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diff --git a/doc/explanation/index.md b/doc/explanation/index.md
new file mode 100644
index 000000000..5af3e7348
--- /dev/null
+++ b/doc/explanation/index.md
@@ -0,0 +1,10 @@
+# Explanation
+
+Explanation guides provide in-depth understanding of key concepts in hvPlot. These guides help you understand the reasoning behind design decisions and when to use different approaches.
+
+```{toctree}
+:titlesonly:
+:hidden:
+:maxdepth: 2
+
+```
diff --git a/doc/how_to/display_plots.ipynb b/doc/how_to/display_plots.ipynb
new file mode 100644
index 000000000..c81ff63a4
--- /dev/null
+++ b/doc/how_to/display_plots.ipynb
@@ -0,0 +1,499 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f75f8cba",
+   "metadata": {},
+   "source": [
+    "# How to Display Plots in Different Environments\n",
+    "\n",
+    "This guide shows you how to display hvPlot visualizations in various environments: Jupyter notebooks, Python scripts, and interactive command prompts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6b83489",
+   "metadata": {},
+   "source": [
+    "## Jupyter Notebook Environment\n",
+    "\n",
+    "In Jupyter notebooks, hvPlot displays plots automatically when you create them. The easiest way to get started is to import the plotting extensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "718036cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas # noqa\n",
+    "\n",
+    "hvplot.extension('bokeh', 'matplotlib')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f177e50e",
+   "metadata": {},
+   "source": [
+    "Now let's load some sample datasets and create plots that will display automatically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc508d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguins = hvplot.sampledata.penguins(\"pandas\").dropna()\n",
+    "large_data = hvplot.sampledata.synthetic_clusters(\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "316188d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create individual plots\n",
+    "bill_length_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    by='species',\n",
+    "    width=400,\n",
+    "    height=300,\n",
+    "    title=\"Bill Length vs Depth by Species\",\n",
+    "    alpha=0.7\n",
+    ")\n",
+    "\n",
+    "body_mass_plot = penguins.hvplot.hist(\n",
+    "    'body_mass_g',\n",
+    "    by='species',\n",
+    "    width=400,\n",
+    "    height=300,\n",
+    "    title=\"Body Mass Distribution by Species\",\n",
+    "    alpha=0.7\n",
+    ")\n",
+    "\n",
+    "# Display individual plots\n",
+    "bill_length_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9dc8ec42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "body_mass_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8223d3f7",
+   "metadata": {},
+   "source": [
+    "### Creating Layouts in Notebooks\n",
+    "\n",
+    "You can combine plots into layouts using the `+` (side-by-side) and `*` (overlay) operators:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cf593db-acff-4f1e-b21a-66fcdc3d5b45",
+   "metadata": {},
+   "source": [
+    "### Side-by-side layout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd73cd88-963d-4259-9d7b-1a940e1f4453",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "side_by_side = bill_length_plot + body_mass_plot\n",
+    "side_by_side"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97d2955c-6ee4-427b-8ea7-cd815c5ca86d",
+   "metadata": {},
+   "source": [
+    "### Overlays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc51a68c-9f55-47b1-9810-5d561f478db9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stocks = hvplot.sampledata.stocks(\"pandas\")\n",
+    "\n",
+    "tech_stocks_1 = stocks.hvplot.line(\n",
+    "    x='date',\n",
+    "    y=['Apple', 'Google'],\n",
+    "    title=\"Tech Stock Comparison (Normalized Prices)\",\n",
+    "    width=600,\n",
+    "    height=400,\n",
+    "    alpha=0.8\n",
+    ")\n",
+    "\n",
+    "tech_stocks_2 = stocks.hvplot.line(\n",
+    "    x='date',\n",
+    "    y=['Microsoft', 'Amazon'],\n",
+    "    width=600,\n",
+    "    height=400,\n",
+    "    alpha=0.8\n",
+    ")\n",
+    "\n",
+    "overlay = tech_stocks_1 * tech_stocks_2\n",
+    "\n",
+    "overlay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b9c6ad0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_scatter = large_data.hvplot.scatter(\n",
+    "    x='x',\n",
+    "    y='y',\n",
+    "    by='cat',\n",
+    "    datashade=True,\n",
+    "    alpha=0.6,\n",
+    "    width=400,\n",
+    "    height=300,\n",
+    "    title=\"Synthetic Clusters\"\n",
+    ")\n",
+    "\n",
+    "cluster_hist = large_data.hvplot.hist(\n",
+    "    'x',\n",
+    "    bins=30,\n",
+    "    width=400,\n",
+    "    height=300,\n",
+    "    title=\"X Distribution in Clusters\"\n",
+    ")\n",
+    "\n",
+    "# Create a 2x2 layout\n",
+    "layout = (bill_length_plot +\n",
+    "          body_mass_plot +\n",
+    "          cluster_scatter +\n",
+    "          cluster_hist).opts(shared_axes=False).cols(2)\n",
+    "layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da43b41f",
+   "metadata": {},
+   "source": [
+    "## Python Scripts and Command Line\n",
+    "\n",
+    "When working outside Jupyter notebooks, plots won't display automatically. You need to use the `hvplot.show()` function to open plots in a browser window."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8870874c",
+   "metadata": {},
+   "source": [
+    "### Interactive Python Command Prompt\n",
+    "\n",
+    "When working in an interactive Python session (IPython, Python REPL), using `hvplot.show()` in the ipython environment automatically opens a bokeh server, displaying with plot with all the interactive features enabled:\n",
+    "\n",
+    "<img src=\"../assets/combined_penguin.png\" style=\"display: table; margin: 0 auto;\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a7cfd6a",
+   "metadata": {},
+   "source": [
+    "### Scripts\n",
+    "\n",
+    "When using python scripts like in vscode or other IDEs, the display behavior is also identical, as it also opens up a bokeh server:\n",
+    "\n",
+    "<img src=\"../assets/vscode_penguin.png\" style=\"display: table; margin: 0 auto;\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1cb758a",
+   "metadata": {},
+   "source": [
+    "## Environment-Specific Tips\n",
+    "\n",
+    "### Jupyter Lab vs Jupyter Notebook\n",
+    "\n",
+    "hvPlot works seamlessly in both Jupyter Lab and Jupyter Notebook. The display behavior is identical:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73048fd3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguin_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    by='species',\n",
+    "    title=\"Penguin Bill Dimensions\",\n",
+    "    alpha=0.7\n",
+    ")\n",
+    "\n",
+    "penguin_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f3282ca",
+   "metadata": {},
+   "source": [
+    "### VS Code with Jupyter Extension\n",
+    "\n",
+    "When using VS Code with the Jupyter extension, hvPlot plots display inline just like in traditional Jupyter environments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49258fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vscode_plot = penguins.hvplot.violin(\n",
+    "    y='body_mass_g',\n",
+    "    by='species',\n",
+    "    title=\"Penguin Body Mass Distribution by Species\",\n",
+    ")\n",
+    "\n",
+    "vscode_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e0fc4a3",
+   "metadata": {},
+   "source": [
+    "### Google Colab\n",
+    "\n",
+    "In Google Colab, you may need to enable the Bokeh extension explicitly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deabc63b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hvplot.extension('bokeh')  # Explicitly enable Bokeh\n",
+    "\n",
+    "colab_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    by='species',\n",
+    "    title=\"Penguin Measurements (Colab Compatible)\",\n",
+    "    alpha=0.7\n",
+    ")\n",
+    "\n",
+    "colab_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "686792dd",
+   "metadata": {},
+   "source": [
+    "### Terminal/SSH Environments\n",
+    "\n",
+    "When working on remote servers or in terminal-only environments, you have several options:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "823e3f5d-2196-4eec-9f4f-ad3ba5572247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 1: Save plots to files instead of displaying\n",
+    "terminal_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    by='species',\n",
+    "    title=\"Penguin Bill Dimensions (for Terminal Environment)\",\n",
+    "    alpha=0.7\n",
+    ")\n",
+    "\n",
+    "hvplot.save(terminal_plot, 'terminal_plot.html')\n",
+    "print(\"Plot saved as 'terminal_plot.html'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0aad103-32df-4856-ac57-281c5db385d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 2: Use matplotlib backend for direct PNG export\n",
+    "hvplot.output(backend='matplotlib')\n",
+    "terminal_plot_mpl = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    by='species',\n",
+    "    title=\"Penguin Bill Dimensions (Matplotlib)\",\n",
+    ")\n",
+    "\n",
+    "hvplot.save(terminal_plot_mpl, 'terminal_plot.png')\n",
+    "print(\"Plot saved as 'terminal_plot.png'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d8dea3d",
+   "metadata": {},
+   "source": [
+    "## Troubleshooting Display Issues\n",
+    "\n",
+    "### Plots Not Displaying in Notebooks\n",
+    "\n",
+    "If plots aren't showing in your notebook, try these solutions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb8e1d53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution 1: Explicitly enable the extension\n",
+    "hvplot.extension('bokeh')\n",
+    "\n",
+    "# Solution 2: Check if the import worked\n",
+    "import hvplot.pandas # noqa\n",
+    "print(\"hvplot.pandas imported successfully\")\n",
+    "\n",
+    "# Solution 3: Create a test plot\n",
+    "test_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    color='species',\n",
+    "    title=\"Test Plot\"\n",
+    ")\n",
+    "\n",
+    "test_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a34f3ed2",
+   "metadata": {},
+   "source": [
+    "### Browser Issues with hvplot.show()\n",
+    "\n",
+    "If `hvplot.show()` isn't opening your browser, you can save the plot as a html file and open it manually:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "672d941a-0857-4ae5-9f11-41c883b4b8f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backup_plot = penguins.hvplot.scatter(x='bill_length_mm', y='flipper_length_mm', by='species')\n",
+    "hvplot.save(backup_plot, 'backup_plot.html')\n",
+    "print(\"Manually open 'backup_plot.html'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd040662",
+   "metadata": {},
+   "source": [
+    "## Cleanup\n",
+    "\n",
+    "Let's clean up the files we created:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4fa37ee2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "files_to_clean = [\n",
+    "    'terminal_plot.html',\n",
+    "    'terminal_plot.png',\n",
+    "    'backup_plot.html'\n",
+    "]\n",
+    "\n",
+    "for filename in files_to_clean:\n",
+    "    file_path = Path(filename)\n",
+    "    if file_path.exists():\n",
+    "        file_path.unlink()\n",
+    "        print(f\"Removed {filename}\")\n",
+    "    else:\n",
+    "        print(f\"{filename} not found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "005c4d00",
+   "metadata": {},
+   "source": [
+    ":::{admonition} Summary\n",
+    "\n",
+    "Here's a quick reference for displaying plots in different environments:\n",
+    "\n",
+    "| Environment | How to Display | Notes |\n",
+    "|-------------|----------------|-------|\n",
+    "| **Jupyter Notebook/Lab** | Automatic (just create the plot) | Import `hvplot.pandas` or similar |\n",
+    "| **VS Code + Jupyter** | Automatic (just create the plot) | Same as Jupyter |\n",
+    "| **Python Scripts** | `hvplot.show(plot)` | Opens in browser |\n",
+    "| **Interactive Python** | `hvplot.show(plot)` | Opens in browser |\n",
+    "| **Google Colab** | Automatic (may need `hvplot.extension('bokeh')`) | Import hvplot first |\n",
+    "| **Terminal/SSH** | Save to file: `hvplot.save(plot, 'file.html')` | Transfer file to view locally |\n",
+    "| **Offline/Airgapped** | Use matplotlib backend or save with `INLINE` resources | No internet required |\n",
+    ":::\n",
+    "\n",
+    ":::{admonition} Next Steps\n",
+    ":class: seealso\n",
+    "\n",
+    "- Learn how to:\n",
+    "  - [Save Bokeh plots](save_bokeh_plots.ipynb)\n",
+    "  - [Save Matplotlib plots](save_matplotlib_plots.ipynb)\n",
+    "- Explore [using Panel for advanced layouts and interactivity](use_panel_for_display.ipynb)\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/how_to/index.md b/doc/how_to/index.md
new file mode 100644
index 000000000..658c49de4
--- /dev/null
+++ b/doc/how_to/index.md
@@ -0,0 +1,14 @@
+# How-To Guides
+
+How-to guides are practical, problem-oriented instructions that help you accomplish specific tasks with hvPlot. These guides assume you're already familiar with the basics and want to solve particular problems or achieve specific goals.
+
+```{toctree}
+:titlesonly:
+:hidden:
+:maxdepth: 2
+
+display_plots
+save_bokeh_plots
+save_matplotlib_plots
+use_panel_for_display
+```
diff --git a/doc/how_to/save_bokeh_plots.ipynb b/doc/how_to/save_bokeh_plots.ipynb
new file mode 100644
index 000000000..5507dd447
--- /dev/null
+++ b/doc/how_to/save_bokeh_plots.ipynb
@@ -0,0 +1,438 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d3667843",
+   "metadata": {},
+   "source": [
+    "# How to Save Interactive Bokeh Plots\n",
+    "\n",
+    "This guide shows you how to export hvPlot visualizations using the Bokeh backend to various file formats, with a focus on interactive HTML exports and static PNG images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cd5a9f8",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's set up hvPlot with the default Bokeh backend and create a sample plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9979426d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas # noqa\n",
+    "\n",
+    "penguins = hvplot.sampledata.penguins(\"pandas\").dropna()\n",
+    "\n",
+    "plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    color='species',\n",
+    "    title=\"Penguin Bill Dimensions\"\n",
+    ")\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cc859c2",
+   "metadata": {},
+   "source": [
+    "## HTML Export (Recommended)\n",
+    "\n",
+    "The primary strength of Bokeh plots is their interactivity. HTML export preserves all interactive features like zooming, panning, and hover tooltips:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebd7e190",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.save(plot, 'penguins_bokeh.html')\n",
+    "print(\"Interactive HTML plot saved as 'penguins_bokeh.html'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e41cd8e0",
+   "metadata": {},
+   "source": [
+    "## HTML with Inline Resources\n",
+    "\n",
+    "By default, HTML files depend on loading JavaScript from online CDN repositories. For offline or airgapped environments, use inline resources:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e50cecd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.save(plot, 'penguins_offline.html', resources='inline')\n",
+    "print(\"Self-contained HTML plot saved as 'penguins_offline.html'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03308dda",
+   "metadata": {},
+   "source": [
+    "## PNG Export\n",
+    "\n",
+    "For static images, Bokeh can export to PNG, but this requires additional browser driver dependencies."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e9d4450",
+   "metadata": {},
+   "source": [
+    "### Installing PNG Export Dependencies\n",
+    "\n",
+    "Choose one of these options:\n",
+    "\n",
+    "**Option 1: Chrome/Chromium (Recommended)**\n",
+    "\n",
+    "Install Chrome or Chromium browser first, then install the Python package.\n",
+    "\n",
+    "```bash\n",
+    "pip install selenium chromedriver-autoinstaller\n",
+    "```\n",
+    "\n",
+    "**Option 2: Firefox**\n",
+    "\n",
+    "Install Firefox browser first, then install geckodriver.\n",
+    "```bash\n",
+    "pip install selenium\n",
+    "```\n",
+    "Download geckodriver from the official [GitHub releases page](https://github.com/mozilla/geckodriver), then add geckodriver to your PATH\n",
+    "\n",
+    "**Option 3: Using conda**\n",
+    "```bash\n",
+    "conda install selenium\n",
+    "```\n",
+    "\n",
+    "\n",
+    ":::{tip}\n",
+    "For help adding geckodriver to your PATH:\n",
+    "- **Windows:**\n",
+    "  - Place `geckodriver.exe` in a folder (e.g., `C:\\\\tools\\\\geckodriver`)\n",
+    "  - Add that folder to your System PATH:\n",
+    "    - _System Properties_ > _Environment Variables_.\n",
+    "    - Edit the Path variable and add the folder path.\n",
+    "- **macOS/Linux:**\n",
+    "  - Move the `geckodriver` binary to `/usr/local/bin` or another directory already in your PATH.\n",
+    "  - Alternatively, update your PATH manually in your shell config (e.g., `export PATH=\"$PATH:/path/to/geckodriver\"`)\n",
+    "\n",
+    "If unsure, consult your operating system documentation for instructions on modifying the PATH environment variable.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1c82dc",
+   "metadata": {},
+   "source": [
+    "### Saving PNG Files\n",
+    "\n",
+    "Once dependencies are installed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d9a4667",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    hvplot.save(plot, 'penguins_bokeh.png')\n",
+    "    print(\"PNG saved successfully as 'penguins_bokeh.png'\")\n",
+    "except Exception as e:\n",
+    "    print(f\"PNG export failed: {e}\")\n",
+    "    print(\"Make sure you have selenium and a browser driver installed.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e869e235",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "This will only work if you have selenium and a browser driver installed\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e36e5455",
+   "metadata": {},
+   "source": [
+    "### High-Resolution PNG Export\n",
+    "\n",
+    "For presentations or print, you may want higher resolution images:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82961f10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "high_res_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    width=1200,\n",
+    "    height=800,\n",
+    "    color='species',\n",
+    "    title=\"High-Resolution Penguin Plot\"\n",
+    ")\n",
+    "\n",
+    "high_res_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5162834",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    hvplot.save(high_res_plot, 'penguins_high_res.png')\n",
+    "    print(\"High-resolution PNG saved\")\n",
+    "except Exception as e:\n",
+    "    print(f\"PNG export failed: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86012fc5",
+   "metadata": {},
+   "source": [
+    "## Batch Saving Multiple Plots\n",
+    "\n",
+    "You can efficiently save multiple interactive plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88b92170",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plots = {\n",
+    "    'scatter': penguins.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', color='species', width=400),\n",
+    "    'histogram': penguins.hvplot.hist('body_mass_g', by='species', alpha=0.7, width=400),\n",
+    "    'box': penguins.hvplot.box(y='flipper_length_mm', by='species', width=400),\n",
+    "}\n",
+    "\n",
+    "# Display the plots\n",
+    "(plots['scatter'] + plots['histogram'] + plots['box']).cols(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb0f717b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name, plot in plots.items():\n",
+    "    # Save interactive HTML\n",
+    "    hvplot.save(plot, f'{name}_bokeh.html')\n",
+    "    print(f\"Saved {name}_bokeh.html\")\n",
+    "\n",
+    "    # Try PNG export\n",
+    "    try:\n",
+    "        hvplot.save(plot, f'{name}_bokeh.png')\n",
+    "        print(f\"Saved {name}_bokeh.png\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"PNG export failed for {name}: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60f34a01",
+   "metadata": {},
+   "source": [
+    "## Testing PNG Export Setup\n",
+    "\n",
+    "Here's how to test if your PNG export setup is working:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2d327f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test if selenium can find your browser\n",
+    "try:\n",
+    "    from selenium import webdriver\n",
+    "    from selenium.webdriver.chrome.options import Options\n",
+    "\n",
+    "    # Test Chrome\n",
+    "    chrome_options = Options()\n",
+    "    chrome_options.add_argument(\"--headless\")  # Run in background\n",
+    "    driver = webdriver.Chrome(options=chrome_options)\n",
+    "    driver.quit()\n",
+    "    print(\"✓ Chrome driver is working\")\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(f\"✗ Chrome driver issue: {e}\")\n",
+    "\n",
+    "    # Try Firefox as fallback\n",
+    "    try:\n",
+    "        from selenium.webdriver.firefox.options import Options as FirefoxOptions\n",
+    "        firefox_options = FirefoxOptions()\n",
+    "        firefox_options.add_argument(\"--headless\")\n",
+    "        driver = webdriver.Firefox(options=firefox_options)\n",
+    "        driver.quit()\n",
+    "        print(\"✓ Firefox driver is working\")\n",
+    "    except Exception as e2:\n",
+    "        print(f\"✗ Firefox driver issue: {e2}\")\n",
+    "        print(\"Consider installing browser drivers or using matplotlib backend for PNG export\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44dbf404",
+   "metadata": {},
+   "source": [
+    "## Working with Large Datasets\n",
+    "\n",
+    "For very large datasets, consider using datashader for efficient rendering:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6aca1b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "large_data = hvplot.sampledata.synthetic_clusters(\"pandas\")\n",
+    "print(f\"Dataset size: {len(large_data):,} points\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14276034",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datashaded_plot = large_data.hvplot.scatter(\n",
+    "    x='x',\n",
+    "    y='y',\n",
+    "    by='cat',\n",
+    "    datashade=True,\n",
+    "    width=400,\n",
+    "    height=400,\n",
+    "    title=\"Large Dataset with Datashader\"\n",
+    ")\n",
+    "\n",
+    "datashaded_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcad710e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.save(datashaded_plot, 'large_dataset_bokeh.html')\n",
+    "print(\"Large dataset plot saved successfully\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71a6cd15",
+   "metadata": {},
+   "source": [
+    "## Cleanup\n",
+    "\n",
+    "Let's clean up the files we created during this demonstration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b66bfd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "files_to_clean = [\n",
+    "    'penguins_bokeh.html', 'penguins_offline.html', 'penguins_bokeh.png',\n",
+    "    'penguins_high_res.png',\n",
+    "    'scatter_bokeh.html', 'histogram_bokeh.html', 'box_bokeh.html',\n",
+    "    'scatter_bokeh.png', 'histogram_bokeh.png', 'box_bokeh.png',\n",
+    "    'large_dataset_bokeh.html'\n",
+    "]\n",
+    "\n",
+    "for filename in files_to_clean:\n",
+    "    file_path = Path(filename)\n",
+    "    if file_path.exists():\n",
+    "        file_path.unlink()\n",
+    "        print(f\"Removed {filename}\")\n",
+    "    else:\n",
+    "        print(f\"{filename} not found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25125417",
+   "metadata": {},
+   "source": [
+    "## Best Practices for Bokeh Plots\n",
+    "\n",
+    "1. **Prioritize HTML export**: Bokeh's strength is interactivity, so HTML should be your primary export format\n",
+    "\n",
+    "2. **Use inline resources for offline sharing**: Include `resources=INLINE` when sharing files that won't have internet access\n",
+    "\n",
+    "3. **Test PNG export setup**: Always verify your selenium and browser driver installation\n",
+    "\n",
+    "4. **Consider file sizes**: Interactive HTML files can be large for complex plots, but preserve all functionality\n",
+    "\n",
+    "5. **Handle large datasets**: Use `datashade=True` or `rasterize=True` for datasets with many points\n",
+    "\n",
+    "6. **Optimize for your use case**:\n",
+    "   - **Web embedding**: HTML with CDN resources (default)\n",
+    "   - **Offline sharing**: HTML with inline resources\n",
+    "   - **Presentations/documents**: PNG (requires browser driver)\n",
+    "   - **Email attachments**: PNG (smaller, universally viewable)\n",
+    "\n",
+    ":::{seealso}\n",
+    "- Learn about [saving matplotlib plots](save_matplotlib_plots.ipynb) for publication-quality static images\n",
+    "- Explore [using Panel for advanced layouts and interactivity](use_panel_for_display.ipynb)\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/how_to/save_matplotlib_plots.ipynb b/doc/how_to/save_matplotlib_plots.ipynb
new file mode 100644
index 000000000..0c866f10f
--- /dev/null
+++ b/doc/how_to/save_matplotlib_plots.ipynb
@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "72979cef",
+   "metadata": {},
+   "source": [
+    "# How to Save Publication-Quality Matplotlib Plots\n",
+    "\n",
+    "This guide shows you how to export hvPlot visualizations using the matplotlib backend to create high-quality static images perfect for publications, presentations, and print media."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7238e264",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's set up hvPlot with the matplotlib backend:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c33ba493",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas # noqa\n",
+    "hvplot.extension('matplotlib')\n",
+    "\n",
+    "penguins = hvplot.sampledata.penguins(\"pandas\").dropna()\n",
+    "\n",
+    "plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    color='species',\n",
+    "    title=\"Penguin Bill Dimensions (Matplotlib)\",\n",
+    ")\n",
+    "\n",
+    "plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5c5fcb4",
+   "metadata": {},
+   "source": [
+    "## PNG Export (No Browser Driver Required!)\n",
+    "\n",
+    "The main advantage of matplotlib is that it generates PNG files natively without requiring browser drivers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c29a135",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.save(plot, 'mpl_plot.png')\n",
+    "print(\"Matplotlib plot saved as PNG\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63253c78",
+   "metadata": {},
+   "source": [
+    "## High-Quality Publication Figures\n",
+    "\n",
+    "For publications, you can control the DPI and other matplotlib settings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55f3db15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.rcParams['figure.dpi'] = 150  # High DPI for better quality\n",
+    "\n",
+    "publication_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    color='species',\n",
+    "    width=1200,\n",
+    "    height=800,\n",
+    "    title=\"Penguin Bill Dimensions\",\n",
+    "    xlabel=\"Bill Length (mm)\",\n",
+    "    ylabel=\"Bill Depth (mm)\"\n",
+    ")\n",
+    "\n",
+    "publication_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3053915c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.save(publication_plot, 'publication_figure.png')\n",
+    "print(\"Publication-quality figure saved as 'publication_figure.png'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b9b5e2d",
+   "metadata": {},
+   "source": [
+    "## Customizing Output Quality\n",
+    "\n",
+    "You can fine-tune various matplotlib parameters for optimal output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4727d75e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure matplotlib for high-quality output\n",
+    "matplotlib.rcParams.update({\n",
+    "    'figure.dpi': 300,        # High DPI for print\n",
+    "    'savefig.dpi': 300,       # DPI for saved figures\n",
+    "    'font.size': 12,          # Base font size\n",
+    "    'axes.labelsize': 14,     # Axis label font size\n",
+    "    'axes.titlesize': 16,     # Title font size\n",
+    "    'legend.fontsize': 11,    # Legend font size\n",
+    "    'figure.figsize': (10, 6) # Default figure size\n",
+    "})\n",
+    "\n",
+    "print(\"Matplotlib configured for high-quality output\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77f11648",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a professional-looking plot\n",
+    "professional_plot = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    color='species',\n",
+    "    title=\"Bill Length vs Bill Depth by Species\",\n",
+    "    xlabel=\"Bill Length (mm)\",\n",
+    "    ylabel=\"Bill Depth (mm)\",\n",
+    "    legend='top_right'\n",
+    ")\n",
+    "\n",
+    "professional_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8d9afde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.save(professional_plot, 'professional_plot.png')\n",
+    "print(\"Professional plot saved as 'professional_plot.png'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b12a439d",
+   "metadata": {},
+   "source": [
+    "## Multiple Plot Types\n",
+    "\n",
+    "Matplotlib also works well with all hvPlot chart types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54cc8595",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plots = {\n",
+    "    'scatter': penguins.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm', color='species', width=400),\n",
+    "    'histogram': penguins.hvplot.hist('body_mass_g', by='species', alpha=0.7, width=400),\n",
+    "    'box': penguins.hvplot.box(y='flipper_length_mm', by='species', width=400),\n",
+    "    'violin': penguins.hvplot.violin(y='bill_length_mm', by='species', width=400),\n",
+    "}\n",
+    "\n",
+    "layout = (plots['scatter'] + plots['histogram'] + plots['box'] + plots['violin']).cols(2)\n",
+    "layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a77790e",
+   "metadata": {},
+   "source": [
+    "## Batch Saving Multiple Static Plots\n",
+    "\n",
+    "Save multiple matplotlib plots efficiently:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d65c7475",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name, plot in plots.items():\n",
+    "    filename = f'{name}_matplotlib.png'\n",
+    "    hvplot.save(plot, filename)\n",
+    "    print(f\"Saved {filename}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "915440bc-e652-48e5-a2cf-f9a87b88c84e",
+   "metadata": {},
+   "source": [
+    "## Saving as Multi-panels for Publications\n",
+    "\n",
+    "For scientific publications, you might need to create multi-panel figures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72367442-63bf-496a-a875-4313d4cb1cf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.save(layout, 'figure_panels.png')\n",
+    "print(\"Multi-panel figure saved as 'figure_panels.png'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a9c5ad0",
+   "metadata": {},
+   "source": [
+    "## Different File Formats\n",
+    "\n",
+    "Matplotlib supports various file formats beyond PNG:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95f8b9ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save in different formats\n",
+    "formats = ['png', 'pdf', 'svg', 'gif']\n",
+    "\n",
+    "for fmt in formats:\n",
+    "    try:\n",
+    "        filename = f'penguin_plot.{fmt}'\n",
+    "        hvplot.save(plot, filename)\n",
+    "        print(f\"Saved {filename}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Failed to save {fmt}: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8ade29b",
