python/pyspark/pandas/series.py - master - githubmirror/spark

python/pyspark/pandas/series.py
7 577 строк238 KB
Ruifeng Zheng
[MINOR][PS][DOC] Fix typos in pandas-on-Spark docstrings
07 июл 2026, 09:51
07 июл 2026, 09:51e00505a
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
A wrapper class for Spark Column to behave like pandas Series.
"""

import datetime
import re
import inspect
import warnings
from collections.abc import Mapping
from functools import partial, reduce, wraps
from typing import (
    Any,
    Callable,
    Dict,
    Generic,
    IO,
    Iterable,
    List,
    Literal,
    Optional,
    Sequence,
    Tuple,
    Type,
    TypeVar,
    Union,
    cast,
    no_type_check,
    overload,
    TYPE_CHECKING,
)

import numpy as np
import pandas as pd
from pandas.core.accessor import CachedAccessor  # type: ignore[attr-defined]
from pandas.io.formats.printing import pprint_thing  # type: ignore[import-not-found]
from pandas.api.extensions import no_default
from pandas.api.types import (
    is_list_like,
    is_hashable,
    is_numeric_dtype,
    CategoricalDtype,
)
from pandas.tseries.frequencies import DateOffset  # type: ignore[attr-defined]

from pyspark._globals import _NoValue, _NoValueType
from pyspark.loose_version import LooseVersion
from pyspark.sql import (
    functions as F,
    Column as PySparkColumn,
    DataFrame as SparkDataFrame,
    Window as PySparkWindow,
)
from pyspark.sql.internal import InternalFunction as SF
from pyspark.sql.types import (
    ArrayType,
    BooleanType,
    DecimalType,
    DoubleType,
    FloatType,
    IntegerType,
    LongType,
    NumericType,
    Row,
    StructType,
    TimestampType,
    NullType,
)
from pyspark.sql.window import Window
from pyspark import pandas as ps  # For running doctests and reference resolution in PyCharm.
from pyspark.pandas._typing import Axis, Dtype, Label, Name, Scalar, T
from pyspark.pandas.accessors import PandasOnSparkSeriesMethods
from pyspark.pandas.categorical import CategoricalAccessor
from pyspark.pandas.config import get_option
from pyspark.pandas.correlation import (
    compute,
    CORRELATION_VALUE_1_COLUMN,
    CORRELATION_VALUE_2_COLUMN,
    CORRELATION_CORR_OUTPUT_COLUMN,
    CORRELATION_COUNT_OUTPUT_COLUMN,
)
from pyspark.pandas.base import IndexOpsMixin
from pyspark.pandas.exceptions import SparkPandasIndexingError
from pyspark.pandas.frame import DataFrame
from pyspark.pandas.generic import Frame
from pyspark.pandas.internal import (
    InternalField,
    InternalFrame,
    DEFAULT_SERIES_NAME,
    NATURAL_ORDER_COLUMN_NAME,
    SPARK_DEFAULT_INDEX_NAME,
    SPARK_DEFAULT_SERIES_NAME,
)
from pyspark.pandas.missing.series import MissingPandasLikeSeries
from pyspark.pandas.plot import PandasOnSparkPlotAccessor
from pyspark.pandas.utils import (
    ansi_mode_context,
    combine_frames,
    is_ansi_mode_enabled,
    is_name_like_tuple,
    is_name_like_value,
    name_like_string,
    same_anchor,
    scol_for,
    sql_conf,
    validate_arguments_and_invoke_function,
    validate_axis,
    validate_bool_kwarg,
    verify_temp_column_name,
    SPARK_CONF_ARROW_ENABLED,
    log_advice,
)
from pyspark.pandas.datetimes import DatetimeMethods
from pyspark.pandas.spark.accessors import SparkSeriesMethods
from pyspark.pandas.strings import StringMethods
from pyspark.pandas.typedef import (
    infer_return_type,
    spark_type_to_pandas_dtype,
    ScalarType,
    SeriesType,
    create_type_for_series_type,
)
from pyspark.pandas.typedef.typehints import as_spark_type

if TYPE_CHECKING:
    from pyspark.sql._typing import ColumnOrName

    from pyspark.pandas.groupby import SeriesGroupBy
    from pyspark.pandas.resample import SeriesResampler
    from pyspark.pandas.indexes import Index
    from pyspark.pandas.spark.accessors import SparkIndexOpsMethods

# This regular expression pattern is compiled and defined here to avoid to compile the same
# pattern every time it is used in _repr_ in Series.
# This pattern basically seeks the footer string from pandas'
REPR_PATTERN = re.compile(r"Length: (?P<length>[0-9]+)")

FuncT = TypeVar("FuncT", bound=Callable[..., Any])


def with_ansi_mode_context(f: FuncT) -> FuncT:
    @wraps(f)
    def _with_ansi_mode_context(self: "Series", *args: Any, **kwargs: Any) -> Any:
        with ansi_mode_context(self._internal.spark_frame.sparkSession):
            return f(self, *args, **kwargs)

    return cast(FuncT, _with_ansi_mode_context)


_flex_doc_SERIES = """
Return {desc} of series and other, element-wise (binary operator `{op_name}`).

Equivalent to ``{equiv}``

Parameters
----------
other : Series or scalar value
fill_value : Scalar value, default None

Returns
-------
Series
    The result of the operation.

See Also
--------
Series.{reverse}

{series_examples}
"""

_add_example_SERIES = """
Examples
--------
>>> df = ps.DataFrame({'a': [2, 2, 4, np.nan],
...                    'b': [2, np.nan, 2, np.nan]},
...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df
     a    b
a  2.0  2.0
b  2.0  NaN
c  4.0  2.0
d  NaN  NaN

>>> df.a.add(df.b)
a    4.0
b    NaN
c    6.0
d    NaN
dtype: float64

>>> df.a.radd(df.b)
a    4.0
b    NaN
c    6.0
d    NaN
dtype: float64
"""

_sub_example_SERIES = """
Examples
--------
>>> df = ps.DataFrame({'a': [2, 2, 4, np.nan],
...                    'b': [2, np.nan, 2, np.nan]},
...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df
     a    b
a  2.0  2.0
b  2.0  NaN
c  4.0  2.0
d  NaN  NaN

>>> df.a.subtract(df.b)
a    0.0
b    NaN
c    2.0
d    NaN
dtype: float64

>>> df.a.rsub(df.b)
a    0.0
b    NaN
c   -2.0
d    NaN
dtype: float64
"""

_mul_example_SERIES = """
Examples
--------
>>> df = ps.DataFrame({'a': [2, 2, 4, np.nan],
...                    'b': [2, np.nan, 2, np.nan]},
...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df
     a    b
a  2.0  2.0
b  2.0  NaN
c  4.0  2.0
d  NaN  NaN

>>> df.a.multiply(df.b)
a    4.0
b    NaN
c    8.0
d    NaN
dtype: float64

>>> df.a.rmul(df.b)
a    4.0
b    NaN
c    8.0
d    NaN
dtype: float64
"""

_div_example_SERIES = """
Examples
--------
>>> df = ps.DataFrame({'a': [2, 2, 4, np.nan],
...                    'b': [2, np.nan, 2, np.nan]},
...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df
     a    b
a  2.0  2.0
b  2.0  NaN
c  4.0  2.0
d  NaN  NaN

>>> df.a.divide(df.b)
a    1.0
b    NaN
c    2.0
d    NaN
dtype: float64

>>> df.a.rdiv(df.b)
a    1.0
b    NaN
c    0.5
d    NaN
dtype: float64
"""

_pow_example_SERIES = """
Examples
--------
>>> df = ps.DataFrame({'a': [2, 2, 4, np.nan],
...                    'b': [2, np.nan, 2, np.nan]},
...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df
     a    b
a  2.0  2.0
b  2.0  NaN
c  4.0  2.0
d  NaN  NaN

>>> df.a.pow(df.b)
a     4.0
b     NaN
c    16.0
d     NaN
dtype: float64

>>> df.a.rpow(df.b)
a     4.0
b     NaN
c    16.0
d     NaN
dtype: float64
"""

_mod_example_SERIES = """
Examples
--------
>>> df = ps.DataFrame({'a': [2, 2, 4, np.nan],
...                    'b': [2, np.nan, 2, np.nan]},
...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df
     a    b
a  2.0  2.0
b  2.0  NaN
c  4.0  2.0
d  NaN  NaN

>>> df.a.mod(df.b)
a    0.0
b    NaN
c    0.0
d    NaN
dtype: float64

>>> df.a.rmod(df.b)
a    0.0
b    NaN
c    2.0
d    NaN
dtype: float64
"""

_floordiv_example_SERIES = """
Examples
--------
>>> df = ps.DataFrame({'a': [2, 2, 4, np.nan],
...                    'b': [2, np.nan, 2, np.nan]},
...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])
>>> df
     a    b
a  2.0  2.0
b  2.0  NaN
c  4.0  2.0
d  NaN  NaN

>>> df.a.floordiv(df.b)
a    1.0
b    NaN
c    2.0
d    NaN
dtype: float64

>>> df.a.rfloordiv(df.b)
a    1.0
b    NaN
c    0.0
d    NaN
dtype: float64
"""

# Needed to disambiguate Series.str and str type
str_type = str


class Series(Frame, IndexOpsMixin, Generic[T]):
    """
    pandas-on-Spark Series that corresponds to pandas Series logically. This holds Spark Column
    internally.

    .. versionchanged:: 4.1.0
        Support construction from a pandas-on-Spark Series input, which can be used with
        additional parameters index, dtype, and name for overriding the original value.

    :ivar _internal: an internal immutable Frame to manage metadata.
    :type _internal: InternalFrame
    :ivar _psdf: Parent's pandas-on-Spark DataFrame
    :type _psdf: ps.DataFrame

    Parameters
    ----------
    data : array-like, dict, or scalar value, pandas Series, pandas-on-Spark Series
        Contains data stored in Series
        Note that if `data` is a Series, index, dtype, or name can also be
        specified to override the original value.
    index : array-like or Index (1d)
        Values must be hashable and have the same length as `data`.
        Non-unique index values are allowed. Will default to
        RangeIndex (0, 1, 2, ..., n) if not provided. If both a dict and index
        sequence is used, the index will override the keys found in the
        dict.
    dtype : numpy.dtype or None
        If None, dtype will be inferred
    name : str, default None
        The name to give to the Series.
    copy : boolean, default False
        Copy input data
    """

    def __init__(  # type: ignore[no-untyped-def]
        self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=no_default
    ):
        assert data is not None

        self._anchor: DataFrame  # type: ignore[annotation-unchecked]
        self._col_label: Label  # type: ignore[annotation-unchecked]
        if isinstance(data, DataFrame):
            assert dtype is None
            assert name is None
            assert not copy
            assert fastpath is no_default

            self._anchor = data
            self._col_label = index

        elif isinstance(data, Series):
            assert not copy
            assert fastpath is no_default

            if name:
                data = data.rename(name)

            if index:
                data = data.reindex(index)

            if dtype:
                data = data.astype(dtype)

            anchor = DataFrame(data)
            self._anchor = anchor
            self._col_label = anchor._internal.column_labels[0]
            object.__setattr__(anchor, "_psseries", {self._column_label: self})
        else:
            if isinstance(data, pd.Series):
                assert index is None
                assert dtype is None
                assert name is None
                assert not copy
                assert fastpath is no_default
                s = data
            else:
                from pyspark.pandas.indexes.base import Index

                if isinstance(index, Index):
                    raise TypeError(
                        "The given index cannot be a pandas-on-Spark index. "
                        "Try pandas index or array-like."
                    )

                if LooseVersion(pd.__version__) < LooseVersion("3.0.0"):
                    s = pd.Series(
                        data=data, index=index, dtype=dtype, name=name, copy=copy, fastpath=fastpath
                    )
                else:
                    # fastpath is removed since pandas 3.0.0
                    s = pd.Series(data=data, index=index, dtype=dtype, name=name, copy=copy)
            internal = InternalFrame.from_pandas(pd.DataFrame(s))
            if s.name is None:
                internal = internal.copy(column_labels=[None])
            anchor = DataFrame(internal)

            self._anchor = anchor
            self._col_label = anchor._internal.column_labels[0]
            object.__setattr__(anchor, "_psseries", {self._column_label: self})

    @property
    def _psdf(self) -> DataFrame:
        return self._anchor

    @property
    def _internal(self) -> InternalFrame:
        return self._psdf._internal.select_column(self._column_label)

    @property
    def _column_label(self) -> Optional[Label]:
        return self._col_label

    def _update_internal_frame(
        self, internal: InternalFrame, check_same_anchor: bool = True
    ) -> None:
        if LooseVersion(pd.__version__) < "3.0.0":
            self._psdf._update_internal_frame(internal, check_same_anchor=check_same_anchor)
        else:
            self._update_anchor(DataFrame(internal.select_column(self._column_label)))

    def _update_anchor(self, psdf: DataFrame) -> None:
        assert psdf._internal.column_labels == [self._column_label], (
            psdf._internal.column_labels,
            [self._column_label],
        )
        self._anchor = psdf
        object.__setattr__(psdf, "_psseries", {self._column_label: self})

    def _with_new_scol(
        self, scol: PySparkColumn, *, field: Optional[InternalField] = None
    ) -> "Series":
        """
        Copy pandas-on-Spark Series with the new Spark Column.

        :param scol: the new Spark Column
        :return: the copied Series
        """
        name = name_like_string(self._column_label)
        internal = self._internal.copy(
            data_spark_columns=[scol.alias(name)],
            data_fields=[
                field if field is None or field.struct_field is None else field.copy(name=name)
            ],
        )
        return first_series(DataFrame(internal))

    spark: "SparkIndexOpsMethods" = CachedAccessor("spark", SparkSeriesMethods)

    @property
    def dtypes(self) -> Dtype:
        """Return the dtype object of the underlying data.

        >>> s = ps.Series(list('abc'))
        >>> s.dtype == s.dtypes
        True
        """
        return self.dtype

    @property
    def axes(self) -> List["Index"]:
        """
        Return a list of the row axis labels.

        Examples
        --------

        >>> psser = ps.Series([1, 2, 3])
        >>> psser.axes
        [Index([0, 1, 2], dtype='int64')]
        """
        return [self.index]

    # Arithmetic Operators
    def add(self, other: Any, fill_value: Union[int, str, float] = None) -> "Series":
        if fill_value is not None:
            if isinstance(other, (int, str, float)):
                scol = self.spark.column
                scol = F.when(scol.isNull() | F.isnan(scol), fill_value).otherwise(scol)
                self = self._with_new_scol(scol)
            else:
                raise NotImplementedError(
                    "`fill_value` currently only works when type of `other` is in (int, str, float)"
                )

        return self + other

    add.__doc__ = _flex_doc_SERIES.format(
        desc="Addition",
        op_name="+",
        equiv="series + other",
        reverse="radd",
        series_examples=_add_example_SERIES,
    )

    def radd(self, other: Any, fill_value: Union[int, str, float] = None) -> "Series":
        if fill_value is not None:
            if isinstance(other, (int, str, float)):
                scol = self.spark.column
                scol = F.when(scol.isNull() | F.isnan(scol), fill_value).otherwise(scol)
                self = self._with_new_scol(scol)
            else:
                raise NotImplementedError(
                    "`fill_value` currently only works when type of `other` is in (int, str, float)"
                )

        return other + self

    radd.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Addition",
        op_name="+",
        equiv="other + series",
        reverse="add",
        series_examples=_add_example_SERIES,
    )

    def div(self, other: Any) -> "Series":
        return self / other

    div.__doc__ = _flex_doc_SERIES.format(
        desc="Floating division",
        op_name="/",
        equiv="series / other",
        reverse="rdiv",
        series_examples=_div_example_SERIES,
    )

    divide = div

    def rdiv(self, other: Any) -> "Series":
        return other / self

    rdiv.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Floating division",
        op_name="/",
        equiv="other / series",
        reverse="div",
        series_examples=_div_example_SERIES,
    )

    def truediv(self, other: Any) -> "Series":
        return self / other

    truediv.__doc__ = _flex_doc_SERIES.format(
        desc="Floating division",
        op_name="/",
        equiv="series / other",
        reverse="rtruediv",
        series_examples=_div_example_SERIES,
    )

    def rtruediv(self, other: Any) -> "Series":
        return other / self

    rtruediv.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Floating division",
        op_name="/",
        equiv="other / series",
        reverse="truediv",
        series_examples=_div_example_SERIES,
    )

    def mul(self, other: Any) -> "Series":
        return self * other

    mul.__doc__ = _flex_doc_SERIES.format(
        desc="Multiplication",
        op_name="*",
        equiv="series * other",
        reverse="rmul",
        series_examples=_mul_example_SERIES,
    )

    multiply = mul

    def rmul(self, other: Any) -> "Series":
        return other * self

    rmul.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Multiplication",
        op_name="*",
        equiv="other * series",
        reverse="mul",
        series_examples=_mul_example_SERIES,
    )

    def sub(self, other: Any) -> "Series":
        return self - other

    sub.__doc__ = _flex_doc_SERIES.format(
        desc="Subtraction",
        op_name="-",
        equiv="series - other",
        reverse="rsub",
        series_examples=_sub_example_SERIES,
    )

    subtract = sub

    def rsub(self, other: Any) -> "Series":
        return other - self

    rsub.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Subtraction",
        op_name="-",
        equiv="other - series",
        reverse="sub",
        series_examples=_sub_example_SERIES,
    )

    def mod(self, other: Any) -> "Series":
        return self % other

    mod.__doc__ = _flex_doc_SERIES.format(
        desc="Modulo",
        op_name="%",
        equiv="series % other",
        reverse="rmod",
        series_examples=_mod_example_SERIES,
    )

    def rmod(self, other: Any) -> "Series":
        return other % self

    rmod.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Modulo",
        op_name="%",
        equiv="other % series",
        reverse="mod",
        series_examples=_mod_example_SERIES,
    )

    def pow(self, other: Any) -> "Series":
        return self**other

    pow.__doc__ = _flex_doc_SERIES.format(
        desc="Exponential power of series",
        op_name="**",
        equiv="series ** other",
        reverse="rpow",
        series_examples=_pow_example_SERIES,
    )

    def rpow(self, other: Any) -> "Series":
        return other**self

    rpow.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Exponential power",
        op_name="**",
        equiv="other ** series",
        reverse="pow",
        series_examples=_pow_example_SERIES,
    )

    def floordiv(self, other: Any) -> "Series":
        return self // other

    floordiv.__doc__ = _flex_doc_SERIES.format(
        desc="Integer division",
        op_name="//",
        equiv="series // other",
        reverse="rfloordiv",
        series_examples=_floordiv_example_SERIES,
    )

    def rfloordiv(self, other: Any) -> "Series":
        return other // self

    rfloordiv.__doc__ = _flex_doc_SERIES.format(
        desc="Reverse Integer division",
        op_name="//",
        equiv="other // series",
        reverse="floordiv",
        series_examples=_floordiv_example_SERIES,
    )

    # create accessor for pandas-on-Spark specific methods.
    pandas_on_spark = CachedAccessor("pandas_on_spark", PandasOnSparkSeriesMethods)

    # Comparison Operators
    def eq(self, other: Any) -> "Series":
        """
        Compare if the current value is equal to the other.

        >>> df = ps.DataFrame({'a': [1, 2, 3, 4],
        ...                    'b': [1, np.nan, 1, np.nan]},
        ...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])

        >>> df.a == 1
        a     True
        b    False
        c    False
        d    False
        Name: a, dtype: bool

        >>> df.b.eq(1)
        a     True
        b    False
        c     True
        d    False
        Name: b, dtype: bool
        """
        return self == other

    equals = eq

    def gt(self, other: Any) -> "Series":
        """
        Compare if the current value is greater than the other.

        >>> df = ps.DataFrame({'a': [1, 2, 3, 4],
        ...                    'b': [1, np.nan, 1, np.nan]},
        ...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])

        >>> df.a > 1
        a    False
        b     True
        c     True
        d     True
        Name: a, dtype: bool

        >>> df.b.gt(1)
        a    False
        b    False
        c    False
        d    False
        Name: b, dtype: bool
        """
        return self > other

    def ge(self, other: Any) -> "Series":
        """
        Compare if the current value is greater than or equal to the other.

        >>> df = ps.DataFrame({'a': [1, 2, 3, 4],
        ...                    'b': [1, np.nan, 1, np.nan]},
        ...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])

        >>> df.a >= 2
        a    False
        b     True
        c     True
        d     True
        Name: a, dtype: bool

        >>> df.b.ge(2)
        a    False
        b    False
        c    False
        d    False
        Name: b, dtype: bool
        """
        return self >= other

    def lt(self, other: Any) -> "Series":
        """
        Compare if the current value is less than the other.

        >>> df = ps.DataFrame({'a': [1, 2, 3, 4],
        ...                    'b': [1, np.nan, 1, np.nan]},
        ...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])

        >>> df.a < 1
        a    False
        b    False
        c    False
        d    False
        Name: a, dtype: bool

        >>> df.b.lt(2)
        a     True
        b    False
        c     True
        d    False
        Name: b, dtype: bool
        """
        return self < other

    def le(self, other: Any) -> "Series":
        """
        Compare if the current value is less than or equal to the other.

        >>> df = ps.DataFrame({'a': [1, 2, 3, 4],
        ...                    'b': [1, np.nan, 1, np.nan]},
        ...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])

        >>> df.a <= 2
        a     True
        b     True
        c    False
        d    False
        Name: a, dtype: bool

        >>> df.b.le(2)
        a     True
        b    False
        c     True
        d    False
        Name: b, dtype: bool
        """
        return self <= other

    def ne(self, other: Any) -> "Series":
        """
        Compare if the current value is not equal to the other.

        >>> df = ps.DataFrame({'a': [1, 2, 3, 4],
        ...                    'b': [1, np.nan, 1, np.nan]},
        ...                   index=['a', 'b', 'c', 'd'], columns=['a', 'b'])

        >>> df.a != 1
        a    False
        b     True
        c     True
        d     True
        Name: a, dtype: bool

        >>> df.b.ne(1)
        a    False
        b     True
        c    False
        d     True
        Name: b, dtype: bool
        """
        return self != other

    def divmod(self, other: Any) -> Tuple["Series", "Series"]:
        """
        Return Integer division and modulo of series and other, element-wise
        (binary operator `divmod`).

        Parameters
        ----------
        other : Series or scalar value

        Returns
        -------
        2-Tuple of Series
            The result of the operation.

        See Also
        --------
        Series.rdivmod
        """
        return self.floordiv(other), self.mod(other)

    def rdivmod(self, other: Any) -> Tuple["Series", "Series"]:
        """
        Return Integer division and modulo of series and other, element-wise
        (binary operator `rdivmod`).

        Parameters
        ----------
        other : Series or scalar value

        Returns
        -------
        2-Tuple of Series
            The result of the operation.

        See Also
        --------
        Series.divmod
        """
        return self.rfloordiv(other), self.rmod(other)

    def between(self, left: Any, right: Any, inclusive: str = "both") -> "Series":
        """
        Return boolean Series equivalent to left <= series <= right.
        This function returns a boolean vector containing `True` wherever the
        corresponding Series element is between the boundary values `left` and
        `right`. NA values are treated as `False`.

        Parameters
        ----------
        left : scalar or list-like
            Left boundary.
        right : scalar or list-like
            Right boundary.
        inclusive : {"both", "neither", "left", "right"}
            Include boundaries. Whether to set each bound as closed or open.

            .. versionchanged:: 4.0.0

        Returns
        -------
        Series
            Series representing whether each element is between left and
            right (inclusive).

        See Also
        --------
        Series.gt : Greater than of series and other.
        Series.lt : Less than of series and other.

        Notes
        -----
        This function is equivalent to ``(left <= ser) & (ser <= right)``

        Examples
        --------
        >>> s = ps.Series([2, 0, 4, 8, np.nan])

        Boundary values are included by default:

        >>> s.between(0, 4)
        0     True
        1     True
        2     True
        3    False
        4    False
        dtype: bool

        With `inclusive` set to "neither" boundary values are excluded:

        >>> s.between(0, 4, inclusive="neither")
        0     True
        1    False
        2    False
        3    False
        4    False
        dtype: bool

        With `inclusive` set to "right" only right boundary value is included:

        >>> s.between(0, 4, inclusive="right")
        0     True
        1    False
        2     True
        3    False
        4    False
        dtype: bool

        With `inclusive` set to "left" only left boundary value is included:

        >>> s.between(0, 4, inclusive="left")
        0     True
        1     True
        2    False
        3    False
        4    False
        dtype: bool

        `left` and `right` can be any scalar value:

        >>> s = ps.Series(['Alice', 'Bob', 'Carol', 'Eve'])
        >>> s.between('Anna', 'Daniel')
        0    False
        1     True
        2     True
        3    False
        dtype: bool
        """
        if inclusive == "both":
            lmask = self >= left
            rmask = self <= right
        elif inclusive == "left":
            lmask = self >= left
            rmask = self < right
        elif inclusive == "right":
            lmask = self > left
            rmask = self <= right
        elif inclusive == "neither":
            lmask = self > left
            rmask = self < right
        else:
            raise ValueError(
                "Inclusive has to be either string of 'both','left', 'right', or 'neither'."
            )

        return lmask & rmask

    def cov(self, other: "Series", min_periods: Optional[int] = None, ddof: int = 1) -> float:
        """
        Compute covariance with Series, excluding missing values.

