Gain Loss Analysis

Gain loss (switching) analysis of customer movement between a focus and a comparison group across two periods.

Pandas-only; period and group selectors are positional boolean masks.

GainLoss

Gain loss (switching) analysis between a focus group and a comparison group.

Results are computed at initialization; there are no df/table attributes.

gain_loss_df has one row per customer, indexed by customer_id (by (group_col, customer_id) when grouped), so customer_id is not a column. Its 15 columns are focus/comparison/total for _p1, _p2 and _diff, then the six components new, lost, increased_focus, decreased_focus, switch_from_comparison and switch_to_comparison, which sum to focus_diff. Customers whose values are all 0 are dropped.

gain_loss_table_df has the same columns summed over customers: one row when ungrouped, one row per group_col value otherwise.

Source code in openretailscience/analysis/gain_loss.py

class GainLoss:
    """Gain loss (switching) analysis between a focus group and a comparison group.

    Results are computed at initialization; there are no ``df``/``table`` attributes.

    ``gain_loss_df`` has one row per customer, indexed by ``customer_id`` (by
    ``(group_col, customer_id)`` when grouped), so ``customer_id`` is not a column. Its 15
    columns are ``focus``/``comparison``/``total`` for ``_p1``, ``_p2`` and ``_diff``, then the
    six components ``new``, ``lost``, ``increased_focus``, ``decreased_focus``,
    ``switch_from_comparison`` and ``switch_to_comparison``, which sum to ``focus_diff``.
    Customers whose values are all 0 are dropped.

    ``gain_loss_table_df`` has the same columns summed over customers: one row when
    ungrouped, one row per ``group_col`` value otherwise.
    """

    def __init__(
        self,
        df: pd.DataFrame,
        p1_index: Mask,
        p2_index: Mask,
        focus_group_index: Mask,
        focus_group_name: str,
        comparison_group_index: Mask,
        comparison_group_name: str,
        group_col: str | None = None,
        value_col: str | None = None,
        agg_func: str = "sum",
    ) -> None:
        """Calculate the gain loss decomposition at the customer level.

        The four masks have one entry per row of ``df`` and are matched by position, so ``df``
        may repeat index labels.

        Args:
            df (pd.DataFrame): Transactions with the option-derived customer_id column and ``value_col``.
            p1_index (list[bool] | np.ndarray | pd.Series): Rows in the first period.
            p2_index (list[bool] | np.ndarray | pd.Series): Rows in the second period.
            focus_group_index (list[bool] | np.ndarray | pd.Series): Rows in the focus group.
            focus_group_name (str): Display name of the focus group.
            comparison_group_index (list[bool] | np.ndarray | pd.Series): Rows in the comparison group.
            comparison_group_name (str): Display name of the comparison group.
            group_col (str | None): Optional column to break the analysis down by.
            value_col (str | None): Value column to aggregate; defaults to option ``column.unit_spend``.
            agg_func (str): A pandas groupby aggregation name, such as "sum", "mean" or "nunique"
                (case-sensitive). Defaults to "sum".

        Raises:
            TypeError: If a mask is not boolean.
            ValueError: If ``p1_index`` and ``p2_index`` overlap.
            ValueError: If ``focus_group_index`` and ``comparison_group_index`` overlap.
            ValueError: If a mask does not have one entry per row of ``df``, or is a Series whose
                index differs from ``df.index``.
            ValueError: If the option-derived customer_id or ``value_col`` columns are missing,
                or if a given ``group_col`` is not in ``df``.
        """
        value_col = get_option("column.unit_spend") if value_col is None else value_col
        if not len(df) == len(p1_index) == len(p2_index) == len(focus_group_index) == len(comparison_group_index):
            raise ValueError(
                "p1_index, p2_index, focus_group_index, and comparison_group_index must each have one entry per row "
                "of df",
            )
        raw_masks = (p1_index, p2_index, focus_group_index, comparison_group_index)
        if any(isinstance(mask, pd.Series) and not mask.index.equals(df.index) for mask in raw_masks):
            raise ValueError("A Series mask's index must match df.index; build the masks from df itself")
        # Positional masks, so a df index with repeated labels (e.g. customer_id) can't misalign them.
        # infer_objects accepts an object Series of plain bools but, unlike pd.array, keeps a None/NaN hole non-boolean
        masks = tuple(pd.Series(mask).infer_objects().array for mask in raw_masks)
        if not all(pd.api.types.is_bool_dtype(mask.dtype) for mask in masks):
            raise TypeError(
                "p1_index, p2_index, focus_group_index, and comparison_group_index must be boolean; a mask with "
                'missing values needs the nullable "boolean" dtype, where <NA> selects nothing'
            )
        # A nullable mask's <NA> (e.g. a comparison against a missing brand) selects nothing, as in pandas indexing
        p1_index, p2_index, focus_group_index, comparison_group_index = (
            mask.to_numpy(dtype=bool, na_value=False) for mask in masks
        )