+   "metadata": {},
+   "source": [
+    "## Cleanup\n",
+    "\n",
+    "Let's clean up the files we created during this demonstration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5074ea76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "files_to_clean = [\n",
+    "    'mpl_plot.png', 'publication_figure.png', 'professional_plot.png',\n",
+    "    'scatter_matplotlib.png', 'histogram_matplotlib.png', 'box_matplotlib.png', 'violin_matplotlib.png',\n",
+    "    'penguin_plot.png', 'penguin_plot.pdf', 'penguin_plot.svg', 'penguin_plot.gif',\n",
+    "    'figure_panels.png',\n",
+    "]\n",
+    "\n",
+    "for filename in files_to_clean:\n",
+    "    file_path = Path(filename)\n",
+    "    if file_path.exists():\n",
+    "        file_path.unlink()\n",
+    "        print(f\"Removed {filename}\")\n",
+    "    else:\n",
+    "        print(f\"{filename} not found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "934eac39-1983-492b-a7d8-3a0766b4f7e4",
+   "metadata": {},
+   "source": [
+    "## Advantages of Matplotlib for Static Export\n",
+    "\n",
+    "- **No browser driver required**: Matplotlib generates PNG files natively\n",
+    "- **Better text rendering**: Superior font handling for publications\n",
+    "- **Smaller file sizes**: More efficient PNG compression\n",
+    "- **Scientific publishing**: Better suited for academic papers\n",
+    "- **Consistent output**: Reproducible across different systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97e8d3f3",
+   "metadata": {},
+   "source": [
+    "## Best Practices for Matplotlib Plots\n",
+    "\n",
+    "1. **No browser dependencies**: Matplotlib generates images natively, making it ideal for server environments\n",
+    "\n",
+    "2. **Configure DPI appropriately**:\n",
+    "   - **Screen display**: 72-96 DPI\n",
+    "   - **Web use**: 96-150 DPI\n",
+    "   - **Print/publications**: 300+ DPI\n",
+    "\n",
+    "3. **Choose the right format**:\n",
+    "   - **PNG**: Best for complex plots with many colors\n",
+    "   - **PDF**: Vector format, ideal for publications\n",
+    "   - **SVG**: Vector format, good for web embedding\n",
+    "\n",
+    "4. **Optimize for your use case**:\n",
+    "   - **Scientific papers**: High DPI PNG or PDF\n",
+    "   - **Presentations**: Medium DPI PNG\n",
+    "   - **Web display**: Lower DPI PNG\n",
+    "   - **Print materials**: 300+ DPI PDF or PNG\n",
+    "\n",
+    "5. **Font and sizing**:\n",
+    "   - Use appropriate font sizes for your target medium\n",
+    "   - Test readability at the intended viewing size\n",
+    "   - Consider colorblind-friendly palettes\n",
+    "\n",
+    ":::{seealso}\n",
+    "- Learn about [saving interactive Bokeh plots](save_bokeh_plots.ipynb) for web sharing and interactive exploration\n",
+    "- Explore [using Panel for advanced layouts and interactivity](use_panel_for_display.ipynb)\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/how_to/use_panel_for_display.ipynb b/doc/how_to/use_panel_for_display.ipynb
new file mode 100644
index 000000000..4d00925a2
--- /dev/null
+++ b/doc/how_to/use_panel_for_display.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "290fd872",
+   "metadata": {},
+   "source": [
+    "# How to Use Panel for Interactive Display and Layouts\n",
+    "\n",
+    "This guide shows you how to use [Panel](https://panel.holoviz.org) to create interactive displays, update plots dynamically, and build sophisticated layouts with hvPlot visualizations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "223bea4a",
+   "metadata": {},
+   "source": [
+    "## Why Use Panel with hvPlot?\n",
+    "\n",
+    "Panel extends hvPlot's capabilities by providing:\n",
+    "\n",
+    "- **Dynamic updates**: Change plot content without recreating the entire visualization\n",
+    "- **Advanced layouts**: More sophisticated arrangements than simple `+` and `*` operators\n",
+    "- **Interactive widgets**: Add controls like sliders, dropdowns, and buttons\n",
+    "- **Dashboard creation**: Build full applications and dashboards\n",
+    "- **Server deployment**: Easily serve interactive applications\n",
+    "\n",
+    ":::{note}\n",
+    "Some of the dynamic functionality in this notebook require a live python process to work.\n",
+    ":::\n",
+    "\n",
+    "Let's start with the basics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8527e864",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "import hvplot.pandas # noqa\n",
+    "\n",
+    "# Enable Panel extension for notebooks\n",
+    "pn.extension('tabulator')\n",
+    "\n",
+    "# Load sample data from hvsampledata\n",
+    "earthquakes = hvplot.sampledata.earthquakes(\"pandas\")\n",
+    "penguins = hvplot.sampledata.penguins(\"pandas\").dropna()\n",
+    "stocks = hvplot.sampledata.stocks(\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8908e40a",
+   "metadata": {},
+   "source": [
+    "## Basic Panel Usage\n",
+    "\n",
+    "### Creating a Panel Pane\n",
+    "\n",
+    "The fundamental concept in Panel is a \"pane\" - a container that can hold and display objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2905dcca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a simple plot\n",
+    "earthquake_magnitude_plot = earthquakes.hvplot.line(\n",
+    "    x='time',\n",
+    "    y='mag',\n",
+    "    title=\"Earthquake Magnitude Over Time\",\n",
+    "    color='red',\n",
+    ")\n",
+    "\n",
+    "# Wrap it in a Panel pane\n",
+    "pane = pn.panel(earthquake_magnitude_plot)\n",
+    "pane"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58f9f1f1",
+   "metadata": {},
+   "source": [
+    "### Dynamic Plot Updates\n",
+    "\n",
+    "One of Panel's key features is the ability to update plot content dynamically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "795b1c98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earthquake_depth_plot = earthquakes.hvplot.line(\n",
+    "    x='time',\n",
+    "    y='depth',\n",
+    "    title=\"Earthquake Depth Over Time\",\n",
+    ")\n",
+    "\n",
+    "pane.object = earthquake_depth_plot\n",
+    "print(\"Pane updated with earthquake depth data - check the plot above!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7db4dc6",
