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API Reference

Main Entry Point

FinancialSankey

finflow_sankey.FinancialSankey

Main entry point for FinFlow Sankey.

Source code in finflow_sankey/__init__.py 
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class FinancialSankey:
    """Main entry point for FinFlow Sankey."""

    @classmethod
    def income_statement(
        cls,
        data: pl.DataFrame | pl.LazyFrame,
        *,
        period: str | None = None,
        currency: str | None = None,
        mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
        layout: str | None = None,
    ) -> SankeyPipeline:
        """Create an income statement Sankey pipeline."""
        template = IncomeStatementTemplate(layout=layout)
        return SankeyPipeline(
            data=data,
            template=template,
            period=period,
            currency=currency,
            mapping=mapping,
            layout=layout,
        )

    @classmethod
    def cash_flow_statement(
        cls,
        data: pl.DataFrame | pl.LazyFrame,
        *,
        period: str | None = None,
        currency: str | None = None,
        mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
        layout: str | None = None,
    ) -> SankeyPipeline:
        """Create a cash flow statement Sankey pipeline."""
        from finflow_sankey.templates.cash_flow import CashFlowStatementTemplate

        template = CashFlowStatementTemplate(layout=layout)
        return SankeyPipeline(
            data=data,
            template=template,
            period=period,
            currency=currency,
            mapping=mapping,
            layout=layout,
        )

    @classmethod
    def balance_sheet_reconciliation(
        cls,
        data: pl.DataFrame | pl.LazyFrame,
        *,
        as_of: str | None = None,
        currency: str | None = None,
        mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
        layout: str | None = None,
    ) -> SankeyPipeline:
        """Create a balance sheet reconciliation Sankey pipeline."""
        from finflow_sankey.templates.balance_sheet import BalanceSheetReconciliationTemplate

        template = BalanceSheetReconciliationTemplate(layout=layout)
        return SankeyPipeline(
            data=data,
            template=template,
            period=as_of,
            currency=currency,
            mapping=mapping,
            layout=layout,
        )

    @classmethod
    def multi_period_compare(
        cls,
        data: pl.DataFrame | pl.LazyFrame,
        *,
        currency: str | None = None,
        mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
        layout: str | None = None,
    ) -> SankeyPipeline:
        """Create a multi-period comparison Sankey pipeline."""
        from finflow_sankey.templates.multi_period import MultiPeriodComparisonTemplate

        template = MultiPeriodComparisonTemplate(layout=layout)
        return SankeyPipeline(
            data=data,
            template=template,
            period=None,
            currency=currency,
            mapping=mapping,
            layout=layout,
        )

income_statement(data, *, period=None, currency=None, mapping=None, layout=None) classmethod

Create an income statement Sankey pipeline.

Source code in finflow_sankey/__init__.py 
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@classmethod
def income_statement(
    cls,
    data: pl.DataFrame | pl.LazyFrame,
    *,
    period: str | None = None,
    currency: str | None = None,
    mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
    layout: str | None = None,
) -> SankeyPipeline:
    """Create an income statement Sankey pipeline."""
    template = IncomeStatementTemplate(layout=layout)
    return SankeyPipeline(
        data=data,
        template=template,
        period=period,
        currency=currency,
        mapping=mapping,
        layout=layout,
    )

cash_flow_statement(data, *, period=None, currency=None, mapping=None, layout=None) classmethod

Create a cash flow statement Sankey pipeline.

Source code in finflow_sankey/__init__.py 
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@classmethod
def cash_flow_statement(
    cls,
    data: pl.DataFrame | pl.LazyFrame,
    *,
    period: str | None = None,
    currency: str | None = None,
    mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
    layout: str | None = None,
) -> SankeyPipeline:
    """Create a cash flow statement Sankey pipeline."""
    from finflow_sankey.templates.cash_flow import CashFlowStatementTemplate

    template = CashFlowStatementTemplate(layout=layout)
    return SankeyPipeline(
        data=data,
        template=template,
        period=period,
        currency=currency,
        mapping=mapping,
        layout=layout,
    )

balance_sheet_reconciliation(data, *, as_of=None, currency=None, mapping=None, layout=None) classmethod

Create a balance sheet reconciliation Sankey pipeline.

