index(start_slice) & Index(end_slice)

    def slice_locs(self, start=None, end=None):
        """
        For an ordered Index, compute the slice locations for input labels

        Parameters
        ----------
        start : label, default None
            If None, defaults to the beginning
        end : label, default None
            If None, defaults to the end

        Returns
        -------
        (start, end) : (int, int)

        Notes
        -----
        This function assumes that the data is sorted, so use at your own peril
        """

        is_unique = self.is_unique

        def _get_slice(starting_value, offset, search_side, slice_property,
                       search_value):
            if search_value is None:
                return starting_value

            try:
                slc = self.get_loc(search_value)

                if not is_unique:

                    # get_loc will return a boolean array for non_uniques
                    # if we are not monotonic
                    if isinstance(slc, np.ndarray):
                        raise KeyError("cannot peform a slice operation "
                                       "on a non-unique non-monotonic index")

                if isinstance(slc, slice):
                    slc = getattr(slc, slice_property)
                else:
                    slc += offset

            except KeyError:
                if self.is_monotonic:

                    # we are duplicated but non-unique
                    # so if we have an indexer then we are done
                    # else search for it (GH 7523)
                    if not is_unique and is_integer(search_value):
                        slc = search_value
                    else:
                        slc = self.searchsorted(search_value,
                                                side=search_side)
                else:
                    raise
            return slc

        start_slice = _get_slice(0, offset=0, search_side='left',
                                 slice_property='start', search_value=start)
        end_slice = _get_slice(len(self), offset=1, search_side='right',
                               slice_property='stop', search_value=end)

        return start_slice, end_slice

    def delete(self, loc):
        """
        Make new Index with passed location(-s) deleted

        Returns
        -------
        new_index : Index
        """
        return np.delete(self, loc)

    def insert(self, loc, item):
        """
        Make new Index inserting new item at location. Follows
        Python list.append semantics for negative values

        Parameters
        ----------
        loc : int
        item : object

        Returns
        -------
        new_index : Index
        """
        _self = np.asarray(self)
        item_idx = Index([item], dtype=self.dtype).values
        idx = np.concatenate(
            (_self[:loc], item_idx, _self[loc:]))
        return Index(idx, name=self.name)

    def drop(self, labels):
        """
        Make new Index with passed list of labels deleted

        Parameters
        ----------
        labels : array-like

        Returns
        -------
        dropped : Index
        """
        labels = com._index_labels_to_array(labels)
        indexer = self.get_indexer(labels)
        mask = indexer == -1
        if mask.any():
            raise ValueError('labels %s not contained in axis' % labels[mask])
        return self.delete(indexer)


class Int64Index(Index):

    """
    Immutable ndarray implementing an ordered, sliceable set. The basic object
    storing axis labels for all pandas objects. Int64Index is a special case
    of `Index` with purely integer labels. This is the default index type used
    by the DataFrame and Series ctors when no explicit index is provided by the
    user.

    Parameters
    ----------
    data : array-like (1-dimensional)
    dtype : NumPy dtype (default: int64)
    copy : bool
        Make a copy of input ndarray
    name : object
        Name to be stored in the index

    Notes
    -----
    An Index instance can **only** contain hashable objects
    """

    _groupby = _algos.groupby_int64
    _arrmap = _algos.arrmap_int64
    _left_indexer_unique = _algos.left_join_indexer_unique_int64
    _left_indexer = _algos.left_join_indexer_int64
    _inner_indexer = _algos.inner_join_indexer_int64
    _outer_indexer = _algos.outer_join_indexer_int64

    _engine_type = _index.Int64Engine

    def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False):

        if fastpath:
            subarr = data.view(cls)
            subarr.name = name
            return subarr

        # isscalar, generators handled in coerce_to_ndarray
        data = cls._coerce_to_ndarray(data)

        if issubclass(data.dtype.type, compat.string_types):
            cls._string_data_error(data)

        elif issubclass(data.dtype.type, np.integer):
            # don't force the upcast as we may be dealing
            # with a platform int
            if dtype is None or not issubclass(np.dtype(dtype).type,
                                               np.integer):
                dtype = np.int64

            subarr = np.array(data, dtype=dtype, copy=copy)
        else:
            subarr = np.array(data, dtype=np.int64, copy=copy)
            if len(data) > 0:
                if (subarr != data).any():
                    raise TypeError('Unsafe NumPy casting to integer, you must'
                                    ' explicitly cast')

        subarr = subarr.view(cls)
        subarr.name = name
        return subarr

    @property
    def inferred_type(self):
        return 'integer'

    @property
    def asi8(self):
        # do not cache or you'll create a memory leak
        return self.values.view('i8')

    @property
    def is_all_dates(self):
        """
        Checks that all the labels are datetime objects
        """
        return False

    def equals(self, other):
        """
        Determines if two Index objects contain the same elements.
        """
        if self.is_(other):
            return True

        # if not isinstance(other, Int64Index):
        #     return False

        try:
            return array_equivalent(self, other)
        except TypeError:
            # e.g. fails in numpy 1.6 with DatetimeIndex #1681
            return False

    def _wrap_joined_index(self, joined, other):
        name = self.name if self.name == other.name else None
        return Int64Index(joined, name=name)


class Float64Index(Index):

    """
    Immutable ndarray implementing an ordered, sliceable set. The basic object
    storing axis labels for all pandas objects. Float64Index is a special case
    of `Index` with purely floating point labels.

    Parameters
    ----------
    data : array-like (1-dimensional)
    dtype : NumPy dtype (default: object)
    copy : bool
        Make a copy of input ndarray
    name : object
        Name to be stored in the index

    Notes
    -----
    An Float64Index instance can **only** contain hashable objects
    """

    # when this is not longer object dtype this can be changed
    _engine_type = _index.Float64Engine
    _groupby = _algos.groupby_float64
    _arrmap = _algos.arrmap_float64
    _left_indexer_unique = _algos.left_join_indexer_unique_float64
    _left_indexer = _algos.left_join_indexer_float64
    _inner_indexer = _algos.inner_join_indexer_float64
    _outer_indexer = _algos.outer_join_indexer_float64

    def __new__(cls, data, dtype=None, copy=False, name=None, fastpath=False):

        if fastpath:
            subarr = data.view(cls)
            subarr.name = name
            return subarr

        data = cls._coerce_to_ndarray(data)

        if issubclass(data.dtype.type, compat.string_types):
            cls._string_data_error(data)

        if dtype is None:
            dtype = np.float64

        try:
            subarr = np.array(data, dtype=dtype, copy=copy)
        except:
            raise TypeError('Unsafe NumPy casting, you must '
                            'explicitly cast')

        # coerce to float64 for storage
        if subarr.dtype != np.float64:
            subarr = subarr.astype(np.float64)

        subarr = subarr.view(cls)
        subarr.name = name
        return subarr

    @property
    def inferred_type(self):
        return 'floating'

    def astype(self, dtype):
        if np.dtype(dtype) not in (np.object, np.float64):
            raise TypeError('Setting %s dtype to anything other than '
                            'float64 or object is not supported' %
                            self.__class__)
        return Index(self.values, name=self.name, dtype=dtype)

    def _convert_scalar_indexer(self, key, typ=None):
        if typ == 'iloc':
            return super(Float64Index, self)._convert_scalar_indexer(key,
                                                                     typ=typ)
        return key

    def _convert_slice_indexer(self, key, typ=None):
        """ convert a slice indexer, by definition these are labels
            unless we are iloc """

        # if we are not a slice, then we are done
        if not isinstance(key, slice):
            return key

        if typ == 'iloc':
            return super(Float64Index, self)._convert_slice_indexer(key,
                                                                    typ=typ)

        # allow floats here
        validator = lambda v: v is None or is_integer(v) or is_float(v)
        self._validate_slicer(key, validator)

