Cleaned up Plotting Pitfalls

examples/user_guide/1_Plotting_Pitfalls.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "## Common plotting pitfalls that get worse with large data\n",
     "\n",
-    "When working with large datasets, visualizations are often the only way available to understand the properties of that dataset -- there are simply too many data points to examine each one!  Thus it is very important to be aware of some common plotting problems that are minor inconveniences with small datasets but very serious problems with larger ones.\n",
+    "When working with large datasets, visualizations are often the only practical way to understand the properties of that dataset -- it's too easy to get fooled by statistical measures computed blindly, yet too many data points to examine each one!  Thus it is very important to be aware of some common plotting problems that are minor inconveniences with small datasets but very serious problems with larger ones.\n",
     "\n",
     "We'll cover:\n",
     "\n",
@@ -454,7 +454,7 @@
    "source": [
     "### [Datashader](https://github.com/holoviz/datashader)\n",
     "\n",
-    "The steps above show how to avoid the six main plotting pitfalls by hand, but it can be awkward and relatively slow to do so.  Luckily there is a new Python library available to automate and optimize these steps, named [Datashader](https://github.com/bokeh/datashader).  Datashader avoids users having to make dataset-dependent decisions and parameter settings when visualizing a new dataset.  Datashader makes it practical to create accurate visualizations of datasets too large to understand directly, up to a billion points on a normal laptop and larger datasets on a compute cluster.  As a simple teaser, the above steps can be expressed very concisely using the Datashader interface provided by [HoloViews](https://holoviews.org):"
+    "The steps above show how to avoid the six main plotting pitfalls by hand, but it can be awkward and relatively slow to do so.  Luckily there is now a highly optimized Python library available to automate these steps, named [Datashader](https://github.com/bokeh/datashader).  Datashader avoids users having to make dataset-dependent decisions and parameter settings when visualizing a new dataset.  Datashader makes it practical to create accurate visualizations of datasets too large to understand directly, up to a billion points on a normal laptop and larger datasets on a compute cluster.  As a simple teaser, the above steps can be expressed very concisely using the Datashader interface provided by [HoloViews](https://holoviews.org):"
    ]
   },
   {
@@ -488,7 +488,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "See the [Datashader web site](https://datashader.org) for details and examples to help you get started."
+    "Notice how in the same plot you can see all the peaks, the random sampling toward the edges, and gradual changes in structure in between, all without adjusting a single plotting parameter. See the [Datashader web site](https://datashader.org) for details and examples to help you get started."
    ]
   }
  ],