From 0da30f9421c4c240822d3e91ce29f6dccf9a41f3 Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 10:05:21 +0100
Subject: [PATCH 1/8] Add barlike option to area plot   Summary of changes:

  1. plotting.py: Added barlike parameter to area() with helper _apply_barlike_style()
  2. accessor.py: Updated both DataArrayPlotlyAccessor.area() and DatasetPlotlyAccessor.area()
  3. Tests: Added 3 tests for barlike (basic, trace styling, animation frames)
  4. Notebook: Created docs/examples/barlike.ipynb demonstrating the feature

  Usage:
  xpx(da).area(barlike=True)  # Looks like bars, renders faster
---
 docs/examples/barlike.ipynb | 193 ++++++++++++++++++++++++++++++++++++
 tests/test_accessor.py      |  27 +++++
 xarray_plotly/accessor.py   |   6 ++
 xarray_plotly/plotting.py   |  25 ++++-
 4 files changed, 250 insertions(+), 1 deletion(-)
 create mode 100644 docs/examples/barlike.ipynb

diff --git a/docs/examples/barlike.ipynb b/docs/examples/barlike.ipynb
new file mode 100644
index 0000000..2979e65
--- /dev/null
+++ b/docs/examples/barlike.ipynb
@@ -0,0 +1,193 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Bar-like Area Charts\n",
+    "\n",
+    "Area charts can be styled to look like bar charts using `barlike=True`. This renders much faster than actual bar charts for large datasets because it uses a single polygon per trace instead of individual rectangles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "from xarray_plotly import config, xpx\n",
+    "\n",
+    "config.notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sample data\n",
+    "da = xr.DataArray(\n",
+    "    np.random.rand(12, 3) * 100,\n",
+    "    dims=[\"month\", \"category\"],\n",
+    "    coords={\n",
+    "        \"month\": [\n",
+    "            \"Jan\",\n",
+    "            \"Feb\",\n",
+    "            \"Mar\",\n",
+    "            \"Apr\",\n",
+    "            \"May\",\n",
+    "            \"Jun\",\n",
+    "            \"Jul\",\n",
+    "            \"Aug\",\n",
+    "            \"Sep\",\n",
+    "            \"Oct\",\n",
+    "            \"Nov\",\n",
+    "            \"Dec\",\n",
+    "        ],\n",
+    "        \"category\": [\"A\", \"B\", \"C\"],\n",
+    "    },\n",
+    "    name=\"sales\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "## Regular Area Chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xpx(da).area()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "## Bar-like Area Chart\n",
+    "\n",
+    "With `barlike=True`, the area chart uses stepped lines and removes outlines to look like bars:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xpx(da).area(barlike=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## Comparison with Actual Bar Chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xpx(da).bar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## With Animation\n",
+    "\n",
+    "The `barlike` styling also applies to animation frames:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data with animation dimension\n",
+    "da_anim = xr.DataArray(\n",
+    "    np.random.rand(12, 3, 5) * 100,\n",
+    "    dims=[\"month\", \"category\", \"year\"],\n",
+    "    coords={\n",
+    "        \"month\": [\n",
+    "            \"Jan\",\n",
+    "            \"Feb\",\n",
+    "            \"Mar\",\n",
+    "            \"Apr\",\n",
+    "            \"May\",\n",
+    "            \"Jun\",\n",
+    "            \"Jul\",\n",
+    "            \"Aug\",\n",
+    "            \"Sep\",\n",
+    "            \"Oct\",\n",
+    "            \"Nov\",\n",
+    "            \"Dec\",\n",
+    "        ],\n",
+    "        \"category\": [\"A\", \"B\", \"C\"],\n",
+    "        \"year\": [2020, 2021, 2022, 2023, 2024],\n",
+    "    },\n",
+    "    name=\"sales\",\n",
+    ")\n",
+    "\n",
+    "xpx(da_anim).area(animation_frame=\"year\", barlike=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "## When to Use\n",
+    "\n",
+    "Use `barlike=True` when:\n",
+    "- You have large datasets where bar charts render slowly\n",
+    "- You want stacked bar-like visualization with better performance\n",
+    "- You need animations with many frames\n",
+    "\n",
+    "Use actual `bar()` when:\n",
+    "- You need precise bar positioning (grouped bars)\n",
+    "- You need pattern fills\n",
+    "- Visual accuracy is more important than performance"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/test_accessor.py b/tests/test_accessor.py
index 9786112..3bd17d9 100644
--- a/tests/test_accessor.py
+++ b/tests/test_accessor.py
@@ -146,6 +146,33 @@ def test_area_returns_figure(self) -> None:
         fig = self.da_2d.plotly.area()
         assert isinstance(fig, go.Figure)
 
+    def test_area_barlike_returns_figure(self) -> None:
+        """Test that area(barlike=True) returns a Plotly Figure."""
+        fig = self.da_2d.plotly.area(barlike=True)
+        assert isinstance(fig, go.Figure)
+
+    def test_area_barlike_trace_styling(self) -> None:
+        """Test that barlike applies correct trace styling."""
+        fig = self.da_2d.plotly.area(barlike=True)
+        for trace in fig.data:
+            assert trace.line.width == 0
+            assert trace.line.shape == "hv"
+            assert trace.fillcolor is not None
+
+    def test_area_barlike_animation_frames(self) -> None:
+        """Test that barlike styling applies to animation frames."""
+        da = xr.DataArray(
+            np.random.rand(5, 3, 4),
+            dims=["time", "city", "year"],
+        )
+        fig = da.plotly.area(animation_frame="year", barlike=True)
+        assert len(fig.frames) > 0
+        for frame in fig.frames:
+            for trace in frame.data:
+                assert trace.line.width == 0
+                assert trace.line.shape == "hv"
+                assert trace.fillcolor is not None
+
     def test_scatter_returns_figure(self) -> None:
         """Test that scatter() returns a Plotly Figure."""
         fig = self.da_2d.plotly.scatter()
diff --git a/xarray_plotly/accessor.py b/xarray_plotly/accessor.py
index ff45d26..b02b6a5 100644
--- a/xarray_plotly/accessor.py
+++ b/xarray_plotly/accessor.py
@@ -131,6 +131,7 @@ def area(
         facet_col: SlotValue = auto,
         facet_row: SlotValue = auto,
         animation_frame: SlotValue = auto,
+        barlike: bool = False,
         **px_kwargs: Any,
     ) -> go.Figure:
         """Create an interactive stacked area chart.
@@ -144,6 +145,7 @@ def area(
             facet_col: Dimension for subplot columns. Default: fourth dimension.
             facet_row: Dimension for subplot rows. Default: fifth dimension.
             animation_frame: Dimension for animation. Default: sixth dimension.
+            barlike: If True, style as bar chart (stepped, no outline). Faster than bars.
             **px_kwargs: Additional arguments passed to `plotly.express.area()`.
 
         Returns:
@@ -157,6 +159,7 @@ def area(
             facet_col=facet_col,
             facet_row=facet_row,
             animation_frame=animation_frame,
+            barlike=barlike,
             **px_kwargs,
         )
 
@@ -472,6 +475,7 @@ def area(
         facet_col: SlotValue = auto,
         facet_row: SlotValue = auto,
         animation_frame: SlotValue = auto,
+        barlike: bool = False,
         **px_kwargs: Any,
     ) -> go.Figure:
         """Create an interactive stacked area chart.
@@ -484,6 +488,7 @@ def area(
             facet_col: Dimension for subplot columns.
             facet_row: Dimension for subplot rows.
             animation_frame: Dimension for animation.
+            barlike: If True, style as bar chart (stepped, no outline). Faster than bars.
             **px_kwargs: Additional arguments passed to `plotly.express.area()`.
 
         Returns:
@@ -498,6 +503,7 @@ def area(
             facet_col=facet_col,
             facet_row=facet_row,
             animation_frame=animation_frame,
+            barlike=barlike,
             **px_kwargs,
         )
 
diff --git a/xarray_plotly/plotting.py b/xarray_plotly/plotting.py
index 638de94..d4247c6 100644
--- a/xarray_plotly/plotting.py
+++ b/xarray_plotly/plotting.py
@@ -167,6 +167,14 @@ def bar(
     )
 
 
+def _apply_barlike_style(traces: tuple) -> None:
+    """Apply bar-like styling to area traces (in-place)."""
+    for trace in traces:
+        color = trace.line.color
+        trace.fillcolor = color
+        trace.line = {"width": 0, "color": color, "shape": "hv"}
+
+
 def area(
     darray: DataArray,
     *,
@@ -176,6 +184,7 @@ def area(
     facet_col: SlotValue = auto,
     facet_row: SlotValue = auto,
     animation_frame: SlotValue = auto,
+    barlike: bool = False,
     **px_kwargs: Any,
 ) -> go.Figure:
     """
@@ -200,6 +209,10 @@ def area(
         Dimension for subplot rows. Default: fifth dimension.
     animation_frame
         Dimension for animation. Default: sixth dimension.
+    barlike
+        If True, style the area chart to look like a bar chart using stepped
+        lines with no outline. Renders faster than bar charts for large data.
+        Default: False.
     **px_kwargs
         Additional arguments passed to `plotly.express.area()`.
 
