Sort_by providing zero-copy views (and more)

doc/getting_started/tutorials/13.ctable-basics.ipynb

@@ -774,12 +774,7 @@
    "cell_type": "markdown",
    "id": "4f466e5d",
    "metadata": {},
-   "source": [
-    "### 3.3 Sorting\n",
-    "\n",
-    "`sort_by()` returns a sorted copy by default (or sorts in-place with `inplace=True`).\n",
-    "Multi-column sorting is supported — primary key first."
-   ]
+   "source": "### 3.3 Sorting\n\n`sort_by()` returns a sorted copy by default (or sorts in-place with `inplace=True`).\nPass `view=True` for a zero-copy sorted **view** that shares the table's data and gathers\nrows on demand — ideal for reading a sorted slice of a large table without copying it.\nMulti-column sorting is supported — primary key first."
   },
   {
    "cell_type": "code",
@@ -1197,37 +1192,9 @@
      "start_time": "2026-05-21T09:38:01.039615Z"
     }
    },
-   "source": [
-    "# Top 10 hottest days in Madrid across the whole year\n",
-    "# Sort the full table, then filter — views cannot be sorted directly\n",
-    "hottest_all = climate.sort_by(\"temperature\", ascending=False)\n",
-    "madrid_sorted = hottest_all.where(hottest_all.city == \"Madrid\")\n",
-    "print(\"10 hottest days in Madrid:\")\n",
-    "print(madrid_sorted.select([\"city\", \"day\", \"temperature\", \"humidity\"]).head(10))"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 hottest days in Madrid:\n",
-      "     city  day  temperature   humidity\n",
-      "0  Madrid  191    31.399208  42.543335\n",
-      "1  Madrid  190    31.232576  44.303246\n",
-      "2  Madrid  227    31.227442  46.992290\n",
-      "3  Madrid  194    30.915184  35.044228\n",
-      "4  Madrid  186    30.879374  48.080303\n",
-      "5  Madrid  202    30.745684  43.722813\n",
-      "6  Madrid  177    30.469023  38.390163\n",
-      "7  Madrid  163    30.215179  46.051888\n",
-      "8  Madrid  181    30.181025  43.726521\n",
-      "9  Madrid  184    29.936199  50.654797\n",
-      "\n",
-      "[10 rows x 4 columns]\n"
-     ]
-    }
-   ],
-   "execution_count": 21
+   "source": "# Top 10 hottest days in Madrid across the whole year.\n# Views *can* be sorted: sort_by() on a where()-view returns a zero-copy sorted\n# view — it shares the table's columns and gathers rows on demand, no full-table\n# copy. (On a base table, pass view=True for the same lazy behaviour.)\nmadrid = climate.where(climate.city == \"Madrid\")\nmadrid_sorted = madrid.sort_by(\"temperature\", ascending=False)\nprint(\"10 hottest days in Madrid:\")\nprint(madrid_sorted.select([\"city\", \"day\", \"temperature\", \"humidity\"]).head(10))",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -2876,30 +2843,7 @@
    "cell_type": "markdown",
    "id": "405cd155",
    "metadata": {},
-   "source": [
-    "---\n",
-    "## Summary\n",
-    "\n",
-    "Here's everything we covered:\n",
-    "\n",
-    "| Feature | API |\n",
-    "|---------|-----|\n",
-    "| Create | `CTable(Schema)`, `CTable(Schema, new_data=...)` |\n",
-    "| Insert | `append(row)`, `extend(list_or_array)` |\n",
-    "| View | `head()`, `tail()`, `print(t)`, `t.info()` |\n",
-    "| Filter | `where(expr)` → view |\n",
-    "| Project | `select([cols])` → view |\n",
-    "| Sort | `sort_by(cols)`, `sort_by(cols, inplace=True)` |\n",
-    "| Aggregates | `col.sum()`, `.mean()`, `.std()`, `.min()`, `.max()` |\n",
-    "| Stats | `describe()`, `cov()` |\n",
-    "| Mutate | `delete()`, `compact()`, `add_column()`, `drop_column()`, `assign()` |\n",
-    "| Persist | `save(path)`, `to_b2z()`, `to_b2d()`, `CTable.open(path)`, `CTable.load(path)` |\n",
-    "| Interop | `to_arrow()`, `from_arrow()`, `to_csv()`, `from_csv()` |\n",
-    "| Nullable | `null_value=` on spec, `is_null()`, `notnull()`, `null_count()` |\n",
-    "\n",
-    "CTable is designed for **compressed analytical workloads** — large tables that need to stay small in RAM\n",
-    "while still being fast to query and easy to persist."
-   ]
+   "source": "---\n## Summary\n\nHere's everything we covered:\n\n| Feature | API |\n|---------|-----|\n| Create | `CTable(Schema)`, `CTable(Schema, new_data=...)` |\n| Insert | `append(row)`, `extend(list_or_array)` |\n| View | `head()`, `tail()`, `print(t)`, `t.info()` |\n| Filter | `where(expr)` → view |\n| Project | `select([cols])` → view |\n| Sort | `sort_by(cols)`, `sort_by(cols, view=True)`, `sort_by(cols, inplace=True)` |\n| Aggregates | `col.sum()`, `.mean()`, `.std()`, `.min()`, `.max()` |\n| Stats | `describe()`, `cov()` |\n| Mutate | `delete()`, `compact()`, `add_column()`, `drop_column()`, `assign()` |\n| Persist | `save(path)`, `to_b2z()`, `to_b2d()`, `CTable.open(path)`, `CTable.load(path)` |\n| Interop | `to_arrow()`, `from_arrow()`, `to_csv()`, `from_csv()` |\n| Nullable | `null_value=` on spec, `is_null()`, `notnull()`, `null_count()` |\n\nCTable is designed for **compressed analytical workloads** — large tables that need to stay small in RAM\nwhile still being fast to query and easy to persist."
   }
  ],
  "metadata": {