series.py

# Copyright 2023 Google LLC
#
# Licensed 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.

"""Series is a 1 dimensional data structure."""

from __future__ import annotations

import datetime
import functools
import itertools
import numbers
import textwrap
import typing
from typing import (
    Any,
    Callable,
    cast,
    Iterable,
    List,
    Literal,
    Mapping,
    Optional,
    overload,
    Sequence,
    Tuple,
    TypeVar,
    Union,
)
import warnings

import bigframes_vendored.constants as constants
import bigframes_vendored.pandas.core.series as vendored_pandas_series
import google.cloud.bigquery as bigquery
import numpy
import pandas
from pandas.api import extensions as pd_ext
import pyarrow as pa
import typing_extensions

from bigframes._tools import docs
import bigframes.core
from bigframes.core import agg_expressions, groupby
import bigframes.core.block_transforms as block_ops
import bigframes.core.blocks as blocks
import bigframes.core.expression as ex
import bigframes.core.identifiers as ids
import bigframes.core.indexers
import bigframes.core.indexes as indexes
from bigframes.core.logging import log_adapter
import bigframes.core.ordering as order
import bigframes.core.scalar as scalars
import bigframes.core.utils as utils
import bigframes.core.validations as validations
import bigframes.core.window
from bigframes.core.window import rolling
import bigframes.core.window_spec as windows
import bigframes.dataframe
import bigframes.dtypes
import bigframes.exceptions as bfe
import bigframes.formatting_helpers as formatter
import bigframes.functions
import bigframes.operations as ops
import bigframes.operations.aggregations as agg_ops
import bigframes.operations.blob as blob
import bigframes.operations.lists as lists
import bigframes.operations.plotting as plotting
import bigframes.operations.python_op_maps as python_ops
import bigframes.operations.structs as structs
import bigframes.session

if typing.TYPE_CHECKING:
    import bigframes.geopandas.geoseries
    import bigframes.operations.datetimes as datetimes
    import bigframes.operations.strings as strings


U = TypeVar("U")
LevelType = typing.Union[str, int]
LevelsType = typing.Union[LevelType, typing.Sequence[LevelType]]


_bigquery_function_recommendation_message = (
    "Your functions could not be applied directly to the Series."
    " Try converting it to a BigFrames BigQuery function."
)

_list = list  # Type alias to escape Series.list property


@log_adapter.class_logger
@docs.inherit_docs(vendored_pandas_series.Series)
class Series:
    # Must be above 5000 for pandas to delegate to bigframes for binops
    __pandas_priority__ = 13000

    # Ensure mypy can more robustly determine the type of self._block since it
    # gets set in various places.
    _block: blocks.Block

    def __init__(
        self,
        data=None,
        index=None,
        dtype: Optional[bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype] = None,
        name: str | None = None,
        copy: Optional[bool] = None,
        *,
        session: Optional[bigframes.session.Session] = None,
    ):
        self._query_job: Optional[bigquery.QueryJob] = None
        import bigframes.pandas

        # Ignore object dtype if provided, as it provides no additional
        # information about what BigQuery type to use.
        if dtype is not None and bigframes.dtypes.is_object_like(dtype):
            dtype = None

        read_pandas_func = (
            session.read_pandas
            if (session is not None)
            else (lambda x: bigframes.pandas.read_pandas(x))
        )

        block: typing.Optional[blocks.Block] = None
        if (name is not None) and not isinstance(name, typing.Hashable):
            raise ValueError(
                f"BigQuery DataFrames only supports hashable series names. {constants.FEEDBACK_LINK}"
            )
        if copy is not None and not copy:
            raise ValueError(
                f"Series constructor only supports copy=True. {constants.FEEDBACK_LINK}"
            )

        if isinstance(data, blocks.Block):
            block = data
        elif isinstance(data, bigframes.pandas.Series):
            block = data._get_block()
        # special case where data is local scalar, but index is bigframes index (maybe very big)
        elif (
            not utils.is_list_like(data) and not isinstance(data, indexes.Index)
        ) and isinstance(index, indexes.Index):
            block = index._block
            block, _ = block.create_constant(data)
            block = block.with_column_labels([None])
            # prevents no-op reindex later
            index = None
        elif isinstance(data, indexes.Index) or isinstance(index, indexes.Index):
            data = indexes.Index(data, dtype=dtype, name=name, session=session)
            # set to none as it has already been applied, avoid re-cast later
            if data.nlevels != 1:
                raise NotImplementedError("Cannot interpret multi-index as Series.")
            # Reset index to promote index columns to value columns, set default index
            data_block = data._block.reset_index(drop=False).with_column_labels(
                data.names
            )
            if index is not None:  # Align data and index by offset
                bf_index = indexes.Index(index, session=session)
                idx_block = bf_index._block.reset_index(
                    drop=False
                )  # reset to align by offsets, and then reset back
                idx_cols = idx_block.value_columns
                data_block, (l_mapping, _) = idx_block.join(data_block, how="left")
                data_block = data_block.set_index([l_mapping[col] for col in idx_cols])
                data_block = data_block.with_index_labels(bf_index.names)
                # prevents no-op reindex later
                index = None
            block = data_block

        if block:
            assert len(block.value_columns) == 1
            assert len(block.column_labels) == 1
            if index is not None:  # reindexing operation
                bf_index = indexes.Index(index)
                idx_block = bf_index._block
                idx_cols = idx_block.index_columns
                block, _ = idx_block.join(block, how="left")
                block = block.with_index_labels(bf_index.names)
            if name:
                block = block.with_column_labels([name])
            if dtype:
                bf_dtype = bigframes.dtypes.bigframes_type(dtype)
                block = block.multi_apply_unary_op(ops.AsTypeOp(to_type=bf_dtype))
        else:
            if isinstance(dtype, str) and dtype.lower() == "json":
                dtype = bigframes.dtypes.JSON_DTYPE
            pd_series = pandas.Series(
                data=data,
                index=index,  # type:ignore
                dtype=dtype,  # type:ignore
                name=name,
            )
            block = read_pandas_func(pd_series)._get_block()  # type:ignore

        assert block is not None
        self._block: blocks.Block = block

        self._block.session._register_object(self)

    @property
    def dt(self) -> datetimes.DatetimeMethods:
        import bigframes.operations.datetimes as datetimes

        return datetimes.DatetimeMethods(self)

    @property
    def dtype(self):
        bigframes.dtypes.warn_on_db_dtypes_json_dtype([self._dtype])
        return self._dtype

    @property
    def dtypes(self):
        bigframes.dtypes.warn_on_db_dtypes_json_dtype([self._dtype])
        return self._dtype

    @property
    def geo(self) -> bigframes.geopandas.geoseries.GeoSeries:
        """
        Accessor object for geography properties of the Series values.

        Returns:
            bigframes.geopandas.geoseries.GeoSeries:
                An accessor containing geography methods.

        """
        import bigframes.geopandas.geoseries

        return bigframes.geopandas.geoseries.GeoSeries(self)

    @property
    @validations.requires_index
    def loc(self) -> bigframes.core.indexers.LocSeriesIndexer:
        return bigframes.core.indexers.LocSeriesIndexer(self)

    @property
    @validations.requires_ordering()
    def iloc(self) -> bigframes.core.indexers.IlocSeriesIndexer:
        return bigframes.core.indexers.IlocSeriesIndexer(self)

    @property
    @validations.requires_ordering()
    def iat(self) -> bigframes.core.indexers.IatSeriesIndexer:
        return bigframes.core.indexers.IatSeriesIndexer(self)

    @property
    @validations.requires_index
    def at(self) -> bigframes.core.indexers.AtSeriesIndexer:
        return bigframes.core.indexers.AtSeriesIndexer(self)

    @property
    def name(self) -> blocks.Label:
        return self._name

    @name.setter
    def name(self, label: blocks.Label):
        new_block = self._block.with_column_labels([label])
        self._set_block(new_block)

    @property
    def shape(self) -> typing.Tuple[int]:
        return (self._block.shape[0],)

    @property
    def size(self) -> int:
        return self.shape[0]

    @property
    def ndim(self) -> int:
        return 1

    @property
    def empty(self) -> bool:
        return self.shape[0] == 0

    @property
    def hasnans(self) -> bool:
        # Note, hasnans is actually a null check, and NaNs don't count for nullable float
        return self.isnull().any()

    @property
    def values(self) -> numpy.ndarray:
        return self.to_numpy()

    @property
    @validations.requires_index
    def index(self) -> indexes.Index:
        return indexes.Index.from_frame(self)

    @validations.requires_index
    def keys(self) -> indexes.Index:
        return self.index

    @property
    def query_job(self) -> Optional[bigquery.QueryJob]:
        """BigQuery job metadata for the most recent query.

        Returns:
            The most recent `QueryJob
            <https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob>`_.
        """
        if self._query_job is None:
            self._set_internal_query_job(self._compute_dry_run())
        return self._query_job

    @property
    def struct(self) -> structs.StructAccessor:
        return structs.StructAccessor(self)

    @property
    def list(self) -> lists.ListAccessor:
        return lists.ListAccessor(self)

    @property
    def blob(self) -> blob.BlobAccessor:
        """
        Accessor for Blob operations.
        """
        warnings.warn(
            "The blob accessor is deprecated and will be removed in a future release. Use bigframes.bigquery.obj functions instead.",
            category=bfe.ApiDeprecationWarning,
            stacklevel=2,
        )
        return blob.BlobAccessor(self)

    @property
    @validations.requires_ordering()
    def T(self) -> Series:
        return self.transpose()

    @property
    def _info_axis(self) -> indexes.Index:
        return self.index

    @property
    def _session(self) -> bigframes.Session:
        return self._get_block().expr.session

    @property
    def _struct_fields(self) -> List[str]:
        if not bigframes.dtypes.is_struct_like(self._dtype):
            return []

        struct_type = typing.cast(pa.StructType, self._dtype.pyarrow_dtype)
        return [struct_type.field(i).name for i in range(struct_type.num_fields)]

    @validations.requires_ordering()
    def transpose(self) -> Series:
        return self

    def _set_internal_query_job(self, query_job: Optional[bigquery.QueryJob]):
        self._query_job = query_job

    def __len__(self):
        return self.shape[0]

    def __bool__(self):
        raise ValueError(
            "Cannot convert Series into bool. Consider using .empty(), .item(), .any(), or .all() methods."
        )

    def __iter__(self) -> typing.Iterator:
        return itertools.chain.from_iterable(
            map(lambda x: x.squeeze(axis=1), self._block.to_pandas_batches())
        )

    def __contains__(self, key) -> bool:
        return key in self.index

    def copy(self) -> Series:
        return Series(self._block)

    @overload
    def rename(
        self,
        index: Union[blocks.Label, Mapping[Any, Any]] = None,
    ) -> Series:
        ...