@@ -218,11 +231,14 @@ def area(
         animation_frame=animation_frame,
     )
 
+    if barlike:
+        px_kwargs.setdefault("line_shape", "hv")
+
     df = to_dataframe(darray)
     value_col = get_value_col(darray)
     labels = {**build_labels(darray, slots, value_col), **px_kwargs.pop("labels", {})}
 
-    return px.area(
+    fig = px.area(
         df,
         x=slots.get("x"),
         y=value_col,
@@ -235,6 +251,13 @@ def area(
         **px_kwargs,
     )
 
+    if barlike:
+        _apply_barlike_style(fig.data)
+        for frame in fig.frames:
+            _apply_barlike_style(frame.data)
+
+    return fig
+
 
 def box(
     darray: DataArray,

From 9e2d00a3e77e35dfda266454abdee0e735b715a0 Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 10:14:39 +0100
Subject: [PATCH 2/8] Add barlike option to area plot   Summary of changes:

  1. plotting.py: Added barlike parameter to area() with helper _apply_barlike_style()
  2. accessor.py: Updated both DataArrayPlotlyAccessor.area() and DatasetPlotlyAccessor.area()
  3. Tests: Added 3 tests for barlike (basic, trace styling, animation frames)
  4. Notebook: Created docs/examples/barlike.ipynb demonstrating the feature

  Usage:
  xpx(da).area(barlike=True)  # Looks like bars, renders faster
---
 .../{barlike.ipynb => fast_bar.ipynb}         | 36 +++-----
 tests/test_accessor.py                        | 18 ++--
 xarray_plotly/accessor.py                     | 77 ++++++++++++++--
 xarray_plotly/config.py                       |  1 +
 xarray_plotly/plotting.py                     | 92 +++++++++++++++----
 5 files changed, 172 insertions(+), 52 deletions(-)
 rename docs/examples/{barlike.ipynb => fast_bar.ipynb} (76%)

diff --git a/docs/examples/barlike.ipynb b/docs/examples/fast_bar.ipynb
similarity index 76%
rename from docs/examples/barlike.ipynb
rename to docs/examples/fast_bar.ipynb
index 2979e65..6537730 100644
--- a/docs/examples/barlike.ipynb
+++ b/docs/examples/fast_bar.ipynb
@@ -5,9 +5,9 @@
    "id": "0",
    "metadata": {},
    "source": [
-    "# Bar-like Area Charts\n",
+    "# Fast Bar Charts\n",
     "\n",
-    "Area charts can be styled to look like bar charts using `barlike=True`. This renders much faster than actual bar charts for large datasets because it uses a single polygon per trace instead of individual rectangles."
+    "The `fast_bar()` method creates bar-like visualizations using stacked areas. This renders much faster than actual bar charts for large datasets because it uses a single polygon per trace instead of individual rectangles."
    ]
   },
   {
@@ -62,7 +62,7 @@
    "id": "3",
    "metadata": {},
    "source": [
-    "## Regular Area Chart"
+    "## Fast Bar Chart"
    ]
   },
   {
@@ -72,7 +72,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xpx(da).area()"
+    "xpx(da).fast_bar()"
    ]
   },
   {
@@ -80,9 +80,7 @@
    "id": "5",
    "metadata": {},
    "source": [
-    "## Bar-like Area Chart\n",
-    "\n",
-    "With `barlike=True`, the area chart uses stepped lines and removes outlines to look like bars:"
+    "## Comparison with Regular Bar Chart"
    ]
   },
   {
@@ -92,7 +90,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xpx(da).area(barlike=True)"
+    "xpx(da).bar()"
    ]
   },
   {
@@ -100,7 +98,7 @@
    "id": "7",
    "metadata": {},
    "source": [
-    "## Comparison with Actual Bar Chart"
+    "## Comparison with Area Chart"
    ]
   },
   {
@@ -110,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xpx(da).bar()"
+    "xpx(da).area()"
    ]
   },
   {
@@ -120,7 +118,7 @@
    "source": [
     "## With Animation\n",
     "\n",
-    "The `barlike` styling also applies to animation frames:"
+    "The `fast_bar` styling also applies to animation frames:"
    ]
   },
   {
@@ -155,7 +153,7 @@
     "    name=\"sales\",\n",
     ")\n",
     "\n",
-    "xpx(da_anim).area(animation_frame=\"year\", barlike=True)"
+    "xpx(da_anim).fast_bar(animation_frame=\"year\")"
    ]
   },
   {
@@ -165,15 +163,11 @@
    "source": [
     "## When to Use\n",
     "\n",
-    "Use `barlike=True` when:\n",
-    "- You have large datasets where bar charts render slowly\n",
-    "- You want stacked bar-like visualization with better performance\n",
-    "- You need animations with many frames\n",
-    "\n",
-    "Use actual `bar()` when:\n",
-    "- You need precise bar positioning (grouped bars)\n",
-    "- You need pattern fills\n",
-    "- Visual accuracy is more important than performance"
+    "| Method | Use when... |\n",
+    "|--------|-------------|\n",
+    "| `fast_bar()` | Large datasets, animations with many frames, performance matters |\n",
+    "| `bar()` | Need precise bar positioning, grouped bars, pattern fills |\n",
+    "| `area()` | Want smooth continuous fills, standard area chart appearance |"
    ]
   }
  ],
diff --git a/tests/test_accessor.py b/tests/test_accessor.py
index 3bd17d9..74b89bc 100644
--- a/tests/test_accessor.py
+++ b/tests/test_accessor.py
@@ -146,26 +146,26 @@ def test_area_returns_figure(self) -> None:
         fig = self.da_2d.plotly.area()
         assert isinstance(fig, go.Figure)
 
-    def test_area_barlike_returns_figure(self) -> None:
-        """Test that area(barlike=True) returns a Plotly Figure."""
-        fig = self.da_2d.plotly.area(barlike=True)
+    def test_fast_bar_returns_figure(self) -> None:
+        """Test that fast_bar() returns a Plotly Figure."""
+        fig = self.da_2d.plotly.fast_bar()
         assert isinstance(fig, go.Figure)
 
-    def test_area_barlike_trace_styling(self) -> None:
-        """Test that barlike applies correct trace styling."""
-        fig = self.da_2d.plotly.area(barlike=True)
+    def test_fast_bar_trace_styling(self) -> None:
+        """Test that fast_bar applies correct trace styling."""
+        fig = self.da_2d.plotly.fast_bar()
         for trace in fig.data:
             assert trace.line.width == 0
             assert trace.line.shape == "hv"
             assert trace.fillcolor is not None
 
-    def test_area_barlike_animation_frames(self) -> None:
-        """Test that barlike styling applies to animation frames."""
+    def test_fast_bar_animation_frames(self) -> None:
+        """Test that fast_bar styling applies to animation frames."""
         da = xr.DataArray(
             np.random.rand(5, 3, 4),
             dims=["time", "city", "year"],
         )
-        fig = da.plotly.area(animation_frame="year", barlike=True)
+        fig = da.plotly.fast_bar(animation_frame="year")
         assert len(fig.frames) > 0
         for frame in fig.frames:
             for trace in frame.data:
diff --git a/xarray_plotly/accessor.py b/xarray_plotly/accessor.py
index b02b6a5..80b604c 100644
--- a/xarray_plotly/accessor.py
+++ b/xarray_plotly/accessor.py
@@ -131,7 +131,6 @@ def area(
         facet_col: SlotValue = auto,
         facet_row: SlotValue = auto,
         animation_frame: SlotValue = auto,
-        barlike: bool = False,
         **px_kwargs: Any,
     ) -> go.Figure:
         """Create an interactive stacked area chart.
@@ -145,7 +144,6 @@ def area(
             facet_col: Dimension for subplot columns. Default: fourth dimension.
             facet_row: Dimension for subplot rows. Default: fifth dimension.
             animation_frame: Dimension for animation. Default: sixth dimension.
-            barlike: If True, style as bar chart (stepped, no outline). Faster than bars.
             **px_kwargs: Additional arguments passed to `plotly.express.area()`.
 
         Returns:
@@ -159,7 +157,41 @@ def area(
             facet_col=facet_col,
             facet_row=facet_row,
             animation_frame=animation_frame,
-            barlike=barlike,
+            **px_kwargs,
+        )
+
+    def fast_bar(
+        self,
+        *,
+        x: SlotValue = auto,
+        color: SlotValue = auto,
+        facet_col: SlotValue = auto,
+        facet_row: SlotValue = auto,
+        animation_frame: SlotValue = auto,
+        **px_kwargs: Any,
+    ) -> go.Figure:
+        """Create a bar-like chart using stacked areas for better performance.
+
+        Slot order: x -> color -> facet_col -> facet_row -> animation_frame
+
+        Args:
+            x: Dimension for x-axis. Default: first dimension.
+            color: Dimension for color/stacking. Default: second dimension.
+            facet_col: Dimension for subplot columns. Default: third dimension.
+            facet_row: Dimension for subplot rows. Default: fourth dimension.
+            animation_frame: Dimension for animation. Default: fifth dimension.
+            **px_kwargs: Additional arguments passed to `plotly.express.area()`.
+
+        Returns:
+            Interactive Plotly Figure.
+        """
+        return plotting.fast_bar(
+            self._da,
+            x=x,
+            color=color,
+            facet_col=facet_col,
+            facet_row=facet_row,
+            animation_frame=animation_frame,
             **px_kwargs,
         )
 
@@ -475,7 +507,6 @@ def area(
         facet_col: SlotValue = auto,
         facet_row: SlotValue = auto,
         animation_frame: SlotValue = auto,
-        barlike: bool = False,
         **px_kwargs: Any,
     ) -> go.Figure:
         """Create an interactive stacked area chart.
@@ -488,7 +519,6 @@ def area(
             facet_col: Dimension for subplot columns.
             facet_row: Dimension for subplot rows.
             animation_frame: Dimension for animation.
-            barlike: If True, style as bar chart (stepped, no outline). Faster than bars.
             **px_kwargs: Additional arguments passed to `plotly.express.area()`.
 
         Returns:
@@ -503,7 +533,42 @@ def area(
             facet_col=facet_col,
             facet_row=facet_row,
             animation_frame=animation_frame,
-            barlike=barlike,
+            **px_kwargs,
+        )
+
+    def fast_bar(
+        self,
+        var: str | None = None,
+        *,
+        x: SlotValue = auto,
+        color: SlotValue = auto,
+        facet_col: SlotValue = auto,
+        facet_row: SlotValue = auto,
+        animation_frame: SlotValue = auto,
+        **px_kwargs: Any,
+    ) -> go.Figure:
+        """Create a bar-like chart using stacked areas for better performance.
+
+        Args:
+            var: Variable to plot. If None, plots all variables with "variable" dimension.
+            x: Dimension for x-axis.
+            color: Dimension for color/stacking.
+            facet_col: Dimension for subplot columns.
+            facet_row: Dimension for subplot rows.
+            animation_frame: Dimension for animation.
+            **px_kwargs: Additional arguments passed to `plotly.express.area()`.
+
+        Returns:
+            Interactive Plotly Figure.
+        """
+        da = self._get_dataarray(var)
+        return plotting.fast_bar(
+            da,
+            x=x,
+            color=color,
+            facet_col=facet_col,
+            facet_row=facet_row,
+            animation_frame=animation_frame,
             **px_kwargs,
         )
 
diff --git a/xarray_plotly/config.py b/xarray_plotly/config.py
index e28cab0..0f704a3 100644
--- a/xarray_plotly/config.py
+++ b/xarray_plotly/config.py
@@ -26,6 +26,7 @@
         "animation_frame",
     ),
     "bar": ("x", "color", "pattern_shape", "facet_col", "facet_row", "animation_frame"),
+    "fast_bar": ("x", "color", "facet_col", "facet_row", "animation_frame"),
     "area": (
         "x",
         "color",
diff --git a/xarray_plotly/plotting.py b/xarray_plotly/plotting.py
index d4247c6..cdf7d1d 100644
--- a/xarray_plotly/plotting.py
+++ b/xarray_plotly/plotting.py
@@ -175,6 +175,81 @@ def _apply_barlike_style(traces: tuple) -> None:
         trace.line = {"width": 0, "color": color, "shape": "hv"}
 
 
+def fast_bar(
+    darray: DataArray,
+    *,
+    x: SlotValue = auto,
+    color: SlotValue = auto,
+    facet_col: SlotValue = auto,
+    facet_row: SlotValue = auto,
+    animation_frame: SlotValue = auto,
+    **px_kwargs: Any,
+) -> go.Figure:
+    """
+    Create a bar-like chart using stacked areas for better performance.
+
+    Uses `px.area` with stepped lines and no outline to create a bar-like
+    appearance. Renders faster than `bar()` for large datasets because it
+    uses a single polygon per trace instead of individual rectangles.
+
+    The y-axis shows DataArray values. Dimensions fill slots in order:
+    x -> color -> facet_col -> facet_row -> animation_frame
+
+    Parameters
+    ----------
+    darray
+        The DataArray to plot.
+    x
+        Dimension for x-axis. Default: first dimension.
+    color
+        Dimension for color/stacking. Default: second dimension.
+    facet_col
+        Dimension for subplot columns. Default: third dimension.
+    facet_row
+        Dimension for subplot rows. Default: fourth dimension.
+    animation_frame
+        Dimension for animation. Default: fifth dimension.
+    **px_kwargs
+        Additional arguments passed to `plotly.express.area()`.
+
+    Returns
+    -------
+    plotly.graph_objects.Figure
+    """
+    slots = assign_slots(
+        list(darray.dims),
+        "fast_bar",
+        x=x,
+        color=color,
+        facet_col=facet_col,
+        facet_row=facet_row,
+        animation_frame=animation_frame,
+    )
+
+    df = to_dataframe(darray)
+    value_col = get_value_col(darray)
+    labels = {**build_labels(darray, slots, value_col), **px_kwargs.pop("labels", {})}
+
+    fig = px.area(
+        df,
+        x=slots.get("x"),
+        y=value_col,
+        color=slots.get("color"),
+        facet_col=slots.get("facet_col"),
+        facet_row=slots.get("facet_row"),
+        animation_frame=slots.get("animation_frame"),
+        line_shape="hv",
+        labels=labels,
+        **px_kwargs,
+    )
+
+    _apply_barlike_style(fig.data)
+    for frame in fig.frames:
+        _apply_barlike_style(frame.data)
+
+    return fig
+
+
 def area(
     darray: DataArray,
     *,
@@ -184,7 +259,6 @@ def area(
     facet_col: SlotValue = auto,
     facet_row: SlotValue = auto,
     animation_frame: SlotValue = auto,
-    barlike: bool = False,
     **px_kwargs: Any,
 ) -> go.Figure:
     """
@@ -209,10 +283,6 @@ def area(
         Dimension for subplot rows. Default: fifth dimension.
     animation_frame
         Dimension for animation. Default: sixth dimension.
-    barlike
-        If True, style the area chart to look like a bar chart using stepped
-        lines with no outline. Renders faster than bar charts for large data.
-        Default: False.
     **px_kwargs
         Additional arguments passed to `plotly.express.area()`.
 
@@ -231,14 +301,11 @@ def area(
         animation_frame=animation_frame,
     )
 
-    if barlike:
-        px_kwargs.setdefault("line_shape", "hv")
-
     df = to_dataframe(darray)
     value_col = get_value_col(darray)
     labels = {**build_labels(darray, slots, value_col), **px_kwargs.pop("labels", {})}
 
-    fig = px.area(
+    return px.area(
         df,
         x=slots.get("x"),
         y=value_col,
@@ -251,13 +318,6 @@ def area(
         **px_kwargs,
     )
 
-    if barlike:
-        _apply_barlike_style(fig.data)
-        for frame in fig.frames:
-            _apply_barlike_style(frame.data)
-
-    return fig
-
 
 def box(
     darray: DataArray,

From 9606c0961524bd3a58543ef96beebd229fb82353 Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 10:26:31 +0100
Subject: [PATCH 3/8] =?UTF-8?q?=E2=8F=BA=20Done.=20The=20fast=5Fbar()=20me?=
 =?UTF-8?q?thod=20now=20handles=20all=20cases:=20=20=20=E2=94=8C=E2=94=80?=
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 =?UTF-8?q?ackgroup=3D1)=20=20=20=20=20=20=20=20=20=20=20=20=20=20?=
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 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=BC=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=A4?=
 =?UTF-8?q?=20=20=20=E2=94=82=20All=20negative=20=E2=94=82=20Stacked=20(st?=
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 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=BC=E2=94=80=E2=94=80=E2=94=80?=
 =?UTF-8?q?=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80=E2=94=80?=
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 =?UTF-8?q?=20=20=20=E2=94=82=20Mixed=20+/-=20=20=20=20=E2=94=82=20No=20st?=
 =?UTF-8?q?acking,=20fill=20to=20zero=20(tozeroy)=20=E2=94=82=20=20=20?=
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 =?UTF-8?q?gns()=20detection,=20updated=20=5Fapply=5Fbarlike=5Fstyle()=20t?=
 =?UTF-8?q?o=20handle=20mixed=20signs=20=20=20-=20test=5Faccessor.py:=20Ad?=
 =?UTF-8?q?ded=202=20new=20tests=20for=20mixed/same-sign=20behavior=20=20?=
 =?UTF-8?q?=20-=20fast=5Fbar.ipynb:=20Updated=20with=20negative=20and=20mi?=
 =?UTF-8?q?xed=20value=20examples,=20documented=20the=20behavior?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

  Note: For mixed data, series will overlap since stacking doesn't work well with mixed signs. The notebook documents that bar() should be used if proper stacking of mixed data
   is needed.
---
 docs/examples/fast_bar.ipynb | 97 +++++++++++++++++++++++++++++++++++-
 mkdocs.yml                   |  3 ++
 tests/test_accessor.py       | 21 ++++++++
 xarray_plotly/plotting.py    | 34 +++++++++++--
 4 files changed, 149 insertions(+), 6 deletions(-)

diff --git a/docs/examples/fast_bar.ipynb b/docs/examples/fast_bar.ipynb
index 6537730..f4acacc 100644
--- a/docs/examples/fast_bar.ipynb
+++ b/docs/examples/fast_bar.ipynb
@@ -165,10 +165,103 @@
     "\n",
     "| Method | Use when... |\n",
     "|--------|-------------|\n",
-    "| `fast_bar()` | Large datasets, animations with many frames, performance matters |\n",
-    "| `bar()` | Need precise bar positioning, grouped bars, pattern fills |\n",
+    "| `fast_bar()` | Large datasets, animations with many frames, same-sign data (all positive or all negative) |\n",
+    "| `bar()` | Need precise bar positioning, grouped bars, pattern fills, or mixed positive/negative stacking |\n",
     "| `area()` | Want smooth continuous fills, standard area chart appearance |"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "## Negative Values\n",
+    "\n",
+    "Testing with all negative values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All negative values\n",
+    "da_negative = xr.DataArray(\n",
+    "    -np.random.rand(6, 3) * 100,\n",
+    "    dims=[\"month\", \"category\"],\n",
+    "    coords={\n",
+    "        \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"],\n",
+    "        \"category\": [\"A\", \"B\", \"C\"],\n",
+    "    },\n",
+    "    name=\"loss\",\n",
+    ")\n",
+    "\n",
+    "xpx(da_negative).fast_bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comparison: regular bar with negative values\n",
+    "xpx(da_negative).bar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "## Mixed Values (Positive and Negative)\n",
+    "\n",
+    "For mixed positive/negative data, `fast_bar()` automatically disables stacking and fills each series to zero independently. This prevents visual artifacts from stacked areas but means series will overlap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mixed positive and negative values\n",
+    "da_mixed = xr.DataArray(\n",
+    "    np.array(\n",
+    "        [\n",
+    "            [50, -30, 20],\n",
+    "            [-40, 60, -10],\n",
+    "            [30, -50, 40],\n",
+    "            [-20, 70, -30],\n",
+    "            [60, -40, 50],\n",
+    "            [-30, 80, -20],\n",
+    "        ]\n",
+    "    ),\n",
+    "    dims=[\"month\", \"category\"],\n",
+    "    coords={\n",
+    "        \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"],\n",
+    "        \"category\": [\"A\", \"B\", \"C\"],\n",
+    "    },\n",
+    "    name=\"profit_loss\",\n",
+    ")\n",
+    "\n",
+    "xpx(da_mixed).fast_bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Comparison: regular bar with mixed values\n",
+    "xpx(da_mixed).bar()"
+   ]
   }
  ],
  "metadata": {
diff --git a/mkdocs.yml b/mkdocs.yml
index 1a1f0f1..5a38157 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -73,4 +73,7 @@ nav:
       - Dimensions & Facets: examples/dimensions.ipynb
       - Plotly Express Options: examples/kwargs.ipynb
       - Figure Customization: examples/figure.ipynb
+      - Combining Figures: examples/combining.ipynb
+      - Figure Manipulation: examples/manipulation.ipynb
+      - Fast Bar Charts: examples/fast_bar.ipynb
   - API Reference: api.md
diff --git a/tests/test_accessor.py b/tests/test_accessor.py
index 74b89bc..7b6ed33 100644
--- a/tests/test_accessor.py
+++ b/tests/test_accessor.py
@@ -173,6 +173,27 @@ def test_fast_bar_animation_frames(self) -> None:
                 assert trace.line.shape == "hv"
                 assert trace.fillcolor is not None
 
+    def test_fast_bar_mixed_signs_no_stacking(self) -> None:
+        """Test that fast_bar disables stacking for mixed positive/negative data."""
+        da = xr.DataArray(
+            np.array([[50, -30], [-40, 60]]),
+            dims=["time", "category"],
+        )
+        fig = da.plotly.fast_bar()
+        for trace in fig.data:
+            assert trace.stackgroup is None
+            assert trace.fill == "tozeroy"
+
+    def test_fast_bar_same_sign_stacks(self) -> None:
+        """Test that fast_bar uses stacking for same-sign data."""
+        da = xr.DataArray(
+            np.random.rand(5, 3) * 100,
+            dims=["time", "category"],
+        )
+        fig = da.plotly.fast_bar()
+        for trace in fig.data:
+            assert trace.stackgroup is not None
+
     def test_scatter_returns_figure(self) -> None:
         """Test that scatter() returns a Plotly Figure."""
         fig = self.da_2d.plotly.scatter()
diff --git a/xarray_plotly/plotting.py b/xarray_plotly/plotting.py
index cdf7d1d..b4feaaa 100644
--- a/xarray_plotly/plotting.py
+++ b/xarray_plotly/plotting.py
@@ -167,12 +167,31 @@ def bar(
     )
 
 
-def _apply_barlike_style(traces: tuple) -> None:
-    """Apply bar-like styling to area traces (in-place)."""
+def _apply_barlike_style(traces: tuple, mixed_signs: bool = False) -> None:
+    """Apply bar-like styling to area traces (in-place).
+
+    Parameters
+    ----------
+    traces
+        The traces to style.
+    mixed_signs
+        If True, disable stacking and fill to zero for mixed positive/negative data.
+    """
     for trace in traces:
         color = trace.line.color
         trace.fillcolor = color
         trace.line = {"width": 0, "color": color, "shape": "hv"}
+        if mixed_signs:
+            trace.stackgroup = None
+            trace.fill = "tozeroy"
+
+
+def _has_mixed_signs(values: np.ndarray) -> bool:
+    """Check if data contains both positive and negative values."""
+    finite = values[np.isfinite(values)]
+    if len(finite) == 0:
+        return False
+    return bool(np.any(finite > 0) and np.any(finite < 0))
 
 
 def fast_bar(
@@ -195,6 +214,10 @@ def fast_bar(
     The y-axis shows DataArray values. Dimensions fill slots in order:
     x -> color -> facet_col -> facet_row -> animation_frame
 
+    Note: For mixed positive/negative data, stacking is disabled and each
+    series fills independently to zero. For best stacking behavior with
+    mixed data, use `bar()` instead.
+
     Parameters
     ----------
     darray
@@ -226,6 +249,9 @@ def fast_bar(
         animation_frame=animation_frame,
     )
 
+    # Check for mixed positive/negative values
+    mixed_signs = _has_mixed_signs(darray.values)
+
     df = to_dataframe(darray)
     value_col = get_value_col(darray)
     labels = {**build_labels(darray, slots, value_col), **px_kwargs.pop("labels", {})}
@@ -243,9 +269,9 @@ def fast_bar(
         **px_kwargs,
     )
 
-    _apply_barlike_style(fig.data)
+    _apply_barlike_style(fig.data, mixed_signs)
     for frame in fig.frames:
-        _apply_barlike_style(frame.data)
+        _apply_barlike_style(frame.data, mixed_signs)
 
     return fig
 

From 548041167614903f72299b383f1743a1748113b2 Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 10:37:08 +0100
Subject: [PATCH 4/8] Summary of changes:

  fast_bar() now properly handles mixed positive/negative data:
  - Same-sign data (all positive or all negative): Uses single stackgroup for normal stacking
  - Mixed-sign data: Splits each trace into positive and negative parts with separate stackgroups
    - Positives stack upward from zero (stackgroup='positive')
    - Negatives stack downward from zero (stackgroup='negative')
    - This matches bar chart behavior with barmode='relative'

