diff --git a/.ipynb_checkpoints/lab-sql-9-checkpoint.ipynb b/.ipynb_checkpoints/lab-sql-9-checkpoint.ipynb
new file mode 100644
index 0000000..0dd5a22
--- /dev/null
+++ b/.ipynb_checkpoints/lab-sql-9-checkpoint.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "716cf0f1",
+   "metadata": {},
+   "source": [
+    "Create a Python connection with SQL database and retrieve the results of the last two queries (also mentioned below) as dataframes:\n",
+    "\n",
+    "Check the number of rentals for each customer for May\n",
+    "\n",
+    "Check the number of rentals for each customer for June\n",
+    "\n",
+    "Hint: You can store the results from the two queries in two separate dataframes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2e6f857e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "283c46dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass  # To get the password without showing the input\n",
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "84e25554",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "engine = create_engine(connection_string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e6ab611b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CUSTOMER_ID</th>\n",
+       "      <th>RENTAL_COUNT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>431</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>351</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CUSTOMER_ID  RENTAL_COUNT\n",
+       "0            197             8\n",
+       "1            506             7\n",
+       "2            109             7\n",
+       "3            269             6\n",
+       "4            239             6\n",
+       "..           ...           ...\n",
+       "515          431             1\n",
+       "516          351             1\n",
+       "517           10             1\n",
+       "518          136             1\n",
+       "519           61             1\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_may = \"SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT FROM SAKILA.RENTALS_MAY GROUP BY CUSTOMER_ID ORDER BY RENTAL_COUNT DESC\"\n",
+    "df_may = pd.read_sql_query(query_may, engine)\n",
+    "\n",
+    "# Execute the second query and store the result in a dataframe\n",
+    "query_june = \"SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT FROM SAKILA.RENTALS_JUNE GROUP BY CUSTOMER_ID ORDER BY RENTAL_COUNT DESC\"\n",
+    "df_june = pd.read_sql_query(query_june, engine)\n",
+    "\n",
+    "\n",
+    "\n",
+    "df_may"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "13da5007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CUSTOMER_ID</th>\n",
+       "      <th>RENTAL_COUNT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>431</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>351</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CUSTOMER_ID  RENTAL_COUNT\n",
+       "0            197             8\n",
+       "1            506             7\n",
+       "2            109             7\n",
+       "3            269             6\n",
+       "4            239             6\n",
+       "..           ...           ...\n",
+       "515          431             1\n",
+       "516          351             1\n",
+       "517           10             1\n",
+       "518          136             1\n",
+       "519           61             1\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_may"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16474d15",
+   "metadata": {},
+   "source": [
+    "Write a function that checks if customer borrowed more or less films in the month of June as compared to May.\n",
+    "\n",
+    "Hint: For this part, you can create a join between the two dataframes created before, using the merge function available for pandas dataframes. Here is a link to the documentation for the merge function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b17fd5a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Customer borrowed more films in June compared to May\n"
+     ]
+    }
+   ],
+   "source": [
+    "def compare_rentals(df_may, df_june, customer_id):\n",
+    "\n",
+    "    may_rentals = df_may[df_may['CUSTOMER_ID'] == customer_id]['RENTAL_COUNT'].values\n",
+    "    june_rentals = df_june[df_june['CUSTOMER_ID'] == customer_id]['RENTAL_COUNT'].values\n",
+    "\n",
+    "    if len(may_rentals) > 0 and len(june_rentals) > 0:\n",
+    "        if may_rentals[0] < june_rentals[0]:\n",
+    "            return \"Customer borrowed more films in June compared to May\"\n",
+    "        elif may_rentals[0] > june_rentals[0]:\n",
+    "            return \"Customer borrowed fewer films in June compared to May\"\n",
+    "        else:\n",
+    "            return \"Customer borrowed the same number of films in May and June\"\n",
+    "    else:\n",
+    "        return \"Customer ID not found in one or both datasets\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "09ad1f81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Customer ID not found in one or both datasets'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_rentals(df_may, df_june, 34)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-sql-9.ipynb b/lab-sql-9.ipynb
new file mode 100644
index 0000000..0dd5a22
--- /dev/null
+++ b/lab-sql-9.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "716cf0f1",
+   "metadata": {},
+   "source": [
+    "Create a Python connection with SQL database and retrieve the results of the last two queries (also mentioned below) as dataframes:\n",
+    "\n",
+    "Check the number of rentals for each customer for May\n",
+    "\n",
+    "Check the number of rentals for each customer for June\n",
+    "\n",
+    "Hint: You can store the results from the two queries in two separate dataframes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2e6f857e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "283c46dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass  # To get the password without showing the input\n",