    @overload
    def rename(
        self,
        index: Union[blocks.Label, Mapping[Any, Any]] = None,
        *,
        inplace: Literal[False],
        **kwargs,
    ) -> Series:
        ...

    @overload
    def rename(
        self,
        index: Union[blocks.Label, Mapping[Any, Any]] = None,
        *,
        inplace: Literal[True],
        **kwargs,
    ) -> None:
        ...

    def rename(
        self,
        index: Union[blocks.Label, Mapping[Any, Any]] = None,
        *,
        inplace: bool = False,
        **kwargs,
    ) -> Optional[Series]:
        if len(kwargs) != 0:
            raise NotImplementedError(
                f"rename does not currently support any keyword arguments. {constants.FEEDBACK_LINK}"
            )

        # rename the index
        if isinstance(index, Mapping):
            index = typing.cast(Mapping[Any, Any], index)
            block = self._block
            for k, v in index.items():
                new_idx_ids = []
                for idx_id, idx_dtype in zip(block.index_columns, block.index.dtypes):
                    # Will throw if key type isn't compatible with index type, which leads to invalid SQL.
                    block.create_constant(k, dtype=idx_dtype)

                    # Will throw if value type isn't compatible with index type.
                    block, const_id = block.create_constant(v, dtype=idx_dtype)
                    block, cond_id = block.project_expr(
                        ops.ne_op.as_expr(idx_id, ex.const(k))
                    )
                    block, new_idx_id = block.apply_ternary_op(
                        idx_id, cond_id, const_id, ops.where_op
                    )

                    new_idx_ids.append(new_idx_id)
                    block = block.drop_columns([const_id, cond_id])

                block = block.set_index(new_idx_ids, index_labels=block.index.names)

            if inplace:
                self._block = block
                return None
            else:
                return Series(block)

        # rename the Series name
        if isinstance(index, typing.Hashable):
            index = typing.cast(Optional[str], index)
            block = self._block.with_column_labels([index])

            if inplace:
                self._block = block
                return None
            else:
                return Series(block)

        raise ValueError(f"Unsupported type of parameter index: {type(index)}")

    @overload
    def rename_axis(
        self,
        mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]],
    ) -> Series:
        ...

    @overload
    def rename_axis(
        self,
        mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]],
        *,
        inplace: Literal[False],
        **kwargs,
    ) -> Series:
        ...

    @overload
    def rename_axis(
        self,
        mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]],
        *,
        inplace: Literal[True],
        **kwargs,
    ) -> None:
        ...

    @validations.requires_index
    def rename_axis(
        self,
        mapper: typing.Union[blocks.Label, typing.Sequence[blocks.Label]],
        *,
        inplace: bool = False,
        **kwargs,
    ) -> Optional[Series]:
        if len(kwargs) != 0:
            raise NotImplementedError(
                f"rename_axis does not currently support any keyword arguments. {constants.FEEDBACK_LINK}"
            )
        # limited implementation: the new index name is simply the 'mapper' parameter
        if _is_list_like(mapper):
            labels = mapper
        else:
            labels = [mapper]

        block = self._block.with_index_labels(labels)
        if inplace:
            self._block = block
            return None
        else:
            return Series(block)

    def equals(
        self, other: typing.Union[Series, bigframes.dataframe.DataFrame]
    ) -> bool:
        # Must be same object type, same column dtypes, and same label values
        if not isinstance(other, Series):
            return False
        return block_ops.equals(self._block, other._block)

    @overload  # type: ignore[override]
    def reset_index(
        self,
        level: blocks.LevelsType = ...,
        *,
        name: typing.Optional[str] = ...,
        drop: Literal[False] = ...,
        inplace: Literal[False] = ...,
        allow_duplicates: Optional[bool] = ...,
    ) -> bigframes.dataframe.DataFrame:
        ...

    @overload
    def reset_index(
        self,
        level: blocks.LevelsType = ...,
        *,
        name: typing.Optional[str] = ...,
        drop: Literal[True] = ...,
        inplace: Literal[False] = ...,
        allow_duplicates: Optional[bool] = ...,
    ) -> Series:
        ...

    @overload
    def reset_index(
        self,
        level: blocks.LevelsType = ...,
        *,
        name: typing.Optional[str] = ...,
        drop: bool = ...,
        inplace: Literal[True] = ...,
        allow_duplicates: Optional[bool] = ...,
    ) -> None:
        ...

    @validations.requires_ordering()
    def reset_index(
        self,
        level: blocks.LevelsType = None,
        *,
        name: typing.Optional[str] = None,
        drop: bool = False,
        inplace: bool = False,
        allow_duplicates: Optional[bool] = None,
    ) -> bigframes.dataframe.DataFrame | Series | None:
        if allow_duplicates is None:
            allow_duplicates = False
        block = self._block.reset_index(level, drop, allow_duplicates=allow_duplicates)
        if drop:
            if inplace:
                self._set_block(block)
                return None
            return Series(block)
        else:
            if inplace:
                raise ValueError(
                    "Series.reset_index cannot combine inplace=True and drop=False"
                )
            if name:
                block = block.assign_label(self._value_column, name)
            return bigframes.dataframe.DataFrame(block)

    def _repr_mimebundle_(self, include=None, exclude=None):
        """
        Custom display method for IPython/Jupyter environments.
        This is called by IPython's display system when the object is displayed.
        """
        # TODO(b/467647693): Anywidget integration has been tested in Jupyter, VS Code, and
        # BQ Studio, but there is a known compatibility issue with Marimo that needs to be addressed.
        from bigframes.display import html

        return html.repr_mimebundle(self, include=include, exclude=exclude)

    def __repr__(self) -> str:
        # Protect against errors with uninitialized Series. See:
        # https://github.com/googleapis/python-bigquery-dataframes/issues/728
        if not hasattr(self, "_block"):
            return object.__repr__(self)

        # TODO(swast): Add a timeout here? If the query is taking a long time,
        # maybe we just print the job metadata that we have so far?
        # TODO(swast): Avoid downloading the whole series by using job
        # metadata, like we do with DataFrame.
        opts = bigframes.options.display
        if opts.repr_mode == "deferred":
            return formatter.repr_query_job(self._compute_dry_run())

        self._cached()
        pandas_df, row_count, query_job = self._block.retrieve_repr_request_results(
            opts.max_rows
        )
        self._set_internal_query_job(query_job)
        from bigframes.display import plaintext

        return plaintext.create_text_representation(
            pandas_df,
            row_count,
            is_series=True,
            has_index=len(self._block.index_columns) > 0,
        )

    def astype(
        self,
        dtype: Union[bigframes.dtypes.DtypeString, bigframes.dtypes.Dtype],
        *,
        errors: Literal["raise", "null"] = "raise",
    ) -> Series:
        if errors not in ["raise", "null"]:
            raise ValueError("Argument 'errors' must be one of 'raise' or 'null'")
        dtype = bigframes.dtypes.bigframes_type(dtype)
        return self._apply_unary_op(
            bigframes.operations.AsTypeOp(to_type=dtype, safe=(errors == "null"))
        )

    def to_pandas(
        self,
        max_download_size: Optional[int] = None,
        sampling_method: Optional[str] = None,
        random_state: Optional[int] = None,
        *,
        ordered: bool = True,
        dry_run: bool = False,
        allow_large_results: Optional[bool] = None,
    ) -> pandas.Series:
        """Writes Series to pandas Series.

        **Examples:**

            >>> s = bpd.Series([4, 3, 2])

        Download the data from BigQuery and convert it into an in-memory pandas Series.

            >>> s.to_pandas()
            0    4
            1    3
            2    2
            dtype: Int64

        Estimate job statistics without processing or downloading data by using `dry_run=True`.

            >>> s.to_pandas(dry_run=True) # doctest: +SKIP
            columnCount                                                            1
            columnDtypes                                               {None: Int64}
            indexLevel                                                             1
            indexDtypes                                                      [Int64]
            projectId                                                  bigframes-dev
            location                                                              US
            jobType                                                            QUERY
            destinationTable       {'projectId': 'bigframes-dev', 'datasetId': '_...
            useLegacySql                                                       False
            referencedTables                                                    None
            totalBytesProcessed                                                    0
            cacheHit                                                           False
            statementType                                                     SELECT
            creationTime                            2025-04-03 18:54:59.219000+00:00
            dtype: object

        Args:
            max_download_size (int, default None):
                .. deprecated:: 2.0.0
                    ``max_download_size`` parameter is deprecated. Please use ``to_pandas_batches()``
                    method instead.

                Download size threshold in MB. If ``max_download_size`` is exceeded when downloading data,
                the data will be downsampled if ``bigframes.options.sampling.enable_downsampling`` is
                ``True``, otherwise, an error will be raised. If set to a value other than ``None``,
                this will supersede the global config.
            sampling_method (str, default None):
                .. deprecated:: 2.0.0
                    ``sampling_method`` parameter is deprecated. Please use ``sample()`` method instead.

                Downsampling algorithms to be chosen from, the choices are: "head": This algorithm
                returns a portion of the data from the beginning. It is fast and requires minimal
                computations to perform the downsampling; "uniform": This algorithm returns uniform
                random samples of the data. If set to a value other than None, this will supersede
                the global config.
            random_state (int, default None):
                .. deprecated:: 2.0.0
                    ``random_state`` parameter is deprecated. Please use ``sample()`` method instead.

                The seed for the uniform downsampling algorithm. If provided, the uniform method may
                take longer to execute and require more computation. If set to a value other than
                None, this will supersede the global config.
            ordered (bool, default True):
                Determines whether the resulting pandas series will be  ordered.
                In some cases, unordered may result in a faster-executing query.
            dry_run (bool, default False):
                If this argument is true, this method will not process the data. Instead, it returns
                a Pandas Series containing dry run job statistics
            allow_large_results (bool, default None):
                If not None, overrides the global setting to allow or disallow large query results
                over the default size limit of 10 GB.

