diff --git a/lab_sql_7.ipynb b/lab_sql_7.ipynb
new file mode 100644
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--- /dev/null
+++ b/lab_sql_7.ipynb
@@ -0,0 +1,851 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a93ae413",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a Python connection with SQL database and retrieve the results of the last two queries in sql file as dataframes.\n",
+    "# Hint: You can store the results from the two queries in two separate dataframes.\n",
+    "\n",
+    "# Write a function that checks if customer borrowed more or less films in the month of June as compared to May.\n",
+    "# Hint: For this part, you can create a join between the two dataframes created before, using the merge function available for pandas dataframes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f8e81b34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pymysql\n",
+    "from sqlalchemy import create_engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1f33781a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fea6ab84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/sakila'\n",
+    "engine = create_engine(connection_string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a69c861b",
+   "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>rental_id</th>\n",
+       "      <th>rental_date</th>\n",
+       "      <th>inventory_id</th>\n",
+       "      <th>customer_id</th>\n",
+       "      <th>return_date</th>\n",
+       "      <th>staff_id</th>\n",
+       "      <th>last_update</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2005-05-24 22:53:30</td>\n",
+       "      <td>367</td>\n",
+       "      <td>130</td>\n",
+       "      <td>2005-05-26 22:04:30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2005-05-24 22:54:33</td>\n",
+       "      <td>1525</td>\n",
+       "      <td>459</td>\n",
+       "      <td>2005-05-28 19:40:33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2005-05-24 23:03:39</td>\n",
+       "      <td>1711</td>\n",
+       "      <td>408</td>\n",
+       "      <td>2005-06-01 22:12:39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2005-05-24 23:04:41</td>\n",
+       "      <td>2452</td>\n",
+       "      <td>333</td>\n",
+       "      <td>2005-06-03 01:43:41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2005-05-24 23:05:21</td>\n",
+       "      <td>2079</td>\n",
+       "      <td>222</td>\n",
+       "      <td>2005-06-02 04:33:21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16039</th>\n",
+       "      <td>16045</td>\n",
+       "      <td>2005-08-23 22:25:26</td>\n",
+       "      <td>772</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2005-08-25 23:54:26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16040</th>\n",
+       "      <td>16046</td>\n",
+       "      <td>2005-08-23 22:26:47</td>\n",
+       "      <td>4364</td>\n",
+       "      <td>74</td>\n",
+       "      <td>2005-08-27 18:02:47</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16041</th>\n",
+       "      <td>16047</td>\n",
+       "      <td>2005-08-23 22:42:48</td>\n",
+       "      <td>2088</td>\n",
+       "      <td>114</td>\n",
+       "      <td>2005-08-25 02:48:48</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16042</th>\n",
+       "      <td>16048</td>\n",
+       "      <td>2005-08-23 22:43:07</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>103</td>\n",
+       "      <td>2005-08-31 21:33:07</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16043</th>\n",
+       "      <td>16049</td>\n",
+       "      <td>2005-08-23 22:50:12</td>\n",
+       "      <td>2666</td>\n",
+       "      <td>393</td>\n",
+       "      <td>2005-08-30 01:01:12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16044 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       rental_id         rental_date  inventory_id  customer_id  \\\n",
+       "0              1 2005-05-24 22:53:30           367          130   \n",
+       "1              2 2005-05-24 22:54:33          1525          459   \n",
+       "2              3 2005-05-24 23:03:39          1711          408   \n",
+       "3              4 2005-05-24 23:04:41          2452          333   \n",
+       "4              5 2005-05-24 23:05:21          2079          222   \n",
+       "...          ...                 ...           ...          ...   \n",
+       "16039      16045 2005-08-23 22:25:26           772           14   \n",
+       "16040      16046 2005-08-23 22:26:47          4364           74   \n",
+       "16041      16047 2005-08-23 22:42:48          2088          114   \n",
+       "16042      16048 2005-08-23 22:43:07          2019          103   \n",
+       "16043      16049 2005-08-23 22:50:12          2666          393   \n",
+       "\n",
+       "              return_date  staff_id         last_update  \n",
+       "0     2005-05-26 22:04:30         1 2006-02-15 21:30:53  \n",
+       "1     2005-05-28 19:40:33         1 2006-02-15 21:30:53  \n",
+       "2     2005-06-01 22:12:39         1 2006-02-15 21:30:53  \n",
+       "3     2005-06-03 01:43:41         2 2006-02-15 21:30:53  \n",
+       "4     2005-06-02 04:33:21         1 2006-02-15 21:30:53  \n",
+       "...                   ...       ...                 ...  \n",
+       "16039 2005-08-25 23:54:26         1 2006-02-15 21:30:53  \n",