  Implementation:
  - Added _split_traces_by_sign() helper that creates separate traces for positive and negative values
  - Updated fast_bar() to detect mixed signs and apply the split
  - Animation frames are also handled correctly
---
 docs/examples/fast_bar.ipynb |  6 +--
 tests/test_accessor.py       | 11 ++--
 xarray_plotly/plotting.py    | 98 ++++++++++++++++++++++++++++--------
 3 files changed, 87 insertions(+), 28 deletions(-)

diff --git a/docs/examples/fast_bar.ipynb b/docs/examples/fast_bar.ipynb
index f4acacc..356ea3f 100644
--- a/docs/examples/fast_bar.ipynb
+++ b/docs/examples/fast_bar.ipynb
@@ -165,8 +165,8 @@
     "\n",
     "| Method | Use when... |\n",
     "|--------|-------------|\n",
-    "| `fast_bar()` | Large datasets, animations with many frames, same-sign data (all positive or all negative) |\n",
-    "| `bar()` | Need precise bar positioning, grouped bars, pattern fills, or mixed positive/negative stacking |\n",
+    "| `fast_bar()` | Large datasets, animations with many frames, performance matters |\n",
+    "| `bar()` | Need precise bar positioning, grouped bars, pattern fills |\n",
     "| `area()` | Want smooth continuous fills, standard area chart appearance |"
    ]
   },
@@ -219,7 +219,7 @@
    "source": [
     "## Mixed Values (Positive and Negative)\n",
     "\n",
-    "For mixed positive/negative data, `fast_bar()` automatically disables stacking and fills each series to zero independently. This prevents visual artifacts from stacked areas but means series will overlap."
+    "For mixed positive/negative data, `fast_bar()` automatically splits traces into separate stackgroups: positives stack upward from zero, negatives stack downward. This matches bar chart stacking behavior."
    ]
   },
   {
diff --git a/tests/test_accessor.py b/tests/test_accessor.py
index 7b6ed33..4e71e96 100644
--- a/tests/test_accessor.py
+++ b/tests/test_accessor.py
@@ -173,16 +173,17 @@ def test_fast_bar_animation_frames(self) -> None:
                 assert trace.line.shape == "hv"
                 assert trace.fillcolor is not None
 
-    def test_fast_bar_mixed_signs_no_stacking(self) -> None:
-        """Test that fast_bar disables stacking for mixed positive/negative data."""
+    def test_fast_bar_mixed_signs_separate_stacks(self) -> None:
+        """Test that fast_bar uses separate stackgroups for mixed positive/negative data."""
         da = xr.DataArray(
             np.array([[50, -30], [-40, 60]]),
             dims=["time", "category"],
         )
         fig = da.plotly.fast_bar()
-        for trace in fig.data:
-            assert trace.stackgroup is None
-            assert trace.fill == "tozeroy"
+        # Should have separate positive and negative stackgroups
+        stackgroups = {trace.stackgroup for trace in fig.data}
+        assert "positive" in stackgroups
+        assert "negative" in stackgroups
 
     def test_fast_bar_same_sign_stacks(self) -> None:
         """Test that fast_bar uses stacking for same-sign data."""
diff --git a/xarray_plotly/plotting.py b/xarray_plotly/plotting.py
index b4feaaa..9cf8bb1 100644
--- a/xarray_plotly/plotting.py
+++ b/xarray_plotly/plotting.py
@@ -167,23 +167,12 @@ def bar(
     )
 
 
-def _apply_barlike_style(traces: tuple, mixed_signs: bool = False) -> None:
-    """Apply bar-like styling to area traces (in-place).
-
-    Parameters
-    ----------
-    traces
-        The traces to style.
-    mixed_signs
-        If True, disable stacking and fill to zero for mixed positive/negative data.
-    """
+def _apply_barlike_style(traces: tuple) -> None:
+    """Apply bar-like styling to area traces (in-place)."""
     for trace in traces:
         color = trace.line.color
         trace.fillcolor = color
         trace.line = {"width": 0, "color": color, "shape": "hv"}
-        if mixed_signs:
-            trace.stackgroup = None
-            trace.fill = "tozeroy"
 
 
 def _has_mixed_signs(values: np.ndarray) -> bool:
@@ -194,6 +183,60 @@ def _has_mixed_signs(values: np.ndarray) -> bool:
     return bool(np.any(finite > 0) and np.any(finite < 0))
 
 
+def _split_traces_by_sign(traces: tuple) -> list:
+    """Split traces into separate positive and negative stackgroups.
+
+    For mixed positive/negative data, this creates proper bar-like stacking
+    where positives stack upward and negatives stack downward from zero.
+    """
+    import plotly.graph_objects as go
+
+    new_traces = []
+    for trace in traces:
+        y_values = np.array(trace.y)
+        color = trace.line.color
+        name = trace.name
+
+        # Positive trace (negatives become 0)
+        y_pos = np.clip(y_values, 0, None)
+        if np.any(y_pos > 0):
+            new_traces.append(
+                go.Scatter(
+                    x=trace.x,
+                    y=y_pos,
+                    name=name,
+                    stackgroup="positive",
+                    line={"width": 0, "shape": "hv", "color": color},
+                    fillcolor=color,
+                    mode="lines",
+                    legendgroup=name,
+                    xaxis=trace.xaxis,
+                    yaxis=trace.yaxis,
+                )
+            )
+
+        # Negative trace (positives become 0)
+        y_neg = np.clip(y_values, None, 0)
+        if np.any(y_neg < 0):
+            new_traces.append(
+                go.Scatter(
+                    x=trace.x,
+                    y=y_neg,
+                    name=name,
+                    stackgroup="negative",
+                    line={"width": 0, "shape": "hv", "color": color},
+                    fillcolor=color,
+                    mode="lines",
+                    legendgroup=name,
+                    showlegend=False,
+                    xaxis=trace.xaxis,
+                    yaxis=trace.yaxis,
+                )
+            )
+
+    return new_traces
+
+
 def fast_bar(
     darray: DataArray,
     *,
@@ -214,9 +257,8 @@ def fast_bar(
     The y-axis shows DataArray values. Dimensions fill slots in order:
     x -> color -> facet_col -> facet_row -> animation_frame
 
-    Note: For mixed positive/negative data, stacking is disabled and each
-    series fills independently to zero. For best stacking behavior with
-    mixed data, use `bar()` instead.
+    For mixed positive/negative data, positives stack upward and negatives
+    stack downward from zero, similar to bar chart behavior.
 
     Parameters
     ----------
@@ -239,6 +281,8 @@ def fast_bar(
     -------
     plotly.graph_objects.Figure
     """
+    import plotly.graph_objects as go
+
     slots = assign_slots(
         list(darray.dims),
         "fast_bar",
@@ -256,7 +300,7 @@ def fast_bar(
     value_col = get_value_col(darray)
     labels = {**build_labels(darray, slots, value_col), **px_kwargs.pop("labels", {})}
 
-    fig = px.area(
+    base_fig = px.area(
         df,
         x=slots.get("x"),
         y=value_col,
@@ -269,9 +313,23 @@ def fast_bar(
         **px_kwargs,
     )
 
-    _apply_barlike_style(fig.data, mixed_signs)
-    for frame in fig.frames:
-        _apply_barlike_style(frame.data, mixed_signs)
+    if mixed_signs:
+        # Split traces into positive/negative stackgroups for proper stacking
+        new_traces = _split_traces_by_sign(base_fig.data)
+        fig = go.Figure(data=new_traces, layout=base_fig.layout)
+
+        # Handle animation frames
+        new_frames = []
+        for frame in base_fig.frames:
+            frame_traces = _split_traces_by_sign(frame.data)
+            new_frames.append(go.Frame(data=frame_traces, name=frame.name))
+        fig.frames = new_frames
+    else:
+        # Simple case: just apply bar-like styling
+        fig = base_fig
+        _apply_barlike_style(fig.data)
+        for frame in fig.frames:
+            _apply_barlike_style(frame.data)
 
     return fig
 

From 127ff951e95f4f5f41a57750e0569baae46c44d0 Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 10:45:10 +0100
Subject: [PATCH 5/8] Done. The implementation is now much cleaner.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

  Summary:
  ┌─────────────────────┬─────────────────────────────────────────┐
  │     Trace type      │                Behavior                 │
  ├─────────────────────┼─────────────────────────────────────────┤
  │ All positive values │ stackgroup='positive' - stacks upward   │
  ├─────────────────────┼─────────────────────────────────────────┤
  │ All negative values │ stackgroup='negative' - stacks downward │
  ├─────────────────────┼─────────────────────────────────────────┤
  │ Mixed +/- values    │ stackgroup=None, dashed line, no fill   │
  └─────────────────────┴─────────────────────────────────────────┘
  Code is simpler:
  - _style_traces_as_bars() - single function that classifies and styles all traces
  - No trace splitting needed
  - Works correctly for facets and animations

  Notebook updated with examples showing:
  1. Split columns (Profit positive, Loss negative) → proper stacking
  2. Truly mixed columns → dashed lines indicating user should use bar()
---
 docs/examples/fast_bar.ipynb |  63 +++++++++++---
 tests/test_accessor.py       |  19 +++-
 xarray_plotly/plotting.py    | 164 ++++++++++++++++-------------------
 3 files changed, 140 insertions(+), 106 deletions(-)

diff --git a/docs/examples/fast_bar.ipynb b/docs/examples/fast_bar.ipynb
index 356ea3f..b345c89 100644
--- a/docs/examples/fast_bar.ipynb
+++ b/docs/examples/fast_bar.ipynb
@@ -219,7 +219,12 @@
    "source": [
     "## Mixed Values (Positive and Negative)\n",
     "\n",
-    "For mixed positive/negative data, `fast_bar()` automatically splits traces into separate stackgroups: positives stack upward from zero, negatives stack downward. This matches bar chart stacking behavior."
+    "`fast_bar()` classifies each trace (color group) by its values:\n",
+    "- **Purely positive** → stacks upward (stackgroup='positive')\n",
+    "- **Purely negative** → stacks downward (stackgroup='negative')  \n",
+    "- **Mixed signs** → shown as dashed line, no stacking\n",
+    "\n",
+    "When one column is all positive and another all negative, they stack correctly in separate groups:"
    ]
   },
   {
@@ -229,24 +234,58 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Mixed positive and negative values\n",
-    "da_mixed = xr.DataArray(\n",
+    "# Column A positive, Column B negative - stacks correctly\n",
+    "da_split = xr.DataArray(\n",
     "    np.array(\n",
     "        [\n",
-    "            [50, -30, 20],\n",
-    "            [-40, 60, -10],\n",
-    "            [30, -50, 40],\n",
-    "            [-20, 70, -30],\n",
-    "            [60, -40, 50],\n",
-    "            [-30, 80, -20],\n",
+    "            [50, -20],\n",
+    "            [60, -40],\n",
+    "            [30, -30],\n",
+    "            [70, -25],\n",
+    "            [40, -35],\n",
+    "            [55, -15],\n",
     "        ]\n",
     "    ),\n",
     "    dims=[\"month\", \"category\"],\n",
     "    coords={\n",
     "        \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"],\n",
-    "        \"category\": [\"A\", \"B\", \"C\"],\n",
+    "        \"category\": [\"Profit\", \"Loss\"],\n",
+    "    },\n",
+    "    name=\"financials\",\n",
+    ")\n",
+    "\n",
+    "xpx(da_split).fast_bar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "Truly mixed columns (each has both + and -) are shown as dashed lines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Truly mixed columns - shown as dashed lines (use bar() for these)\n",
+    "da_mixed = xr.DataArray(\n",
+    "    np.array(\n",
+    "        [\n",
+    "            [50, -30],\n",
+    "            [-40, 60],\n",
+    "            [30, -50],\n",
+    "        ]\n",
+    "    ),\n",
+    "    dims=[\"month\", \"category\"],\n",
+    "    coords={\n",
+    "        \"month\": [\"Jan\", \"Feb\", \"Mar\"],\n",
+    "        \"category\": [\"A\", \"B\"],\n",
     "    },\n",
-    "    name=\"profit_loss\",\n",
     ")\n",
     "\n",
     "xpx(da_mixed).fast_bar()"
@@ -255,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "17",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tests/test_accessor.py b/tests/test_accessor.py
index 4e71e96..a8f23d2 100644
--- a/tests/test_accessor.py
+++ b/tests/test_accessor.py
@@ -173,14 +173,25 @@ def test_fast_bar_animation_frames(self) -> None:
                 assert trace.line.shape == "hv"
                 assert trace.fillcolor is not None
 
-    def test_fast_bar_mixed_signs_separate_stacks(self) -> None:
-        """Test that fast_bar uses separate stackgroups for mixed positive/negative data."""
+    def test_fast_bar_mixed_signs_dashed(self) -> None:
+        """Test that fast_bar shows mixed-sign traces as dashed lines."""
         da = xr.DataArray(
-            np.array([[50, -30], [-40, 60]]),
+            np.array([[50, -30], [-40, 60]]),  # Both columns have mixed signs
+            dims=["time", "category"],
+        )
+        fig = da.plotly.fast_bar()
+        # Mixed traces should have no stacking and dashed lines
+        for trace in fig.data:
+            assert trace.stackgroup is None
+            assert trace.line.dash == "dash"
+
+    def test_fast_bar_separate_sign_columns(self) -> None:
+        """Test that fast_bar uses separate stackgroups when columns have different signs."""
+        da = xr.DataArray(
+            np.array([[50, -30], [60, -40]]),  # Column 0 positive, column 1 negative
             dims=["time", "category"],
         )
         fig = da.plotly.fast_bar()
-        # Should have separate positive and negative stackgroups
         stackgroups = {trace.stackgroup for trace in fig.data}
         assert "positive" in stackgroups
         assert "negative" in stackgroups
diff --git a/xarray_plotly/plotting.py b/xarray_plotly/plotting.py
index 9cf8bb1..7ea590a 100644
--- a/xarray_plotly/plotting.py
+++ b/xarray_plotly/plotting.py
@@ -167,74 +167,78 @@ def bar(
     )
 
 
-def _apply_barlike_style(traces: tuple) -> None:
-    """Apply bar-like styling to area traces (in-place)."""
-    for trace in traces:
-        color = trace.line.color
-        trace.fillcolor = color
-        trace.line = {"width": 0, "color": color, "shape": "hv"}
-
-
-def _has_mixed_signs(values: np.ndarray) -> bool:
-    """Check if data contains both positive and negative values."""
-    finite = values[np.isfinite(values)]
-    if len(finite) == 0:
-        return False
-    return bool(np.any(finite > 0) and np.any(finite < 0))
-
-
-def _split_traces_by_sign(traces: tuple) -> list:
-    """Split traces into separate positive and negative stackgroups.
-
-    For mixed positive/negative data, this creates proper bar-like stacking
-    where positives stack upward and negatives stack downward from zero.
+def _classify_trace_sign(y_values: np.ndarray) -> str:
+    """Classify a trace as 'positive', 'negative', or 'mixed' based on its values."""
+    y_arr = np.asarray(y_values)
+    y_clean = y_arr[np.isfinite(y_arr) & (np.abs(y_arr) > 1e-9)]
+    if len(y_clean) == 0:
+        return "zero"
+    has_pos = bool(np.any(y_clean > 0))
+    has_neg = bool(np.any(y_clean < 0))
+    if has_pos and has_neg:
+        return "mixed"
+    elif has_neg:
+        return "negative"
+    elif has_pos:
+        return "positive"
+    return "zero"
+
+
+def _style_traces_as_bars(fig: go.Figure) -> None:
+    """Style area chart traces to look like bar charts with proper pos/neg stacking.
+
+    Classifies each trace (by name) across all data and animation frames,
+    then assigns stackgroups: positive traces stack upward, negative stack downward.
     """
-    import plotly.graph_objects as go
+    # Collect all traces (main + animation frames)
+    all_traces = list(fig.data)
+    for frame in fig.frames:
+        all_traces.extend(frame.data)
+
+    # Classify each trace name by aggregating sign info across all occurrences
+    sign_flags: dict[str, dict[str, bool]] = {}
+    for trace in all_traces:
+        if trace.name not in sign_flags:
+            sign_flags[trace.name] = {"has_pos": False, "has_neg": False}
+        if trace.y is not None and len(trace.y) > 0:
+            y_arr = np.asarray(trace.y)
+            y_clean = y_arr[np.isfinite(y_arr) & (np.abs(y_arr) > 1e-9)]
+            if len(y_clean) > 0:
+                if np.any(y_clean > 0):
+                    sign_flags[trace.name]["has_pos"] = True
+                if np.any(y_clean < 0):
+                    sign_flags[trace.name]["has_neg"] = True
+
+    # Build classification map
+    class_map: dict[str, str] = {}
+    for name, flags in sign_flags.items():
+        if flags["has_pos"] and flags["has_neg"]:
+            class_map[name] = "mixed"
+        elif flags["has_neg"]:
+            class_map[name] = "negative"
+        elif flags["has_pos"]:
+            class_map[name] = "positive"
+        else:
+            class_map[name] = "zero"
 