        # translate to locations
        return self.slice_indexer(key.start, key.stop, key.step)

    def get_value(self, series, key):
        """ we always want to get an index value, never a value """
        if not np.isscalar(key):
            raise InvalidIndexError

        from pandas.core.indexing import _maybe_droplevels
        from pandas.core.series import Series

        k = _values_from_object(key)
        loc = self.get_loc(k)
        new_values = series.values[loc]
        if np.isscalar(new_values) or new_values is None:
            return new_values

        new_index = self[loc]
        new_index = _maybe_droplevels(new_index, k)
        return Series(new_values, index=new_index, name=series.name)

    def equals(self, other):
        """
        Determines if two Index objects contain the same elements.
        """
        if self is other:
            return True

        # need to compare nans locations and make sure that they are the same
        # since nans don't compare equal this is a bit tricky
        try:
            if not isinstance(other, Float64Index):
                other = self._constructor(other)
            if self.dtype != other.dtype or self.shape != other.shape:
                return False
            left, right = self.values, other.values
            return ((left == right) | (self._isnan & other._isnan)).all()
        except TypeError:
            # e.g. fails in numpy 1.6 with DatetimeIndex #1681
            return False

    def __contains__(self, other):
        if super(Float64Index, self).__contains__(other):
            return True

        try:
            # if other is a sequence this throws a ValueError
            return np.isnan(other) and self.hasnans
        except ValueError:
            try:
                return len(other) <= 1 and _try_get_item(other) in self
            except TypeError:
                return False
        except:
            return False

    def get_loc(self, key):
        try:
            if np.all(np.isnan(key)):
                try:
                    return self._nan_idxs.item()
                except ValueError:
                    return self._nan_idxs
        except (TypeError, NotImplementedError):
            pass
        return super(Float64Index, self).get_loc(key)

    @property
    def is_all_dates(self):
        """
        Checks that all the labels are datetime objects
        """
        return False

    @cache_readonly
    def _nan_idxs(self):
        w, = self._isnan.nonzero()
        return w

    @cache_readonly
    def _isnan(self):
        return np.isnan(self.values)

    @cache_readonly
    def hasnans(self):
        return self._isnan.any()

    @cache_readonly
    def is_unique(self):
        return super(Float64Index, self).is_unique and self._nan_idxs.size < 2

    def isin(self, values):
        """
        Compute boolean array of whether each index value is found in the
        passed set of values

        Parameters
        ----------
        values : set or sequence of values

        Returns
        -------
        is_contained : ndarray (boolean dtype)
        """
        value_set = set(values)
        return lib.ismember_nans(self._array_values(), value_set,
                                 isnull(list(value_set)).any())


class MultiIndex(Index):

    """
    Implements multi-level, a.k.a. hierarchical, index object for pandas
    objects

    Parameters
    ----------
    levels : sequence of arrays
        The unique labels for each level
    labels : sequence of arrays
        Integers for each level designating which label at each location
    sortorder : optional int
        Level of sortedness (must be lexicographically sorted by that
        level)
    names : optional sequence of objects
        Names for each of the index levels.
    """
    # initialize to zero-length tuples to make everything work
    _names = FrozenList()
    _levels = FrozenList()
    _labels = FrozenList()
    _comparables = ['names']
    rename = Index.set_names

    def __new__(cls, levels=None, labels=None, sortorder=None, names=None,
                copy=False, verify_integrity=True):
        if levels is None or labels is None:
            raise TypeError("Must pass both levels and labels")
        if len(levels) != len(labels):
            raise ValueError('Length of levels and labels must be the same.')
        if len(levels) == 0:
            raise ValueError('Must pass non-zero number of levels/labels')
        if len(levels) == 1:
            if names:
                name = names[0]
            else:
                name = None

            return Index(levels[0], name=name, copy=True).take(labels[0])

        # v3, 0.8.0
        subarr = np.empty(0, dtype=object).view(cls)
        # we've already validated levels and labels, so shortcut here
        subarr._set_levels(levels, copy=copy, validate=False)
        subarr._set_labels(labels, copy=copy, validate=False)

        if names is not None:
            # handles name validation
            subarr._set_names(names)

        if sortorder is not None:
            subarr.sortorder = int(sortorder)
        else:
            subarr.sortorder = sortorder

        if verify_integrity:
            subarr._verify_integrity()

        return subarr

    def _verify_integrity(self):
        """Raises ValueError if length of levels and labels don't match or any
        label would exceed level bounds"""
        # NOTE: Currently does not check, among other things, that cached
        # nlevels matches nor that sortorder matches actually sortorder.
        labels, levels = self.labels, self.levels
        if len(levels) != len(labels):
            raise ValueError("Length of levels and labels must match. NOTE:"
                             " this index is in an inconsistent state.")
        label_length = len(self.labels[0])
        for i, (level, label) in enumerate(zip(levels, labels)):
            if len(label) != label_length:
                raise ValueError("Unequal label lengths: %s" % (
                                 [len(lab) for lab in labels]))
            if len(label) and label.max() >= len(level):
                raise ValueError("On level %d, label max (%d) >= length of"
                                 " level  (%d). NOTE: this index is in an"
                                 " inconsistent state" % (i, label.max(),
                                                          len(level)))

    def _get_levels(self):
        return self._levels

    def _set_levels(self, levels, copy=False, validate=True,
                    verify_integrity=False):
        # This is NOT part of the levels property because it should be
        # externally not allowed to set levels. User beware if you change
        # _levels directly
        if validate and len(levels) == 0:
            raise ValueError('Must set non-zero number of levels.')
        if validate and len(levels) != len(self._labels):
            raise ValueError('Length of levels must match length of labels.')
        levels = FrozenList(_ensure_index(lev, copy=copy)._shallow_copy()
                            for lev in levels)
        names = self.names
        self._levels = levels
        if any(names):
            self._set_names(names)

        self._tuples = None
        self._reset_cache()

        if verify_integrity:
            self._verify_integrity()

    def set_levels(self, levels, inplace=False, verify_integrity=True):
        """
        Set new levels on MultiIndex. Defaults to returning
        new index.

        Parameters
        ----------
        levels : sequence
            new levels to apply
        inplace : bool
            if True, mutates in place
        verify_integrity : bool (default True)
            if True, checks that levels and labels are compatible

        Returns
        -------
        new index (of same type and class...etc)
        """
        if not com.is_list_like(levels) or not com.is_list_like(levels[0]):
            raise TypeError("Levels must be list of lists-like")
        if inplace:
            idx = self
        else:
            idx = self._shallow_copy()
        idx._reset_identity()
        idx._set_levels(levels, validate=True,
                        verify_integrity=verify_integrity)
        if not inplace:
            return idx

    # remove me in 0.14 and change to read only property
    __set_levels = deprecate("setting `levels` directly",
                             partial(set_levels, inplace=True,
                                     verify_integrity=True),
                             alt_name="set_levels")
    levels = property(fget=_get_levels, fset=__set_levels)

    def _get_labels(self):
        return self._labels

    def _set_labels(self, labels, copy=False, validate=True,
                    verify_integrity=False):
        if validate and len(labels) != self.nlevels:
            raise ValueError("Length of labels must match length of levels")
        self._labels = FrozenList(
            _ensure_frozen(labs, copy=copy)._shallow_copy() for labs in labels)
        self._tuples = None
        self._reset_cache()

        if verify_integrity:
            self._verify_integrity()

    def set_labels(self, labels, inplace=False, verify_integrity=True):
        """
        Set new labels on MultiIndex. Defaults to returning
        new index.

        Parameters
        ----------
        labels : sequence of arrays
            new labels to apply
        inplace : bool
            if True, mutates in place
        verify_integrity : bool (default True)
            if True, checks that levels and labels are compatible

        Returns
        -------
        new index (of same type and class...etc)
        """
        if not com.is_list_like(labels) or not com.is_list_like(labels[0]):
            raise TypeError("Labels must be list of lists-like")
        if inplace:
            idx = self
        else:
            idx = self._shallow_copy()
        idx._reset_identity()
        idx._set_labels(labels, verify_integrity=verify_integrity)
        if not inplace:
            return idx

    # remove me in 0.14 and change to readonly property
    __set_labels = deprecate("setting labels directly",
                             partial(set_labels, inplace=True,
                                     verify_integrity=True),
                             alt_name="set_labels")
    labels = property(fget=_get_labels, fset=__set_labels)

    def copy(self, names=None, dtype=None, levels=None, labels=None,
             deep=False):
        """
        Make a copy of this object. Names, dtype, levels and labels can be
        passed and will be set on new copy.

