try except for failing case & typo fix

Author: Graciaaa3

docs/tutorials/groupby_doc.ipynb

@@ -8,7 +8,6 @@
     "# GroupBy for NestedPandas\n",
     "\n",
     "This notebook explores how Pandas' built-in `groupby` interacts with `NestedPandas` structures.\n",
-    "<!-- highlight what works, what doesn’t, and why — with clear examples and explanations. -->\n",
     "\n",
     "Because Nested-Pandas extends the Pandas library, native ``pandas.DataFrame.groupby`` works with nested-pandas out of the box in some ways. "
    ]
@@ -59,7 +58,7 @@
     "\n",
     "- Some built-in methods like `count` work but not as expected (view nested column as a single object).\n",
     "- Others (`min`, `max`, `mean`) fail on nested columns.\n",
-    "- Interestingly, `describe` will work as expcted with the automatic flattened nested column."
+    "- Interestingly, `describe` will work as expected with the automatic flattened nested column."
    ]
   },
   {
@@ -69,7 +68,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# count is viewing nested columns as signle objects\n",
+    "# count is viewing nested columns as single objects\n",
     "nf.groupby(\"c\").count()"
    ]
   },
@@ -81,7 +80,11 @@
    "outputs": [],
    "source": [
     "# min/max/mean fail on nested columns\n",
-    "nf.groupby(\"c\").min()  # will produce error"
+    "try:\n",
+    "    grouped_min = nf.groupby(\"c\").min()\n",
+    "    print(grouped_min)\n",
+    "except TypeError as e:\n",
+    "    print(f\"Cannot compute min on nested columns: {e}\")"
    ]
   },
   {
@@ -101,7 +104,7 @@
    "metadata": {},
    "source": [
     "## Type Preservation\n",
-    "Within each group, the object remains accessible as ``NestedFrame`` object and the nested columns remain ``NestedSeries``.\n",
+    "Within each group, the object remains accessible as a ``NestedFrame`` object and the nested columns remain ``NestedSeries``.\n",
     "\n",
     "We can check this by applying a custom function on our 2-group `groupby` object:"
    ]
@@ -208,7 +211,7 @@
     "\n",
     "`.apply()` for nested operations is supported natively. It generally works if the function flattens or use index slicing to ensure matching type for operations. \n",
     "\n",
-    "Some potential exmaples:"
+    "Some potential examples:"
    ]
   },
   {
@@ -255,13 +258,15 @@
     "- Use **slice-based indexing** (.iloc[0:1]) to preserve nested types.\n",
     "- Use **.nest.to_flat()** to flatten a nested column when needed for numerical or aggregating operations.\n",
     "\n",
-    "- Nested structures are designed to reduce the need for expensive groupby operations by allowing data to stay organized hierarchically. However, when grouping is necessary, pandas’ groupby still works with nested-pandas and maintains type consistency."
+    "- Nested structures are designed to reduce the need for expensive groupby operations by allowing data to stay organized hierarchically. However, when grouping is necessary, pandas’ groupby still works with nested-pandas and maintains type consistency.\n",
+    "\n",
+    "- Some use cases may behave unexpectedly because of the nested structures. We encourage users to open issues if you run into unexpected behavior or edge cases.\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },