update on missing value function in example doc

docs/example.ipynb

@@ -82,16 +82,35 @@
    "source": [
     "## Check if data file has any missing values\n",
     "\n",
-    "After verifying the data file type, the next step is to check whether the data contains any missing values using `missing_values_summary`."
+    "After verifying the data file type, the next step is to check whether the data contains any missing values using `missing_values_summary`.\n",
+    "\n",
+    "This function will quickly provide a summary including:        \n",
+    "    •   The count of missing values for each column.                             \n",
+    "    •   The percentage of missing values for each column."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Age     1 (25.0%)\n",
+       "City    1 (25.0%)\n",
+       "Name: Missing Count (Percentage), dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "sample_df = pd.read_csv(file_name)"
+    "sample_df = pd.read_csv(file_name)\n",
+    "\n",
+    "missing_values_summary(sample_df)"
    ]
   },
   {
@@ -207,6 +226,13 @@
     "get_summary_statistics(sample_df)\n",
     "get_summary_statistics(sample_df, col=[\"Age\", \"City\"])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -225,7 +251,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,