See Explorer output code within a tab

examples/user_guide/Explorer.ipynb

@@ -8,6 +8,7 @@
    "source": [
     "import hvplot.pandas\n",
     "\n",
+    "import xarray as xr\n",
     "from bokeh.sampledata.penguins import data as df"
    ]
   },
@@ -16,12 +17,13 @@
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" role=\"info\">\n",
-    "    The Explorer has been added to hvPlot in version <code>0.8.0</code>, and improved and documented in version <code>0.8.1</code>. It does not yet support all the data structures supported by hvPlot, for now it works best with Pandas DataFrame objects. Please report any issue or feature request <a href='https://github.com/holoviz/hvplot/'>on GitHub</a>.\n",
+    "    The Explorer has been added to hvPlot in version <code>0.8.0</code>, and improved and documented in version <code>0.8.1</code> and <code>0.8.5</code>. It does not yet support all the data structures supported by hvPlot. Please report any issue or feature request <a href='https://github.com/holoviz/hvplot/'>on GitHub</a>.\n",
     "</div>"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "bd5ab787",
    "metadata": {},
    "source": [
     "hvPlot API provides a simple and intuitive way to create plots. However when you are exploring data you don't always know in advance the best way to display it, or even what kind of plot would be best to visualize the data. You will very likely embark in an iterative process that implies choosing a kind of plot, setting various options, running some code, and repeat until you're satisfied with the output and the insights you get. The *Explorer* is a *Graphical User Interface* that allows you to easily generate customized plots, which in practice gives you the possibility to **explore** both your data and hvPlot's extensive API.\n",
@@ -34,6 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "6d36b75a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +71,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hvexplorer.param.set_param(kind='scatter', x='bill_length_mm', y_multi=['bill_depth_mm'], by=['species'])\n",
+    "hvexplorer.param.update(kind='scatter', x='bill_length_mm', y_multi=['bill_depth_mm'], by=['species'])\n",
     "hvexplorer.labels.title = 'Penguins Scatter'"
    ]
   },
@@ -121,6 +124,14 @@
     "df.hvplot(**settings)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "be5f3cc6",
+   "metadata": {},
+   "source": [
+    "Or you can simply copy/paste the code displayed under the `Code` tab."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -139,6 +150,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "76c92eaf",
    "metadata": {},
    "source": [
     "Copy/pasting this string without the outer quotes into the next cell is all you need to recreate the exact same plot."
@@ -153,6 +165,83 @@
     "df.hvplot(by=['species'], kind='scatter', title='Penguins Scatter', x='bill_length_mm', y=['bill_depth_mm'])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b986b42d",
+   "metadata": {},
+   "source": [
+    "You may also call `hvplot` on the explorer instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db9ada50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvexplorer.hvplot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c183122",
+   "metadata": {},
+   "source": [
+    "It also works for xarray objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f15dde1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
+    "\n",
+    "hvplot.explorer(ds, x=\"lon\", y=\"lat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6d90743",
+   "metadata": {},
+   "source": [
+    "It's also possible to geographically reference the data, with cartopy and geoviews installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87d4ab4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvexplorer = hvplot.explorer(ds, x=\"lon\", y=\"lat\", geo=True)\n",
+    "hvexplorer.geographic.param.update(crs=\"PlateCarree\", tiles=\"CartoDark\")\n",
+    "hvexplorer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4736831",
+   "metadata": {},
+   "source": [
+    "Large datasets can be plotted efficiently using Datashader's `rasterize`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d01077b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvexplorer = hvplot.explorer(ds, x=\"lon\", y=\"lat\", rasterize=True)\n",
+    "hvexplorer"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

hvplot/converter.py

@@ -671,12 +671,12 @@ def _process_crs(self, data, crs):
 
         try:
             return process_crs(_crs)
-        except ValueError:
+        except ValueError as e:
             # only raise error if crs was specified in kwargs
             if crs:
                 raise ValueError(
                     f"'{crs}' must be either a valid crs or an reference to "
-                    "a `data.attr` containing a valid crs.")
+                    f"a `data.attr` containing a valid crs: {e}")
 
     def _process_data(self, kind, data, x, y, by, groupby, row, col,
                       use_dask, persist, backlog, label, group_label,