base.py

"""
Base and utility classes for pandas objects.
"""
import textwrap
import warnings

import numpy as np

import pandas._libs.lib as lib
import pandas.compat as compat
from pandas.compat import PYPY, OrderedDict, builtins
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
from pandas.util._decorators import Appender, Substitution, cache_readonly
from pandas.util._validators import validate_bool_kwarg

from pandas.core.dtypes.common import (
    is_datetime64tz_dtype, is_datetimelike, is_extension_array_dtype,
    is_extension_type, is_list_like, is_object_dtype, is_scalar)
from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
from pandas.core.dtypes.missing import isna

from pandas.core import algorithms, common as com
from pandas.core.accessor import DirNamesMixin
import pandas.core.nanops as nanops

_shared_docs = dict()
_indexops_doc_kwargs = dict(klass='IndexOpsMixin', inplace='',
                            unique='IndexOpsMixin', duplicated='IndexOpsMixin')


class StringMixin(object):
    """implements string methods so long as object defines a `__unicode__`
    method.

    Handles Python2/3 compatibility transparently.
    """
    # side note - this could be made into a metaclass if more than one
    #             object needs

    # ----------------------------------------------------------------------
    # Formatting

    def __unicode__(self):
        raise AbstractMethodError(self)

    def __str__(self):
        """
        Return a string representation for a particular Object

        Invoked by str(df) in both py2/py3.
        Yields Bytestring in Py2, Unicode String in py3.
        """

        if compat.PY3:
            return self.__unicode__()
        return self.__bytes__()

    def __bytes__(self):
        """
        Return a string representation for a particular object.

        Invoked by bytes(obj) in py3 only.
        Yields a bytestring in both py2/py3.
        """
        from pandas.core.config import get_option

        encoding = get_option("display.encoding")
        return self.__unicode__().encode(encoding, 'replace')

    def __repr__(self):
        """
        Return a string representation for a particular object.

        Yields Bytestring in Py2, Unicode String in py3.
        """
        return str(self)


class PandasObject(StringMixin, DirNamesMixin):

    """baseclass for various pandas objects"""

    @property
    def _constructor(self):
        """class constructor (for this class it's just `__class__`"""
        return self.__class__

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

        Invoked by unicode(obj) in py2 only. Yields a Unicode String in both
        py2/py3.
        """
        # Should be overwritten by base classes
        return object.__repr__(self)

    def _reset_cache(self, key=None):
        """
        Reset cached properties. If ``key`` is passed, only clears that key.
        """
        if getattr(self, '_cache', None) is None:
            return
        if key is None:
            self._cache.clear()
        else:
            self._cache.pop(key, None)

    def __sizeof__(self):
        """
        Generates the total memory usage for an object that returns
        either a value or Series of values
        """
        if hasattr(self, 'memory_usage'):
            mem = self.memory_usage(deep=True)
            if not is_scalar(mem):
                mem = mem.sum()
            return int(mem)

        # no memory_usage attribute, so fall back to
        # object's 'sizeof'
        return super(PandasObject, self).__sizeof__()


class NoNewAttributesMixin(object):
    """Mixin which prevents adding new attributes.

    Prevents additional attributes via xxx.attribute = "something" after a
    call to `self.__freeze()`. Mainly used to prevent the user from using
    wrong attributes on a accessor (`Series.cat/.str/.dt`).

    If you really want to add a new attribute at a later time, you need to use
    `object.__setattr__(self, key, value)`.
    """

    def _freeze(self):
        """Prevents setting additional attributes"""
        object.__setattr__(self, "__frozen", True)

    # prevent adding any attribute via s.xxx.new_attribute = ...
    def __setattr__(self, key, value):
        # _cache is used by a decorator
        # We need to check both 1.) cls.__dict__ and 2.) getattr(self, key)
        # because
        # 1.) getattr is false for attributes that raise errors
        # 2.) cls.__dict__ doesn't traverse into base classes
        if (getattr(self, "__frozen", False) and not
                (key == "_cache" or
                 key in type(self).__dict__ or
                 getattr(self, key, None) is not None)):
            raise AttributeError("You cannot add any new attribute '{key}'".
                                 format(key=key))
        object.__setattr__(self, key, value)


class GroupByError(Exception):
    pass


class DataError(GroupByError):
    pass


class SpecificationError(GroupByError):
    pass


class SelectionMixin(object):
    """
    mixin implementing the selection & aggregation interface on a group-like
    object sub-classes need to define: obj, exclusions
    """
    _selection = None
    _internal_names = ['_cache', '__setstate__']
    _internal_names_set = set(_internal_names)

