diff --git a/doc/explanation/index.md b/doc/explanation/index.md
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+++ 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/create_advanced_layouts.ipynb b/doc/how_to/create_advanced_layouts.ipynb
new file mode 100644
index 000000000..803827dea
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8597e294",
+   "metadata": {},
+   "source": [
+    "# How to Create Advanced Plot Layouts\n",
+    "\n",
+    "This guide shows you how to create sophisticated plot layouts using hvPlot and Panel for more complex arrangements beyond basic subplots and grids.\n",
+    "\n",
+    "## When to use advanced layouts\n",
+    "\n",
+    "Use advanced layouts when:\n",
+    "- You need precise control over plot positioning and sizing beyond basic faceting\n",
+    "- You want to combine different types of visualizations (plots, tables, widgets)\n",
+    "- You need interactive dashboards with custom arrangements\n",
+    "- Basic subplots and grids don't provide the flexibility you need"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4ffad99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas  # noqa\n",
+    "import panel as pn\n",
+    "\n",
+    "pn.extension('tabulator')\n",
+    "\n",
+    "penguins = hvplot.sampledata.penguins(\"pandas\").dropna().reset_index(drop=True)\n",
+    "stocks = hvplot.sampledata.stocks(\"pandas\")\n",
+    "earthquakes = hvplot.sampledata.earthquakes(\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96dc3ecf",
+   "metadata": {},
+   "source": [
+    "## Using the `+` operator for side-by-side layouts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ded095ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size_opts = {\"frame_width\": 250, \"aspect\": \"square\"}\n",
+    "\n",
+    "scatter = penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    color='species',\n",
+    "    title='Bill Dimensions',\n",
+    "    **size_opts\n",
+    ")\n",
+    "hist = penguins.hvplot.hist(\n",
+    "    y='body_mass_g',\n",
+    "    by='species',\n",
+    "    title='Body Mass Distribution',\n",
+    "    **size_opts\n",
+    ")\n",
+    "\n",
+    "scatter + hist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "276c2afe",
+   "metadata": {},
+   "source": [
+    "## Using the `*` operator for overlays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6810b4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "line_plot = stocks.hvplot.line(x='date', y='Apple', label=\"Line\")\n",
+    "scatter_plot = stocks[::20].hvplot.scatter(x='date', y='Apple', color=\"red\",\n",
+    "                                           size=50, alpha=0.7, label=\"Marker\")\n",
+    "\n",
+    "line_plot * scatter_plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f377395b",
+   "metadata": {},
+   "source": [
+    "### Complex layouts with custom arrangements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8ccd32a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size_opts = {\"frame_width\": 250, \"aspect\": \"square\"}\n",
+    "\n",
+    "p1 = penguins.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm',\n",
+    "                             color='species', size=80, alpha=0.7,\n",
+    "                             title='Bill Dimensions', **size_opts)\n",
+    "\n",
+    "p2 = penguins.hvplot.scatter(x='flipper_length_mm', y='body_mass_g',\n",
+    "                             color='species', size=80, alpha=0.7,\n",
+    "                             title='Size Measurements',  **size_opts)\n",
+    "\n",
+    "p3 = penguins.hvplot.box(y='bill_length_mm', by='species',\n",
+    "                         title='Bill Length by Species',  **size_opts)\n",
+    "\n",
+    "p4 = penguins.hvplot.violin(y='body_mass_g', by='species',\n",
+    "                            title='Body Mass Distribution',  **size_opts)\n",
+    "\n",
+    "\n",
+    "layout = (p1 + p2 + p3 + p4).cols(2)\n",
+    "layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2105758a",
+   "metadata": {},
+   "source": [
+    "## Panel Layouts for Dashboard Creation\n",
+    "\n",
+    "Panel provides even more flexibility for creating complex dashboards:\n",
+    "\n",
+    "### Basic Panel layouts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df356385",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trend_plot = stocks.hvplot.line(x='date', title='Stock Trends', height=300)\n",
+    "\n",
+    "correlation_plot = hvplot.plotting.scatter_matrix(stocks, alpha=0.5)\n",
+    "\n",
+    "# Create a Panel Column layout\n",
+    "pn.Column(trend_plot, correlation_plot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "736a01b8",
+   "metadata": {},
+   "source": [
+    "### Combining plots with data tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3368427e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary_plot = earthquakes.hvplot.scatter(x='lon', y='lat', color='mag',\n",
+    "                                           size='mag', alpha=0.7,\n",
+    "                                           title='Earthquake Locations',\n",
+    "                                           frame_height=300, aspect='square',\n",
+    "                                           tiles=True)\n",
+    "\n",
+    "data_table = pn.widgets.Tabulator(earthquakes, height=400, width=400,\n",
+    "                                  pagination='remote', page_size=10)\n",
+    "\n",
+    "pn.Row(summary_plot, data_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bf9ee23",
+   "metadata": {},
+   "source": [
+    "### Creating tabbed layouts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b20ecfc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overview_tab = penguins.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm',\n",
+    "                                       color='species', size=80, alpha=0.7,\n",
+    "                                       title='Species Overview')\n",
+    "\n",
+    "detailed_tab = penguins.hvplot.scatter(x='bill_length_mm', y='bill_depth_mm',\n",
+    "                                        by='species', subplots=True,\n",
+    "                                        width=250, height=200, alpha=0.7)\n",
+    "\n",
+    "data_tab = pn.widgets.Tabulator(penguins, pagination='remote', page_size=10)\n",
+    "\n",
+    "tabs = pn.Tabs(\n",
+    "    ('Overview', overview_tab),\n",
+    "    ('Detailed View', detailed_tab),\n",
+    "    ('Data Table', data_tab)\n",
+    ")\n",
+    "tabs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ff9aaa7",
+   "metadata": {},
+   "source": [
+    "### Advanced Panel layouts with custom sizing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a26cb55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "main_plot = stocks.hvplot.line(x='date', y=['Apple', 'Google'], title='Main Trends')\n",
+    "\n",
+    "side_plot1 = stocks.hvplot.hist(y='Apple', title='Apple Distribution',\n",
+    "                                height=170, width=340)\n",
+    "\n",
+    "side_plot2 = stocks.hvplot.hist(y='Google', title='Google Distribution',\n",
+    "                                height=170, width=340)\n",
+    "\n",
+    "# Create complex layout\n",
+    "layout = pn.Column(\n",
+    "    main_plot,\n",
+    "    pn.Row(side_plot1, side_plot2)\n",
+    ")\n",
+    "layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e62ac5",
+   "metadata": {},
+   "source": [
+    "## Best practices for advanced layouts\n",
+    "\n",
+    "1. **Start simple**: Begin with basic layouts and add complexity gradually\n",
+    "2. **Consider screen size**: Test your layouts on different screen sizes\n",
+    "3. **Use consistent sizing**: Maintain visual harmony with consistent dimensions\n",
+    "4. **Optimize for your use case**: Choose HvPlot/HoloViews for analysis and basic faceting, Panel for dashboards\n",
+    "\n",
+    ":::{admonition} Summary\n",
+    "\n",
+    "- Use `+` operator for side-by-side layouts\n",
+    "- Use `*` operator for overlays\n",
+    "- Panel provides `Row`, `Column`, and `Tabs` for complex dashboard layouts\n",
+    "- Panel can combine plots with tables, widgets, and other components\n",
+    "- Use `responsive=True` and Panel's sizing modes for adaptive layouts\n",
+    ":::\n",
+    "\n",
+    ":::{admonition} Further Reading\n",
+    ":class: seealso\n",
+    "See the [Panel layouts tutorial](https://panel.holoviz.org/tutorials/basic/layouts.html) for more on layouting Python objects\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/how_to/create_basic_subplots.ipynb b/doc/how_to/create_basic_subplots.ipynb
new file mode 100644
index 000000000..22ab674af
--- /dev/null
+++ b/doc/how_to/create_basic_subplots.ipynb
@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "23258cf2",
+   "metadata": {},
+   "source": [
+    "# How to Create Basic Subplots\n",
+    "\n",
+    "This guide shows you how to facet your data into separate subplots instead of overlaying them on a single axis. This is useful when you want to compare multiple variables or categories side by side.\n",
+    "\n",
+    "## When to use subplots\n",
+    "\n",
+    "Use subplots when:\n",
+    "- You want to compare multiple variables with different scales\n",
+    "- You need to avoid visual clutter from overlapping lines or points\n",
+    "- You want to highlight patterns in individual variables\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, let's load some sample datasets from the `hvsampledata` module:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27271ae7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas  # noqa\n",
+    "\n",
+    "stocks = hvplot.sampledata.stocks(\"pandas\")\n",
+    "penguins = hvplot.sampledata.penguins(\"pandas\").dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78b0c591",
+   "metadata": {},
+   "source": [
+    "## Default behavior: Overlaid plots\n",
+    "\n",
+    "By default, hvPlot overlays multiple columns onto one axis for easy comparison:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eecbe872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stocks.hvplot(x='date', value_label='Stock Price', title='Relative Stock Prices')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c1e32ef",
+   "metadata": {},
+   "source": [
+    "## Creating subplots with subplots=True\n",
+    "\n",
+    "To split each column into its own separate plot, set `subplots=True`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9183b5c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stocks.hvplot(\n",
+    "    x='date',\n",
+    "    value_label='Stock Price',\n",
+    "    subplots=True,\n",
+    "    width=250,\n",
+    "    height=200,\n",
+    ").cols(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1415cdb5",
+   "metadata": {},
+   "source": [
+    "## Controlling axis sharing\n",
+    "\n",
+    "By default, subplots have linked, normalized axes to facilitate comparison. You can pan or zoom in any plot to see how the others change.\n",
+    "\n",
+    "However, if your data covers widely different numerical ranges, use `shared_axes=False` to give each plot its own range:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "651df41d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguin_cols = penguins[\n",
+    "    ['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']\n",
+    "    ]\n",
+    "\n",
+    "penguin_cols.hvplot(\n",
+    "    kind='hist',\n",
+    "    subplots=True,\n",
+    "    shared_axes=False,\n",
+    "    frame_height=200,\n",
+    "    aspect='square',\n",
+    "    alpha=0.7\n",
+    ").cols(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7dcbb62a",
+   "metadata": {},
+   "source": [
+    "Notice how each histogram has its own appropriate scale, making it easier to see the distribution patterns.\n",
+    "\n",
+    ":::{tip}\n",
+    "Use `.cols(n)` to specify how many plots per row before wrapping to the next line\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63a4539d",
+   "metadata": {},
+   "source": [
+    "## Using subplots with grouping (`by` parameter)\n",
+    "\n",
+    "You can combine `subplots=True` with the `by` keyword to create separate plots for each group. This is a form of categorical faceting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ce5b1b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    by='species',\n",
+    "    subplots=True,\n",
+    "    width=250,\n",
+    "    height=200,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "831403fd",
+   "metadata": {},
+   "source": [
+    ":::{admonition} Summary\n",
+    "\n",
+    "- Use `subplots=True` to create separate plots (facets) instead of overlaying them\n",
+    "- Use `shared_axes=False` when your data has very different scales\n",
+    "- Subplots with shared axes allow linked panning and zooming for easy comparison\n",
+    "- Use `.cols(n)` to control the layout and number of plots per row\n",
+    "- Combine with `by` parameter to create subplots for different groups (categorical faceting)\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/how_to/create_plot_grids.ipynb b/doc/how_to/create_plot_grids.ipynb
new file mode 100644
index 000000000..12295d853
--- /dev/null
+++ b/doc/how_to/create_plot_grids.ipynb
@@ -0,0 +1,218 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2b241557",
+   "metadata": {},
+   "source": [
+    "# How to Create Plot Grids\n",
+    "\n",
+    "This guide shows you how to arrange multidimensional data into explicit grids of plots with shared axes, allowing easier comparisons across large numbers of related plots.\n",
+    "\n",
+    "## When to use plot grids\n",
+    "\n",
+    "Use plot grids when:\n",
+    "- You have multidimensional data that naturally forms a matrix structure\n",
+    "- You want shared axes across rows and columns for easy comparison\n",
+    "- You need to visualize patterns across multiple categorical variables simultaneously\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "Let's load some sample datasets from the `hvsampledata` module:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e99b3c25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas  # noqa\n",
+    "import hvplot.xarray  # noqa\n",
+    "\n",
+    "penguins = hvplot.sampledata.penguins(\"pandas\").dropna()\n",
+    "air_temp = hvplot.sampledata.air_temperature(\"xarray\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6ff304e",
+   "metadata": {},
+   "source": [
+    ":::{note}\n",
+    "`subplots=True` lays out plots sequentially and formats each plot independently, while grids arrange data into explicit rows and columns with shared axes. This is the difference between simple faceting and grid-based faceting.\n",
+    ":::\n",
+    "\n",
+    "## Creating a simple row of plots\n",
+    "\n",
+    "Use the `col` parameter to create a horizontal row of plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d57c2ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    col='species',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e10b6e14",
+   "metadata": {},
+   "source": [
+    "## Creating a 2D grid of plots\n",
+    "\n",
+    "Use both `row` and `col` parameters to create a 2D grid. This is powerful for exploring relationships across multiple categorical dimensions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92c00b59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguins.hvplot.scatter(\n",
+    "    x='bill_length_mm',\n",
+    "    y='bill_depth_mm',\n",
+    "    row='sex',\n",
+    "    col='species',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07363b2e",
+   "metadata": {},
+   "source": [
+    "Notice how the axis ticks and labels are shared across the grid. This saves space and makes comparisons easier compared to individual subplots.\n",
+    "\n",
+    "## Working with gridded data\n",
+    "\n",
+    "Grids are particularly useful for multidimensional arrays. Let's use the air temperature dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "190a86ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select a subset of time steps for visualization\n",
+    "air_subset = air_temp.isel(time=[0, 1])\n",
+    "\n",
+    "air_subset.hvplot(col='time', cmap='viridis', frame_width=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "401833bc",
+   "metadata": {},
+   "source": [
+    "## Customizing grid appearance\n",
+    "\n",
+    "You can disable axes entirely if they're not needed for your visualization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf92fa87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air_subset.hvplot(\n",
+    "    col='time',\n",
+    "    xaxis=False,\n",
+    "    yaxis=False,\n",
+    "    colorbar=False,\n",
+    "    cmap='viridis',\n",
+    "    frame_width=200,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfbb510f",
+   "metadata": {},
+   "source": [
+    "## Advanced grid layouts\n",
+    "\n",
+    "You can create more complex arrangements by combining multiple categorical variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f98864f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguins.hvplot.box(\n",
+    "    y='body_mass_g',\n",
+    "    by='species',\n",
+    "    col='sex',\n",
+    "    frame_width=200,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a41958e",
+   "metadata": {},
+   "source": [
+    "## Combining grid layouts with statistical plots\n",
+    "\n",
+    "Grids work well with various plot types, including statistical visualizations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5636e09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penguins.hvplot.hist(\n",
+    "    y='flipper_length_mm',\n",
+    "    by='species',\n",
+    "    col='sex',\n",
+    "    bins=15,\n",
+    "    frame_width=200,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5a52999",
+   "metadata": {},
+   "source": [
+    ":::{admonition} Summary\n",
+    "\n",
+    "- Use `col` parameter to create a row of plots with shared axes\n",
+    "- Use both `row` and `col` to create a 2D grid of plots\n",
+    "- Grids automatically share axes across rows and columns for easy comparison\n",
+    "- Use `xaxis=False` and `yaxis=False` to create cleaner visualizations when axes aren't needed\n",
+    "- Grids work with any plot type and are particularly powerful for multidimensional data\n",
+    "- Grid layouts save space and facilitate pattern recognition across multiple dimensions"
+   ]
+  }
+ ],
+ "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..7d8c49d53
--- /dev/null
+++ b/doc/how_to/index.md
@@ -0,0 +1,17 @@
+# 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.
+
+## Plot Layout and Organization
+
+Learn how to organize and arrange your plots for better visualization and comparison.
+
+```{toctree}
+:titlesonly:
+:hidden:
+:maxdepth: 2
+
+create_basic_subplots
+create_plot_grids
+create_advanced_layouts
+```
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>