        Parameters
        ----------
        names : sequence, optional
        dtype : numpy dtype or pandas type, optional
        levels : sequence, optional
        labels : sequence, optional

        Returns
        -------
        copy : MultiIndex

        Notes
        -----
        In most cases, there should be no functional difference from using
        ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
        This could be potentially expensive on large MultiIndex objects.
        """
        new_index = np.ndarray.copy(self)
        if deep:
            from copy import deepcopy
            levels = levels if levels is not None else deepcopy(self.levels)
            labels = labels if labels is not None else deepcopy(self.labels)
            names = names if names is not None else deepcopy(self.names)
        if levels is not None:
            new_index = new_index.set_levels(levels)
        if labels is not None:
            new_index = new_index.set_labels(labels)
        if names is not None:
            new_index = new_index.set_names(names)
        if dtype:
            new_index = new_index.astype(dtype)
        return new_index

    def __array_finalize__(self, obj):
        """
        Update custom MultiIndex attributes when a new array is created by
        numpy, e.g. when calling ndarray.view()
        """
        # overriden if a view
        self._reset_identity()
        if not isinstance(obj, type(self)):
            # Only relevant if this array is being created from an Index
            # instance.
            return

        # skip the validation on first, rest will catch the errors
        self._set_levels(getattr(obj, 'levels', []), validate=False)
        self._set_labels(getattr(obj, 'labels', []))
        self._set_names(getattr(obj, 'names', []))
        self.sortorder = getattr(obj, 'sortorder', None)

    def _array_values(self):
        # hack for various methods
        return self.values

    @cache_readonly
    def dtype(self):
        return np.dtype('O')

    def __repr__(self):
        encoding = get_option('display.encoding')
        attrs = [('levels', default_pprint(self.levels)),
                 ('labels', default_pprint(self.labels))]
        if not all(name is None for name in self.names):
            attrs.append(('names', default_pprint(self.names)))
        if self.sortorder is not None:
            attrs.append(('sortorder', default_pprint(self.sortorder)))

        space = ' ' * (len(self.__class__.__name__) + 1)
        prepr = (u(",\n%s") % space).join([u("%s=%s") % (k, v)
                                          for k, v in attrs])
        res = u("%s(%s)") % (self.__class__.__name__, prepr)

        if not compat.PY3:
            # needs to be str in Python 2
            res = res.encode(encoding)
        return res

    def __unicode__(self):
        """
        Return a string representation for a particular Index

        Invoked by unicode(df) in py2 only. Yields a Unicode String in both
        py2/py3.
        """
        rows = self.format(names=True)
        max_rows = get_option('display.max_rows')
        if len(rows) > max_rows:
            spaces = (len(rows[0]) - 3) // 2
            centered = ' ' * spaces
            half = max_rows // 2
            rows = rows[:half] + [centered + '...' + centered] + rows[-half:]
        return "\n".join(rows)

    def __len__(self):
        return len(self.labels[0])

    def _get_names(self):
        return FrozenList(level.name for level in self.levels)

    def _set_names(self, values, validate=True):
        """
        sets names on levels. WARNING: mutates!

        Note that you generally want to set this *after* changing levels, so
        that it only acts on copies"""
        values = list(values)
        if validate and len(values) != self.nlevels:
            raise ValueError('Length of names must match length of levels')
        # set the name
        for name, level in zip(values, self.levels):
            level.rename(name, inplace=True)

    names = property(
        fset=_set_names, fget=_get_names, doc="Names of levels in MultiIndex")

    def _reference_duplicate_name(self, name):
        """
        Returns True if the name refered to in self.names is duplicated.
        """
        # count the times name equals an element in self.names.
        return np.sum(name == np.asarray(self.names)) > 1

    def _format_native_types(self, **kwargs):
        return self.tolist()

    @property
    def _constructor(self):
        return MultiIndex.from_tuples

    @cache_readonly
    def inferred_type(self):
        return 'mixed'

    @staticmethod
    def _from_elements(values, labels=None, levels=None, names=None,
                       sortorder=None):
        index = values.view(MultiIndex)
        index._set_levels(levels)
        index._set_labels(labels)
        index._set_names(names)
        index.sortorder = sortorder
        return index

    def _get_level_number(self, level):
        try:
            count = self.names.count(level)
            if count > 1:
                raise ValueError('The name %s occurs multiple times, use a '
                                 'level number' % level)
            level = self.names.index(level)
        except ValueError:
            if not isinstance(level, int):
                raise KeyError('Level %s not found' % str(level))
            elif level < 0:
                level += self.nlevels
                if level < 0:
                    orig_level = level - self.nlevels
                    raise IndexError(
                        'Too many levels: Index has only %d levels, '
                        '%d is not a valid level number' % (self.nlevels, orig_level)
                    )
            # Note: levels are zero-based
            elif level >= self.nlevels:
                raise IndexError('Too many levels: Index has only %d levels, '
                                 'not %d' % (self.nlevels, level + 1))
        return level

    _tuples = None

    @property
    def values(self):
        if self._is_v2:
            return self.view(np.ndarray)
        else:
            if self._tuples is not None:
                return self._tuples

            values = []
            for lev, lab in zip(self.levels, self.labels):
                taken = com.take_1d(lev.values, lab)
                # Need to box timestamps, etc.
                if hasattr(lev, '_box_values'):
                    taken = lev._box_values(taken)
                values.append(taken)

            self._tuples = lib.fast_zip(values)
            return self._tuples

    # fml
    @property
    def _is_v1(self):
        contents = self.view(np.ndarray)
        return len(contents) > 0 and not isinstance(contents[0], tuple)

    @property
    def _is_v2(self):
        contents = self.view(np.ndarray)
        return len(contents) > 0 and isinstance(contents[0], tuple)

    @property
    def _has_complex_internals(self):
        # to disable groupby tricks
        return True

    @property
    def has_duplicates(self):
        """
        Return True if there are no unique groups
        """
        # has duplicates
        shape = [len(lev) for lev in self.levels]
        group_index = np.zeros(len(self), dtype='i8')
        for i in range(len(shape)):
            stride = np.prod([x for x in shape[i + 1:]], dtype='i8')
            group_index += self.labels[i] * stride

        if len(np.unique(group_index)) < len(group_index):
            return True

        return False

    def get_value(self, series, key):
        # somewhat broken encapsulation
        from pandas.core.indexing import _maybe_droplevels
        from pandas.core.series import Series

        # Label-based
        s = _values_from_object(series)
        k = _values_from_object(key)

        def _try_mi(k):
            # TODO: what if a level contains tuples??
            loc = self.get_loc(k)
            new_values = series.values[loc]
            new_index = self[loc]
            new_index = _maybe_droplevels(new_index, k)
            return Series(new_values, index=new_index, name=series.name)

        try:
            return self._engine.get_value(s, k)
        except KeyError as e1:
            try:
                return _try_mi(key)
            except KeyError:
                pass

            try:
                return _index.get_value_at(s, k)
            except IndexError:
                raise
            except TypeError:
                # generator/iterator-like
                if com.is_iterator(key):
                    raise InvalidIndexError(key)
                else:
                    raise e1
            except Exception:  # pragma: no cover
                raise e1
        except TypeError:

            # a Timestamp will raise a TypeError in a multi-index
            # rather than a KeyError, try it here
            # note that a string that 'looks' like a Timestamp will raise
            # a KeyError! (GH5725)
            if isinstance(key, (datetime.datetime, np.datetime64)) or (
                    compat.PY3 and isinstance(key, compat.string_types)):
                try:
                    return _try_mi(key)
                except (KeyError):
                    raise
                except:
                    pass

                try:
                    return _try_mi(Timestamp(key))
                except:
                    pass

            raise InvalidIndexError(key)

    def get_level_values(self, level):
        """
        Return vector of label values for requested level, equal to the length
        of the index