        Returns:
            pandas.Series: A pandas Series with all rows of this Series if the data_sampling_threshold_mb
                is not exceeded; otherwise, a pandas Series with downsampled rows of the DataFrame. If dry_run
                is set to True, a pandas Series containing dry run statistics will be returned.
        """
        if max_download_size is not None:
            msg = bfe.format_message(
                "DEPRECATED: The `max_download_size` parameters for `Series.to_pandas()` "
                "are deprecated and will be removed soon. Please use `Series.to_pandas_batches()`."
            )
            warnings.warn(msg, category=FutureWarning)
        if sampling_method is not None or random_state is not None:
            msg = bfe.format_message(
                "DEPRECATED: The `sampling_method` and `random_state` parameters for "
                "`Series.to_pandas()` are deprecated and will be removed soon. "
                "Please use `Series.sample().to_pandas()` instead for sampling."
            )
            warnings.warn(msg, category=FutureWarning)

        if dry_run:
            dry_run_stats, dry_run_job = self._block._compute_dry_run(
                max_download_size=max_download_size,
                sampling_method=sampling_method,
                random_state=random_state,
                ordered=ordered,
            )

            self._set_internal_query_job(dry_run_job)
            return dry_run_stats

        # Repeat the to_pandas() call to make mypy deduce type correctly, because mypy cannot resolve
        # Literal[True/False] to bool
        df, query_job = self._block.to_pandas(
            max_download_size=max_download_size,
            sampling_method=sampling_method,
            random_state=random_state,
            ordered=ordered,
            allow_large_results=allow_large_results,
        )

        if query_job:
            self._set_internal_query_job(query_job)

        series = df.squeeze(axis=1)
        series.name = self._name
        return series

    def to_pandas_batches(
        self,
        page_size: Optional[int] = None,
        max_results: Optional[int] = None,
        *,
        allow_large_results: Optional[bool] = None,
    ) -> Iterable[pandas.Series]:
        """Stream Series results to an iterable of pandas Series.

        page_size and max_results determine the size and number of batches,
        see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob#google_cloud_bigquery_job_QueryJob_result

        **Examples:**

            >>> s = bpd.Series([4, 3, 2, 2, 3])

        Iterate through the results in batches, limiting the total rows yielded
        across all batches via `max_results`:

            >>> for s_batch in s.to_pandas_batches(max_results=3):
            ...     print(s_batch)
            0    4
            1    3
            2    2
            dtype: Int64

        Alternatively, control the approximate size of each batch using `page_size`
        and fetch batches manually using `next()`:

            >>> it = s.to_pandas_batches(page_size=2)
            >>> next(it)
            0    4
            1    3
            dtype: Int64
            >>> next(it)
            2    2
            3    2
            dtype: Int64

        Args:
            page_size (int, default None):
                The maximum number of rows of each batch. Non-positive values are ignored.
            max_results (int, default None):
                The maximum total number of rows of all batches.
            allow_large_results (bool, default None):
                If not None, overrides the global setting to allow or disallow large query results
                over the default size limit of 10 GB.

        Returns:
            Iterable[pandas.Series]:
                An iterable of smaller Series which combine to
                form the original Series. Results stream from bigquery,
                see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.table.RowIterator#google_cloud_bigquery_table_RowIterator_to_arrow_iterable
        """
        batches = self._block.to_pandas_batches(
            page_size=page_size,
            max_results=max_results,
            allow_large_results=allow_large_results,
        )
        return map(lambda df: cast(pandas.Series, df.squeeze(1)), batches)

    def _compute_dry_run(self) -> bigquery.QueryJob:
        _, query_job = self._block._compute_dry_run((self._value_column,))
        return query_job

    def drop(
        self,
        labels: typing.Any = None,
        *,
        axis: typing.Union[int, str] = 0,
        index: typing.Any = None,
        columns: Union[blocks.Label, typing.Iterable[blocks.Label]] = None,
        level: typing.Optional[LevelType] = None,
    ) -> Series:
        if (labels is None) == (index is None):
            raise ValueError("Must specify exactly one of 'labels' or 'index'")

        if labels is not None:
            index = labels

        # ignore axis, columns params
        block = self._block
        level_id = self._resolve_levels(level or 0)[0]
        if _is_list_like(index):
            block, inverse_condition_id = block.apply_unary_op(
                level_id, ops.IsInOp(values=tuple(index), match_nulls=True)
            )
            block, condition_id = block.apply_unary_op(
                inverse_condition_id, ops.invert_op
            )
        else:
            block, condition_id = block.project_expr(
                ops.ne_op.as_expr(level_id, ex.const(index))
            )
        block = block.filter_by_id(condition_id, keep_null=True)
        block = block.drop_columns([condition_id])
        return Series(block.select_column(self._value_column))

    @validations.requires_index
    def droplevel(self, level: LevelsType, axis: int | str = 0):
        resolved_level_ids = self._resolve_levels(level)
        return Series(self._block.drop_levels(resolved_level_ids))

    @validations.requires_index
    def swaplevel(self, i: int = -2, j: int = -1):
        level_i = self._block.index_columns[i]
        level_j = self._block.index_columns[j]
        mapping = {level_i: level_j, level_j: level_i}
        reordering = [
            mapping.get(index_id, index_id) for index_id in self._block.index_columns
        ]
        return Series(self._block.reorder_levels(reordering))

    @validations.requires_index
    def reorder_levels(self, order: LevelsType, axis: int | str = 0):
        resolved_level_ids = self._resolve_levels(order)
        return Series(self._block.reorder_levels(resolved_level_ids))

    def _resolve_levels(self, level: LevelsType) -> typing.Sequence[str]:
        return self._block.index.resolve_level(level)

    def between(self, left, right, inclusive="both"):
        if inclusive not in ["both", "neither", "left", "right"]:
            raise ValueError(
                "Must set 'inclusive' to one of 'both', 'neither', 'left', or 'right'"
            )
        left_op = ops.ge_op if (inclusive in ["left", "both"]) else ops.gt_op
        right_op = ops.le_op if (inclusive in ["right", "both"]) else ops.lt_op
        return self._apply_binary_op(left, left_op).__and__(
            self._apply_binary_op(right, right_op)
        )

    def case_when(self, caselist) -> Series:
        cases = []

        for condition, output in itertools.chain(caselist, [(True, self)]):
            cases.append(condition)
            cases.append(output)
            # In pandas, the default value if no case matches is the original value.
            # This makes it impossible to change the type of the column, but if
            # the condition is always True, we know it will match and no subsequent
            # conditions matter (including the fallback to `self`). This break allows
            # the type to change (see: internal issue 349926559).
            if condition is True:
                break

        return self._apply_nary_op(
            ops.case_when_op,
            cases,
            # Self is already included in "others".
            ignore_self=True,
        ).rename(self.name)

    @validations.requires_ordering()
    def cumsum(self) -> Series:
        return self._apply_window_op(agg_ops.sum_op, windows.cumulative_rows())

    @validations.requires_ordering()
    def ffill(self, *, limit: typing.Optional[int] = None) -> Series:
        window = windows.rows(start=None if limit is None else -limit, end=0)
        return self._apply_window_op(agg_ops.LastNonNullOp(), window)

    pad = ffill

    @validations.requires_ordering()
    def bfill(self, *, limit: typing.Optional[int] = None) -> Series:
        window = windows.rows(start=0, end=limit)
        return self._apply_window_op(agg_ops.FirstNonNullOp(), window)

    @validations.requires_ordering()
    def cummax(self) -> Series:
        return self._apply_window_op(agg_ops.max_op, windows.cumulative_rows())

    @validations.requires_ordering()
    def cummin(self) -> Series:
        return self._apply_window_op(agg_ops.min_op, windows.cumulative_rows())

    @validations.requires_ordering()
    def cumprod(self) -> Series:
        return self._apply_window_op(agg_ops.product_op, windows.cumulative_rows())

    @validations.requires_ordering()
    def shift(self, periods: int = 1) -> Series:
        window_spec = windows.rows()
        return self._apply_window_op(agg_ops.ShiftOp(periods), window_spec)

    @validations.requires_ordering()
    def diff(self, periods: int = 1) -> Series:
        window_spec = windows.rows()
        return self._apply_window_op(agg_ops.DiffOp(periods), window_spec)

    @validations.requires_ordering()
    def pct_change(self, periods: int = 1) -> Series:
        # Future versions of pandas will not perfrom ffill automatically
        series = self.ffill()
        return Series(block_ops.pct_change(series._block, periods=periods))

    @validations.requires_ordering()
    def rank(
        self,
        axis=0,
        method: str = "average",
        numeric_only=False,
        na_option: str = "keep",
        ascending: bool = True,
        pct: bool = False,
    ) -> Series:
        return Series(
            block_ops.rank(self._block, method, na_option, ascending, pct=pct)
        )

    def fillna(self, value=None) -> Series:
        return self._apply_binary_op(value, ops.fillna_op)

    def replace(
        self, to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False
    ):
        if regex:
            # No-op unless to_replace and series dtype are both string type
            if not isinstance(to_replace, str) or not isinstance(
                self.dtype, pandas.StringDtype
            ):
                return self
            return self._regex_replace(to_replace, value)
        elif utils.is_dict_like(to_replace):
            return self._mapping_replace(to_replace)  # type: ignore
        elif utils.is_list_like(to_replace):
            replace_list = to_replace
        else:  # Scalar
            replace_list = [to_replace]
        replace_list = [
            i for i in replace_list if bigframes.dtypes.is_compatible(i, self.dtype)
        ]
        return self._simple_replace(replace_list, value) if replace_list else self

    def _regex_replace(self, to_replace: str, value: str):
        if not bigframes.dtypes.is_dtype(value, self.dtype):
            raise NotImplementedError(
                f"Cannot replace {self.dtype} elements with incompatible item {value} as mixed-type columns not supported. {constants.FEEDBACK_LINK}"
            )
        block, result_col = self._block.apply_unary_op(
            self._value_column,
            ops.RegexReplaceStrOp(to_replace, value),
            result_label=self.name,
        )
        return Series(block.select_column(result_col))

    def _simple_replace(self, to_replace_list: typing.Sequence, value):
        result_type = bigframes.dtypes.is_compatible(value, self.dtype)
        if not result_type:
            raise NotImplementedError(
                f"Cannot replace {self.dtype} elements with incompatible item {value} as mixed-type columns not supported. {constants.FEEDBACK_LINK}"
            )

        if result_type != self.dtype:
            return self.astype(result_type)._simple_replace(to_replace_list, value)

        block, cond = self._block.apply_unary_op(
            self._value_column, ops.IsInOp(tuple(to_replace_list))
        )
        block, result_col = block.project_expr(
            ops.where_op.as_expr(ex.const(value), cond, self._value_column), self.name
        )
        return Series(block.select_column(result_col))

    def _mapping_replace(self, mapping: dict[typing.Hashable, typing.Hashable]):
        if not mapping:
            return self.copy()

        tuples = []
        lcd_types: list[typing.Optional[bigframes.dtypes.Dtype]] = []
        for key, value in mapping.items():
            lcd_type = bigframes.dtypes.is_compatible(key, self.dtype)
            if not lcd_type:
                continue
            if not bigframes.dtypes.is_dtype(value, self.dtype):
                raise NotImplementedError(
                    f"Cannot replace {self.dtype} elements with incompatible item {value} as mixed-type columns not supported. {constants.FEEDBACK_LINK}"
                )
            tuples.append((key, value))
            lcd_types.append(lcd_type)

        result_dtype = functools.reduce(
            lambda t1, t2: bigframes.dtypes.lcd_type(t1, t2) if (t1 and t2) else None,
            lcd_types,
            self.dtype,
        )
        if not result_dtype:
            raise NotImplementedError(
                f"Cannot replace {self.dtype} elements with incompatible mapping {mapping} as mixed-type columns not supported. {constants.FEEDBACK_LINK}"
            )
        block, result = self._block.apply_unary_op(
            self._value_column, ops.MapOp(tuple(tuples))
        )
        replaced = Series(block.select_column(result))
        replaced.name = self.name
        return replaced

    @validations.requires_ordering()
    @validations.requires_index
    def interpolate(self, method: str = "linear") -> Series:
        if method == "pad":
            return self.ffill()
        result = block_ops.interpolate(self._block, method)
        return Series(result)

    def dropna(
        self,
        *,
        axis: int = 0,
        inplace: bool = False,
        how: typing.Optional[str] = None,
        ignore_index: bool = False,
    ) -> Series:
        if inplace:
            raise NotImplementedError("'inplace'=True not supported")
        result = block_ops.dropna(self._block, [self._value_column], how="any")
        if ignore_index:
            result = result.reset_index()
        return Series(result)

    @validations.requires_ordering(bigframes.constants.SUGGEST_PEEK_PREVIEW)
    def head(self, n: int = 5) -> Series:
        return typing.cast(Series, self.iloc[0:n])

    @validations.requires_ordering()
    def tail(self, n: int = 5) -> Series:
        return typing.cast(Series, self.iloc[-n:])

    def peek(
        self, n: int = 5, *, force: bool = True, allow_large_results=None
    ) -> pandas.Series:
        """
        Preview n arbitrary elements from the series without guarantees about row selection or ordering.

