diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..2e8b124
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,25 @@
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+    groups:
+      dev-dependencies:
+        patterns:
+          - "pytest*"
+          - "mypy"
+          - "ruff"
+          - "pre-commit"
+          - "nbstripout"
+      docs-dependencies:
+        patterns:
+          - "mkdocs*"
+          - "pooch"
+          - "netcdf4"
+          - "jupyter"
+
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"
diff --git a/.github/workflows/dependabot-auto-merge.yml b/.github/workflows/dependabot-auto-merge.yml
new file mode 100644
index 0000000..3a701d5
--- /dev/null
+++ b/.github/workflows/dependabot-auto-merge.yml
@@ -0,0 +1,24 @@
+name: Dependabot auto-merge
+on: pull_request
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  dependabot:
+    runs-on: ubuntu-latest
+    if: github.actor == 'dependabot[bot]'
+    steps:
+      - name: Dependabot metadata
+        id: metadata
+        uses: dependabot/fetch-metadata@v2
+        with:
+          github-token: "${{ secrets.GITHUB_TOKEN }}"
+
+      - name: Auto-merge minor and patch updates
+        if: steps.metadata.outputs.update-type == 'version-update:semver-patch' || steps.metadata.outputs.update-type == 'version-update:semver-minor'
+        run: gh pr merge --auto --squash "$PR_URL"
+        env:
+          PR_URL: ${{ github.event.pull_request.html_url }}
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/docs/examples/advanced.ipynb b/docs/examples/advanced.ipynb
index 1b23f46..71bda7c 100644
--- a/docs/examples/advanced.ipynb
+++ b/docs/examples/advanced.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Advanced Usage\n",
     "\n",
-    "This notebook covers advanced patterns and customization options."
+    "This notebook covers advanced Plotly customization. All kwargs are passed directly to [Plotly Express](https://plotly.com/python/plotly-express/), and figures can be modified using the full [Plotly Graph Objects API](https://plotly.com/python/graph-objects/)."
    ]
   },
   {
@@ -15,22 +15,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
     "import xarray as xr\n",
     "\n",
     "from xarray_plotly import config, xpx\n",
     "\n",
-    "config.notebook()  # Configure Plotly for notebook rendering"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Working with xarray Attributes\n",
-    "\n",
-    "xarray_plotly automatically uses metadata from xarray attributes for labels:"
+    "config.notebook()"
    ]
   },
   {
@@ -39,37 +30,37 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "da = xr.DataArray(\n",
-    "    np.random.randn(30, 3).cumsum(axis=0) + 15,\n",
-    "    dims=[\"time\", \"station\"],\n",
-    "    coords={\n",
-    "        \"time\": pd.date_range(\"2024-01-01\", periods=30, freq=\"D\"),\n",
-    "        \"station\": [\"Alpine\", \"Coastal\", \"Urban\"],\n",
-    "    },\n",
-    "    name=\"temperature\",\n",
-    "    attrs={\n",
-    "        \"long_name\": \"Air Temperature\",\n",
-    "        \"units\": \"°C\",\n",
-    "        \"standard_name\": \"air_temperature\",\n",
-    "    },\n",
-    ")\n",
+    "# Load sample data\n",
+    "df_stocks = px.data.stocks().set_index(\"date\")\n",
+    "df_stocks.index = df_stocks.index.astype(\"datetime64[ns]\")\n",
     "\n",
-    "# Add coordinate attributes\n",
-    "da.coords[\"time\"].attrs = {\"long_name\": \"Time\", \"units\": \"days\"}\n",
-    "da.coords[\"station\"].attrs = {\"long_name\": \"Measurement Station\"}\n",
+    "stocks = xr.DataArray(\n",
+    "    df_stocks.values,\n",
+    "    dims=[\"date\", \"company\"],\n",
+    "    coords={\"date\": df_stocks.index, \"company\": df_stocks.columns.tolist()},\n",
+    "    name=\"price\",\n",
+    ")\n",
     "\n",
-    "# Labels are automatically extracted from attrs\n",
-    "fig = xpx(da).line(title=\"Temperature with Auto-Labels\")\n",
-    "fig"
+    "df_gap = px.data.gapminder()\n",
+    "countries = [\"United States\", \"China\", \"Germany\", \"Brazil\", \"Nigeria\"]\n",
+    "df_life = df_gap[df_gap[\"country\"].isin(countries)].pivot(\n",
+    "    index=\"year\", columns=\"country\", values=\"lifeExp\"\n",
+    ")\n",
+    "life_exp = xr.DataArray(\n",
+    "    df_life.values,\n",
+    "    dims=[\"year\", \"country\"],\n",
+    "    coords={\"year\": df_life.index, \"country\": df_life.columns.tolist()},\n",
+    "    name=\"life_exp\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Configuring Label Behavior\n",
+    "## Passing Kwargs to Plotly Express\n",
     "\n",
-    "Use `config.set_options()` to control how labels are extracted from attributes:"
+    "All keyword arguments are passed directly to [Plotly Express functions](https://plotly.com/python-api-reference/plotly.express.html):"
    ]
   },
   {
@@ -78,72 +69,47 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Disable units in labels\n",
-    "with config.set_options(label_include_units=False):\n",
-    "    fig = xpx(da).line(title=\"Without Units in Labels\")\n",
+    "# All px.line kwargs work: template, labels, colors, etc.\n",
+    "fig = xpx(stocks).line(\n",
+    "    title=\"Stock Performance\",\n",
+    "    template=\"plotly_white\",\n",
+    "    labels={\"price\": \"Normalized Price\", \"date\": \"Date\", \"company\": \"Ticker\"},\n",
+    "    color_discrete_sequence=px.colors.qualitative.Set2,\n",
+    ")\n",
     "fig"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Overriding Labels\n",
-    "\n",
-    "You can override the automatic labels:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da).line(\n",
-    "    labels={\n",
-    "        \"temperature\": \"Temp (°C)\",\n",
-    "        \"time\": \"Date\",\n",
-    "        \"station\": \"Location\",\n",
-    "    },\n",
-    "    title=\"Custom Labels\",\n",
+    "# px.imshow kwargs: colorscale, midpoint, aspect\n",
+    "# See: https://plotly.com/python/imshow/\n",
+    "life_change = life_exp - life_exp.isel(year=0)\n",
+    "\n",
+    "fig = xpx(life_change).imshow(\n",
+    "    color_continuous_scale=\"RdBu\",\n",
+    "    color_continuous_midpoint=0,\n",
+    "    aspect=\"auto\",\n",
+    "    title=\"Life Expectancy Change Since 1952\",\n",
     ")\n",
     "fig"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Advanced Dimension Assignment\n",
-    "\n",
-    "### Complex Slot Assignments"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.random.seed(42)\n",
-    "\n",
-    "da_complex = xr.DataArray(\n",
-    "    np.random.randn(20, 3, 2, 2),\n",
-    "    dims=[\"time\", \"city\", \"scenario\", \"model\"],\n",
-    "    coords={\n",
-    "        \"time\": pd.date_range(\"2024-01-01\", periods=20),\n",
-    "        \"city\": [\"NYC\", \"LA\", \"Chicago\"],\n",
-    "        \"scenario\": [\"SSP2\", \"SSP5\"],\n",
-    "        \"model\": [\"GCM-A\", \"GCM-B\"],\n",
-    "    },\n",
-    "    name=\"projection\",\n",
-    ")\n",
-    "\n",
-    "# Use line_dash for one dimension, color for another\n",
-    "fig = xpx(da_complex.sel(city=\"NYC\")).line(\n",
-    "    color=\"scenario\",\n",
-    "    line_dash=\"model\",\n",
-    "    title=\"Multiple Visual Encodings\",\n",
+    "# px.bar kwargs: barmode, text_auto\n",
+    "# See: https://plotly.com/python/bar-charts/\n",
+    "fig = xpx(life_exp.sel(year=[1952, 1982, 2007])).bar(\n",
+    "    barmode=\"group\",\n",
+    "    text_auto=\".1f\",\n",
+    "    title=\"Life Expectancy\",\n",
     ")\n",
     "fig"
    ]
@@ -152,23 +118,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Reducing Dimensions Before Plotting\n",
+    "## Modifying Figures After Creation\n",
     "\n",
-    "When you have more dimensions than slots, reduce them first:"
+    "All methods return a Plotly `Figure`. Use the [Figure API](https://plotly.com/python/creating-and-updating-figures/) to customize:"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Average over model dimension\n",
-    "fig = xpx(da_complex.mean(\"model\")).line(\n",
-    "    facet_col=\"city\",\n",
-    "    title=\"Ensemble Mean by City\",\n",
-    ")\n",
-    "fig"
+    "### update_layout\n",
+    "\n",
+    "Modify [layout properties](https://plotly.com/python/reference/layout/): legend, margins, fonts, etc."
    ]
   },
   {
@@ -177,10 +138,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Select a specific slice\n",
-    "fig = xpx(da_complex.sel(scenario=\"SSP5\", model=\"GCM-A\")).line(\n",
-    "    facet_col=\"city\",\n",
-    "    title=\"SSP5 / GCM-A Projections\",\n",
+    "fig = xpx(stocks).line()\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title={\"text\": \"Stock Prices\", \"x\": 0.5, \"font\": {\"size\": 24}},\n",
+    "    legend={\n",
+    "        \"orientation\": \"h\",\n",
+    "        \"yanchor\": \"bottom\",\n",
+    "        \"y\": 1.02,\n",
+    "        \"xanchor\": \"center\",\n",
+    "        \"x\": 0.5,\n",
+    "    },\n",
+    "    margin={\"t\": 80},\n",
     ")\n",
     "fig"
    ]
@@ -189,14 +158,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Custom Styling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Themes"
+    "### update_traces\n",
+    "\n",
+    "Modify [trace properties](https://plotly.com/python/reference/): line width, markers, opacity, etc."
    ]
   },
   {
@@ -205,34 +169,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "da_simple = da.sel(station=\"Urban\")\n",
-    "\n",
-    "fig = xpx(da_simple).line(\n",
-    "    template=\"plotly_dark\",\n",
-    "    title=\"Dark Theme\",\n",
-    ")\n",
+    "fig = xpx(stocks).line()\n",
+    "fig.update_traces(line={\"width\": 3})\n",
+    "fig.update_layout(title=\"Thicker Lines\")\n",
     "fig"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Custom Colors"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import plotly.express as px\n",
-    "\n",
-    "fig = xpx(da).line(\n",
-    "    color_discrete_sequence=px.colors.qualitative.Set2,\n",
-    "    title=\"Custom Color Palette\",\n",
-    ")\n",
+    "fig = xpx(stocks).scatter()\n",
+    "fig.update_traces(marker={\"size\": 12, \"opacity\": 0.7, \"line\": {\"width\": 1, \"color\": \"white\"}})\n",
+    "fig.update_layout(title=\"Custom Markers\")\n",
     "fig"
    ]
   },
@@ -240,7 +191,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Heatmap Colorscales"
+    "### update_xaxes / update_yaxes\n",
+    "\n",
+    "Modify [axis properties](https://plotly.com/python/axes/): range slider, tick format, etc."
    ]
   },
   {
@@ -249,18 +202,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "da_2d = xr.DataArray(\n",
-    "    np.random.randn(20, 30),\n",
-    "    dims=[\"lat\", \"lon\"],\n",
-    "    name=\"anomaly\",\n",
-    ")\n",
-    "\n",
-    "# Diverging colorscale centered at zero\n",
-    "fig = xpx(da_2d).imshow(\n",
-    "    color_continuous_scale=\"RdBu_r\",\n",
-    "    color_continuous_midpoint=0,\n",
-    "    title=\"Diverging Colorscale\",\n",
-    ")\n",
+    "fig = xpx(stocks).line(title=\"With Range Slider\")\n",
+    "fig.update_xaxes(rangeslider_visible=True)\n",
+    "fig.update_yaxes(tickformat=\".0%\")\n",
     "fig"
    ]
   },
@@ -268,9 +212,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Post-Creation Customization\n",
+    "### Adding Shapes and Annotations\n",
     "\n",
-    "All plots return Plotly `Figure` objects that can be extensively customized:"
+    "Add reference lines, [shapes](https://plotly.com/python/shapes/), and [annotations](https://plotly.com/python/text-and-annotations/):"
    ]
   },
   {
@@ -279,23 +223,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da).line()\n",
+    "fig = xpx(stocks).line(title=\"With Reference Lines and Annotations\")\n",
     "\n",
-    "# Add horizontal reference line\n",
-    "fig.add_hline(y=15, line_dash=\"dash\", line_color=\"gray\", annotation_text=\"Reference\")\n",
+    "fig.add_hline(y=1.0, line_dash=\"dash\", line_color=\"gray\", annotation_text=\"Baseline\")\n",
+    "fig.add_vline(x=\"2018-10-01\", line_dash=\"dot\", line_color=\"red\")\n",
     "\n",
-    "# Update layout\n",
-    "fig.update_layout(\n",
-    "    title=\"Temperature with Reference Line\",\n",
-    "    legend={\n",
-    "        \"orientation\": \"h\",\n",
-    "        \"yanchor\": \"bottom\",\n",
-    "        \"y\": 1.02,\n",
-    "        \"xanchor\": \"right\",\n",
-    "        \"x\": 1,\n",
-    "    },\n",
+    "fig.add_annotation(\n",
+    "    x=\"2018-10-01\",\n",
+    "    y=1.4,\n",
+    "    text=\"Market correction\",\n",
+    "    showarrow=True,\n",
+    "    arrowhead=2,\n",
+    "    ax=40,\n",
+    "    ay=-40,\n",
     ")\n",
-    "\n",
     "fig"
    ]
   },
@@ -303,7 +244,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Modifying Traces"
+    "### Adding Traces with Graph Objects\n",
+    "\n",
+    "Add custom traces using [Plotly Graph Objects](https://plotly.com/python/graph-objects/):"
    ]
   },
   {
@@ -312,12 +255,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da).line()\n",
+    "fig = xpx(stocks.sel(company=\"GOOG\")).line(title=\"GOOG with Moving Average\")\n",
     "\n",
-    "# Make all lines thicker\n",
-    "fig.update_traces(line_width=3)\n",
+    "# Add moving average as a new trace\n",
+    "goog = stocks.sel(company=\"GOOG\")\n",
+    "ma_20 = goog.rolling(date=20, center=True).mean()\n",
     "\n",
-    "fig.update_layout(title=\"Thicker Lines\")\n",
+    "fig.add_trace(\n",
+    "    go.Scatter(\n",
+    "        x=ma_20.coords[\"date\"].values,\n",
+    "        y=ma_20.values,\n",
+    "        mode=\"lines\",\n",
+    "        name=\"20-day MA\",\n",
+    "        line={\"dash\": \"dash\", \"color\": \"red\", \"width\": 2},\n",
+    "    )\n",
+    ")\n",
     "fig"
    ]
   },
@@ -325,27 +277,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exporting Figures"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interactive HTML\n",
+    "## Exporting Figures\n",
     "\n",
-    "```python\n",
-    "fig.write_html(\"interactive_plot.html\")\n",
-    "```\n",
-    "\n",
-    "### Static Images\n",
-    "\n",
-    "Requires `kaleido`: `pip install kaleido`\n",
+    "See [static image export](https://plotly.com/python/static-image-export/) and [HTML export](https://plotly.com/python/interactive-html-export/).\n",
     "\n",
     "```python\n",
-    "fig.write_image(\"plot.png\", scale=2)  # High resolution\n",
-    "fig.write_image(\"plot.svg\")  # Vector format\n",
-    "fig.write_image(\"plot.pdf\")  # PDF\n",
+    "# Interactive HTML\n",
+    "fig.write_html(\"plot.html\")\n",
+    "\n",
+    "# Static images (requires: pip install kaleido)\n",
+    "fig.write_image(\"plot.png\", scale=2)\n",
+    "fig.write_image(\"plot.svg\")\n",
+    "fig.write_image(\"plot.pdf\")\n",
     "```"
    ]
   },
@@ -353,59 +296,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Integration Examples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### With xarray operations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Rolling mean\n",
-    "da_smooth = da.rolling(time=7, center=True).mean()\n",
+    "## More Resources\n",
     "\n",
-    "fig = xpx(da_smooth).line(\n",
-    "    title=\"7-Day Rolling Mean\",\n",
-    ")\n",
-    "fig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Groupby operations\n",
-    "da_monthly = xr.DataArray(\n",
-    "    np.random.randn(365, 3).cumsum(axis=0),\n",
-    "    dims=[\"time\", \"category\"],\n",
-    "    coords={\n",
-    "        \"time\": pd.date_range(\"2024-01-01\", periods=365),\n",
-    "        \"category\": [\"A\", \"B\", \"C\"],\n",
-    "    },\n",
-    "    name=\"value\",\n",
-    ")\n",
-    "\n",
-    "monthly_mean = da_monthly.groupby(\"time.month\").mean()\n",
-    "\n",
-    "fig = xpx(monthly_mean).line(\n",
-    "    title=\"Monthly Climatology\",\n",
-    ")\n",
-    "fig.update_xaxes(\n",
-    "    tickmode=\"array\",\n",
-    "    tickvals=list(range(1, 13)),\n",
-    "    ticktext=[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"],\n",
-    ")\n",
-    "fig"
+    "- [Plotly Express API](https://plotly.com/python-api-reference/plotly.express.html)\n",
+    "- [Figure Reference](https://plotly.com/python/reference/)\n",
+    "- [Plotly Tutorials](https://plotly.com/python/)"
    ]
   }
  ],
diff --git a/docs/examples/plot-types.ipynb b/docs/examples/plot-types.ipynb
index 94ab5b7..1f068a7 100644
--- a/docs/examples/plot-types.ipynb
+++ b/docs/examples/plot-types.ipynb
@@ -15,8 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
+    "import plotly.express as px\n",
     "import xarray as xr\n",
     "\n",
     "from xarray_plotly import config, xpx\n",
@@ -28,9 +27,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Sample Data\n",
+    "## Load Sample Data\n",
     "\n",
-    "Let's create some sample data to work with:"
+    "We'll use plotly's built-in datasets converted to xarray:\n",
+    "- **stocks**: Tech company stock prices over time\n",
+    "- **gapminder**: Country statistics (life expectancy, GDP, population) by year"
    ]
   },
   {
@@ -39,40 +40,62 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.random.seed(42)\n",
+    "# Stock prices: 2D (date, company)\n",
+    "df_stocks = px.data.stocks().set_index(\"date\")\n",
+    "df_stocks.index = df_stocks.index.astype(\"datetime64[ns]\")\n",
     "\n",
-    "# Time series data\n",
-    "da_ts = xr.DataArray(\n",
-    "    np.random.randn(30, 3).cumsum(axis=0),\n",
-    "    dims=[\"time\", \"category\"],\n",
-    "    coords={\n",
-    "        \"time\": pd.date_range(\"2024-01-01\", periods=30),\n",
-    "        \"category\": [\"A\", \"B\", \"C\"],\n",
-    "    },\n",
-    "    name=\"value\",\n",
+    "stocks = xr.DataArray(\n",
+    "    df_stocks.values,\n",
+    "    dims=[\"date\", \"company\"],\n",
+    "    coords={\"date\": df_stocks.index, \"company\": df_stocks.columns.tolist()},\n",
+    "    name=\"price\",\n",
+    ")\n",
+    "print(f\"stocks: {dict(stocks.sizes)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gapminder: pivot to create multi-dimensional arrays\n",
+    "df_gap = px.data.gapminder()\n",
+    "\n",
+    "# Life expectancy: 2D (year, country) - select a few countries\n",
+    "countries = [\"United States\", \"China\", \"Germany\", \"Brazil\", \"Nigeria\"]\n",
+    "df_life = df_gap[df_gap[\"country\"].isin(countries)].pivot(\n",
+    "    index=\"year\", columns=\"country\", values=\"lifeExp\"\n",
     ")\n",
     "\n",
-    "# 2D grid data\n",
-    "da_2d = xr.DataArray(\n",
-    "    np.random.rand(20, 30),\n",
-    "    dims=[\"lat\", \"lon\"],\n",
-    "    coords={\n",
-    "        \"lat\": np.linspace(-90, 90, 20),\n",
-    "        \"lon\": np.linspace(-180, 180, 30),\n",
-    "    },\n",
-    "    name=\"temperature\",\n",
+    "life_exp = xr.DataArray(\n",
+    "    df_life.values,\n",
+    "    dims=[\"year\", \"country\"],\n",
+    "    coords={\"year\": df_life.index, \"country\": df_life.columns.tolist()},\n",
+    "    name=\"life_expectancy\",\n",
+    "    attrs={\"units\": \"years\"},\n",
     ")\n",
+    "print(f\"life_exp: {dict(life_exp.sizes)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# GDP per capita by continent and year (aggregated)\n",
+    "df_continent = df_gap.groupby([\"continent\", \"year\"])[\"gdpPercap\"].mean().reset_index()\n",
+    "df_gdp = df_continent.pivot(index=\"year\", columns=\"continent\", values=\"gdpPercap\")\n",
     "\n",
-    "# Categorical data\n",
-    "da_cat = xr.DataArray(\n",
-    "    np.random.rand(4, 3) * 100,\n",
-    "    dims=[\"product\", \"region\"],\n",
-    "    coords={\n",
-    "        \"product\": [\"Widget\", \"Gadget\", \"Gizmo\", \"Thingamajig\"],\n",
-    "        \"region\": [\"North\", \"South\", \"West\"],\n",
-    "    },\n",
-    "    name=\"sales\",\n",
-    ")"
+    "gdp = xr.DataArray(\n",
+    "    df_gdp.values,\n",
+    "    dims=[\"year\", \"continent\"],\n",
+    "    coords={\"year\": df_gdp.index, \"continent\": df_gdp.columns.tolist()},\n",
+    "    name=\"gdp_per_capita\",\n",
+    "    attrs={\"units\": \"USD\"},\n",
+    ")\n",
+    "print(f\"gdp: {dict(gdp.sizes)}\")"
    ]
   },
   {
@@ -90,24 +113,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_ts).line(title=\"Line Plot\")\n",
+    "fig = xpx(stocks).line(title=\"Stock Prices Over Time\")\n",
     "fig"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### With markers"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_ts).line(markers=True, title=\"Line Plot with Markers\")\n",
+    "fig = xpx(life_exp).line(title=\"Life Expectancy by Country\", markers=True)\n",
     "fig"
    ]
   },
@@ -126,7 +142,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_cat).bar(title=\"Stacked Bar Chart\")\n",
+    "# GDP by continent for selected years\n",
+    "fig = xpx(gdp.sel(year=[1952, 1977, 2007])).bar(title=\"GDP per Capita by Continent\")\n",
     "fig"
    ]
   },
@@ -143,7 +160,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_cat).bar(barmode=\"group\", title=\"Grouped Bar Chart\")\n",
+    "fig = xpx(gdp.sel(year=[1952, 1977, 2007])).bar(barmode=\"group\", title=\"GDP per Capita (Grouped)\")\n",
     "fig"
    ]
   },
@@ -162,18 +179,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Use absolute values for stacking to make sense\n",
-    "da_positive = xr.DataArray(\n",
-    "    np.abs(np.random.randn(30, 3)) * 10,\n",
-    "    dims=[\"time\", \"source\"],\n",
-    "    coords={\n",
-    "        \"time\": pd.date_range(\"2024-01-01\", periods=30),\n",
-    "        \"source\": [\"Solar\", \"Wind\", \"Hydro\"],\n",
-    "    },\n",
-    "    name=\"energy\",\n",
+    "# Population by continent over time\n",
+    "df_pop = df_gap.groupby([\"continent\", \"year\"])[\"pop\"].sum().reset_index()\n",
+    "df_pop_pivot = df_pop.pivot(index=\"year\", columns=\"continent\", values=\"pop\")\n",
+    "\n",
+    "population = xr.DataArray(\n",
+    "    df_pop_pivot.values / 1e9,  # Convert to billions\n",
+    "    dims=[\"year\", \"continent\"],\n",
+    "    coords={\"year\": df_pop_pivot.index, \"continent\": df_pop_pivot.columns.tolist()},\n",
+    "    name=\"population\",\n",
+    "    attrs={\"units\": \"billions\"},\n",
     ")\n",
     "\n",
-    "fig = xpx(da_positive).area(title=\"Stacked Area Chart\")\n",
+    "fig = xpx(population).area(title=\"World Population by Continent\")\n",
     "fig"
    ]
   },
@@ -192,7 +210,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_ts).scatter(title=\"Scatter Plot\")\n",
+    "fig = xpx(stocks).scatter(title=\"Stock Prices Scatter\")\n",
     "fig"
    ]
   },
@@ -200,9 +218,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Dimension vs Dimension (Geographic style)\n",
+    "## Box Plot\n",
     "\n",
-    "You can plot one dimension against another, with values shown as color:"
+    "Best for showing distributions:"
    ]
   },
   {
@@ -211,34 +229,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_2d).scatter(x=\"lon\", y=\"lat\", color=\"value\", title=\"Lat/Lon Scatter\")\n",
+    "# Stock price distributions by company\n",
+    "fig = xpx(stocks).box(title=\"Stock Price Distributions\")\n",
     "fig"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Box Plot\n",
-    "\n",
-    "Best for showing distributions:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create data with more samples\n",
-    "da_dist = xr.DataArray(\n",
-    "    np.random.randn(100, 4) + np.array([0, 1, 2, 3]),\n",
-    "    dims=[\"sample\", \"group\"],\n",
-    "    coords={\"group\": [\"Control\", \"Treatment A\", \"Treatment B\", \"Treatment C\"]},\n",
-    "    name=\"response\",\n",
-    ")\n",
-    "\n",
-    "fig = xpx(da_dist).box(title=\"Box Plot\")\n",
+    "# Life expectancy distributions by country\n",
+    "fig = xpx(life_exp).box(title=\"Life Expectancy Distribution by Country\")\n",
     "fig"
    ]
   },
@@ -257,7 +260,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_2d).imshow(title=\"Heatmap\")\n",
+    "fig = xpx(life_exp).imshow(title=\"Life Expectancy Heatmap\")\n",
     "fig"
    ]
   },
@@ -274,9 +277,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da_2d).imshow(\n",
+    "fig = xpx(gdp).imshow(\n",
     "    color_continuous_scale=\"Viridis\",\n",
-    "    title=\"Heatmap with Viridis colorscale\",\n",
+    "    title=\"GDP per Capita by Continent and Year\",\n",
     ")\n",
     "fig"
    ]
@@ -296,22 +299,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# 3D data for faceting\n",
-    "da_3d = xr.DataArray(\n",
-    "    np.random.randn(30, 3, 2).cumsum(axis=0),\n",
-    "    dims=[\"time\", \"city\", \"scenario\"],\n",
-    "    coords={\n",
-    "        \"time\": pd.date_range(\"2024-01-01\", periods=30),\n",
-    "        \"city\": [\"NYC\", \"LA\", \"Chicago\"],\n",
-    "        \"scenario\": [\"Low\", \"High\"],\n",
-    "    },\n",
-    "    name=\"value\",\n",
+    "# Create 3D data: life expectancy by year, country, and metric\n",
+    "# We'll add GDP as another \"metric\" dimension\n",
+    "df_metrics = df_gap[df_gap[\"country\"].isin(countries)].pivot(\n",
+    "    index=\"year\", columns=\"country\", values=\"gdpPercap\"\n",
+    ")\n",
+    "gdp_countries = xr.DataArray(\n",
+    "    df_metrics.values,\n",
+    "    dims=[\"year\", \"country\"],\n",
+    "    coords={\"year\": df_metrics.index, \"country\": df_metrics.columns.tolist()},\n",
+    "    name=\"gdp_per_capita\",\n",
+    ")\n",
+    "\n",
+    "# Combine into 3D array\n",
+    "combined = xr.concat(\n",
+    "    [life_exp, gdp_countries / 1000],  # Scale GDP to thousands\n",
+    "    dim=xr.Variable(\"metric\", [\"Life Exp (years)\", \"GDP (thousands USD)\"]),\n",
     ")\n",
     "\n",
-    "fig = xpx(da_3d).line(\n",
-    "    facet_col=\"city\",\n",
-    "    facet_row=\"scenario\",\n",
-    "    title=\"Faceted Line Plot\",\n",
+    "fig = xpx(combined).line(\n",
+    "    facet_col=\"metric\",\n",
+    "    title=\"Country Comparison: Life Expectancy and GDP\",\n",
     ")\n",
     "fig"
    ]
@@ -331,35 +339,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create monthly data\n",
-    "da_monthly = xr.DataArray(\n",
-    "    np.random.rand(12, 4) * 100,\n",
-    "    dims=[\"month\", \"product\"],\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",
-    "        \"product\": [\"A\", \"B\", \"C\", \"D\"],\n",
-    "    },\n",
-    "    name=\"sales\",\n",
+    "# Animated bar chart of GDP by continent over time\n",
+    "fig = xpx(gdp).bar(\n",
+    "    x=\"continent\",\n",
+    "    animation_frame=\"year\",\n",
+    "    title=\"GDP per Capita by Continent (Animated)\",\n",
+    "    range_y=[0, 35000],\n",
     ")\n",
-    "\n",
-    "fig = xpx(da_monthly).bar(\n",
-    "    x=\"product\",\n",
-    "    animation_frame=\"month\",\n",
-    "    title=\"Monthly Sales (Animated)\",\n",
-    "    range_y=[0, 120],\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Animated line showing life expectancy evolution\n",
+    "fig = xpx(life_exp).bar(\n",
+    "    x=\"country\",\n",
+    "    animation_frame=\"year\",\n",
+    "    title=\"Life Expectancy by Country (Animated)\",\n",
+    "    range_y=[0, 85],\n",
     ")\n",
     "fig"
    ]
diff --git a/docs/getting-started.ipynb b/docs/getting-started.ipynb
index cd80907..11c0bfa 100644
--- a/docs/getting-started.ipynb
+++ b/docs/getting-started.ipynb
@@ -41,8 +41,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
+    "import plotly.express as px\n",
     "import xarray as xr\n",
     "\n",
     "from xarray_plotly import config, xpx\n",
@@ -54,9 +53,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create Sample Data\n",
+    "## Load Sample Data\n",
     "\n",
-    "Let's create a DataArray with multiple dimensions:"
+    "We'll use plotly's built-in stock price data and convert it to an xarray DataArray:"
    ]
   },
   {
@@ -65,22 +64,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create sample climate data\n",
-    "np.random.seed(42)\n",
-    "\n",
-    "da = xr.DataArray(\n",
-    "    np.random.randn(50, 3, 2).cumsum(axis=0),  # Random walk\n",
-    "    dims=[\"time\", \"city\", \"scenario\"],\n",
-    "    coords={\n",
-    "        \"time\": pd.date_range(\"2020-01-01\", periods=50, freq=\"D\"),\n",