    _builtin_table = OrderedDict((
        (builtins.sum, np.sum),
        (builtins.max, np.max),
        (builtins.min, np.min),
    ))

    _cython_table = OrderedDict((
        (builtins.sum, 'sum'),
        (builtins.max, 'max'),
        (builtins.min, 'min'),
        (np.all, 'all'),
        (np.any, 'any'),
        (np.sum, 'sum'),
        (np.nansum, 'sum'),
        (np.mean, 'mean'),
        (np.nanmean, 'mean'),
        (np.prod, 'prod'),
        (np.nanprod, 'prod'),
        (np.std, 'std'),
        (np.nanstd, 'std'),
        (np.var, 'var'),
        (np.nanvar, 'var'),
        (np.median, 'median'),
        (np.nanmedian, 'median'),
        (np.max, 'max'),
        (np.nanmax, 'max'),
        (np.min, 'min'),
        (np.nanmin, 'min'),
        (np.cumprod, 'cumprod'),
        (np.nancumprod, 'cumprod'),
        (np.cumsum, 'cumsum'),
        (np.nancumsum, 'cumsum'),
    ))

    @property
    def _selection_name(self):
        """
        return a name for myself; this would ideally be called
        the 'name' property, but we cannot conflict with the
        Series.name property which can be set
        """
        if self._selection is None:
            return None  # 'result'
        else:
            return self._selection

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

    @cache_readonly
    def _selected_obj(self):

        if self._selection is None or isinstance(self.obj, ABCSeries):
            return self.obj
        else:
            return self.obj[self._selection]

    @cache_readonly
    def ndim(self):
        return self._selected_obj.ndim

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

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

    def __getitem__(self, key):
        if self._selection is not None:
            raise IndexError('Column(s) {selection} already selected'
                             .format(selection=self._selection))

        if isinstance(key, (list, tuple, ABCSeries, ABCIndexClass,
                            np.ndarray)):
            if len(self.obj.columns.intersection(key)) != len(key):
                bad_keys = list(set(key).difference(self.obj.columns))
                raise KeyError("Columns not found: {missing}"
                               .format(missing=str(bad_keys)[1:-1]))
            return self._gotitem(list(key), ndim=2)

        elif not getattr(self, 'as_index', False):
            if key not in self.obj.columns:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=2)

        else:
            if key not in self.obj:
                raise KeyError("Column not found: {key}".format(key=key))
            return self._gotitem(key, ndim=1)

    def _gotitem(self, key, ndim, subset=None):
        """
        sub-classes to define
        return a sliced object

        Parameters
        ----------
        key : string / list of selections
        ndim : 1,2
            requested ndim of result
        subset : object, default None
            subset to act on

        """
        raise AbstractMethodError(self)

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

    agg = aggregate

    def _try_aggregate_string_function(self, arg, *args, **kwargs):
        """
        if arg is a string, then try to operate on it:
        - try to find a function (or attribute) on ourselves
        - try to find a numpy function
        - raise

        """
        assert isinstance(arg, compat.string_types)

        f = getattr(self, arg, None)
        if f is not None:
            if callable(f):
                return f(*args, **kwargs)

            # people may try to aggregate on a non-callable attribute
            # but don't let them think they can pass args to it
            assert len(args) == 0
            assert len([kwarg for kwarg in kwargs
                        if kwarg not in ['axis', '_level']]) == 0
            return f

        f = getattr(np, arg, None)
        if f is not None:
            return f(self, *args, **kwargs)

        raise ValueError("{arg} is an unknown string function".format(arg=arg))

    def _aggregate(self, arg, *args, **kwargs):
        """
        provide an implementation for the aggregators

        Parameters
        ----------
        arg : string, dict, function
        *args : args to pass on to the function
        **kwargs : kwargs to pass on to the function

        Returns
        -------
        tuple of result, how

        Notes
        -----
        how can be a string describe the required post-processing, or
        None if not required
        """
        is_aggregator = lambda x: isinstance(x, (list, tuple, dict))
        is_nested_renamer = False

        _axis = kwargs.pop('_axis', None)
        if _axis is None:
            _axis = getattr(self, 'axis', 0)
        _level = kwargs.pop('_level', None)

        if isinstance(arg, compat.string_types):
            return self._try_aggregate_string_function(arg, *args,
                                                       **kwargs), None

        if isinstance(arg, dict):