        ``Series.peek(force=False)`` will always be very fast, but will not succeed if data requires
        full data scanning. Using ``force=True`` will always succeed, but may be perform queries.
        Query results will be cached so that future steps will benefit from these queries.

        Args:
            n (int, default 5):
                The number of rows to select from the series. Which N rows are returned is non-deterministic.
            force (bool, default True):
                If the data cannot be peeked efficiently, the series will instead be fully materialized as part
                of the operation if ``force=True``. If ``force=False``, the operation will throw a ValueError.
            allow_large_results (bool, default None):
                If not None, overrides the global setting to allow or disallow large query results
                over the default size limit of 10 GB.
        Returns:
            pandas.Series: A pandas Series with n rows.

        Raises:
            ValueError: If force=False and data cannot be efficiently peeked.
        """
        maybe_result = self._block.try_peek(n, allow_large_results=allow_large_results)
        if maybe_result is None:
            if force:
                self._cached()
                maybe_result = self._block.try_peek(
                    n, force=True, allow_large_results=allow_large_results
                )
                assert maybe_result is not None
            else:
                raise ValueError(
                    "Cannot peek efficiently when data has aggregates, joins or window functions applied. Use force=True to fully compute dataframe."
                )
        as_series = maybe_result.squeeze(axis=1)
        as_series.name = self.name
        return as_series

    def item(self):
        # Docstring is in third_party/bigframes_vendored/pandas/core/series.py
        return self.peek(2).item()

    def nlargest(self, n: int = 5, keep: str = "first") -> Series:
        if keep not in ("first", "last", "all"):
            raise ValueError("'keep must be one of 'first', 'last', or 'all'")
        if keep != "all":
            validations.enforce_ordered(self, "nlargest(keep != 'all')")
        return Series(
            block_ops.nlargest(self._block, n, [self._value_column], keep=keep)
        )

    def nsmallest(self, n: int = 5, keep: str = "first") -> Series:
        if keep not in ("first", "last", "all"):
            raise ValueError("'keep must be one of 'first', 'last', or 'all'")
        if keep != "all":
            validations.enforce_ordered(self, "nsmallest(keep != 'all')")
        return Series(
            block_ops.nsmallest(self._block, n, [self._value_column], keep=keep)
        )

    def isin(self, values) -> "Series":
        if isinstance(values, Series):
            return Series(self._block.isin(values._block))
        if isinstance(values, indexes.Index):
            return Series(self._block.isin(values.to_series()._block))
        if not _is_list_like(values):
            raise TypeError(
                "only list-like objects are allowed to be passed to "
                f"isin(), you passed a [{type(values).__name__}]"
            )
        return self._apply_unary_op(
            ops.IsInOp(values=tuple(values), match_nulls=True)
        ).fillna(value=False)

    def isna(self) -> "Series":
        return self._apply_unary_op(ops.isnull_op)

    isnull = isna

    def notna(self) -> "Series":
        return self._apply_unary_op(ops.notnull_op)

    notnull = notna

    def __and__(self, other: bool | int | Series) -> Series:
        return self._apply_binary_op(other, ops.and_op)

    __rand__ = __and__

    def __or__(self, other: bool | int | Series) -> Series:
        return self._apply_binary_op(other, ops.or_op)

    __ror__ = __or__

    def __xor__(self, other: bool | int | Series) -> Series:
        return self._apply_binary_op(other, ops.xor_op)

    __rxor__ = __xor__

    def __add__(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self.add(other)

    def __radd__(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self.radd(other)

    def add(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self._apply_binary_op(other, ops.add_op)

    def radd(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self._apply_binary_op(other, ops.add_op, reverse=True)

    def __sub__(self, other: float | int | Series) -> Series:
        return self.sub(other)

    def __rsub__(self, other: float | int | Series) -> Series:
        return self.rsub(other)

    def sub(self, other) -> Series:
        return self._apply_binary_op(other, ops.sub_op)

    def rsub(self, other) -> Series:
        return self._apply_binary_op(other, ops.sub_op, reverse=True)

    subtract = sub

    def __mul__(self, other: float | int | Series) -> Series:
        return self.mul(other)

    def __rmul__(self, other: float | int | Series) -> Series:
        return self.rmul(other)

    def mul(self, other: float | int | Series) -> Series:
        return self._apply_binary_op(other, ops.mul_op)

    def rmul(self, other: float | int | Series) -> Series:
        return self._apply_binary_op(other, ops.mul_op, reverse=True)

    multiply = mul

    def __truediv__(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self.truediv(other)

    def __rtruediv__(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self.rtruediv(other)

    def truediv(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self._apply_binary_op(other, ops.div_op)

    def rtruediv(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self._apply_binary_op(other, ops.div_op, reverse=True)

    div = divide = truediv

    rdiv = rtruediv

    def __floordiv__(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self.floordiv(other)

    def __rfloordiv__(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self.rfloordiv(other)

    def floordiv(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self._apply_binary_op(other, ops.floordiv_op)

    def rfloordiv(self, other: float | int | pandas.Timedelta | Series) -> Series:
        return self._apply_binary_op(other, ops.floordiv_op, reverse=True)

    def __pow__(self, other: float | int | Series) -> Series:
        return self.pow(other)

    def __rpow__(self, other: float | int | Series) -> Series:
        return self.rpow(other)

    def pow(self, other: float | int | Series) -> Series:
        return self._apply_binary_op(other, ops.pow_op)

    def rpow(self, other: float | int | Series) -> Series:
        return self._apply_binary_op(other, ops.pow_op, reverse=True)

    def __lt__(self, other: float | int | str | Series) -> Series:
        return self.lt(other)

    def __le__(self, other: float | int | str | Series) -> Series:
        return self.le(other)

    def lt(self, other) -> Series:
        return self._apply_binary_op(other, ops.lt_op)

    def le(self, other) -> Series:
        return self._apply_binary_op(other, ops.le_op)

    def __gt__(self, other: float | int | str | Series) -> Series:
        return self.gt(other)

    def __ge__(self, other: float | int | str | Series) -> Series:
        return self.ge(other)

    def gt(self, other) -> Series:
        return self._apply_binary_op(other, ops.gt_op)

    def ge(self, other) -> Series:
        return self._apply_binary_op(other, ops.ge_op)

    def __mod__(self, other) -> Series:  # type: ignore
        return self.mod(other)

    def __rmod__(self, other) -> Series:  # type: ignore
        return self.rmod(other)

    def mod(self, other) -> Series:  # type: ignore
        return self._apply_binary_op(other, ops.mod_op)

    def rmod(self, other) -> Series:  # type: ignore
        return self._apply_binary_op(other, ops.mod_op, reverse=True)

    def divmod(self, other) -> Tuple[Series, Series]:  # type: ignore
        # TODO(huanc): when self and other both has dtype int and other contains zeros,
        # the output should be dtype float, both floordiv and mod returns dtype int in this case.
        return (self.floordiv(other), self.mod(other))

    def rdivmod(self, other) -> Tuple[Series, Series]:  # type: ignore
        # TODO(huanc): when self and other both has dtype int and self contains zeros,
        # the output should be dtype float, both floordiv and mod returns dtype int in this case.
        return (self.rfloordiv(other), self.rmod(other))

    def dot(self, other):
        return (self * other).sum()

    def __matmul__(self, other):
        return self.dot(other)

    def __rmatmul__(self, other):
        return self.dot(other)

    def combine_first(self, other: Series) -> Series:
        result = self._apply_binary_op(other, ops.coalesce_op)
        result.name = self.name
        return result

    def update(self, other: Union[Series, Sequence, Mapping]) -> None:
        result = self._apply_binary_op(
            other, ops.coalesce_op, reverse=True, alignment="left"
        )
        self._set_block(result._get_block())

    def __abs__(self) -> Series:
        return self.abs()

    def abs(self) -> Series:
        return self._apply_unary_op(ops.abs_op)

    def round(self, decimals=0) -> "Series":
        return self._apply_binary_op(decimals, ops.round_op)

    def corr(self, other: Series, method="pearson", min_periods=None) -> float:
        # TODO(tbergeron): Validate early that both are numeric
        # TODO(tbergeron): Handle partially-numeric columns
        if method != "pearson":
            raise NotImplementedError(
                f"Only Pearson correlation is currently supported. {constants.FEEDBACK_LINK}"
            )
        if min_periods:
            raise NotImplementedError(
                f"min_periods not yet supported. {constants.FEEDBACK_LINK}"
            )
        return self._apply_binary_aggregation(other, agg_ops.CorrOp())

    def autocorr(self, lag: int = 1) -> float:
        return self.corr(self.shift(lag))

    def cov(self, other: Series) -> float:
        return self._apply_binary_aggregation(other, agg_ops.CovOp())

    def all(self) -> bool:
        return typing.cast(bool, self._apply_aggregation(agg_ops.all_op))

    def any(self) -> bool:
        return typing.cast(bool, self._apply_aggregation(agg_ops.any_op))

    def count(self) -> int:
        return typing.cast(int, self._apply_aggregation(agg_ops.count_op))

    def nunique(self) -> int:
        return typing.cast(int, self._apply_aggregation(agg_ops.nunique_op))

    def max(self) -> scalars.Scalar:
        return self._apply_aggregation(agg_ops.max_op)

    def min(self) -> scalars.Scalar:
        return self._apply_aggregation(agg_ops.min_op)

    def std(self) -> float:
        return typing.cast(float, self._apply_aggregation(agg_ops.std_op))

    def var(self) -> float:
        return typing.cast(float, self._apply_aggregation(agg_ops.var_op))

    def _central_moment(self, n: int) -> float:
        """Useful helper for calculating central moment statistics"""
        # Nth central moment is mean((x-mean(x))^n)
        # See: https://en.wikipedia.org/wiki/Moment_(mathematics)
        mean_deltas = self - self.mean()
        delta_powers = mean_deltas**n
        return delta_powers.mean()

    def agg(self, func: str | typing.Sequence[str]) -> scalars.Scalar | Series:
        if _is_list_like(func):
            if self.dtype not in bigframes.dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE:
                raise NotImplementedError(
                    f"Multiple aggregations only supported on numeric series. {constants.FEEDBACK_LINK}"
                )
            aggregations = [agg_ops.lookup_agg_func(f)[0] for f in func]
            return Series(
                self._block.summarize(
                    [self._value_column],
                    aggregations,
                )
            )
        else:
            return self._apply_aggregation(agg_ops.lookup_agg_func(func)[0])

    aggregate = agg

    def describe(self) -> Series:
        from bigframes.pandas.core.methods import describe

        return cast(Series, describe.describe(self, include="all"))

    def skew(self):
        count = self.count()
        if count < 3:
            return pandas.NA

        moment3 = self._central_moment(3)
        moment2 = self.var() * (count - 1) / count  # Convert sample var to pop var

