From b3f46a3c675d5c9b7ced26a466a8646ca09d0c09 Mon Sep 17 00:00:00 2001
From: Julia Kent <46687291+jukent@users.noreply.github.com>
Date: Fri, 10 Jan 2025 10:09:09 -0700
Subject: [PATCH] rm from comparison

---
 notebooks/1-comparison.ipynb | 114 +----------------------------------
 1 file changed, 1 insertion(+), 113 deletions(-)

diff --git a/notebooks/1-comparison.ipynb b/notebooks/1-comparison.ipynb
index 70c2608..a72ffc8 100644
--- a/notebooks/1-comparison.ipynb
+++ b/notebooks/1-comparison.ipynb
@@ -260,119 +260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plotly\n",
-    "\n",
-    "<img src=\"images/logos/plotly.png\" width=250 alt=\"Plotly Logo\"></img>\n",
-    "\n",
-    "Plotly is solid choice for interactive plotting. Plotly has functionality in several languags. Here is the [Plotly Python documentation](https://plotly.com/python/).\n",
-    "\n",
-    "Here is an example using their \"Express\" functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import plotly.express as px\n",
-    "\n",
-    "fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Seaborn\n",
-    "\n",
-    "<img src=\"images/logos/seaborn.svg\" width=250 alt=\"Seaborn Logo\"></img>\n",
-    "\n",
-    "Seaborn is a high level interactive interface for creating statistical visualizations built on matplotlib. Check out the [Seaborn documentation](https://seaborn.pydata.org/).\n",
-    "\n",
-    "Here is their [heatmap example](https://seaborn.pydata.org/examples/spreadsheet_heatmap.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "sns.set_theme()\n",
-    "\n",
-    "# Load the example flights dataset and convert to long-form\n",
-    "flights_long = sns.load_dataset(\"flights\")\n",
-    "flights = flights_long.pivot(index=\"month\", columns=\"year\", values=\"passengers\")\n",
-    "\n",
-    "# Draw a heatmap with the numeric values in each cell\n",
-    "f, ax = plt.subplots(figsize=(9, 6))\n",
-    "sns.heatmap(flights, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n",
-    "\n",
-    "plt.show();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bokeh\n",
-    "\n",
-    "<img src=\"images/logos/bokeh.jpeg\" width=250 alt=\"Bokeh Logo\"></img>\n",
-    "\n",
-    "Bokeh is a Javascript-powered tool for creating interactive visualizations in modern web browsers. Check out the [Bokeh documentation](https://bokeh.org/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## UXarray\n",
-    "\n",
-    "<img src=\"images/logos/uxarray.png\" width=250 alt=\"UXarray Logo\"></img>\n",
-    "\n",
-    "UXarray specializes in unstructured grids, built around [UGRID conventions](https://ugrid-conventions.github.io/ugrid-conventions/) and Xarray syntax. See the [UXarray documentation](https://uxarray.readthedocs.io/en/latest/) and check out the the [UXarray Cookbook](https://projectpythia.org/unstructured-grid-viz-cookbook)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## hvPlot\n",
-    "\n",
-    "<img src=\"images/logos/hvplot.svg\" width=250 alt=\"Datashader Logo\"></img>\n",
-    "\n",
-    "hvPlot wraps both [Datashader](https://datashader.org/), a graphics pipeline, and [Holoviews](https://holoviews.org/), a tool for bundling data and metadata for intuitive interactive plotting, at a higher level. All 3 tools are by [Holoviz](https://holoviz.org/). Reference the [hvPlot documentation](https://hvplot.holoviz.org/).\n",
-    "\n",
-    "Here is a simple example from their [user guide](https://hvplot.holoviz.org/user_guide/Introduction.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import hvplot.pandas\n",
-    "\n",
-    "pd.options.plotting.backend = 'holoviews'\n",
-    "\n",
-    "index = pd.date_range('1/1/2000', periods=1000)\n",
-    "df = pd.DataFrame(np.random.randn(1000, 4), index=index, columns=list('ABCD')).cumsum()\n",
-    "\n",
-    "df.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This useful diagram from [hvPlot's documentation](https://hvplot.holoviz.org/index.html) details how different high-level tools for data visualization interact.\n",
-    "\n",
-    "<img src=\"images/hvplot_diagram.svg\" alt=\"Datashader Logo\"></img>"
+    "Interactive visualization libraries such as Plotly, UXarray, seaborn, bokeh, and hvplot will be explored in a separate interactive plotting Cookbook."
    ]
   },
   {