diff --git a/notebooks/01_thicket_tutorial.ipynb b/notebooks/01_thicket_tutorial.ipynb
index 3e8d1c78..339eb7ad 100644
--- a/notebooks/01_thicket_tutorial.ipynb
+++ b/notebooks/01_thicket_tutorial.ipynb
@@ -181,8 +181,55 @@
     "tags": []
    },
    "source": [
-    "## 4. Thicket Components\n",
+    "## 4. Thicket Components"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fe4f805",
+   "metadata": {},
+   "source": [
+    "### 4.i Profiles\n",
     "\n",
+    "The file paths for performance profiles are represented as integer hashes in Thicket. The mapping between the integer hashes and the performance profile paths can be accessed in the `Thicket.profile_mapping` dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "332798db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "th_lassen.profile_mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "569b2677",
+   "metadata": {},
+   "source": [
+    "#### Filtering Profiles\n",
+    "\n",
+    "We can filter for specific profiles using the `Thicket.filter_profiles()` function. Lets say we only want the profiles with a problem size less than 4 milion. We can either read in the files for 1 and 2 million into a new Thicket, or we can filter the existing Thicket object, which is shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2127de91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "th_less_4m = th_lassen.filter_profile([1814734126, 3448088579])\n",
+    "th_less_4m.profile_mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9572ecd",
+   "metadata": {},
+   "source": [
     "### 4.1 Performance Data\n",
     "\n",
     "The performance data table is a multi-dimensional, multi-indexed component of thicket. The rows represent nodes that each contain a different execution (i.e., profile index) of the associated call tree node. \n",
@@ -1172,7 +1219,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.7"
   },
   "papermill": {
    "default_parameters": {},