        # See G1 estimator:
        # https://en.wikipedia.org/wiki/Skewness#Sample_skewness
        numerator = moment3
        denominator = moment2 ** (3 / 2)
        adjustment = (count * (count - 1)) ** 0.5 / (count - 2)

        return (numerator / denominator) * adjustment

    def kurt(self):
        count = self.count()
        if count < 4:
            return pandas.NA

        moment4 = self._central_moment(4)
        moment2 = self.var() * (count - 1) / count  # Convert sample var to pop var

        # Kurtosis is often defined as the second standardize moment: moment(4)/moment(2)**2
        # Pandas however uses Fisher’s estimator, implemented below
        numerator = (count + 1) * (count - 1) * moment4
        denominator = (count - 2) * (count - 3) * moment2**2
        adjustment = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))

        return (numerator / denominator) - adjustment

    kurtosis = kurt

    def mode(self) -> Series:
        block = self._block
        # Approach: Count each value, return each value for which count(x) == max(counts))
        block = block.aggregate(
            by_column_ids=[self._value_column],
            aggregations=(
                agg_expressions.UnaryAggregation(
                    agg_ops.count_op, ex.deref(self._value_column)
                ),
            ),
        )
        value_count_col_id = block.value_columns[0]
        block, max_value_count_col_id = block.apply_window_op(
            value_count_col_id,
            agg_ops.max_op,
            window_spec=windows.unbound(),
        )
        block, is_mode_col_id = block.apply_binary_op(
            value_count_col_id,
            max_value_count_col_id,
            ops.eq_op,
        )
        block = block.filter_by_id(is_mode_col_id)
        # use temporary name for reset_index to avoid collision, restore after dropping extra columns
        block = (
            block.with_index_labels(["mode_temp_internal"])
            .order_by([order.ascending_over(self._value_column)])
            .reset_index(drop=False)
        )
        block = block.select_column(self._value_column).with_column_labels([self.name])
        mode_values_series = Series(block.select_column(self._value_column))
        return typing.cast(Series, mode_values_series)

    def mean(self) -> float:
        return typing.cast(float, self._apply_aggregation(agg_ops.mean_op))

    def median(self, *, exact: bool = True) -> float:
        if exact:
            return typing.cast(float, self.quantile(0.5))
        else:
            return typing.cast(float, self._apply_aggregation(agg_ops.median_op))

    def quantile(self, q: Union[float, Sequence[float]] = 0.5) -> Union[Series, float]:
        qs = tuple(q) if utils.is_list_like(q) else (q,)
        result = block_ops.quantile(self._block, (self._value_column,), qs=qs)
        if utils.is_list_like(q):
            # Drop the first level, since only one column
            result = result.with_column_labels(result.column_labels.droplevel(0))
            result, index_col = result.create_constant(self.name, None)
            result = result.set_index([index_col])
            return Series(
                result.transpose(original_row_index=pandas.Index([self.name]))
            )
        else:
            return cast(float, Series(result).to_pandas().squeeze())

    def sum(self) -> float:
        return typing.cast(float, self._apply_aggregation(agg_ops.sum_op))

    def prod(self) -> float:
        return typing.cast(float, self._apply_aggregation(agg_ops.product_op))

    product = prod

    def __eq__(self, other: object) -> Series:  # type: ignore
        return self.eq(other)

    def __ne__(self, other: object) -> Series:  # type: ignore
        return self.ne(other)

    def __invert__(self) -> Series:
        return self._apply_unary_op(ops.invert_op)

    def __pos__(self) -> Series:
        return self._apply_unary_op(ops.pos_op)

    def __neg__(self) -> Series:
        return self._apply_unary_op(ops.neg_op)

    def __dir__(self) -> List[str]:
        return dir(type(self)) + self._struct_fields

    def eq(self, other: object) -> Series:
        # TODO: enforce stricter alignment
        return self._apply_binary_op(other, ops.eq_op)

    def ne(self, other: object) -> Series:
        # TODO: enforce stricter alignment
        return self._apply_binary_op(other, ops.ne_op)

    def items(self):
        for batch_df in self._block.to_pandas_batches():
            assert (
                batch_df.shape[1] == 1
            ), f"Expected 1 column in the dataframe, but got {batch_df.shape[1]}."
            for item in batch_df.squeeze(axis=1).items():
                yield item

    def _apply_callable(self, condition):
        """ "Executes the possible callable condition as needed."""
        if callable(condition):
            # When it's a bigframes function.
            if isinstance(condition, bigframes.functions.Udf):
                return self.apply(condition)
            # When it's a plain Python function.
            else:
                return self.apply(condition, by_row=False)

        # When it's not a callable.
        return condition

    def where(self, cond, other=None):
        cond = self._apply_callable(cond)
        other = self._apply_callable(other)

        value_id, cond_id, other_id, block = self._align3(cond, other)
        block, result_id = block.project_expr(
            ops.where_op.as_expr(value_id, cond_id, other_id)
        )
        return Series(block.select_column(result_id).with_column_labels([self.name]))

    def clip(self, lower=None, upper=None):
        if lower is None and upper is None:
            return self
        if lower is None:
            return self._apply_binary_op(upper, ops.minimum_op, alignment="left")
        if upper is None:
            return self._apply_binary_op(lower, ops.maximum_op, alignment="left")
        # special rule to coerce scalar string args to date
        value_id, lower_id, upper_id, block = self._align3(
            lower, upper, cast_scalars=(bigframes.dtypes.is_date_like(self.dtype))
        )
        block, result_id = block.project_expr(
            ops.clip_op.as_expr(value_id, lower_id, upper_id),
        )
        return Series(block.select_column(result_id).with_column_labels([self.name]))

    @validations.requires_ordering()
    def argmax(self) -> int:
        block, row_nums = self._block.promote_offsets()
        block = block.order_by(
            [
                order.descending_over(self._value_column),
                order.ascending_over(row_nums),
            ]
        )
        return typing.cast(
            scalars.Scalar, Series(block.select_column(row_nums)).iloc[0]
        )

    @validations.requires_ordering()
    def argmin(self) -> int:
        block, row_nums = self._block.promote_offsets()
        block = block.order_by(
            [
                order.ascending_over(self._value_column),
                order.ascending_over(row_nums),
            ]
        )
        return typing.cast(
            scalars.Scalar, Series(block.select_column(row_nums)).iloc[0]
        )

    @validations.requires_index
    def unstack(self, level: LevelsType = -1):
        if isinstance(level, int) or isinstance(level, str):
            level = [level]

        block = self._block

        if self.index.nlevels == 1:
            raise ValueError("Series must have multi-index to unstack")

        # Pivot by index levels
        unstack_ids = self._resolve_levels(level)
        block = block.reset_index(drop=False)
        block = block.set_index(
            [col for col in self._block.index_columns if col not in unstack_ids]
        )

        pivot_block = block.pivot(
            columns=unstack_ids,
            values=self._block.value_columns,
            values_in_index=False,
        )
        return bigframes.dataframe.DataFrame(pivot_block)

    @validations.requires_index
    def idxmax(self) -> blocks.Label:
        block = self._block.order_by(
            [
                order.descending_over(self._value_column),
                *[
                    order.ascending_over(idx_col)
                    for idx_col in self._block.index_columns
                ],
            ]
        )
        block = block.slice(0, 1)
        return indexes.Index(block).to_pandas()[0]

    @validations.requires_index
    def idxmin(self) -> blocks.Label:
        block = self._block.order_by(
            [
                order.ascending_over(self._value_column),
                *[
                    order.ascending_over(idx_col)
                    for idx_col in self._block.index_columns
                ],
            ]
        )
        block = block.slice(0, 1)
        return indexes.Index(block).to_pandas()[0]

    @property
    @validations.requires_ordering()
    def is_monotonic_increasing(self) -> bool:
        return typing.cast(
            bool, self._block.is_monotonic_increasing(self._value_column)
        )

    @property
    @validations.requires_ordering()
    def is_monotonic_decreasing(self) -> bool:
        return typing.cast(
            bool, self._block.is_monotonic_decreasing(self._value_column)
        )

    def __getitem__(self, indexer):
        # TODO: enforce stricter alignment, should fail if indexer is missing any keys.
        use_iloc = (
            isinstance(indexer, slice)
            and all(
                isinstance(x, numbers.Integral) or (x is None)
                for x in [indexer.start, indexer.stop, indexer.step]
            )
        ) or (
            isinstance(indexer, numbers.Integral)
            and not isinstance(self._block.index.dtypes[0], pandas.Int64Dtype)
        )
        if use_iloc:
            return self.iloc[indexer]
        if isinstance(indexer, Series):
            (left, right, block) = self._align(indexer, "left")
            block = block.filter(right)
            block = block.select_column(left.id.name)
            return Series(block)
        return self.loc[indexer]

    def __getattr__(self, key: str):
        # Protect against recursion errors with uninitialized Series objects.
        # We use "_block" attribute to check whether the instance is initialized.
        # See:
        # https://github.com/googleapis/python-bigquery-dataframes/issues/728
        # and
        # https://nedbatchelder.com/blog/201010/surprising_getattr_recursion.html
        if key == "_block":
            raise AttributeError(key)
        elif hasattr(pandas.Series, key):
            log_adapter.submit_pandas_labels(
                self._block.session.bqclient, self.__class__.__name__, key
            )
            raise AttributeError(
                textwrap.dedent(
                    f"""
                    BigQuery DataFrames has not yet implemented an equivalent to
                    'pandas.Series.{key}'. {constants.FEEDBACK_LINK}
                    """
                )
            )
        elif key in self._struct_fields:
            return self.struct.field(key)
        else:
            raise AttributeError(key)

    def __setitem__(self, key, value) -> None:
        """Set item using direct assignment, delegating to .loc indexer."""
        self.loc[key] = value

    def _apply_aggregation(
        self, op: agg_ops.UnaryAggregateOp | agg_ops.NullaryAggregateOp
    ) -> Any:
        return self._block.get_stat(self._value_column, op)

    def _apply_window_op(
        self, op: agg_ops.UnaryWindowOp, window_spec: windows.WindowSpec
    ):
        block = self._block
        block, result_id = block.apply_window_op(
            self._value_column, op, window_spec=window_spec, result_label=self.name
        )
        result = Series(block.select_column(result_id))
        if op.skips_nulls:
            return result.where(self.notna(), None)
        else:
            return result

    def value_counts(
        self,
        normalize: bool = False,
        sort: bool = True,
        ascending: bool = False,
        *,
        dropna: bool = True,
    ):
        block = block_ops.value_counts(
            self._block,
            [self._value_column],
            normalize=normalize,
            ascending=ascending,
            drop_na=dropna,
        )
        return Series(block)

    @typing.overload  # type: ignore[override]
    def sort_values(
        self,
        *,
        axis=...,
        inplace: Literal[True] = ...,
        ascending: bool | typing.Sequence[bool] = ...,
        kind: str = ...,
        na_position: typing.Literal["first", "last"] = ...,
    ) -> None:
        ...

    @typing.overload
    def sort_values(
        self,
        *,
        axis=...,
        inplace: Literal[False] = ...,
        ascending: bool | typing.Sequence[bool] = ...,
        kind: str = ...,
        na_position: typing.Literal["first", "last"] = ...,
    ) -> Series:
        ...

    def sort_values(
        self,
        *,
        axis=0,
        inplace: bool = False,
        ascending=True,
        kind: str = "quicksort",
        na_position: typing.Literal["first", "last"] = "last",
    ) -> Optional[Series]:
        if axis != 0 and axis != "index":
            raise ValueError(f"No axis named {axis} for object type Series")
        if na_position not in ["first", "last"]:
            raise ValueError("Param na_position must be one of 'first' or 'last'")
        block = self._block.order_by(
            [
                order.ascending_over(self._value_column, (na_position == "last"))
                if ascending
                else order.descending_over(self._value_column, (na_position == "last"))
            ],
        )
        if inplace:
            self._set_block(block)
            return None
        else:
            return Series(block)

    @typing.overload  # type: ignore[override]
    def sort_index(
        self, *, axis=..., inplace: Literal[False] = ..., ascending=..., na_position=...
    ) -> Series:
        ...

    @typing.overload
    def sort_index(
        self, *, axis=0, inplace: Literal[True] = ..., ascending=..., na_position=...
    ) -> None:
        ...

    @validations.requires_index
    def sort_index(
        self, *, axis=0, inplace: bool = False, ascending=True, na_position="last"
    ) -> Optional[Series]:
        # TODO(tbergeron): Support level parameter once multi-index introduced.
        if axis != 0 and axis != "index":
            raise ValueError(f"No axis named {axis} for object type Series")
        if na_position not in ["first", "last"]:
            raise ValueError("Param na_position must be one of 'first' or 'last'")
        block = self._block
        na_last = na_position == "last"
        ordering = [
            order.ascending_over(column, na_last)
            if ascending
            else order.descending_over(column, na_last)
            for column in block.index_columns
        ]
        block = block.order_by(ordering)
        if inplace:
            self._set_block(block)
            return None
        else:
            return Series(block)

    @validations.requires_ordering()
    def rolling(
        self,
        window: int | pandas.Timedelta | numpy.timedelta64 | datetime.timedelta | str,
        min_periods: int | None = None,
        closed: Literal["right", "left", "both", "neither"] = "right",
    ) -> bigframes.core.window.Window:
        if isinstance(window, int):
            # Rows rolling
            window_spec = windows.WindowSpec(
                bounds=windows.RowsWindowBounds.from_window_size(window, closed),
                min_periods=window if min_periods is None else min_periods,
            )
            return bigframes.core.window.Window(
                self._block, window_spec, self._block.value_columns, is_series=True
            )

        return rolling.create_range_window(
            block=self._block,
            window=window,
            min_periods=min_periods,
            closed=closed,
            is_series=True,
        )

    @validations.requires_ordering()
    def expanding(self, min_periods: int = 1) -> bigframes.core.window.Window:
        window_spec = windows.cumulative_rows(min_periods=min_periods)
        return bigframes.core.window.Window(
            self._block, window_spec, self._block.value_columns, is_series=True
        )

    def pipe(
        self,
        func: Union[Callable[..., U], tuple[Callable[..., U], str]],
        *args,
        **kwargs,
    ) -> U:
        import bigframes_vendored.pandas.core.common as common

        return common.pipe(self, func, *args, **kwargs)

    def get(self, key, default=None):
        try:
            return self[key]
        except (KeyError, ValueError, IndexError):
            return default

    def groupby(
        self,
        by: typing.Union[
            blocks.Label, Series, typing.Sequence[typing.Union[blocks.Label, Series]]
        ] = None,
        axis=0,
        level: typing.Optional[
            int | str | typing.Sequence[int] | typing.Sequence[str]
        ] = None,
        as_index: bool = True,
        *,
        dropna: bool = True,
    ) -> bigframes.core.groupby.SeriesGroupBy:
        if (by is not None) and (level is not None):
            raise ValueError("Do not specify both 'by' and 'level'")
        if not as_index:
            raise ValueError("as_index=False only valid with DataFrame")
        if axis:
            raise ValueError("No axis named {} for object type Series".format(level))
        if not as_index:
            raise ValueError("'as_index'=False only applies to DataFrame")
        if by is not None:
            return self._groupby_values(by, dropna)
        if level is not None:
            return self._groupby_level(level, dropna)
        else:
            raise TypeError("You have to supply one of 'by' and 'level'")

    @validations.requires_index
    def _groupby_level(
        self,
        level: int | str | typing.Sequence[int] | typing.Sequence[str],
        dropna: bool = True,
    ) -> bigframes.core.groupby.SeriesGroupBy:
        if utils.is_list_like(level):
            by_key_is_singular = False
        else:
            by_key_is_singular = True

        return groupby.SeriesGroupBy(
            self._block,
            self._value_column,
            by_col_ids=self._resolve_levels(level),
            value_name=self.name,
            dropna=dropna,
            by_key_is_singular=by_key_is_singular,
        )

    def _groupby_values(
        self,
        by: typing.Union[
            blocks.Label, Series, typing.Sequence[typing.Union[blocks.Label, Series]]
        ],
        dropna: bool = True,
    ) -> bigframes.core.groupby.SeriesGroupBy:
        if not isinstance(by, Series) and _is_list_like(by):
            by = list(by)
            by_key_is_singular = False
        else:
            by = [typing.cast(typing.Union[blocks.Label, Series], by)]
            by_key_is_singular = True

        block = self._block
        grouping_cols: typing.Sequence[str] = []
        value_col = self._value_column
        for key in by:
            if isinstance(key, Series):
                block, (
                    get_column_left,
                    get_column_right,
                ) = block.join(key._block, how="inner" if dropna else "left")

                value_col = get_column_left[value_col]
                grouping_cols = [
                    *[get_column_left[value] for value in grouping_cols],
                    get_column_right[key._value_column],
                ]
            else:
                # Interpret as index level
                matches = block.index_name_to_col_id.get(key, [])
                if len(matches) != 1:
                    raise ValueError(
                        f"GroupBy key {key} does not match a unique index level. BigQuery DataFrames only interprets lists of strings as index level names, not directly as per-row group assignments."
                    )
                grouping_cols = [*grouping_cols, matches[0]]

        return groupby.SeriesGroupBy(
            block,
            value_col,
            by_col_ids=grouping_cols,
            value_name=self.name,
            dropna=dropna,
            by_key_is_singular=by_key_is_singular,
        )

    def apply(
        self,
        func,
        by_row: typing.Union[typing.Literal["compat"], bool] = "compat",
        *,
        args: typing.Tuple = (),
    ) -> Series:
        # Note: This signature differs from pandas.Series.apply. Specifically,
        # `args` is keyword-only and `by_row` is a custom parameter here. Full
        # alignment would involve breaking changes. However, given that by_row
        # is not frequently used, we defer any such changes until there is a
        # clear need based on user feedback.
        #
        # See pandas docs for reference:
        # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html

        # TODO(shobs, b/274645634): Support convert_dtype, **kwargs
        # is actually a ternary op

        if by_row not in ["compat", False]:
            raise ValueError("Param by_row must be one of 'compat' or False")

        if not callable(func) and not isinstance(func, numpy.ufunc):
            raise ValueError(
                "Only a ufunc (a function that applies to the entire Series) or"
                " a BigFrames BigQuery function that only works on single values"
                " are supported."
            )

        if isinstance(
            func,
            (bigframes.functions.Udf,),
        ):
            # We are working with bigquery function at this point
            if args:
                result_series = self._apply_nary_op(
                    ops.NaryRemoteFunctionOp(function_def=func.udf_def), args
                )
                # TODO(jialuo): Investigate why `_apply_nary_op` drops the series
                # `name`. Manually reassigning it here as a temporary fix.
                result_series.name = self.name
            else:
                result_series = self._apply_unary_op(
                    ops.RemoteFunctionOp(function_def=func.udf_def, apply_on_null=True)
                )

            return result_series

        bf_op = python_ops.python_callable_to_op(func)
        if bf_op and isinstance(bf_op, ops.UnaryOp):
            return self._apply_unary_op(bf_op)

        # It is neither a remote function nor a managed function.
        # Then it must be a vectorized function that applies to the Series
        # as a whole.
        if by_row:
            raise ValueError(
                "You have passed a function as-is. If your intention is to "
                "apply this function in a vectorized way (i.e. to the "
                "entire Series as a whole, and you are sure that it "
                "performs only the operations that are implemented for a "
                "Series (e.g. a chain of arithmetic/logical operations, "
                "such as `def foo(s): return s % 2 == 1`), please also "
                "specify `by_row=False`. If your function contains "
                "arbitrary code, it can only be applied to every element "
                "in the Series individually, in which case you must "
                "convert it to a BigFrames BigQuery function using "
                "`bigframes.pandas.udf`, "
                "or `bigframes.pandas.remote_function` before passing."
            )

        try:
            return func(self)  # type: ignore
        except Exception as ex:
            # This could happen if any of the operators in func is not
            # supported on a Series. Let's guide the customer to use a
            # bigquery function instead
            if hasattr(ex, "message"):
                ex.message += f"\n{_bigquery_function_recommendation_message}"
            raise

    def combine(
        self,
        other,
        func,
    ) -> Series:
        if not callable(func) and not isinstance(func, numpy.ufunc):
            raise ValueError(
                "Only a ufunc (a function that applies to the entire Series) or"
                " a BigFrames BigQuery function that only works on single values"
                " are supported."
            )

        if isinstance(
            func,
            bigframes.functions.Udf,
        ):
            result_series = self._apply_binary_op(
                other, ops.BinaryRemoteFunctionOp(function_def=func.udf_def)
            )
            return result_series

        bf_op = python_ops.python_callable_to_op(func)
        if bf_op and isinstance(bf_op, ops.BinaryOp):
            result_series = self._apply_binary_op(other, bf_op)
            return result_series

        # Keep this in sync with .apply
        try:
            return func(self, other)
        except Exception as ex:
            # This could happen if any of the operators in func is not
            # supported on a Series. Let's guide the customer to use a
            # bigquery function instead
            if hasattr(ex, "message"):
                ex.message += f"\n{_bigquery_function_recommendation_message}"
            raise

    @validations.requires_index
    def add_prefix(self, prefix: str, axis: int | str | None = None) -> Series:
        return Series(self._get_block().add_prefix(prefix))

    @validations.requires_index
    def add_suffix(self, suffix: str, axis: int | str | None = None) -> Series:
        return Series(self._get_block().add_suffix(suffix))

    def take(
        self, indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs
    ) -> Series:
        if not utils.is_list_like(indices):
            raise ValueError("indices should be a list-like object.")
        return typing.cast(Series, self.iloc[indices])

    def filter(
        self,
        items: typing.Optional[typing.Iterable] = None,
        like: typing.Optional[str] = None,
        regex: typing.Optional[str] = None,
        axis: typing.Optional[typing.Union[str, int]] = None,
    ) -> Series:
        if (axis is not None) and utils.get_axis_number(axis) != 0:
            raise ValueError(f"Invalid axis for series: {axis}")
        if sum([(items is not None), (like is not None), (regex is not None)]) != 1:
            raise ValueError(
                "Need to provide exactly one of 'items', 'like', or 'regex'"
            )
        if len(self._block.index_columns) > 1:
            raise NotImplementedError(
                f"Method filter does not support rows multiindex. {constants.FEEDBACK_LINK}"
            )
        if (like is not None) or (regex is not None):
            block = self._block
            block, label_string_id = block.apply_unary_op(
                self._block.index_columns[0],
                ops.AsTypeOp(to_type=pandas.StringDtype(storage="pyarrow")),
            )
            if like is not None:
                block, mask_id = block.apply_unary_op(
                    label_string_id, ops.StrContainsOp(pat=like)
                )
            else:  # regex
                assert regex is not None
                block, mask_id = block.apply_unary_op(
                    label_string_id, ops.StrContainsRegexOp(pat=regex)
                )

            block = block.filter_by_id(mask_id)
            block = block.select_columns([self._value_column])
            return Series(block)
        elif items is not None:
            # Behavior matches pandas 2.1+, older pandas versions would reindex
            block = self._block
            block, mask_id = block.apply_unary_op(
                self._block.index_columns[0], ops.IsInOp(values=tuple(items))
            )
            block = block.filter_by_id(mask_id)
            block = block.select_columns([self._value_column])
            return Series(block)
        else:
            raise ValueError("Need to provide 'items', 'like', or 'regex'")

    @validations.requires_index
    def reindex(self, index=None, *, validate: typing.Optional[bool] = None):
        if validate and not self.index.is_unique:
            raise ValueError("Original index must be unique to reindex")
        keep_original_names = False
        if isinstance(index, indexes.Index):
            new_indexer = bigframes.dataframe.DataFrame(data=index._block)[[]]
        else:
            if not isinstance(index, pandas.Index):
                keep_original_names = True
                index = pandas.Index(index)
            if index.nlevels != self.index.nlevels:
                raise NotImplementedError(
                    "Cannot reindex with index with different nlevels"
                )
            new_indexer = bigframes.dataframe.DataFrame(
                index=index, session=self._get_block().expr.session
            )[[]]
        # multiindex join is senstive to index names, so we will set all these
        result = new_indexer.rename_axis(range(new_indexer.index.nlevels)).join(
            self.to_frame().rename_axis(range(self.index.nlevels)),
            how="left",
        )
        # and then reset the names after the join
        result_block = result.rename_axis(
            self.index.names if keep_original_names else index.names
        )._block
        return Series(result_block)

    @validations.requires_index
    def reindex_like(self, other: Series, *, validate: typing.Optional[bool] = None):
        return self.reindex(other.index, validate=validate)

    def drop_duplicates(self, *, keep: str = "first") -> Series:
        block = block_ops.drop_duplicates(self._block, (self._value_column,), keep)
        return Series(block)

    def unique(self, keep_order=True) -> Series:
        if keep_order:
            validations.enforce_ordered(self, "unique(keep_order != False)")
            return self.drop_duplicates()
        block = self._block.aggregate(
            [
                agg_expressions.UnaryAggregation(
                    agg_ops.AnyValueOp(), ex.deref(self._value_column)
                )
            ],
            [self._value_column],
            column_labels=self._block.column_labels,
            dropna=False,
        )
        return Series(block.reset_index())

    def duplicated(self, keep: str = "first") -> Series:
        block, indicator = block_ops.indicate_duplicates(
            self._block, (self._value_column,), keep
        )
        return Series(
            block.select_column(
                indicator,
            ).with_column_labels([self.name])
        )

    def mask(self, cond, other=None) -> Series:
        cond = self._apply_callable(cond)
        other = self._apply_callable(other)
        if not isinstance(cond, Series):
            raise TypeError(
                f"Only bigframes series condition is supported, received {type(cond).__name__}. "
                f"{constants.FEEDBACK_LINK}"
            )
        return self.where(~cond, other)

    def to_frame(self, name: blocks.Label = None) -> bigframes.dataframe.DataFrame:
        provided_name = name if name else self.name
        # To be consistent with Pandas, it assigns 0 as the column name if missing. 0 is the first element of RangeIndex.
        block = self._block.with_column_labels(
            [provided_name] if provided_name else [0]
        )
        return bigframes.dataframe.DataFrame(block)

    def to_csv(
        self,
        path_or_buf=None,
        sep=",",
        *,
        header: bool = True,
        index: bool = True,
        allow_large_results: Optional[bool] = None,
    ) -> Optional[str]:
        if utils.is_gcs_path(path_or_buf):
            return self.to_frame().to_csv(
                path_or_buf,
                sep=sep,
                header=header,
                index=index,
                allow_large_results=allow_large_results,
            )
        else:
            pd_series = self.to_pandas(allow_large_results=allow_large_results)
            return pd_series.to_csv(
                path_or_buf=path_or_buf, sep=sep, header=header, index=index
            )

    def to_dict(
        self,
        into: type[dict] = dict,
        *,
        allow_large_results: Optional[bool] = None,
    ) -> typing.Mapping:
        return typing.cast(dict, self.to_pandas(allow_large_results=allow_large_results).to_dict(into=into))  # type: ignore

    def to_excel(
        self, excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs
    ) -> None:
        return self.to_pandas(allow_large_results=allow_large_results).to_excel(
            excel_writer, sheet_name=sheet_name, **kwargs
        )

    def to_json(
        self,
        path_or_buf=None,
        orient: Optional[
            typing.Literal["split", "records", "index", "columns", "values", "table"]
        ] = None,
        *,
        lines: bool = False,
        index: bool = True,
        allow_large_results: Optional[bool] = None,
    ) -> Optional[str]:
        if utils.is_gcs_path(path_or_buf):
            return self.to_frame().to_json(
                path_or_buf=path_or_buf,
                orient=orient,
                lines=lines,
                index=index,
                allow_large_results=allow_large_results,
            )
        else:
            pd_series = self.to_pandas(allow_large_results=allow_large_results)
            return pd_series.to_json(
                path_or_buf=path_or_buf, orient=orient, lines=lines, index=index  # type: ignore
            )

    def to_latex(
        self,
        buf=None,
        columns=None,
        header=True,
        index=True,
        *,
        allow_large_results=None,
        **kwargs,
    ) -> typing.Optional[str]:
        return self.to_pandas(allow_large_results=allow_large_results).to_latex(
            buf, columns=columns, header=header, index=index, **kwargs
        )

    def tolist(
        self,
        *,
        allow_large_results: Optional[bool] = None,
    ) -> _list:
        return self.to_pandas(allow_large_results=allow_large_results).to_list()

    to_list = tolist

    def to_markdown(
        self,
        buf: typing.IO[str] | None = None,
        mode: str = "wt",
        index: bool = True,
        *,
        allow_large_results: Optional[bool] = None,
        **kwargs,
    ) -> typing.Optional[str]:
        return self.to_pandas(allow_large_results=allow_large_results).to_markdown(buf, mode=mode, index=index, **kwargs)  # type: ignore

    def to_numpy(
        self,
        dtype=None,
        copy=False,
        na_value=pd_ext.no_default,
        *,
        allow_large_results=None,
        **kwargs,
    ) -> numpy.ndarray:
        return self.to_pandas(allow_large_results=allow_large_results).to_numpy(
            dtype, copy, na_value, **kwargs
        )

    def __array__(self, dtype=None, copy: Optional[bool] = None) -> numpy.ndarray:
        if copy is False:
            raise ValueError("Cannot convert to array without copy.")
        return self.to_numpy(dtype=dtype)

    def to_pickle(self, path, *, allow_large_results=None, **kwargs) -> None:
        return self.to_pandas(allow_large_results=allow_large_results).to_pickle(
            path, **kwargs
        )

    def to_string(
        self,
        buf=None,
        na_rep="NaN",
        float_format=None,
        header=True,
        index=True,
        length=False,
        dtype=False,
        name=False,
        max_rows=None,
        min_rows=None,
        *,
        allow_large_results=None,
    ) -> typing.Optional[str]:
        return self.to_pandas(allow_large_results=allow_large_results).to_string(
            buf,
            na_rep,
            float_format,
            header,
            index,
            length,
            dtype,
            name,
            max_rows,
            min_rows,
        )

    def to_xarray(
        self,
        *,
        allow_large_results: Optional[bool] = None,
    ):
        return self.to_pandas(allow_large_results=allow_large_results).to_xarray()

    def _throw_if_index_contains_duplicates(
        self, error_message: typing.Optional[str] = None
    ) -> None:
        if not self.index.is_unique:
            error_message = (
                error_message
                if error_message
                else "Index contains duplicate entries, but uniqueness is required."
            )
            raise pandas.errors.InvalidIndexError(error_message)

    def map(
        self,
        arg: typing.Union[Mapping, Series, Callable],
        na_action: Optional[str] = None,
        *,
        verify_integrity: bool = False,
    ) -> Series:
        if na_action:
            raise NotImplementedError(
                f"Non-None na_action argument is not yet supported for Series.map. {constants.FEEDBACK_LINK}"
            )
        if isinstance(arg, Series):
            if verify_integrity:
                error_message = "When verify_integrity is True in Series.map, index of arg parameter must not have duplicate entries."
                arg._throw_if_index_contains_duplicates(error_message=error_message)
            map_df = bigframes.dataframe.DataFrame(arg._block)
            map_df = map_df.rename(columns={arg.name: self.name})
        elif isinstance(arg, Mapping):
            map_df = bigframes.dataframe.DataFrame(
                {"keys": list(arg.keys()), self.name: list(arg.values())},  # type: ignore
                session=self._get_block().expr.session,
            )
            map_df = map_df.set_index("keys")
        elif callable(arg):
            # This is for remote function and managed funtion.
            return self.apply(arg)
        else:
            # Mirroring pandas, call the uncallable object
            arg()  # throws TypeError: object is not callable

        self_df = self.to_frame(name="series")
        result_df = self_df.join(map_df, on="series")
        return result_df[self.name]