Source code in finflow_sankey/__init__.py 
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@classmethod
def balance_sheet_reconciliation(
    cls,
    data: pl.DataFrame | pl.LazyFrame,
    *,
    as_of: str | None = None,
    currency: str | None = None,
    mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
    layout: str | None = None,
) -> SankeyPipeline:
    """Create a balance sheet reconciliation Sankey pipeline."""
    from finflow_sankey.templates.balance_sheet import BalanceSheetReconciliationTemplate

    template = BalanceSheetReconciliationTemplate(layout=layout)
    return SankeyPipeline(
        data=data,
        template=template,
        period=as_of,
        currency=currency,
        mapping=mapping,
        layout=layout,
    )

multi_period_compare(data, *, currency=None, mapping=None, layout=None) classmethod

Create a multi-period comparison Sankey pipeline.

Source code in finflow_sankey/__init__.py 
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@classmethod
def multi_period_compare(
    cls,
    data: pl.DataFrame | pl.LazyFrame,
    *,
    currency: str | None = None,
    mapping: AccountMapper | dict[str, Any] | str | Path | None = None,
    layout: str | None = None,
) -> SankeyPipeline:
    """Create a multi-period comparison Sankey pipeline."""
    from finflow_sankey.templates.multi_period import MultiPeriodComparisonTemplate

    template = MultiPeriodComparisonTemplate(layout=layout)
    return SankeyPipeline(
        data=data,
        template=template,
        period=None,
        currency=currency,
        mapping=mapping,
        layout=layout,
    )

Pipeline

SankeyPipeline

finflow_sankey.core.pipeline.SankeyPipeline

Pipeline for building and rendering financial Sankey diagrams.

Source code in finflow_sankey/core/pipeline.py 
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class SankeyPipeline:
    """Pipeline for building and rendering financial Sankey diagrams."""

    def __init__(
        self,
        data: pl.DataFrame | pl.LazyFrame,
        template: StatementTemplate,
        period: str | None = None,
        currency: str | None = None,
        default_palette: ColorPalette | None = None,
        mapping: AccountMapper | dict | str | Path | None = None,
        layout: str | None = None,
    ):
        self._lf = self._to_lazy_frame(data)
        self.template = template
        self.period = period
        self.currency = currency
        self.default_palette = default_palette
        self.layout = layout
        self._mapper = self._resolve_mapper(mapping)
        self._validated_df: pl.DataFrame | None = None
        self._graph: FinancialGraph | None = None
        self._sign_normalizer = SignNormalizer(expense_sign="positive")
        self._unit_normalizer = UnitNormalizer()

    def _to_lazy_frame(self, data: pl.DataFrame | pl.LazyFrame) -> pl.LazyFrame:
        """Convert input data to Polars LazyFrame."""
        if isinstance(data, pl.LazyFrame):
            return data
        if isinstance(data, pl.DataFrame):
            return data.lazy()

        raise TypeError(
            "Input data must be a Polars DataFrame or Polars LazyFrame."
        )

    def _resolve_mapper(
        self, mapping: AccountMapper | dict | str | Path | None
    ) -> AccountMapper | None:
        if mapping is None:
            return None
        if isinstance(mapping, AccountMapper):
            return mapping
        if isinstance(mapping, dict):
            return AccountMapper.from_dict(mapping)
        return AccountMapper.from_yaml(mapping)

    def validate(self, tolerance: float = 0.01) -> "SankeyPipeline":
        """Validate input data."""
        df = self._prepare_df()
        validator = FinancialValidator(self.template.statement_type)
        self._validated_df = validator.validate(
            df,
            required_roles=self.template.required_roles(),
            tolerance=tolerance,
        )
        return self

    def normalize_signs(self) -> "SankeyPipeline":
        """Normalize signs (already done in _prepare_df by default)."""
        return self

    def group_minor(
        self,
        min_pct: float | None = None,
        min_value: float | None = None,
        top_n: int | None = None,
        label: str = "Other",
    ) -> "SankeyPipeline":
        """Group minor accounts into Other.