            # aggregate based on the passed dict
            if _axis != 0:  # pragma: no cover
                raise ValueError('Can only pass dict with axis=0')

            obj = self._selected_obj

            def nested_renaming_depr(level=4):
                # deprecation of nested renaming
                # GH 15931
                warnings.warn(
                    ("using a dict with renaming "
                     "is deprecated and will be removed in a future "
                     "version"),
                    FutureWarning, stacklevel=level)

            # if we have a dict of any non-scalars
            # eg. {'A' : ['mean']}, normalize all to
            # be list-likes
            if any(is_aggregator(x) for x in compat.itervalues(arg)):
                new_arg = compat.OrderedDict()
                for k, v in compat.iteritems(arg):
                    if not isinstance(v, (tuple, list, dict)):
                        new_arg[k] = [v]
                    else:
                        new_arg[k] = v

                    # the keys must be in the columns
                    # for ndim=2, or renamers for ndim=1

                    # ok for now, but deprecated
                    # {'A': { 'ra': 'mean' }}
                    # {'A': { 'ra': ['mean'] }}
                    # {'ra': ['mean']}

                    # not ok
                    # {'ra' : { 'A' : 'mean' }}
                    if isinstance(v, dict):
                        is_nested_renamer = True

                        if k not in obj.columns:
                            msg = ('cannot perform renaming for {key} with a '
                                   'nested dictionary').format(key=k)
                            raise SpecificationError(msg)
                        nested_renaming_depr(4 + (_level or 0))

                    elif isinstance(obj, ABCSeries):
                        nested_renaming_depr()
                    elif (isinstance(obj, ABCDataFrame) and
                          k not in obj.columns):
                        raise KeyError(
                            "Column '{col}' does not exist!".format(col=k))

                arg = new_arg

            else:
                # deprecation of renaming keys
                # GH 15931
                keys = list(compat.iterkeys(arg))
                if (isinstance(obj, ABCDataFrame) and
                        len(obj.columns.intersection(keys)) != len(keys)):
                    nested_renaming_depr()

            from pandas.core.reshape.concat import concat

            def _agg_1dim(name, how, subset=None):
                """
                aggregate a 1-dim with how
                """
                colg = self._gotitem(name, ndim=1, subset=subset)
                if colg.ndim != 1:
                    raise SpecificationError("nested dictionary is ambiguous "
                                             "in aggregation")
                return colg.aggregate(how, _level=(_level or 0) + 1)

            def _agg_2dim(name, how):
                """
                aggregate a 2-dim with how
                """
                colg = self._gotitem(self._selection, ndim=2,
                                     subset=obj)
                return colg.aggregate(how, _level=None)

            def _agg(arg, func):
                """
                run the aggregations over the arg with func
                return an OrderedDict
                """
                result = compat.OrderedDict()
                for fname, agg_how in compat.iteritems(arg):
                    result[fname] = func(fname, agg_how)
                return result

            # set the final keys
            keys = list(compat.iterkeys(arg))
            result = compat.OrderedDict()

            # nested renamer
            if is_nested_renamer:
                result = list(_agg(arg, _agg_1dim).values())

                if all(isinstance(r, dict) for r in result):

                    result, results = compat.OrderedDict(), result
                    for r in results:
                        result.update(r)
                    keys = list(compat.iterkeys(result))

                else:

                    if self._selection is not None:
                        keys = None

            # some selection on the object
            elif self._selection is not None:

                sl = set(self._selection_list)

                # we are a Series like object,
                # but may have multiple aggregations
                if len(sl) == 1:

                    result = _agg(arg, lambda fname,
                                  agg_how: _agg_1dim(self._selection, agg_how))

                # we are selecting the same set as we are aggregating
                elif not len(sl - set(keys)):

                    result = _agg(arg, _agg_1dim)

                # we are a DataFrame, with possibly multiple aggregations
                else:

                    result = _agg(arg, _agg_2dim)

            # no selection
            else:

                try:
                    result = _agg(arg, _agg_1dim)
                except SpecificationError:

                    # we are aggregating expecting all 1d-returns
                    # but we have 2d
                    result = _agg(arg, _agg_2dim)

            # combine results

            def is_any_series():
                # return a boolean if we have *any* nested series
                return any(isinstance(r, ABCSeries)
                           for r in compat.itervalues(result))

            def is_any_frame():
                # return a boolean if we have *any* nested series
                return any(isinstance(r, ABCDataFrame)
                           for r in compat.itervalues(result))

            if isinstance(result, list):
                return concat(result, keys=keys, axis=1, sort=True), True

            elif is_any_frame():
                # we have a dict of DataFrames
                # return a MI DataFrame

                return concat([result[k] for k in keys],
                              keys=keys, axis=1), True

            elif isinstance(self, ABCSeries) and is_any_series():

