base.py

"""
Base and utility classes for pandas objects.
"""

import builtins
import textwrap
from typing import Any, Callable, Dict, FrozenSet, Optional, TypeVar, Union

import numpy as np

import pandas._libs.lib as lib
from pandas._typing import IndexLabel
from pandas.compat import PYPY
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
from pandas.util._decorators import cache_readonly, doc

from pandas.core.dtypes.common import (
    is_categorical_dtype,
    is_dict_like,
    is_extension_array_dtype,
    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
from pandas.core.accessor import DirNamesMixin
from pandas.core.algorithms import duplicated, unique1d, value_counts
from pandas.core.arraylike import OpsMixin
from pandas.core.arrays import ExtensionArray
from pandas.core.construction import create_series_with_explicit_dtype
import pandas.core.nanops as nanops

_shared_docs: Dict[str, str] = dict()
_indexops_doc_kwargs = dict(
    klass="IndexOpsMixin",
    inplace="",
    unique="IndexOpsMixin",
    duplicated="IndexOpsMixin",
)

_T = TypeVar("_T", bound="IndexOpsMixin")


class PandasObject(DirNamesMixin):
    """
    Baseclass for various pandas objects.
    """

    _cache: Dict[str, Any]

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

    def __repr__(self) -> str:
        """
        Return a string representation for a particular object.
        """
        # Should be overwritten by base classes
        return object.__repr__(self)

    def _reset_cache(self, key: Optional[str] = None) -> 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)
            return int(mem if is_scalar(mem) else mem.sum())

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


class NoNewAttributesMixin:
    """
    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 an 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: str, 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(f"You cannot add any new attribute '{key}'")
        object.__setattr__(self, key, value)


class DataError(Exception):
    pass


class SpecificationError(Exception):
    pass


class SelectionMixin:
    """
    mixin implementing the selection & aggregation interface on a group-like
    object sub-classes need to define: obj, exclusions
    """

    _selection: Optional[IndexLabel] = None
    _internal_names = ["_cache", "__setstate__"]
    _internal_names_set = set(_internal_names)

    _builtin_table = {builtins.sum: np.sum, builtins.max: np.max, builtins.min: np.min}

    _cython_table = {
        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.
        """
        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) -> int:
        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(f"Column(s) {self._selection} already selected")

        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(f"Columns not found: {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(f"Column not found: {key}")
            return self._gotitem(key, ndim=2)

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

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

        Parameters
        ----------
        key : str / 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: str, *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, str)

        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"]]) == 0
            return f

        f = getattr(np, arg, None)
        if f is not None:
            if hasattr(self, "__array__"):
                # in particular exclude Window
                return f(self, *args, **kwargs)

        raise AttributeError(
            f"'{arg}' is not a valid function for '{type(self).__name__}' object"
        )

    def _get_cython_func(self, arg: Callable) -> Optional[str]:
        """
        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(OpsMixin):
    """
    Common ops mixin to support a unified interface / docs for Series / Index
    """

    # ndarray compatibility
    __array_priority__ = 1000
    _deprecations: FrozenSet[str] = frozenset(
        ["tolist"]  # tolist is not deprecated, just suppressed in the __dir__
    )

    @property
    def _values(self) -> Union[ExtensionArray, np.ndarray]:
        # must be defined here as a property for mypy
        raise AbstractMethodError(self)

    def transpose(self: _T, *args, **kwargs) -> _T:
        """
        Return the transpose, which is by definition self.

        Returns
        -------
        %(klass)s
        """
        nv.validate_transpose(args, kwargs)
        return self

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

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

    def __len__(self) -> int:
        # We need this defined here for mypy
        raise AbstractMethodError(self)

    @property
    def ndim(self) -> int:
        """
        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.

        Returns
        -------
        scalar
            The first element of %(klass)s.

        Raises
        ------
        ValueError
            If the data is not length-1.
        """
        if len(self) == 1:
            return next(iter(self))
        raise ValueError("can only convert an array of size 1 to a Python scalar")

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

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

    @property
    def array(self) -> ExtensionArray:
        """
        The ExtensionArray of the data backing this Series or Index.

        .. versionadded:: 0.24.0

        Returns
        -------
        ExtensionArray
            An ExtensionArray of the values stored within. For extension
            types, this is the actual array. For NumPy native types, this
            is a thin (no copy) wrapper around :class:`numpy.ndarray`.

            ``.array`` differs ``.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
        string             StringArray
        boolean            BooleanArray
        datetime64[ns, tz] DatetimeArray
        ================== =============================

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

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

        Examples
        --------
        For regular NumPy types like int, and float, a PandasArray
        is returned.

        >>> pd.Series([1, 2, 3]).array
        <PandasArray>
        [1, 2, 3]
        Length: 3, dtype: int64

        For extension types, like Categorical, the actual ExtensionArray
        is returned

        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
        >>> ser.array
        ['a', 'b', 'a']
        Categories (2, object): ['a', 'b']
        """
        raise AbstractMethodError(self)

    def to_numpy(self, dtype=None, copy=False, na_value=lib.no_default, **kwargs):
        """
        A NumPy ndarray representing the values in this Series or Index.

        .. versionadded:: 0.24.0

        Parameters
        ----------
        dtype : str or numpy.dtype, optional
            The dtype to pass to :meth:`numpy.asarray`.
        copy : bool, default False
            Whether to ensure that the returned value is not a view on
            another array. Note that ``copy=False`` does not *ensure* that
            ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
            a copy is made, even if not strictly necessary.
        na_value : Any, optional
            The value to use for missing values. The default value depends
            on `dtype` and the type of the array.

            .. versionadded:: 1.0.0

        **kwargs
            Additional keywords passed through to the ``to_numpy`` method
            of the underlying array (for extension arrays).

            .. versionadded:: 1.0.0

        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
        -----
        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.

        For NumPy dtypes, this will be a reference to the actual data stored
        in this Series or Index (assuming ``copy=False``). 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 default 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]     datetime64[ns]
        datetime64[ns, tz] ndarray[object] (Timestamps)
        ================== ================================

        Examples
        --------
        >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a']))
        >>> ser.to_numpy()
        array(['a', 'b', 'a'], dtype=object)

        Specify the `dtype` to control how datetime-aware data is represented.
        Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp`
        objects, each with the correct ``tz``.

        >>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
        >>> ser.to_numpy(dtype=object)
        array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),
               Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
              dtype=object)

        Or ``dtype='datetime64[ns]'`` to return an ndarray of native
        datetime64 values. The values are converted to UTC and the timezone
        info is dropped.

        >>> ser.to_numpy(dtype="datetime64[ns]")
        ... # doctest: +ELLIPSIS
        array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
              dtype='datetime64[ns]')
        """
        if is_extension_array_dtype(self.dtype):
            return self.array.to_numpy(dtype, copy=copy, na_value=na_value, **kwargs)
        elif kwargs:
            bad_keys = list(kwargs.keys())[0]
            raise TypeError(
                f"to_numpy() got an unexpected keyword argument '{bad_keys}'"
            )

        result = np.asarray(self._values, dtype=dtype)
        # TODO(GH-24345): Avoid potential double copy
        if copy or na_value is not lib.no_default:
            result = result.copy()
            if na_value is not lib.no_default:
                result[self.isna()] = na_value
        return result

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

    def max(self, axis=None, skipna: bool = True, *args, **kwargs):
        """
        Return the maximum value of the Index.

        Parameters
        ----------
        axis : int, optional
            For compatibility with NumPy. Only 0 or None are allowed.
        skipna : bool, default True
            Exclude NA/null values when showing the result.
        *args, **kwargs
            Additional arguments and keywords for compatibility with NumPy.

        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)
        """
        nv.validate_minmax_axis(axis)
        nv.validate_max(args, kwargs)
        return nanops.nanmax(self._values, skipna=skipna)

    @doc(op="max", oppose="min", value="largest")
    def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
        """
        Return int position of the {value} value in the Series.

        If the {op}imum is achieved in multiple locations,
        the first row position is returned.

        Parameters
        ----------
        axis : {{None}}
            Dummy argument for consistency with Series.
        skipna : bool, default True
            Exclude NA/null values when showing the result.
        *args, **kwargs
            Additional arguments and keywords for compatibility with NumPy.

        Returns
        -------
        int
            Row position of the {op}imum value.

        See Also
        --------
        Series.arg{op} : Return position of the {op}imum value.
        Series.arg{oppose} : Return position of the {oppose}imum value.
        numpy.ndarray.arg{op} : Equivalent method for numpy arrays.
        Series.idxmax : Return index label of the maximum values.
        Series.idxmin : Return index label of the minimum values.

        Examples
        --------
        Consider dataset containing cereal calories

        >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,
        ...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})
        >>> s
        Corn Flakes              100.0
        Almond Delight           110.0
        Cinnamon Toast Crunch    120.0
        Cocoa Puff               110.0
        dtype: float64

        >>> s.argmax()
        2
        >>> s.argmin()
        0

        The maximum cereal calories is the third element and
        the minimum cereal calories is the first element,
        since series is zero-indexed.
        """
        nv.validate_minmax_axis(axis)
        nv.validate_argmax_with_skipna(skipna, args, kwargs)
        return nanops.nanargmax(self._values, skipna=skipna)

    def min(self, axis=None, skipna: bool = True, *args, **kwargs):
        """
        Return the minimum value of the Index.

        Parameters
        ----------
        axis : {None}
            Dummy argument for consistency with Series.
        skipna : bool, default True
            Exclude NA/null values when showing the result.
        *args, **kwargs
            Additional arguments and keywords for compatibility with NumPy.

        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)
        """
        nv.validate_minmax_axis(axis)
        nv.validate_min(args, kwargs)
        return nanops.nanmin(self._values, skipna=skipna)

    @doc(argmax, op="min", oppose="max", value="smallest")
    def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int:
        nv.validate_minmax_axis(axis)
        nv.validate_argmax_with_skipna(skipna, args, kwargs)
        return nanops.nanargmin(self._values, skipna=skipna)

    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)

        Returns
        -------
        list

        See Also
        --------
        numpy.ndarray.tolist : Return the array as an a.ndim-levels deep
            nested list of Python scalars.
        """
        if not isinstance(self._values, np.ndarray):
            # check for ndarray instead of dtype to catch DTA/TDA
            return list(self._values)
        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)

        Returns
        -------
        iterator
        """
        # We are explicitly making element iterators.
        if not isinstance(self._values, np.ndarray):
            # Check type instead of dtype to catch DTA/TDA
            return iter(self._values)
        else:
            return map(self._values.item, range(self._values.size))

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

    def isna(self):
        return isna(self._values)

    def _reduce(
        self,
        op,
        name: str,
        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(
                f"{type(self).__name__} cannot perform the operation {name}"
            )
        return func(skipna=skipna, **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
        -------
        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 is_dict_like(mapper):
            if isinstance(mapper, dict) and 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

                # The return value of mapping with an empty mapper is
                # expected to be pd.Series(np.nan, ...). As np.nan is
                # of dtype float64 the return value of this method should
                # be float64 as well
                mapper = create_series_with_explicit_dtype(
                    mapper, dtype_if_empty=np.float64
                )

        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_categorical_dtype(self.dtype):
                # use the built in categorical series mapper which saves
                # time by mapping the categories instead of all values
                return self._values.map(mapper)

            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_array_dtype(self.dtype) and hasattr(self._values, "map"):
            # GH#23179 some EAs do not have `map`
            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
            if na_action == "ignore":

                def map_f(values, f):
                    return lib.map_infer_mask(values, f, isna(values).view(np.uint8))

            elif na_action is None:
                map_f = lib.map_infer
            else:
                msg = (
                    "na_action must either be 'ignore' or None, "
                    f"{na_action} was passed"
                )
                raise ValueError(msg)

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

        return new_values

    def value_counts(
        self,
        normalize: bool = False,
        sort: bool = True,
        ascending: bool = False,
        bins=None,
        dropna: bool = 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 : bool, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : bool, default True
            Sort by frequencies.
        ascending : bool, default False
            Sort in ascending order.
        bins : int, optional
            Rather than count values, group them into half-open bins,
            a convenience for ``pd.cut``, only works with numeric data.
        dropna : bool, default True
            Don't include counts of NaN.

        Returns
        -------
        Series

        See Also
        --------
        Series.count: Number of non-NA elements in a Series.
        DataFrame.count: Number of non-NA elements in a DataFrame.
        DataFrame.value_counts: Equivalent method on DataFrames.

        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
        """
        result = value_counts(
            self,
            sort=sort,
            ascending=ascending,
            normalize=normalize,
            bins=bins,
            dropna=dropna,
        )
        return result

    def unique(self):
        values = self._values

        if not isinstance(values, np.ndarray):
            result = values.unique()
            if self.dtype.kind in ["m", "M"] and isinstance(self, ABCSeries):
                # GH#31182 Series._values returns EA, unpack for backward-compat
                if getattr(self.dtype, "tz", None) is None:
                    result = np.asarray(result)
        else:
            result = unique1d(values)

        return result

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

        Excludes NA values by default.

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

        Returns
        -------
        int

        See Also
        --------
        DataFrame.nunique: Method nunique for DataFrame.
        Series.count: Count non-NA/null observations in the Series.

        Examples
        --------
        >>> s = pd.Series([1, 3, 5, 7, 7])
        >>> s
        0    1
        1    3
        2    5
        3    7
        4    7
        dtype: int64

        >>> s.nunique()
        4
        """
        uniqs = self.unique()
        n = len(uniqs)
        if dropna and isna(uniqs).any():
            n -= 1
        return n

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

        Returns
        -------
        bool
        """
        return self.nunique(dropna=False) == len(self)

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

        Returns
        -------
        bool
        """
        from pandas import Index

        return Index(self).is_monotonic

    @property
    def is_monotonic_increasing(self) -> bool:
        """
        Alias for is_monotonic.
        """
        # mypy complains if we alias directly
        return self.is_monotonic

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

        Returns
        -------
        bool
        """
        from pandas import Index

        return Index(self).is_monotonic_decreasing

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

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

        Returns
        -------
        bytes used

        See Also
        --------
        numpy.ndarray.nbytes : Total bytes consumed by the elements of the
            array.

        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._values)
        return v

    @doc(
        algorithms.factorize,
        values="",
        order="",
        size_hint="",
        sort=textwrap.dedent(
            """\
            sort : bool, default False
                Sort `uniques` and shuffle `codes` to maintain the
                relationship.
            """
        ),
    )
    def factorize(self, sort: bool = False, na_sentinel: Optional[int] = -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} `self` such that, if the
        corresponding elements in `value` were inserted before the indices,
        the order of `self` would be preserved.

        .. note::

            The {klass} *must* be monotonically sorted, otherwise
            wrong locations will likely be returned. Pandas does *not*
            check this for you.

        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
        --------
        sort_values : Sort by the values along either axis.
        numpy.searchsorted : Similar method from NumPy.

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

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

        >>> ser.searchsorted(4)
        3

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

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

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

        >>> ser = pd.Series(pd.to_datetime(['21.10.2020', '22.10.2020', '23.10.2020']))
        >>> ser
        0   2020-10-21
        1   2020-10-22
        2   2020-10-23
        dtype: datetime64[ns]

        >>> ser.searchsorted('3/14/2000')
        3

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

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

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

        If the values are not monotonically sorted, wrong locations
        may be returned:

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

        >>> ser.searchsorted(1)  # doctest: +SKIP
        0  # wrong result, correct would be 1
        """

    @doc(_shared_docs["searchsorted"], klass="Index")
    def searchsorted(self, value, side="left", sorter=None) -> np.ndarray:
        return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)

    def drop_duplicates(self, keep="first"):
        duplicated = self.duplicated(keep=keep)
        result = self[np.logical_not(duplicated)]
        return result

    def duplicated(self, keep="first"):
        return duplicated(self._values, keep=keep)