    @validations.requires_ordering()
    def sample(
        self,
        n: Optional[int] = None,
        frac: Optional[float] = None,
        *,
        random_state: Optional[int] = None,
        sort: Optional[bool | Literal["random"]] = "random",
    ) -> Series:
        if n is not None and frac is not None:
            raise ValueError("Only one of 'n' or 'frac' parameter can be specified.")

        ns = (n,) if n is not None else ()
        fracs = (frac,) if frac is not None else ()
        return Series(
            self._block.split(ns=ns, fracs=fracs, random_state=random_state, sort=sort)[
                0
            ]
        )

    def explode(self, *, ignore_index: Optional[bool] = False) -> Series:
        return Series(
            self._block.explode(
                column_ids=[self._value_column], ignore_index=ignore_index
            )
        )

    @validations.requires_ordering()
    def resample(
        self,
        rule: str,
        *,
        closed: Optional[Literal["right", "left"]] = None,
        label: Optional[Literal["right", "left"]] = None,
        level: Optional[LevelsType] = None,
        origin: Union[
            Union[
                pandas.Timestamp, datetime.datetime, numpy.datetime64, int, float, str
            ],
            Literal["epoch", "start", "start_day", "end", "end_day"],
        ] = "start_day",
    ) -> bigframes.core.groupby.SeriesGroupBy:
        block = self._block._generate_resample_label(
            rule=rule,
            closed=closed,
            label=label,
            on=None,
            level=level,
            origin=origin,
        )
        series = Series(block)
        return series.groupby(level=0)

    def __array_ufunc__(
        self, ufunc: numpy.ufunc, method: str, *inputs, **kwargs
    ) -> Series:
        """Used to support numpy ufuncs.
        See: https://numpy.org/doc/stable/reference/ufuncs.html
        """
        # Only __call__ supported with zero arguments
        if method != "__call__" or len(inputs) > 2 or len(kwargs) > 0:
            return NotImplemented

        if len(inputs) == 1 and ufunc in ops.NUMPY_TO_OP:
            return self._apply_unary_op(ops.NUMPY_TO_OP[ufunc])
        if len(inputs) == 2 and ufunc in ops.NUMPY_TO_BINOP:
            binop = ops.NUMPY_TO_BINOP[ufunc]
            if inputs[0] is self:
                return self._apply_binary_op(inputs[1], binop)
            else:
                return self._apply_binary_op(inputs[0], binop, reverse=True)

        return NotImplemented

    @property
    def plot(self):
        return plotting.PlotAccessor(self)

    def hist(
        self, by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs
    ):
        return self.plot.hist(by=by, bins=bins, **kwargs)

    def line(
        self,
        x: typing.Optional[typing.Hashable] = None,
        y: typing.Optional[typing.Hashable] = None,
        **kwargs,
    ):
        return self.plot.line(x=x, y=y, **kwargs)

    def area(
        self,
        x: typing.Optional[typing.Hashable] = None,
        y: typing.Optional[typing.Hashable] = None,
        stacked: bool = True,
        **kwargs,
    ):
        return self.plot.area(x=x, y=y, stacked=stacked, **kwargs)

    def bar(
        self,
        x: typing.Optional[typing.Hashable] = None,
        y: typing.Optional[typing.Hashable] = None,
        **kwargs,
    ):
        return self.plot.bar(x=x, y=y, **kwargs)

    def _slice(
        self,
        start: typing.Optional[int] = None,
        stop: typing.Optional[int] = None,
        step: typing.Optional[int] = None,
    ) -> Series:
        return Series(
            self._block.slice(
                start=start, stop=stop, step=step if (step is not None) else 1
            ).select_column(self._value_column),
        )

    def cache(self):
        """
        Materializes the Series to a temporary table.

        Useful if the series will be used multiple times, as this will avoid recomputating the shared intermediate value.

        Returns:
            Series: Self
        """
        # Do not use session-aware cashing if user-requested
        return self._cached(force=True, session_aware=False)

    def _cached(self, *, force: bool = True, session_aware: bool = True) -> Series:
        self._block.cached(force=force, session_aware=session_aware)
        return self

    # Keep this at the bottom of the Series class to avoid
    # confusing type checker by overriding str
    @property
    def str(self) -> strings.StringMethods:
        import bigframes.operations.strings as strings

        return strings.StringMethods(self)

    @property
    def _value_column(self) -> __builtins__.str:
        return self._block.value_columns[0]

    @property
    def _name(self) -> blocks.Label:
        return self._block.column_labels[0]

    @property
    def _dtype(self):
        return self._block.dtypes[0]

    def _set_block(self, block: blocks.Block):
        self._block = block

    def _get_block(self) -> blocks.Block:
        return self._block

    def _apply_unary_op(
        self,
        op: ops.UnaryOp,
    ) -> Series:
        """Applies a unary operator to the series."""
        block, result_id = self._block.apply_unary_op(
            self._value_column,
            op,
        )
        return Series(block.select_column(result_id), name=self.name)  # type: ignore

    def _apply_binary_op(
        self,
        other: typing.Any,
        op: ops.BinaryOp,
        alignment: typing.Literal["outer", "left"] = "outer",
        reverse: bool = False,
    ) -> Series:
        """Applies a binary operator to the series and other."""
        if bigframes.core.convert.can_convert_to_series(other):
            self_index = indexes.Index(self._block)
            other_series = bigframes.core.convert.to_bf_series(
                other, self_index, self._block.session
            )
            (self_col, other_col, block) = self._align(other_series, how=alignment)

            name = self._name
            # Drop name if both objects have name attr, but they don't match
            if (
                hasattr(other, "name")
                and other_series.name != self._name
                and alignment == "outer"
            ):
                name = None
            expr = op.as_expr(
                other_col if reverse else self_col, self_col if reverse else other_col
            )
            block, result_id = block.project_expr(expr)
            block = block.select_column(result_id).with_column_labels([name])
            return Series(block)  # type: ignore

        else:  # Scalar binop
            name = self._name
            expr = op.as_expr(
                ex.const(other) if reverse else self._value_column,
                self._value_column if reverse else ex.const(other),
            )
            block, result_id = self._block.project_expr(expr)
            block = block.select_column(result_id).with_column_labels([name])
            return Series(block)  # type: ignore

    def _apply_nary_op(
        self,
        op: ops.NaryOp,
        others: Sequence[typing.Union[Series, scalars.Scalar]],
        ignore_self=False,
    ):
        """Applies an n-ary operator to the series and others."""
        values, block = self._align_n(
            others, ignore_self=ignore_self, cast_scalars=False
        )
        block, result_id = block.project_expr(op.as_expr(*values))
        return Series(block.select_column(result_id))

    def _apply_binary_aggregation(
        self, other: Series, stat: agg_ops.BinaryAggregateOp
    ) -> float:
        (left, right, block) = self._align(other, how="outer")
        assert isinstance(left, ex.DerefOp)
        assert isinstance(right, ex.DerefOp)
        return block.get_binary_stat(left.id.name, right.id.name, stat)

    AlignedExprT = Union[ex.ScalarConstantExpression, ex.DerefOp]

    @typing.overload
    def _align(
        self, other: Series, how="outer"
    ) -> tuple[ex.DerefOp, ex.DerefOp, blocks.Block,]:
        ...

    @typing.overload
    def _align(
        self, other: typing.Union[Series, scalars.Scalar], how="outer"
    ) -> tuple[ex.DerefOp, AlignedExprT, blocks.Block,]:
        ...

    def _align(
        self, other: typing.Union[Series, scalars.Scalar], how="outer"
    ) -> tuple[ex.DerefOp, AlignedExprT, blocks.Block,]:
        """Aligns the series value with another scalar or series object. Returns new left column id, right column id and joined tabled expression."""
        values, block = self._align_n(
            [
                other,
            ],
            how,
        )
        return (typing.cast(ex.DerefOp, values[0]), values[1], block)

    def _align3(self, other1: Series | scalars.Scalar, other2: Series | scalars.Scalar, how="left", cast_scalars: bool = True) -> tuple[ex.DerefOp, AlignedExprT, AlignedExprT, blocks.Block]:  # type: ignore
        """Aligns the series value with 2 other scalars or series objects. Returns new values and joined tabled expression."""
        values, index = self._align_n([other1, other2], how, cast_scalars=cast_scalars)
        return (
            typing.cast(ex.DerefOp, values[0]),
            values[1],
            values[2],
            index,
        )

    def _align_n(
        self,
        others: typing.Sequence[typing.Union[Series, scalars.Scalar]],
        how="outer",
        ignore_self=False,
        cast_scalars: bool = False,
    ) -> tuple[
        typing.Sequence[Union[ex.ScalarConstantExpression, ex.DerefOp]],
        blocks.Block,
    ]:
        if ignore_self:
            value_ids: List[Union[ex.ScalarConstantExpression, ex.DerefOp]] = []
        else:
            value_ids = [ex.deref(self._value_column)]

        block = self._block
        for other in others:
            if isinstance(other, Series):
                block, (
                    get_column_left,
                    get_column_right,
                ) = block.join(other._block, how=how)
                rebindings = {
                    ids.ColumnId(old): ids.ColumnId(new)
                    for old, new in get_column_left.items()
                }
                remapped_value_ids = (
                    value.remap_column_refs(rebindings) for value in value_ids
                )
                value_ids = [
                    *remapped_value_ids,  # type: ignore
                    ex.deref(get_column_right[other._value_column]),
                ]
            else:
                # Will throw if can't interpret as scalar.
                dtype = typing.cast(bigframes.dtypes.Dtype, self._dtype)
                value_ids = [
                    *value_ids,
                    ex.const(other, dtype=dtype if cast_scalars else None),
                ]
        return (value_ids, block)

    def _throw_if_null_index(self, opname: __builtins__.str):
        if len(self._block.index_columns) == 0:
            raise bigframes.exceptions.NullIndexError(
                f"Series cannot perform {opname} as it has no index. Set an index using set_index."
            )


def _is_list_like(obj: typing.Any) -> typing_extensions.TypeGuard[typing.Sequence]:
    return pandas.api.types.is_list_like(obj)