                # we have a dict of Series
                # return a MI Series
                try:
                    result = concat(result)
                except TypeError:
                    # we want to give a nice error here if
                    # we have non-same sized objects, so
                    # we don't automatically broadcast

                    raise ValueError("cannot perform both aggregation "
                                     "and transformation operations "
                                     "simultaneously")

                return result, True

            # fall thru
            from pandas import DataFrame, Series
            try:
                result = DataFrame(result)
            except ValueError:

                # we have a dict of scalars
                result = Series(result,
                                name=getattr(self, 'name', None))

            return result, True
        elif is_list_like(arg) and arg not in compat.string_types:
            # we require a list, but not an 'str'
            return self._aggregate_multiple_funcs(arg,
                                                  _level=_level,
                                                  _axis=_axis), None
        else:
            result = None

        f = self._is_cython_func(arg)
        if f and not args and not kwargs:
            return getattr(self, f)(), None

        # caller can react
        return result, True

    def _aggregate_multiple_funcs(self, arg, _level, _axis):
        from pandas.core.reshape.concat import concat

        if _axis != 0:
            raise NotImplementedError("axis other than 0 is not supported")

        if self._selected_obj.ndim == 1:
            obj = self._selected_obj
        else:
            obj = self._obj_with_exclusions

        results = []
        keys = []

        # degenerate case
        if obj.ndim == 1:
            for a in arg:
                try:
                    colg = self._gotitem(obj.name, ndim=1, subset=obj)
                    results.append(colg.aggregate(a))

                    # make sure we find a good name
                    name = com.get_callable_name(a) or a
                    keys.append(name)
                except (TypeError, DataError):
                    pass
                except SpecificationError:
                    raise

        # multiples
        else:
            for index, col in enumerate(obj):
                try:
                    colg = self._gotitem(col, ndim=1,
                                         subset=obj.iloc[:, index])
                    results.append(colg.aggregate(arg))
                    keys.append(col)
                except (TypeError, DataError):
                    pass
                except ValueError:
                    # cannot aggregate
                    continue
                except SpecificationError:
                    raise

        # if we are empty
        if not len(results):
            raise ValueError("no results")

        try:
            return concat(results, keys=keys, axis=1, sort=False)
        except TypeError:

            # we are concatting non-NDFrame objects,
            # e.g. a list of scalars

            from pandas.core.dtypes.cast import is_nested_object
            from pandas import Series
            result = Series(results, index=keys, name=self.name)
            if is_nested_object(result):
                raise ValueError("cannot combine transform and "
                                 "aggregation operations")
            return result

    def _shallow_copy(self, obj=None, obj_type=None, **kwargs):
        """
        return a new object with the replacement attributes
        """
        if obj is None:
            obj = self._selected_obj.copy()
        if obj_type is None:
            obj_type = self._constructor
        if isinstance(obj, obj_type):
            obj = obj.obj
        for attr in self._attributes:
            if attr not in kwargs:
                kwargs[attr] = getattr(self, attr)
        return obj_type(obj, **kwargs)

    def _is_cython_func(self, arg):
        """
        if we define an internal function for this argument, return it
        """
        return self._cython_table.get(arg)

    def _is_builtin_func(self, arg):
        """
        if we define an builtin function for this argument, return it,
        otherwise return the arg
        """
        return self._builtin_table.get(arg, arg)


class IndexOpsMixin(object):
    """ common ops mixin to support a unified interface / docs for Series /
    Index
    """

    # ndarray compatibility
    __array_priority__ = 1000

    def transpose(self, *args, **kwargs):
        """
        Return the transpose, which is by definition self.
        """
        nv.validate_transpose(args, kwargs)
        return self

    T = property(transpose, doc="Return the transpose, which is by "
                                "definition self.")

    @property
    def _is_homogeneous_type(self):
        """
        Whether the object has a single dtype.

        By definition, Series and Index are always considered homogeneous.
        A MultiIndex may or may not be homogeneous, depending on the
        dtypes of the levels.

        See Also
        --------
        DataFrame._is_homogeneous_type
        MultiIndex._is_homogeneous_type
        """
        return True

    @property
    def shape(self):
        """
        Return a tuple of the shape of the underlying data.
        """
        return self._values.shape

    @property
    def ndim(self):
        """
        Number of dimensions of the underlying data, by definition 1.
        """
        return 1

    def item(self):
        """
        Return the first element of the underlying data as a python scalar.
        """
        try:
            return self.values.item()
        except IndexError:
            # copy numpy's message here because Py26 raises an IndexError
            raise ValueError('can only convert an array of size 1 to a '
                             'Python scalar')

    @property
    def data(self):
        """
        Return the data pointer of the underlying data.
        """
        warnings.warn("{obj}.data is deprecated and will be removed "
                      "in a future version".format(obj=type(self).__name__),
                      FutureWarning, stacklevel=2)
        return self.values.data

    @property
    def itemsize(self):
        """
        Return the size of the dtype of the item of the underlying data.
        """
        warnings.warn("{obj}.itemsize is deprecated and will be removed "
                      "in a future version".format(obj=type(self).__name__),
                      FutureWarning, stacklevel=2)
        return self._ndarray_values.itemsize

    @property
    def nbytes(self):
        """
        Return the number of bytes in the underlying data.
        """
        return self._values.nbytes

    @property
    def strides(self):
        """
        Return the strides of the underlying data.
        """
        warnings.warn("{obj}.strides is deprecated and will be removed "
                      "in a future version".format(obj=type(self).__name__),
                      FutureWarning, stacklevel=2)
        return self._ndarray_values.strides

    @property
    def size(self):
        """
        Return the number of elements in the underlying data.
        """
        return self._values.size

    @property
    def flags(self):
        """
        Return the ndarray.flags for the underlying data.
        """
        warnings.warn("{obj}.flags is deprecated and will be removed "
                      "in a future version".format(obj=type(self).__name__),
                      FutureWarning, stacklevel=2)
        return self.values.flags

    @property
    def base(self):
        """
        Return the base object if the memory of the underlying data is shared.
        """
        warnings.warn("{obj}.base is deprecated and will be removed "
                      "in a future version".format(obj=type(self).__name__),
                      FutureWarning, stacklevel=2)
        return self.values.base

    @property
    def array(self):
        # type: () -> Union[np.ndarray, ExtensionArray]
        """
        The actual Array backing this Series or Index.

        .. versionadded:: 0.24.0

        Returns
        -------
        array : numpy.ndarray or ExtensionArray
            This is the actual array stored within this object. This differs
            from ``.values`` which may require converting the data
            to a different form.

        See Also
        --------
        Index.to_numpy : Similar method that always returns a NumPy array.
        Series.to_numpy : Similar method that always returns a NumPy array.

        Notes
        -----
        This table lays out the different array types for each extension
        dtype within pandas.

        ================== =============================
        dtype              array type
        ================== =============================
        category           Categorical
        period             PeriodArray
        interval           IntervalArray
        IntegerNA          IntegerArray
        datetime64[ns, tz] DatetimeArray
        ================== =============================

        For any 3rd-party extension types, the array type will be an
        ExtensionArray.

        For all remaining dtypes ``.array`` will be the :class:`numpy.ndarray`
        stored within. If you absolutely need a NumPy array (possibly with
        copying / coercing data), then use :meth:`Series.to_numpy` instead.

        .. note::

           ``.array`` will always return the underlying object backing the
           Series or Index. If a future version of pandas adds a specialized
           extension type for a data type, then the return type of ``.array``
           for that data type will change from an object-dtype ndarray to the
           new ExtensionArray.

        Examples
        --------
        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
        >>> ser.array
        [a, b, a]
        Categories (2, object): [a, b]
        """
        return self._values

    def to_numpy(self):
        """
        A NumPy ndarray representing the values in this Series or Index.

        .. versionadded:: 0.24.0

        The returned array will be the same up to equality (values equal
        in `self` will be equal in the returned array; likewise for values
        that are not equal). When `self` contains an ExtensionArray, the
        dtype may be different. For example, for a category-dtype Series,
        ``to_numpy()`` will return a NumPy array and the categorical dtype
        will be lost.

        Returns
        -------
        numpy.ndarray

        See Also
        --------
        Series.array : Get the actual data stored within.
        Index.array : Get the actual data stored within.
        DataFrame.to_numpy : Similar method for DataFrame.

        Notes
        -----
        For NumPy dtypes, this will be a reference to the actual data stored
        in this Series or Index. Modifying the result in place will modify
        the data stored in the Series or Index (not that we recommend doing
        that).

        For extension types, ``to_numpy()`` *may* require copying data and
        coercing the result to a NumPy type (possibly object), which may be
        expensive. When you need a no-copy reference to the underlying data,
        :attr:`Series.array` should be used instead.

        This table lays out the different dtypes and return types of
        ``to_numpy()`` for various dtypes within pandas.

        ================== ================================
        dtype              array type
        ================== ================================
        category[T]        ndarray[T] (same dtype as input)
        period             ndarray[object] (Periods)
        interval           ndarray[object] (Intervals)
        IntegerNA          ndarray[object]
        datetime64[ns, tz] ndarray[object] (Timestamps)
        ================== ================================

        Examples
        --------
        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
        >>> ser.to_numpy()
        array(['a', 'b', 'a'], dtype=object)
        """
        if (is_extension_array_dtype(self.dtype) or
                is_datetime64tz_dtype(self.dtype)):
            # TODO(DatetimeArray): remove the second clause.
            return np.asarray(self._values)
        return self._values

    @property
    def _ndarray_values(self):
        # type: () -> np.ndarray
        """
        The data as an ndarray, possibly losing information.

        The expectation is that this is cheap to compute, and is primarily
        used for interacting with our indexers.

        - categorical -> codes
        """
        if is_extension_array_dtype(self):
            return self.array._ndarray_values
        return self.values

    @property
    def empty(self):
        return not self.size

    def max(self):
        """
        Return the maximum value of the Index.

        Returns
        -------
        scalar
            Maximum value.

        See Also
        --------
        Index.min : Return the minimum value in an Index.
        Series.max : Return the maximum value in a Series.
        DataFrame.max : Return the maximum values in a DataFrame.

        Examples
        --------
        >>> idx = pd.Index([3, 2, 1])
        >>> idx.max()
        3

        >>> idx = pd.Index(['c', 'b', 'a'])
        >>> idx.max()
        'c'

        For a MultiIndex, the maximum is determined lexicographically.

        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
        >>> idx.max()
        ('b', 2)
        """
        return nanops.nanmax(self.values)

    def argmax(self, axis=None):
        """
        Return a ndarray of the maximum argument indexer.

        See Also
        --------
        numpy.ndarray.argmax
        """
        return nanops.nanargmax(self.values)

    def min(self):
        """
        Return the minimum value of the Index.

        Returns
        -------
        scalar
            Minimum value.

        See Also
        --------
        Index.max : Return the maximum value of the object.
        Series.min : Return the minimum value in a Series.
        DataFrame.min : Return the minimum values in a DataFrame.

        Examples
        --------
        >>> idx = pd.Index([3, 2, 1])
        >>> idx.min()
        1

        >>> idx = pd.Index(['c', 'b', 'a'])
        >>> idx.min()
        'a'

        For a MultiIndex, the minimum is determined lexicographically.

        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
        >>> idx.min()
        ('a', 1)
        """
        return nanops.nanmin(self.values)

    def argmin(self, axis=None):
        """
        Return a ndarray of the minimum argument indexer.

        See Also
        --------
        numpy.ndarray.argmin
        """
        return nanops.nanargmin(self.values)

    def tolist(self):
        """
        Return a list of the values.

        These are each a scalar type, which is a Python scalar
        (for str, int, float) or a pandas scalar
        (for Timestamp/Timedelta/Interval/Period)

        See Also
        --------
        numpy.ndarray.tolist
        """
        if is_datetimelike(self._values):
            return [com.maybe_box_datetimelike(x) for x in self._values]
        elif is_extension_array_dtype(self._values):
            return list(self._values)
        else:
            return self._values.tolist()

    to_list = tolist

    def __iter__(self):
        """
        Return an iterator of the values.

        These are each a scalar type, which is a Python scalar
        (for str, int, float) or a pandas scalar
        (for Timestamp/Timedelta/Interval/Period)
        """
        return iter(self.tolist())

    @cache_readonly
    def hasnans(self):
        """
        Return if I have any nans; enables various perf speedups.
        """
        return bool(isna(self).any())

    def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
                filter_type=None, **kwds):
        """ perform the reduction type operation if we can """
        func = getattr(self, name, None)
        if func is None:
            raise TypeError("{klass} cannot perform the operation {op}".format(
                            klass=self.__class__.__name__, op=name))
        return func(**kwds)

    def _map_values(self, mapper, na_action=None):
        """
        An internal function that maps values using the input
        correspondence (which can be a dict, Series, or function).

        Parameters
        ----------
        mapper : function, dict, or Series
            The input correspondence object
        na_action : {None, 'ignore'}
            If 'ignore', propagate NA values, without passing them to the
            mapping function

        Returns
        -------
        applied : Union[Index, MultiIndex], inferred
            The output of the mapping function applied to the index.
            If the function returns a tuple with more than one element
            a MultiIndex will be returned.

