Repository: ploomber/jupysql (language: python)

Issue:
allow the schema to be specified in --persist
Allow schema to be specified when saving a df using `--persist`

Context :
![image](https://github.com/ploomber/jupysql/assets/29096135/2a4a9cda-2ec8-4ada-9653-85527d744ebc)



Discussion / hints:
if the user passes a `something.another`, we should interpret `something` as the schema, then validate that `another` is a valid identifier

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4842ec4d7..ae65968c8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,7 @@
 * [Fix] Fixed bug that returns empty results when exception is raised from DB driver
 * [Fix] Added guards to check and raise errors when arguments are entered twice in %sql, %sqlcmd and %sqlplot (#806)
 * [Fix] Fixed bug that returns snippet typo error message when another table is misspelled (#940)
+* [Feature] Allow user to specify the schema when saving dataframes using `--persist` (#945)
 
 ## 0.10.3 (2023-11-06)
 
diff --git a/doc/integrations/pandas.md b/doc/integrations/pandas.md
index 868231f83..b2cd887d1 100644
--- a/doc/integrations/pandas.md
+++ b/doc/integrations/pandas.md
@@ -127,3 +127,23 @@ df
 ```{code-cell} ipython3
 %sql SELECT * FROM df;
 ```
+
+### `--persist` in schema
+
+A schema can also be specified when persisting a dataframe.
+
+```{code-cell} ipython3
+%%sql duckdb://
+CREATE SCHEMA IF NOT EXISTS schema1;
+CREATE TABLE numbers (num INTEGER);
+INSERT INTO numbers VALUES (1);
+INSERT INTO numbers VALUES (2);
+```
+
+```{code-cell} ipython3
+results = %sql SELECT * FROM numbers;
+```
+
+```{code-cell} ipython3
+%sql --persist schema1.results
+```
diff --git a/doc/integrations/trinodb.ipynb b/doc/integrations/trinodb.ipynb
index 7228c4c45..464e50f33 100644
--- a/doc/integrations/trinodb.ipynb
+++ b/doc/integrations/trinodb.ipynb
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "tags": []
    },
@@ -87,7 +87,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "f9e0890fa2dea4de751347f4c6a4194dbbbdc7865535caeea0ae27e1f20245e0\n"
+      "cba8365556d3f35dd56cfd06747276ef1c7b7661eb4268b74e665d8d4d44a7e7\n"
      ]
     }
    ],
@@ -168,20 +168,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/r6/q0qh39ts70582wth4g1dhjhm0000gn/T/ipykernel_48433/191114213.py:3: SADeprecationWarning: The dbapi() classmethod on dialect classes has been renamed to import_dbapi().  Implement an import_dbapi() classmethod directly on class <class 'trino.sqlalchemy.dialect.TrinoDialect'> to remove this warning; the old .dbapi() classmethod may be maintained for backwards compatibility.\n",
-      "  engine = create_engine(\"trino://user@localhost:8080/memory\", connect_args={\"user\": \"user\"})\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sqlalchemy import create_engine\n",
     "\n",
@@ -212,22 +203,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "Tip: You may define configurations in /Users/neelashasen/Dev/jupysql_master/jupysql/pyproject.toml. Please review our <a href='https://jupysql.ploomber.io/en/latest/api/configuration.html#loading-from-a-file'>configuration guideline</a>."
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Connecting to &#x27;default&#x27;</span>"
+      ],
+      "text/plain": [
+       "Connecting to 'default'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%load_ext sql"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Connecting and switching to connection &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Connecting and switching to connection 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%sql trino://user@localhost:8080/memory"
    ]
@@ -252,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -281,7 +310,7 @@
        "+------+"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -300,7 +329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "tags": []
    },
@@ -461,7 +490,7 @@
        "+-----------------------+-----------+----------+---------+"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -480,18 +509,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*  trino://user@localhost:8080/memory\n",
-      "Done.\n"
-     ]
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
@@ -517,7 +550,7 @@
        "+-------+"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "tags": []
    },
@@ -548,18 +581,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*  trino://user@localhost:8080/memory\n",
-      "Done.\n"
-     ]
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
@@ -585,7 +622,7 @@
        "+-------+"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -598,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "tags": []
    },
@@ -609,18 +646,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*  trino://user@localhost:8080/memory\n",