        if (p1_index & p2_index).any():
            raise ValueError("p1_index and p2_index should not overlap")

        if (focus_group_index & comparison_group_index).any():
            raise ValueError("focus_group_index and comparison_group_index should not overlap")

        if group_col is not None:
            # group_col is typed str | None here, so unpack the single-element list back to a string.
            group_col = ensure_columns(df, group_col, "group_col")[0]
        ensure_data_has_columns(df, [get_option("column.customer_id"), value_col])

        self.focus_group_name = focus_group_name
        self.comparison_group_name = comparison_group_name
        self.group_col = group_col
        self.value_col = value_col

        self.gain_loss_df = self._calc_gain_loss(
            df=df,
            p1_index=p1_index,
            p2_index=p2_index,
            focus_group_index=focus_group_index,
            comparison_group_index=comparison_group_index,
            group_col=group_col,
            value_col=value_col,
            agg_func=agg_func,
        )
        self.gain_loss_table_df = self._calc_gains_loss_table(
            gain_loss_df=self.gain_loss_df,
            group_col=group_col,
        )

    @staticmethod
    def process_customer_group(
        focus_p1: float,
        comparison_p1: float,
        focus_p2: float,
        comparison_p2: float,
        focus_diff: float,
        comparison_diff: float,
    ) -> tuple[float, float, float, float, float, float]:
        """Decompose a group's focus-period change into the six gain loss components.

        Returns ``(new, lost, increased_focus, decreased_focus, switch_from_comparison,
        switch_to_comparison)`` in that order. ``new``, ``increased_focus``, and
        ``switch_from_comparison`` are >= 0; ``lost``, ``decreased_focus``, and
        ``switch_to_comparison`` are <= 0. When both groups are zero in p1 the entire
        focus p2 value counts as ``new``; when both are zero in p2 the entire focus
        p1 value counts as ``lost``.

        Args:
            focus_p1 (float): Focus group value in period 1.
            comparison_p1 (float): Comparison group value in period 1.
            focus_p2 (float): Focus group value in period 2.
            comparison_p2 (float): Comparison group value in period 2.
            focus_diff (float): Focus group change, p2 - p1.
            comparison_diff (float): Comparison group change, p2 - p1.

        Returns:
            tuple[float, float, float, float, float, float]: The six components, in the unpack order above.
        """
        if focus_p1 == 0 and comparison_p1 == 0:
            return focus_p2, 0, 0, 0, 0, 0
        if focus_p2 == 0 and comparison_p2 == 0:
            return 0, -1 * focus_p1, 0, 0, 0, 0

        if focus_diff > 0:
            focus_inc_dec = focus_diff if comparison_diff > 0 else max(0, comparison_diff + focus_diff)
        elif comparison_diff < 0:
            focus_inc_dec = focus_diff
        else:
            focus_inc_dec = min(0, comparison_diff + focus_diff)

        increased_focus = max(0, focus_inc_dec)
        decreased_focus = min(0, focus_inc_dec)

        transfer = focus_diff - focus_inc_dec
        switch_from_comparison = max(0, transfer)
        switch_to_comparison = min(0, transfer)

        return 0, 0, increased_focus, decreased_focus, switch_from_comparison, switch_to_comparison

    @staticmethod
    def _calc_gain_loss(
        df: pd.DataFrame,
        p1_index: np.ndarray,
        p2_index: np.ndarray,
        focus_group_index: np.ndarray,
        comparison_group_index: np.ndarray,
        group_col: str | None,
        value_col: str,
        agg_func: str,
    ) -> pd.DataFrame:
        """Build ``gain_loss_df`` (described on the class) from positional boolean masks.

        Args:
            df (pd.DataFrame): The transactions.
            p1_index (np.ndarray): Rows in the first period.
            p2_index (np.ndarray): Rows in the second period.
            focus_group_index (np.ndarray): Rows in the focus group.
            comparison_group_index (np.ndarray): Rows in the comparison group.
            group_col (str | None): Column to break the analysis down by, if any.
            value_col (str): Value column to aggregate.
            agg_func (str): pandas groupby aggregation name.

        Returns:
            pd.DataFrame: The ``gain_loss_df``.
        """
        cols = ColumnHelper()
        in_periods = p1_index | p2_index
        df = df[in_periods].copy()
        p1_index, p2_index, focus_group_index, comparison_group_index = (
            mask[in_periods] for mask in (p1_index, p2_index, focus_group_index, comparison_group_index)
        )
        df[cols.customer_id] = df[cols.customer_id].astype("category")

        grp_cols = [cols.customer_id] if group_col is None else [group_col, cols.customer_id]

        p1_df = pd.concat(
            [
                df[focus_group_index & p1_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[comparison_group_index & p1_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[(focus_group_index | comparison_group_index) & p1_index]
                .groupby(grp_cols, observed=False)[value_col]
                .agg(agg_func),
            ],
            axis=1,
        )
        p1_df.columns = ["focus", "comparison", "total"]

        p2_df = pd.concat(
            [
                df[focus_group_index & p2_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[comparison_group_index & p2_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[(focus_group_index | comparison_group_index) & p2_index]
                .groupby(grp_cols, observed=False)[value_col]
                .agg(agg_func),
            ],
            axis=1,
        )
        p2_df.columns = ["focus", "comparison", "total"]

        gl_df = p1_df.merge(p2_df, on=grp_cols, how="outer", suffixes=("_p1", "_p2")).fillna(0)

        # Remove rows that are all 0 due to grouping by customer_id as a categorical with observed=False
        gl_df = gl_df[~(gl_df == 0).all(axis=1)]

        gl_df["focus_diff"] = gl_df["focus_p2"] - gl_df["focus_p1"]
        gl_df["comparison_diff"] = gl_df["comparison_p2"] - gl_df["comparison_p1"]
        gl_df["total_diff"] = gl_df["total_p2"] - gl_df["total_p1"]

        if len(gl_df) == 0:
            # The zip below has no rows to unpack
            return gl_df.assign(
                new=0, lost=0, increased_focus=0, decreased_focus=0, switch_from_comparison=0, switch_to_comparison=0
            )

        (
            gl_df["new"],
            gl_df["lost"],
            gl_df["increased_focus"],
            gl_df["decreased_focus"],
            gl_df["switch_from_comparison"],
            gl_df["switch_to_comparison"],
        ) = zip(
            *gl_df.apply(
                lambda x: GainLoss.process_customer_group(
                    focus_p1=x["focus_p1"],
                    comparison_p1=x["comparison_p1"],
                    focus_p2=x["focus_p2"],
                    comparison_p2=x["comparison_p2"],
                    focus_diff=x["focus_diff"],
                    comparison_diff=x["comparison_diff"],
                ),
                axis=1,
            ),
            strict=False,
        )

        return gl_df

    @staticmethod
    def _calc_gains_loss_table(
        gain_loss_df: pd.DataFrame,
        group_col: str | None = None,
    ) -> pd.DataFrame:
        """Aggregate the per-customer gain loss table to total gains and losses.

        Args:
            gain_loss_df (pd.DataFrame): The per-customer gain loss table.
            group_col (str | None): Grouping column, if any.

        Returns:
            pd.DataFrame: A single-row frame when ``group_col`` is None, otherwise one row per group.
        """
        if group_col is None:
            return gain_loss_df.sum().to_frame("").T

        return gain_loss_df.groupby(level=0).sum()

    def plot(
        self,
        title: str | None = None,
        eyebrow: str | None = None,
        subtitle: str | None = None,
        x_label: str | None = None,
        y_label: str | None = None,
        ax: Axes | None = None,
        source_text: str | None = None,
        move_legend_outside: bool = False,
        **kwargs: Any,  # noqa: ANN401
    ) -> SubplotBase:
        """Plot the gain loss table as a stacked horizontal bar of the six decomposition columns.

        Green segments are ``new`` / ``increased_focus`` / ``switch_from_comparison``; red
        segments are ``lost`` / ``decreased_focus`` / ``switch_to_comparison``. Legend labels
        use the focus and comparison group names. ``stacked`` and ``color`` kwargs are
        silently ignored.

        Args:
            title (str | None): Plot title; defaults to "Gain Loss from {focus} to {comparison}".
            eyebrow (str | None): Small uppercase label rendered above the title.
            subtitle (str | None): Supporting copy rendered below the title.
            x_label (str | None): X-axis label; defaults to the value column name.
            y_label (str | None): Y-axis label; defaults to the focus group name, or ``group_col`` when grouped.
            ax (Axes | None): Axes to plot on.
            source_text (str | None): Source annotation.
            move_legend_outside (bool): Whether to move the legend outside the plot.
            **kwargs (Any): Additional keyword arguments forwarded to the bar plot.

        Returns:
            SubplotBase: The plot axes.
        """
        increase_cols = ["new", "increased_focus", "switch_from_comparison"]
        decrease_cols = ["lost", "decreased_focus", "switch_to_comparison"]
        all_cols = increase_cols + decrease_cols
        segment_colors = [
            COLORS["green"][700],
            COLORS["green"][500],
            COLORS["green"][300],
            COLORS["red"][700],
            COLORS["red"][500],
            COLORS["red"][300],
        ]
        legend_labels = [
            "New",
            f"Increased {self.focus_group_name}",
            f"Switch From {self.comparison_group_name}",
            "Lost",
            f"Decreased {self.focus_group_name}",
            f"Switch To {self.comparison_group_name}",
        ]

        default_y_label = self.focus_group_name if self.group_col is None else self.group_col
        plot_data = self.gain_loss_table_df.copy()

        kwargs.pop("stacked", None)
        kwargs.pop("color", None)

        resolved_title = (
            title if title is not None else f"Gain Loss from {self.focus_group_name} to {self.comparison_group_name}"
        )

        ax = bar.plot(
            df=plot_data,
            value_col=all_cols,
            title=resolved_title,
            eyebrow=eyebrow,
            subtitle=subtitle,
            y_label=y_label if y_label is not None else default_y_label,
            x_label=x_label if x_label is not None else self.value_col,
            orientation="horizontal",
            ax=ax,
            source_text=source_text,
            move_legend_outside=move_legend_outside,
            legend_labels=legend_labels,
            stacked=True,
            color=segment_colors,
            **kwargs,
        )

        ax.axvline(0, color="black", linewidth=0.5)

        return ax

__init__(df, p1_index, p2_index, focus_group_index, focus_group_name, comparison_group_index, comparison_group_name, group_col=None, value_col=None, agg_func='sum')

Calculate the gain loss decomposition at the customer level.

The four masks have one entry per row of df and are matched by position, so df may repeat index labels.

Parameters:

Name	Type	Description	Default
df	DataFrame	Transactions with the option-derived customer_id column and value_col.	required
p1_index	list[bool] | ndarray | Series	Rows in the first period.	required
p2_index	list[bool] | ndarray | Series	Rows in the second period.	required
focus_group_index	list[bool] | ndarray | Series	Rows in the focus group.	required
focus_group_name	str	Display name of the focus group.	required
comparison_group_index	list[bool] | ndarray | Series	Rows in the comparison group.	required
comparison_group_name	str	Display name of the comparison group.	required
group_col	str | None	Optional column to break the analysis down by.	None
value_col	str | None	Value column to aggregate; defaults to option column.unit_spend.	None
agg_func	str	A pandas groupby aggregation name, such as "sum", "mean" or "nunique" (case-sensitive). Defaults to "sum".	'sum'

Raises:

Type	Description
TypeError	If a mask is not boolean.
ValueError	If p1_index and p2_index overlap.
ValueError	If focus_group_index and comparison_group_index overlap.
ValueError	If a mask does not have one entry per row of df, or is a Series whose index differs from df.index.
ValueError	If the option-derived customer_id or value_col columns are missing, or if a given group_col is not in df.

Source code in openretailscience/analysis/gain_loss.py

def __init__(
    self,
    df: pd.DataFrame,
    p1_index: Mask,
    p2_index: Mask,
    focus_group_index: Mask,
    focus_group_name: str,
    comparison_group_index: Mask,
    comparison_group_name: str,
    group_col: str | None = None,
    value_col: str | None = None,
    agg_func: str = "sum",
) -> None:
    """Calculate the gain loss decomposition at the customer level.

    The four masks have one entry per row of ``df`` and are matched by position, so ``df``
    may repeat index labels.

    Args:
        df (pd.DataFrame): Transactions with the option-derived customer_id column and ``value_col``.
        p1_index (list[bool] | np.ndarray | pd.Series): Rows in the first period.
        p2_index (list[bool] | np.ndarray | pd.Series): Rows in the second period.
        focus_group_index (list[bool] | np.ndarray | pd.Series): Rows in the focus group.
        focus_group_name (str): Display name of the focus group.
        comparison_group_index (list[bool] | np.ndarray | pd.Series): Rows in the comparison group.
        comparison_group_name (str): Display name of the comparison group.
        group_col (str | None): Optional column to break the analysis down by.
        value_col (str | None): Value column to aggregate; defaults to option ``column.unit_spend``.
        agg_func (str): A pandas groupby aggregation name, such as "sum", "mean" or "nunique"
            (case-sensitive). Defaults to "sum".

    Raises:
        TypeError: If a mask is not boolean.
        ValueError: If ``p1_index`` and ``p2_index`` overlap.
        ValueError: If ``focus_group_index`` and ``comparison_group_index`` overlap.
        ValueError: If a mask does not have one entry per row of ``df``, or is a Series whose
            index differs from ``df.index``.
        ValueError: If the option-derived customer_id or ``value_col`` columns are missing,
            or if a given ``group_col`` is not in ``df``.
    """
    value_col = get_option("column.unit_spend") if value_col is None else value_col
    if not len(df) == len(p1_index) == len(p2_index) == len(focus_group_index) == len(comparison_group_index):
        raise ValueError(
            "p1_index, p2_index, focus_group_index, and comparison_group_index must each have one entry per row "
            "of df",
        )
    raw_masks = (p1_index, p2_index, focus_group_index, comparison_group_index)
    if any(isinstance(mask, pd.Series) and not mask.index.equals(df.index) for mask in raw_masks):
        raise ValueError("A Series mask's index must match df.index; build the masks from df itself")
    # Positional masks, so a df index with repeated labels (e.g. customer_id) can't misalign them.
    # infer_objects accepts an object Series of plain bools but, unlike pd.array, keeps a None/NaN hole non-boolean
    masks = tuple(pd.Series(mask).infer_objects().array for mask in raw_masks)
    if not all(pd.api.types.is_bool_dtype(mask.dtype) for mask in masks):
        raise TypeError(
            "p1_index, p2_index, focus_group_index, and comparison_group_index must be boolean; a mask with "
            'missing values needs the nullable "boolean" dtype, where <NA> selects nothing'
        )
    # A nullable mask's <NA> (e.g. a comparison against a missing brand) selects nothing, as in pandas indexing
    p1_index, p2_index, focus_group_index, comparison_group_index = (
        mask.to_numpy(dtype=bool, na_value=False) for mask in masks
    )

    if (p1_index & p2_index).any():
        raise ValueError("p1_index and p2_index should not overlap")

    if (focus_group_index & comparison_group_index).any():
        raise ValueError("focus_group_index and comparison_group_index should not overlap")

    if group_col is not None:
        # group_col is typed str | None here, so unpack the single-element list back to a string.
        group_col = ensure_columns(df, group_col, "group_col")[0]
    ensure_data_has_columns(df, [get_option("column.customer_id"), value_col])

    self.focus_group_name = focus_group_name
    self.comparison_group_name = comparison_group_name
    self.group_col = group_col
    self.value_col = value_col

    self.gain_loss_df = self._calc_gain_loss(
        df=df,
        p1_index=p1_index,
        p2_index=p2_index,
        focus_group_index=focus_group_index,
        comparison_group_index=comparison_group_index,
        group_col=group_col,
        value_col=value_col,
        agg_func=agg_func,
    )
    self.gain_loss_table_df = self._calc_gains_loss_table(
        gain_loss_df=self.gain_loss_df,
        group_col=group_col,
    )

plot(title=None, eyebrow=None, subtitle=None, x_label=None, y_label=None, ax=None, source_text=None, move_legend_outside=False, **kwargs)

Plot the gain loss table as a stacked horizontal bar of the six decomposition columns.

Green segments are new / increased_focus / switch_from_comparison; red segments are lost / decreased_focus / switch_to_comparison. Legend labels use the focus and comparison group names. stacked and color kwargs are silently ignored.

Parameters:

Name	Type	Description	Default
title	str | None	Plot title; defaults to "Gain Loss from {focus} to {comparison}".	None
eyebrow	str | None	Small uppercase label rendered above the title.	None
subtitle	str | None	Supporting copy rendered below the title.	None
x_label	str | None	X-axis label; defaults to the value column name.	None
y_label	str | None	Y-axis label; defaults to the focus group name, or group_col when grouped.	None
ax	Axes | None	Axes to plot on.	None
source_text	str | None	Source annotation.	None
move_legend_outside	bool	Whether to move the legend outside the plot.	False
**kwargs	Any	Additional keyword arguments forwarded to the bar plot.	{}

Returns:

Name	Type	Description
SubplotBase	SubplotBase	The plot axes.

Source code in openretailscience/analysis/gain_loss.py

def plot(
    self,
    title: str | None = None,
    eyebrow: str | None = None,
    subtitle: str | None = None,
    x_label: str | None = None,
    y_label: str | None = None,
    ax: Axes | None = None,
    source_text: str | None = None,
    move_legend_outside: bool = False,
    **kwargs: Any,  # noqa: ANN401
) -> SubplotBase:
    """Plot the gain loss table as a stacked horizontal bar of the six decomposition columns.

    Green segments are ``new`` / ``increased_focus`` / ``switch_from_comparison``; red
    segments are ``lost`` / ``decreased_focus`` / ``switch_to_comparison``. Legend labels
    use the focus and comparison group names. ``stacked`` and ``color`` kwargs are
    silently ignored.

    Args:
        title (str | None): Plot title; defaults to "Gain Loss from {focus} to {comparison}".
        eyebrow (str | None): Small uppercase label rendered above the title.
        subtitle (str | None): Supporting copy rendered below the title.
        x_label (str | None): X-axis label; defaults to the value column name.
        y_label (str | None): Y-axis label; defaults to the focus group name, or ``group_col`` when grouped.
        ax (Axes | None): Axes to plot on.
        source_text (str | None): Source annotation.
        move_legend_outside (bool): Whether to move the legend outside the plot.
        **kwargs (Any): Additional keyword arguments forwarded to the bar plot.

    Returns:
        SubplotBase: The plot axes.
    """
    increase_cols = ["new", "increased_focus", "switch_from_comparison"]
    decrease_cols = ["lost", "decreased_focus", "switch_to_comparison"]
    all_cols = increase_cols + decrease_cols
    segment_colors = [
        COLORS["green"][700],
        COLORS["green"][500],
        COLORS["green"][300],
        COLORS["red"][700],
        COLORS["red"][500],
        COLORS["red"][300],
    ]
    legend_labels = [
        "New",
        f"Increased {self.focus_group_name}",
        f"Switch From {self.comparison_group_name}",
        "Lost",
        f"Decreased {self.focus_group_name}",
        f"Switch To {self.comparison_group_name}",
    ]

    default_y_label = self.focus_group_name if self.group_col is None else self.group_col
    plot_data = self.gain_loss_table_df.copy()

    kwargs.pop("stacked", None)
    kwargs.pop("color", None)

    resolved_title = (
        title if title is not None else f"Gain Loss from {self.focus_group_name} to {self.comparison_group_name}"
    )

    ax = bar.plot(
        df=plot_data,
        value_col=all_cols,
        title=resolved_title,
        eyebrow=eyebrow,
        subtitle=subtitle,
        y_label=y_label if y_label is not None else default_y_label,
        x_label=x_label if x_label is not None else self.value_col,
        orientation="horizontal",
        ax=ax,
        source_text=source_text,
        move_legend_outside=move_legend_outside,
        legend_labels=legend_labels,
        stacked=True,
        color=segment_colors,
        **kwargs,
    )

    ax.axvline(0, color="black", linewidth=0.5)

    return ax

process_customer_group(focus_p1, comparison_p1, focus_p2, comparison_p2, focus_diff, comparison_diff) staticmethod

Decompose a group's focus-period change into the six gain loss components.

Returns (new, lost, increased_focus, decreased_focus, switch_from_comparison, switch_to_comparison) in that order. new, increased_focus, and switch_from_comparison are >= 0; lost, decreased_focus, and switch_to_comparison are <= 0. When both groups are zero in p1 the entire focus p2 value counts as new; when both are zero in p2 the entire focus p1 value counts as lost.

Parameters:

Name	Type	Description	Default
focus_p1	float	Focus group value in period 1.	required
comparison_p1	float	Comparison group value in period 1.	required
focus_p2	float	Focus group value in period 2.	required
comparison_p2	float	Comparison group value in period 2.	required
focus_diff	float	Focus group change, p2 - p1.	required
comparison_diff	float	Comparison group change, p2 - p1.	required

Returns:

Type	Description
tuple[float, float, float, float, float, float]	tuple[float, float, float, float, float, float]: The six components, in the unpack order above.

Source code in openretailscience/analysis/gain_loss.py

@staticmethod
def process_customer_group(
    focus_p1: float,
    comparison_p1: float,
    focus_p2: float,
    comparison_p2: float,
    focus_diff: float,
    comparison_diff: float,
) -> tuple[float, float, float, float, float, float]:
    """Decompose a group's focus-period change into the six gain loss components.

    Returns ``(new, lost, increased_focus, decreased_focus, switch_from_comparison,
    switch_to_comparison)`` in that order. ``new``, ``increased_focus``, and
    ``switch_from_comparison`` are >= 0; ``lost``, ``decreased_focus``, and
    ``switch_to_comparison`` are <= 0. When both groups are zero in p1 the entire
    focus p2 value counts as ``new``; when both are zero in p2 the entire focus
    p1 value counts as ``lost``.

    Args:
        focus_p1 (float): Focus group value in period 1.
        comparison_p1 (float): Comparison group value in period 1.
        focus_p2 (float): Focus group value in period 2.
        comparison_p2 (float): Comparison group value in period 2.
        focus_diff (float): Focus group change, p2 - p1.
        comparison_diff (float): Comparison group change, p2 - p1.

    Returns:
        tuple[float, float, float, float, float, float]: The six components, in the unpack order above.
    """
    if focus_p1 == 0 and comparison_p1 == 0:
        return focus_p2, 0, 0, 0, 0, 0
    if focus_p2 == 0 and comparison_p2 == 0:
        return 0, -1 * focus_p1, 0, 0, 0, 0

    if focus_diff > 0:
        focus_inc_dec = focus_diff if comparison_diff > 0 else max(0, comparison_diff + focus_diff)
    elif comparison_diff < 0:
        focus_inc_dec = focus_diff
    else:
        focus_inc_dec = min(0, comparison_diff + focus_diff)

    increased_focus = max(0, focus_inc_dec)
    decreased_focus = min(0, focus_inc_dec)

    transfer = focus_diff - focus_inc_dec
    switch_from_comparison = max(0, transfer)
    switch_to_comparison = min(0, transfer)

    return 0, 0, increased_focus, decreased_focus, switch_from_comparison, switch_to_comparison