update dask tutorial (#309)

* saving

* updates

tutorials/using-dask/1.intro-to-dask.ipynb

@@ -83,7 +83,7 @@
     "from distributed import Client\n",
     "\n",
     "c = Client()\n",
-    "# c.restart()\n",
+    "c.restart()\n",
     "c"
    ]
   },
@@ -201,8 +201,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "container_name = \"isdweatherdatacontainer\"\n",
-    "\n",
+    "color = \"green\"\n",
+    "container_name = \"nyctlc\"\n",
     "storage_options = {\"account_name\": \"azureopendatastorage\"}"
    ]
   },
@@ -222,8 +222,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%time\n",
-    "fs.ls(f\"{container_name}/ISDWeather/year=2020\")"
+    "fs.ls(f\"{container_name}\")"
    ]
   },
   {
@@ -232,7 +231,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "files = fs.glob(f\"{container_name}/ISDWeather/year=2020/month=2/*.parquet\")\n",
+    "fs.ls(f\"{container_name}/{color}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fs.ls(f\"{container_name}/{color}/puYear=2016/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = fs.glob(f\"{container_name}/{color}/puYear=2016/puMonth=12/*.parquet\")\n",
     "files = [f\"az://{file}\" for file in files]\n",
     "files[-5:]"
    ]
@@ -240,25 +257,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "%%time\n",
-    "ddf = dd.read_parquet(\n",
-    "    files, storage_options=storage_options, chunksize=\"100MB\"\n",
+    "ddf = (\n",
+    "    dd.read_parquet(files, storage_options=storage_options)\n",
+    "    .repartition(npartitions=8)\n",
+    "    .persist()\n",
     ")\n",
     "ddf"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ddf = ddf.persist()  # persist all or some of data in RAM"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -276,7 +288,62 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "ddf.describe().compute()"
+    "len(ddf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ddf.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.style.use(\"dark_background\")\n",
+    "\n",
+    "ddf[\"tipAmount\"].compute().hist(\n",
+    "    figsize=(16, 8), bins=256, range=(0.1, 20),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = ddf.compute()\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "gbs = round(df.memory_usage(index=True, deep=True).sum() / 1e9, 2)\n",
+    "print(f\"df is {gbs} GBs\")"
    ]
   },
   {