+   "metadata": {},
+   "source": [
+    "### In-Place Updates with Overlays\n",
+    "\n",
+    "You can also update the plot content in-place using overlay operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a3ded04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the earthquake magnitude plot as an overlay\n",
+    "pane.object *= earthquake_magnitude_plot\n",
+    "print(\"Added earthquake magnitude as overlay - now showing both trends!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0259a1d6",
+   "metadata": {},
+   "source": [
+    "## Advanced Layouts with Panel\n",
+    "\n",
+    "### Column and Row Layouts\n",
+    "\n",
+    "Panel provides more sophisticated layout options than hvPlot's basic `+` operator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9031c551",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create multiple plots\n",
+    "\n",
+    "size_opts = {\"width\": 350, \"height\": 250}\n",
+    "\n",
+    "plot1 = earthquakes.hvplot(\n",
+    "    x='time', y='mag',\n",
+    "    title=\"Magnitude\", **size_opts,\n",
+    ")\n",
+    "\n",
+    "plot2 = earthquakes.hvplot(\n",
+    "    x='time', y='depth',\n",
+    "    title=\"Depth\", **size_opts,\n",
+    ")\n",
+    "\n",
+    "plot3 = earthquakes.hvplot.scatter(\n",
+    "    x='lon', y='lat',\n",
+    "    title=\"Geographic Distribution\", **size_opts,\n",
+    ")\n",
+    "\n",
+    "plot4 = earthquakes.hvplot.hist(\n",
+    "    'mag', bins=20,\n",
+    "    title=\"Magnitude Distribution\", **size_opts,\n",
+    ")\n",
+    "\n",
+    "# Create a row layout\n",
+    "row_layout = pn.Row(plot1, plot2, plot3)\n",
+    "row_layout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37fc5176",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a column layout\n",
+    "column_layout = pn.Column(plot1, plot2, plot3)\n",
+    "column_layout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "461cc037",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a nested layout (2x2 grid)\n",
+    "grid_layout = pn.Column(\n",
+    "    pn.Row(plot1, plot2),\n",
+    "    pn.Row(plot3, plot4)\n",
+    ")\n",
+    "grid_layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c355d2c5",
+   "metadata": {},
+   "source": [
+    "### Tabs for Organized Content\n",
+    "\n",
+    "Tabs are perfect for organizing multiple related visualizations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83685ed5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create different types of plots for the penguin data\n",
+    "size_opts = {\"width\": 600, \"height\": 400}\n",
+    "\n",
+    "penguin_scatter = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm', y='bill_depth_mm',\n",
+    "    by='species', title=\"Bill Dimensions\",\n",
+    "    **size_opts,\n",
+    ")\n",
+    "\n",
+    "penguin_hist = penguins.hvplot.hist(\n",
+    "    'body_mass_g', by='species',\n",
+    "    title=\"Body Mass Distribution\",\n",
+    "    alpha=0.7, **size_opts,\n",
+    ")\n",
+    "\n",
+    "penguin_box = penguins.hvplot.box(\n",
+    "    y='flipper_length_mm', by='species',\n",
+    "    title=\"Flipper Length by Species\",\n",
+    "    **size_opts,\n",
+    ")\n",
+    "\n",
+    "# Create tabbed interface\n",
+    "tabs = pn.Tabs(\n",
+    "    (\"Scatter Plot\", penguin_scatter),\n",
+    "    (\"Histogram\", penguin_hist),\n",
+    "    (\"Box Plot\", penguin_box)\n",
+    ")\n",
+    "\n",
+    "tabs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31c1b0f3",
+   "metadata": {},
+   "source": [
+    "## Interactive Widgets and Controls\n",
+    "\n",
+    "### Simple Widget Example\n",
+    "\n",
+    "Let's create a dropdown to switch between different earthquake metrics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f02a00c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get available earthquake numeric columns\n",
+    "earthquake_columns = ['mag', 'depth']\n",
+    "\n",
+    "# Create a dropdown widget\n",
+    "earthquake_selector = pn.widgets.Select(\n",
+    "    name='Earthquake Metric',\n",
+    "    value=earthquake_columns[0],\n",
+    "    options=earthquake_columns,\n",
+    "    width=200\n",
+    ")\n",
+    "\n",
+    "# Function to create plot based on selection\n",
+    "def create_earthquake_plot(metric):\n",
+    "    return earthquakes.hvplot(\n",
+    "        x='time',\n",
+    "        y=metric,\n",
+    "        title=f\"{metric.title()} Over Time\",\n",
+    "        line_width=3\n",
+    "    )\n",
+    "\n",
+    "# Create reactive plot that updates when widget changes\n",
+    "interactive_plot = pn.bind(create_earthquake_plot, earthquake_selector)\n",
+    "\n",
+    "# Combine widget and plot\n",
+    "widget_layout = pn.Column(\n",
+    "    \"## Interactive Earthquake Data Explorer\",\n",
+    "    earthquake_selector,\n",
+    "    interactive_plot\n",
+    ")\n",
+    "\n",
+    "widget_layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a88f06dd",
+   "metadata": {},
+   "source": [
+    "### Multiple Widgets Example\n",
+    "\n",
+    "Let's create a more complex example with multiple controls for the penguin data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3349a60f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create multiple widgets\n",
+    "x_axis = pn.widgets.Select(\n",
+    "    name='X Axis',\n",
+    "    value='bill_length_mm',\n",
+    "    options=['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']\n",
+    ")\n",
+    "\n",
+    "y_axis = pn.widgets.Select(\n",
+    "    name='Y Axis',\n",
+    "    value='bill_depth_mm',\n",
+    "    options=['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']\n",
+    ")\n",
+    "\n",
+    "plot_type = pn.widgets.Select(\n",
+    "    name='Plot Type',\n",
+    "    value='scatter',\n",
+    "    options=['scatter', 'line', 'bar']\n",
+    ")\n",
+    "\n",
+    "color_by = pn.widgets.Select(\n",
+    "    name='Color By',\n",
+    "    value='species',\n",
+    "    options=['species', 'island', 'sex', None]\n",
+    ")\n",
+    "\n",
+    "alpha_slider = pn.widgets.FloatSlider(\n",
+    "    name='Transparency',\n",
+    "    start=0.1,\n",
+    "    end=1.0,\n",
+    "    value=0.7,\n",