-      "Done.\n"
-     ]
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
@@ -646,7 +687,7 @@
        "+-------+"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -667,18 +708,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*  trino://user@localhost:8080/memory\n",
-      "Skipping execution...\n"
-     ]
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Skipping execution...</span>"
+      ],
+      "text/plain": [
+       "Skipping execution..."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -692,18 +749,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*  trino://user@localhost:8080/memory\n",
-      "Done.\n"
-     ]
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
@@ -733,7 +794,7 @@
        "+-------+------------------+-------+"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -754,7 +815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "tags": []
    },
@@ -767,7 +828,7 @@
       "SELECT *\n",
       "FROM default.taxi\n",
       "WHERE passenger_count > 3\n",
-      "-- remove top 1% outliers for better visualization\n",
+      "\n",
       "AND trip_distance < 18.93)\n",
       "SELECT MIN(trip_distance), AVG(trip_distance), MAX(trip_distance)\n",
       "FROM many_passengers\n"
@@ -797,22 +858,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*  trino://user@localhost:8080/memory\n",
-      "Done.\n"
-     ]
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
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",
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+duxYue05evSoceuttxp+fn6Gr6+vcdtttxlpaWnnvAX77GWc69bqivZTRYqLi42ZM2cabdq0MTw9PY2WLVsacXFxRkFBQaXWcyHnuvU8JiamwvHn2u49e/YYw4YNM3x8fIxGjRoZsbGxxqlTpy64/ujoaKfHFjRo0MAIDg42oqKijA8++MAoKSkp95mzbz3/z3/+Y/Tr189o3LixYbVajXbt2hlTpkwxcnJynD73xBNPGFdccYXh4eHhdBv6p59+anTv3t2oV6+eERwcbDzzzDPG66+/Xu5W9datW1f4yISz6zEMw/j111+N2NhY44orrjC8vLyMFi1aGNHR0cbx48cdY4qKioxnnnnG6Nq1q2G1Wo1GjRoZoaGhxsyZM8vVDlwqi2Fc5MwyAJetnTt36i9/+YveeustjRw50t3lXPZmzJihmTNn6tixY+XuOANQezBnBzCpU6dOlWubN2+ePDw8LvjkYgAwE+bsACY1e/Zsbd++XTfccIPq1q2rFStWaMWKFRo3bhy39gL4UyHsACb1t7/9TYmJiXriiSeUl5enVq1aacaMGXr00UfdXRoA1Cjm7AAAAFNjzg4AADA1wg4AADA15uzozPe0pKWlycfHh0eXAwBwmTAMQydPnlTz5s3l4XHu8zeEHUlpaWncnQIAwGXqyJEjatGixTn7CTuS45HqR44ckc1mc3M1AACgMnJzc9WyZUunL0auCGFH/+97gmw2G2EHAIDLzIWmoDBBGQAAmBphBwAAmJpbw05wcLAsFku5V0xMjCSpoKBAMTExaty4sby9vRUVFaXMzEynZaSmpioyMlINGjRQQECApkyZotOnT7tjcwAAQC3k1rDzzTffKD093fFKTEyUJN12222SpAkTJuizzz7TsmXLtH79eqWlpWno0KGOz5eUlCgyMlJFRUXatGmTlixZosWLF2vatGlu2R4AAFD71Kqvi3jooYe0fPlyHThwQLm5uWratKneeecdDRs2TJK0b98+denSRcnJyerdu7dWrFihv//970pLS1NgYKAkaeHChZo6daqOHTsmLy+vSq03NzdXvr6+ysnJYYIyAACXicr+/a41c3aKior01ltv6e6775bFYtH27dtVXFys8PBwx5jOnTurVatWSk5OliQlJyerW7dujqAjSREREcrNzdXu3bvPua7CwkLl5uY6vQAAgDnVmrDz8ccfKzs7W6NHj5YkZWRkyMvLS35+fk7jAgMDlZGR4Rjzx6BT1l/Wdy7x8fHy9fV1vHigIAAA5lVrws5rr72mgQMHqnnz5tW+rri4OOXk5DheR44cqfZ1AgAA96gVDxX8+eeftXr1an344YeOtqCgIBUVFSk7O9vp7E5mZqaCgoIcY7Zu3eq0rLK7tcrGVMRqtcpqtVbhFgAAgNqqVpzZWbRokQICAhQZGeloCw0Nlaenp5KSkhxtKSkpSk1Nld1ulyTZ7XZ9//33ysrKcoxJTEyUzWZTSEhIzW0AAACotdx+Zqe0tFSLFi1SdHS06tb9f+X4+vpq7Nixmjhxovz9/WWz2XT//ffLbrerd+/ekqT+/fsrJCREo0aN0uzZs5WRkaHHHntMMTExnLkBAACSakHYWb16tVJTU3X33XeX65s7d648PDwUFRWlwsJCRURE6OWXX3b016lTR8uXL9f48eNlt9vVsGFDRUdHa9asWTW5CQAAoBarVc/ZcReeswMAwOXnsnvODgAAQHUg7AAAAFNz+5wdswt+5HN3l3DRfkqIvPAgAAAuE5zZAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApub2sPPLL7/oH//4hxo3bqz69eurW7du2rZtm6PfMAxNmzZNzZo1U/369RUeHq4DBw44LePEiRMaOXKkbDab/Pz8NHbsWOXl5dX0pgAAgFrIrWHnt99+U58+feTp6akVK1Zoz549eu6559SoUSPHmNmzZ2v+/PlauHChtmzZooYNGyoiIkIFBQWOMSNHjtTu3buVmJio5cuXa8OGDRo3bpw7NgkAANQyFsMwDHet/JFHHtHGjRv11VdfVdhvGIaaN2+uSZMmafLkyZKknJwcBQYGavHixRoxYoT27t2rkJAQffPNN+rZs6ckaeXKlRo0aJCOHj2q5s2bX7CO3Nxc+fr6KicnRzabreo2UFLwI59X6fJqwk8Jke4uAQCAC6rs32+3ntn59NNP1bNnT912220KCAjQX/7yF7366quO/sOHDysjI0Ph4eGONl9fX/Xq1UvJycmSpOTkZPn5+TmCjiSFh4fLw8NDW7ZsqXC9hYWFys3NdXoBAABzcmvY+fHHH7VgwQJ16NBBX375pcaPH68HHnhAS5YskSRlZGRIkgIDA50+FxgY6OjLyMhQQECAU3/dunXl7+/vGHO2+Ph4+fr6Ol4tW7as6k0DAAC1hFvDTmlpqa6++mo9/fTT+stf/qJx48bpnnvu0cKFC6t1vXFxccrJyXG8jhw5Uq3rAwAA7uPWsNOsWTOFhIQ4tXXp0kWpqamSpKCgIElSZmam05jMzExHX1BQkLKyspz6T58+rRMnTjjGnM1qtcpmszm9AACAObk17PTp00cpKSlObfv371fr1q0lSW3atFFQUJCSkpIc/bm5udqyZYvsdrskyW63Kzs7W9u3b3eMWbNmjUpLS9WrV68a2AoAAFCb1XXnyidMmKC//e1vevrpp3X77bdr69ateuWVV/TKK69IkiwWix566CE9+eST6tChg9q0aaPHH39czZs315AhQySdORM0YMAAx+Wv4uJixcbGasSIEZW6EwsAAJibW8POX//6V3300UeKi4vTrFmz1KZNG82bN08jR450jHn44YeVn5+vcePGKTs7W3379tXKlStVr149x5i3335bsbGxCgsLk4eHh6KiojR//nx3bBIAAKhl3PqcndqC5+w44zk7AIDLwWXxnB0AAIDqRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACm5tawM2PGDFksFqdX586dHf0FBQWKiYlR48aN5e3traioKGVmZjotIzU1VZGRkWrQoIECAgI0ZcoUnT59uqY3BQAA1FJ13V1A165dtXr1asf7unX/X0kTJkzQ559/rmXLlsnX11exsbEaOnSoNm7cKEkqKSlRZGSkgoKCtGnTJqWnp+uuu+6Sp6ennn766RrfFgAAUPu4PezUrVtXQUFB5dpzcnL02muv6Z133tGNN94oSVq0aJG6dOmizZs3q3fv3lq1apX27Nmj1atXKzAwUFdddZWeeOIJTZ06VTNmzJCXl1dNbw4AAKhl3D5n58CBA2revLnatm2rkSNHKjU1VZK0fft2FRcXKzw83DG2c+fOatWqlZKTkyVJycnJ6tatmwIDAx1jIiIilJubq927d59znYWFhcrNzXV6AQAAc3Jr2OnVq5cWL16slStXasGCBTp8+LCuvfZanTx5UhkZGfLy8pKfn5/TZwIDA5WRkSFJysjIcAo6Zf1lfecSHx8vX19fx6tly5ZVu2EAAKDWcOtlrIEDBzp+7t69u3r16qXWrVvr/fffV/369attvXFxcZo4caLjfW5uLoEHAACTcvtlrD/y8/NTx44ddfDgQQUFBamoqEjZ2dlOYzIzMx1zfIKCgsrdnVX2vqJ5QGWsVqtsNpvTCwAAmFOtCjt5eXk6dOiQmjVrptDQUHl6eiopKcnRn5KSotTUVNntdkmS3W7X999/r6ysLMeYxMRE2Ww2hYSE1Hj9AACg9nHrZazJkyfr5ptvVuvWrZWWlqbp06erTp06uuOOO+Tr66uxY8dq4sSJ8vf3l81m0/333y+73a7evXtLkvr376+QkBCNGjVKs2fPVkZGhh577DHFxMTIarW6c9MAAEAt4dawc/ToUd1xxx369ddf1bRpU/Xt21ebN29W06ZNJUlz586Vh4eHoqKiVFhYqIiICL388suOz9epU0fLly/X+PHjZbfb1bBhQ0VHR2vWrFnu2iQAAFDLWAzDMNxdhLvl5ubK19dXOTk5VT5/J/iRz6t0eTXhp4RId5cAAMAFVfbvd62aswMAAFDVCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUXAo7P/74Y1XXAQAAUC1cCjvt27fXDTfcoLfeeksFBQVVXRMAAECVcSnsfPvtt+revbsmTpyooKAg/etf/9LWrVurujYAAIBL5lLYueqqq/T8888rLS1Nr7/+utLT09W3b19deeWVmjNnjo4dO1bVdQIAALjkkiYo161bV0OHDtWyZcv0zDPP6ODBg5o8ebJatmypu+66S+np6VVVJwAAgEsuKexs27ZN9913n5o1a6Y5c+Zo8uTJOnTokBITE5WWlqbBgwdXVZ0AAAAuqevKh+bMmaNFixYpJSVFgwYN0htvvKFBgwbJw+NMdmrTpo0WL16s4ODgqqwVAADgorkUdhYsWKC7775bo0ePVrNmzSocExAQoNdee+2SigMAALhULl3GOnDggOLi4s4ZdCTJy8tL0dHRlV5mQkKCLBaLHnroIUdbQUGBYmJi1LhxY3l7eysqKkqZmZlOn0tNTVVkZKQaNGiggIAATZkyRadPn77obQIAAObkUthZtGiRli1bVq592bJlWrJkyUUv75tvvtF//vMfde/e3al9woQJ+uyzz7Rs2TKtX79eaWlpGjp0qKO/pKREkZGRKioq0qZNm7RkyRItXrxY06ZNu/iNAgAApuRS2ImPj1eTJk3KtQcEBOjpp5++qGXl5eVp5MiRevXVV9WoUSNHe05Ojl577TXNmTNHN954o0JDQ7Vo0SJt2rRJmzdvliStWrVKe/bs0VtvvaWrrrpKAwcO1BNPPKGXXnpJRUVFrmwaAAAwGZfCTmpqqtq0aVOuvXXr1kpNTb2oZcXExCgyMlLh4eFO7du3b1dxcbFTe+fOndWqVSslJydLkpKTk9WtWzcFBgY6xkRERCg3N1e7d+8+5zoLCwuVm5vr9AIAAObkUtgJCAjQrl27yrV/9913aty4caWXs3TpUn377beKj48v15eRkSEvLy/5+fk5tQcGBiojI8Mx5o9Bp6y/rO9c4uPj5evr63i1bNmy0jUDAIDLi0th54477tADDzygtWvXqqSkRCUlJVqzZo0efPBBjRgxolLLOHLkiB588EG9/fbbqlevnitluCwuLk45OTmO15EjR2p0/QAAoOa4dOv5E088oZ9++klhYWGqW/fMIkpLS3XXXXdVes7O9u3blZWVpauvvtrRVlJSog0bNujFF1/Ul19+qaKiImVnZzud3cnMzFRQUJAkKSgoqNx3cpXdrVU2piJWq1VWq7VSdQIAgMubS2HHy8tL7733np544gl99913ql+/vrp166bWrVtXehlhYWH6/vvvndrGjBmjzp07a+rUqWrZsqU8PT2VlJSkqKgoSVJKSopSU1Nlt9slSXa7XU899ZSysrIUEBAgSUpMTJTNZlNISIgrmwYAAEzGpbBTpmPHjurYsaNLn/Xx8dGVV17p1NawYUM1btzY0T527FhNnDhR/v7+stlsuv/++2W329W7d29JUv/+/RUSEqJRo0Zp9uzZysjI0GOPPaaYmBjO3AAAAEkuhp2SkhItXrxYSUlJysrKUmlpqVP/mjVrqqS4uXPnysPDQ1FRUSosLFRERIRefvllR3+dOnW0fPlyjR8/Xna7XQ0bNlR0dLRmzZpVJesHAACXP4thGMbFfig2NlaLFy9WZGSkmjVrJovF4tQ/d+7cKiuwJuTm5srX11c5OTmy2WxVuuzgRz6v0uXVhJ8SIt1dAgAAF1TZv98undlZunSp3n//fQ0aNMjlAgEAAGqCS7eee3l5qX379lVdCwAAQJVzKexMmjRJzz//vFy4AgYAAFCjXLqM9fXXX2vt2rVasWKFunbtKk9PT6f+Dz/8sEqKAwAAuFQuhR0/Pz/deuutVV0LAABAlXMp7CxatKiq6wAAAKgWLs3ZkaTTp09r9erV+s9//qOTJ09KktLS0pSXl1dlxQEAAFwql87s/PzzzxowYIBSU1NVWFiom266ST4+PnrmmWdUWFiohQsXVnWdAAAALnHpzM6DDz6onj176rffflP9+vUd7bfeequSkpKqrDgAAIBL5dKZna+++kqbNm2Sl5eXU3twcLB++eWXKikMAACgKrh0Zqe0tFQlJSXl2o8ePSofH59LLgoAAKCquBR2+vfvr3nz5jneWywW5eXlafr06XyFBAAAqFVcuoz13HPPKSIiQiEhISooKNCdd96pAwcOqEmTJnr33XerukYAAACXuRR2WrRooe+++05Lly7Vrl27lJeXp7Fjx2rkyJFOE5YBAADczaWwI0l169bVP/7xj6qsBQAAoMq5FHbeeOON8/bfddddLhUDAABQ1VwKOw8++KDT++LiYv3+++/y8vJSgwYNCDsAAKDWcOlurN9++83plZeXp5SUFPXt25cJygAAoFZx+buxztahQwclJCSUO+sDAADgTlUWdqQzk5bT0tKqcpEAAACXxKU5O59++qnTe8MwlJ6erhdffFF9+vSpksIAAACqgkthZ8iQIU7vLRaLmjZtqhtvvFHPPfdcVdQFAABQJVwKO6WlpVVdBwAAQLWo0jk7AAAAtY1LZ3YmTpxY6bFz5sxxZRUAAABVwqWws2PHDu3YsUPFxcXq1KmTJGn//v2qU6eOrr76asc4i8VSNVUCAAC4yKWwc/PNN8vHx0dLlixRo0aNJJ150OCYMWN07bXXatKkSVVaJAAAgKtcmrPz3HPPKT4+3hF0JKlRo0Z68sknuRsLAADUKi6FndzcXB07dqxc+7Fjx3Ty5MlLLgoAAKCquBR2br31Vo0ZM0Yffvihjh49qqNHj+p///ufxo4dq6FDh1Z1jQAAAC5zac7OwoULNXnyZN15550qLi4+s6C6dTV27Fg9++yzVVogAADApXAp7DRo0EAvv/yynn32WR06dEiS1K5dOzVs2LBKiwMAALhUl/RQwfT0dKWnp6tDhw5q2LChDMOoqroAAACqhEth59dff1VYWJg6duyoQYMGKT09XZI0duxYbjsHAAC1ikthZ8KECfL09FRqaqoaNGjgaB8+fLhWrlxZZcUBAABcKpfm7KxatUpffvmlWrRo4dTeoUMH/fzzz1VSGAAAQFVw6cxOfn6+0xmdMidOnJDVar3kogAAAKqKS2Hn2muv1RtvvOF4b7FYVFpaqtmzZ+uGG26osuIAAAAulUuXsWbPnq2wsDBt27ZNRUVFevjhh7V7926dOHFCGzdurOoaAQAAXObSmZ0rr7xS+/fvV9++fTV48GDl5+dr6NCh2rFjh9q1a1fVNQIAALjsosNOcXGxwsLClJWVpUcffVTvv/++vvjiCz355JNq1qzZRS1rwYIF6t69u2w2m2w2m+x2u1asWOHoLygoUExMjBo3bixvb29FRUUpMzPTaRmpqamKjIxUgwYNFBAQoClTpuj06dMXu1kAAMCkLjrseHp6ateuXVWy8hYtWighIUHbt2/Xtm3bdOONN2rw4MHavXu3pDO3uH/22WdatmyZ1q9fr7S0NKfv3iopKVFkZKSKioq0adMmLVmyRIsXL9a0adOqpD4AAHD5sxguPPZ4woQJslqtSkhIqPKC/P399eyzz2rYsGFq2rSp3nnnHQ0bNkyStG/fPnXp0kXJycnq3bu3VqxYob///e9KS0tTYGCgpDPf2zV16lQdO3ZMXl5elVpnbm6ufH19lZOTI5vNVqXbE/zI51W6vJrwU0Kku0sAAOCCKvv326UJyqdPn9brr7+u1atXKzQ0tNx3Ys2ZM+eil1lSUqJly5YpPz9fdrtd27dvV3FxscLDwx1jOnfurFatWjnCTnJysrp16+YIOpIUERGh8ePHa/fu3frLX/5S4boKCwtVWFjoeJ+bm3vR9QIAgMvDRYWdH3/8UcHBwfrhhx909dVXS5L279/vNMZisVxUAd9//73sdrsKCgrk7e2tjz76SCEhIdq5c6e8vLzk5+fnND4wMFAZGRmSpIyMDKegU9Zf1ncu8fHxmjlz5kXVCQAALk8XFXY6dOig9PR0rV27VtKZr4eYP39+ucBxMTp16qSdO3cqJydHH3zwgaKjo7V+/XqXl1cZcXFxmjhxouN9bm6uWrZsWa3rBAAA7nFRYefs6T0rVqxQfn7+JRXg5eWl9u3bS5JCQ0P1zTff6Pnnn9fw4cNVVFSk7Oxsp7M7mZmZCgoKkiQFBQVp69atTssru1urbExFrFYrT3oGAOBPwqXn7JRxYW7zBZWWlqqwsFChoaHy9PRUUlKSoy8lJUWpqamy2+2SJLvdru+//15ZWVmOMYmJibLZbAoJCany2gAAwOXnos7sWCyWcnNyLnaOzh/FxcVp4MCBatWqlU6ePKl33nlH69at05dffilfX1+NHTtWEydOlL+/v2w2m+6//37Z7Xb17t1bktS/f3+FhIRo1KhRmj17tjIyMvTYY48pJiaGMzcAAECSC5exRo8e7QgSBQUFuvfee8vdjfXhhx9WanlZWVm66667lJ6eLl9fX3Xv3l1ffvmlbrrpJknS3Llz5eHhoaioKBUWFioiIkIvv/yy4/N16tTR8uXLNX78eNntdjVs2FDR0dGaNWvWxWwWAAAwsYt6zs6YMWMqNW7RokUuF+QOPGfHGc/ZAQBcDqrlOTuXW4gBAAC4pAnKAAAAtR1hBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmJpbw058fLz++te/ysfHRwEBARoyZIhSUlKcxhQUFCgmJkaNGzeWt7e3oqKilJmZ6TQmNTVVkZGRatCggQICAjRlyhSdPn26JjcFAADUUm4NO+vXr1dMTIw2b96sxMREFRcXq3///srPz3eMmTBhgj777DMtW7ZM69evV1pamoYOHeroLykpUWRkpIqKirRp0yYtWbJEixcv1rRp09yxSQAAoJaxGIZhuLuIMseOHVNAQIDWr1+vfv36KScnR02bNtU777yjYcOGSZL27dunLl26KDk5Wb1799aKFSv097//XWlpaQoMDJQkLVy4UFOnTtWxY8fk5eV1wfXm5ubK19dXOTk5stlsVbpNwY98XqXLqwk/JUS6uwQAAC6osn+/a9WcnZycHEmSv7+/JGn79u0qLi5WeHi4Y0znzp3VqlUrJScnS5KSk5PVrVs3R9CRpIiICOXm5mr37t0VrqewsFC5ublOLwAAYE513V1AmdLSUj300EPq06ePrrzySklSRkaGvLy85Ofn5zQ2MDBQGRkZjjF/DDpl/WV9FYmPj9fMmTOreAvMg7NRAAAzqTVndmJiYvTDDz9o6dKl1b6uuLg45eTkOF5Hjhyp9nUCAAD3qBVndmJjY7V8+XJt2LBBLVq0cLQHBQWpqKhI2dnZTmd3MjMzFRQU5BizdetWp+WV3a1VNuZsVqtVVqu1ircCAADURm49s2MYhmJjY/XRRx9pzZo1atOmjVN/aGioPD09lZSU5GhLSUlRamqq7Ha7JMlut+v7779XVlaWY0xiYqJsNptCQkJqZkMAAECt5dYzOzExMXrnnXf0ySefyMfHxzHHxtfXV/Xr15evr6/Gjh2riRMnyt/fXzabTffff7/sdrt69+4tSerfv79CQkI0atQozZ49WxkZGXrssccUExPD2RsAAODesLNgwQJJ0vXXX+/UvmjRIo0ePVqSNHfuXHl4eCgqKkqFhYWKiIjQyy+/7Bhbp04dLV++XOPHj5fdblfDhg0VHR2tWbNm1dRmAACAWqxWPWfHXXjOzuWPu7EA4M/nsnzODgAAQFUj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFNza9jZsGGDbr75ZjVv3lwWi0Uff/yxU79hGJo2bZqaNWum+vXrKzw8XAcOHHAac+LECY0cOVI2m01+fn4aO3as8vLyanArAABAbebWsJOfn68ePXropZdeqrB/9uzZmj9/vhYuXKgtW7aoYcOGioiIUEFBgWPMyJEjtXv3biUmJmr58uXasGGDxo0bV1ObAAAAarm67lz5wIEDNXDgwAr7DMPQvHnz9Nhjj2nw4MGSpDfeeEOBgYH6+OOPNWLECO3du1crV67UN998o549e0qSXnjhBQ0aNEj//ve/1bx58xrbFgAAUDvV2jk7hw8fVkZGhsLDwx1tvr6+6tWrl5KTkyVJycnJ8vPzcwQdSQoPD5eHh4e2bNlyzmUXFhYqNzfX6QUAAMyp1oadjIwMSVJgYKBTe2BgoKMvIyNDAQEBTv1169aVv7+/Y0xF4uPj5evr63i1bNmyiqsHAAC1Ra0NO9UpLi5OOTk5jteRI0fcXRIAAKgmtTbsBAUFSZIyMzOd2jMzMx19QUFBysrKcuo/ffq0Tpw44RhTEavVKpvN5vQCAADmVGvDTps2bRQUFKSkpCRHW25urrZs2SK73S5Jstvtys7O1vbt2x1j1qxZo9LSUvXq1avGawYAALWPW+/GysvL08GDBx3vDx8+rJ07d8rf31+tWrXSQw89pCeffFIdOnRQmzZt9Pjjj6t58+YaMmSIJKlLly4aMGCA7rnnHi1cuFDFxcWKjY3ViBEjuBMLAABIcnPY2bZtm2644QbH+4kTJ0qSoqOjtXjxYj388MPKz8/XuHHjlJ2drb59+2rlypWqV6+e4zNvv/22YmNjFRYWJg8PD0VFRWn+/Pk1vi0AAKB2shiGYbi7CHfLzc2Vr6+vcnJyqnz+TvAjn1fp8lCxnxIi3V0CAKCGVfbvd62dswMAAFAVCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDU3PrdWEBVuRy/loOvuACAmsGZHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGp13V0A8GcV/Mjn7i7BJT8lRLq7BAC4KJzZAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApsbdWAAuyuV6F9nlhrvegKpjmjM7L730koKDg1WvXj316tVLW7dudXdJAACgFjBF2Hnvvfc0ceJETZ8+Xd9++6169OihiIgIZWVlubs0AADgZqYIO3PmzNE999yjMWPGKCQkRAsXLlSDBg30+uuvu7s0AADgZpd92CkqKtL27dsVHh7uaPPw8FB4eLiSk5PdWBkAAKgNLvsJysePH1dJSYkCAwOd2gMDA7Vv374KP1NYWKjCwkLH+5ycHElSbm5ulddXWvh7lS8TgPm1mrDM3SUAVeaHmRHVstyyv9uGYZx33GUfdlwRHx+vmTNnlmtv2bKlG6oBAMDcfOdV7/JPnjwpX1/fc/Zf9mGnSZMmqlOnjjIzM53aMzMzFRQUVOFn4uLiNHHiRMf70tJSnThxQo0bN5bFYqmy2nJzc9WyZUsdOXJENputypZ7uWJ/OGN/OGN/OGN/OGN/OGN/nGEYhk6ePKnmzZufd9xlH3a8vLwUGhqqpKQkDRkyRNKZ8JKUlKTY2NgKP2O1WmW1Wp3a/Pz8qq1Gm832pz4Yz8b+cMb+cMb+cMb+cMb+cMb+0HnP6JS57MOOJE2cOFHR0dHq2bOnrrnmGs2bN0/5+fkaM2aMu0sDAABuZoqwM3z4cB07dkzTpk1TRkaGrrrqKq1cubLcpGUAAPDnY4qwI0mxsbHnvGzlLlarVdOnTy93yezPiv3hjP3hjP3hjP3hjP3hjP1xcSzGhe7XAgAAuIxd9g8VBAAAOB/CDgAAMDXCDgAAMDXCDgAAMDXCTjV66aWXFBwcrHr16qlXr17aunWru0tyixkzZshisTi9Onfu7O6yasyGDRt08803q3nz5rJYLPr444+d+g3D0LRp09SsWTPVr19f4eHhOnDggHuKrQEX2h+jR48ud7wMGDDAPcVWs/j4eP31r3+Vj4+PAgICNGTIEKWkpDiNKSgoUExMjBo3bixvb29FRUWVe2K8WVRmf1x//fXljo97773XTRVXrwULFqh79+6OBwfa7XatWLHC0f9nOjYuFWGnmrz33nuaOHGipk+frm+//VY9evRQRESEsrKy3F2aW3Tt2lXp6emO19dff+3ukmpMfn6+evTooZdeeqnC/tmzZ2v+/PlauHChtmzZooYNGyoiIkIFBQU1XGnNuND+kKQBAwY4HS/vvvtuDVZYc9avX6+YmBht3rxZiYmJKi4uVv/+/ZWfn+8YM2HCBH322WdatmyZ1q9fr7S0NA0dOtSNVVefyuwPSbrnnnucjo/Zs2e7qeLq1aJFCyUkJGj79u3atm2bbrzxRg0ePFi7d++W9Oc6Ni6ZgWpxzTXXGDExMY73JSUlRvPmzY34+Hg3VuUe06dPN3r06OHuMmoFScZHH33keF9aWmoEBQUZzz77rKMtOzvbsFqtxrvvvuuGCmvW2fvDMAwjOjraGDx4sFvqcbesrCxDkrF+/XrDMM4cC56ensayZcscY/bu3WtIMpKTk91VZo05e38YhmFcd911xoMPPui+otysUaNGxn//+98//bFxsTizUw2Kioq0fft2hYeHO9o8PDwUHh6u5ORkN1bmPgcOHFDz5s3Vtm1bjRw5Uqmpqe4uqVY4fPiwMjIynI4VX19f9erV6097rEjSunXrFBAQoE6dOmn8+PH69ddf3V1SjcjJyZEk+fv7S5K2b9+u4uJip+Ojc+fOatWq1Z/i+Dh7f5R5++231aRJE1155ZWKi4vT77//7o7yalRJSYmWLl2q/Px82e32P/2xcbFM8wTl2uT48eMqKSkp93UVgYGB2rdvn5uqcp9evXpp8eLF6tSpk9LT0zVz5kxde+21+uGHH+Tj4+Pu8twqIyNDkio8Vsr6/mwGDBigoUOHqk2bNjp06JD+7//+TwMHDlRycrLq1Knj7vKqTWlpqR566CH16dNHV155paQzx4eXl1e5Lyr+MxwfFe0PSbrzzjvVunVrNW/eXLt27dLUqVOVkpKiDz/80I3VVp/vv/9edrtdBQUF8vb21kcffaSQkBDt3LnzT3tsuIKwg2o3cOBAx8/du3dXr1691Lp1a73//vsaO3asGytDbTRixAjHz926dVP37t3Vrl07rVu3TmFhYW6srHrFxMTohx9++FPNZzufc+2PcePGOX7u1q2bmjVrprCwMB06dEjt2rWr6TKrXadOnbRz507l5OTogw8+UHR0tNavX+/usi47XMaqBk2aNFGdOnXKzYrPzMxUUFCQm6qqPfz8/NSxY0cdPHjQ3aW4XdnxwLFybm3btlWTJk1MfbzExsZq+fLlWrt2rVq0aOFoDwoKUlFRkbKzs53Gm/34ONf+qEivXr0kybTHh5eXl9q3b6/Q0FDFx8erR48eev755/+0x4arCDvVwMvLS6GhoUpKSnK0lZaWKikpSXa73Y2V1Q55eXk6dOiQmjVr5u5S3K5NmzYKCgpyOlZyc3O1ZcsWjpX/39GjR/Xrr7+a8ngxDEOxsbH66KOPtGbNGrVp08apPzQ0VJ6enk7HR0pKilJTU015fFxof1Rk586dkmTK46MipaWlKiws/NMdG5fM3TOkzWrp0qWG1Wo1Fi9ebOzZs8cYN26c4efnZ2RkZLi7tBo3adIkY926dcbhw4eNjRs3GuHh4UaTJk2MrKwsd5dWI06ePGns2LHD2LFjhyHJmDNnjrFjxw7j559/NgzDMBISEgw/Pz/jk08+MXbt2mUMHjzYaNOmjXHq1Ck3V149zrc/Tp48aUyePNlITk42Dh8+bKxevdq4+uqrjQ4dOhgFBQXuLr3KjR8/3vD19TXWrVtnpKenO16///67Y8y9995rtGrVylizZo2xbds2w263G3a73Y1VV58L7Y+DBw8as2bNMrZt22YcPnzY+OSTT4y2bdsa/fr1c3Pl1eORRx4x1q9fbxw+fNjYtWuX8cgjjxgWi8VYtWqVYRh/rmPjUhF2qtELL7xgtGrVyvDy8jKuueYaY/Pmze4uyS2GDx9uNGvWzPDy8jKuuOIKY/jw4cbBgwfdXVaNWbt2rSGp3Cs6OtowjDO3nz/++ONGYGCgYbVajbCwMCMlJcW9RVej8+2P33//3ejfv7/RtGlTw9PT02jdurVxzz33mPYfCRXtB0nGokWLHGNOnTpl3HfffUajRo2MBg0aGLfeequRnp7uvqKr0YX2R2pqqtGvXz/D39/fsFqtRvv27Y0pU6YYOTk57i28mtx9991G69atDS8vL6Np06ZGWFiYI+gYxp/r2LhUFsMwjJo7jwQAAFCzmLMDAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADoNrNmDFDV111VY2tb/To0RoyZEiNrQ9A7cZDBQFcFIvFct7+6dOna8aMGU5teXl5KiwsVOPGjV1e77p163TDDTc4avDx8VHbtm110003acKECU7fjZSTkyPDMOTn53fB5Y4ePVrZ2dn6+OOPXa4NQO1W190FALi8pKenO35+7733NG3aNKWkpDjavL29HT8bhqGSkhJ5e3s7tV+KlJQU2Ww25ebm6ttvv9Xs2bP12muvad26derWrZskydfXt0rWBcAcuIwF4KIEBQU5Xr6+vrJYLI73+/btk4+Pj1asWKHQ0FBZrVZ9/fXX5S5jlV1mmjlzppo2bSqbzaZ7771XRUVFF1x/QECAgoKC1LFjR40YMUIbN25U06ZNNX78+HLLL/PBBx+oW7duql+/vho3bqzw8HDl5+drxowZWrJkiT755BNZLBZZLBatW7dOkjR16lR17NhRDRo0UNu2bfX444+ruLjYscyybXrzzTcVHBwsX19fjRgxQidPnnSMKS0t1ezZs9W+fXtZrVa1atVKTz31lKP/yJEjuv322+Xn5yd/f38NHjxYP/3008X/UgCcF2EHQJV75JFHlJCQoL1796p79+4VjklKStLevXu1bt06vfvuu/rwww81c+bMi15X/fr1de+992rjxo3Kysoq15+enq477rhDd999t2N9Q4cOlWEYmjx5sm6//XYNGDBA6enpSk9P19/+9jdJko+PjxYvXqw9e/bo+eef16uvvqq5c+c6LfvQoUP6+OOPtXz5ci1fvlzr169XQkKCoz8uLk4JCQl6/PHHtWfPHr3zzjsKDAyUJBUXFysiIkI+Pj766quvtHHjRnl7e2vAgAGVCn0AKo/LWACq3KxZs3TTTTedd4yXl5def/11NWjQQF27dtWsWbM0ZcoUPfHEE/LwuLh/h3Xu3FmS9NNPPykgIMCpLz09XadPn9bQoUPVunVrSXJc7pLOhKXCwkIFBQU5fe6xxx5z/BwcHKzJkydr6dKlevjhhx3tpaWlWrx4sXx8fCRJo0aNUlJSkp566imdPHlSzz//vF588UVFR0dLktq1a6e+fftKOnMJsLS0VP/9738d86AWLVokPz8/rVu3Tv3797+ofQDg3Ag7AKpcz549LzimR48eatCggeO93W5XXl6ejhw54ggllVV2n0VFk6d79OihsLAwdevWTREREerfv7+GDRumRo0anXeZ7733nubPn69Dhw4pLy9Pp0+fls1mcxoTHBzsCDqS1KxZM8fZpb1796qwsFBhYWEVLv+7777TwYMHnT4vSQUFBTp06NCFNxpApXEZC0CVa9iwYY2ub+/evZLOhI+z1alTR4mJiVqxYoVCQkL0wgsvqFOnTjp8+PA5l5ecnKyRI0dq0KBBWr58uXbs2KFHH3203OUlT09Pp/cWi0WlpaWSzpwxOp+8vDyFhoZq586dTq/9+/frzjvvrMxmA6gkwg4At/juu+906tQpx/vNmzfL29tbLVu2vKjlnDp1Sq+88or69eunpk2bVjjGYrGoT58+mjlzpnbs2CEvLy999NFHks5cTispKXEav2nTJrVu3VqPPvqoevbsqQ4dOujnn3++qLo6dOig+vXrKykpqcL+q6++WgcOHFBAQIDat2/v9OJuMqBqEXYAuEVRUZHGjh2rPXv26IsvvtD06dMVGxt7wfk6WVlZysjI0IEDB7R06VL16dNHx48f14IFCyocv2XLFj399NPatm2bUlNT9eGHH+rYsWPq0qWLpDNng3bt2qWUlBQdP35cxcXF6tChg1JTU7V06VIdOnRI8+fPd4SjyqpXr56mTp2qhx9+WG+88YYOHTqkzZs367XXXpMkjRw5Uk2aNNHgwYP11Vdf6fDhw1q3bp0eeOABHT169KLWBeD8mLMDwC3CwsLUoUMH9evXT4WFhbrjjjvKPYywIp06dZLFYpG3t7fatm2r/v37a+LEieUmGJex2WzasGGD5s2bp9zcXLVu3VrPPfecBg4cKEm65557tG7dOvXs2VN5eXlau3atbrnlFk2YMEGxsbEqLCxUZGSkHn/88UrV90ePP/646tatq2nTpiktLU3NmjXTvffeK0lq0KCBNmzYoKlTp2ro0KE6efKkrrjiCoWFhZWbGwTg0vAEZQA1jqcWA6hJXMYCAACmRtgBAACmxmUsAABgapzZAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApvb/AQi3lQkmdslEAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -837,22 +902,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "*  trino://user@localhost:8080/memory\n",
-      "Done.\n"
-     ]
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -875,6 +944,259 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Persist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = %sql SELECT * FROM default.taxi WHERE passenger_count > 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = result.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We need to pass `--no-index` since index creation is not supported in `Trino DB`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<span style=\"color: green\">Success! Persisted df to the database.</span>"
+      ],
+      "text/plain": [
+       "Success! Persisted df to the database."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%sql --persist default.df --no-index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<span style=\"None\">Running query in &#x27;trino://user@localhost:8080/memory&#x27;</span>"
+      ],
+      "text/plain": [
+       "Running query in 'trino://user@localhost:8080/memory'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <thead>\n",
+       "        <tr>\n",
+       "            <th>vendorid</th>\n",
+       "            <th>tpep_pickup_datetime</th>\n",
+       "            <th>tpep_dropoff_datetime</th>\n",
+       "            <th>passenger_count</th>\n",
+       "            <th>trip_distance</th>\n",
+       "            <th>ratecodeid</th>\n",
+       "            <th>store_and_fwd_flag</th>\n",
+       "            <th>pulocationid</th>\n",
+       "            <th>dolocationid</th>\n",
+       "            <th>payment_type</th>\n",
+       "            <th>fare_amount</th>\n",
+       "            <th>extra</th>\n",
+       "            <th>mta_tax</th>\n",
+       "            <th>tip_amount</th>\n",
+       "            <th>tolls_amount</th>\n",
+       "            <th>improvement_surcharge</th>\n",
+       "            <th>total_amount</th>\n",
+       "            <th>congestion_surcharge</th>\n",
+       "            <th>airport_fee</th>\n",
+       "        </tr>\n",
+       "    </thead>\n",
+       "    <tbody>\n",
+       "        <tr>\n",
+       "            <td>2</td>\n",
+       "            <td>2021-01-01 00:31:06</td>\n",
+       "            <td>2021-01-01 00:38:52</td>\n",
+       "            <td>5.0</td>\n",
+       "            <td>1.7</td>\n",
+       "            <td>1.0</td>\n",
+       "            <td>N</td>\n",
+       "            <td>142</td>\n",
+       "            <td>50</td>\n",
+       "            <td>1</td>\n",
+       "            <td>8.0</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>2.36</td>\n",
+       "            <td>0.0</td>\n",
+       "            <td>0.3</td>\n",
+       "            <td>14.16</td>\n",
+       "            <td>2.5</td>\n",
+       "            <td>None</td>\n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <td>2</td>\n",
+       "            <td>2021-01-01 00:42:11</td>\n",
+       "            <td>2021-01-01 00:44:24</td>\n",
+       "            <td>5.0</td>\n",
+       "            <td>0.81</td>\n",
+       "            <td>1.0</td>\n",
+       "            <td>N</td>\n",
+       "            <td>50</td>\n",
+       "            <td>142</td>\n",
+       "            <td>2</td>\n",
+       "            <td>4.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>0.0</td>\n",
+       "            <td>0.0</td>\n",
+       "            <td>0.3</td>\n",
+       "            <td>8.3</td>\n",
+       "            <td>2.5</td>\n",
+       "            <td>None</td>\n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <td>2</td>\n",
+       "            <td>2021-01-01 00:31:06</td>\n",
+       "            <td>2021-01-01 00:38:52</td>\n",
+       "            <td>5.0</td>\n",
+       "            <td>1.7</td>\n",
+       "            <td>1.0</td>\n",
+       "            <td>N</td>\n",
+       "            <td>142</td>\n",
+       "            <td>50</td>\n",
+       "            <td>1</td>\n",
+       "            <td>8.0</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>2.36</td>\n",
+       "            <td>0.0</td>\n",
+       "            <td>0.3</td>\n",
+       "            <td>14.16</td>\n",
+       "            <td>2.5</td>\n",
+       "            <td>None</td>\n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <td>2</td>\n",
+       "            <td>2021-01-01 00:42:11</td>\n",
+       "            <td>2021-01-01 00:44:24</td>\n",
+       "            <td>5.0</td>\n",
+       "            <td>0.81</td>\n",
+       "            <td>1.0</td>\n",
+       "            <td>N</td>\n",
+       "            <td>50</td>\n",
+       "            <td>142</td>\n",
+       "            <td>2</td>\n",
+       "            <td>4.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>0.0</td>\n",
+       "            <td>0.0</td>\n",
+       "            <td>0.3</td>\n",
+       "            <td>8.3</td>\n",
+       "            <td>2.5</td>\n",
+       "            <td>None</td>\n",
+       "        </tr>\n",
+       "        <tr>\n",
+       "            <td>2</td>\n",
+       "            <td>2021-01-01 00:34:37</td>\n",
+       "            <td>2021-01-01 00:47:22</td>\n",
+       "            <td>4.0</td>\n",
+       "            <td>3.15</td>\n",
+       "            <td>1.0</td>\n",
+       "            <td>N</td>\n",
+       "            <td>238</td>\n",
+       "            <td>162</td>\n",
+       "            <td>1</td>\n",
+       "            <td>12.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>0.5</td>\n",
+       "            <td>2.0</td>\n",
+       "            <td>0.0</td>\n",
+       "            <td>0.3</td>\n",
+       "            <td>18.3</td>\n",
+       "            <td>2.5</td>\n",
+       "            <td>None</td>\n",
+       "        </tr>\n",
+       "    </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+\n",
+       "| vendorid | tpep_pickup_datetime | tpep_dropoff_datetime | passenger_count | trip_distance | ratecodeid | store_and_fwd_flag | pulocationid | dolocationid | payment_type | fare_amount | extra | mta_tax | tip_amount | tolls_amount | improvement_surcharge | total_amount | congestion_surcharge | airport_fee |\n",
+       "+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+\n",
+       "|    2     | 2021-01-01 00:31:06  |  2021-01-01 00:38:52  |       5.0       |      1.7      |    1.0     |         N          |     142      |      50      |      1       |     8.0     |  0.5  |   0.5   |    2.36    |     0.0      |          0.3          |    14.16     |         2.5          |     None    |\n",
+       "|    2     | 2021-01-01 00:42:11  |  2021-01-01 00:44:24  |       5.0       |      0.81     |    1.0     |         N          |      50      |     142      |      2       |     4.5     |  0.5  |   0.5   |    0.0     |     0.0      |          0.3          |     8.3      |         2.5          |     None    |\n",
+       "|    2     | 2021-01-01 00:31:06  |  2021-01-01 00:38:52  |       5.0       |      1.7      |    1.0     |         N          |     142      |      50      |      1       |     8.0     |  0.5  |   0.5   |    2.36    |     0.0      |          0.3          |    14.16     |         2.5          |     None    |\n",
+       "|    2     | 2021-01-01 00:42:11  |  2021-01-01 00:44:24  |       5.0       |      0.81     |    1.0     |         N          |      50      |     142      |      2       |     4.5     |  0.5  |   0.5   |    0.0     |     0.0      |          0.3          |     8.3      |         2.5          |     None    |\n",
+       "|    2     | 2021-01-01 00:34:37  |  2021-01-01 00:47:22  |       4.0       |      3.15     |    1.0     |         N          |     238      |     162      |      1       |     12.5    |  0.5  |   0.5   |    2.0     |     0.0      |          0.3          |     18.3     |         2.5          |     None    |\n",
+       "+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%sql SELECT * FROM default.df LIMIT 5"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -887,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "metadata": {
     "tags": []
    },
@@ -896,8 +1218,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CONTAINER ID   IMAGE           COMMAND                  CREATED          STATUS                    PORTS                    NAMES\n",
-      "819a48a82c7d   trinodb/trino   \"/usr/lib/trino/bin/…\"   33 seconds ago   Up 33 seconds (healthy)   0.0.0.0:8080->8080/tcp   trino\n"
+      "CONTAINER ID   IMAGE           COMMAND                  CREATED         STATUS                   PORTS                    NAMES\n",
+      "cba8365556d3   trinodb/trino   \"/usr/lib/trino/bin/…\"   2 minutes ago   Up 2 minutes (healthy)   0.0.0.0:8080->8080/tcp   trino\n"
      ]
     }
    ],
@@ -907,7 +1229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "metadata": {
     "tags": []
    },
@@ -919,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "metadata": {
     "tags": []
    },
@@ -928,7 +1250,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Container id: 819a48a82c7d\n"
+      "Container id: cba8365556d3\n"
      ]
     }
    ],
@@ -939,7 +1261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "metadata": {
     "tags": []
    },
@@ -948,7 +1270,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "819a48a82c7d\n"
+      "cba8365556d3\n"
      ]
     }
    ],
@@ -958,7 +1280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "metadata": {
     "tags": []
    },
@@ -967,7 +1289,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "819a48a82c7d\n"
+      "cba8365556d3\n"
      ]
     }
    ],
@@ -977,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 31,
    "metadata": {
     "tags": []
    },
@@ -1011,7 +1333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.12"
   },
   "myst": {
    "html_meta": {
diff --git a/src/sql/connection/connection.py b/src/sql/connection/connection.py
index 1d98c4cde..678ca30de 100644
--- a/src/sql/connection/connection.py
+++ b/src/sql/connection/connection.py
@@ -492,7 +492,7 @@ def _get_database_information(self):
         pass
 
     @abc.abstractmethod
-    def to_table(self, table_name, data_frame, if_exists, index):
+    def to_table(self, table_name, data_frame, if_exists, index, schema=None):
         """Create a table from a pandas DataFrame"""
         pass
 
@@ -941,7 +941,7 @@ def _start_sqlalchemy_connection(cls, engine, connect_str):
         except Exception as e:
             raise _error_invalid_connection_info(e, connect_str) from e
 
-    def to_table(self, table_name, data_frame, if_exists, index):
+    def to_table(self, table_name, data_frame, if_exists, index, schema=None):
         """Create a table from a pandas DataFrame"""
         operation = partial(
             data_frame.to_sql,
@@ -949,6 +949,7 @@ def to_table(self, table_name, data_frame, if_exists, index):
             self.connection_sqlalchemy,
             if_exists=if_exists,
             index=index,
+            schema=schema,
         )
 
         try:
@@ -1052,7 +1053,7 @@ def connection_sqlalchemy(self):
             "This feature is only available for SQLAlchemy connections"
         )
 
-    def to_table(self, table_name, data_frame, if_exists, index):
+    def to_table(self, table_name, data_frame, if_exists, index, schema=None):
         raise exceptions.NotImplementedError(
             "--persist/--persist-replace is not available for DBAPI connections"
             " (only available for SQLAlchemy connections)"
diff --git a/src/sql/magic.py b/src/sql/magic.py
index c9c75fff6..2a2d23e2a 100644
--- a/src/sql/magic.py
+++ b/src/sql/magic.py
@@ -628,6 +628,14 @@ def _persist_dataframe(
 
         frame_name = raw.strip(";")
 
+        # user may pass schema.dataframe (required for certain DBs
+        # like Trino)
+        schema_name = None
+        if "." in frame_name:
+            schema_frame = frame_name.split(".")
+            schema_name = schema_frame[0]
+            frame_name = schema_frame[1]
+
         # invalid identifier
         if not frame_name.isidentifier():
             raise exceptions.UsageError(
@@ -666,7 +674,11 @@ def _persist_dataframe(
             if_exists = "fail"
 
         conn.to_table(
-            table_name=table_name, data_frame=frame, if_exists=if_exists, index=index
+            table_name=table_name,
+            data_frame=frame,
+            if_exists=if_exists,
+            index=index,
+            schema=schema_name,
         )
 
 


Test patch:
diff --git a/src/tests/integration/test_generic_db_operations.py b/src/tests/integration/test_generic_db_operations.py
index 7322756b9..47325ed16 100644
--- a/src/tests/integration/test_generic_db_operations.py
+++ b/src/tests/integration/test_generic_db_operations.py
@@ -1459,3 +1459,93 @@ def test_query_snippet_invalid_function_error_message(
 
     assert error_type == result_error
     assert all(msg in result_msg for msg in error_msgs)
+
+
+@pytest.mark.parametrize(
+    "ip_with_dynamic_db, args",
+    [
+        ("ip_with_postgreSQL", ""),
+        ("ip_with_duckDB", ""),
+        # snowflake does not support "CREATE INDEX", so we need to
+        # pass --no-index
+        ("ip_with_Snowflake", "--no-index"),
+        pytest.param(
+            "ip_with_mySQL",
+            "",
+            marks=pytest.mark.xfail(reason="Access denied for user"),
+        ),
+        pytest.param(
+            "ip_with_mariaDB",
+            "",
+            marks=pytest.mark.xfail(reason="Access denied for user"),
+        ),
+        pytest.param(
+            "ip_with_SQLite", "", marks=pytest.mark.xfail(reason="schema not supported")
+        ),
+        pytest.param(
+            "ip_with_duckDB_native",
+            "",
+            marks=pytest.mark.xfail(
+                reason="'duckdb.DuckDBPyConnection' object has no attribute 'rowcount'"
+            ),
+        ),
+        pytest.param(
+            "ip_with_redshift",
+            "",
+            marks=pytest.mark.xfail(reason="permission denied for database dev"),
+        ),
+        pytest.param(
+            "ip_with_clickhouse",
+            "",
+            marks=pytest.mark.xfail(
+                reason="sqlalchemy.exc.CompileError: "
+                "No engine for table <table_name>"
+            ),
+        ),
+    ],
+)
+def test_persist_in_schema(ip_with_dynamic_db, args, request, test_table_name_dict):
+    limit = 15
+    expected = 15
+
+    ip_with_dynamic_db = request.getfixturevalue(ip_with_dynamic_db)
+    # Clean up
+
+    ip_with_dynamic_db.run_cell("%config SqlMagic.displaylimit = 0")
+
+    ip_with_dynamic_db.run_cell("%sql CREATE SCHEMA IF NOT EXISTS schema1;")
+
+    ip_with_dynamic_db.run_cell(
+        f"%sql DROP TABLE IF EXISTS "
+        f"schema1.{test_table_name_dict['new_table_from_df']}"
+    )
+
+    # Prepare DF
+    ip_with_dynamic_db.run_cell(
+        f"results = %sql SELECT * FROM {test_table_name_dict['taxi']}\
+          LIMIT {limit}"
+    )
+
+    # Prepare expected df
+    expected_df = ip_with_dynamic_db.run_cell(
+        f"%sql SELECT * FROM {test_table_name_dict['taxi']}\
+          LIMIT {limit}"
+    )
+
+    ip_with_dynamic_db.run_cell(
+        f"{test_table_name_dict['new_table_from_df']} = results.DataFrame()"
+    )
+    # Create table from DF
+    persist_out = ip_with_dynamic_db.run_cell(
+        f"%sql --persist schema1.{test_table_name_dict['new_table_from_df']} {args}"
+    )
+    out_df = ip_with_dynamic_db.run_cell(
+        f"%sql SELECT * FROM schema1.{test_table_name_dict['new_table_from_df']}"
+    )
+    assert persist_out.error_in_exec is None and out_df.error_in_exec is None
+    assert len(out_df.result) == expected
+
+    expected_df_ = expected_df.result.DataFrame()
+    out_df_ = out_df.result.DataFrame()
+
+    assert expected_df_.equals(out_df_.loc[:, out_df_.columns != "level_0"])
diff --git a/src/tests/test_magic.py b/src/tests/test_magic.py
index 9df621e31..dfff1baba 100644
--- a/src/tests/test_magic.py
+++ b/src/tests/test_magic.py
@@ -240,6 +240,42 @@ def test_persist(ip):
     assert persisted == [(0, 1, "foo"), (1, 2, "bar")]
 
 
+def test_persist_in_schema(ip_empty):
+    ip_empty.run_cell("%sql duckdb://")
+    ip_empty.run_cell("%sql CREATE SCHEMA IF NOT EXISTS schema1;")
+    df = pd.DataFrame({"a": [1, 2, 3]})
+    ip_empty.push({"df": df})
+    ip_empty.run_cell("%sql --persist schema1.df")
+    persisted = ip_empty.run_cell("%sql SELECT * FROM schema1.df;").result.DataFrame()
+    assert persisted["a"].tolist() == [1, 2, 3]
+
+
+def test_persist_replace_in_schema(ip_empty):
+    ip_empty.run_cell("%sql duckdb://")
+    ip_empty.run_cell("%sql CREATE SCHEMA IF NOT EXISTS schema1;")
+    df = pd.DataFrame({"a": [1, 2, 3]})
+    ip_empty.push({"df": df})
+    ip_empty.run_cell("%sql --persist schema1.df")
+    df = pd.DataFrame({"a": [6, 7]})
+    ip_empty.push({"df": df})
+    ip_empty.run_cell("%sql --perist-replace schema1.df")
+    persisted = ip_empty.run_cell("%sql SELECT * FROM schema1.df;").result.DataFrame()
+    assert persisted["a"].tolist() == [1, 2, 3]
+
+
+def test_append_in_schema(ip_empty):
+    ip_empty.run_cell("%sql duckdb://")
+    ip_empty.run_cell("%sql CREATE SCHEMA IF NOT EXISTS schema1;")
+    df = pd.DataFrame({"a": [1, 2, 3]})
+    ip_empty.push({"df": df})
+    ip_empty.run_cell("%sql --persist schema1.df")
+    df = pd.DataFrame({"a": [6, 7]})
+    ip_empty.push({"df": df})
+    ip_empty.run_cell("%sql --append schema1.df")
+    persisted = ip_empty.run_cell("%sql SELECT * FROM schema1.df;").result.DataFrame()
+    assert persisted["a"].tolist() == [1, 2, 3, 6, 7]
+
+
 def test_persist_no_index(ip):
     runsql(ip, "")
     ip.run_cell("results = %sql SELECT * FROM test;")