        .. versionadded:: 3.3.0

        Parameters
        ----------
        other : Series
            Series with which to compute the covariance.
        min_periods : int, optional
            Minimum number of observations needed to have a valid result.
        ddof : int, default 1
            Delta degrees of freedom. The divisor used in calculations
            is ``N - ddof``, where ``N`` represents the number of elements.

            .. versionadded:: 3.4.0

        Returns
        -------
        float
            Covariance between Series and other

        Examples
        --------
        >>> from pyspark.pandas.config import set_option, reset_option
        >>> s1 = ps.Series([0.90010907, 0.13484424, 0.62036035])
        >>> s2 = ps.Series([0.12528585, 0.26962463, 0.51111198])
        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s1.cov(s2)
        -0.016857...
        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s1.cov(s2, ddof=2)
        -0.033715...
        """
        if not isinstance(other, Series):
            raise TypeError("unsupported type: %s" % type(other))
        if not is_numeric_dtype(self.dtype):
            raise TypeError("unsupported dtype: %s" % self.dtype)
        if not is_numeric_dtype(other.dtype):
            raise TypeError("unsupported dtype: %s" % other.dtype)
        if not isinstance(ddof, int):
            raise TypeError("ddof must be integer")

        min_periods = 1 if min_periods is None else min_periods

        if same_anchor(self, other):
            sdf = self._internal.spark_frame.select(self.spark.column, other.spark.column)
        else:
            combined = combine_frames(self.to_frame(), other.to_frame())
            sdf = combined._internal.spark_frame.select(*combined._internal.data_spark_columns)

        sdf = sdf.dropna()

        if len(sdf.head(min_periods)) < min_periods:
            return np.nan
        else:
            sdf = sdf.select(SF.covar(F.col(sdf.columns[0]), F.col(sdf.columns[1]), ddof))
            return sdf.head(1)[0][0]

    # TODO: Support ps.Series ``arg``
    def map(
        self, arg: Union[Dict, Callable[[Any], Any], pd.Series], na_action: Optional[str] = None
    ) -> "Series":
        """
        Map values of Series according to input correspondence.

        Used for substituting each value in a Series with another value,
        that may be derived from a function, a ``dict``.

        .. note:: make sure the size of the dictionary is not huge because it could
            downgrade the performance or throw OutOfMemoryError due to a huge
            expression within Spark. Consider the input as a function as an
            alternative instead in this case.

        Parameters
        ----------
        arg : function, dict or pd.Series
            Mapping correspondence.
        na_action :
            If `ignore`, propagate NA values, without passing them to the mapping correspondence.

        Returns
        -------
        Series
            Same index as caller.

        See Also
        --------
        Series.apply : For applying more complex functions on a Series.
        DataFrame.applymap : Apply a function element-wise on a whole DataFrame.

        Notes
        -----
        When ``arg`` is a dictionary, values in Series that are not in the
        dictionary (as keys) is converted to ``None``. However, if the
        dictionary is a ``dict`` subclass that defines ``__missing__`` (i.e.
        provides a method for default values), then this default is used
        rather than ``None``.

        Examples
        --------
        >>> s = ps.Series(['cat', 'dog', None, 'rabbit'])
        >>> s
        0       cat
        1       dog
        2      None
        3    rabbit
        dtype: object

        ``map`` accepts a ``dict``. Values that are not found
        in the ``dict`` are converted to ``None``, unless the dict has a default
        value (e.g. ``defaultdict``):

        >>> s.map({'cat': 'kitten', 'dog': 'puppy'})
        0    kitten
        1     puppy
        2      None
        3      None
        dtype: object

        It also accepts a pandas Series:

        >>> pser = pd.Series(['kitten', 'puppy'], index=['cat', 'dog'])
        >>> s.map(pser)
        0    kitten
        1     puppy
        2      None
        3      None
        dtype: object

        It also accepts a function:

        >>> def format(x) -> str:
        ...     return 'I am a {}'.format(x)

        >>> s.map(format)
        0       I am a cat
        1       I am a dog
        2      I am a None
        3    I am a rabbit
        dtype: object

        To avoid applying the function to missing values (and keep them as NaN)
        na_action='ignore' can be used:

        >>> s.map('I am a {}'.format, na_action='ignore')
        0       I am a cat
        1       I am a dog
        2             None
        3    I am a rabbit
        dtype: object
        """
        if isinstance(arg, (dict, pd.Series)):
            if (
                isinstance(arg, dict)
                and len(arg) == 0
                and not hasattr(arg, "__missing__")
                and LooseVersion(pd.__version__) >= "3.0.0"
            ):
                # pandas 3 returns an all-NaN float64 Series for ``Series.map({})``.
                return self._with_new_scol(F.lit(None).cast(DoubleType()))

            is_start = True
            # In case dictionary is empty.
            current = F.when(F.lit(False), F.lit(None).cast(self.spark.data_type))

            for to_replace, value in arg.items():
                if is_start:
                    current = F.when(self.spark.column == F.lit(to_replace), value)
                    is_start = False
                else:
                    current = current.when(self.spark.column == F.lit(to_replace), value)

            if isinstance(arg, dict) and hasattr(arg, "__missing__"):
                tmp_val = arg[np._NoValue]
                # Remove in case it's set in defaultdict.
                del arg[np._NoValue]
                current = current.otherwise(F.lit(tmp_val))
            else:
                current = current.otherwise(F.lit(None).cast(self.spark.data_type))
            return self._with_new_scol(current)
        else:
            return self.pandas_on_spark.transform_batch(lambda pser: pser.map(arg, na_action))

    @property
    def shape(self) -> Tuple[int]:
        """Return a tuple of the shape of the underlying data."""
        return (len(self),)

    @property
    def name(self) -> Name:
        """Return name of the Series."""
        name = self._column_label
        if name is not None and len(name) == 1:
            return name[0]
        else:
            return name

    @name.setter
    def name(self, name: Name) -> None:
        self.rename(name, inplace=True)

    # TODO: Currently, changing index labels taking dictionary/Series is not supported.
    def rename(
        self, index: Optional[Union[Name, Callable[[Any], Any]]] = None, **kwargs: Any
    ) -> "Series":
        """
        Alter Series index labels or name.

        Parameters
        ----------
        index : scalar or function, optional
            Functions are transformations to apply to the index.
            Scalar will alter the Series.name attribute.

        inplace : bool, default False
            Whether to return a new Series. If True then value of copy is
            ignored.

        Returns
        -------
        Series
            Series with index labels or name altered.

        Examples
        --------

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

        >>> s.rename("my_name")  # scalar, changes Series.name
        0    1
        1    2
        2    3
        Name: my_name, dtype: int64

        >>> s.rename(lambda x: x ** 2)  # function, changes labels
        0    1
        1    2
        4    3
        dtype: int64
        """
        if index is None:
            pass
        if callable(index):
            if kwargs.get("inplace", False):
                raise ValueError("inplace True is not supported yet for a function 'index'")
            frame = self.to_frame()
            new_index_name = verify_temp_column_name(frame, "__index_name__")
            frame[new_index_name] = self.index.map(index)
            frame.set_index(new_index_name, inplace=True)
            frame.index.name = self.index.name
            return first_series(frame).rename(self.name)
        elif isinstance(index, (pd.Series, dict)):
            raise ValueError("'index' of %s type is not supported yet" % type(index).__name__)
        elif not is_hashable(index):
            raise TypeError("Series.name must be a hashable type")
        elif not isinstance(index, tuple):
            index = (index,)
        name = name_like_string(index)
        scol = self.spark.column.alias(name)
        field = self._internal.data_fields[0].copy(name=name)

        internal = self._internal.copy(
            column_labels=[index],
            data_spark_columns=[scol],
            data_fields=[field],
            column_label_names=None,
        )
        psdf: DataFrame = DataFrame(internal)

        if kwargs.get("inplace", False):
            self._col_label = index
            self._update_anchor(psdf)
            return self
        else:
            return first_series(psdf)

    def rename_axis(
        self, mapper: Optional[Any] = None, index: Optional[Any] = None, inplace: bool = False
    ) -> Optional["Series"]:
        """
        Set the name of the axis for the index or columns.

        Parameters
        ----------
        mapper, index :  scalar, list-like, dict-like or function, optional
            A scalar, list-like, dict-like or functions transformations to
            apply to the index values.
        inplace : bool, default False
            Modifies the object directly, instead of creating a new Series.

        Returns
        -------
        Series, or None if `inplace` is True.

        See Also
        --------
        Series.rename : Alter Series index labels or name.
        DataFrame.rename : Alter DataFrame index labels or name.
        Index.rename : Set new names on index.

        Examples
        --------
        >>> s = ps.Series(["dog", "cat", "monkey"], name="animal")
        >>> s  # doctest: +NORMALIZE_WHITESPACE
        0       dog
        1       cat
        2    monkey
        Name: animal, dtype: object
        >>> s.rename_axis("index").sort_index()  # doctest: +NORMALIZE_WHITESPACE
        index
        0       dog
        1       cat
        2    monkey
        Name: animal, dtype: object

        **MultiIndex**

        >>> index = pd.MultiIndex.from_product([['mammal'],
        ...                                        ['dog', 'cat', 'monkey']],
        ...                                       names=['type', 'name'])
        >>> s = ps.Series([4, 4, 2], index=index, name='num_legs')
        >>> s  # doctest: +NORMALIZE_WHITESPACE
        type    name
        mammal  dog       4
                cat       4
                monkey    2
        Name: num_legs, dtype: int64
        >>> s.rename_axis(index={'type': 'class'}).sort_index()  # doctest: +NORMALIZE_WHITESPACE
        class   name
        mammal  cat       4
                dog       4
                monkey    2
        Name: num_legs, dtype: int64
        >>> s.rename_axis(index=str.upper).sort_index()  # doctest: +NORMALIZE_WHITESPACE
        TYPE    NAME
        mammal  cat       4
                dog       4
                monkey    2
        Name: num_legs, dtype: int64
        """
        psdf = self.to_frame().rename_axis(mapper=mapper, index=index, inplace=False)
        if inplace:
            self._update_anchor(psdf)
            return None
        else:
            return first_series(psdf)

    @property
    def index(self) -> "ps.Index":
        """The index (axis labels) Column of the Series.

        See Also
        --------
        Index
        """
        return self._psdf.index

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

        Returns
        -------
        is_unique : boolean

        >>> ps.Series([1, 2, 3]).is_unique
        True
        >>> ps.Series([1, 2, 2]).is_unique
        False
        >>> ps.Series([1, 2, 3, None]).is_unique
        True
        """
        scol = self.spark.column

        # Here we check:
        #   1. the distinct count without nulls and count without nulls for non-null values
        #   2. count null values and see if null is a distinct value.
        #
        # This workaround is to calculate the distinct count including nulls in
        # single pass. Note that COUNT(DISTINCT expr) in Spark is designed to ignore nulls.
        return self._internal.spark_frame.select(
            (F.count(scol) == F.countDistinct(scol))
            & (F.count(F.when(scol.isNull(), 1).otherwise(None)) <= 1)
        ).collect()[0][0]

    def reset_index(
        self,
        level: Optional[Union[int, Name, Sequence[Union[int, Name]]]] = None,
        drop: bool = False,
        name: Optional[Name] = None,
        inplace: bool = False,
    ) -> Optional[Union["Series", DataFrame]]:
        """
        Generate a new DataFrame or Series with the index reset.

        This is useful when the index needs to be treated as a column,
        or when the index is meaningless and needs to be reset
        to the default before another operation.

        Parameters
        ----------
        level : int, str, tuple, or list, default optional
            For a Series with a MultiIndex, only remove the specified levels from the index.
            Removes all levels by default.
        drop : bool, default False
            Just reset the index, without inserting it as a column in the new DataFrame.
        name : object, optional
            The name to use for the column containing the original Series values.
            Uses self.name by default. This argument is ignored when drop is True.
        inplace : bool, default False
            Modify the Series in place (do not create a new object).

        Returns
        -------
        Series or DataFrame
            When `drop` is False (the default), a DataFrame is returned.
            The newly created columns will come first in the DataFrame,
            followed by the original Series values.
            When `drop` is True, a `Series` is returned.
            In either case, if ``inplace=True``, no value is returned.

        Examples
        --------
        >>> s = ps.Series([1, 2, 3, 4], index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))

        Generate a DataFrame with default index.

        >>> s.reset_index()
          idx  0
        0   a  1
        1   b  2
        2   c  3
        3   d  4

        To specify the name of the new column use `name`.

        >>> s.reset_index(name='values')
          idx  values
        0   a       1
        1   b       2
        2   c       3
        3   d       4

        To generate a new Series with the default set `drop` to True.

        >>> s.reset_index(drop=True)
        0    1
        1    2
        2    3
        3    4
        dtype: int64

        To update the Series in place, without generating a new one
        set `inplace` to True. Note that it also requires ``drop=True``.

        >>> s.reset_index(inplace=True, drop=True)
        >>> s
        0    1
        1    2
        2    3
        3    4
        dtype: int64
        """
        inplace = validate_bool_kwarg(inplace, "inplace")
        if inplace and not drop:
            raise TypeError("Cannot reset_index inplace on a Series to create a DataFrame")

        if drop:
            psdf = self._psdf[[self.name]]
        else:
            psser = self
            if name is not None:
                psser = psser.rename(name)
            psdf = psser.to_frame()
        psdf = psdf.reset_index(level=level, drop=drop)
        if drop:
            if inplace:
                self._update_anchor(psdf)
                return None
            else:
                return first_series(psdf)
        else:
            return psdf

    def to_frame(self, name: Optional[Name] = None) -> DataFrame:
        """
        Convert Series to DataFrame.

        Parameters
        ----------
        name : object, default None
            The passed name should substitute for the series name (if it has
            one).

        Returns
        -------
        DataFrame
            DataFrame representation of Series.

        Examples
        --------
        >>> s = ps.Series(["a", "b", "c"])
        >>> s.to_frame()
           0
        0  a
        1  b
        2  c

        >>> s = ps.Series(["a", "b", "c"], name="vals")
        >>> s.to_frame()
          vals
        0    a
        1    b
        2    c
        """
        if name is not None:
            renamed = self.rename(name)
        elif self._column_label is None:
            renamed = self.rename(DEFAULT_SERIES_NAME)
        else:
            renamed = self
        return DataFrame(renamed._internal)

    to_dataframe = to_frame

    def to_string(
        self,
        buf: Optional[IO[str]] = None,
        na_rep: str = "NaN",
        float_format: Optional[Callable[[float], str]] = None,
        header: bool = True,
        index: bool = True,
        length: bool = False,
        dtype: bool = False,
        name: bool = False,
        max_rows: Optional[int] = None,
    ) -> Optional[str]:
        """
        Render a string representation of the Series.

        .. note:: This method should only be used if the resulting pandas object is expected
                  to be small, as all the data is loaded into the driver's memory. If the input
                  is large, set max_rows parameter.

        Parameters
        ----------
        buf : StringIO-like, optional
            buffer to write to
        na_rep : string, optional
            string representation of NAN to use, default 'NaN'
        float_format : one-parameter function, optional
            formatter function to apply to columns' elements if they are floats
            default None
        header : boolean, default True
            Add the Series header (index name)
        index : bool, optional
            Add index (row) labels, default True
        length : boolean, default False
            Add the Series length
        dtype : boolean, default False
            Add the Series dtype
        name : boolean, default False
            Add the Series name if not None
        max_rows : int, optional
            Maximum number of rows to show before truncating. If None, show
            all.

        Returns
        -------
        formatted : string (if not buffer passed)

        Examples
        --------
        >>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)], columns=['dogs', 'cats'])
        >>> print(df['dogs'].to_string())
        0    0.2
        1    0.0
        2    0.6
        3    0.2

        >>> print(df['dogs'].to_string(max_rows=2))
        0    0.2
        1    0.0
        """
        # Make sure locals() call is at the top of the function so we don't capture local variables.
        args = locals()
        if max_rows is not None:
            psseries = self.head(max_rows)
        else:
            psseries = self

        return validate_arguments_and_invoke_function(
            psseries._to_internal_pandas(), self.to_string, pd.Series.to_string, args
        )

    def to_clipboard(self, excel: bool = True, sep: Optional[str] = None, **kwargs: Any) -> None:
        # Docstring defined below by reusing DataFrame.to_clipboard's.
        args = locals()
        psseries = self

        return validate_arguments_and_invoke_function(
            psseries._to_internal_pandas(), self.to_clipboard, pd.Series.to_clipboard, args
        )

    to_clipboard.__doc__ = DataFrame.to_clipboard.__doc__

    def to_dict(self, into: Type = dict) -> Mapping:
        """
        Convert Series to {label -> value} dict or dict-like object.

        .. note:: This method should only be used if the resulting pandas DataFrame is expected
            to be small, as all the data is loaded into the driver's memory.

        Parameters
        ----------
        into : class, default dict
            The collections.abc.Mapping subclass to use as the return
            object. Can be the actual class or an empty
            instance of the mapping type you want.  If you want a
            collections.defaultdict, you must pass it initialized.

        Returns
        -------
        collections.abc.Mapping
            Key-value representation of Series.

        Examples
        --------
        >>> s = ps.Series([1, 2, 3, 4])
        >>> s_dict = s.to_dict()
        >>> sorted(s_dict.items())
        [(0, 1), (1, 2), (2, 3), (3, 4)]

        >>> from collections import OrderedDict, defaultdict
        >>> s.to_dict(OrderedDict)
        OrderedDict(...)

        >>> dd = defaultdict(list)
        >>> s.to_dict(dd)  # doctest: +ELLIPSIS
        defaultdict(<class 'list'>, {...})
        """
        # Make sure locals() call is at the top of the function so we don't capture local variables.
        args = locals()
        psseries = self
        return validate_arguments_and_invoke_function(
            psseries._to_internal_pandas(), self.to_dict, pd.Series.to_dict, args
        )

    def to_latex(
        self,
        buf: Optional[IO[str]] = None,
        columns: Optional[List[Name]] = None,
        header: bool = True,
        index: bool = True,
        na_rep: str = "NaN",
        formatters: Optional[
            Union[List[Callable[[Any], str]], Dict[Name, Callable[[Any], str]]]
        ] = None,
        float_format: Optional[Callable[[float], str]] = None,
        sparsify: Optional[bool] = None,
        index_names: bool = True,
        bold_rows: bool = False,
        column_format: Optional[str] = None,
        longtable: Optional[bool] = None,
        escape: Optional[bool] = None,
        encoding: Optional[str] = None,
        decimal: str = ".",
        multicolumn: Optional[bool] = None,
        multicolumn_format: Optional[str] = None,
        multirow: Optional[bool] = None,
    ) -> Optional[str]:
        args = locals()
        psseries = self
        return validate_arguments_and_invoke_function(
            psseries._to_internal_pandas(), self.to_latex, pd.Series.to_latex, args
        )

    to_latex.__doc__ = DataFrame.to_latex.__doc__

    def to_pandas(self) -> pd.Series:
        """
        Return a pandas Series.

        .. note:: This method should only be used if the resulting pandas object is expected
                  to be small, as all the data is loaded into the driver's memory.

        Examples
        --------
        >>> df = ps.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)], columns=['dogs', 'cats'])
        >>> df['dogs'].to_pandas()
        0    0.2
        1    0.0
        2    0.6
        3    0.2
        Name: dogs, dtype: float64
        """
        log_advice(
            "`to_pandas` loads all data into the driver's memory. "
            "It should only be used if the resulting pandas Series is expected to be small."
        )
        return self._to_pandas()

    def _to_pandas(self) -> pd.Series:
        """
        Same as `to_pandas()`, without issuing the advice log for internal usage.
        """
        return self._to_internal_pandas().copy()

    def to_list(self) -> List:
        """
        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)

        .. note:: This method should only be used if the resulting list is expected
            to be small, as all the data is loaded into the driver's memory.

        """
        log_advice(
            "`to_list` loads all data into the driver's memory. "
            "It should only be used if the resulting list is expected to be small."
        )
        return self._to_internal_pandas().tolist()

    tolist = to_list

    def duplicated(self, keep: Union[bool, str] = "first") -> "Series":
        """
        Indicate duplicate Series values.

        Duplicated values are indicated as ``True`` values in the resulting
        Series. Either all duplicates, all except the first or all except the
        last occurrence of duplicates can be indicated.

        .. versionadded:: 3.4.0

        Parameters
        ----------
        keep : {'first', 'last', False}, default 'first'
            Method to handle marking duplicates:
            - 'first' : Mark duplicates as ``True`` except for the first occurrence.
            - 'last' : Mark duplicates as ``True`` except for the last occurrence.
            - ``False`` : Mark all duplicates as ``True``.

        Returns
        -------
        Series
            Series indicating whether each value has occurred in the
            preceding values

        See Also
        --------
        Index.drop_duplicates : Remove duplicate values from Index.
        DataFrame.duplicated : Equivalent method on DataFrame.
        Series.drop_duplicates : Remove duplicate values from Series.

        Examples
        --------
        By default, for each set of duplicated values, the first occurrence is
        set on False and all others on True:

        >>> animals = ps.Series(['lama', 'cow', 'lama', 'beetle', 'lama'])
        >>> animals.duplicated().sort_index()
        0    False
        1    False
        2     True
        3    False
        4     True
        dtype: bool

        which is equivalent to

        >>> animals.duplicated(keep='first').sort_index()
        0    False
        1    False
        2     True
        3    False
        4     True
        dtype: bool

        By using 'last', the last occurrence of each set of duplicated values
        is set on False and all others on True:

        >>> animals.duplicated(keep='last').sort_index()
        0     True
        1    False
        2     True
        3    False
        4    False
        dtype: bool

        By setting keep on ``False``, all duplicates are True:

        >>> animals.duplicated(keep=False).sort_index()
        0     True
        1    False
        2     True
        3    False
        4     True
        dtype: bool
        """
        return self._psdf[[self.name]].duplicated(keep=keep).rename(self.name)

    def drop_duplicates(
        self, keep: Union[bool, str] = "first", inplace: bool = False
    ) -> Optional["Series"]:
        """
        Return Series with duplicate values removed.

        Parameters
        ----------
        keep : {'first', 'last', ``False``}, default 'first'
            Method to handle dropping duplicates:
            - 'first' : Drop duplicates except for the first occurrence.
            - 'last' : Drop duplicates except for the last occurrence.
            - ``False`` : Drop all duplicates.
        inplace : bool, default ``False``
            If ``True``, performs operation inplace and returns None.

        Returns
        -------
        Series
            Series with duplicates dropped.

        Examples
        --------
        Generate a Series with duplicated entries.

        >>> s = ps.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'],
        ...               name='animal')
        >>> s.sort_index()
        0      lama
        1       cow
        2      lama
        3    beetle
        4      lama
        5     hippo
        Name: animal, dtype: object

        With the 'keep' parameter, the selection behavior of duplicated values
        can be changed. The value 'first' keeps the first occurrence for each
        set of duplicated entries. The default value of keep is 'first'.

        >>> s.drop_duplicates().sort_index()
        0      lama
        1       cow
        3    beetle
        5     hippo
        Name: animal, dtype: object

        The value 'last' for parameter 'keep' keeps the last occurrence for
        each set of duplicated entries.

        >>> s.drop_duplicates(keep='last').sort_index()
        1       cow
        3    beetle
        4      lama
        5     hippo
        Name: animal, dtype: object

        The value ``False`` for parameter 'keep' discards all sets of
        duplicated entries. Setting the value of 'inplace' to ``True`` performs
        the operation inplace and returns ``None``.

        >>> s.drop_duplicates(keep=False, inplace=True)
        >>> s.sort_index()
        1       cow
        3    beetle
        5     hippo
        Name: animal, dtype: object
        """
        inplace = validate_bool_kwarg(inplace, "inplace")
        psdf = self._psdf[[self.name]].drop_duplicates(keep=keep)

        if inplace:
            self._update_anchor(psdf)
            return None
        else:
            return first_series(psdf)

    def reindex(self, index: Optional[Any] = None, fill_value: Optional[Any] = None) -> "Series":
        """
        Conform Series to new index with optional filling logic, placing
        NA/NaN in locations having no value in the previous index. A new object
        is produced.

        Parameters
        ----------
        index: array-like, optional
            New labels / index to conform to, should be specified using keywords.
            Preferably an Index object to avoid duplicating data
        fill_value : scalar, default np.nan
            Value to use for missing values. Defaults to NaN, but can be any
            "compatible" value.

        Returns
        -------
        Series with changed index.

        See Also
        --------
        Series.reset_index : Remove row labels or move them to new columns.

        Examples
        --------

        Create a series with some fictional data.

        >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
        >>> ser = ps.Series([200, 200, 404, 404, 301],
        ...                 index=index, name='http_status')
        >>> ser
        Firefox      200
        Chrome       200
        Safari       404
        IE10         404
        Konqueror    301
        Name: http_status, dtype: int64

        Create a new index and reindex the Series. By default
        values in the new index that do not have corresponding
        records in the Series are assigned ``NaN``.

        >>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
        ...             'Chrome']
        >>> ser.reindex(new_index).sort_index()
        Chrome           200.0
        Comodo Dragon      NaN
        IE10             404.0
        Iceweasel          NaN
        Safari           404.0
        Name: http_status, dtype: float64

        We can fill in the missing values by passing a value to
        the keyword ``fill_value``.

        >>> ser.reindex(new_index, fill_value=0).sort_index()
        Chrome           200
        Comodo Dragon      0
        IE10             404
        Iceweasel          0
        Safari           404
        Name: http_status, dtype: int64

        To further illustrate the filling functionality in
        ``reindex``, we will create a Series with a
        monotonically increasing index (for example, a sequence
        of dates).

        >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
        >>> ser2 = ps.Series([100, 101, np.nan, 100, 89, 88],
        ...                  name='prices', index=date_index)
        >>> ser2.sort_index()
        2010-01-01    100.0
        2010-01-02    101.0
        2010-01-03      NaN
        2010-01-04    100.0
        2010-01-05     89.0
        2010-01-06     88.0
        Name: prices, dtype: float64

        Suppose we decide to expand the series to cover a wider
        date range.

        >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
        >>> ser2.reindex(date_index2).sort_index()
        2009-12-29      NaN
        2009-12-30      NaN
        2009-12-31      NaN
        2010-01-01    100.0
        2010-01-02    101.0
        2010-01-03      NaN
        2010-01-04    100.0
        2010-01-05     89.0
        2010-01-06     88.0
        2010-01-07      NaN
        Name: prices, dtype: float64
        """

        return first_series(self.to_frame().reindex(index=index, fill_value=fill_value)).rename(
            self.name
        )

    def reindex_like(self, other: Union["Series", "DataFrame"]) -> "Series":
        """
        Return a Series with matching indices as other object.

        Conform the object to the same index on all axes. Places NA/NaN in locations
        having no value in the previous index.

        Parameters
        ----------
        other : Series or DataFrame
            Its row and column indices are used to define the new indices
            of this object.

        Returns
        -------
        Series
            Series with changed indices on each axis.

        See Also
        --------
        DataFrame.set_index : Set row labels.
        DataFrame.reset_index : Remove row labels or move them to new columns.
        DataFrame.reindex : Change to new indices or expand indices.

        Notes
        -----
        Same as calling
        ``.reindex(index=other.index, ...)``.

        Examples
        --------

        >>> s1 = ps.Series([24.3, 31.0, 22.0, 35.0],
        ...                index=pd.date_range(start='2014-02-12',
        ...                                    end='2014-02-15', freq='D'),
        ...                name="temp_celsius")
        >>> s1
        2014-02-12    24.3
        2014-02-13    31.0
        2014-02-14    22.0
        2014-02-15    35.0
        Name: temp_celsius, dtype: float64

        >>> s2 = ps.Series(["low", "low", "medium"],
        ...                index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
        ...                                        '2014-02-15']),
        ...                name="winspeed")
        >>> s2
        2014-02-12       low
        2014-02-13       low
        2014-02-15    medium
        Name: winspeed, dtype: object

        >>> s2.reindex_like(s1).sort_index()
        2014-02-12       low
        2014-02-13       low
        2014-02-14      None
        2014-02-15    medium
        Name: winspeed, dtype: object
        """
        if isinstance(other, (Series, DataFrame)):
            return self.reindex(index=other.index)
        else:
            raise TypeError("other must be a pandas-on-Spark Series or DataFrame")

    def fillna(
        self,
        value: Optional[Any] = None,
        method: Union[Optional[str], _NoValueType] = _NoValue,
        axis: Optional[Axis] = None,
        inplace: bool = False,
        limit: Optional[int] = None,
    ) -> Optional["Series"]:
        """Fill NA/NaN values.

        .. note:: the current implementation of 'method' parameter in fillna uses Spark's Window
            without specifying partition specification. This leads to moving all data into
            a single partition in a single machine and could cause serious
            performance degradation. Avoid this method with very large datasets.

        Parameters
        ----------
        value : scalar, dict, Series
            Value to use to fill holes. alternately a dict/Series of values
            specifying which value to use for each column.
            DataFrame is not supported.
        method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
            Method to use for filling holes in reindexed Series pad / ffill: propagate last valid
            observation forward to next valid backfill / bfill:
            use NEXT valid observation to fill gap

            .. deprecated:: 4.0.0

        axis : {0 or `index`}
            1 and `columns` are not supported.
        inplace : boolean, default False
            Fill in place (do not create a new object)
        limit : int, default None
            If method is specified, this is the maximum number of consecutive NaN values to
            forward/backward fill. In other words, if there is a gap with more than this number of
            consecutive NaNs, it will only be partially filled. If method is not specified,
            this is the maximum number of entries along the entire axis where NaNs will be filled.
            Must be greater than 0 if not None

            .. deprecated:: 4.0.0


        Returns
        -------
        Series
            Series with NA entries filled.

        Examples
        --------
        >>> s = ps.Series([np.nan, 2, 3, 4, np.nan, 6], name='x')
        >>> s
        0    NaN
        1    2.0
        2    3.0
        3    4.0
        4    NaN
        5    6.0
        Name: x, dtype: float64

        Replace all NaN elements with 0s.

        >>> s.fillna(0)
        0    0.0
        1    2.0
        2    3.0
        3    4.0
        4    0.0
        5    6.0
        Name: x, dtype: float64

        We can also propagate non-null values forward or backward.

        >>> s.fillna(method='ffill')  # doctest: +SKIP
        0    NaN
        1    2.0
        2    3.0
        3    4.0
        4    4.0
        5    6.0
        Name: x, dtype: float64

        >>> s = ps.Series([np.nan, 'a', 'b', 'c', np.nan], name='x')
        >>> s.fillna(method='ffill')  # doctest: +SKIP
        0    None
        1       a
        2       b
        3       c
        4       c
        Name: x, dtype: object
        """
        if LooseVersion(pd.__version__) < "3.0.0":
            if method is _NoValue:
                method = None
        else:
            if method is not _NoValue:
                raise TypeError(
                    "The `method` parameter is not supported in pandas 3.0.0 and later. "
                )
            method = None

        return self._fillna_with_method(
            value=value,
            method=method,  # type: ignore[arg-type]
            axis=axis,
            inplace=inplace,
            limit=limit,
        )

    def _fillna_with_method(
        self,
        value: Optional[Any] = None,
        method: Optional[str] = None,
        axis: Optional[Axis] = None,
        inplace: bool = False,
        limit: Optional[int] = None,
    ) -> Optional["Series"]:
        psser = self._fillna(value=value, method=method, axis=axis, limit=limit)

        if method is not None:
            warnings.warn(
                "Series.fillna with 'method' is deprecated and will raise in a future version. "
                "Use Series.ffill() or Series.bfill() instead.",
                FutureWarning,
            )
            psser = DataFrame(psser._psdf._internal.resolved_copy)._psser_for(self._column_label)

        inplace = validate_bool_kwarg(inplace, "inplace")
        if inplace:
            self._update_internal_frame(psser._psdf._internal, check_same_anchor=False)
            return None
        else:
            return psser.copy()

    def _fillna(
        self,
        value: Optional[Any] = None,
        method: Optional[str] = None,
        axis: Optional[Axis] = None,
        limit: Optional[int] = None,
        part_cols: Sequence["ColumnOrName"] = (),
    ) -> "Series":
        axis = validate_axis(axis)
        if axis != 0:
            raise NotImplementedError("fillna currently only works for axis=0 or axis='index'")
        if (value is None) and (method is None):
            raise ValueError("Must specify a fillna 'value' or 'method' parameter.")
        if (method is not None) and (method not in ["ffill", "pad", "backfill", "bfill"]):
            raise ValueError("Expecting 'pad', 'ffill', 'backfill' or 'bfill'.")

        scol = self.spark.column

        if not self.spark.nullable and not isinstance(
            self.spark.data_type, (FloatType, DoubleType)
        ):
            return self._psdf.copy()._psser_for(self._column_label)

        cond = self.isnull().spark.column

        if value is not None:
            if not isinstance(value, (float, int, str, bool)):
                raise TypeError("Unsupported type %s" % type(value).__name__)
            if limit is not None:
                raise NotImplementedError("limit parameter for value is not support now")
            scol = F.when(cond, value).otherwise(scol)
        else:
            if method in ["ffill", "pad"]:
                func = F.last
                end = Window.currentRow - 1
                if limit is not None:
                    begin = Window.currentRow - limit
                else:
                    begin = Window.unboundedPreceding
            elif method in ["bfill", "backfill"]:
                func = F.first
                begin = Window.currentRow + 1
                if limit is not None:
                    end = Window.currentRow + limit
                else:
                    end = Window.unboundedFollowing

            window = (
                Window.partitionBy(*part_cols)
                .orderBy(NATURAL_ORDER_COLUMN_NAME)
                .rowsBetween(begin, end)
            )
            scol = F.when(cond, func(scol, True).over(window)).otherwise(scol)

        return DataFrame(
            self._psdf._internal.with_new_spark_column(
                self._column_label,
                scol.alias(name_like_string(self.name)),  # TODO: dtype?
            )
        )._psser_for(self._column_label)

    def interpolate(
        self,
        method: str = "linear",
        limit: Optional[int] = None,
        limit_direction: Optional[str] = None,
        limit_area: Optional[str] = None,
    ) -> "Series":
        return self._interpolate(
            method=method, limit=limit, limit_direction=limit_direction, limit_area=limit_area
        )

    def _interpolate(
        self,
        method: str = "linear",
        limit: Optional[int] = None,
        limit_direction: Optional[str] = None,
        limit_area: Optional[str] = None,
    ) -> "Series":
        if self.dtype == "object":
            warnings.warn(
                "Series.interpolate with object dtype is deprecated and will raise in a "
                "future version. Convert to a specific numeric type before interpolating.",
                FutureWarning,
            )
        if method not in ["linear"]:
            raise NotImplementedError("interpolate currently works only for method='linear'")
        if (limit is not None) and (not limit > 0):
            raise ValueError("limit must be > 0.")
        if (limit_direction is not None) and (
            limit_direction not in ["forward", "backward", "both"]
        ):
            raise ValueError("invalid limit_direction: '{}'".format(limit_direction))
        if (limit_area is not None) and (limit_area not in ["inside", "outside"]):
            raise ValueError("invalid limit_area: '{}'".format(limit_area))

        if not self.spark.nullable and not isinstance(
            self.spark.data_type, (FloatType, DoubleType)
        ):
            return self._psdf.copy()._psser_for(self._column_label)

        scol = self.spark.column
        last_non_null = F.last(scol, True)
        null_index = SF.null_index(scol)

        window_forward = PySparkWindow.orderBy(NATURAL_ORDER_COLUMN_NAME).rowsBetween(
            PySparkWindow.unboundedPreceding, PySparkWindow.currentRow
        )
        last_non_null_forward = last_non_null.over(window_forward)
        null_index_forward = null_index.over(window_forward)

        window_backward = PySparkWindow.orderBy(F.desc(NATURAL_ORDER_COLUMN_NAME)).rowsBetween(
            PySparkWindow.unboundedPreceding, PySparkWindow.currentRow
        )
        last_non_null_backward = last_non_null.over(window_backward)
        null_index_backward = null_index.over(window_backward)

        fill = (last_non_null_backward - last_non_null_forward) / (
            null_index_backward + null_index_forward
        ) * null_index_forward + last_non_null_forward

        fill_cond = ~F.isnull(last_non_null_backward) & ~F.isnull(last_non_null_forward)

        pad_head = F.lit(None)
        pad_head_cond = F.lit(False)
        pad_tail = F.lit(None)
        pad_tail_cond = F.lit(False)

        # inputs  -> NaN, NaN, 1.0, NaN, NaN, NaN, 5.0, NaN, NaN
        if limit_direction is None or limit_direction == "forward":
            # outputs -> NaN, NaN, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0
            pad_tail = last_non_null_forward
            pad_tail_cond = F.isnull(last_non_null_backward) & ~F.isnull(last_non_null_forward)
            if limit is not None:
                # outputs (limit=1) -> NaN, NaN, 1.0, 2.0, NaN, NaN, 5.0, 5.0, NaN
                fill_cond = fill_cond & (null_index_forward <= F.lit(limit))
                pad_tail_cond = pad_tail_cond & (null_index_forward <= F.lit(limit))

        elif limit_direction == "backward":
            # outputs -> 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, NaN, NaN
            pad_head = last_non_null_backward
            pad_head_cond = ~F.isnull(last_non_null_backward) & F.isnull(last_non_null_forward)
            if limit is not None:
                # outputs (limit=1) -> NaN, 1.0, 1.0, NaN, NaN, 4.0, 5.0, NaN, NaN
                fill_cond = fill_cond & (null_index_backward <= F.lit(limit))
                pad_head_cond = pad_head_cond & (null_index_backward <= F.lit(limit))

        else:
            # outputs -> 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0
            pad_head = last_non_null_backward
            pad_head_cond = ~F.isnull(last_non_null_backward) & F.isnull(last_non_null_forward)
            pad_tail = last_non_null_forward
            pad_tail_cond = F.isnull(last_non_null_backward) & ~F.isnull(last_non_null_forward)
            if limit is not None:
                # outputs (limit=1) -> NaN, 1.0, 1.0, 2.0, NaN, 4.0, 5.0, 5.0, NaN
                fill_cond = fill_cond & (
                    (null_index_forward <= F.lit(limit)) | (null_index_backward <= F.lit(limit))
                )
                pad_head_cond = pad_head_cond & (null_index_backward <= F.lit(limit))
                pad_tail_cond = pad_tail_cond & (null_index_forward <= F.lit(limit))

        if limit_area == "inside":
            pad_head_cond = F.lit(False)
            pad_tail_cond = F.lit(False)
        elif limit_area == "outside":
            fill_cond = F.lit(False)

        cond = self.isnull().spark.column
        scol = (
            F.when(cond & fill_cond, fill)
            .when(cond & pad_head_cond, pad_head)
            .when(cond & pad_tail_cond, pad_tail)
            .otherwise(scol)
        )

        return DataFrame(
            self._psdf._internal.with_new_spark_column(self._column_label, scol)  # TODO: dtype?
        )._psser_for(self._column_label)

    def dropna(self, axis: Axis = 0, inplace: bool = False, **kwargs: Any) -> Optional["Series"]:
        """
        Return a new Series with missing values removed.

        Parameters
        ----------
        axis : {0 or 'index'}, default 0
            There is only one axis to drop values from.
        inplace : bool, default False
            If True, do operation inplace and return None.
        **kwargs
            Not in use.

        Returns
        -------
        Series
            Series with NA entries dropped from it.

        Examples
        --------
        >>> ser = ps.Series([1., 2., np.nan])
        >>> ser
        0    1.0
        1    2.0
        2    NaN
        dtype: float64

        Drop NA values from a Series.

        >>> ser.dropna()
        0    1.0
        1    2.0
        dtype: float64

        Keep the Series with valid entries in the same variable.

        >>> ser.dropna(inplace=True)
        >>> ser
        0    1.0
        1    2.0
        dtype: float64
        """
        inplace = validate_bool_kwarg(inplace, "inplace")
        # TODO: last two examples from pandas produce different results.
        psdf = self._psdf[[self.name]].dropna(axis=axis, inplace=False)
        if inplace:
            self._update_anchor(psdf)
            return None
        else:
            return first_series(psdf)

    def clip(
        self,
        lower: Union[float, int] = None,
        upper: Union[float, int] = None,
        inplace: bool = False,
    ) -> "Series":
        """
        Trim values at input threshold(s).

        Assigns values outside boundary-to-boundary values.

        Parameters
        ----------
        lower : float or int, default None
            Minimum threshold value. All values below this threshold will be set to it.
        upper : float or int, default None
            Maximum threshold value. All values above this threshold will be set to it.
        inplace : bool, default False
             if True, perform operation in-place

        Returns
        -------
        Series
            Series with the values outside the clip boundaries replaced

        Examples
        --------
        >>> psser = ps.Series([0, 2, 4])
        >>> psser
        0    0
        1    2
        2    4
        dtype: int64

        >>> psser.clip(1, 3)
        0    1
        1    2
        2    3
        dtype: int64

        Clip can be performed in-place.

        >>> psser.clip(2, 3, inplace=True)
        >>> psser
        0    2
        1    2
        2    3
        dtype: int64

        Notes
        -----
        One difference between this implementation and pandas is that running
        `pd.Series(['a', 'b']).clip(0, 1)` will crash with "TypeError: '<=' not supported between
        instances of 'str' and 'int'" while `ps.Series(['a', 'b']).clip(0, 1)` will output the
        original Series, simply ignoring the incompatible types.
        """
        if is_list_like(lower) or is_list_like(upper):
            raise TypeError(
                "List-like value are not supported for 'lower' and 'upper' at the " + "moment"
            )

        if lower is None and upper is None:
            return self

        if isinstance(self.spark.data_type, NumericType):
            scol = self.spark.column
            if lower is not None:
                scol = F.when(scol < lower, lower).otherwise(scol)
            if upper is not None:
                scol = F.when(scol > upper, upper).otherwise(scol)
            if inplace:
                internal = self._internal.copy(
                    data_spark_columns=[scol.alias(self._internal.data_spark_column_names[0])],
                    data_fields=[self._internal.data_fields[0]],
                )
                self._update_internal_frame(internal, check_same_anchor=False)
                return None
            else:
                return self._with_new_scol(
                    scol.alias(self._internal.data_spark_column_names[0]),
                    field=self._internal.data_fields[0],
                )
        else:
            return self

    def drop(
        self,
        labels: Optional[Union[Name, List[Name]]] = None,
        index: Optional[Union[Name, List[Name]]] = None,
        columns: Optional[Union[Name, List[Name]]] = None,
        level: Optional[int] = None,
        inplace: bool = False,
        errors: str = "raise",
    ) -> "Series":
        """
        Return Series with specified index labels removed.

        Remove elements of a Series based on specifying the index labels.
        When using a multi-index, labels on different levels can be removed by specifying the level.

        Parameters
        ----------
        labels : single label or list-like
            Index labels to drop.
        index : single label or list-like
            Redundant for application on Series, but index can be used instead of labels.
        columns : single label or list-like
            No change is made to the Series; use 'index' or 'labels' instead.

            .. versionadded:: 3.4.0
        level : int or level name, optional
            For MultiIndex, level for which the labels will be removed.
        inplace: bool, default False
            If True, do operation inplace and return None

            .. versionadded:: 3.4.0
        errors : {{'ignore', 'raise'}}, default 'raise'
            If 'ignore', suppress error and only existing labels are dropped.

            .. versionadded:: 4.1.0

        Returns
        -------
        Series
            Series with specified index labels removed.

        See Also
        --------
        Series.dropna

        Examples
        --------
        >>> s = ps.Series(data=np.arange(3), index=['A', 'B', 'C'])
        >>> s
        A    0
        B    1
        C    2
        dtype: int64

        Drop single label A

        >>> s.drop('A')
        B    1
        C    2
        dtype: int64

        Drop labels B and C

        >>> s.drop(labels=['B', 'C'])
        A    0
        dtype: int64

        With 'index' rather than 'labels' returns exactly same result.

        >>> s.drop(index='A')
        B    1
        C    2
        dtype: int64

        >>> s.drop(index=['B', 'C'])
        A    0
        dtype: int64

        With 'columns', no change is made to the Series.

        >>> s.drop(columns=['A'])
        A    0
        B    1
        C    2
        dtype: int64

        With 'inplace=True', do operation inplace and return None.

        >>> s.drop(index=['B', 'C'], inplace=True)
        >>> s
        A    0
        dtype: int64

        Also support for MultiIndex

        >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
        ...                       ['speed', 'weight', 'length']],
        ...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                       [0, 1, 2, 0, 1, 2, 0, 1, 2]])
        >>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
        ...               index=midx)
        >>> s
        lama    speed      45.0
                weight    200.0
                length      1.2
        cow     speed      30.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                weight      1.0
                length      0.3
        dtype: float64

        >>> s.drop(labels='weight', level=1)
        lama    speed      45.0
                length      1.2
        cow     speed      30.0
                length      1.5
        falcon  speed     320.0
                length      0.3
        dtype: float64

        >>> s.drop(('lama', 'weight'))
        lama    speed      45.0
                length      1.2
        cow     speed      30.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                weight      1.0
                length      0.3
        dtype: float64

        >>> s.drop([('lama', 'speed'), ('falcon', 'weight')])
        lama    weight    200.0
                length      1.2
        cow     speed      30.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                length      0.3
        dtype: float64
        """
        if errors not in ("raise", "ignore"):
            raise ValueError("errors must be either 'raise' or 'ignore'")
        dropped = self._drop(
            labels=labels, index=index, level=level, inplace=inplace, columns=columns
        )
        return None if dropped is None else first_series(dropped)

    def _drop(
        self,
        labels: Optional[Union[Name, List[Name]]] = None,
        index: Optional[Union[Name, List[Name]]] = None,
        level: Optional[int] = None,
        inplace: bool = False,
        columns: Optional[Union[Name, List[Name]]] = None,
    ) -> Optional[DataFrame]:
        if labels is not None:
            if columns is not None or index is not None:
                raise ValueError("Cannot specify both 'labels' and 'index'/'columns'")
            return self._drop(index=labels, level=level, inplace=inplace, columns=columns)
        if index is not None:
            internal = self._internal
            if level is None:
                level = 0
            if level >= internal.index_level:
                raise ValueError("'level' should be less than the number of indexes")

            if is_name_like_tuple(index):
                index_list = [cast(Label, index)]
            elif is_name_like_value(index):
                index_list = [(index,)]
            elif all(is_name_like_value(idxes, allow_tuple=False) for idxes in index):
                index_list = [(idex,) for idex in index]
            elif not all(is_name_like_tuple(idxes) for idxes in index):
                raise ValueError(
                    "If the given index is a list, it "
                    "should only contains names as all tuples or all non tuples "
                    "that contain index names"
                )
            else:
                index_list = cast(List[Label], index)

            drop_index_scols = []
            for idxes in index_list:
                try:
                    index_scols = [
                        internal.index_spark_columns[lvl] == idx
                        for lvl, idx in enumerate(idxes, level)
                    ]
                except IndexError:
                    raise KeyError(
                        "Key length ({}) exceeds index depth ({})".format(
                            internal.index_level, len(idxes)
                        )
                    )
                drop_index_scols.append(reduce(lambda x, y: x & y, index_scols))

            cond = ~reduce(lambda x, y: x | y, drop_index_scols)
            dropped_internal = internal.with_filter(cond)
            if inplace:
                self._update_anchor(DataFrame(dropped_internal))
                return None
            else:
                return DataFrame(dropped_internal)
        elif columns is not None:
            return self._psdf
        else:
            raise ValueError("Need to specify at least one of 'labels', 'index' or 'columns'")

    def head(self, n: int = 5) -> "Series":
        """
        Return the first n rows.

        This function returns the first n rows for the object based on position.
        It is useful for quickly testing if your object has the right type of data in it.

        Parameters
        ----------
        n : Integer, default =  5

        Returns
        -------
        The first n rows of the caller object.

        Examples
        --------
        >>> df = ps.DataFrame({'animal':['alligator', 'bee', 'falcon', 'lion']})
        >>> df.animal.head(2)  # doctest: +NORMALIZE_WHITESPACE
        0     alligator
        1     bee
        Name: animal, dtype: object
        """
        return first_series(self.to_frame().head(n)).rename(self.name)

    def last(self, offset: Union[str, DateOffset]) -> "Series":
        """
        Select final periods of time series data based on a date offset.

        When having a Series with dates as index, this function can
        select the last few elements based on a date offset.

        .. deprecated:: 4.0.0

        Parameters
        ----------
        offset : str or DateOffset
            The offset length of the data that will be selected. For instance,
            '3D' will display all the rows having their index within the last 3 days.

        Returns
        -------
        Series
            A subset of the caller.

        Raises
        ------
        TypeError
            If the index is not a :class:`DatetimeIndex`

        Examples
        --------
        >>> index = pd.date_range('2018-04-09', periods=4, freq='2D')
        >>> psser = ps.Series([1, 2, 3, 4], index=index)
        >>> psser
        2018-04-09    1
        2018-04-11    2
        2018-04-13    3
        2018-04-15    4
        dtype: int64

        Get the rows for the last 3 days:

        >>> psser.last('3D')  # doctest: +SKIP
        2018-04-13    3
        2018-04-15    4
        dtype: int64

        Notice the data for 3 last calendar days were returned, not the last
        3 observed days in the dataset, and therefore data for 2018-04-11 was
        not returned.
        """
        if LooseVersion(pd.__version__) < "3.0.0":
            return self._last(offset)
        else:
            raise AttributeError(
                "The `last` method is not supported in pandas 3.0.0 and later. "
                "Please create a mask and filter using `.loc` instead"
            )

    def _last(self, offset: Union[str, DateOffset]) -> "Series":
        warnings.warn(
            "last is deprecated and will be removed in a future version. "
            "Please create a mask and filter using `.loc` instead",
            FutureWarning,
        )
        return first_series(self.to_frame().last(offset)).rename(self.name)

    def first(self, offset: Union[str, DateOffset]) -> "Series":
        """
        Select first periods of time series data based on a date offset.

        When having a Series with dates as index, this function can
        select the first few elements based on a date offset.

        .. deprecated:: 4.0.0

        Parameters
        ----------
        offset : str or DateOffset
            The offset length of the data that will be selected. For instance,
            '3D' will display all the rows having their index within the first 3 days.

        Returns
        -------
        Series
            A subset of the caller.

        Raises
        ------
        TypeError
            If the index is not a :class:`DatetimeIndex`

        Examples
        --------
        >>> index = pd.date_range('2018-04-09', periods=4, freq='2D')
        >>> psser = ps.Series([1, 2, 3, 4], index=index)
        >>> psser
        2018-04-09    1
        2018-04-11    2
        2018-04-13    3
        2018-04-15    4
        dtype: int64

        Get the rows for the first 3 days:

        >>> psser.first('3D')  # doctest: +SKIP
        2018-04-09    1
        2018-04-11    2
        dtype: int64

        Notice the data for 3 first calendar days were returned, not the first
        3 observed days in the dataset, and therefore data for 2018-04-13 was
        not returned.
        """
        if LooseVersion(pd.__version__) < "3.0.0":
            return self._first(offset)
        else:
            raise AttributeError(
                "The `first` method is not supported in pandas 3.0.0 and later. "
                "Please create a mask and filter using `.loc` instead"
            )

    def _first(self, offset: Union[str, DateOffset]) -> "Series":
        warnings.warn(
            "first is deprecated and will be removed in a future version. "
            "Please create a mask and filter using `.loc` instead",
            FutureWarning,
        )
        return first_series(self.to_frame().first(offset)).rename(self.name)

    # TODO: Categorical type isn't supported (due to PySpark's limitation) and
    # some doctests related with timestamps were not added.
    def unique(self) -> "Series":
        """
        Return unique values of Series object.

        Uniques are returned in order of appearance. Hash table-based unique,
        therefore does NOT sort.

        .. note:: This method returns newly created Series whereas pandas returns
                  the unique values as a NumPy array.

        Returns
        -------
        Returns the unique values as a Series.

        See Also
        --------
        Index.unique
        groupby.SeriesGroupBy.unique

        Examples
        --------
        >>> psser = ps.Series([2, 1, 3, 3], name='A')
        >>> psser.unique().sort_values()
        1    1
        0    2
        2    3
        Name: A, dtype: int64

        >>> ps.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()
        0   2016-01-01
        dtype: datetime64[ns]

        >>> psser.name = ('x', 'a')
        >>> psser.unique().sort_values()
        1    1
        0    2
        2    3
        Name: (x, a), dtype: int64
        """
        sdf = self._internal.spark_frame.select(self.spark.column).distinct()
        internal = InternalFrame(
            spark_frame=sdf,
            index_spark_columns=None,
            column_labels=[self._column_label],
            data_spark_columns=[scol_for(sdf, self._internal.data_spark_column_names[0])],
            data_fields=[self._internal.data_fields[0]],
            column_label_names=self._internal.column_label_names,
        )
        return first_series(DataFrame(internal))

    def sort_values(
        self,
        ascending: bool = True,
        inplace: bool = False,
        na_position: str = "last",
        ignore_index: bool = False,
    ) -> Optional["Series"]:
        """
        Sort by the values.

        Sort a Series in ascending or descending order by some criterion.

        Parameters
        ----------
        ascending : bool or list of bool, default True
             Sort ascending vs. descending. Specify list for multiple sort
             orders.  If this is a list of bools, must match the length of
             the by.
        inplace : bool, default False
             if True, perform operation in-place
        na_position : {'first', 'last'}, default 'last'
             `first` puts NaNs at the beginning, `last` puts NaNs at the end
        ignore_index : bool, default False
             If True, the resulting axis will be labeled 0, 1, ..., n - 1.

             .. versionadded:: 3.4.0

        Returns
        -------
        sorted_obj : Series ordered by values.

        Examples
        --------
        >>> s = ps.Series([np.nan, 1, 3, 10, 5])
        >>> s
        0     NaN
        1     1.0
        2     3.0
        3    10.0
        4     5.0
        dtype: float64

        Sort values ascending order (default behaviour)

        >>> s.sort_values(ascending=True)
        1     1.0
        2     3.0
        4     5.0
        3    10.0
        0     NaN
        dtype: float64

        Sort values descending order

        >>> s.sort_values(ascending=False)
        3    10.0
        4     5.0
        2     3.0
        1     1.0
        0     NaN
        dtype: float64

        Sort values descending order and ignoring index

        >>> s.sort_values(ascending=False, ignore_index=True)
        0    10.0
        1     5.0
        2     3.0
        3     1.0
        4     NaN
        dtype: float64

        Sort values inplace

        >>> s.sort_values(ascending=False, inplace=True)
        >>> s
        3    10.0
        4     5.0
        2     3.0
        1     1.0
        0     NaN
        dtype: float64

        Sort values putting NAs first

        >>> s.sort_values(na_position='first')
        0     NaN
        1     1.0
        2     3.0
        4     5.0
        3    10.0
        dtype: float64

        Sort a series of strings

        >>> s = ps.Series(['z', 'b', 'd', 'a', 'c'])
        >>> s
        0    z
        1    b
        2    d
        3    a
        4    c
        dtype: object

        >>> s.sort_values()
        3    a
        1    b
        4    c
        2    d
        0    z
        dtype: object
        """
        inplace = validate_bool_kwarg(inplace, "inplace")
        psdf = self._psdf[[self.name]]._sort(
            by=[self.spark.column], ascending=ascending, na_position=na_position
        )

        if inplace:
            if ignore_index:
                psdf.reset_index(drop=True, inplace=inplace)
            self._update_anchor(psdf)
            return None
        else:
            return first_series(psdf.reset_index(drop=True)) if ignore_index else first_series(psdf)

    def sort_index(
        self,
        axis: Axis = 0,
        level: Optional[Union[int, List[int]]] = None,
        ascending: bool = True,
        inplace: bool = False,
        kind: str = None,
        na_position: str = "last",
        ignore_index: bool = False,
    ) -> Optional["Series"]:
        """
        Sort object by labels (along an axis)

        Parameters
        ----------
        axis : index, columns to direct sorting. Currently, only axis = 0 is supported.
        level : int or level name or list of ints or list of level names
            if not None, sort on values in specified index level(s)
        ascending : boolean, default True
            Sort ascending vs. descending
        inplace : bool, default False
            if True, perform operation in-place
        kind : str, default None
            pandas-on-Spark does not allow specifying the sorting algorithm now,
            default None
        na_position : {'first', 'last'}, default 'last'
            first puts NaNs at the beginning, last puts NaNs at the end. Not implemented for
            MultiIndex.
        ignore_index : bool, default False
            If True, the resulting axis will be labeled 0, 1, ..., n - 1.

            .. versionadded:: 3.4.0

        Returns
        -------
        sorted_obj : Series

        Examples
        --------
        >>> s = ps.Series([2, 1, np.nan], index=['b', 'a', np.nan])

        >>> s.sort_index()  # doctest: +SKIP
        a       1.0
        b       2.0
        None    NaN
        dtype: float64

        >>> s.sort_index(ignore_index=True)
        0    1.0
        1    2.0
        2    NaN
        dtype: float64

        >>> s.sort_index(ascending=False)  # doctest: +SKIP
        b       2.0
        a       1.0
        None    NaN
        dtype: float64

        >>> s.sort_index(na_position='first')  # doctest: +SKIP
        None    NaN
        a       1.0
        b       2.0
        dtype: float64

        >>> s.sort_index(inplace=True)
        >>> s  # doctest: +SKIP
        a       1.0
        b       2.0
        None    NaN
        dtype: float64

        Multi-index series.

        >>> s = ps.Series(range(4), index=[['b', 'b', 'a', 'a'], [1, 0, 1, 0]], name='0')

        >>> s.sort_index()
        a  0    3
           1    2
        b  0    1
           1    0
        Name: 0, dtype: int64

        >>> s.sort_index(level=1)  # doctest: +SKIP
        a  0    3
        b  0    1
        a  1    2
        b  1    0
        Name: 0, dtype: int64

        >>> s.sort_index(level=[1, 0])
        a  0    3
        b  0    1
        a  1    2
        b  1    0
        Name: 0, dtype: int64
        """
        inplace = validate_bool_kwarg(inplace, "inplace")
        psdf = self._psdf[[self.name]].sort_index(
            axis=axis, level=level, ascending=ascending, kind=kind, na_position=na_position
        )

        if inplace:
            if ignore_index:
                psdf.reset_index(drop=True, inplace=inplace)
            self._update_anchor(psdf)
            return None
        else:
            return first_series(psdf.reset_index(drop=True)) if ignore_index else first_series(psdf)

    def swaplevel(
        self, i: Union[int, Name] = -2, j: Union[int, Name] = -1, copy: bool = True
    ) -> "Series":
        """
        Swap levels i and j in a MultiIndex.
        Default is to swap the two innermost levels of the index.

        Parameters
        ----------
        i, j : int, str
            Level of the indices to be swapped. Can pass level name as string.
        copy : bool, default True
            Whether to copy underlying data. Must be True.

        Returns
        -------
        Series
            Series with levels swapped in MultiIndex.

        Examples
        --------
        >>> midx = pd.MultiIndex.from_arrays([['a', 'b'], [1, 2]], names = ['word', 'number'])
        >>> midx  # doctest: +SKIP
        MultiIndex([('a', 1),
                    ('b', 2)],
                   names=['word', 'number'])
        >>> psser = ps.Series(['x', 'y'], index=midx)
        >>> psser
        word  number
        a     1         x
        b     2         y
        dtype: object
        >>> psser.swaplevel()
        number  word
        1       a       x
        2       b       y
        dtype: object
        >>> psser.swaplevel(0, 1)
        number  word
        1       a       x
        2       b       y
        dtype: object
        >>> psser.swaplevel('number', 'word')
        number  word
        1       a       x
        2       b       y
        dtype: object
        """
        assert copy is True

        return first_series(self.to_frame().swaplevel(i, j, axis=0)).rename(self.name)

    def swapaxes(self, i: Axis, j: Axis, copy: bool = True) -> "Series":
        """
        Interchange axes and swap values axes appropriately.

        .. deprecated:: 4.0.0

        Parameters
        ----------
        i: {0 or 'index', 1 or 'columns'}. The axis to swap.
        j: {0 or 'index', 1 or 'columns'}. The axis to swap.
        copy : bool, default True.

        Returns
        -------
        Series

        Examples
        --------
        >>> psser = ps.Series([1, 2, 3], index=["x", "y", "z"])
        >>> psser
        x    1
        y    2
        z    3
        dtype: int64
        >>>
        >>> psser.swapaxes(0, 0)  # doctest: +SKIP
        x    1
        y    2
        z    3
        dtype: int64
        """
        if LooseVersion(pd.__version__) < "3.0.0":
            return self._swapaxes(i, j, copy)
        else:
            raise AttributeError(
                "The `swapaxes` method is not supported in pandas 3.0.0 and later. "
                "Please use the `transpose` method instead"
            )

    def _swapaxes(self, i: Axis, j: Axis, copy: bool = True) -> "Series":
        warnings.warn(
            "'Series.swapaxes' is deprecated and will be removed in a future version. "
            "Please use 'Series.transpose' instead.",
            FutureWarning,
        )
        assert copy is True

        i = validate_axis(i)
        j = validate_axis(j)
        if not i == j == 0:
            raise ValueError("Axis must be 0 for Series")

        return self.copy()

    def add_prefix(self, prefix: str) -> "Series":
        """
        Prefix labels with string `prefix`.

        For Series, the row labels are prefixed.
        For DataFrame, the column labels are prefixed.

        Parameters
        ----------
        prefix : str
           The string to add before each label.

        Returns
        -------
        Series
           New Series with updated labels.

        See Also
        --------
        Series.add_suffix: Suffix column labels with string `suffix`.
        DataFrame.add_suffix: Suffix column labels with string `suffix`.
        DataFrame.add_prefix: Prefix column labels with string `prefix`.

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

        >>> s.add_prefix('item_')
        item_0    1
        item_1    2
        item_2    3
        item_3    4
        dtype: int64
        """
        assert isinstance(prefix, str)
        internal = self._internal.resolved_copy
        sdf = internal.spark_frame.select(
            [
                F.concat(F.lit(prefix), index_spark_column).alias(index_spark_column_name)
                for index_spark_column, index_spark_column_name in zip(
                    internal.index_spark_columns, internal.index_spark_column_names
                )
            ]
            + internal.data_spark_columns
        )
        return first_series(
            DataFrame(internal.with_new_sdf(sdf, index_fields=([None] * internal.index_level)))
        )

    def add_suffix(self, suffix: str) -> "Series":
        """
        Suffix labels with string suffix.

        For Series, the row labels are suffixed.
        For DataFrame, the column labels are suffixed.

        Parameters
        ----------
        suffix : str
           The string to add after each label.

        Returns
        -------
        Series
           New Series with updated labels.

        See Also
        --------
        Series.add_prefix: Prefix row labels with string `prefix`.
        DataFrame.add_prefix: Prefix column labels with string `prefix`.
        DataFrame.add_suffix: Suffix column labels with string `suffix`.

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

        >>> s.add_suffix('_item')
        0_item    1
        1_item    2
        2_item    3
        3_item    4
        dtype: int64
        """
        assert isinstance(suffix, str)
        internal = self._internal.resolved_copy
        sdf = internal.spark_frame.select(
            [
                F.concat(index_spark_column, F.lit(suffix)).alias(index_spark_column_name)
                for index_spark_column, index_spark_column_name in zip(
                    internal.index_spark_columns, internal.index_spark_column_names
                )
            ]
            + internal.data_spark_columns
        )
        return first_series(
            DataFrame(internal.with_new_sdf(sdf, index_fields=([None] * internal.index_level)))
        )

    def autocorr(self, lag: int = 1) -> float:
        """
        Compute the lag-N autocorrelation.

        This method computes the Pearson correlation between
        the Series and its shifted self.

        .. note:: the current implementation of rank uses Spark's Window without
            specifying partition specification. This leads to moving all data into
            a single partition in a single machine and could cause serious
            performance degradation. Avoid this method with very large datasets.

        .. versionadded:: 3.4.0

        Parameters
        ----------
        lag : int, default 1
            Number of lags to apply before performing autocorrelation.

        Returns
        -------
        float
            The Pearson correlation between self and self.shift(lag).

        See Also
        --------
        Series.corr : Compute the correlation between two Series.
        Series.shift : Shift index by desired number of periods.
        DataFrame.corr : Compute pairwise correlation of columns.

        Notes
        -----
        If the Pearson correlation is not well defined return 'NaN'.

        Examples
        --------
        >>> s = ps.Series([.2, .0, .6, .2, np.nan, .5, .6])
        >>> s.autocorr()  # doctest: +ELLIPSIS
        -0.141219...
        >>> s.autocorr(0)  # doctest: +ELLIPSIS
        1.0...
        >>> s.autocorr(2)  # doctest: +ELLIPSIS
        0.970725...
        >>> s.autocorr(-3)  # doctest: +ELLIPSIS
        0.277350...
        >>> s.autocorr(5)  # doctest: +ELLIPSIS
        -1.000000...
        >>> s.autocorr(6)  # doctest: +ELLIPSIS
        nan

        If the Pearson correlation is not well defined, then 'NaN' is returned.

        >>> s = ps.Series([1, 0, 0, 0])
        >>> s.autocorr()
        nan
        """
        # This implementation is suboptimal because it moves all data to a single partition,
        # global sort should be used instead of window, but it should be a start
        if not isinstance(lag, int):
            raise TypeError("lag should be an int; however, got [%s]" % type(lag).__name__)

        sdf = self._internal.spark_frame
        scol = self.spark.column
        if lag == 0:
            corr = sdf.select(F.corr(scol, scol)).head()[0]
        else:
            lag_scol = F.lag(scol, lag).over(Window.orderBy(NATURAL_ORDER_COLUMN_NAME))
            lag_col_name = verify_temp_column_name(sdf, "__autocorr_lag_tmp_col__")

            sdf_lag = sdf.withColumn(lag_col_name, lag_scol)
            if is_ansi_mode_enabled(sdf.sparkSession):
                # Compute covariance between the original and lagged columns.
                # If the covariance is None or zero (indicating no linear relationship),
                # return NaN, otherwise, proceeding to compute correlation may raise
                # DIVIDE_BY_ZERO under ANSI mode.
                cov_value = sdf_lag.select(F.covar_samp(scol, F.col(lag_col_name))).head()[0]
                if cov_value is None or cov_value == 0.0:
                    return np.nan
            corr = sdf_lag.select(F.corr(scol, F.col(lag_col_name))).head()[0]

        return np.nan if corr is None else corr

    @with_ansi_mode_context
    def corr(
        self, other: "Series", method: str = "pearson", min_periods: Optional[int] = None
    ) -> float:
        """
        Compute correlation with `other` Series, excluding missing values.

        .. versionadded:: 3.3.0

        Parameters
        ----------
        other : Series
        method : {'pearson', 'spearman', 'kendall'}
            * pearson : standard correlation coefficient
            * spearman : Spearman rank correlation
            * kendall : Kendall Tau correlation coefficient

            .. versionchanged:: 3.4.0
               support 'kendall' for method parameter
        min_periods : int, optional
            Minimum number of observations needed to have a valid result.

            .. versionadded:: 3.4.0

        Returns
        -------
        correlation : float

        Notes
        -----
        The complexity of Kendall correlation is O(#row * #row), if the dataset is too
        large, sampling ahead of correlation computation is recommended.

        Examples
        --------
        >>> df = ps.DataFrame({'s1': [.2, .0, .6, .2],
        ...                    's2': [.3, .6, .0, .1]})
        >>> s1 = df.s1
        >>> s2 = df.s2
        >>> s1.corr(s2, method='pearson')
        -0.85106...

        >>> s1.corr(s2, method='spearman')
        -0.94868...

        >>> s1.corr(s2, method='kendall')
        -0.91287...

        >>> s1 = ps.Series([1, np.nan, 2, 1, 1, 2, 3])
        >>> s2 = ps.Series([3, 4, 1, 1, 5])

        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s1.corr(s2, method="pearson")
        -0.52223...

        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s1.corr(s2, method="spearman")
        -0.54433...

        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s1.corr(s2, method="kendall")
        -0.51639...

        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s1.corr(s2, method="kendall", min_periods=5)
        nan
        """
        if method not in ["pearson", "spearman", "kendall"]:
            raise ValueError(f"Invalid method {method}")
        if not isinstance(other, Series):
            raise TypeError("'other' must be a Series")
        if min_periods is not None and not isinstance(min_periods, int):
            raise TypeError(f"Invalid min_periods type {type(min_periods).__name__}")

        min_periods = 1 if min_periods is None else min_periods

        if same_anchor(self, other):
            combined = self
            this = self
            that = other
        else:
            combined = combine_frames(self._psdf, other._psdf)  # type: ignore[assignment]
            this = combined["this"]
            that = combined["that"]

        sdf = combined._internal.spark_frame
        index_col_name = verify_temp_column_name(sdf, "__ser_corr_index_temp_column__")
        this_scol = this._internal.spark_column_for(this._internal.column_labels[0])
        that_scol = that._internal.spark_column_for(that._internal.column_labels[0])

        sdf = sdf.select(
            F.lit(0).alias(index_col_name),
            this_scol.cast("double").alias(CORRELATION_VALUE_1_COLUMN),
            that_scol.cast("double").alias(CORRELATION_VALUE_2_COLUMN),
        )

        sdf = compute(sdf=sdf, groupKeys=[index_col_name], method=method).select(
            F.when(
                F.col(CORRELATION_COUNT_OUTPUT_COLUMN) < min_periods, F.lit(None).cast("double")
            ).otherwise(F.col(CORRELATION_CORR_OUTPUT_COLUMN))
        )

        results = sdf.take(1)
        if len(results) == 0:
            raise ValueError("attempt to get corr of an empty sequence")
        else:
            return np.nan if results[0][0] is None else results[0][0]

    def nsmallest(self, n: int = 5) -> "Series":
        """
        Return the smallest `n` elements.

        Parameters
        ----------
        n : int, default 5
            Return this many ascending sorted values.

        Returns
        -------
        Series
            The `n` smallest values in the Series, sorted in increasing order.

        See Also
        --------
        Series.nlargest: Get the `n` largest elements.
        Series.sort_values: Sort Series by values.
        Series.head: Return the first `n` rows.

        Notes
        -----
        Faster than ``.sort_values().head(n)`` for small `n` relative to
        the size of the ``Series`` object.
        In pandas-on-Spark, thanks to Spark's lazy execution and query optimizer,
        the two would have same performance.

        Examples
        --------
        >>> data = [1, 2, 3, 4, np.nan ,6, 7, 8]
        >>> s = ps.Series(data)
        >>> s
        0    1.0
        1    2.0
        2    3.0
        3    4.0
        4    NaN
        5    6.0
        6    7.0
        7    8.0
        dtype: float64

        The `n` largest elements where ``n=5`` by default.

        >>> s.nsmallest()
        0    1.0
        1    2.0
        2    3.0
        3    4.0
        5    6.0
        dtype: float64

        >>> s.nsmallest(3)
        0    1.0
        1    2.0
        2    3.0
        dtype: float64
        """
        return self.sort_values(ascending=True).head(n)

    def nlargest(self, n: int = 5) -> "Series":
        """
        Return the largest `n` elements.

        Parameters
        ----------
        n : int, default 5

        Returns
        -------
        Series
            The `n` largest values in the Series, sorted in decreasing order.

        See Also
        --------
        Series.nsmallest: Get the `n` smallest elements.
        Series.sort_values: Sort Series by values.
        Series.head: Return the first `n` rows.

        Notes
        -----
        Faster than ``.sort_values(ascending=False).head(n)`` for small `n`
        relative to the size of the ``Series`` object.

        In pandas-on-Spark, thanks to Spark's lazy execution and query optimizer,
        the two would have same performance.

        Examples
        --------
        >>> data = [1, 2, 3, 4, np.nan ,6, 7, 8]
        >>> s = ps.Series(data)
        >>> s
        0    1.0
        1    2.0
        2    3.0
        3    4.0
        4    NaN
        5    6.0
        6    7.0
        7    8.0
        dtype: float64

        The `n` largest elements where ``n=5`` by default.

        >>> s.nlargest()
        7    8.0
        6    7.0
        5    6.0
        3    4.0
        2    3.0
        dtype: float64

        >>> s.nlargest(n=3)
        7    8.0
        6    7.0
        5    6.0
        dtype: float64


        """
        return self.sort_values(ascending=False).head(n)

    def sample(
        self,
        n: Optional[int] = None,
        frac: Optional[float] = None,
        replace: bool = False,
        random_state: Optional[int] = None,
        ignore_index: bool = False,
    ) -> "Series":
        return first_series(
            self.to_frame().sample(
                n=n,
                frac=frac,
                replace=replace,
                random_state=random_state,
                ignore_index=ignore_index,
            )
        ).rename(self.name)

    sample.__doc__ = DataFrame.sample.__doc__

    @no_type_check
    def hist(self, bins=10, **kwds):
        return self.plot.hist(bins, **kwds)

    hist.__doc__ = PandasOnSparkPlotAccessor.hist.__doc__

    def apply(self, func: Callable, args: Sequence[Any] = (), **kwds: Any) -> "Series":
        """
        Invoke function on values of Series.

        Can be a Python function that only works on the Series.

        .. note:: this API executes the function once to infer the type which is
             potentially expensive, for instance, when the dataset is created after
             aggregations or sorting.

             To avoid this, specify return type in ``func``, for instance, as below:

             >>> def square(x) -> np.int32:
             ...     return x ** 2

             pandas-on-Spark uses return type hint and does not try to infer the type.

        Parameters
        ----------
        func : function
            Python function to apply. Note that type hint for return type is required.
        args : tuple
            Positional arguments passed to func after the series value.
        **kwds
            Additional keyword arguments passed to func.

        Returns
        -------
        Series

        See Also
        --------
        Series.aggregate : Only perform aggregating type operations.
        Series.transform : Only perform transforming type operations.
        DataFrame.apply : The equivalent function for DataFrame.

        Examples
        --------
        Create a Series with typical summer temperatures for each city.

        >>> s = ps.Series([20, 21, 12],
        ...               index=['London', 'New York', 'Helsinki'])
        >>> s
        London      20
        New York    21
        Helsinki    12
        dtype: int64


        Square the values by defining a function and passing it as an
        argument to ``apply()``.

        >>> def square(x) -> np.int64:
        ...     return x ** 2
        >>> s.apply(square)
        London      400
        New York    441
        Helsinki    144
        dtype: int64


        Define a custom function that needs additional positional
        arguments and pass these additional arguments using the
        ``args`` keyword

        >>> def subtract_custom_value(x, custom_value) -> np.int64:
        ...     return x - custom_value

        >>> s.apply(subtract_custom_value, args=(5,))
        London      15
        New York    16
        Helsinki     7
        dtype: int64


        Define a custom function that takes keyword arguments
        and pass these arguments to ``apply``

        >>> def add_custom_values(x, **kwargs) -> np.int64:
        ...     for month in kwargs:
        ...         x += kwargs[month]
        ...     return x

        >>> s.apply(add_custom_values, june=30, july=20, august=25)
        London      95
        New York    96
        Helsinki    87
        dtype: int64


        Use a function from the Numpy library

        >>> def numpy_log(col) -> np.float64:
        ...     return np.log(col)
        >>> s.apply(numpy_log)
        London      2.995732
        New York    3.044522
        Helsinki    2.484907
        dtype: float64


        You can omit the type hint and let pandas-on-Spark infer its type.

        >>> s.apply(np.log)
        London      2.995732
        New York    3.044522
        Helsinki    2.484907
        dtype: float64

        """
        assert callable(func), "the first argument should be a callable function."
        try:
            spec = inspect.getfullargspec(func)
            return_sig = spec.annotations.get("return", None)
            should_infer_schema = return_sig is None
        except TypeError:
            # Falls back to schema inference if it fails to get signature.
            should_infer_schema = True

        def apply_each(s: Any) -> pd.Series:
            return s.apply(func, args=args, **kwds)

        if should_infer_schema:
            return self.pandas_on_spark._transform_batch(apply_each, None)
        else:
            sig_return = infer_return_type(func)
            if not isinstance(sig_return, ScalarType):
                raise ValueError(
                    "Expected the return type of this function to be of scalar type, "
                    "but found type {}".format(sig_return)
                )
            return_type = sig_return
            return self.pandas_on_spark._transform_batch(apply_each, return_type)

    # TODO: not all arguments are implemented comparing to pandas' for now.
    def aggregate(self, func: Union[str, List[str]]) -> Union[Scalar, "Series"]:
        """Aggregate using one or more operations over the specified axis.

        Parameters
        ----------
        func : str or a list of str
            function name(s) as string apply to series.

        Returns
        -------
        scalar, Series
            The return can be:
            - scalar : when Series.agg is called with single function
            - Series : when Series.agg is called with several functions

        Notes
        -----
        `agg` is an alias for `aggregate`. Use the alias.

        See Also
        --------
        Series.apply : Invoke function on a Series.
        Series.transform : Only perform transforming type operations.
        Series.groupby : Perform operations over groups.
        DataFrame.aggregate : The equivalent function for DataFrame.

        Examples
        --------
        >>> s = ps.Series([1, 2, 3, 4])
        >>> s.agg('min')
        1

        >>> s.agg(['min', 'max']).sort_index()
        max    4
        min    1
        dtype: int64
        """
        if isinstance(func, list):
            return first_series(self.to_frame().aggregate(func)).rename(self.name)
        elif isinstance(func, str):
            return getattr(self, func)()
        else:
            raise TypeError("func must be a string or list of strings")

    agg = aggregate

    def transpose(self, *args: Any, **kwargs: Any) -> "Series":
        """
        Return the transpose, which is self.

        Examples
        --------
        It returns the same object as the transpose of the given series object, which is by
        definition self.

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

        >>> s.transpose()
        0    1
        1    2
        2    3
        dtype: int64
        """
        return self.copy()

    T = property(transpose)

    def transform(
        self, func: Union[Callable, List[Callable]], axis: Axis = 0, *args: Any, **kwargs: Any
    ) -> Union["Series", DataFrame]:
        """
        Call ``func`` producing the same type as `self` with transformed values
        and that has the same axis length as input.

        .. note:: this API executes the function once to infer the type which is
             potentially expensive, for instance, when the dataset is created after
             aggregations or sorting.

             To avoid this, specify return type in ``func``, for instance, as below:

             >>> def square(x) -> np.int32:
             ...     return x ** 2

             pandas-on-Spark uses return type hint and does not try to infer the type.

        Parameters
        ----------
        func : function or list
            A function or a list of functions to use for transforming the data.
        axis : int, default 0 or 'index'
            Can only be set to 0 now.
        *args
            Positional arguments to pass to `func`.
        **kwargs
            Keyword arguments to pass to `func`.

        Returns
        -------
        An instance of the same type with `self` that must have the same length as input.

        See Also
        --------
        Series.aggregate : Only perform aggregating type operations.
        Series.apply : Invoke function on Series.
        DataFrame.transform : The equivalent function for DataFrame.

        Examples
        --------

        >>> s = ps.Series(range(3))
        >>> s
        0    0
        1    1
        2    2
        dtype: int64

        >>> def sqrt(x) -> float:
        ...     return np.sqrt(x)
        >>> s.transform(sqrt)
        0    0.000000
        1    1.000000
        2    1.414214
        dtype: float64

        Even though the resulting instance must have the same length as the
        input, it is possible to provide several input functions:

        >>> def exp(x) -> float:
        ...     return np.exp(x)
        >>> s.transform([sqrt, exp])
               sqrt       exp
        0  0.000000  1.000000
        1  1.000000  2.718282
        2  1.414214  7.389056

        You can omit the type hint and let pandas-on-Spark infer its type.

        >>> s.transform([np.sqrt, np.exp])
               sqrt       exp
        0  0.000000  1.000000
        1  1.000000  2.718282
        2  1.414214  7.389056
        """
        axis = validate_axis(axis)
        if axis != 0:
            raise NotImplementedError('axis should be either 0 or "index" currently.')

        if isinstance(func, list):
            applied = []
            for f in func:
                applied.append(self.apply(f, args=args, **kwargs).rename(f.__name__))

            internal = self._internal.with_new_columns(applied)
            return DataFrame(internal)
        else:
            return self.apply(func, args=args, **kwargs)

    def round(self, decimals: int = 0) -> "Series":
        """
        Round each value in a Series to the given number of decimals.

        Parameters
        ----------
        decimals : int
            Number of decimal places to round to (default: 0).
            If decimals are negative, it specifies the number of
            positions to the left of the decimal point.

        Returns
        -------
        Series object

        See Also
        --------
        DataFrame.round

        Examples
        --------
        >>> df = ps.Series([0.028208, 0.038683, 0.877076], name='x')
        >>> df
        0    0.028208
        1    0.038683
        2    0.877076
        Name: x, dtype: float64

        >>> df.round(2)
        0    0.03
        1    0.04
        2    0.88
        Name: x, dtype: float64
        """
        if not isinstance(decimals, int):
            raise TypeError("decimals must be an integer")
        scol = F.round(self.spark.column, decimals)
        return self._with_new_scol(
            scol,
            field=(
                self._internal.data_fields[0].copy(nullable=True)
                if not isinstance(self.spark.data_type, DecimalType)
                else None
            ),
        )

    # TODO: add 'interpolation' parameter.
    def quantile(
        self, q: Union[float, Iterable[float]] = 0.5, accuracy: int = 10000
    ) -> Union[Scalar, "Series"]:
        """
        Return value at the given quantile.

        .. note:: Unlike pandas', the quantile in pandas-on-Spark is an approximated quantile
            based upon approximate percentile computation because computing quantile across
            a large dataset is extremely expensive.

        Parameters
        ----------
        q : float or array-like, default 0.5 (50% quantile)
            0 <= q <= 1, the quantile(s) to compute.
        accuracy : int, optional
            Default accuracy of approximation. Larger value means better accuracy.
            The relative error can be deduced by 1.0 / accuracy.

        Returns
        -------
        float or Series
            If the current object is a Series and ``q`` is an array, a Series will be
            returned where the index is ``q`` and the values are the quantiles, otherwise
            a float will be returned.

        Examples
        --------
        >>> s = ps.Series([1, 2, 3, 4, 5])
        >>> s.quantile(.5)
        3.0

        >>> (s + 1).quantile(.5)
        4.0

        >>> s.quantile([.25, .5, .75])
        0.25    2.0
        0.50    3.0
        0.75    4.0
        dtype: float64

        >>> (s + 1).quantile([.25, .5, .75])
        0.25    3.0
        0.50    4.0
        0.75    5.0
        dtype: float64
        """
        if isinstance(q, Iterable):
            return first_series(
                cast(
                    "ps.DataFrame",
                    self.to_frame().quantile(q=q, axis=0, numeric_only=False, accuracy=accuracy),
                )
            ).rename(self.name)
        else:
            if not isinstance(accuracy, int):
                raise TypeError(
                    "accuracy must be an integer; however, got [%s]" % type(accuracy).__name__
                )

            if not isinstance(q, float):
                raise TypeError(
                    "q must be a float or an array of floats; however, [%s] found." % type(q)
                )
            q_float = q
            if q_float < 0.0 or q_float > 1.0:
                raise ValueError("percentiles should all be in the interval [0, 1].")

            def quantile(psser: Series) -> PySparkColumn:
                spark_type = psser.spark.data_type
                spark_column = psser.spark.column
                if isinstance(spark_type, (BooleanType, NumericType, NullType)):
                    return F.percentile_approx(spark_column.cast(DoubleType()), q_float, accuracy)
                else:
                    raise TypeError(
                        "Could not convert {} ({}) to numeric".format(
                            spark_type_to_pandas_dtype(spark_type), spark_type.simpleString()
                        )
                    )

            return self._reduce_for_stat_function(quantile, name="quantile")

    # TODO: add axis, pct, na_option parameter
    def rank(
        self, method: str = "average", ascending: bool = True, numeric_only: bool = False
    ) -> "Series":
        """
        Compute numerical data ranks (1 through n) along axis. Equal values are
        assigned a rank that is the average of the ranks of those values.

        .. note:: the current implementation of rank uses Spark's Window without
            specifying partition specification. This leads to moving all data into
            a single partition in a single machine and could cause serious
            performance degradation. Avoid this method with very large datasets.

        Parameters
        ----------
        method : {'average', 'min', 'max', 'first', 'dense'}
            * average: average rank of group
            * min: lowest rank in group
            * max: highest rank in group
            * first: ranks assigned in order they appear in the array
            * dense: like 'min', but rank always increases by 1 between groups
        ascending : boolean, default True
            False for ranks by high (1) to low (N)
        numeric_only : bool, default False
            For DataFrame objects, rank only numeric columns if set to True.

            .. versionchanged:: 4.0.0
                The default value of ``numeric_only`` is now ``False``.


        Returns
        -------
        ranks : same type as caller

        Examples
        --------
        >>> s = ps.Series([1, 2, 2, 3], name='A')
        >>> s
        0    1
        1    2
        2    2
        3    3
        Name: A, dtype: int64

        >>> s.rank()
        0    1.0
        1    2.5
        2    2.5
        3    4.0
        Name: A, dtype: float64

        If method is set to 'min', it uses lowest rank in group.

        >>> s.rank(method='min')
        0    1.0
        1    2.0
        2    2.0
        3    4.0
        Name: A, dtype: float64

        If method is set to 'max', it uses highest rank in group.

        >>> s.rank(method='max')
        0    1.0
        1    3.0
        2    3.0
        3    4.0
        Name: A, dtype: float64

        If method is set to 'first', it is assigned rank in order without groups.

        >>> s.rank(method='first')
        0    1.0
        1    2.0
        2    3.0
        3    4.0
        Name: A, dtype: float64

        If method is set to 'dense', it leaves no gaps in group.

        >>> s.rank(method='dense')
        0    1.0
        1    2.0
        2    2.0
        3    3.0
        Name: A, dtype: float64

        If numeric_only is set to 'True', rank only numeric Series,
        return an empty Series otherwise.

        >>> s = ps.Series(['a', 'b', 'c'], name='A', index=['x', 'y', 'z'])
        >>> s
        x    a
        y    b
        z    c
        Name: A, dtype: object
        """
        is_numeric = isinstance(self.spark.data_type, (NumericType, BooleanType))
        if numeric_only and not is_numeric:
            raise TypeError("Series.rank does not allow numeric_only=True with non-numeric dtype.")
        else:
            return self._rank(method, ascending).spark.analyzed

    def _rank(
        self,
        method: str = "average",
        ascending: bool = True,
        *,
        part_cols: Sequence["ColumnOrName"] = (),
    ) -> "Series":
        if method not in ["average", "min", "max", "first", "dense"]:
            msg = "method must be one of 'average', 'min', 'max', 'first', 'dense'"
            raise ValueError(msg)

        if self._internal.index_level > 1:
            raise NotImplementedError("rank do not support MultiIndex now")

        if ascending:
            asc_func = PySparkColumn.asc
        else:
            asc_func = PySparkColumn.desc

        if method == "first":
            window = (
                Window.orderBy(
                    asc_func(self.spark.column),
                    asc_func(F.col(NATURAL_ORDER_COLUMN_NAME)),
                )
                .partitionBy(*part_cols)
                .rowsBetween(Window.unboundedPreceding, Window.currentRow)
            )
            scol = F.row_number().over(window)
        elif method == "dense":
            window = (
                Window.orderBy(asc_func(self.spark.column))
                .partitionBy(*part_cols)
                .rowsBetween(Window.unboundedPreceding, Window.currentRow)
            )
            scol = F.dense_rank().over(window)
        else:
            if method == "average":
                stat_func = F.mean
            elif method == "min":
                stat_func = F.min
            elif method == "max":
                stat_func = F.max
            window1 = (
                Window.orderBy(asc_func(self.spark.column))
                .partitionBy(*part_cols)
                .rowsBetween(Window.unboundedPreceding, Window.currentRow)
            )

            window2 = Window.partitionBy(
                cast("List[ColumnOrName]", [self.spark.column]) + list(part_cols)
            ).rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)
            scol = stat_func(F.row_number().over(window1)).over(window2)
        return self._with_new_scol(scol.cast(DoubleType()))

    def filter(
        self,
        items: Optional[Sequence[Any]] = None,
        like: Optional[str] = None,
        regex: Optional[str] = None,
        axis: Optional[Axis] = None,
    ) -> "Series":
        axis = validate_axis(axis)
        if axis == 1:
            raise ValueError("Series does not support columns axis.")
        return first_series(
            self.to_frame().filter(items=items, like=like, regex=regex, axis=axis),
        ).rename(self.name)

    filter.__doc__ = DataFrame.filter.__doc__

    def describe(self, percentiles: Optional[List[float]] = None) -> "Series":
        return first_series(self.to_frame().describe(percentiles)).rename(self.name)

    describe.__doc__ = DataFrame.describe.__doc__

    def diff(self, periods: int = 1) -> "Series":
        """
        First discrete difference of element.

        Calculates the difference of a Series element compared with another element in the
        DataFrame (default is the element in the same column of the previous row).

        .. note:: the current implementation of diff uses Spark's Window without
            specifying partition specification. This leads to moving all data into
            a single partition in a single machine and could cause serious
            performance degradation. Avoid this method with very large datasets.

        Parameters
        ----------
        periods : int, default 1
            Periods to shift for calculating difference, accepts negative values.

        Returns
        -------
        diffed : Series

        Examples
        --------
        >>> df = ps.DataFrame({'a': [1, 2, 3, 4, 5, 6],
        ...                    'b': [1, 1, 2, 3, 5, 8],
        ...                    'c': [1, 4, 9, 16, 25, 36]}, columns=['a', 'b', 'c'])
        >>> df
           a  b   c
        0  1  1   1
        1  2  1   4
        2  3  2   9
        3  4  3  16
        4  5  5  25
        5  6  8  36

        >>> df.b.diff()
        0    NaN
        1    0.0
        2    1.0
        3    1.0
        4    2.0
        5    3.0
        Name: b, dtype: float64

        Difference with previous value

        >>> df.c.diff(periods=3)
        0     NaN
        1     NaN
        2     NaN
        3    15.0
        4    21.0
        5    27.0
        Name: c, dtype: float64

        Difference with following value

        >>> df.c.diff(periods=-1)
        0    -3.0
        1    -5.0
        2    -7.0
        3    -9.0
        4   -11.0
        5     NaN
        Name: c, dtype: float64
        """
        return self._diff(periods).spark.analyzed

    def _diff(self, periods: int, *, part_cols: Sequence["ColumnOrName"] = ()) -> "Series":
        if not isinstance(periods, int):
            raise TypeError("periods should be an int; however, got [%s]" % type(periods).__name__)
        window = (
            Window.partitionBy(*part_cols)
            .orderBy(NATURAL_ORDER_COLUMN_NAME)
            .rowsBetween(-periods, -periods)
        )
        scol = self.spark.column - F.lag(self.spark.column, periods).over(window)
        return self._with_new_scol(scol, field=self._internal.data_fields[0].copy(nullable=True))

    def idxmax(self, skipna: bool = True) -> Union[Tuple, Any]:
        """
        Return the row label of the maximum value.

        If multiple values equal the maximum, the first row label with that
        value is returned.

        Parameters
        ----------
        skipna : bool, default True
            Exclude NA/null values. If the entire Series is NA, the result
            will be NA.

        Returns
        -------
        Index
            Label of the maximum value.

        Raises
        ------
        ValueError
            If the Series is empty.

        See Also
        --------
        Series.idxmin : Return index *label* of the first occurrence
            of minimum of values.

        Examples
        --------
        >>> s = ps.Series(data=[1, None, 4, 3, 5],
        ...               index=['A', 'B', 'C', 'D', 'E'])
        >>> s
        A    1.0
        B    NaN
        C    4.0
        D    3.0
        E    5.0
        dtype: float64

        >>> s.idxmax()
        'E'

        If `skipna` is False and there is an NA value in the data,
        the function returns ``nan``.

        >>> s.idxmax(skipna=False)
        nan

        In case of multi-index, you get a tuple:

        >>> index = pd.MultiIndex.from_arrays([
        ...     ['a', 'a', 'b', 'b'], ['c', 'd', 'e', 'f']], names=('first', 'second'))
        >>> s = ps.Series(data=[1, None, 4, 5], index=index)
        >>> s
        first  second
        a      c         1.0
               d         NaN
        b      e         4.0
               f         5.0
        dtype: float64

        >>> s.idxmax()
        ('b', 'f')

        If multiple values equal the maximum, the first row label with that
        value is returned.

        >>> s = ps.Series([1, 100, 1, 100, 1, 100], index=[10, 3, 5, 2, 1, 8])
        >>> s
        10      1
        3     100
        5       1
        2     100
        1       1
        8     100
        dtype: int64

        >>> s.idxmax()
        3
        """
        sdf = self._internal.spark_frame
        scol = self.spark.column
        index_scols = self._internal.index_spark_columns

        if skipna:
            sdf = sdf.orderBy(scol.desc_nulls_last(), NATURAL_ORDER_COLUMN_NAME)
        else:
            sdf = sdf.orderBy(scol.desc_nulls_first(), NATURAL_ORDER_COLUMN_NAME)

        results = sdf.select([scol] + index_scols).take(1)
        if len(results) == 0:
            raise ValueError("attempt to get idxmin of an empty sequence")
        if results[0][0] is None:
            if LooseVersion(pd.__version__) < "3.0.0":
                # This will only happen when skipna is False because we will
                # place nulls first.
                warnings.warn(
                    "The behavior of Series.idxmax with all-NA values, or any-NA and skipna=False, "
                    "is deprecated. In a future version this will raise ValueError",
                    FutureWarning,
                )
                return np.nan
            else:
                if skipna:
                    raise ValueError("Encountered all NA values")
                else:
                    raise ValueError("Encountered an NA value with skipna=False")
        values = list(results[0][1:])
        if len(values) == 1:
            return values[0]
        else:
            return tuple(values)

    def idxmin(self, skipna: bool = True) -> Union[Tuple, Any]:
        """
        Return the row label of the minimum value.

        If multiple values equal the minimum, the first row label with that
        value is returned.

        Parameters
        ----------
        skipna : bool, default True
            Exclude NA/null values. If the entire Series is NA, the result
            will be NA.

        Returns
        -------
        Index
            Label of the minimum value.

        Raises
        ------
        ValueError
            If the Series is empty.

        See Also
        --------
        Series.idxmax : Return index *label* of the first occurrence
            of maximum of values.

        Notes
        -----
        This method is the Series version of ``ndarray.argmin``. This method
        returns the label of the minimum, while ``ndarray.argmin`` returns
        the position. To get the position, use ``series.values.argmin()``.

        Examples
        --------
        >>> s = ps.Series(data=[1, None, 4, 0],
        ...               index=['A', 'B', 'C', 'D'])
        >>> s
        A    1.0
        B    NaN
        C    4.0
        D    0.0
        dtype: float64

        >>> s.idxmin()
        'D'

        If `skipna` is False and there is an NA value in the data,
        the function returns ``nan``.

        >>> s.idxmin(skipna=False)
        nan

        In case of multi-index, you get a tuple:

        >>> index = pd.MultiIndex.from_arrays([
        ...     ['a', 'a', 'b', 'b'], ['c', 'd', 'e', 'f']], names=('first', 'second'))
        >>> s = ps.Series(data=[1, None, 4, 0], index=index)
        >>> s
        first  second
        a      c         1.0
               d         NaN
        b      e         4.0
               f         0.0
        dtype: float64

        >>> s.idxmin()
        ('b', 'f')

        If multiple values equal the minimum, the first row label with that
        value is returned.

        >>> s = ps.Series([1, 100, 1, 100, 1, 100], index=[10, 3, 5, 2, 1, 8])
        >>> s
        10      1
        3     100
        5       1
        2     100
        1       1
        8     100
        dtype: int64

        >>> s.idxmin()
        10
        """
        sdf = self._internal.spark_frame
        scol = self.spark.column
        index_scols = self._internal.index_spark_columns

        if skipna:
            sdf = sdf.orderBy(scol.asc_nulls_last(), NATURAL_ORDER_COLUMN_NAME)
        else:
            sdf = sdf.orderBy(scol.asc_nulls_first(), NATURAL_ORDER_COLUMN_NAME)

        results = sdf.select([scol] + index_scols).take(1)
        if len(results) == 0:
            raise ValueError("attempt to get idxmin of an empty sequence")
        if results[0][0] is None:
            if LooseVersion(pd.__version__) < "3.0.0":
                # This will only happen when skipna is False because we will
                # place nulls first.
                warnings.warn(
                    "The behavior of Series.idxmin with all-NA values, or any-NA and skipna=False, "
                    "is deprecated. In a future version this will raise ValueError",
                    FutureWarning,
                )
                return np.nan
            else:
                if skipna:
                    raise ValueError("Encountered all NA values")
                else:
                    raise ValueError("Encountered an NA value with skipna=False")
        values = list(results[0][1:])
        if len(values) == 1:
            return values[0]
        else:
            return tuple(values)

    def pop(self, item: Name) -> Union["Series", Scalar]:
        """
        Return item and drop from series.

        Parameters
        ----------
        item : label
            Label of index to be popped.

        Returns
        -------
        Value that is popped from series.

        Examples
        --------
        >>> s = ps.Series(data=np.arange(3), index=['A', 'B', 'C'])
        >>> s
        A    0
        B    1
        C    2
        dtype: int64

        >>> int(s.pop('A'))
        0

        >>> s
        B    1
        C    2
        dtype: int64

        >>> s = ps.Series(data=np.arange(3), index=['A', 'A', 'C'])
        >>> s
        A    0
        A    1
        C    2
        dtype: int64

        >>> s.pop('A')
        A    0
        A    1
        dtype: int64

        >>> s
        C    2
        dtype: int64

        Also support for MultiIndex

        >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
        ...                       ['speed', 'weight', 'length']],
        ...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                       [0, 1, 2, 0, 1, 2, 0, 1, 2]])
        >>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
        ...               index=midx)
        >>> s
        lama    speed      45.0
                weight    200.0
                length      1.2
        cow     speed      30.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                weight      1.0
                length      0.3
        dtype: float64

        >>> s.pop('lama')
        speed      45.0
        weight    200.0
        length      1.2
        dtype: float64

        >>> s
        cow     speed      30.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                weight      1.0
                length      0.3
        dtype: float64

        Also support for MultiIndex with several indexes.

        >>> midx = pd.MultiIndex([['a', 'b', 'c'],
        ...                       ['lama', 'cow', 'falcon'],
        ...                       ['speed', 'weight', 'length']],
        ...                      [[0, 0, 0, 0, 0, 0, 1, 1, 1],
        ...                       [0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                       [0, 1, 2, 0, 1, 2, 0, 0, 2]]
        ...  )
        >>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
        ...              index=midx)
        >>> s
        a  lama    speed      45.0
                   weight    200.0
                   length      1.2
           cow     speed      30.0
                   weight    250.0
                   length      1.5
        b  falcon  speed     320.0
                   speed       1.0
                   length      0.3
        dtype: float64

        >>> s.pop(('a', 'lama'))
        speed      45.0
        weight    200.0
        length      1.2
        dtype: float64

        >>> s
        a  cow     speed      30.0
                   weight    250.0
                   length      1.5
        b  falcon  speed     320.0
                   speed       1.0
                   length      0.3
        dtype: float64

        >>> s.pop(('b', 'falcon', 'speed'))
        (b, falcon, speed)    320.0
        (b, falcon, speed)      1.0
        dtype: float64
        """
        if not is_name_like_value(item):
            raise TypeError("'key' should be string or tuple that contains strings")
        if not is_name_like_tuple(item):
            item = (item,)
        if self._internal.index_level < len(item):
            raise KeyError(
                "Key length ({}) exceeds index depth ({})".format(
                    len(item), self._internal.index_level
                )
            )

        internal = self._internal
        scols = internal.index_spark_columns[len(item) :] + [self.spark.column]
        rows = [internal.spark_columns[level] == index for level, index in enumerate(item)]
        sdf = internal.spark_frame.filter(reduce(lambda x, y: x & y, rows)).select(scols)

        psdf = self._drop(item)
        self._update_anchor(psdf)

        if self._internal.index_level == len(item):
            # if spark_frame has one column and one data, return data only without frame
            pdf = sdf.limit(2).toPandas()
            length = len(pdf)
            if length == 1:
                val = pdf[internal.data_spark_column_names[0]].iloc[0]
                if isinstance(self.dtype, CategoricalDtype):
                    return self.dtype.categories[val]
                else:
                    return val

            item_string = name_like_string(item)
            sdf = sdf.withColumn(SPARK_DEFAULT_INDEX_NAME, F.lit(str(item_string)))
            internal = InternalFrame(
                spark_frame=sdf,
                index_spark_columns=[scol_for(sdf, SPARK_DEFAULT_INDEX_NAME)],
                column_labels=[self._column_label],
                data_fields=[self._internal.data_fields[0]],
            )
            return first_series(DataFrame(internal))
        else:
            internal = internal.copy(
                spark_frame=sdf,
                index_spark_columns=[
                    scol_for(sdf, col) for col in internal.index_spark_column_names[len(item) :]
                ],
                index_fields=internal.index_fields[len(item) :],
                index_names=self._internal.index_names[len(item) :],
                data_spark_columns=[scol_for(sdf, internal.data_spark_column_names[0])],
            )
            return first_series(DataFrame(internal))

    def copy(self, deep: bool = True) -> "Series":
        """
        Make a copy of this object's indices and data.

        Parameters
        ----------
        deep : bool, default True
            this parameter is not supported but just dummy parameter to match pandas.