        Parameters
        ----------
        level : int or level name

        Returns
        -------
        values : ndarray
        """
        num = self._get_level_number(level)
        unique = self.levels[num]  # .values
        labels = self.labels[num]
        filled = com.take_1d(unique.values, labels, fill_value=unique._na_value)
        values = unique._simple_new(filled, self.names[num],
                                    freq=getattr(unique, 'freq', None),
                                    tz=getattr(unique, 'tz', None))
        return values

    def format(self, space=2, sparsify=None, adjoin=True, names=False,
               na_rep=None, formatter=None):
        if len(self) == 0:
            return []

        stringified_levels = []
        for lev, lab in zip(self.levels, self.labels):
            na = na_rep if na_rep is not None else _get_na_rep(lev.dtype.type)

            if len(lev) > 0:

                formatted = lev.take(lab).format(formatter=formatter)

                # we have some NA
                mask = lab == -1
                if mask.any():
                    formatted = np.array(formatted, dtype=object)
                    formatted[mask] = na
                    formatted = formatted.tolist()

            else:
                # weird all NA case
                formatted = [com.pprint_thing(na if isnull(x) else x,
                                              escape_chars=('\t', '\r', '\n'))
                             for x in com.take_1d(lev.values, lab)]
            stringified_levels.append(formatted)

        result_levels = []
        for lev, name in zip(stringified_levels, self.names):
            level = []

            if names:
                level.append(com.pprint_thing(name,
                                              escape_chars=('\t', '\r', '\n'))
                             if name is not None else '')

            level.extend(np.array(lev, dtype=object))
            result_levels.append(level)

        if sparsify is None:
            sparsify = get_option("display.multi_sparse")

        if sparsify:
            sentinel = ''
            # GH3547
            # use value of sparsify as sentinel,  unless it's an obvious
            # "Truthey" value
            if sparsify not in [True, 1]:
                sentinel = sparsify
            # little bit of a kludge job for #1217
            result_levels = _sparsify(result_levels,
                                      start=int(names),
                                      sentinel=sentinel)

        if adjoin:
            return com.adjoin(space, *result_levels).split('\n')
        else:
            return result_levels

    def to_hierarchical(self, n_repeat, n_shuffle=1):
        """
        Return a MultiIndex reshaped to conform to the
        shapes given by n_repeat and n_shuffle.

        Useful to replicate and rearrange a MultiIndex for combination
        with another Index with n_repeat items.

        Parameters
        ----------
        n_repeat : int
            Number of times to repeat the labels on self
        n_shuffle : int
            Controls the reordering of the labels. If the result is going
            to be an inner level in a MultiIndex, n_shuffle will need to be
            greater than one. The size of each label must divisible by
            n_shuffle.

        Returns
        -------
        MultiIndex

        Examples
        --------
        >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
                                          (2, u'one'), (2, u'two')])
        >>> idx.to_hierarchical(3)
        MultiIndex(levels=[[1, 2], [u'one', u'two']],
                   labels=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
                           [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1]])
        """
        levels = self.levels
        labels = [np.repeat(x, n_repeat) for x in self.labels]
        # Assumes that each label is divisible by n_shuffle
        labels = [x.reshape(n_shuffle, -1).ravel(1) for x in labels]
        names = self.names
        return MultiIndex(levels=levels, labels=labels, names=names)

    @property
    def is_all_dates(self):
        return False

    def is_lexsorted(self):
        """
        Return True if the labels are lexicographically sorted
        """
        return self.lexsort_depth == self.nlevels

    def is_lexsorted_for_tuple(self, tup):
        """
        Return True if we are correctly lexsorted given the passed tuple
        """
        return len(tup) <= self.lexsort_depth

    @cache_readonly
    def lexsort_depth(self):
        if self.sortorder is not None:
            if self.sortorder == 0:
                return self.nlevels
            else:
                return 0

        int64_labels = [com._ensure_int64(lab) for lab in self.labels]
        for k in range(self.nlevels, 0, -1):
            if lib.is_lexsorted(int64_labels[:k]):
                return k

        return 0

    @classmethod
    def from_arrays(cls, arrays, sortorder=None, names=None):
        """
        Convert arrays to MultiIndex

        Parameters
        ----------
        arrays : list / sequence of array-likes
            Each array-like gives one level's value for each data point.
            len(arrays) is the number of levels.
        sortorder : int or None
            Level of sortedness (must be lexicographically sorted by that
            level)

        Returns
        -------
        index : MultiIndex

        Examples
        --------
        >>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
        >>> MultiIndex.from_arrays(arrays, names=('number', 'color'))

        See Also
        --------
        MultiIndex.from_tuples : Convert list of tuples to MultiIndex
        MultiIndex.from_product : Make a MultiIndex from cartesian product
                                  of iterables
        """
        from pandas.core.categorical import Categorical

        if len(arrays) == 1:
            name = None if names is None else names[0]
            return Index(arrays[0], name=name)

        cats = [Categorical.from_array(arr) for arr in arrays]
        levels = [c.levels for c in cats]
        labels = [c.codes for c in cats]
        if names is None:
            names = [c.name for c in cats]

        return MultiIndex(levels=levels, labels=labels,
                          sortorder=sortorder, names=names,
                          verify_integrity=False)

    @classmethod
    def from_tuples(cls, tuples, sortorder=None, names=None):
        """
        Convert list of tuples to MultiIndex

        Parameters
        ----------
        tuples : list / sequence of tuple-likes
            Each tuple is the index of one row/column.
        sortorder : int or None
            Level of sortedness (must be lexicographically sorted by that
            level)

        Returns
        -------
        index : MultiIndex

        Examples
        --------
        >>> tuples = [(1, u'red'), (1, u'blue'),
                      (2, u'red'), (2, u'blue')]
        >>> MultiIndex.from_tuples(tuples, names=('number', 'color'))

        See Also
        --------
        MultiIndex.from_arrays : Convert list of arrays to MultiIndex
        MultiIndex.from_product : Make a MultiIndex from cartesian product
                                  of iterables
        """
        if len(tuples) == 0:
            # I think this is right? Not quite sure...
            raise TypeError('Cannot infer number of levels from empty list')

        if isinstance(tuples, np.ndarray):
            if isinstance(tuples, Index):
                tuples = tuples.values

            arrays = list(lib.tuples_to_object_array(tuples).T)
        elif isinstance(tuples, list):
            arrays = list(lib.to_object_array_tuples(tuples).T)
        else:
            arrays = lzip(*tuples)

        return MultiIndex.from_arrays(arrays, sortorder=sortorder,
                                      names=names)

    @classmethod
    def from_product(cls, iterables, sortorder=None, names=None):
        """
        Make a MultiIndex from the cartesian product of multiple iterables

        Parameters
        ----------
        iterables : list / sequence of iterables
            Each iterable has unique labels for each level of the index.
        sortorder : int or None
            Level of sortedness (must be lexicographically sorted by that
            level).
        names : list / sequence of strings or None
            Names for the levels in the index.

        Returns
        -------
        index : MultiIndex

        Examples
        --------
        >>> numbers = [0, 1, 2]
        >>> colors = [u'green', u'purple']
        >>> MultiIndex.from_product([numbers, colors],
                                     names=['number', 'color'])
        MultiIndex(levels=[[0, 1, 2], [u'green', u'purple']],
                   labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
                   names=[u'number', u'color'])

        See Also
        --------
        MultiIndex.from_arrays : Convert list of arrays to MultiIndex
        MultiIndex.from_tuples : Convert list of tuples to MultiIndex
        """
        from pandas.core.categorical import Categorical
        from pandas.tools.util import cartesian_product

        categoricals = [Categorical.from_array(it) for it in iterables]
        labels = cartesian_product([c.codes for c in categoricals])

        return MultiIndex(levels=[c.levels for c in categoricals],
                          labels=labels, sortorder=sortorder, names=names)

    @property
    def nlevels(self):
        return len(self.levels)

    @property
    def levshape(self):
        return tuple(len(x) for x in self.levels)

    def __contains__(self, key):
        hash(key)
        # work around some kind of odd cython bug
        try:
            self.get_loc(key)
            return True
        except KeyError:
            return False

    def __reduce__(self):
        """Necessary for making this object picklable"""
        object_state = list(np.ndarray.__reduce__(self))
        subclass_state = ([lev.view(np.ndarray) for lev in self.levels],
                          [label.view(np.ndarray) for label in self.labels],
                          self.sortorder, list(self.names))
        object_state[2] = (object_state[2], subclass_state)
        return tuple(object_state)

    def __setstate__(self, state):
        """Necessary for making this object picklable"""
        nd_state, own_state = state
        np.ndarray.__setstate__(self, nd_state)
        levels, labels, sortorder, names = own_state

        self._set_levels([Index(x) for x in levels], validate=False)
        self._set_labels(labels)
        self._set_names(names)
        self.sortorder = sortorder
        self._verify_integrity()

    def __getitem__(self, key):
        if np.isscalar(key):
            retval = []
            for lev, lab in zip(self.levels, self.labels):
                if lab[key] == -1:
                    retval.append(np.nan)
                else:
                    retval.append(lev[lab[key]])

            return tuple(retval)
        else:
            if com._is_bool_indexer(key):
                key = np.asarray(key)
                sortorder = self.sortorder
            else:
                # cannot be sure whether the result will be sorted
                sortorder = None

            result = np.empty(0, dtype=object).view(type(self))
            new_labels = [lab[key] for lab in self.labels]

            # an optimization
            result._set_levels(self.levels, validate=False)
            result._set_labels(new_labels)
            result.sortorder = sortorder
            result._set_names(self.names)

            return result

    def take(self, indexer, axis=None):
        """
        Analogous to ndarray.take
        """
        indexer = com._ensure_platform_int(indexer)
        new_labels = [lab.take(indexer) for lab in self.labels]
        return MultiIndex(levels=self.levels, labels=new_labels,
                          names=self.names, verify_integrity=False)

    def append(self, other):
        """
        Append a collection of Index options together

        Parameters
        ----------
        other : Index or list/tuple of indices

        Returns
        -------
        appended : Index
        """
        if not isinstance(other, (list, tuple)):
            other = [other]

        if all((isinstance(o, MultiIndex) and o.nlevels >= self.nlevels) for o in other):
            arrays = []
            for i in range(self.nlevels):
                label = self.get_level_values(i)
                appended = [o.get_level_values(i) for o in other]
                arrays.append(label.append(appended))
            return MultiIndex.from_arrays(arrays, names=self.names)

        to_concat = (self.values,) + tuple(k.values for k in other)
        new_tuples = np.concatenate(to_concat)

        # if all(isinstance(x, MultiIndex) for x in other):
        try:
            return MultiIndex.from_tuples(new_tuples, names=self.names)
        except:
            return Index(new_tuples)

    def argsort(self, *args, **kwargs):
        return self.values.argsort()

    def drop(self, labels, level=None):
        """
        Make new MultiIndex with passed list of labels deleted

        Parameters
        ----------
        labels : array-like
            Must be a list of tuples
        level : int or level name, default None

        Returns
        -------
        dropped : MultiIndex
        """
        if level is not None:
            return self._drop_from_level(labels, level)

        try:
            if not isinstance(labels, np.ndarray):
                labels = com._index_labels_to_array(labels)
            indexer = self.get_indexer(labels)
            mask = indexer == -1
            if mask.any():
                raise ValueError('labels %s not contained in axis'
                                 % labels[mask])
            return self.delete(indexer)
        except Exception:
            pass

        inds = []
        for label in labels:
            loc = self.get_loc(label)
            if isinstance(loc, int):
                inds.append(loc)
            else:
                inds.extend(lrange(loc.start, loc.stop))

        return self.delete(inds)

    def _drop_from_level(self, labels, level):
        labels = com._index_labels_to_array(labels)
        i = self._get_level_number(level)
        index = self.levels[i]
        values = index.get_indexer(labels)

        mask = ~lib.ismember(self.labels[i], set(values))

        return self[mask]

    def droplevel(self, level=0):
        """
        Return Index with requested level removed. If MultiIndex has only 2
        levels, the result will be of Index type not MultiIndex.

        Parameters
        ----------
        level : int/level name or list thereof

        Notes
        -----
        Does not check if result index is unique or not

        Returns
        -------
        index : Index or MultiIndex
        """
        levels = level
        if not isinstance(levels, (tuple, list)):
            levels = [level]

        new_levels = list(self.levels)
        new_labels = list(self.labels)
        new_names = list(self.names)

        levnums = sorted(self._get_level_number(lev) for lev in levels)[::-1]

        for i in levnums:
            new_levels.pop(i)
            new_labels.pop(i)
            new_names.pop(i)

        if len(new_levels) == 1:

            # set nan if needed
            mask = new_labels[0] == -1
            result = new_levels[0].take(new_labels[0])
            if mask.any():
                np.putmask(result, mask, np.nan)

            result.name = new_names[0]
            return result
        else:
            return MultiIndex(levels=new_levels, labels=new_labels,
                              names=new_names, verify_integrity=False)

    def swaplevel(self, i, j):
        """
        Swap level i with level j. Do not change the ordering of anything

        Parameters
        ----------
        i, j : int, string (can be mixed)
            Level of index to be swapped. Can pass level name as string.

        Returns
        -------
        swapped : MultiIndex
        """
        new_levels = list(self.levels)
        new_labels = list(self.labels)
        new_names = list(self.names)

        i = self._get_level_number(i)
        j = self._get_level_number(j)

        new_levels[i], new_levels[j] = new_levels[j], new_levels[i]
        new_labels[i], new_labels[j] = new_labels[j], new_labels[i]
        new_names[i], new_names[j] = new_names[j], new_names[i]

        return MultiIndex(levels=new_levels, labels=new_labels,
                          names=new_names, verify_integrity=False)

    def reorder_levels(self, order):
        """
        Rearrange levels using input order. May not drop or duplicate levels

        Parameters
        ----------
        """
        order = [self._get_level_number(i) for i in order]
        if len(order) != self.nlevels:
            raise AssertionError(('Length of order must be same as '
                                  'number of levels (%d), got %d')
                                 % (self.nlevels, len(order)))
        new_levels = [self.levels[i] for i in order]
        new_labels = [self.labels[i] for i in order]
        new_names = [self.names[i] for i in order]

        return MultiIndex(levels=new_levels, labels=new_labels,
                          names=new_names, verify_integrity=False)

    def __getslice__(self, i, j):
        return self.__getitem__(slice(i, j))

    def sortlevel(self, level=0, ascending=True, sort_remaining=True):
        """
        Sort MultiIndex at the requested level. The result will respect the
        original ordering of the associated factor at that level.

        Parameters
        ----------
        level : list-like, int or str, default 0
            If a string is given, must be a name of the level
            If list-like must be names or ints of levels.
        ascending : boolean, default True
            False to sort in descending order
        sort_remaining : sort by the remaining levels after level.