+    "    step=0.1\n",
+    ")\n",
+    "\n",
+    "# Function to create plot based on all widgets\n",
+    "def create_penguin_plot(x, y, plot_kind, color, alpha):\n",
+    "    plot_method = getattr(penguins.hvplot, plot_kind)\n",
+    "\n",
+    "    kwargs = {\n",
+    "        'x': x,\n",
+    "        'y': y,\n",
+    "        'title': f\"{plot_kind.title()} Plot: {y} vs {x}\",\n",
+    "        'width': 600,\n",
+    "        'height': 400,\n",
+    "        'alpha': alpha\n",
+    "    }\n",
+    "\n",
+    "    if color:\n",
+    "        kwargs['by'] = color\n",
+    "\n",
+    "    return plot_method(**kwargs)\n",
+    "\n",
+    "# Bind the function to all widgets\n",
+    "complex_interactive_plot = pn.bind(\n",
+    "    create_penguin_plot,\n",
+    "    x_axis, y_axis,\n",
+    "    plot_type, color_by, alpha_slider\n",
+    ")\n",
+    "\n",
+    "# Create control panel\n",
+    "controls = pn.Card(\n",
+    "    x_axis, y_axis, plot_type, color_by, alpha_slider,\n",
+    "    title=\"Plot Controls\",\n",
+    ")\n",
+    "\n",
+    "# Combine controls and plot side by side\n",
+    "complex_layout = pn.Row(\n",
+    "    controls,\n",
+    "    pn.Column(\n",
+    "        \"## Dynamic Penguin Data Explorer\",\n",
+    "        complex_interactive_plot\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "complex_layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35019415",
+   "metadata": {},
+   "source": [
+    "## Dashboard Creation\n",
+    "\n",
+    "### Multi-Dataset Dashboard\n",
+    "\n",
+    "Let's create a comprehensive dashboard combining multiple datasets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c34bd450",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create summary statistics\n",
+    "earthquake_summary = pn.widgets.Tabulator(\n",
+    "    earthquakes.describe().round(2),\n",
+    "    width=400,\n",
+    "    height=200\n",
+    ")\n",
+    "\n",
+    "penguin_summary = pn.widgets.Tabulator(\n",
+    "    penguins.describe().round(2),\n",
+    "    width=400,\n",
+    "    height=200\n",
+    ")\n",
+    "\n",
+    "# Create overview plots\n",
+    "earthquake_overview = earthquakes.hvplot.line(\n",
+    "    x='time', y=['mag', 'depth'],\n",
+    "    title=\"Earthquake Trends Overview\",\n",
+    "    width=500, height=300,\n",
+    ")\n",
+    "\n",
+    "penguin_overview = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm', y='body_mass_g',\n",
+    "    by='species', title=\"Penguin Overview\",\n",
+    "    width=500, height=300,\n",
+    ")\n",
+    "\n",
+    "summary = pn.pane.Markdown(\"\"\"\n",
+    "        ## Multi-Dataset Analytics Dashboard\n",
+    "\n",
+    "        This dashboard demonstrates:\n",
+    "        - Multiple dataset integration\n",
+    "        - Tables with summary statistics\n",
+    "        - Responsive layout\n",
+    "        - Mixed content types (plots, tables, text)\n",
+    "        \"\"\")\n",
+    "\n",
+    "tables = pn.Row(\n",
+    "    earthquake_summary,\n",
+    "    penguin_summary\n",
+    ")\n",
+    "\n",
+    "plots = pn.Row(\n",
+    "        earthquake_overview,\n",
+    "        penguin_overview\n",
+    "    )\n",
+    "\n",
+    "layout = pn.Column(summary, tables, plots)\n",
+    "\n",
+    "layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20cc245a",
+   "metadata": {},
+   "source": [
+    "## Panel Templates and Styling\n",
+    "\n",
+    "### Using Built-in Templates\n",
+    "\n",
+    "Panel provides several built-in templates for professional-looking applications:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5775a7ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Material Design Template\n",
+    "material_template = pn.template.MaterialTemplate(\n",
+    "    title=\"Material Design Dashboard\",\n",
+    "    sidebar=[\n",
+    "        \"## Controls\",\n",
+    "        earthquake_selector,\n",
+    "        pn.widgets.Button(name=\"Refresh\", button_type='primary')\n",
+    "    ],\n",
+    "    main=[interactive_plot],\n",
+    "    header_background='#3f51b5'\n",
+    ")\n",
+    "\n",
+    "material_template.servable();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "063b9fb6-5010-43db-ae1e-3d4c9e4fbd79",
+   "metadata": {},
+   "source": [
+    ":::{tip}\n",
+    "You can use `.show()` to display the dashboard in a new tab.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e6af9b",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "Panel extends hvPlot's capabilities in several key ways:\n",
+    "\n",
+    "### Key Features:\n",
+    "- **Dynamic Updates**: Change plot content without page reloads\n",
+    "- **Advanced Layouts**: Rows, columns, tabs, and templates\n",
+    "- **Interactive Widgets**: Dropdowns, sliders, buttons, and more\n",
+    "- **Dashboard Creation**: Professional applications with minimal code\n",
+    "\n",
+    "### When to Use Panel:\n",
+    "- Building interactive dashboards\n",
+    "- Creating applications for non-technical users\n",
+    "- Need for real-time data updates\n",
+    "- Complex layouts beyond simple plot combinations\n",
+    "- Deploying web applications\n",
+    "\n",
+    "### Quick Reference:\n",
+    "\n",
+    "| Task | Panel Code |\n",
+    "|------|------------|\n",
+    "| Basic pane | `pn.panel(plot)` |\n",
+    "| Update plot | `pane.object = new_plot` |\n",
+    "| Row layout | `pn.Row(plot1, plot2)` |\n",
+    "| Column layout | `pn.Column(plot1, plot2)` |\n",
+    "| Tabs | `pn.Tabs((\"Tab1\", plot1), (\"Tab2\", plot2))` |\n",
+    "| Interactive | `pn.bind(function, widget)` |\n",
+    "\n",
+    ":::{seealso}\n",
+    "- Check out other [Panel templates](https://panel.holoviz.org/tutorials/basic/templates.html) here\n",
+    "\n",
+    "- Check out the full [Panel documentation](https://panel.holoviz.org) for advanced features!\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/index.md b/doc/index.md
index e868f5cec..5340961fd 100644
--- a/doc/index.md
+++ b/doc/index.md
@@ -434,8 +434,10 @@ align: center
 
 Tutorials <tutorials/index>
 User Guide <user_guide/index>
+How-To Guides <how_to/index>
 Gallery <reference/index>
 Reference <ref/index>
+Explanation <explanation/index>
 Developer Guide <developer_guide>
 Releases <releases>
 Roadmap <roadmap>