        Returns
        -------
        copy : Series

        Examples
        --------
        >>> s = ps.Series([1, 2], index=["a", "b"])
        >>> s
        a    1
        b    2
        dtype: int64
        >>> s_copy = s.copy()
        >>> s_copy
        a    1
        b    2
        dtype: int64
        """
        return first_series(DataFrame(self._internal))

    def mode(self, dropna: bool = True) -> "Series":
        """
        Return the mode(s) of the dataset.

        Always returns Series even if only one value is returned.

        .. versionchanged:: 3.4.0
           Series name is preserved to follow pandas 1.4+ behavior.

        Parameters
        ----------
        dropna : bool, default True
            Don't consider counts of NaN/NaT.

        Returns
        -------
        Series
            Modes of the Series.

        Examples
        --------
        >>> s = ps.Series([0, 0, 1, 1, 1, np.nan, np.nan, np.nan])
        >>> s
        0    0.0
        1    0.0
        2    1.0
        3    1.0
        4    1.0
        5    NaN
        6    NaN
        7    NaN
        dtype: float64

        >>> s.mode()
        0    1.0
        dtype: float64

        If there are several same modes, all items are shown

        >>> s = ps.Series([0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3,
        ...                np.nan, np.nan, np.nan])
        >>> s
        0     0.0
        1     0.0
        2     1.0
        3     1.0
        4     1.0
        5     2.0
        6     2.0
        7     2.0
        8     3.0
        9     3.0
        10    3.0
        11    NaN
        12    NaN
        13    NaN
        dtype: float64

        >>> s.mode().sort_values()
        0    1.0
        1    2.0
        2    3.0
        dtype: float64

        With 'dropna' set to 'False', we can also see NaN in the result

        >>> s.mode(False).sort_values()
        0    1.0
        1    2.0
        2    3.0
        3    NaN
        dtype: float64
        """
        scol = self.spark.column
        name = self._internal.data_spark_column_names[0]
        sdf = (
            self._internal.spark_frame.select(SF.mode(scol, dropna).alias(name))
            .select(F.array_sort(F.col(name)).alias(name))
            .select(F.explode(F.col(name)).alias(name))
        )
        internal = InternalFrame(spark_frame=sdf, index_spark_columns=None, column_labels=[None])
        ser_mode = first_series(DataFrame(internal))
        ser_mode.name = self.name
        return ser_mode

    def keys(self) -> "ps.Index":
        """
        Return alias for index.

        Returns
        -------
        Index
            Index of the Series.

        Examples
        --------
        >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
        ...                       ['speed', 'weight', 'length']],
        ...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                       [0, 1, 2, 0, 1, 2, 0, 1, 2]])
        >>> psser = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)

        >>> psser.keys()  # doctest: +SKIP
        MultiIndex([(  'lama',  'speed'),
                    (  'lama', 'weight'),
                    (  'lama', 'length'),
                    (   'cow',  'speed'),
                    (   'cow', 'weight'),
                    (   'cow', 'length'),
                    ('falcon',  'speed'),
                    ('falcon', 'weight'),
                    ('falcon', 'length')],
                   )
        """
        return self.index

    # TODO: introduce 'in_place'; fully support 'regex'
    @with_ansi_mode_context
    def replace(
        self,
        to_replace: Optional[Union[Any, List, Tuple, Dict]] = None,
        value: Optional[Union[List, Tuple]] = None,
        regex: Union[str, bool] = False,
    ) -> "Series":
        """
        Replace values given in to_replace with value.
        Values of the Series are replaced with other values dynamically.

        .. note:: For partial pattern matching, the replacement is against the whole string,
            which is different from pandas. That's by the nature of underlying Spark API.

        Parameters
        ----------
        to_replace : str, list, tuple, dict, Series, int, float, or None
            How to find the values that will be replaced.
            * numeric, str:

                - numeric: numeric values equal to to_replace will be replaced with value
                - str: string exactly matching to_replace will be replaced with value

            * list of str or numeric:

                - if to_replace and value are both lists or tuples, they must be the same length.
                - str and numeric rules apply as above.

            * dict:

                - Dicts can be used to specify different replacement values for different
                  existing values.
                  For example, {'a': 'b', 'y': 'z'} replaces the value 'a' with 'b' and 'y'
                  with 'z'. To use a dict in this way the value parameter should be None.
                - For a DataFrame a dict can specify that different values should be replaced
                  in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1
                  in column 'a' and the value 'z' in column 'b' and replaces these values with
                  whatever is specified in value.
                  The value parameter should not be None in this case.
                  You can treat this as a special case of passing two lists except that you are
                  specifying the column to search in.

            See the examples section for examples of each of these.

        value : scalar, dict, list, tuple, str default None
            Value to replace any values matching to_replace with.
            For a DataFrame a dict of values can be used to specify which value to use
            for each column (columns not in the dict will not be filled).
            Regular expressions, strings and lists or dicts of such objects are also allowed.

        regex: bool or str, default False
            Whether to interpret to_replace and/or value as regular expressions.
            If this is True then to_replace must be a string.
            Alternatively, this could be a regular expression in which case to_replace must be None.


        Returns
        -------
        Series
            Object after replacement.

        Examples
        --------

        Scalar `to_replace` and `value`

        >>> s = ps.Series([0, 1, 2, 3, 4])
        >>> s
        0    0
        1    1
        2    2
        3    3
        4    4
        dtype: int64

        >>> s.replace(0, 5)
        0    5
        1    1
        2    2
        3    3
        4    4
        dtype: int64

        List-like `to_replace`

        >>> s.replace([0, 4], 5000)
        0    5000
        1       1
        2       2
        3       3
        4    5000
        dtype: int64

        >>> s.replace([1, 2, 3], [10, 20, 30])
        0     0
        1    10
        2    20
        3    30
        4     4
        dtype: int64

        Dict-like `to_replace`

        >>> s.replace({1: 1000, 2: 2000, 3: 3000, 4: 4000})
        0       0
        1    1000
        2    2000
        3    3000
        4    4000
        dtype: int64

        Also support for MultiIndex

        >>> midx = pd.MultiIndex([['lama', 'cow', 'falcon'],
        ...                       ['speed', 'weight', 'length']],
        ...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                       [0, 1, 2, 0, 1, 2, 0, 1, 2]])
        >>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
        ...               index=midx)
        >>> s
        lama    speed      45.0
                weight    200.0
                length      1.2
        cow     speed      30.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                weight      1.0
                length      0.3
        dtype: float64

        >>> s.replace(45, 450)
        lama    speed     450.0
                weight    200.0
                length      1.2
        cow     speed      30.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                weight      1.0
                length      0.3
        dtype: float64

        >>> s.replace([45, 30, 320], 500)
        lama    speed     500.0
                weight    200.0
                length      1.2
        cow     speed     500.0
                weight    250.0
                length      1.5
        falcon  speed     500.0
                weight      1.0
                length      0.3
        dtype: float64

        >>> s.replace({45: 450, 30: 300})
        lama    speed     450.0
                weight    200.0
                length      1.2
        cow     speed     300.0
                weight    250.0
                length      1.5
        falcon  speed     320.0
                weight      1.0
                length      0.3
        dtype: float64

        Regular expression `to_replace`

        >>> psser = ps.Series(['bat', 'foo', 'bait', 'abc', 'bar', 'zoo'])
        >>> psser.replace(to_replace=r'^ba.$', value='new', regex=True)
        0     new
        1     foo
        2    bait
        3     abc
        4     new
        5     zoo
        dtype: object

        >>> psser.replace(value='new', regex=r'^.oo$')
        0     bat
        1     new
        2    bait
        3     abc
        4     bar
        5     new
        dtype: object

        For partial pattern matching, the replacement is against the whole string

        >>> psser.replace('ba', 'xx', regex=True)
        0     xx
        1    foo
        2     xx
        3    abc
        4     xx
        5    zoo
        dtype: object
        """
        if isinstance(regex, str):
            if to_replace is not None:
                raise ValueError("'to_replace' must be 'None' if 'regex' is not a bool")
            to_replace = regex
            regex = True
        elif not isinstance(regex, bool):
            raise NotImplementedError("'regex' of %s type is not supported" % type(regex).__name__)
        elif regex is True:
            assert isinstance(to_replace, str), (
                "If 'regex' is True then 'to_replace' must be a string"
            )

        if to_replace is None:
            if LooseVersion(pd.__version__) < "3.0.0":
                return self._fillna_with_method(method="ffill")
            else:
                if value is None and regex is False:
                    raise ValueError(
                        "Series.replace must specify either 'value', a dict-like "
                        "'to_replace', or dict-like 'regex'."
                    )

        if not isinstance(to_replace, (str, list, tuple, dict, int, float)):
            raise TypeError("'to_replace' should be one of str, list, tuple, dict, int, float")

        to_replace = list(to_replace) if isinstance(to_replace, tuple) else to_replace
        value = list(value) if isinstance(value, tuple) else value
        if isinstance(to_replace, list) and isinstance(value, list):
            if not len(to_replace) == len(value):
                raise ValueError(
                    "Replacement lists must match in length. Expecting {} got {}".format(
                        len(to_replace), len(value)
                    )
                )
            to_replace = {k: v for k, v in zip(to_replace, value)}

        spark_session = self._internal.spark_frame.sparkSession
        ansi_mode = is_ansi_mode_enabled(spark_session)
        col_type = self.spark.data_type

        if isinstance(to_replace, dict):
            is_start = True
            if len(to_replace) == 0:
                current = self.spark.column
            else:
                for to_replace_, value in to_replace.items():
                    if pd.isna(to_replace_):
                        if ansi_mode and isinstance(col_type, NumericType):
                            cond = F.isnan(self.spark.column) | self.spark.column.isNull()
                        else:
                            cond = self.spark.column.isNull()
                    else:
                        to_replace_lit = (
                            F.lit(to_replace_).try_cast(col_type)
                            if ansi_mode
                            else F.lit(to_replace_)
                        )
                        cond = self.spark.column == to_replace_lit
                    value_expr = F.lit(value).try_cast(col_type) if ansi_mode else F.lit(value)
                    if is_start:
                        current = F.when(cond, value_expr)
                        is_start = False
                    else:
                        current = current.when(cond, value_expr)
                current = current.otherwise(self.spark.column)
        else:
            if regex:
                # to_replace must be a string
                cond = self.spark.column.rlike(cast(str, to_replace))
            else:
                if ansi_mode:
                    to_replace_values = (
                        [to_replace]
                        if not is_list_like(to_replace) or isinstance(to_replace, str)
                        else to_replace
                    )
                    to_replace_values = cast(List[Any], to_replace_values)
                    literals = [F.lit(v).try_cast(col_type) for v in to_replace_values]
                    cond = self.spark.column.isin(literals)
                else:
                    cond = self.spark.column.isin(to_replace)
                # to_replace may be a scalar
                if np.array(pd.isna(to_replace)).any():
                    if ansi_mode:
                        if isinstance(col_type, NumericType):
                            cond = cond | F.isnan(self.spark.column) | self.spark.column.isNull()
                        else:
                            cond = cond | self.spark.column.isNull()
                    else:
                        cond = cond | F.isnan(self.spark.column) | self.spark.column.isNull()

            if ansi_mode:
                value_expr = F.lit(value).try_cast(col_type)
                current = F.when(cond, value_expr).otherwise(self.spark.column.try_cast(col_type))

            else:
                current = F.when(cond, value).otherwise(self.spark.column)

        return self._with_new_scol(current)  # TODO: dtype?

    def update(self, other: "Series") -> None:
        """
        Modify Series in place using non-NA values from passed Series. Aligns on index.

        Parameters
        ----------
        other : Series

        Examples
        --------
        >>> from pyspark.pandas.config import set_option, reset_option
        >>> set_option("compute.ops_on_diff_frames", True)
        >>> s = ps.Series([1, 2, 3])
        >>> s.update(ps.Series([4, 5, 6]))
        >>> s.sort_index()
        0    4
        1    5
        2    6
        dtype: int64

        >>> s = ps.Series(['a', 'b', 'c'])
        >>> s.update(ps.Series(['d', 'e'], index=[0, 2]))
        >>> s.sort_index()
        0    d
        1    b
        2    e
        dtype: object

        >>> s = ps.Series([1, 2, 3])
        >>> s.update(ps.Series([4, 5, 6, 7, 8]))
        >>> s.sort_index()
        0    4
        1    5
        2    6
        dtype: int64

        >>> s = ps.Series([1, 2, 3], index=[10, 11, 12])
        >>> s
        10    1
        11    2
        12    3
        dtype: int64

        >>> s.update(ps.Series([4, 5, 6]))
        >>> s.sort_index()
        10    1
        11    2
        12    3
        dtype: int64

        >>> s.update(ps.Series([4, 5, 6], index=[11, 12, 13]))
        >>> s.sort_index()
        10    1
        11    4
        12    5
        dtype: int64

        If ``other`` contains NaNs the corresponding values are not updated
        in the original Series.

        >>> s = ps.Series([1, 2, 3])
        >>> s.update(ps.Series([4, np.nan, 6]))
        >>> s.sort_index()
        0    4.0
        1    2.0
        2    6.0
        dtype: float64

        >>> reset_option("compute.ops_on_diff_frames")
        """
        if not isinstance(other, Series):
            raise TypeError("'other' must be a Series")

        if same_anchor(self, other):
            scol = (
                F.when(other.spark.column.isNotNull(), other.spark.column)
                .otherwise(self.spark.column)
                .alias(self._psdf._internal.spark_column_name_for(self._column_label))
            )
            internal = self._psdf._internal.with_new_spark_column(
                self._column_label,
                scol,  # TODO: dtype?
            )
            self._update_internal_frame(internal)
        else:
            combined = combine_frames(self._psdf, other._psdf, how="leftouter")

            this_scol = combined["this"]._internal.spark_column_for(self._column_label)
            that_scol = combined["that"]._internal.spark_column_for(other._column_label)

            scol = (
                F.when(that_scol.isNotNull(), that_scol)
                .otherwise(this_scol)
                .alias(self._psdf._internal.spark_column_name_for(self._column_label))
            )

            internal = combined["this"]._internal.with_new_spark_column(
                self._column_label,
                scol,  # TODO: dtype?
            )

            self._update_internal_frame(internal.resolved_copy, check_same_anchor=False)

    def where(self, cond: "Series", other: Any = np.nan) -> "Series":
        """
        Replace values where the condition is False.

        Parameters
        ----------
        cond : boolean Series
            Where cond is True, keep the original value. Where False,
            replace with corresponding value from other.
        other : scalar, Series
            Entries where cond is False are replaced with corresponding value from other.

        Returns
        -------
        Series

        Examples
        --------

        >>> from pyspark.pandas.config import set_option, reset_option
        >>> set_option("compute.ops_on_diff_frames", True)
        >>> s1 = ps.Series([0, 1, 2, 3, 4])
        >>> s2 = ps.Series([100, 200, 300, 400, 500])
        >>> s1.where(s1 > 0).sort_index()
        0    NaN
        1    1.0
        2    2.0
        3    3.0
        4    4.0
        dtype: float64

        >>> s1.where(s1 > 1, 10).sort_index()
        0    10
        1    10
        2     2
        3     3
        4     4
        dtype: int64

        >>> s1.where(s1 > 1, s1 + 100).sort_index()
        0    100
        1    101
        2      2
        3      3
        4      4
        dtype: int64

        >>> s1.where(s1 > 1, s2).sort_index()
        0    100
        1    200
        2      2
        3      3
        4      4
        dtype: int64

        >>> reset_option("compute.ops_on_diff_frames")
        """
        assert isinstance(cond, Series)

        # We should check the DataFrame from both `cond` and `other`.
        should_try_ops_on_diff_frame = not same_anchor(cond, self) or (
            isinstance(other, Series) and not same_anchor(other, self)
        )

        if should_try_ops_on_diff_frame:
            # Try to perform it with 'compute.ops_on_diff_frame' option.
            psdf = self.to_frame()
            tmp_cond_col = verify_temp_column_name(psdf, "__tmp_cond_col__")
            tmp_other_col = verify_temp_column_name(psdf, "__tmp_other_col__")

            psdf[tmp_cond_col] = cond
            psdf[tmp_other_col] = other

            # above logic makes a Spark DataFrame looks like below:
            # +-----------------+---+----------------+-----------------+
            # |__index_level_0__|  0|__tmp_cond_col__|__tmp_other_col__|
            # +-----------------+---+----------------+-----------------+
            # |                0|  0|           false|              100|
            # |                1|  1|           false|              200|
            # |                3|  3|            true|              400|
            # |                2|  2|            true|              300|
            # |                4|  4|            true|              500|
            # +-----------------+---+----------------+-----------------+
            condition = (
                F.when(
                    psdf[tmp_cond_col].spark.column,
                    psdf._psser_for(psdf._internal.column_labels[0]).spark.column,
                )
                .otherwise(psdf[tmp_other_col].spark.column)
                .alias(psdf._internal.data_spark_column_names[0])
            )

            internal = psdf._internal.with_new_columns(
                [condition], column_labels=self._internal.column_labels
            )
            return first_series(DataFrame(internal))
        else:
            if isinstance(other, Series):
                other = other.spark.column
            condition = (
                F.when(cond.spark.column, self.spark.column)
                .otherwise(other)
                .alias(self._internal.data_spark_column_names[0])
            )
            return self._with_new_scol(condition)

    def mask(self, cond: "Series", other: Any = np.nan) -> "Series":
        """
        Replace values where the condition is True.

        Parameters
        ----------
        cond : boolean Series
            Where cond is False, keep the original value. Where True,
            replace with corresponding value from other.
        other : scalar, Series
            Entries where cond is True are replaced with corresponding value from other.

        Returns
        -------
        Series

        Examples
        --------

        >>> from pyspark.pandas.config import set_option, reset_option
        >>> set_option("compute.ops_on_diff_frames", True)
        >>> s1 = ps.Series([0, 1, 2, 3, 4])
        >>> s2 = ps.Series([100, 200, 300, 400, 500])
        >>> s1.mask(s1 > 0).sort_index()
        0    0.0
        1    NaN
        2    NaN
        3    NaN
        4    NaN
        dtype: float64

        >>> s1.mask(s1 > 1, 10).sort_index()
        0     0
        1     1
        2    10
        3    10
        4    10
        dtype: int64

        >>> s1.mask(s1 > 1, s1 + 100).sort_index()
        0      0
        1      1
        2    102
        3    103
        4    104
        dtype: int64

        >>> s1.mask(s1 > 1, s2).sort_index()
        0      0
        1      1
        2    300
        3    400
        4    500
        dtype: int64

        >>> reset_option("compute.ops_on_diff_frames")
        """
        return self.where(~cond, other)

    def xs(self, key: Name, level: Optional[int] = None) -> "Series":
        """
        Return cross-section from the Series.

        This method takes a `key` argument to select data at a particular
        level of a MultiIndex.

        Parameters
        ----------
        key : label or tuple of label
            Label contained in the index, or partially in a MultiIndex.
        level : object, defaults to first n levels (n=1 or len(key))
            In case of a key partially contained in a MultiIndex, indicate
            which levels are used. Levels can be referred by label or position.

        Returns
        -------
        Series
            Cross-section from the original Series
            corresponding to the selected index levels.

        Examples
        --------
        >>> midx = pd.MultiIndex([['a', 'b', 'c'],
        ...                       ['lama', 'cow', 'falcon'],
        ...                       ['speed', 'weight', 'length']],
        ...                      [[0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                       [0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                       [0, 1, 2, 0, 1, 2, 0, 1, 2]])
        >>> s = ps.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
        ...               index=midx)
        >>> s
        a  lama    speed      45.0
                   weight    200.0
                   length      1.2
        b  cow     speed      30.0
                   weight    250.0
                   length      1.5
        c  falcon  speed     320.0
                   weight      1.0
                   length      0.3
        dtype: float64

        Get values at specified index

        >>> s.xs('a')
        lama  speed      45.0
              weight    200.0
              length      1.2
        dtype: float64

        Get values at several indexes

        >>> s.xs(('a', 'lama'))
        speed      45.0
        weight    200.0
        length      1.2
        dtype: float64

        Get values at specified index and level

        >>> s.xs('lama', level=1)
        a  speed      45.0
           weight    200.0
           length      1.2
        dtype: float64
        """
        if not isinstance(key, tuple):
            key = (key,)
        if level is None:
            level = 0

        internal = self._internal
        scols = (
            internal.index_spark_columns[:level]
            + internal.index_spark_columns[level + len(key) :]
            + [self.spark.column]
        )
        rows = [internal.spark_columns[lvl] == index for lvl, index in enumerate(key, level)]
        sdf = internal.spark_frame.filter(reduce(lambda x, y: x & y, rows)).select(scols)

        if internal.index_level == len(key):
            # if spark_frame has one column and one data, return data only without frame
            pdf = sdf.limit(2).toPandas()
            length = len(pdf)
            if length == 1:
                return pdf[self._internal.data_spark_column_names[0]].iloc[0]

        index_spark_column_names = (
            internal.index_spark_column_names[:level]
            + internal.index_spark_column_names[level + len(key) :]
        )
        index_names = internal.index_names[:level] + internal.index_names[level + len(key) :]
        index_fields = internal.index_fields[:level] + internal.index_fields[level + len(key) :]

        internal = internal.copy(
            spark_frame=sdf,
            index_spark_columns=[scol_for(sdf, col) for col in index_spark_column_names],
            index_names=index_names,
            index_fields=index_fields,
            data_spark_columns=[scol_for(sdf, internal.data_spark_column_names[0])],
        )
        return first_series(DataFrame(internal))

    def pct_change(self, periods: int = 1) -> "Series":
        """
        Percentage change between the current and a prior element.

        .. note:: the current implementation of this API uses Spark's Window without
            specifying partition specification. This leads to moving all data into
            a single partition in a single machine and could cause serious
            performance degradation. Avoid this method with very large datasets.

        Parameters
        ----------
        periods : int, default 1
            Periods to shift for forming percent change.

        Returns
        -------
        Series

        Examples
        --------

        >>> psser = ps.Series([90, 91, 85], index=[2, 4, 1])
        >>> psser
        2    90
        4    91
        1    85
        dtype: int64

        >>> psser.pct_change()
        2         NaN
        4    0.011111
        1   -0.065934
        dtype: float64

        >>> psser.sort_index().pct_change()
        1         NaN
        2    0.058824
        4    0.011111
        dtype: float64

        >>> psser.pct_change(periods=2)
        2         NaN
        4         NaN
        1   -0.055556
        dtype: float64
        """
        scol = self.spark.column

        window = Window.orderBy(NATURAL_ORDER_COLUMN_NAME).rowsBetween(-periods, -periods)
        prev_row = F.lag(scol, periods).over(window)

        return self._with_new_scol((scol - prev_row) / prev_row).spark.analyzed

    def combine_first(self, other: "Series") -> "Series":
        """
        Combine Series values, choosing the calling Series's values first.

        Parameters
        ----------
        other : Series
            The value(s) to be combined with the `Series`.

        Returns
        -------
        Series
            The result of combining the Series with the other object.

        See Also
        --------
        Series.combine : Perform element-wise operation on two Series
            using a given function.

        Notes
        -----
        Result index will be the union of the two indexes.

        Examples
        --------
        >>> s1 = ps.Series([1, np.nan])
        >>> s2 = ps.Series([3, 4])
        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s1.combine_first(s2)
        0    1.0
        1    4.0
        dtype: float64
        """
        if not isinstance(other, ps.Series):
            raise TypeError("`combine_first` only allows `Series` for parameter `other`")
        if same_anchor(self, other):
            this = self.spark.column
            that = other.spark.column
            combined = self._psdf
        else:
            combined = combine_frames(self._psdf, other._psdf)
            this = combined["this"]._internal.spark_column_for(self._column_label)
            that = combined["that"]._internal.spark_column_for(other._column_label)
        # If `self` has missing value, use value of `other`
        cond = F.when(this.isNull(), that).otherwise(this)
        # If `self` and `other` come from same frame, the anchor should be kept
        if same_anchor(self, other):
            return self._with_new_scol(cond)  # TODO: dtype?
        index_scols = combined._internal.index_spark_columns
        sdf = combined._internal.spark_frame.select(
            *index_scols, cond.alias(self._internal.data_spark_column_names[0])
        ).distinct()
        internal = self._internal.with_new_sdf(
            sdf,
            index_fields=combined._internal.index_fields,
            data_fields=[None],  # TODO: dtype?
        )
        return first_series(DataFrame(internal))

    def dot(self, other: Union["Series", DataFrame]) -> Union[Scalar, "Series"]:
        """
        Compute the dot product between the Series and the columns of other.

        This method computes the dot product between the Series and another
        one, or the Series and each columns of a DataFrame.

        It can also be called using `self @ other` in Python >= 3.5.

        .. note:: This API is slightly different from pandas when indexes from both Series
            are not aligned and config 'compute.eager_check' is False. pandas raise an exception;
            however, pandas-on-Spark just proceeds and performs by ignoring mismatches with NaN
            permissively.

            >>> pdf1 = pd.Series([1, 2, 3], index=[0, 1, 2])
            >>> pdf2 = pd.Series([1, 2, 3], index=[0, 1, 3])
            >>> pdf1.dot(pdf2)  # doctest: +SKIP
            ...
            ValueError: matrices are not aligned

            >>> psdf1 = ps.Series([1, 2, 3], index=[0, 1, 2])
            >>> psdf2 = ps.Series([1, 2, 3], index=[0, 1, 3])
            >>> with ps.option_context("compute.eager_check", False):
            ...     psdf1.dot(psdf2)  # doctest: +SKIP
            ...
            5

        Parameters
        ----------
        other : Series, DataFrame.
            The other object to compute the dot product with its columns.

        Returns
        -------
        scalar, Series
            Return the dot product of the Series and other if other is a
            Series, the Series of the dot product of Series and each row of
            other if other is a DataFrame.

        Notes
        -----
        The Series and other must share the same index if other are a Series
        or a DataFrame.

        Examples
        --------
        >>> s = ps.Series([0, 1, 2, 3])

        >>> s.dot(s)
        14

        >>> s @ s
        14

        >>> psdf = ps.DataFrame({'x': [0, 1, 2, 3], 'y': [0, -1, -2, -3]})
        >>> psdf
           x  y
        0  0  0
        1  1 -1
        2  2 -2
        3  3 -3

        >>> with ps.option_context("compute.ops_on_diff_frames", True):
        ...     s.dot(psdf)
        ...
        x    14
        y   -14
        dtype: int64
        """
        if not same_anchor(self, other):
            if get_option("compute.eager_check") and not cast(
                ps.Index, self.index.sort_values()
            ).equals(cast(ps.Index, other.index.sort_values())):
                raise ValueError("matrices are not aligned")
            elif len(self.index) != len(other.index):
                raise ValueError("matrices are not aligned")

        if isinstance(other, DataFrame):
            other_copy: DataFrame = other.copy()
            column_labels = other_copy._internal.column_labels

            self_column_label = verify_temp_column_name(other_copy, "__self_column__")
            other_copy[self_column_label] = self
            self_psser = other_copy._psser_for(self_column_label)

            product_pssers = [
                cast(Series, other_copy._psser_for(label) * self_psser) for label in column_labels
            ]

            dot_product_psser = DataFrame(
                other_copy._internal.with_new_columns(product_pssers, column_labels=column_labels)
            ).sum()

            return cast(Series, dot_product_psser).rename(self.name)

        else:
            assert isinstance(other, Series)
            return (self * other).sum()

    def __matmul__(self, other: Union["Series", DataFrame]) -> Union[Scalar, "Series"]:
        """
        Matrix multiplication using binary `@` operator in Python>=3.5.
        """
        return self.dot(other)

    def repeat(self, repeats: Union[int, "Series"]) -> "Series":
        """
        Repeat elements of a Series.

        Returns a new Series where each element of the current Series
        is repeated consecutively a given number of times.

        Parameters
        ----------
        repeats : int or Series
            The number of repetitions for each element. This should be a
            non-negative integer. Repeating 0 times will return an empty
            Series.

        Returns
        -------
        Series
            Newly created Series with repeated elements.

        See Also
        --------
        Index.repeat : Equivalent function for Index.

        Examples
        --------
        >>> s = ps.Series(['a', 'b', 'c'])
        >>> s
        0    a
        1    b
        2    c
        dtype: object
        >>> s.repeat(2)
        0    a
        1    b
        2    c
        0    a
        1    b
        2    c
        dtype: object
        >>> ps.Series([1, 2, 3]).repeat(0)
        Series([], dtype: int64)
        """
        if not isinstance(repeats, (int, Series)):
            raise TypeError(
                "`repeats` argument must be integer or Series, but got {}".format(type(repeats))
            )

        if isinstance(repeats, Series):
            if not same_anchor(self, repeats):
                psdf = self.to_frame()
                temp_repeats = verify_temp_column_name(psdf, "__temp_repeats__")
                psdf[temp_repeats] = repeats
                return (
                    psdf._psser_for(psdf._internal.column_labels[0])
                    .repeat(psdf[temp_repeats])
                    .rename(self.name)
                )
            else:
                scol = F.explode(
                    F.array_repeat(self.spark.column, repeats.astype("int32").spark.column)
                ).alias(name_like_string(self.name))
                sdf = self._internal.spark_frame.select(self._internal.index_spark_columns + [scol])
                internal = self._internal.copy(
                    spark_frame=sdf,
                    index_spark_columns=[
                        scol_for(sdf, col) for col in self._internal.index_spark_column_names
                    ],
                    data_spark_columns=[scol_for(sdf, name_like_string(self.name))],
                )
                return first_series(DataFrame(internal))
        else:
            if repeats < 0:
                raise ValueError("negative dimensions are not allowed")

            psdf = self._psdf[[self.name]]
            if repeats == 0:
                return first_series(DataFrame(psdf._internal.with_filter(F.lit(False))))
            else:
                return first_series(cast("ps.DataFrame", ps.concat([psdf] * repeats)))

    def asof(self, where: Union[Any, List]) -> Union[Scalar, "Series"]:
        """
        Return the last row(s) without any NaNs before `where`.

        The last row (for each element in `where`, if list) without any
        NaN is taken.

        If there is no good value, NaN is returned.

        .. note:: This API is dependent on :meth:`Index.is_monotonic_increasing`
            which is expensive.

        Parameters
        ----------
        where : index or array-like of indices

        Returns
        -------
        scalar or Series

            The return can be:

            * scalar : when `self` is a Series and `where` is a scalar
            * Series: when `self` is a Series and `where` is an array-like

            Return scalar or Series

        Notes
        -----
        Indices are assumed to be sorted. Raises if this is not the case and config
        'compute.eager_check' is True. If 'compute.eager_check' is False pandas-on-Spark just
        proceeds and performs by ignoring the indices' order

        Examples
        --------
        >>> s = ps.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
        >>> s
        10    1.0
        20    2.0
        30    NaN
        40    4.0
        dtype: float64

        A scalar `where`.

        >>> float(s.asof(20))
        2.0

        For a sequence `where`, a Series is returned. The first value is
        NaN, because the first element of `where` is before the first
        index value.

        >>> s.asof([5, 20]).sort_index()
        5     NaN
        20    2.0
        dtype: float64

        Missing values are not considered. The following is ``2.0``, not
        NaN, even though NaN is at the index location for ``30``.

        >>> float(s.asof(30))
        2.0

        >>> s = ps.Series([1, 2, np.nan, 4], index=[10, 30, 20, 40])
        >>> with ps.option_context("compute.eager_check", False):
        ...     float(s.asof(20))
        ...
        1.0
        """
        should_return_series = True
        if isinstance(self.index, ps.MultiIndex):
            raise ValueError("asof is not supported for a MultiIndex")
        if isinstance(where, (ps.Index, ps.Series, DataFrame)):
            raise ValueError("where cannot be an Index, Series or a DataFrame")
        if get_option("compute.eager_check") and not self.index.is_monotonic_increasing:
            raise ValueError("asof requires a sorted index")
        if not is_list_like(where):
            should_return_series = False
            where = [where]
        internal = self._internal.resolved_copy
        index_scol = internal.index_spark_columns[0]
        index_type = internal.spark_type_for(index_scol)
        spark_column = internal.data_spark_columns[0]
        monotonically_increasing_id_column = verify_temp_column_name(
            internal.spark_frame, "__monotonically_increasing_id__"
        )
        cond = [
            F.max_by(
                spark_column,
                F.when(
                    (
                        (index_scol <= F.lit(index).cast(index_type)) & spark_column.isNotNull()
                        if pd.notna(index)
                        # If index is nan and the value of the col is not null
                        # then return monotonically_increasing_id. This will let max by
                        # to return last index value, which is the behaviour of pandas
                        else spark_column.isNotNull()
                    ),
                    F.col(monotonically_increasing_id_column),
                ),
            )
            for index in where
        ]

        sdf = internal.spark_frame.withColumn(
            monotonically_increasing_id_column, F.monotonically_increasing_id()
        ).select(cond)

        if not should_return_series:
            with sql_conf({SPARK_CONF_ARROW_ENABLED: False}):
                # Disable Arrow to keep row ordering.
                result = sdf.limit(1).toPandas().iloc[0, 0]
            return result if result is not None else np.nan

        # The data is expected to be small so it's fine to transpose/use default index.
        with ps.option_context("compute.default_index_type", "distributed", "compute.max_rows", 1):
            if len(where) == len(set(where)) and not isinstance(index_type, TimestampType):
                psdf: DataFrame = DataFrame(sdf)
                psdf.columns = pd.Index(where)
                return first_series(psdf.transpose()).rename(self.name)
            else:
                # If `where` has duplicate items, leverage the pandas directly
                # since pandas API on Spark doesn't support the duplicate column name.
                pdf: pd.DataFrame = sdf.limit(1).toPandas()
                pdf.columns = pd.Index(where)
                return first_series(DataFrame(pdf.transpose())).rename(self.name)

    def unstack(self, level: int = -1) -> DataFrame:
        """
        Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame.
        The level involved will automatically get sorted.

        Notes
        -----
        Unlike pandas, pandas-on-Spark doesn't check whether an index is duplicated or not
        because the checking of duplicated index requires scanning whole data which
        can be quite expensive.

        Parameters
        ----------
        level : int, str, or list of these, default last level
            Level(s) to unstack, can pass level name.

        Returns
        -------
        DataFrame
            Unstacked Series.

        Examples
        --------
        >>> s = ps.Series([1, 2, 3, 4],
        ...               index=pd.MultiIndex.from_product([['one', 'two'],
        ...                                                 ['a', 'b']]))
        >>> s
        one  a    1
             b    2
        two  a    3
             b    4
        dtype: int64

        >>> s.unstack(level=-1).sort_index()
             a  b
        one  1  2
        two  3  4

        >>> s.unstack(level=0).sort_index()
           one  two
        a    1    3
        b    2    4
        """
        if not isinstance(self.index, ps.MultiIndex):
            raise ValueError("Series.unstack only support for a MultiIndex")
        index_nlevels = self.index.nlevels
        if level > 0 and (level > index_nlevels - 1):
            raise IndexError(
                "Too many levels: Index has only {} levels, not {}".format(index_nlevels, level + 1)
            )
        elif level < 0 and (level < -index_nlevels):
            raise IndexError(
                "Too many levels: Index has only {} levels, {} is not a valid level number".format(
                    index_nlevels, level
                )
            )

        internal = self._internal.resolved_copy

        index_map = list(
            zip(internal.index_spark_column_names, internal.index_names, internal.index_fields)
        )
        pivot_col, column_label_names, _ = index_map.pop(level)
        index_scol_names, index_names, index_fields = zip(*index_map)
        col = internal.data_spark_column_names[0]

        sdf = internal.spark_frame
        sdf = sdf.groupby(list(index_scol_names)).pivot(pivot_col).agg(F.first(scol_for(sdf, col)))

        internal = InternalFrame(
            spark_frame=sdf,
            index_spark_columns=[scol_for(sdf, col) for col in index_scol_names],
            index_names=list(index_names),
            index_fields=list(index_fields),
            column_label_names=[column_label_names],
        )
        internal = internal.copy(
            data_fields=[
                field.copy(dtype=self._internal.data_fields[0].dtype)
                for field in internal.data_fields
            ]
        )
        return DataFrame(internal)

    def item(self) -> Scalar:
        """
        Return the first element of the underlying data as a Python scalar.

        Returns
        -------
        scalar
            The first element of Series.

        Raises
        ------
        ValueError
            If the data is not length-1.

        Examples
        --------
        >>> psser = ps.Series([10])
        >>> psser.item()
        10
        """
        return self.head(2)._to_internal_pandas().item()

    def items(self) -> Iterable[Tuple[Name, Any]]:
        """
        Lazily iterate over (index, value) tuples.

        This method returns an iterable tuple (index, value). This is
        convenient if you want to create a lazy iterator.

        .. note:: Unlike pandas', the itmes in pandas-on-Spark returns generator rather
            zip object

        Returns
        -------
        iterable
            Iterable of tuples containing the (index, value) pairs from a
            Series.

        See Also
        --------
        DataFrame.items : Iterate over (column name, Series) pairs.
        DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.

        Examples
        --------
        >>> s = ps.Series(['A', 'B', 'C'])
        >>> for index, value in s.items():
        ...     print("Index : {}, Value : {}".format(index, value))
        Index : 0, Value : A
        Index : 1, Value : B
        Index : 2, Value : C
        """
        internal_index_columns = self._internal.index_spark_column_names
        internal_data_column = self._internal.data_spark_column_names[0]

        def extract_kv_from_spark_row(row: Row) -> Tuple[Name, Any]:
            k = (
                row[internal_index_columns[0]]
                if len(internal_index_columns) == 1
                else tuple(row[c] for c in internal_index_columns)
            )
            v = row[internal_data_column]
            return k, v

        for k, v in map(
            extract_kv_from_spark_row, self._internal.resolved_copy.spark_frame.toLocalIterator()
        ):
            yield k, v

    def droplevel(self, level: Union[int, Name, List[Union[int, Name]]]) -> "Series":
        """
        Return Series with requested index level(s) removed.

        Parameters
        ----------
        level : int, str, or list-like
            If a string is given, must be the name of a level
            If list-like, elements must be names or positional indexes
            of levels.

        Returns
        -------
        Series
            Series with requested index level(s) removed.

        Examples
        --------
        >>> psser = ps.Series(
        ...     [1, 2, 3],
        ...     index=pd.MultiIndex.from_tuples(
        ...         [("x", "a"), ("x", "b"), ("y", "c")], names=["level_1", "level_2"]
        ...     ),
        ... )
        >>> psser
        level_1  level_2
        x        a          1
                 b          2
        y        c          3
        dtype: int64

        Removing specific index level by level

        >>> psser.droplevel(0)
        level_2
        a    1
        b    2
        c    3
        dtype: int64

        Removing specific index level by name

        >>> psser.droplevel("level_2")
        level_1
        x    1
        x    2
        y    3
        dtype: int64
        """
        return first_series(self.to_frame().droplevel(level=level, axis=0)).rename(self.name)

    def tail(self, n: int = 5) -> "Series":
        """
        Return the last `n` rows.

        This function returns last `n` rows from the object based on
        position. It is useful for quickly verifying data, for example,
        after sorting or appending rows.

        For negative values of `n`, this function returns all rows except
        the first `n` rows, equivalent to ``df[n:]``.

        Parameters
        ----------
        n : int, default 5
            Number of rows to select.

        Returns
        -------
        type of caller
            The last `n` rows of the caller object.

        See Also
        --------
        DataFrame.head : The first `n` rows of the caller object.

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

        >>> psser.tail(3)  # doctest: +SKIP
        2    3
        3    4
        4    5
        dtype: int64
        """
        return first_series(self.to_frame().tail(n=n)).rename(self.name)

    def explode(self) -> "Series":
        """
        Transform each element of a list-like to a row.

        Returns
        -------
        Series
            Exploded lists to rows; index will be duplicated for these rows.

        See Also
        --------
        Series.str.split : Split string values on specified separator.
        Series.unstack : Unstack, a.k.a. pivot, Series with MultiIndex
            to produce DataFrame.
        DataFrame.melt : Unpivot a DataFrame from wide format to long format.
        DataFrame.explode : Explode a DataFrame from list-like
            columns to long format.

        Examples
        --------
        >>> psser = ps.Series([[1, 2, 3], [], [3, 4]])
        >>> psser
        0    [1, 2, 3]
        1           []
        2       [3, 4]
        dtype: object

        >>> psser.explode()  # doctest: +SKIP
        0    1.0
        0    2.0
        0    3.0
        1    NaN
        2    3.0
        2    4.0
        dtype: float64
        """
        if not isinstance(self.spark.data_type, ArrayType):
            return self.copy()

        scol = F.explode_outer(self.spark.column).alias(name_like_string(self._column_label))

        internal = self._internal.with_new_columns([scol], keep_order=False)
        return first_series(DataFrame(internal))

    def argsort(self) -> "Series":
        """
        Return the integer indices that would sort the Series values.
        Unlike pandas, the index order is not preserved in the result.

        Returns
        -------
        Series
            Positions of values within the sort order with -1 indicating
            nan values.

        Examples
        --------
        >>> psser = ps.Series([3, 3, 4, 1, 6, 2, 3, 7, 8, 7, 10])
        >>> psser
        0      3
        1      3
        2      4
        3      1
        4      6
        5      2
        6      3
        7      7
        8      8
        9      7
        10    10
        dtype: int64

        >>> psser.argsort().sort_index()
        0      3
        1      5
        2      0
        3      1
        4      6
        5      2
        6      4
        7      7
        8      9
        9      8
        10    10
        dtype: int64
        """
        if LooseVersion(pd.__version__) < "3.0.0":
            warnings.warn(
                "The behavior of Series.argsort in the presence of NA values is deprecated. "
                "In a future version, NA values will be ordered last instead of set to -1.",
                FutureWarning,
            )
            source = self.loc[self.notnull()]
        else:
            source = self

        sdf_for_index = source._internal.spark_frame.select(source._internal.index_spark_columns)

        tmp_join_key = verify_temp_column_name(sdf_for_index, "__tmp_join_key__")
        sdf_for_index = InternalFrame.attach_distributed_sequence_column(
            sdf_for_index, tmp_join_key
        )
        # sdf_for_index:
        # +----------------+-----------------+
        # |__tmp_join_key__|__index_level_0__|
        # +----------------+-----------------+
        # |               0|                0|
        # |               1|                1|
        # |               2|                2|
        # |               3|                3|
        # |               4|                4|
        # +----------------+-----------------+

        sdf_for_data = source._internal.spark_frame.select(
            source.spark.column.alias("values"), NATURAL_ORDER_COLUMN_NAME
        )
        sdf_for_data = InternalFrame.attach_distributed_sequence_column(
            sdf_for_data, SPARK_DEFAULT_SERIES_NAME
        )
        # sdf_for_data:
        # +---+------+-----------------+
        # |  0|values|__natural_order__|
        # +---+------+-----------------+
        # |  0|     3|      25769803776|
        # |  1|     3|      51539607552|
        # |  2|     4|      77309411328|
        # |  3|     1|     103079215104|
        # |  4|     2|     128849018880|
        # +---+------+-----------------+

        value_scol = scol_for(sdf_for_data, "values")
        if LooseVersion(pd.__version__) < "3.0.0":
            sdf_for_data = sdf_for_data.sort(value_scol, NATURAL_ORDER_COLUMN_NAME)
        else:
            sdf_for_data = sdf_for_data.sort(value_scol.asc_nulls_last(), NATURAL_ORDER_COLUMN_NAME)
        sdf_for_data = sdf_for_data.drop("values", NATURAL_ORDER_COLUMN_NAME)

        tmp_join_key = verify_temp_column_name(sdf_for_data, "__tmp_join_key__")
        sdf_for_data = InternalFrame.attach_distributed_sequence_column(sdf_for_data, tmp_join_key)
        # sdf_for_index:                         sdf_for_data:
        # +----------------+-----------------+   +----------------+---+
        # |__tmp_join_key__|__index_level_0__|   |__tmp_join_key__|  0|
        # +----------------+-----------------+   +----------------+---+
        # |               0|                0|   |               0|  3|
        # |               1|                1|   |               1|  4|
        # |               2|                2|   |               2|  0|
        # |               3|                3|   |               3|  1|
        # |               4|                4|   |               4|  2|
        # +----------------+-----------------+   +----------------+---+

        sdf = sdf_for_index.join(sdf_for_data, on=tmp_join_key).drop(tmp_join_key)

        internal = self._internal.with_new_sdf(
            spark_frame=sdf,
            data_columns=[SPARK_DEFAULT_SERIES_NAME],
            index_fields=[
                InternalField(dtype=field.dtype) for field in self._internal.index_fields
            ],
            data_fields=[None],
        )
        psser = first_series(DataFrame(internal))

        if LooseVersion(pd.__version__) < "3.0.0":
            return cast(
                Series,
                ps.concat([psser, self.loc[self.isnull()].spark.transform(lambda _: F.lit(-1))]),
            )
        else:
            return psser

    def argmax(self, axis: Axis = None, skipna: bool = True) -> int:
        """
        Return int position of the largest value in the Series.

        If the maximum 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.

        Returns
        -------
        int
            Row position of the maximum value.

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

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

        >>> s.argmax()
        3
        """
        axis = validate_axis(axis, none_axis=0)
        if axis == 1:
            raise ValueError("axis can only be 0 or 'index'")
        sdf = self._internal.spark_frame.select(self.spark.column, NATURAL_ORDER_COLUMN_NAME)
        seq_col_name = verify_temp_column_name(sdf, "__distributed_sequence_column__")
        sdf = InternalFrame.attach_distributed_sequence_column(
            sdf,
            seq_col_name,
        )
        scol = scol_for(sdf, self._internal.data_spark_column_names[0])

        if skipna:
            sdf = sdf.orderBy(scol.desc_nulls_last(), NATURAL_ORDER_COLUMN_NAME, seq_col_name)
        else:
            sdf = sdf.orderBy(scol.desc_nulls_first(), NATURAL_ORDER_COLUMN_NAME, seq_col_name)

        results = sdf.select(scol, seq_col_name).take(1)

        if len(results) == 0:
            raise ValueError("attempt to get argmax of an empty sequence")
        else:
            max_value = results[0]
            # If the maximum is achieved in multiple locations, the first row position is returned.
            if max_value[0] is None:
                if LooseVersion(pd.__version__) < "3.0.0":
                    warnings.warn(
                        "The behavior of Series.argmax/argmin "
                        "with skipna=False and NAs, or with all-NAs is deprecated. "
                        "In a future version this will raise ValueError.",
                        FutureWarning,
                    )
                    return -1
                else:
                    if skipna:
                        raise ValueError("Encountered all NA values")
                    else:
                        raise ValueError("Encountered an NA value with skipna=False")
            else:
                return max_value[1]

    def argmin(self, axis: Axis = None, skipna: bool = True) -> int:
        """
        Return int position of the smallest value in the Series.

        If the minimum 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.

        Returns
        -------
        int
            Row position of the minimum value.

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

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

        >>> s.argmin()  # doctest: +SKIP
        0
        """
        axis = validate_axis(axis, none_axis=0)
        if axis == 1:
            raise ValueError("axis can only be 0 or 'index'")
        sdf = self._internal.spark_frame.select(self.spark.column, NATURAL_ORDER_COLUMN_NAME)
        seq_col_name = verify_temp_column_name(sdf, "__distributed_sequence_column__")
        sdf = InternalFrame.attach_distributed_sequence_column(
            sdf,
            seq_col_name,
        )
        scol = scol_for(sdf, self._internal.data_spark_column_names[0])

        if skipna:
            sdf = sdf.orderBy(scol.asc_nulls_last(), NATURAL_ORDER_COLUMN_NAME, seq_col_name)
        else:
            sdf = sdf.orderBy(scol.asc_nulls_first(), NATURAL_ORDER_COLUMN_NAME, seq_col_name)

        results = sdf.select(scol, seq_col_name).take(1)

        if len(results) == 0:
            raise ValueError("attempt to get argmin of an empty sequence")
        else:
            min_value = results[0]
            # If the maximum is achieved in multiple locations, the first row position is returned.
            if min_value[0] is None:
                if LooseVersion(pd.__version__) < "3.0.0":
                    warnings.warn(
                        "The behavior of Series.argmax/argmin "
                        "with skipna=False and NAs, or with all-NAs is deprecated. "
                        "In a future version this will raise ValueError.",
                        FutureWarning,
                    )
                    return -1
                else:
                    if skipna:
                        raise ValueError("Encountered all NA values")
                    else:
                        raise ValueError("Encountered an NA value with skipna=False")
            else:
                return min_value[1]

    def compare(
        self, other: "Series", keep_shape: bool = False, keep_equal: bool = False
    ) -> DataFrame:
        """
        Compare to another Series and show the differences.

        .. note:: This API is slightly different from pandas when indexes from both Series
            are not identical and config 'compute.eager_check' is False. pandas raise an exception;
            however, pandas-on-Spark just proceeds and performs by ignoring mismatches.

            >>> psser1 = ps.Series([1, 2, 3, 4, 5], index=pd.Index([1, 2, 3, 4, 5]))
            >>> psser2 = ps.Series([1, 2, 3, 4, 5], index=pd.Index([1, 2, 4, 3, 6]))
            >>> psser1.compare(psser2)  # doctest: +SKIP
            ...
            ValueError: Can only compare identically-labeled Series objects

            >>> with ps.option_context("compute.eager_check", False):
            ...     psser1.compare(psser2)  # doctest: +SKIP
            ...
               self  other
            3   3.0    4.0
            4   4.0    3.0
            5   5.0    NaN
            6   NaN    5.0

        Parameters
        ----------
        other : Series
            Object to compare with.
        keep_shape : bool, default False
            If true, all rows and columns are kept.
            Otherwise, only the ones with different values are kept.
        keep_equal : bool, default False
            If true, the result keeps values that are equal.
            Otherwise, equal values are shown as NaNs.

        Returns
        -------
        DataFrame

        Notes
        -----
        Matching NaNs will not appear as a difference.