-    new_traces = []
-    for trace in traces:
-        y_values = np.array(trace.y)
+    # Apply styling to all traces
+    for trace in all_traces:
         color = trace.line.color
-        name = trace.name
-
-        # Positive trace (negatives become 0)
-        y_pos = np.clip(y_values, 0, None)
-        if np.any(y_pos > 0):
-            new_traces.append(
-                go.Scatter(
-                    x=trace.x,
-                    y=y_pos,
-                    name=name,
-                    stackgroup="positive",
-                    line={"width": 0, "shape": "hv", "color": color},
-                    fillcolor=color,
-                    mode="lines",
-                    legendgroup=name,
-                    xaxis=trace.xaxis,
-                    yaxis=trace.yaxis,
-                )
-            )
-
-        # Negative trace (positives become 0)
-        y_neg = np.clip(y_values, None, 0)
-        if np.any(y_neg < 0):
-            new_traces.append(
-                go.Scatter(
-                    x=trace.x,
-                    y=y_neg,
-                    name=name,
-                    stackgroup="negative",
-                    line={"width": 0, "shape": "hv", "color": color},
-                    fillcolor=color,
-                    mode="lines",
-                    legendgroup=name,
-                    showlegend=False,
-                    xaxis=trace.xaxis,
-                    yaxis=trace.yaxis,
-                )
-            )
-
-    return new_traces
+        cls = class_map.get(trace.name, "positive")
+
+        if cls in ("positive", "negative"):
+            trace.stackgroup = cls
+            trace.fillcolor = color
+            trace.line = {"width": 0, "color": color, "shape": "hv"}
+        elif cls == "mixed":
+            # Mixed: no stacking, show as dashed line
+            trace.stackgroup = None
+            trace.fill = None
+            trace.line = {"width": 2, "color": color, "shape": "hv", "dash": "dash"}
+        else:  # zero
+            trace.stackgroup = None
+            trace.fill = None
+            trace.line = {"width": 0, "color": color, "shape": "hv"}
 
 
 def fast_bar(
@@ -257,8 +261,9 @@ def fast_bar(
     The y-axis shows DataArray values. Dimensions fill slots in order:
     x -> color -> facet_col -> facet_row -> animation_frame
 
-    For mixed positive/negative data, positives stack upward and negatives
-    stack downward from zero, similar to bar chart behavior.
+    Traces are classified by their values: purely positive traces stack upward,
+    purely negative traces stack downward. Traces with mixed signs are shown
+    as dashed lines without stacking.
 
     Parameters
     ----------
@@ -281,8 +286,6 @@ def fast_bar(
     -------
     plotly.graph_objects.Figure
     """
-    import plotly.graph_objects as go
-
     slots = assign_slots(
         list(darray.dims),
         "fast_bar",
@@ -293,14 +296,11 @@ def fast_bar(
         animation_frame=animation_frame,
     )
 
-    # Check for mixed positive/negative values
-    mixed_signs = _has_mixed_signs(darray.values)
-
     df = to_dataframe(darray)
     value_col = get_value_col(darray)
     labels = {**build_labels(darray, slots, value_col), **px_kwargs.pop("labels", {})}
 
-    base_fig = px.area(
+    fig = px.area(
         df,
         x=slots.get("x"),
         y=value_col,
@@ -313,23 +313,7 @@ def fast_bar(
         **px_kwargs,
     )
 
-    if mixed_signs:
-        # Split traces into positive/negative stackgroups for proper stacking
-        new_traces = _split_traces_by_sign(base_fig.data)
-        fig = go.Figure(data=new_traces, layout=base_fig.layout)
-
-        # Handle animation frames
-        new_frames = []
-        for frame in base_fig.frames:
-            frame_traces = _split_traces_by_sign(frame.data)
-            new_frames.append(go.Frame(data=frame_traces, name=frame.name))
-        fig.frames = new_frames
-    else:
-        # Simple case: just apply bar-like styling
-        fig = base_fig
-        _apply_barlike_style(fig.data)
-        for frame in fig.frames:
-            _apply_barlike_style(frame.data)
+    _style_traces_as_bars(fig)
 
     return fig
 

From 963f5a236789f6a72f4ee56acb16948fa3f0ab89 Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 10:55:28 +0100
Subject: [PATCH 6/8] Add warning for mixed columns

---
 xarray_plotly/plotting.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/xarray_plotly/plotting.py b/xarray_plotly/plotting.py
index 7ea590a..69acf6c 100644
--- a/xarray_plotly/plotting.py
+++ b/xarray_plotly/plotting.py
@@ -4,6 +4,7 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import TYPE_CHECKING, Any
 
 import numpy as np
@@ -211,9 +212,11 @@ def _style_traces_as_bars(fig: go.Figure) -> None:
 
     # Build classification map
     class_map: dict[str, str] = {}
+    mixed_traces: list[str] = []
     for name, flags in sign_flags.items():
         if flags["has_pos"] and flags["has_neg"]:
             class_map[name] = "mixed"
+            mixed_traces.append(name)
         elif flags["has_neg"]:
             class_map[name] = "negative"
         elif flags["has_pos"]:
@@ -221,6 +224,16 @@ def _style_traces_as_bars(fig: go.Figure) -> None:
         else:
             class_map[name] = "zero"
 
+    # Warn about mixed traces
+    if mixed_traces:
+        warnings.warn(
+            f"fast_bar: traces {mixed_traces} have mixed positive/negative values "
+            "and cannot be stacked. They are shown as dashed lines. "
+            "Consider using bar() for proper stacking of mixed data.",
+            UserWarning,
+            stacklevel=3,
+        )
+
     # Apply styling to all traces
     for trace in all_traces:
         color = trace.line.color

From fbd6e8de91e97ec71b9334b67d674859e50d655f Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 10:57:40 +0100
Subject: [PATCH 7/8] Improve notebook

---
 docs/examples/fast_bar.ipynb | 234 ++++++++++++++++-------------------
 1 file changed, 106 insertions(+), 128 deletions(-)

diff --git a/docs/examples/fast_bar.ipynb b/docs/examples/fast_bar.ipynb
index b345c89..0f4d2df 100644
--- a/docs/examples/fast_bar.ipynb
+++ b/docs/examples/fast_bar.ipynb
@@ -26,43 +26,34 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "id": "2",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Sample data\n",
-    "da = xr.DataArray(\n",
-    "    np.random.rand(12, 3) * 100,\n",
-    "    dims=[\"month\", \"category\"],\n",
-    "    coords={\n",
-    "        \"month\": [\n",
-    "            \"Jan\",\n",
-    "            \"Feb\",\n",
-    "            \"Mar\",\n",
-    "            \"Apr\",\n",
-    "            \"May\",\n",
-    "            \"Jun\",\n",
-    "            \"Jul\",\n",
-    "            \"Aug\",\n",
-    "            \"Sep\",\n",
-    "            \"Oct\",\n",
-    "            \"Nov\",\n",
-    "            \"Dec\",\n",
-    "        ],\n",
-    "        \"category\": [\"A\", \"B\", \"C\"],\n",
-    "    },\n",
-    "    name=\"sales\",\n",
-    ")"
+    "## Basic Example"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "3",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Fast Bar Chart"
+    "# Quarterly revenue data by product and region\n",
+    "np.random.seed(42)\n",
+    "da = xr.DataArray(\n",
+    "    np.random.rand(4, 3, 2) * 100 + 50,\n",
+    "    dims=[\"quarter\", \"product\", \"region\"],\n",
+    "    coords={\n",
+    "        \"quarter\": [\"Q1\", \"Q2\", \"Q3\", \"Q4\"],\n",
+    "        \"product\": [\"Widgets\", \"Gadgets\", \"Gizmos\"],\n",
+    "        \"region\": [\"North\", \"South\"],\n",
+    "    },\n",
+    "    name=\"revenue\",\n",
+    ")\n",
+    "\n",
+    "xpx(da).fast_bar()"
    ]
   },
   {
@@ -72,7 +63,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xpx(da).fast_bar()"
+    "# Comparison with regular bar()\n",
+    "xpx(da).bar()"
    ]
   },
   {
@@ -80,7 +72,7 @@
    "id": "5",
    "metadata": {},
    "source": [
-    "## Comparison with Regular Bar Chart"
+    "## With Faceting"
    ]
   },
   {
@@ -90,7 +82,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xpx(da).bar()"
+    "xpx(da).fast_bar(facet_col=\"region\")"
    ]
   },
   {
@@ -98,7 +90,7 @@
    "id": "7",
    "metadata": {},
    "source": [
-    "## Comparison with Area Chart"
+    "## With Animation"
    ]
   },
   {
@@ -108,7 +100,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xpx(da).area()"
+    "# Multi-year data for animation\n",
+    "np.random.seed(123)\n",
+    "da_anim = xr.DataArray(\n",
+    "    np.random.rand(4, 3, 5) * 100 + 20,\n",
+    "    dims=[\"quarter\", \"product\", \"year\"],\n",
+    "    coords={\n",
+    "        \"quarter\": [\"Q1\", \"Q2\", \"Q3\", \"Q4\"],\n",
+    "        \"product\": [\"Widgets\", \"Gadgets\", \"Gizmos\"],\n",
+    "        \"year\": [2020, 2021, 2022, 2023, 2024],\n",
+    "    },\n",
+    "    name=\"revenue\",\n",
+    ")\n",
+    "\n",
+    "xpx(da_anim).fast_bar(animation_frame=\"year\")"
    ]
   },
   {
@@ -116,9 +121,7 @@
    "id": "9",
    "metadata": {},
    "source": [
-    "## With Animation\n",
-    "\n",
-    "The `fast_bar` styling also applies to animation frames:"
+    "## Faceting + Animation"
    ]
   },
   {
@@ -128,32 +131,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Data with animation dimension\n",
-    "da_anim = xr.DataArray(\n",
-    "    np.random.rand(12, 3, 5) * 100,\n",
-    "    dims=[\"month\", \"category\", \"year\"],\n",
+    "# 4D data: quarter x product x region x year\n",
+    "np.random.seed(456)\n",
+    "da_4d = xr.DataArray(\n",
+    "    np.random.rand(4, 3, 2, 4) * 80 + 30,\n",
+    "    dims=[\"quarter\", \"product\", \"region\", \"year\"],\n",
     "    coords={\n",
-    "        \"month\": [\n",
-    "            \"Jan\",\n",
-    "            \"Feb\",\n",
-    "            \"Mar\",\n",
-    "            \"Apr\",\n",
-    "            \"May\",\n",
-    "            \"Jun\",\n",
-    "            \"Jul\",\n",
-    "            \"Aug\",\n",
-    "            \"Sep\",\n",
-    "            \"Oct\",\n",
-    "            \"Nov\",\n",
-    "            \"Dec\",\n",
-    "        ],\n",
-    "        \"category\": [\"A\", \"B\", \"C\"],\n",
-    "        \"year\": [2020, 2021, 2022, 2023, 2024],\n",
+    "        \"quarter\": [\"Q1\", \"Q2\", \"Q3\", \"Q4\"],\n",
+    "        \"product\": [\"Widgets\", \"Gadgets\", \"Gizmos\"],\n",
+    "        \"region\": [\"North\", \"South\"],\n",
+    "        \"year\": [2021, 2022, 2023, 2024],\n",
     "    },\n",
-    "    name=\"sales\",\n",
+    "    name=\"revenue\",\n",
     ")\n",
     "\n",
-    "xpx(da_anim).fast_bar(animation_frame=\"year\")"
+    "xpx(da_4d).fast_bar(facet_col=\"region\", animation_frame=\"year\")"
    ]
   },
   {
@@ -161,146 +153,132 @@
    "id": "11",
    "metadata": {},
    "source": [
-    "## When to Use\n",
-    "\n",
-    "| Method | Use when... |\n",
-    "|--------|-------------|\n",
-    "| `fast_bar()` | Large datasets, animations with many frames, performance matters |\n",
-    "| `bar()` | Need precise bar positioning, grouped bars, pattern fills |\n",
-    "| `area()` | Want smooth continuous fills, standard area chart appearance |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12",
-   "metadata": {},
-   "source": [
-    "## Negative Values\n",
+    "## Positive and Negative Values\n",
     "\n",
-    "Testing with all negative values:"
+    "`fast_bar()` classifies each trace by its values:\n",
+    "- **Purely positive** → stacks upward\n",
+    "- **Purely negative** → stacks downward\n",
+    "- **Mixed signs** → warning + dashed line (use `bar()` instead)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "13",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# All negative values\n",
-    "da_negative = xr.DataArray(\n",
-    "    -np.random.rand(6, 3) * 100,\n",
+    "# Profit (positive) and Loss (negative) - stacks correctly\n",
+    "np.random.seed(789)\n",
+    "da_split = xr.DataArray(\n",
+    "    np.column_stack(\n",
+    "        [\n",
+    "            np.random.rand(6) * 80 + 20,  # Revenue: positive\n",
+    "            -np.random.rand(6) * 50 - 10,  # Costs: negative\n",
+    "        ]\n",
+    "    ),\n",
     "    dims=[\"month\", \"category\"],\n",
     "    coords={\n",
     "        \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"],\n",
-    "        \"category\": [\"A\", \"B\", \"C\"],\n",
+    "        \"category\": [\"Revenue\", \"Costs\"],\n",
     "    },\n",
-    "    name=\"loss\",\n",
+    "    name=\"financials\",\n",
     ")\n",
     "\n",
-    "xpx(da_negative).fast_bar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Comparison: regular bar with negative values\n",
-    "xpx(da_negative).bar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "15",
-   "metadata": {},
-   "source": [
-    "## Mixed Values (Positive and Negative)\n",
-    "\n",
-    "`fast_bar()` classifies each trace (color group) by its values:\n",
-    "- **Purely positive** → stacks upward (stackgroup='positive')\n",
-    "- **Purely negative** → stacks downward (stackgroup='negative')  \n",
-    "- **Mixed signs** → shown as dashed line, no stacking\n",
-    "\n",
-    "When one column is all positive and another all negative, they stack correctly in separate groups:"
+    "xpx(da_split).fast_bar()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "16",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Column A positive, Column B negative - stacks correctly\n",
-    "da_split = xr.DataArray(\n",
-    "    np.array(\n",
+    "# With animation - sign classification is consistent across frames\n",
+    "np.random.seed(321)\n",
+    "da_split_anim = xr.DataArray(\n",
+    "    np.stack(\n",
     "        [\n",
-    "            [50, -20],\n",
-    "            [60, -40],\n",
-    "            [30, -30],\n",
-    "            [70, -25],\n",
-    "            [40, -35],\n",
-    "            [55, -15],\n",
-    "        ]\n",
+    "            np.column_stack([np.random.rand(6) * 80 + 20, -np.random.rand(6) * 50 - 10])\n",
+    "            for _ in range(4)\n",
+    "        ],\n",
+    "        axis=-1,\n",
     "    ),\n",
-    "    dims=[\"month\", \"category\"],\n",
+    "    dims=[\"month\", \"category\", \"year\"],\n",
     "    coords={\n",
     "        \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"],\n",
-    "        \"category\": [\"Profit\", \"Loss\"],\n",
+    "        \"category\": [\"Revenue\", \"Costs\"],\n",
+    "        \"year\": [2021, 2022, 2023, 2024],\n",
     "    },\n",
     "    name=\"financials\",\n",
     ")\n",
     "\n",
-    "xpx(da_split).fast_bar()"
+    "xpx(da_split_anim).fast_bar(animation_frame=\"year\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "17",
+   "id": "14",
    "metadata": {},
    "source": [
-    "Truly mixed columns (each has both + and -) are shown as dashed lines:"
+    "## Mixed Sign Warning\n",
+    "\n",
+    "When a trace has both positive and negative values, `fast_bar()` shows a warning and displays it as a dashed line:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Truly mixed columns - shown as dashed lines (use bar() for these)\n",
+    "# Both columns have mixed signs - triggers warning\n",
     "da_mixed = xr.DataArray(\n",
     "    np.array(\n",
     "        [\n",
     "            [50, -30],\n",
     "            [-40, 60],\n",
     "            [30, -50],\n",
+    "            [-20, 40],\n",
     "        ]\n",
     "    ),\n",
     "    dims=[\"month\", \"category\"],\n",
     "    coords={\n",
-    "        \"month\": [\"Jan\", \"Feb\", \"Mar\"],\n",
+    "        \"month\": [\"Jan\", \"Feb\", \"Mar\", \"Apr\"],\n",
     "        \"category\": [\"A\", \"B\"],\n",
     "    },\n",
     ")\n",
     "\n",
+    "# This will show a warning\n",
     "xpx(da_mixed).fast_bar()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19",
+   "id": "16",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Comparison: regular bar with mixed values\n",
+    "# For mixed data, use bar() instead\n",
     "xpx(da_mixed).bar()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## When to Use\n",
+    "\n",
+    "| Method | Use when... |\n",
+    "|--------|-------------|\n",
+    "| `fast_bar()` | Large datasets, animations, performance matters, data is same-sign per trace |\n",
+    "| `bar()` | Need grouped bars, pattern fills, or have mixed +/- values per trace |\n",
+    "| `area()` | Want smooth continuous fills |"
+   ]
   }
  ],
  "metadata": {

From 6fdb707a3988a26d7a31b17ae572f8d0e177de2e Mon Sep 17 00:00:00 2001
From: FBumann <117816358+FBumann@users.noreply.github.com>
Date: Thu, 22 Jan 2026 12:48:04 +0100
Subject: [PATCH 8/8] Ad fastbar to accessor.py

---
 xarray_plotly/accessor.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/xarray_plotly/accessor.py b/xarray_plotly/accessor.py
index 80b604c..41f60b4 100644
--- a/xarray_plotly/accessor.py
+++ b/xarray_plotly/accessor.py
@@ -34,7 +34,7 @@ class DataArrayPlotlyAccessor:
         ```
     """
 
-    __all__: ClassVar = ["line", "bar", "area", "scatter", "box", "imshow", "pie"]
+    __all__: ClassVar = ["line", "bar", "fast_bar", "area", "scatter", "box", "imshow", "pie"]
 
     def __init__(self, darray: DataArray) -> None:
         self._da = darray
@@ -384,7 +384,7 @@ class DatasetPlotlyAccessor:
         ```
     """
 
-    __all__: ClassVar = ["line", "bar", "area", "scatter", "box", "pie"]
+    __all__: ClassVar = ["line", "bar", "fast_bar", "area", "scatter", "box", "pie"]
 
     def __init__(self, dataset: Dataset) -> None:
         self._ds = dataset