        Args:
            min_pct: Group accounts below this percentage of total sankey_value.
            min_value: Group accounts below this absolute sankey_value.
            top_n: Keep top_n accounts, group the rest.
            label: Label for the grouped accounts.
        """
        if self._validated_df is None:
            raise RuntimeError("Must call validate() before group_minor().")

        df = self._validated_df

        if min_pct is not None and min_value is not None:
            total = df["sankey_value"].sum()
            threshold = max(total * min_pct, min_value)
            df = group_by_threshold(df, threshold, label)
        elif min_pct is not None:
            total = df["sankey_value"].sum()
            df = group_by_threshold(df, total * min_pct, label)
        elif min_value is not None:
            df = group_by_threshold(df, min_value, label)
        elif top_n is not None:
            df = group_by_top_n(df, top_n, label)

        self._validated_df = df
        return self

    def with_palette(
        self,
        palette: str | Path | dict[str, Any] | ColorPalette | None = None,
    ) -> "SankeyPipeline":
        """Set default palette for rendering."""
        self.default_palette = get_palette(palette)
        return self

    def render(
        self,
        title: str | None = None,
        renderer: str = "plotly",
        palette: str | Path | dict[str, Any] | ColorPalette | None = None,
        theme: str | None = None,
    ) -> Figure:
        """Render the Sankey graph."""
        if self._validated_df is None:
            self.validate()

        graph = self.template.build(self._validated_df, layout=self.layout)

        resolved_palette = self._resolve_palette(palette, theme)

        renderer_obj = self._get_renderer(renderer)
        return renderer_obj.render(graph, resolved_palette, title=title)

    def export_html(
        self,
        path: str | Path,
        title: str | None = None,
        renderer: str = "plotly",
        palette: str | Path | dict[str, Any] | ColorPalette | None = None,
        theme: str | None = None,
        **render_kwargs: Any,
    ) -> str:
        """Render and export the Sankey graph to HTML.

        Args:
            path: Output HTML file path.
            title: Chart title.
            renderer: Renderer name (default: "plotly").
            palette: Custom palette override.
            theme: Built-in theme name.
            **render_kwargs: Additional kwargs passed to render().

        Returns:
            The output file path as a string.
        """
        fig = self.render(
            title=title,
            renderer=renderer,
            palette=palette,
            theme=theme,
            **render_kwargs,
        )
        fig.write_html(str(path))
        return str(path)

    def _resolve_palette(
        self,
        palette: str | Path | dict[str, Any] | ColorPalette | None,
        theme: str | None,
    ) -> ColorPalette:
        """Resolve palette with priority: palette arg > theme > default_palette > built-in."""
        if palette is not None:
            pal = get_palette(palette, theme=theme)
            return pal

        if theme is not None:
            return get_palette(theme=theme)

        if self.default_palette is not None:
            return self.default_palette

        return get_palette()

    def _get_renderer(self, renderer: str) -> Renderer:
        if renderer == "plotly":
            return PlotlyRenderer()
        raise ValueError(f"Unknown renderer: {renderer}")

    def _prepare_df(self) -> pl.DataFrame:
        """Prepare data: schema validation, normalization, sign normalization, mapping."""
        schema = InputSchema(self._lf)
        lf = schema.validate().normalize()
        lf = self._sign_normalizer.normalize(lf)
        lf = self._unit_normalizer.normalize(lf)

        df = lf.collect()

        if self.period:
            df = df.with_columns(pl.lit(self.period).alias("period"))
        if self.currency:
            df = df.with_columns(pl.lit(self.currency).alias("currency"))
        if self._mapper is not None:
            df = self._mapper.apply(df)

        return df

    @property
    def graph(self) -> FinancialGraph:
        if self._graph is None:
            if self._validated_df is None:
                self.validate()
            self._graph = self.template.build(self._validated_df, layout=self.layout)
        return self._graph

validate(tolerance=0.01)

Validate input data.

Source code in finflow_sankey/core/pipeline.py 
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def validate(self, tolerance: float = 0.01) -> "SankeyPipeline":
    """Validate input data."""
    df = self._prepare_df()
    validator = FinancialValidator(self.template.statement_type)
    self._validated_df = validator.validate(
        df,
        required_roles=self.template.required_roles(),
        tolerance=tolerance,
    )
    return self

group_minor(min_pct=None, min_value=None, top_n=None, label='Other')

Group minor accounts into Other.

Parameters:

Name Type Description Default
min_pct float | None

Group accounts below this percentage of total sankey_value.

 None
min_value float | None

Group accounts below this absolute sankey_value.

 None
top_n int | None

Keep top_n accounts, group the rest.

 None
label str

Label for the grouped accounts.

 'Other'
Source code in finflow_sankey/core/pipeline.py 
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def group_minor(
    self,
    min_pct: float | None = None,
    min_value: float | None = None,
    top_n: int | None = None,
    label: str = "Other",
) -> "SankeyPipeline":
    """Group minor accounts into Other.

    Args:
        min_pct: Group accounts below this percentage of total sankey_value.
        min_value: Group accounts below this absolute sankey_value.
        top_n: Keep top_n accounts, group the rest.
        label: Label for the grouped accounts.
    """
    if self._validated_df is None:
        raise RuntimeError("Must call validate() before group_minor().")

    df = self._validated_df

    if min_pct is not None and min_value is not None:
        total = df["sankey_value"].sum()
        threshold = max(total * min_pct, min_value)
        df = group_by_threshold(df, threshold, label)
    elif min_pct is not None:
        total = df["sankey_value"].sum()
        df = group_by_threshold(df, total * min_pct, label)
    elif min_value is not None:
        df = group_by_threshold(df, min_value, label)
    elif top_n is not None:
        df = group_by_top_n(df, top_n, label)

    self._validated_df = df
    return self

with_palette(palette=None)

Set default palette for rendering.

Source code in finflow_sankey/core/pipeline.py 
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def with_palette(
    self,
    palette: str | Path | dict[str, Any] | ColorPalette | None = None,
) -> "SankeyPipeline":
    """Set default palette for rendering."""
    self.default_palette = get_palette(palette)
    return self

render(title=None, renderer='plotly', palette=None, theme=None)

Render the Sankey graph.

Source code in finflow_sankey/core/pipeline.py 
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def render(
    self,
    title: str | None = None,
    renderer: str = "plotly",
    palette: str | Path | dict[str, Any] | ColorPalette | None = None,
    theme: str | None = None,
) -> Figure:
    """Render the Sankey graph."""
    if self._validated_df is None:
        self.validate()

    graph = self.template.build(self._validated_df, layout=self.layout)

    resolved_palette = self._resolve_palette(palette, theme)

    renderer_obj = self._get_renderer(renderer)
    return renderer_obj.render(graph, resolved_palette, title=title)

export_html(path, title=None, renderer='plotly', palette=None, theme=None, **render_kwargs)

Render and export the Sankey graph to HTML.

Parameters:

Name Type Description Default
path str | Path

Output HTML file path.

 required
title str | None

Chart title.

 None
renderer str

Renderer name (default: "plotly").

 'plotly'
palette str | Path | dict[str, Any] | ColorPalette | None

Custom palette override.

 None
theme str | None

Built-in theme name.

 None
**render_kwargs Any

Additional kwargs passed to render().

 {}

Returns:

Type Description
str

The output file path as a string.
Source code in finflow_sankey/core/pipeline.py 
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def export_html(
    self,
    path: str | Path,
    title: str | None = None,
    renderer: str = "plotly",
    palette: str | Path | dict[str, Any] | ColorPalette | None = None,
    theme: str | None = None,
    **render_kwargs: Any,
) -> str:
    """Render and export the Sankey graph to HTML.

    Args:
        path: Output HTML file path.
        title: Chart title.
        renderer: Renderer name (default: "plotly").
        palette: Custom palette override.
        theme: Built-in theme name.
        **render_kwargs: Additional kwargs passed to render().

    Returns:
        The output file path as a string.
    """
    fig = self.render(
        title=title,
        renderer=renderer,
        palette=palette,
        theme=theme,
        **render_kwargs,
    )
    fig.write_html(str(path))
    return str(path)

Core Components

ColorPalette

finflow_sankey.core.palette.ColorPalette dataclass

Complete color palette for rendering.

Source code in finflow_sankey/core/palette.py 
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@dataclass
class ColorPalette:
    """Complete color palette for rendering."""

    name: str = "default"
    version: str = "1.0.0"
    description: str = ""
    roles: RolePalette = field(default_factory=RolePalette)
    semantic: SemanticStyle = field(default_factory=SemanticStyle)

    @classmethod
    def from_yaml(cls, path: str | Path) -> "ColorPalette":
        """Load palette from YAML file."""
        with open(path, "r", encoding="utf-8") as f:
            data = yaml.safe_load(f)

        return cls._from_dict(data)

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "ColorPalette":
        """Load palette from dict."""
        return cls._from_dict(data)

    @classmethod
    def _from_dict(cls, data: dict[str, Any]) -> "ColorPalette":
        roles_data = data.get("roles", {})
        semantic_data = data.get("semantic", {})

        return cls(
            name=data.get("name", "custom"),
            version=data.get("version", "1.0.0"),
            description=data.get("description", ""),
            roles=RolePalette(**roles_data),
            semantic=SemanticStyle(**semantic_data),
        )

    def override(self, overrides: dict[str, Any]) -> "ColorPalette":
        """Create a new palette with partial overrides."""
        role_fields = set(RolePalette.__dataclass_fields__.keys())
        semantic_fields = set(SemanticStyle.__dataclass_fields__.keys())

        role_overrides = {k: v for k, v in overrides.items() if k in role_fields}
        semantic_overrides = {k: v for k, v in overrides.items() if k in semantic_fields}

        return ColorPalette(
            name=f"{self.name}_overridden",
            version=self.version,
            description=self.description,
            roles=RolePalette(**{**self.roles.__dict__, **role_overrides}),
            semantic=SemanticStyle(**{**self.semantic.__dict__, **semantic_overrides}),
        )

    def validate(self) -> None:
        """Validate palette."""
        for role, color in self.roles.__dict__.items():
            if not _is_valid_hex(color):
                raise InvalidColorError(f"Invalid hex color for role '{role}': {color}")

        for semantic_key in ["background", "plot_background", "text", "border"]:
            color = getattr(self.semantic, semantic_key)
            if not _is_valid_hex(color):
                raise InvalidColorError(f"Invalid hex color for semantic '{semantic_key}': {color}")

        if not 0.0 <= self.semantic.link_opacity <= 1.0:
            raise InvalidOpacityError("link_opacity must be between 0 and 1")
        if not 0.0 <= self.semantic.hover_opacity <= 1.0:
            raise InvalidOpacityError("hover_opacity must be between 0 and 1")

        missing = REQUIRED_ROLES - set(self.roles.__dict__.keys())
        if missing:
            raise MissingRoleColorError(f"Missing required role colors: {missing}")

    def get_role_color(self, role: str) -> str:
        """Get color for a role, falling back to 'other'."""
        return getattr(self.roles, role, self.roles.other)

from_yaml(path) classmethod

Load palette from YAML file.

Source code in finflow_sankey/core/palette.py 
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@classmethod
def from_yaml(cls, path: str | Path) -> "ColorPalette":
    """Load palette from YAML file."""
    with open(path, "r", encoding="utf-8") as f:
        data = yaml.safe_load(f)

    return cls._from_dict(data)

from_dict(data) classmethod

Load palette from dict.

Source code in finflow_sankey/core/palette.py 
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@classmethod
def from_dict(cls, data: dict[str, Any]) -> "ColorPalette":
    """Load palette from dict."""
    return cls._from_dict(data)

override(overrides)

Create a new palette with partial overrides.

Source code in finflow_sankey/core/palette.py 
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def override(self, overrides: dict[str, Any]) -> "ColorPalette":
    """Create a new palette with partial overrides."""
    role_fields = set(RolePalette.__dataclass_fields__.keys())
    semantic_fields = set(SemanticStyle.__dataclass_fields__.keys())

    role_overrides = {k: v for k, v in overrides.items() if k in role_fields}
    semantic_overrides = {k: v for k, v in overrides.items() if k in semantic_fields}

    return ColorPalette(
        name=f"{self.name}_overridden",
        version=self.version,
        description=self.description,
        roles=RolePalette(**{**self.roles.__dict__, **role_overrides}),
        semantic=SemanticStyle(**{**self.semantic.__dict__, **semantic_overrides}),
    )

validate()

Validate palette.

Source code in finflow_sankey/core/palette.py 
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def validate(self) -> None:
    """Validate palette."""
    for role, color in self.roles.__dict__.items():
        if not _is_valid_hex(color):
            raise InvalidColorError(f"Invalid hex color for role '{role}': {color}")

    for semantic_key in ["background", "plot_background", "text", "border"]:
        color = getattr(self.semantic, semantic_key)
        if not _is_valid_hex(color):
            raise InvalidColorError(f"Invalid hex color for semantic '{semantic_key}': {color}")

    if not 0.0 <= self.semantic.link_opacity <= 1.0:
        raise InvalidOpacityError("link_opacity must be between 0 and 1")
    if not 0.0 <= self.semantic.hover_opacity <= 1.0:
        raise InvalidOpacityError("hover_opacity must be between 0 and 1")

    missing = REQUIRED_ROLES - set(self.roles.__dict__.keys())
    if missing:
        raise MissingRoleColorError(f"Missing required role colors: {missing}")

get_role_color(role)

Get color for a role, falling back to 'other'.

Source code in finflow_sankey/core/palette.py 
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def get_role_color(self, role: str) -> str:
    """Get color for a role, falling back to 'other'."""
    return getattr(self.roles, role, self.roles.other)

AccountMapper

finflow_sankey.core.mapper.AccountMapper

Maps raw account names to standard financial statement sections.

Source code in finflow_sankey/core/mapper.py 
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class AccountMapper:
    """Maps raw account names to standard financial statement sections."""

    def __init__(self, mapping: dict[str, list[str]]):
        """
        Args:
            mapping: Dict mapping standard section/role to list of raw account names.
                Example: {"revenue": ["Net Sales", "Sales Revenue"], ...}
        """
        self.mapping = mapping
        self._account_to_section: dict[str, str] = {}
        for section, accounts in mapping.items():
            for account in accounts:
                self._account_to_section[account] = section

    @classmethod
    def from_yaml(cls, path: str | Path) -> "AccountMapper":
        """Load mapping from YAML file."""
        with open(path, "r", encoding="utf-8") as f:
            data = yaml.safe_load(f)
        return cls(data)

    @classmethod
    def from_dict(cls, mapping: dict[str, Any]) -> "AccountMapper":
        """Load mapping from dict (values may be list or single string)."""
        normalized: dict[str, list[str]] = {}
        for section, accounts in mapping.items():
            if isinstance(accounts, str):
                normalized[section] = [accounts]
            else:
                normalized[section] = list(accounts)
        return cls(normalized)

    def map_account(self, account: str) -> str | None:
        """Return standard section for a raw account name, or None."""
        return self._account_to_section.get(account)

    def apply(self, df: pl.DataFrame, target_col: str = "section") -> pl.DataFrame:
        """Apply mapping to DataFrame.

        Only overwrites target_col when it is null, preserving explicit sections.
        """
        if not self._account_to_section:
            return df

        return df.with_columns(
            pl.when(pl.col(target_col).is_null())
            .then(
                pl.col("account").replace_strict(
                    self._account_to_section,
                    default=None,
                )
            )
            .otherwise(pl.col(target_col))
            .alias(target_col)
        )

    def list_unmapped(self, df: pl.DataFrame) -> list[str]:
        """Return account names that are not covered by the mapping."""
        return (
            df.filter(
                pl.col("section").is_null()
                & pl.col("account").is_not_null()
            )["account"]
            .unique()
            .to_list()
        )

from_dict(mapping) classmethod

Load mapping from dict (values may be list or single string).

Source code in finflow_sankey/core/mapper.py 
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@classmethod
def from_dict(cls, mapping: dict[str, Any]) -> "AccountMapper":
    """Load mapping from dict (values may be list or single string)."""
    normalized: dict[str, list[str]] = {}
    for section, accounts in mapping.items():
        if isinstance(accounts, str):
            normalized[section] = [accounts]
        else:
            normalized[section] = list(accounts)
    return cls(normalized)

from_yaml(path) classmethod

Load mapping from YAML file.

Source code in finflow_sankey/core/mapper.py 
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@classmethod
def from_yaml(cls, path: str | Path) -> "AccountMapper":
    """Load mapping from YAML file."""
    with open(path, "r", encoding="utf-8") as f:
        data = yaml.safe_load(f)
    return cls(data)

apply(df, target_col='section')

Apply mapping to DataFrame.

Only overwrites target_col when it is null, preserving explicit sections.

Source code in finflow_sankey/core/mapper.py 
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def apply(self, df: pl.DataFrame, target_col: str = "section") -> pl.DataFrame:
    """Apply mapping to DataFrame.

    Only overwrites target_col when it is null, preserving explicit sections.
    """
    if not self._account_to_section:
        return df

    return df.with_columns(
        pl.when(pl.col(target_col).is_null())
        .then(
            pl.col("account").replace_strict(
                self._account_to_section,
                default=None,
            )
        )
        .otherwise(pl.col(target_col))
        .alias(target_col)
    )