        Examples
        --------

        >>> from pyspark.pandas.config import set_option, reset_option
        >>> set_option("compute.ops_on_diff_frames", True)
        >>> s1 = ps.Series(["a", "b", "c", "d", "e"])
        >>> s2 = ps.Series(["a", "a", "c", "b", "e"])

        Align the differences on columns

        >>> s1.compare(s2).sort_index()
          self other
        1    b     a
        3    d     b

        Keep all original rows

        >>> s1.compare(s2, keep_shape=True).sort_index()
           self other
        0  None  None
        1     b     a
        2  None  None
        3     d     b
        4  None  None

        Keep all original rows and all original values

        >>> s1.compare(s2, keep_shape=True, keep_equal=True).sort_index()
          self other
        0    a     a
        1    b     a
        2    c     c
        3    d     b
        4    e     e

        >>> reset_option("compute.ops_on_diff_frames")
        """
        combined: DataFrame
        if same_anchor(self, other):
            self_column_label = verify_temp_column_name(other.to_frame(), "__self_column__")
            other_column_label = verify_temp_column_name(self.to_frame(), "__other_column__")
            combined = DataFrame(
                self._internal.with_new_columns(
                    [self.rename(self_column_label), other.rename(other_column_label)]
                )
            )
        else:
            if get_option("compute.eager_check") and not self.index.equals(other.index):
                raise ValueError("Can only compare identically-labeled Series objects")

            combined = combine_frames(self.to_frame(), other.to_frame())

        this_column_label = "self"
        that_column_label = "other"
        if keep_equal and keep_shape:
            combined.columns = pd.Index([this_column_label, that_column_label])
            return combined

        this_data_scol = combined._internal.data_spark_columns[0]
        that_data_scol = combined._internal.data_spark_columns[1]
        index_scols = combined._internal.index_spark_columns
        sdf = combined._internal.spark_frame
        if keep_shape:
            this_scol = (
                F.when(this_data_scol == that_data_scol, None)
                .otherwise(this_data_scol)
                .alias(this_column_label)
            )
            this_field = combined._internal.data_fields[0].copy(
                name=this_column_label, nullable=True
            )

            that_scol = (
                F.when(this_data_scol == that_data_scol, None)
                .otherwise(that_data_scol)
                .alias(that_column_label)
            )
            that_field = combined._internal.data_fields[1].copy(
                name=that_column_label, nullable=True
            )
        else:
            sdf = sdf.filter(~this_data_scol.eqNullSafe(that_data_scol))

            this_scol = this_data_scol.alias(this_column_label)
            this_field = combined._internal.data_fields[0].copy(name=this_column_label)

            that_scol = that_data_scol.alias(that_column_label)
            that_field = combined._internal.data_fields[1].copy(name=that_column_label)

        sdf = sdf.select(*index_scols, this_scol, that_scol, NATURAL_ORDER_COLUMN_NAME)
        internal = InternalFrame(
            spark_frame=sdf,
            index_spark_columns=[
                scol_for(sdf, col) for col in self._internal.index_spark_column_names
            ],
            index_names=self._internal.index_names,
            index_fields=combined._internal.index_fields,
            column_labels=[(this_column_label,), (that_column_label,)],
            data_spark_columns=[scol_for(sdf, this_column_label), scol_for(sdf, that_column_label)],
            data_fields=[this_field, that_field],
            column_label_names=[None],
        )
        return DataFrame(internal)

    # TODO(SPARK-40553): 1, support array-like 'value'; 2, add parameter 'sorter'
    def searchsorted(self, value: Any, side: str = "left") -> int:
        """
        Find indices where elements should be inserted to maintain order.

        Find the indices into a sorted Series self such that, if the corresponding elements
        in value were inserted before the indices, the order of self would be preserved.

        .. versionadded:: 3.4.0

        Parameters
        ----------
        value : scalar
            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).

        Returns
        -------
        int
            insertion point

        Notes
        -----
        The Series must be monotonically sorted, otherwise wrong locations will likely be returned.

        Examples
        --------
        >>> ser = ps.Series([1, 2, 2, 3])
        >>> ser.searchsorted(0)
        0
        >>> ser.searchsorted(1)
        0
        >>> ser.searchsorted(2)
        1
        >>> ser.searchsorted(5)
        4
        >>> ser.searchsorted(0, side="right")
        0
        >>> ser.searchsorted(1, side="right")
        1
        >>> ser.searchsorted(2, side="right")
        3
        >>> ser.searchsorted(5, side="right")
        4
        """
        if side not in ["left", "right"]:
            raise ValueError(f"Invalid side {side}")

        sdf = self._internal.spark_frame
        index_col_name = verify_temp_column_name(sdf, "__search_sorted_index_col__")
        value_col_name = verify_temp_column_name(sdf, "__search_sorted_value_col__")
        sdf = InternalFrame.attach_distributed_sequence_column(
            sdf.select(self.spark.column.alias(value_col_name)), index_col_name
        )

        if side == "left":
            results = sdf.select(
                F.min(F.when(F.lit(value) <= F.col(value_col_name), F.col(index_col_name))),
                F.count(F.lit(0)),
            ).take(1)
        else:
            results = sdf.select(
                F.min(F.when(F.lit(value) < F.col(value_col_name), F.col(index_col_name))),
                F.count(F.lit(0)),
            ).take(1)

        if len(results) == 0:
            return 0
        else:
            return results[0][1] if results[0][0] is None else results[0][0]

    def align(
        self,
        other: Union[DataFrame, "Series"],
        join: str = "outer",
        axis: Optional[Axis] = None,
        copy: bool = True,
    ) -> Tuple["Series", Union[DataFrame, "Series"]]:
        """
        Align two objects on their axes with the specified join method.

        Join method is specified for each axis Index.

        Parameters
        ----------
        other : DataFrame or Series
        join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
        axis : allowed axis of the other object, default None
            Align on index (0), columns (1), or both (None).
        copy : bool, default True
            Always returns new objects. If copy=False and no reindexing is
            required then original objects are returned.

        Returns
        -------
        (left, right) : (Series, type of other)
            Aligned objects.

        Examples
        --------
        >>> ps.set_option("compute.ops_on_diff_frames", True)
        >>> s1 = ps.Series([7, 8, 9], index=[10, 11, 12])
        >>> s2 = ps.Series(["g", "h", "i"], index=[10, 20, 30])

        >>> aligned_l, aligned_r = s1.align(s2)
        >>> aligned_l.sort_index()
        10    7.0
        11    8.0
        12    9.0
        20    NaN
        30    NaN
        dtype: float64
        >>> aligned_r.sort_index()
        10       g
        11    None
        12    None
        20       h
        30       i
        dtype: object

        Align with the join type "inner":

        >>> aligned_l, aligned_r = s1.align(s2, join="inner")
        >>> aligned_l.sort_index()
        10    7
        dtype: int64
        >>> aligned_r.sort_index()
        10    g
        dtype: object

        Align with a DataFrame:

        >>> df = ps.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}, index=[10, 20, 30])
        >>> aligned_l, aligned_r = s1.align(df)
        >>> aligned_l.sort_index()
        10    7.0
        11    8.0
        12    9.0
        20    NaN
        30    NaN
        dtype: float64
        >>> aligned_r.sort_index()
              a     b
        10  1.0     a
        11  NaN  None
        12  NaN  None
        20  2.0     b
        30  3.0     c

        >>> ps.reset_option("compute.ops_on_diff_frames")
        """
        axis = validate_axis(axis)
        if axis == 1:
            raise ValueError("Series does not support columns axis.")

        self_df = self.to_frame()
        left, right = self_df.align(other, join=join, axis=axis, copy=False)

        if left is self_df:
            left_ser = self
        else:
            left_ser = first_series(left).rename(self.name)

        return (left_ser.copy(), right.copy()) if copy else (left_ser, right)

    def between_time(
        self,
        start_time: Union[datetime.time, str],
        end_time: Union[datetime.time, str],
        inclusive: str = "both",
        axis: Axis = 0,
    ) -> "Series":
        """
        Select values between particular times of the day (example: 9:00-9:30 AM).

        By setting ``start_time`` to be later than ``end_time``,
        you can get the times that are *not* between the two times.

        Parameters
        ----------
        start_time : datetime.time or str
            Initial time as a time filter limit.
        end_time : datetime.time or str
            End time as a time filter limit.
        inclusive : {"both", "neither", "left", "right"}, default "both"
            Include boundaries; whether to set each bound as closed or open.

            .. versionadded:: 4.0.0

        axis : {0 or 'index', 1 or 'columns'}, default 0
            Determine range time on index or columns value.

        Returns
        -------
        Series
            Data from the original object filtered to the specified dates range.

        Raises
        ------
        TypeError
            If the index is not  a :class:`DatetimeIndex`

        See Also
        --------
        at_time : Select values at a particular time of the day.
        last : Select final periods of time series based on a date offset.
        DatetimeIndex.indexer_between_time : Get just the index locations for
            values between particular times of the day.

        Examples
        --------
        >>> idx = pd.date_range('2018-04-09', periods=4, freq='1D20min')
        >>> psser = ps.Series([1, 2, 3, 4], index=idx)
        >>> psser
        2018-04-09 00:00:00    1
        2018-04-10 00:20:00    2
        2018-04-11 00:40:00    3
        2018-04-12 01:00:00    4
        dtype: int64

        >>> psser.between_time('0:15', '0:45')  # doctest: +SKIP
        2018-04-10 00:20:00    2
        2018-04-11 00:40:00    3
        dtype: int64
        """
        return first_series(
            self.to_frame().between_time(start_time, end_time, inclusive, axis)
        ).rename(self.name)

    def at_time(
        self, time: Union[datetime.time, str], asof: bool = False, axis: Axis = 0
    ) -> "Series":
        """
        Select values at particular time of day (example: 9:30AM).

        Parameters
        ----------
        time : datetime.time or str
        axis : {0 or 'index', 1 or 'columns'}, default 0

        Returns
        -------
        Series

        Raises
        ------
        TypeError
            If the index is not  a :class:`DatetimeIndex`

        See Also
        --------
        between_time : Select values between particular times of the day.
        DatetimeIndex.indexer_at_time : Get just the index locations for
            values at particular time of the day.

        Examples
        --------
        >>> idx = pd.date_range('2018-04-09', periods=4, freq='12h')
        >>> psser = ps.Series([1, 2, 3, 4], index=idx)
        >>> psser
        2018-04-09 00:00:00    1
        2018-04-09 12:00:00    2
        2018-04-10 00:00:00    3
        2018-04-10 12:00:00    4
        dtype: int64

        >>> psser.at_time('12:00')
        2018-04-09 12:00:00    2
        2018-04-10 12:00:00    4
        dtype: int64
        """
        return first_series(self.to_frame().at_time(time, asof, axis)).rename(self.name)

    def _cum(
        self,
        func: Callable[[PySparkColumn], PySparkColumn],
        skipna: bool,
        part_cols: Sequence["ColumnOrName"] = (),
        ascending: bool = True,
    ) -> "Series":
        # This is used to cummin, cummax, cumsum, etc.

        if ascending:
            window = (
                Window.orderBy(F.asc(NATURAL_ORDER_COLUMN_NAME))
                .partitionBy(*part_cols)
                .rowsBetween(Window.unboundedPreceding, Window.currentRow)
            )
        else:
            window = (
                Window.orderBy(F.desc(NATURAL_ORDER_COLUMN_NAME))
                .partitionBy(*part_cols)
                .rowsBetween(Window.unboundedPreceding, Window.currentRow)
            )

        if skipna:
            # There is a behavior difference between pandas and PySpark. In case of cummax,
            #
            # Input:
            #      A    B
            # 0  2.0  1.0
            # 1  5.0  NaN
            # 2  1.0  0.0
            # 3  2.0  4.0
            # 4  4.0  9.0
            #
            # pandas:
            #      A    B
            # 0  2.0  1.0
            # 1  5.0  NaN
            # 2  5.0  1.0
            # 3  5.0  4.0
            # 4  5.0  9.0
            #
            # PySpark:
            #      A    B
            # 0  2.0  1.0
            # 1  5.0  1.0
            # 2  5.0  1.0
            # 3  5.0  4.0
            # 4  5.0  9.0

            scol = F.when(
                # Manually sets nulls given the column defined above.
                self.spark.column.isNull(),
                F.lit(None),
            ).otherwise(func(self.spark.column).over(window))
        else:
            # Here, we use two Windows.
            # One for real data.
            # The other one for setting nulls after the first null it meets.
            #
            # There is a behavior difference between pandas and PySpark. In case of cummax,
            #
            # Input:
            #      A    B
            # 0  2.0  1.0
            # 1  5.0  NaN
            # 2  1.0  0.0
            # 3  2.0  4.0
            # 4  4.0  9.0
            #
            # pandas:
            #      A    B
            # 0  2.0  1.0
            # 1  5.0  NaN
            # 2  5.0  NaN
            # 3  5.0  NaN
            # 4  5.0  NaN
            #
            # PySpark:
            #      A    B
            # 0  2.0  1.0
            # 1  5.0  1.0
            # 2  5.0  1.0
            # 3  5.0  4.0
            # 4  5.0  9.0
            scol = F.when(
                # By going through with max, it sets True after the first time it meets null.
                F.max(self.spark.column.isNull()).over(window),
                # Manually sets nulls given the column defined above.
                F.lit(None),
            ).otherwise(func(self.spark.column).over(window))

        return self._with_new_scol(scol)

    def _cumsum(self, skipna: bool, part_cols: Sequence["ColumnOrName"] = ()) -> "Series":
        psser = self
        if isinstance(psser.spark.data_type, BooleanType):
            psser = psser.spark.transform(lambda scol: scol.cast(LongType()))
        elif not isinstance(psser.spark.data_type, NumericType):
            raise TypeError(
                "Could not convert {} ({}) to numeric".format(
                    spark_type_to_pandas_dtype(psser.spark.data_type),
                    psser.spark.data_type.simpleString(),
                )
            )
        return psser._cum(F.sum, skipna, part_cols)

    def _cumprod(self, skipna: bool, part_cols: Sequence["ColumnOrName"] = ()) -> "Series":
        psser = self
        if isinstance(psser.spark.data_type, BooleanType):
            psser = psser.spark.transform(lambda scol: scol.cast(LongType()))
        elif not isinstance(psser.spark.data_type, NumericType):
            raise TypeError(
                "Could not convert {} ({}) to numeric".format(
                    spark_type_to_pandas_dtype(psser.spark.data_type),
                    psser.spark.data_type.simpleString(),
                )
            )
        return psser._cum(lambda c: SF.product(c, skipna), skipna, part_cols)

    # ----------------------------------------------------------------------
    # Accessor Methods
    # ----------------------------------------------------------------------
    dt = CachedAccessor("dt", DatetimeMethods)
    str = CachedAccessor("str", StringMethods)
    cat = CachedAccessor("cat", CategoricalAccessor)
    plot = CachedAccessor("plot", PandasOnSparkPlotAccessor)

    # ----------------------------------------------------------------------

    def _apply_series_op(
        self, op: Callable[["Series"], Union["Series", PySparkColumn]], should_resolve: bool = False
    ) -> "Series":
        psser_or_scol = op(self)
        if isinstance(psser_or_scol, Series):
            psser = psser_or_scol
        else:
            psser = self._with_new_scol(psser_or_scol)
        if should_resolve:
            internal = psser._internal.resolved_copy
            return first_series(DataFrame(internal))
        else:
            return psser.copy()

    def _reduce_for_stat_function(
        self,
        sfun: Callable[["Series"], PySparkColumn],
        name: str_type,
        axis: Optional[Axis] = None,
        numeric_only: bool = False,
        skipna: bool = True,
        **kwargs: Any,
    ) -> Scalar:
        """
        Applies sfun to the column and returns a scalar

        Parameters
        ----------
        sfun : the stats function to be used for aggregation
        name : original pandas API name.
        axis : used only for sanity check because series only support index axis.
        numeric_only : not used by this implementation, but passed down by stats functions.
        skipna: exclude NA/null values when computing the result.
        """
        axis = validate_axis(axis)
        if axis == 1:
            raise NotImplementedError("Series does not support columns axis.")

        if not skipna and get_option("compute.eager_check") and self.hasnans:
            scol = F.first(F.lit(np.nan))
        else:
            scol = sfun(self)

        min_count = kwargs.get("min_count", 0)
        if min_count > 0:
            scol = F.when(Frame._count_expr(self) >= min_count, scol)

        result = unpack_scalar(self._internal.spark_frame.select(scol))
        return result if result is not None else np.nan

    # Override the `groupby` to specify the actual return type annotation.
    def groupby(
        self,
        by: Union[Name, "Series", List[Union[Name, "Series"]]],
        axis: Union[Axis, _NoValueType] = _NoValue,
        as_index: bool = True,
        dropna: bool = True,
    ) -> "SeriesGroupBy":
        return cast(
            "SeriesGroupBy", super().groupby(by=by, axis=axis, as_index=as_index, dropna=dropna)
        )

    groupby.__doc__ = Frame.groupby.__doc__

    def _build_groupby(
        self, by: List[Union["Series", Label]], as_index: bool, dropna: bool
    ) -> "SeriesGroupBy":
        from pyspark.pandas.groupby import SeriesGroupBy

        return SeriesGroupBy._build(self, by, as_index=as_index, dropna=dropna)

    def resample(
        self,
        rule: str_type,
        closed: Optional[Literal["left", "right"]] = None,
        label: Optional[Literal["left", "right"]] = None,
        on: Optional["Series"] = None,
    ) -> "SeriesResampler":
        """
        Resample time-series data.

        Convenience method for frequency conversion and resampling of time series.
        The object must have a datetime-like index (only support `DatetimeIndex` for now),
        or the caller must pass the label of a datetime-like
        series/index to the ``on`` keyword parameter.

        .. versionadded:: 3.4.0

        Parameters
        ----------
        rule : str
            The offset string or object representing target conversion.
            Currently, supported units are {'YE', 'A', 'ME', 'D', 'h',
            'min', 'MIN', 's'}.
        closed : {{'right', 'left'}}, default None
            Which side of bin interval is closed. The default is 'left'
            for all frequency offsets except for 'A', 'YE' and 'ME' which all
            have a default of 'right'.
        label : {{'right', 'left'}}, default None
            Which bin edge label to label bucket with. The default is 'left'
            for all frequency offsets except for 'A', 'YE' and 'ME' which all
            have a default of 'right'.
        on : Series, optional
            For a DataFrame, column to use instead of index for resampling.
            Column must be datetime-like.

        Returns
        -------
        SeriesResampler


        Examples
        --------
        Start by creating a series with 9 one minute timestamps.

        >>> index = pd.date_range('1/1/2000', periods=9, freq='min')
        >>> series = ps.Series(range(9), index=index, name='V')
        >>> series
        2000-01-01 00:00:00    0
        2000-01-01 00:01:00    1
        2000-01-01 00:02:00    2
        2000-01-01 00:03:00    3
        2000-01-01 00:04:00    4
        2000-01-01 00:05:00    5
        2000-01-01 00:06:00    6
        2000-01-01 00:07:00    7
        2000-01-01 00:08:00    8
        Name: V, dtype: int64

        Downsample the series into 3 minute bins and sum the values
        of the timestamps falling into a bin.

        >>> series.resample('3min').sum().sort_index()
        2000-01-01 00:00:00     3.0
        2000-01-01 00:03:00    12.0
        2000-01-01 00:06:00    21.0
        Name: V, dtype: float64

        Downsample the series into 3 minute bins as above, but label each
        bin using the right edge instead of the left. Please note that the
        value in the bucket used as the label is not included in the bucket,
        which it labels. For example, in the original series the
        bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
        value in the resampled bucket with the label ``2000-01-01 00:03:00``
        does not include 3 (if it did, the summed value would be 6, not 3).
        To include this value, close the right side of the bin interval as
        illustrated in the example below this one.

        >>> series.resample('3min', label='right').sum().sort_index()
        2000-01-01 00:03:00     3.0
        2000-01-01 00:06:00    12.0
        2000-01-01 00:09:00    21.0
        Name: V, dtype: float64

        Downsample the series into 3 minute bins as above, but close the right
        side of the bin interval.

        >>> series.resample('3min', label='right', closed='right').sum().sort_index()
        2000-01-01 00:00:00     0.0
        2000-01-01 00:03:00     6.0
        2000-01-01 00:06:00    15.0
        2000-01-01 00:09:00    15.0
        Name: V, dtype: float64

        Upsample the series into 30 second bins.

        >>> series.resample('30s').sum().sort_index()[0:5]   # Select first 5 rows
        2000-01-01 00:00:00    0.0
        2000-01-01 00:00:30    0.0
        2000-01-01 00:01:00    1.0
        2000-01-01 00:01:30    0.0
        2000-01-01 00:02:00    2.0
        Name: V, dtype: float64

        See Also
        --------
        DataFrame.resample : Resample a DataFrame.
        groupby : Group by mapping, function, label, or list of labels.
        """
        from pyspark.pandas.indexes import DatetimeIndex
        from pyspark.pandas.resample import SeriesResampler

        if on is None and not isinstance(self.index, DatetimeIndex):
            raise NotImplementedError("resample currently works only for DatetimeIndex")
        if on is not None and not isinstance(as_spark_type(on.dtype), TimestampType):
            raise NotImplementedError("`on` currently works only for TimestampType")

        agg_columns: List[ps.Series] = []
        column_label = self._internal.column_labels[0]
        if isinstance(self._internal.spark_type_for(column_label), (NumericType, BooleanType)):
            agg_columns.append(self)

        if len(agg_columns) == 0:
            raise ValueError("No available aggregation columns!")

        return SeriesResampler(
            psser=self,
            resamplekey=on,
            rule=rule,
            closed=closed,
            label=label,
            agg_columns=agg_columns,
        )

    def __getitem__(self, key: Any) -> Any:
        if LooseVersion(pd.__version__) < "3.0.0":
            treating_keys_as_positions = isinstance(key, int) and not isinstance(
                self.index.spark.data_type, (IntegerType, LongType)
            )
            if treating_keys_as_positions:
                warnings.warn(
                    "Series.__getitem__ treating keys as positions is deprecated. "
                    "In a future version, integer keys will always be treated as labels "
                    "(consistent with DataFrame behavior). "
                    "To access a value by position, use `ser.iloc[pos]`",
                    FutureWarning,
                )
        else:
            treating_keys_as_positions = False
        try:
            if (
                isinstance(key, slice) and any(isinstance(n, int) for n in [key.start, key.stop])
            ) or treating_keys_as_positions:
                # Seems like pandas Series always uses int as positional search when slicing
                # with ints, searches based on index values when the value is int.
                return self.iloc[key]
            return self.loc[key]
        except SparkPandasIndexingError:
            raise KeyError(
                "Key length ({}) exceeds index depth ({})".format(
                    len(key), self._internal.index_level
                )
            )

    def __getattr__(self, item: str_type) -> Any:
        if item.startswith("__"):
            raise AttributeError(item)
        if hasattr(MissingPandasLikeSeries, item):
            property_or_func = getattr(MissingPandasLikeSeries, item)
            if isinstance(property_or_func, property):
                return property_or_func.fget(self)
            else:
                return partial(property_or_func, self)
        raise AttributeError("'Series' object has no attribute '{}'".format(item))

    def _to_internal_pandas(self) -> pd.Series:
        """
        Return a pandas Series directly from _internal to avoid overhead of copy.

        This method is for internal use only.
        """
        return self._psdf._internal.to_pandas_frame[self.name]

    def __repr__(self) -> str_type:
        max_display_count = get_option("display.max_rows")
        if max_display_count is None:
            return self._to_internal_pandas().to_string(
                name=bool(self.name), dtype=bool(self.dtype)
            )

        pser = self._psdf._get_or_create_repr_pandas_cache(max_display_count)[self.name]
        pser_length = len(pser)
        pser = pser.iloc[:max_display_count]
        if pser_length > max_display_count:
            repr_string = pser.to_string(length=True)
            rest, prev_footer = repr_string.rsplit("\n", 1)
            match = REPR_PATTERN.search(prev_footer)
            if match is not None:
                length = match.group("length")
                dtype_name = str(self.dtype.name)
                if self.name is None:
                    footer = "\ndtype: {dtype}\nShowing only the first {length}".format(
                        length=length, dtype=pprint_thing(dtype_name)
                    )
                else:
                    footer = (
                        "\nName: {name}, dtype: {dtype}\nShowing only the first {length}".format(
                            length=length, name=self.name, dtype=pprint_thing(dtype_name)
                        )
                    )
                return rest + footer
        return pser.to_string(name=self.name, dtype=self.dtype)  # type: ignore[call-overload]

    def __dir__(self) -> Iterable[str_type]:
        if not isinstance(self.spark.data_type, StructType):
            fields = []
        else:
            fields = [f for f in self.spark.data_type.fieldNames() if " " not in f]
        return list(super().__dir__()) + fields

    def __iter__(self) -> None:
        return MissingPandasLikeSeries.__iter__(self)

    # In order to support the type hints such as Series[...]. See DataFrame.__class_getitem__.
    def __class_getitem__(cls, params: Any) -> Type[SeriesType]:
        return create_type_for_series_type(params)


def unpack_scalar(sdf: SparkDataFrame) -> Any:
    """
    Takes a dataframe that is supposed to contain a single row with a single scalar value,
    and returns this value.
    """
    lst = sdf.limit(2).toPandas()
    assert len(lst) == 1, (sdf, lst)
    row = lst.iloc[0]
    lst2 = list(row)
    assert len(lst2) == 1, (row, lst2)
    return lst2[0]


@overload
def first_series(df: DataFrame) -> Series: ...


@overload
def first_series(df: pd.DataFrame) -> pd.Series: ...


def first_series(df: Union[DataFrame, pd.DataFrame]) -> Union[Series, pd.Series]:
    """
    Takes a DataFrame and returns the first column of the DataFrame as a Series
    """
    assert isinstance(df, (DataFrame, pd.DataFrame)), type(df)
    if isinstance(df, DataFrame):
        return df._psser_for(df._internal.column_labels[0])
    else:
        return df[df.columns[0]]


def _test() -> None:
    import os
    import doctest
    import sys
    from pyspark.sql import SparkSession
    import pyspark.pandas.series

    os.chdir(os.environ["SPARK_HOME"])

    globs = pyspark.pandas.series.__dict__.copy()
    globs["ps"] = pyspark.pandas

    spark = (
        SparkSession.builder.master("local[4]").appName("pyspark.pandas.series tests").getOrCreate()
    )
    failure_count, test_count = doctest.testmod(
        pyspark.pandas.series,
        globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
    )
    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()