bayesline.api.equity.ReportAccessorApi

class bayesline.api.equity.ReportAccessorApi

Abstract base class for report accessor APIs.

This class extends BaseReportAccessorApi with additional abstract methods for getting level values and data from portfolio reports.

__init__()

Methods

__init__()
get_data(path, *[, expand, pivot_cols, ...])	Get data for the specified path.
get_data_as_task(path, *[, expand, ...])
get_level_values([levels, include_totals, ...])	Get level values for the specified levels.
is_path_valid(path_levels, *[, expand])	Check if a path is valid.
validate_path(path_levels[, expand])	Validate a path and expand tuple.
with_scenario_settings(scenario_settings, *)	Return a new accessor with the given scenario settings applied.

Attributes

axes	Get the axes configuration for the report.
axes_hashes	Get content hashes of the axis dimensions.
axis_lookup	Get the axis lookup mapping.
dimension_descriptors	Get the semantic descriptors of the dimensions.
identifier	The identifier of the report accessor.
metric_cols	Get the metric column names for the report.
metric_descriptors	Get the semantic descriptors of the metric columns.
pivot_cols	Get the pivot column names for the report.
scenario_settings	Get the scenario settings for the report.
scenario_types	Get the scenario types for the report.

abstract property identifier: int | str

The identifier of the report accessor.

This identifier can be used to reload the underlying report accessor. The identifier is ephemeral, and should only be used for short-lived sessions, and not persisted over time.

Returns

int | str

The identifier of the report accessor.

abstractmethod get_level_values(levels: tuple[str, ...] = (), include_totals: bool = False, filters: list[tuple[str, str, Any] | Sequence[tuple[str, str, Any]]] | None = None) → DataFrame

Get level values for the specified levels.

Parameters

levelstuple[str, …], default=()

The levels to get values for.

include_totalsbool, default=False

Whether to include totals in the results.

filtersDNFFilterExpressions | None, default=None

Optional filters to apply.

Returns

pl.DataFrame

A DataFrame containing the level values.

abstractmethod get_data(path: list[tuple[str, str | int | bool]], *, expand: tuple[str, ...] = (), pivot_cols: tuple[str, ...] = (), value_cols: tuple[str, ...] = (), filters: list[tuple[str, str, Any] | Sequence[tuple[str, str, Any]]] | None = None, pivot_total: bool = False, absent: Literal['drop', 'null', 'keep'] = 'keep') → DataFrame

Get data for the specified path.

Parameters

pathlist[tuple[str, str | int | bool]]

The path to get data for. Entries pin specific values along the report’s dimensions and are AND-ed together as filters.

expandtuple[str, …], default=()

The expand tuple for the path.

pivot_colstuple[str, …], default=()

The columns to pivot on.

value_colstuple[str, …], default=()

The value columns.

filtersDNFFilterExpressions | None, default=None

Optional filters to apply.

pivot_totalbool, default=False

Whether to include pivot totals.

absentLiteral[“drop”, “null”, “keep”], default=”keep”

How to treat cells the report never saw (e.g. an asset a drilled-into portfolio never held, or a report’s degenerate warmup date). "keep" leaves them as-is (the pre-existing behaviour, a rendered 0); "null" blanks the absent cells to NaN but keeps every row; "drop" removes a row when all of its cells are absent.

Returns

pl.DataFrame

A DataFrame containing the requested data.

abstractmethod get_data_as_task(path: list[tuple[str, str | int | bool]], *, expand: tuple[str, ...] = (), pivot_cols: tuple[str, ...] = (), value_cols: tuple[str, ...] = (), filters: list[tuple[str, str, Any] | Sequence[tuple[str, str, Any]]] | None = None, pivot_total: bool = False, absent: Literal['drop', 'null', 'keep'] = 'keep') → Task[DataFrame]

abstractmethod with_scenario_settings(scenario_settings: Annotated[PortfolioHoldingsScenario | BasketFactorScenario, FieldInfo(annotation=NoneType, required=True, discriminator='type')] | Sequence[Annotated[PortfolioHoldingsScenario | BasketFactorScenario, FieldInfo(annotation=NoneType, required=True, discriminator='type')]], *, name: str | None = None) → Self

Return a new accessor with the given scenario settings applied.

Parameters

scenario_settingsAnyScenarioSettings | Sequence[AnyScenarioSettings]

The scenario settings to apply.

namestr | None, default=None

Keep the scenario under this name, scoped to this report. The call returns once the scenario is computed, and every later query of the returned accessor, in any session, reuses it. The same name with the same settings returns the same accessor at once; the same name with different settings replaces what the name held. Without a name the scenario is computed on the first query, and later queries with the same settings, in any session, reuse it.

Returns

Self

A new accessor with the scenario settings applied.