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# 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):