        Returns
        -------
        sorted_index : MultiIndex
        """
        from pandas.core.groupby import _indexer_from_factorized

        labels = list(self.labels)
        shape = list(self.levshape)

        if isinstance(level, (str, int)):
            level = [level]
        level = [self._get_level_number(lev) for lev in level]

        # partition labels and shape
        primary = tuple(labels.pop(lev - i) for i, lev in enumerate(level))
        primshp = tuple(shape.pop(lev - i) for i, lev in enumerate(level))

        if sort_remaining:
            primary += primary + tuple(labels)
            primshp += primshp + tuple(shape)
            sortorder = None
        else:
            sortorder = level[0]

        indexer = _indexer_from_factorized(primary,
                                           primshp,
                                           compress=False)

        if not ascending:
            indexer = indexer[::-1]

        indexer = com._ensure_platform_int(indexer)
        new_labels = [lab.take(indexer) for lab in self.labels]

        new_index = MultiIndex(labels=new_labels, levels=self.levels,
                               names=self.names, sortorder=sortorder,
                               verify_integrity=False)

        return new_index, indexer

    def get_indexer(self, target, method=None, limit=None):
        """
        Compute indexer and mask for new index given the current index. The
        indexer should be then used as an input to ndarray.take to align the
        current data to the new index. The mask determines whether labels are
        found or not in the current index

        Parameters
        ----------
        target : MultiIndex or Index (of tuples)
        method : {'pad', 'ffill', 'backfill', 'bfill'}
            pad / ffill: propagate LAST valid observation forward to next valid
            backfill / bfill: use NEXT valid observation to fill gap

        Notes
        -----
        This is a low-level method and probably should be used at your own risk

        Examples
        --------
        >>> indexer, mask = index.get_indexer(new_index)
        >>> new_values = cur_values.take(indexer)
        >>> new_values[-mask] = np.nan

        Returns
        -------
        (indexer, mask) : (ndarray, ndarray)
        """
        method = self._get_method(method)

        target = _ensure_index(target)

        target_index = target
        if isinstance(target, MultiIndex):
            target_index = target._tuple_index

        if target_index.dtype != object:
            return np.ones(len(target_index)) * -1

        if not self.is_unique:
            raise Exception('Reindexing only valid with uniquely valued Index '
                            'objects')

        self_index = self._tuple_index

        if method == 'pad':
            if not self.is_unique or not self.is_monotonic:
                raise AssertionError(('Must be unique and monotonic to '
                                      'use forward fill getting the indexer'))
            indexer = self_index._engine.get_pad_indexer(target_index,
                                                         limit=limit)
        elif method == 'backfill':
            if not self.is_unique or not self.is_monotonic:
                raise AssertionError(('Must be unique and monotonic to '
                                      'use backward fill getting the indexer'))
            indexer = self_index._engine.get_backfill_indexer(target_index,
                                                              limit=limit)
        else:
            indexer = self_index._engine.get_indexer(target_index)

        return com._ensure_platform_int(indexer)

    def reindex(self, target, method=None, level=None, limit=None,
                copy_if_needed=False):
        """
        Performs any necessary conversion on the input index and calls
        get_indexer. This method is here so MultiIndex and an Index of
        like-labeled tuples can play nice together

        Returns
        -------
        (new_index, indexer, mask) : (MultiIndex, ndarray, ndarray)
        """

        if level is not None:
            if method is not None:
                raise TypeError('Fill method not supported if level passed')
            target = _ensure_index(target)
            target, indexer, _ = self._join_level(target, level, how='right',
                                                  return_indexers=True)
        else:
            if self.equals(target):
                indexer = None
            else:
                if self.is_unique:
                    indexer = self.get_indexer(target, method=method,
                                               limit=limit)
                else:
                    raise Exception(
                        "cannot handle a non-unique multi-index!")

        if not isinstance(target, MultiIndex):
            if indexer is None:
                target = self
            elif (indexer >= 0).all():
                target = self.take(indexer)
            else:
                # hopefully?
                target = MultiIndex.from_tuples(target)

        return target, indexer

    @cache_readonly
    def _tuple_index(self):
        """
        Convert MultiIndex to an Index of tuples

        Returns
        -------
        index : Index
        """
        return Index(self.values)

    def slice_locs(self, start=None, end=None, strict=False):
        """
        For an ordered MultiIndex, compute the slice locations for input
        labels. They can be tuples representing partial levels, e.g. for a
        MultiIndex with 3 levels, you can pass a single value (corresponding to
        the first level), or a 1-, 2-, or 3-tuple.

        Parameters
        ----------
        start : label or tuple, default None
            If None, defaults to the beginning
        end : label or tuple
            If None, defaults to the end
        strict : boolean,

        Returns
        -------
        (start, end) : (int, int)

        Notes
        -----
        This function assumes that the data is sorted by the first level
        """
        if start is None:
            start_slice = 0
        else:
            if not isinstance(start, tuple):
                start = start,
            start_slice = self._partial_tup_index(start, side='left')

        if end is None:
            end_slice = len(self)
        else:
            if not isinstance(end, tuple):
                end = end,
            end_slice = self._partial_tup_index(end, side='right')

        return start_slice, end_slice

    def _partial_tup_index(self, tup, side='left'):
        if len(tup) > self.lexsort_depth:
            raise KeyError('Key length (%d) was greater than MultiIndex'
                           ' lexsort depth (%d)' %
                           (len(tup), self.lexsort_depth))

        n = len(tup)
        start, end = 0, len(self)
        zipped = zip(tup, self.levels, self.labels)
        for k, (lab, lev, labs) in enumerate(zipped):
            section = labs[start:end]

            if lab not in lev:
                if not lev.is_type_compatible(lib.infer_dtype([lab])):
                    raise TypeError('Level type mismatch: %s' % lab)

                # short circuit
                loc = lev.searchsorted(lab, side=side)
                if side == 'right' and loc >= 0:
                    loc -= 1
                return start + section.searchsorted(loc, side=side)

            idx = lev.get_loc(lab)
            if k < n - 1:
                end = start + section.searchsorted(idx, side='right')
                start = start + section.searchsorted(idx, side='left')
            else:
                return start + section.searchsorted(idx, side=side)

    def get_loc(self, key):
        """
        Get integer location slice for requested label or tuple

        Parameters
        ----------
        key : label or tuple

        Returns
        -------
        loc : int or slice object
        """
        if isinstance(key, tuple):
            if len(key) == self.nlevels:
                if self.is_unique:
                    return self._engine.get_loc(_values_from_object(key))
                else:
                    return slice(*self.slice_locs(key, key))
            else:
                # partial selection
                result = slice(*self.slice_locs(key, key))
                if result.start == result.stop:
                    raise KeyError(key)
                return result
        else:
            return self._get_level_indexer(key, level=0)

    def get_loc_level(self, key, level=0, drop_level=True):
        """
        Get integer location slice for requested label or tuple

        Parameters
        ----------
        key : label or tuple
        level : int/level name or list thereof

        Returns
        -------
        loc : int or slice object
        """
        def _maybe_drop_levels(indexer, levels, drop_level):
            if not drop_level:
                return self[indexer]
            # kludgearound
            orig_index = new_index = self[indexer]
            levels = [self._get_level_number(i) for i in levels]
            for i in sorted(levels, reverse=True):
                try:
                    new_index = new_index.droplevel(i)
                except:

                    # no dropping here
                    return orig_index
            return new_index

        if isinstance(level, (tuple, list)):
            if len(key) != len(level):
                raise AssertionError('Key for location must have same '
                                     'length as number of levels')
            result = None
            for lev, k in zip(level, key):
                loc, new_index = self.get_loc_level(k, level=lev)
                if isinstance(loc, slice):
                    mask = np.zeros(len(self), dtype=bool)
                    mask[loc] = True
                    loc = mask

                result = loc if result is None else result & loc

            return result, _maybe_drop_levels(result, level, drop_level)

        level = self._get_level_number(level)

        # kludge for #1796
        if isinstance(key, list):
            key = tuple(key)

        if isinstance(key, tuple) and level == 0:

            try:
                if key in self.levels[0]:
                    indexer = self._get_level_indexer(key, level=level)
                    new_index = _maybe_drop_levels(indexer, [0], drop_level)
                    return indexer, new_index
            except TypeError:
                pass

            if not any(isinstance(k, slice) for k in key):

                # partial selection
                def partial_selection(key):
                    indexer = slice(*self.slice_locs(key, key))
                    if indexer.start == indexer.stop:
                        raise KeyError(key)
                    ilevels = [i for i in range(len(key))
                               if key[i] != slice(None, None)]
                    return indexer, _maybe_drop_levels(indexer, ilevels,
                                                       drop_level)

                if len(key) == self.nlevels:

                    if self.is_unique:

                        # here we have a completely specified key, but are
                        # using some partial string matching here
                        # GH4758
                        can_index_exactly = any([
                            (l.is_all_dates and
                             not isinstance(k, compat.string_types))
                            for k, l in zip(key, self.levels)
                        ])
                        if any([
                            l.is_all_dates for k, l in zip(key, self.levels)
                        ]) and not can_index_exactly:
                            indexer = slice(*self.slice_locs(key, key))

                            # we have a multiple selection here
                            if not indexer.stop - indexer.start == 1:
                                return partial_selection(key)

                            key = tuple(self[indexer].tolist()[0])

                        return (self._engine.get_loc(_values_from_object(key)),
                                None)
                    else:
                        return partial_selection(key)
                else:
                    return partial_selection(key)
            else:
                indexer = None
                for i, k in enumerate(key):
                    if not isinstance(k, slice):
                        k = self._get_level_indexer(k, level=i)
                        if isinstance(k, slice):
                            # everything
                            if k.start == 0 and k.stop == len(self):
                                k = slice(None, None)
                        else:
                            k_index = k

                    if isinstance(k, slice):
                        if k == slice(None, None):
                            continue
                        else:
                            raise TypeError(key)

                    if indexer is None:
                        indexer = k_index
                    else:  # pragma: no cover
                        indexer &= k_index
                if indexer is None:
                    indexer = slice(None, None)
                ilevels = [i for i in range(len(key))
                           if key[i] != slice(None, None)]
                return indexer, _maybe_drop_levels(indexer, ilevels,
                                                   drop_level)
        else:
            indexer = self._get_level_indexer(key, level=level)
            return indexer, _maybe_drop_levels(indexer, [level], drop_level)

    def _get_level_indexer(self, key, level=0):
        # return a boolean indexer or a slice showing where the key is
        # in the totality of values

        level_index = self.levels[level]
        labels = self.labels[level]

        if isinstance(key, slice):
            # handle a slice, returnig a slice if we can
            # otherwise a boolean indexer

            start = level_index.get_loc(key.start or  0)
            stop  = level_index.get_loc(key.stop or len(level_index)-1)
            step = key.step

            if isinstance(start,slice) or isinstance(stop,slice):
                # we have a slice for start and/or stop
                # a partial date slicer on a DatetimeIndex generates a slice
                # note that the stop ALREADY includes the stopped point (if
                # it was a string sliced)
                m = np.zeros(len(labels),dtype=bool)
                m[np.in1d(labels,np.arange(start.start,stop.stop,step))] = True
                return m

            elif level > 0 or self.lexsort_depth == 0 or step is not None:
                # need to have like semantics here to right
                # searching as when we are using a slice
                # so include the stop+1 (so we include stop)
                m = np.zeros(len(labels),dtype=bool)
                m[np.in1d(labels,np.arange(start,stop+1,step))] = True
                return m
            else:
                # sorted, so can return slice object -> view
                i = labels.searchsorted(start, side='left')
                j = labels.searchsorted(stop, side='right')
                return slice(i, j, step)

        else:

            loc = level_index.get_loc(key)
            if level > 0 or self.lexsort_depth == 0:
                return np.array(labels == loc,dtype=bool)
            else:
                # sorted, so can return slice object -> view
                i = labels.searchsorted(loc, side='left')
                j = labels.searchsorted(loc, side='right')
                return slice(i, j)

    def get_locs(self, tup):
        """
        Given a tuple of slices/lists/labels/boolean indexer to a level-wise spec
        produce an indexer to extract those locations

        Parameters
        ----------
        key : tuple of (slices/list/labels)

        Returns
        -------
        locs : integer list of locations or boolean indexer suitable
               for passing to iloc
        """

        from pandas.core.indexing import _is_null_slice

        # must be lexsorted to at least as many levels
        if not self.is_lexsorted_for_tuple(tup):
            raise KeyError('MultiIndex Slicing requires the index to be fully lexsorted'
                           ' tuple len ({0}), lexsort depth ({1})'.format(len(tup), self.lexsort_depth))

        def _convert_indexer(r):
            if isinstance(r, slice):
                m = np.zeros(len(self),dtype=bool)
                m[r] = True
                return m
            return r

        ranges = []
        for i,k in enumerate(tup):

            if com._is_bool_indexer(k):
                # a boolean indexer, must be the same length!
                k = np.asarray(k)
                if len(k) != len(self):
                    raise ValueError("cannot index with a boolean indexer that is"
                                     " not the same length as the index")
                ranges.append(k)
            elif com.is_list_like(k):
                # a collection of labels to include from this level (these are or'd)
                ranges.append(reduce(
                    np.logical_or,[ _convert_indexer(self._get_level_indexer(x, level=i)
                                                     ) for x in k ]))
            elif _is_null_slice(k):
                # empty slice
                pass

            elif isinstance(k,slice):

                # a slice, include BOTH of the labels
                ranges.append(self._get_level_indexer(k,level=i))
            else:
                # a single label
                ranges.append(self.get_loc_level(k,level=i,drop_level=False)[0])

        # identity
        if len(ranges) == 0:
            return slice(0,len(self))

        elif len(ranges) == 1:
            return ranges[0]

        # construct a boolean indexer if we have a slice or boolean indexer
        return reduce(np.logical_and,[ _convert_indexer(r) for r in ranges ])

    def truncate(self, before=None, after=None):
        """
        Slice index between two labels / tuples, return new MultiIndex

        Parameters
        ----------
        before : label or tuple, can be partial. Default None
            None defaults to start
        after : label or tuple, can be partial. Default None
            None defaults to end

        Returns
        -------
        truncated : MultiIndex
        """
        if after and before and after < before:
            raise ValueError('after < before')

        i, j = self.levels[0].slice_locs(before, after)
        left, right = self.slice_locs(before, after)

        new_levels = list(self.levels)
        new_levels[0] = new_levels[0][i:j]

        new_labels = [lab[left:right] for lab in self.labels]
        new_labels[0] = new_labels[0] - i

        return MultiIndex(levels=new_levels, labels=new_labels,
                          verify_integrity=False)

    def equals(self, other):
        """
        Determines if two MultiIndex objects have the same labeling information
        (the levels themselves do not necessarily have to be the same)

        See also
        --------
        equal_levels
        """
        if self.is_(other):
            return True

        if not isinstance(other, MultiIndex):
            return array_equivalent(self.values, _ensure_index(other))

        if self.nlevels != other.nlevels:
            return False

        if len(self) != len(other):
            return False

        for i in range(self.nlevels):
            svalues = com.take_nd(self.levels[i].values, self.labels[i],
                                  allow_fill=False)
            ovalues = com.take_nd(other.levels[i].values, other.labels[i],
                                  allow_fill=False)
            if not array_equivalent(svalues, ovalues):
                return False

        return True

    def equal_levels(self, other):
        """
        Return True if the levels of both MultiIndex objects are the same

        """
        if self.nlevels != other.nlevels:
            return False

        for i in range(self.nlevels):
            if not self.levels[i].equals(other.levels[i]):
                return False
        return True

    def union(self, other):
        """
        Form the union of two MultiIndex objects, sorting if possible

        Parameters
        ----------
        other : MultiIndex or array / Index of tuples

        Returns
        -------
        Index
        """
        self._assert_can_do_setop(other)

        if len(other) == 0 or self.equals(other):
            return self

        result_names = self.names if self.names == other.names else None

        uniq_tuples = lib.fast_unique_multiple([self.values, other.values])
        return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
                                      names=result_names)

    def intersection(self, other):
        """
        Form the intersection of two MultiIndex objects, sorting if possible

        Parameters
        ----------
        other : MultiIndex or array / Index of tuples

        Returns
        -------
        Index
        """
        self._assert_can_do_setop(other)

        if self.equals(other):
            return self

        result_names = self.names if self.names == other.names else None

        self_tuples = self.values
        other_tuples = other.values
        uniq_tuples = sorted(set(self_tuples) & set(other_tuples))
        if len(uniq_tuples) == 0:
            return MultiIndex(levels=[[]] * self.nlevels,
                              labels=[[]] * self.nlevels,
                              names=result_names, verify_integrity=False)
        else:
            return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
                                          names=result_names)

    def diff(self, other):
        """
        Compute sorted set difference of two MultiIndex objects

        Returns
        -------
        diff : MultiIndex
        """
        self._assert_can_do_setop(other)

        if not isinstance(other, MultiIndex):
            if len(other) == 0:
                return self
            try:
                other = MultiIndex.from_tuples(other)
            except:
                raise TypeError('other must be a MultiIndex or a list of'
                                ' tuples')
            result_names = self.names
        else:
            result_names = self.names if self.names == other.names else None

        if self.equals(other):
            return MultiIndex(levels=[[]] * self.nlevels,
                              labels=[[]] * self.nlevels,
                              names=result_names, verify_integrity=False)

        difference = sorted(set(self.values) - set(other.values))

        if len(difference) == 0:
            return MultiIndex(levels=[[]] * self.nlevels,
                              labels=[[]] * self.nlevels,
                              names=result_names, verify_integrity=False)
        else:
            return MultiIndex.from_tuples(difference, sortorder=0,
                                          names=result_names)

    def _assert_can_do_setop(self, other):
        pass

    def astype(self, dtype):
        if np.dtype(dtype) != np.object_:
            raise TypeError('Setting %s dtype to anything other than object '
                            'is not supported' % self.__class__)
        return self._shallow_copy()

    def insert(self, loc, item):
        """
        Make new MultiIndex inserting new item at location

        Parameters
        ----------
        loc : int
        item : tuple
            Must be same length as number of levels in the MultiIndex

        Returns
        -------
        new_index : Index
        """
        # Pad the key with empty strings if lower levels of the key
        # aren't specified:
        if not isinstance(item, tuple):
            item = (item,) + ('',) * (self.nlevels - 1)
        elif len(item) != self.nlevels:
            raise ValueError(
                'Item must have length equal to number of levels.')

        new_levels = []
        new_labels = []
        for k, level, labels in zip(item, self.levels, self.labels):
            if k not in level:
                # have to insert into level
                # must insert at end otherwise you have to recompute all the
                # other labels
                lev_loc = len(level)
                level = level.insert(lev_loc, k)
            else:
                lev_loc = level.get_loc(k)

            new_levels.append(level)
            new_labels.append(np.insert(labels, loc, lev_loc))

        return MultiIndex(levels=new_levels, labels=new_labels,
                          names=self.names, verify_integrity=False)

    def delete(self, loc):
        """
        Make new index with passed location deleted

        Returns
        -------
        new_index : MultiIndex
        """
        new_labels = [np.delete(lab, loc) for lab in self.labels]
        return MultiIndex(levels=self.levels, labels=new_labels,
                          names=self.names, verify_integrity=False)

    get_major_bounds = slice_locs

    __bounds = None

    @property
    def _bounds(self):
        """
        Return or compute and return slice points for level 0, assuming
        sortedness
        """
        if self.__bounds is None:
            inds = np.arange(len(self.levels[0]))
            self.__bounds = self.labels[0].searchsorted(inds)

        return self.__bounds

    def _wrap_joined_index(self, joined, other):
        names = self.names if self.names == other.names else None
        return MultiIndex.from_tuples(joined, names=names)


# For utility purposes

def _sparsify(label_list, start=0, sentinel=''):
    pivoted = lzip(*label_list)
    k = len(label_list)

    result = pivoted[:start + 1]
    prev = pivoted[start]

    for cur in pivoted[start + 1:]:
        sparse_cur = []

        for i, (p, t) in enumerate(zip(prev, cur)):
            if i == k - 1:
                sparse_cur.append(t)
                result.append(sparse_cur)
                break

            if p == t:
                sparse_cur.append(sentinel)
            else:
                sparse_cur.extend(cur[i:])
                result.append(sparse_cur)
                break

        prev = cur

    return lzip(*result)


def _ensure_index(index_like, copy=False):
    if isinstance(index_like, Index):
        if copy:
            index_like = index_like.copy()
        return index_like
    if hasattr(index_like, 'name'):
        return Index(index_like, name=index_like.name, copy=copy)

    # must check for exactly list here because of strict type
    # check in clean_index_list
    if isinstance(index_like, list):
        if type(index_like) != list:
            index_like = list(index_like)
        # 2200 ?
        converted, all_arrays = lib.clean_index_list(index_like)

        if len(converted) > 0 and all_arrays:
            return MultiIndex.from_arrays(converted)
        else:
            index_like = converted
    else:
       # clean_index_list does the equivalent of copying
       # so only need to do this if not list instance
        if copy:
            from copy import copy
            index_like = copy(index_like)

    return Index(index_like)


def _ensure_frozen(array_like, copy=False):
    array_like = np.asanyarray(array_like, dtype=np.int_)
    array_like = array_like.view(FrozenNDArray)
    if copy:
        array_like = array_like.copy()
    return array_like


def _validate_join_method(method):
    if method not in ['left', 'right', 'inner', 'outer']:
        raise ValueError('do not recognize join method %s' % method)


# TODO: handle index names!
def _get_combined_index(indexes, intersect=False):
    indexes = _get_distinct_indexes(indexes)
    if len(indexes) == 0:
        return Index([])
    if len(indexes) == 1:
        return indexes[0]
    if intersect:
        index = indexes[0]
        for other in indexes[1:]:
            index = index.intersection(other)
        return index
    union = _union_indexes(indexes)
    return _ensure_index(union)


def _get_distinct_indexes(indexes):
    return list(dict((id(x), x) for x in indexes).values())


def _union_indexes(indexes):
    if len(indexes) == 0:
        raise AssertionError('Must have at least 1 Index to union')
    if len(indexes) == 1:
        result = indexes[0]
        if isinstance(result, list):
            result = Index(sorted(result))
        return result

    indexes, kind = _sanitize_and_check(indexes)

    if kind == 'special':
        result = indexes[0]

        if hasattr(result, 'union_many'):
            return result.union_many(indexes[1:])
        else:
            for other in indexes[1:]:
                result = result.union(other)
            return result
    elif kind == 'array':
        index = indexes[0]
        for other in indexes[1:]:
            if not index.equals(other):
                return Index(lib.fast_unique_multiple(indexes))

        return index
    else:
        return Index(lib.fast_unique_multiple_list(indexes))


def _trim_front(strings):
    """
    Trims zeros and decimal points
    """
    trimmed = strings
    while len(strings) > 0 and all([x[0] == ' ' for x in trimmed]):
        trimmed = [x[1:] for x in trimmed]
    return trimmed


def _sanitize_and_check(indexes):
    kinds = list(set([type(index) for index in indexes]))

    if list in kinds:
        if len(kinds) > 1:
            indexes = [Index(com._try_sort(x))
                       if not isinstance(x, Index) else x
                       for x in indexes]
            kinds.remove(list)
        else:
            return indexes, 'list'

    if len(kinds) > 1 or Index not in kinds:
        return indexes, 'special'
    else:
        return indexes, 'array'


def _get_consensus_names(indexes):

    # find the non-none names, need to tupleify to make
    # the set hashable, then reverse on return
    consensus_names = set([
        tuple(i.names) for i in indexes if all(n is not None for n in i.names)
    ])
    if len(consensus_names) == 1:
        return list(list(consensus_names)[0])
    return [None] * indexes[0].nlevels


def _maybe_box(idx):
    from pandas.tseries.api import DatetimeIndex, PeriodIndex
    klasses = DatetimeIndex, PeriodIndex

    if isinstance(idx, klasses):
        return idx.asobject
    return idx


def _all_indexes_same(indexes):
    first = indexes[0]
    for index in indexes[1:]:
        if not first.equals(index):
            return False
    return True


def _get_na_rep(dtype):
    return {np.datetime64: 'NaT', np.timedelta64: 'NaT'}.get(dtype, 'NaN')


def _get_na_value(dtype):
    return {np.datetime64: tslib.NaT, np.timedelta64: tslib.NaT}.get(dtype,
                                                                     np.nan)