Nebari demo video (#85)

nebari-dev · Aug 28, 2024 · d930e8c · d930e8c
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diff --git a/02-conda-store.ipynb b/02-conda-store.ipynb
@@ -7,7 +7,9 @@
    "source": [
     "# Manage environments\n",
     "\n",
-    "Create a new data science environment with the libraries you need for your work. Go to \"Services\" -> \"Environment management\" and follow the instructions in this [tutorial](https://conda.store/conda-store-ui/tutorials/create-envs)."
+    "Create a new data science environment with the libraries you need for your work. Go to \"Services\" -> \"Environment management\" and follow the instructions in this [tutorial](https://conda.store/conda-store-ui/tutorials/create-envs).\n",
+    "\n",
+    "Let's try some Polars examples: https://docs.pola.rs/user-guide/getting-started/"
    ]
   },
   {
@@ -17,16 +19,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import polars as pl\n",
-    "\n",
-    "q = (\n",
-    "    pl.scan_csv(\"docs/data/iris.csv\")\n",
-    "    .filter(pl.col(\"sepal_length\") > 5)\n",
-    "    .group_by(\"species\")\n",
-    "    .agg(pl.all().sum())\n",
-    ")\n",
-    "\n",
-    "df = q.collect()"
+    "import polars as pl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc9aee9a-c99f-4749-9d92-45bba4a16ead",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Add your Polars code"
    ]
   },
   {

diff --git a/03-app-sharing.ipynb b/03-app-sharing.ipynb
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7a4de979-d4c3-4e7e-bca1-09122e862e5b",
+   "id": "e4ae0e94-d2ea-431d-a3fd-05b8652148cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,15 +25,77 @@
     "from sklearn.cluster import KMeans\n",
     "from bokeh.sampledata import iris\n",
     "\n",
-    "pn.extension()\n",
-    "\n",
+    "pn.extension()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdaac3fb-adb2-426d-89eb-c34f3a685426",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "flowers = iris.flowers.copy()\n",
-    "cols = list(flowers.columns)[:-1]\n",
-    "\n",
+    "cols = list(flowers.columns)[:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e52853f3-0556-4f9a-a49f-8b5987f57790",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "x = pn.widgets.Select(name='x', options=cols)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23513293-bcbc-4263-b3bd-faf9fe18405f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "y = pn.widgets.Select(name='y', options=cols, value='sepal_width')\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99e6275d-4f2d-45e0-b803-40e2304531e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "n_clusters = pn.widgets.IntSlider(name='n_clusters', start=1, end=5, value=3)\n",
+    "n_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd2382ca-5c85-4fd3-9b75-2b31aac9cc1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "widgets = pn.WidgetBox(\n",
+    "    pn.Column(\n",
+    "        \"\"\"This app provides an example of **building a simple dashboard using Panel**.\\n\\nIt demonstrates how to take the output of **k-means clustering on the Iris dataset** using scikit-learn, parameterizing the number of clusters and the variables to plot.\\n\\nThe entire clustering and plotting pipeline is expressed as a **single reactive function** that responsively returns an updated plot when one of the widgets changes.\\n\\n The **`x` marks the center** of the cluster.\"\"\",\n",
+    "        x, y, n_clusters\n",
+    "    )\n",
+    ")\n",
     "\n",
+    "widgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5102827d-8a22-4e04-81af-fd4b1bc6035a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def get_clusters(x, y, n_clusters):\n",
     "    kmeans = KMeans(n_clusters=n_clusters, n_init='auto')\n",
     "    est = kmeans.fit(iris.flowers.iloc[:, :-1].values)\n",
@@ -48,23 +110,34 @@
     "        )\n",
     "    )\n",
     "\n",
-    "widgets = pn.WidgetBox(\n",
-    "    pn.Column(\n",
-    "        \"\"\"This app provides an example of **building a simple dashboard using Panel**.\\n\\nIt demonstrates how to take the output of **k-means clustering on the Iris dataset** using scikit-learn, parameterizing the number of clusters and the variables to plot.\\n\\nThe entire clustering and plotting pipeline is expressed as a **single reactive function** that responsively returns an updated plot when one of the widgets changes.\\n\\n The **`x` marks the center** of the cluster.\"\"\",\n",
-    "        x, y, n_clusters\n",
-    "    )\n",
-    ")\n",
-    "\n",
     "clusters = pn.pane.HoloViews(\n",
     "    pn.bind(get_clusters, x, y, n_clusters), sizing_mode='stretch_width'\n",
     ")\n",
     "\n",
+    "clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7563992f-cc28-4271-9c65-00b880824c60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "dashboard = pn.template.MaterialTemplate(\n",
     "    title=\"Iris K-Means Clustering\",\n",
     "    sidebar=[widgets],\n",
     "    main=[clusters],\n",
-    ")\n",
-    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49041ce0-0569-4952-8244-341bcf52a621",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "dashboard.servable()"
    ]
   },

diff --git a/04-dask.ipynb b/04-dask.ipynb
@@ -17,7 +17,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from dask_gateway import Gateway"
+    "from dask_gateway import Gateway\n",
+    "import dask.dataframe as dd\n",
+    "\n",
+    "import hvplot\n",
+    "import hvplot.dask"
    ]
   },
   {
@@ -66,16 +70,41 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b15ccbef-9f01-4775-b082-dc24d96c061d",
+   "id": "530dcbdb-5a4f-497e-9d8b-c00a9bb10f60",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import dask.array as da\n",
-    "\n",
-    "x = da.random.random((10000, 10000))\n",
-    "y = (x + x.T) - x.mean(axis=1)\n",
-    "\n",
-    "z = y.var(axis=0).compute()"
+    "ddf = dd.read_parquet(\"gcs://quansight-datasets/airline-ontime-performance/sorted/parquet_by_year\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9da49eb2-ee57-409a-a3bf-73e2c6cc196f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68434394-929a-4503-a149-6bd78bb96ab1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1324da8a-5ac8-4639-85ee-1bc393f27e4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf.groupby(\"FL_DATE\")[\"CANCELLED\"].count().hvplot()"
    ]
   },
   {
@@ -110,9 +139,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "global-global-demo",
+   "display_name": "global-global-demo-dask",
    "language": "python",
-   "name": "conda-env-global-global-demo-py"
+   "name": "conda-env-global-global-demo-dask-py"
   },
   "language_info": {
    "codemirror_mode": {

diff --git a/06-workflows.ipynb b/06-workflows.ipynb
@@ -43,11 +43,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a4918333-770a-4a71-a09a-57c031a5ea71",
+   "id": "5428a803-5ae3-460f-8963-7325282a1e51",
    "metadata": {},
    "outputs": [],
    "source": [
-    "nebari_events[0]"
+    "for i in range(3):\n",
+    "    print(f\"Member: {nebari_events[i]['actor']['login']} \\nEvent: {nebari_events[i][\"type\"]} \\nTime: {nebari_events[i]['created_at']} \\n\")"
    ]
   },
   {
@@ -60,6 +61,14 @@
     "with open('nebari_events.txt', 'a') as f:\n",
     "    f.write(str(nebari_events))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4cd968a-a375-4435-a036-9b0feb20edc9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {