minor edits to the quick intro

notebooks/00-quick_intro.ipynb

@@ -57,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With gliderpy attain the same output as erddapy with fewer lines and a cleaner UI."
+    "And here is how to use gliderpy to obtain the same results but with fewer lines and a cleaner code:"
    ]
   },
   {
@@ -79,34 +79,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The variable names are also standardised by gliderpy\n",
-    "\n",
-    "### Single dataset\n",
+    "The variable names are standardised by gliderpy, making it easier to fetch from different data sources and comparing the results.\n",
     "\n",
     "`gliderpy` can subset the data on the server side by passing a geographic bounding box and time interval."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "glider_grab = GliderDataFetcher()\n",
-    "glider_grab.fetcher.dataset_id = \"whoi_406-20160902T1700\"\n",
-    "glider_grab.query(38, 41, -72, -69, \"2016-09-20T01:00:00\", \"25th sept 2016\")\n",
-    "\n",
-    "df = glider_grab.to_pandas()\n",
-    "df.head()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Multiple datasets\n",
     "\n",
-    "If a user does not specify a `dataset_id`, `gliderpy` will search all datasets for data that falls within the query bounds."
+    "The most common use is to search all datasets for data that falls within the certain space-time bounds."
    ]
   },
   {
@@ -128,7 +112,7 @@
    "source": [
     "### Dataset search\n",
     "\n",
-    "One can query all datasets available."
+    "One can query all dataset_ids available."
    ]
   },
   {
@@ -162,7 +146,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,