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "84e25554",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/bank'\n",
+    "engine = create_engine(connection_string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e6ab611b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CUSTOMER_ID</th>\n",
+       "      <th>RENTAL_COUNT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>431</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>351</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CUSTOMER_ID  RENTAL_COUNT\n",
+       "0            197             8\n",
+       "1            506             7\n",
+       "2            109             7\n",
+       "3            269             6\n",
+       "4            239             6\n",
+       "..           ...           ...\n",
+       "515          431             1\n",
+       "516          351             1\n",
+       "517           10             1\n",
+       "518          136             1\n",
+       "519           61             1\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_may = \"SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT FROM SAKILA.RENTALS_MAY GROUP BY CUSTOMER_ID ORDER BY RENTAL_COUNT DESC\"\n",
+    "df_may = pd.read_sql_query(query_may, engine)\n",
+    "\n",
+    "# Execute the second query and store the result in a dataframe\n",
+    "query_june = \"SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT FROM SAKILA.RENTALS_JUNE GROUP BY CUSTOMER_ID ORDER BY RENTAL_COUNT DESC\"\n",
+    "df_june = pd.read_sql_query(query_june, engine)\n",
+    "\n",
+    "\n",
+    "\n",
+    "df_may"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "13da5007",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CUSTOMER_ID</th>\n",
+       "      <th>RENTAL_COUNT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>269</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>239</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>431</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>351</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>520 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CUSTOMER_ID  RENTAL_COUNT\n",
+       "0            197             8\n",
+       "1            506             7\n",
+       "2            109             7\n",
+       "3            269             6\n",
+       "4            239             6\n",
+       "..           ...           ...\n",
+       "515          431             1\n",
+       "516          351             1\n",
+       "517           10             1\n",
+       "518          136             1\n",
+       "519           61             1\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_may"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16474d15",
+   "metadata": {},
+   "source": [
+    "Write a function that checks if customer borrowed more or less films in the month of June as compared to May.\n",
+    "\n",
+    "Hint: For this part, you can create a join between the two dataframes created before, using the merge function available for pandas dataframes. Here is a link to the documentation for the merge function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b17fd5a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Customer borrowed more films in June compared to May\n"
+     ]
+    }
+   ],
+   "source": [
+    "def compare_rentals(df_may, df_june, customer_id):\n",
+    "\n",
+    "    may_rentals = df_may[df_may['CUSTOMER_ID'] == customer_id]['RENTAL_COUNT'].values\n",
+    "    june_rentals = df_june[df_june['CUSTOMER_ID'] == customer_id]['RENTAL_COUNT'].values\n",
+    "\n",
+    "    if len(may_rentals) > 0 and len(june_rentals) > 0:\n",
+    "        if may_rentals[0] < june_rentals[0]:\n",
+    "            return \"Customer borrowed more films in June compared to May\"\n",
+    "        elif may_rentals[0] > june_rentals[0]:\n",
+    "            return \"Customer borrowed fewer films in June compared to May\"\n",
+    "        else:\n",
+    "            return \"Customer borrowed the same number of films in May and June\"\n",
+    "    else:\n",
+    "        return \"Customer ID not found in one or both datasets\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "09ad1f81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Customer ID not found in one or both datasets'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_rentals(df_may, df_june, 34)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-sql-9.sql b/lab-sql-9.sql
new file mode 100644
index 0000000..68489e1
--- /dev/null
+++ b/lab-sql-9.sql
@@ -0,0 +1,54 @@
+-- In this lab we will find the customers who were active in consecutive months of May and June. Follow the steps to complete the analysis.
+
+-- Create a table rentals_may to store the data from rental table with information for the month of May.
+
+CREATE TABLE SAKILA.RENTALS_MAY AS
+SELECT * FROM SAKILA.RENTAL
+WHERE MONTH(rental_date) = 5;
+
+SELECT * FROM SAKILA.RENTALS_MAY;
+
+-- Insert values in the table rentals_may using the table rental, filtering values only for the month of May.
+
+INSERT INTO rentals_may
+SELECT *
+FROM rental
+WHERE MONTH(rental_date) = 5;
+
+SELECT COUNT(*) FROM SAKILA.RENTALS_MAY;
+
+
+-- Create a table rentals_june to store the data from rental table with information for the month of June.
+
+CREATE TABLE SAKILA.RENTALS_JUNE AS
+SELECT * FROM SAKILA.RENTAL
+WHERE MONTH(rental_date) = 6;
+
+SELECT COUNT(*) FROM SAKILA.RENTALS_JUNE;
+
+
+-- Insert values in the table rentals_june using the table rental, filtering values only for the month of June.
+
+INSERT INTO RENTALS_JUNE
+SELECT *
+FROM rental
+WHERE MONTH(rental_date) = 6;
+
+SELECT COUNT(*) FROM SAKILA.RENTALS_JUNE;
+
+
+-- Check the number of rentals for each customer for May.
+
+SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT
+FROM SAKILA.RENTALS_MAY
+GROUP BY CUSTOMER_ID
+ORDER BY RENTAL_COUNT DESC;
+
+
+
+-- Check the number of rentals for each customer for June.
+
+SELECT CUSTOMER_ID, COUNT(RENTAL_ID) AS RENTAL_COUNT
+FROM SAKILA.RENTALS_JUNE
+GROUP BY CUSTOMER_ID
+ORDER BY RENTAL_COUNT DESC;
\ No newline at end of file