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Improve notebook
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‎docs/examples/fast_bar.ipynb‎

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

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