        """

        # we can fastpath dict/Series to an efficient map
        # as we know that we are not going to have to yield
        # python types
        if isinstance(mapper, dict):
            if hasattr(mapper, '__missing__'):
                # If a dictionary subclass defines a default value method,
                # convert mapper to a lookup function (GH #15999).
                dict_with_default = mapper
                mapper = lambda x: dict_with_default[x]
            else:
                # Dictionary does not have a default. Thus it's safe to
                # convert to an Series for efficiency.
                # we specify the keys here to handle the
                # possibility that they are tuples
                from pandas import Series
                mapper = Series(mapper)

        if isinstance(mapper, ABCSeries):
            # Since values were input this means we came from either
            # a dict or a series and mapper should be an index
            if is_extension_type(self.dtype):
                values = self._values
            else:
                values = self.values

            indexer = mapper.index.get_indexer(values)
            new_values = algorithms.take_1d(mapper._values, indexer)

            return new_values

        # we must convert to python types
        if is_extension_type(self.dtype):
            values = self._values
            if na_action is not None:
                raise NotImplementedError
            map_f = lambda values, f: values.map(f)
        else:
            values = self.astype(object)
            values = getattr(values, 'values', values)
            if na_action == 'ignore':
                def map_f(values, f):
                    return lib.map_infer_mask(values, f,
                                              isna(values).view(np.uint8))
            else:
                map_f = lib.map_infer

        # mapper is a function
        new_values = map_f(values, mapper)

        return new_values

    def value_counts(self, normalize=False, sort=True, ascending=False,
                     bins=None, dropna=True):
        """
        Return a Series containing counts of unique values.

        The resulting object will be in descending order so that the
        first element is the most frequently-occurring element.
        Excludes NA values by default.

        Parameters
        ----------
        normalize : boolean, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : boolean, default True
            Sort by values.
        ascending : boolean, default False
            Sort in ascending order.
        bins : integer, optional
            Rather than count values, group them into half-open bins,
            a convenience for ``pd.cut``, only works with numeric data.
        dropna : boolean, default True
            Don't include counts of NaN.

        Returns
        -------
        counts : Series

        See Also
        --------
        Series.count: Number of non-NA elements in a Series.
        DataFrame.count: Number of non-NA elements in a DataFrame.

        Examples
        --------
        >>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
        >>> index.value_counts()
        3.0    2
        4.0    1
        2.0    1
        1.0    1
        dtype: int64

        With `normalize` set to `True`, returns the relative frequency by
        dividing all values by the sum of values.

        >>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
        >>> s.value_counts(normalize=True)
        3.0    0.4
        4.0    0.2
        2.0    0.2
        1.0    0.2
        dtype: float64

        **bins**

        Bins can be useful for going from a continuous variable to a
        categorical variable; instead of counting unique
        apparitions of values, divide the index in the specified
        number of half-open bins.

        >>> s.value_counts(bins=3)
        (2.0, 3.0]      2
        (0.996, 2.0]    2
        (3.0, 4.0]      1
        dtype: int64

        **dropna**

        With `dropna` set to `False` we can also see NaN index values.

        >>> s.value_counts(dropna=False)
        3.0    2
        NaN    1
        4.0    1
        2.0    1
        1.0    1
        dtype: int64
        """
        from pandas.core.algorithms import value_counts
        result = value_counts(self, sort=sort, ascending=ascending,
                              normalize=normalize, bins=bins, dropna=dropna)
        return result

    def unique(self):
        values = self._values

        if hasattr(values, 'unique'):

            result = values.unique()
        else:
            from pandas.core.algorithms import unique1d
            result = unique1d(values)

        return result

    def nunique(self, dropna=True):
        """
        Return number of unique elements in the object.

        Excludes NA values by default.

        Parameters
        ----------
        dropna : boolean, default True
            Don't include NaN in the count.

        Returns
        -------
        nunique : int
        """
        uniqs = self.unique()
        n = len(uniqs)
        if dropna and isna(uniqs).any():
            n -= 1
        return n

    @property
    def is_unique(self):
        """
        Return boolean if values in the object are unique.

        Returns
        -------
        is_unique : boolean
        """
        return self.nunique() == len(self)

    @property
    def is_monotonic(self):
        """
        Return boolean if values in the object are
        monotonic_increasing.

        .. versionadded:: 0.19.0

        Returns
        -------
        is_monotonic : boolean
        """
        from pandas import Index
        return Index(self).is_monotonic

    is_monotonic_increasing = is_monotonic

    @property
    def is_monotonic_decreasing(self):
        """
        Return boolean if values in the object are
        monotonic_decreasing.

        .. versionadded:: 0.19.0

        Returns
        -------
        is_monotonic_decreasing : boolean
        """
        from pandas import Index
        return Index(self).is_monotonic_decreasing

    def memory_usage(self, deep=False):
        """
        Memory usage of the values

        Parameters
        ----------
        deep : bool
            Introspect the data deeply, interrogate
            `object` dtypes for system-level memory consumption

        Returns
        -------
        bytes used

        See Also
        --------
        numpy.ndarray.nbytes

        Notes
        -----
        Memory usage does not include memory consumed by elements that
        are not components of the array if deep=False or if used on PyPy
        """
        if hasattr(self.array, 'memory_usage'):
            return self.array.memory_usage(deep=deep)

        v = self.array.nbytes
        if deep and is_object_dtype(self) and not PYPY:
            v += lib.memory_usage_of_objects(self.array)
        return v

    @Substitution(
        values='', order='', size_hint='',
        sort=textwrap.dedent("""\
            sort : boolean, default False
                Sort `uniques` and shuffle `labels` to maintain the
                relationship.
            """))
    @Appender(algorithms._shared_docs['factorize'])
    def factorize(self, sort=False, na_sentinel=-1):
        return algorithms.factorize(self, sort=sort, na_sentinel=na_sentinel)

    _shared_docs['searchsorted'] = (
        """
        Find indices where elements should be inserted to maintain order.

        Find the indices into a sorted %(klass)s `self` such that, if the
        corresponding elements in `value` were inserted before the indices,
        the order of `self` would be preserved.

        Parameters
        ----------
        value : array_like
            Values to insert into `self`.
        side : {'left', 'right'}, optional
            If 'left', the index of the first suitable location found is given.
            If 'right', return the last such index.  If there is no suitable
            index, return either 0 or N (where N is the length of `self`).
        sorter : 1-D array_like, optional
            Optional array of integer indices that sort `self` into ascending
            order. They are typically the result of ``np.argsort``.

        Returns
        -------
        int or array of int
            A scalar or array of insertion points with the
            same shape as `value`.

            .. versionchanged :: 0.24.0
                If `value` is a scalar, an int is now always returned.
                Previously, scalar inputs returned an 1-item array for
                :class:`Series` and :class:`Categorical`.

        See Also
        --------
        numpy.searchsorted

        Notes
        -----
        Binary search is used to find the required insertion points.

        Examples
        --------

        >>> x = pd.Series([1, 2, 3])
        >>> x
        0    1
        1    2
        2    3
        dtype: int64

        >>> x.searchsorted(4)
        3

        >>> x.searchsorted([0, 4])
        array([0, 3])

        >>> x.searchsorted([1, 3], side='left')
        array([0, 2])

        >>> x.searchsorted([1, 3], side='right')
        array([1, 3])

        >>> x = pd.Categorical(['apple', 'bread', 'bread',
                                'cheese', 'milk'], ordered=True)
        [apple, bread, bread, cheese, milk]
        Categories (4, object): [apple < bread < cheese < milk]

        >>> x.searchsorted('bread')
        1

        >>> x.searchsorted(['bread'], side='right')
        array([3])
        """)

    @Substitution(klass='IndexOpsMixin')
    @Appender(_shared_docs['searchsorted'])
    def searchsorted(self, value, side='left', sorter=None):
        # needs coercion on the key (DatetimeIndex does already)
        return self.values.searchsorted(value, side=side, sorter=sorter)

    def drop_duplicates(self, keep='first', inplace=False):
        inplace = validate_bool_kwarg(inplace, 'inplace')
        if isinstance(self, ABCIndexClass):
            if self.is_unique:
                return self._shallow_copy()

        duplicated = self.duplicated(keep=keep)
        result = self[np.logical_not(duplicated)]
        if inplace:
            return self._update_inplace(result)
        else:
            return result

    def duplicated(self, keep='first'):
        from pandas.core.algorithms import duplicated
        if isinstance(self, ABCIndexClass):
            if self.is_unique:
                return np.zeros(len(self), dtype=np.bool)
            return duplicated(self, keep=keep)
        else:
            return self._constructor(duplicated(self, keep=keep),
                                     index=self.index).__finalize__(self)

    # ----------------------------------------------------------------------
    # abstracts

    def _update_inplace(self, result, **kwargs):
        raise AbstractMethodError(self)