-    "        \"city\": [\"New York\", \"Los Angeles\", \"Chicago\"],\n",
-    "        \"scenario\": [\"baseline\", \"warming\"],\n",
-    "    },\n",
-    "    name=\"temperature\",\n",
-    "    attrs={\"long_name\": \"Temperature Anomaly\", \"units\": \"°C\"},\n",
+    "# Load stock prices from plotly\n",
+    "df = px.data.stocks()\n",
+    "df = df.set_index(\"date\")\n",
+    "df.index = df.index.astype(\"datetime64[ns]\")\n",
+    "\n",
+    "# Convert to xarray DataArray\n",
+    "stocks = xr.DataArray(\n",
+    "    df.values,\n",
+    "    dims=[\"date\", \"company\"],\n",
+    "    coords={\"date\": df.index, \"company\": df.columns.tolist()},\n",
+    "    name=\"price\",\n",
+    "    attrs={\"long_name\": \"Stock Price\", \"units\": \"normalized\"},\n",
     ")\n",
-    "\n",
-    "da.to_dataframe()"
+    "stocks"
    ]
   },
   {
@@ -98,8 +95,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Dimensions auto-assign: time→x, city→color, scenario→facet_col\n",
-    "fig = xpx(da).line()\n",
+    "# Dimensions auto-assign: date->x, company->color\n",
+    "fig = xpx(stocks).line()\n",
     "fig"
    ]
   },
@@ -124,9 +121,8 @@
     "\n",
     "| Dimension | Slot |\n",
     "|-----------|------|\n",
-    "| time (1st) | x-axis |\n",
-    "| city (2nd) | color |\n",
-    "| scenario (3rd) | facet_col |\n",
+    "| date (1st) | x-axis |\n",
+    "| company (2nd) | color |\n",
     "\n",
     "You can override this with explicit assignments:"
    ]
@@ -137,8 +133,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Put scenario on color, city on facets\n",
-    "fig = xpx(da).line(color=\"scenario\", facet_col=\"city\")\n",
+    "# Put company on x-axis, date on color (just first few dates)\n",
+    "fig = xpx(stocks.isel(date=[0, 25, 50, 75, 100])).bar(x=\"company\", color=\"date\")\n",
     "fig"
    ]
   },
@@ -157,8 +153,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Skip color, so city goes to line_dash instead\n",
-    "fig = xpx(da.sel(scenario=\"baseline\")).line(color=None)\n",
+    "# Skip color, so company goes to line_dash instead\n",
+    "fig = xpx(stocks.sel(company=[\"GOOG\", \"AAPL\", \"MSFT\"])).line(color=None)\n",
     "fig"
    ]
   },
@@ -177,12 +173,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da).line()\n",
+    "fig = xpx(stocks).line()\n",
     "\n",
     "fig.update_layout(\n",
-    "    title=\"Temperature Anomaly Projections\",\n",
+    "    title=\"Tech Stock Performance (2018-2019)\",\n",
     "    template=\"plotly_white\",\n",
-    "    legend_title_text=\"City\",\n",
+    "    legend_title_text=\"Company\",\n",
     ")\n",
     "\n",
     "fig"
@@ -201,9 +197,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig = xpx(da).line(\n",
-    "    title=\"Temperature Trends\",\n",
-    "    color_discrete_sequence=[\"#E63946\", \"#457B9D\", \"#2A9D8F\"],\n",
+    "fig = xpx(stocks).line(\n",
+    "    title=\"Stock Prices\",\n",
+    "    color_discrete_sequence=[\"#E63946\", \"#457B9D\", \"#2A9D8F\", \"#E9C46A\", \"#F4A261\", \"#264653\"],\n",
     "    template=\"simple_white\",\n",
     ")\n",
     "fig"
diff --git a/pyproject.toml b/pyproject.toml
index 5f7f927..145b399 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,19 +32,20 @@ Repository = "https://github.com/FBumann/xarray_plotly"
 
 [project.optional-dependencies]
 dev = [
-    "pytest>=7.0",
-    "pytest-cov>=4.0",
-    "mypy>=1.0",
-    "ruff>=0.4",
-    "pre-commit>=3.0",
-    "nbstripout>=0.6",
+    "pytest==8.3.5",
+    "pytest-cov==6.0.0",
+    "mypy==1.14.1",
+    "ruff==0.9.2",
+    "pre-commit==4.0.1",
+    "nbstripout==0.8.1",
 ]
 docs = [
-    "mkdocs>=1.5",
-    "mkdocs-material>=9.0",
-    "mkdocstrings[python]>=0.24",
-    "mkdocs-jupyter>=0.24",
-    "mkdocs-plotly-plugin>=0.1",
+    "mkdocs==1.6.1",
+    "mkdocs-material==9.5.49",
+    "mkdocstrings[python]==0.27.0",
+    "mkdocs-jupyter==0.25.1",
+    "mkdocs-plotly-plugin==0.1.3",
+    "jupyter==1.1.1",
 ]
 
 [project.entry-points."xarray.backends"]