+       "16040 2005-08-27 18:02:47         2 2006-02-15 21:30:53  \n",
+       "16041 2005-08-25 02:48:48         2 2006-02-15 21:30:53  \n",
+       "16042 2005-08-31 21:33:07         1 2006-02-15 21:30:53  \n",
+       "16043 2005-08-30 01:01:12         2 2006-02-15 21:30:53  \n",
+       "\n",
+       "[16044 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query('select * from sakila.rental', engine)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7c124ce0",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>109</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53</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>580</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>582</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>590</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>595</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>599</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            109             7\n",
+       "2            506             7\n",
+       "3             19             6\n",
+       "4             53             6\n",
+       "..           ...           ...\n",
+       "515          580             1\n",
+       "516          582             1\n",
+       "517          590             1\n",
+       "518          595             1\n",
+       "519          599             1\n",
+       "\n",
+       "[520 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the number of rentals for each customer for May\n",
+    "\n",
+    "# Create temporary table\n",
+    "create_table_query = '''\n",
+    "CREATE TEMPORARY TABLE sakila.rentals_may AS\n",
+    "SELECT \n",
+    "    customer_id, \n",
+    "    COUNT(rental_id) AS rental_count\n",
+    "FROM \n",
+    "    sakila.rental\n",
+    "WHERE \n",
+    "    MONTH(rental_date) = 5\n",
+    "GROUP BY \n",
+    "    customer_id\n",
+    "ORDER BY \n",
+    "    rental_count DESC\n",
+    "'''\n",
+    "\n",
+    "engine = create_engine(connection_string)\n",
+    "with engine.connect() as connection:\n",
+    "    connection.execute(create_table_query)\n",
+    "\n",
+    "# Query the temporary table to get the data\n",
+    "select_query = 'SELECT * FROM sakila.rentals_may'\n",
+    "\n",
+    "rentals_may = pd.read_sql_query(select_query, engine)\n",
+    "rentals_may"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b45cfe27",
+   "metadata": {
+    "scrolled": true
+   },
+   "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>31</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>454</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>213</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>267</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>295</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>549</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>555</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>564</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>588</th>\n",
+       "      <td>580</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>598</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>590 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rental_count\n",
+       "0             31            11\n",
+       "1            454            10\n",
+       "2            213             9\n",
+       "3            267             9\n",
+       "4            295             9\n",
+       "..           ...           ...\n",
+       "585          549             1\n",
+       "586          555             1\n",
+       "587          564             1\n",
+       "588          580             1\n",
+       "589          598             1\n",
+       "\n",
+       "[590 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check the number of rentals for each customer for June\n",
+    "\n",
+    "# Create temporary table\n",
+    "create_table_query2 = '''\n",
+    "CREATE TEMPORARY TABLE sakila.rentals_june AS\n",
+    "SELECT \n",
+    "    customer_id, \n",
+    "    COUNT(rental_id) AS rental_count\n",
+    "FROM \n",
+    "    sakila.rental\n",
+    "WHERE \n",
+    "    MONTH(rental_date) = 6\n",
+    "GROUP BY \n",
+    "    customer_id\n",
+    "ORDER BY \n",
+    "    rental_count DESC\n",
+    "'''\n",
+    "\n",
+    "engine = create_engine(connection_string)\n",
+    "with engine.connect() as connection:\n",
+    "    connection.execute(create_table_query2)\n",
+    "\n",
+    "# Query the temporary table to get the data \n",
+    "select_query2 = 'SELECT * FROM sakila.rentals_june'\n",
+    "\n",
+    "# Corrected variable name\n",
+    "rentals_june = pd.read_sql_query(select_query2, engine)\n",
+    "rentals_june"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5315a6e9",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\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_may</th>\n",
+       "      <th>rental_count_june</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>335</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>370</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>487</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>555</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>598</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>598 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rental_count_may  rental_count_june\n",
+       "0            197               8.0                8.0\n",
+       "1            109               7.0                5.0\n",
+       "2            506               7.0                5.0\n",
+       "3             19               6.0                3.0\n",
+       "4             53               6.0                5.0\n",
+       "..           ...               ...                ...\n",
+       "593          335               NaN                1.0\n",
+       "594          370               NaN                1.0\n",
+       "595          487               NaN                1.0\n",
+       "596          555               NaN                1.0\n",
+       "597          598               NaN                1.0\n",
+       "\n",
+       "[598 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joined_rentals = rentals_may.merge(rentals_june, on='customer_id', suffixes=('_may', '_june'), how='outer')\n",
+    "joined_rentals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "1cbbe8c5",
+   "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_may</th>\n",
+       "      <th>rental_count_june</th>\n",
+       "      <th>rentals_difference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>197</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>borrowed_same_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>109</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>borrowed_less_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>506</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>borrowed_less_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>borrowed_less_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>53</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>borrowed_less_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>515</th>\n",
+       "      <td>580</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>borrowed_same_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>516</th>\n",
+       "      <td>582</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>borrowed_more_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>517</th>\n",
+       "      <td>590</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>borrowed_more_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>595</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>borrowed_more_in_june</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>599</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>borrowed_more_in_june</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>512 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer_id  rental_count_may  rental_count_june     rentals_difference\n",
+       "0            197               8.0                8.0  borrowed_same_in_june\n",
+       "1            109               7.0                5.0  borrowed_less_in_june\n",
+       "2            506               7.0                5.0  borrowed_less_in_june\n",
+       "3             19               6.0                3.0  borrowed_less_in_june\n",
+       "4             53               6.0                5.0  borrowed_less_in_june\n",
+       "..           ...               ...                ...                    ...\n",
+       "515          580               1.0                1.0  borrowed_same_in_june\n",
+       "516          582               1.0                3.0  borrowed_more_in_june\n",
+       "517          590               1.0                5.0  borrowed_more_in_june\n",
+       "518          595               1.0                2.0  borrowed_more_in_june\n",
+       "519          599               1.0                4.0  borrowed_more_in_june\n",
+       "\n",
+       "[512 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Function to compare rental counts for May and June\n",
+    "def compare_rentals(df):\n",
+    "    # Create a new column to indicate the comparison result\n",
+    "    df['rentals_difference'] = df.apply(\n",
+    "        lambda row: 'borrowed_more_in_june' if row['rental_count_june'] > row['rental_count_may'] else (\n",
+    "            'borrowed_less_in_june' if row['rental_count_june'] < row['rental_count_may'] else 'borrowed_same_in_june'), axis=1)\n",
+    "    return df\n",
+    "\n",
+    "rentals_diff = compare_rentals(joined_rentals)\n",
+    "rentals_diff = rentals_diff.dropna()\n",
+    "rentals_diff"
+   ]
+  }
+ ],
+ "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_7.sql b/lab_sql_7.sql
new file mode 100644
index 0000000..39c4e2a
--- /dev/null
+++ b/lab_sql_7.sql
@@ -0,0 +1,46 @@
+-- Create a table rentals_may to store the data from rental table with information for the month of May.
+create temporary table sakila.rentals_may as
+select *
+from sakila.rental
+where month(rental_date) = 5;
+
+select *
+from sakila.rentals_may;
+
+-- Create a table rentals_june to store the data from rental table with information for the month of June.
+create temporary table sakila.rentals_june as
+select *
+from sakila.rental
+where month(rental_date) = 6;
+
+select *
+from sakila.rentals_june;
+
+-- Check the number of rentals for each customer for May.
+select customer_id, count(rental_id)
+from sakila.rentals_may
+group by customer_id
+order by count(rental_id) desc;
+
+-- Check the number of rentals for each customer for June.
+select customer_id, count(rental_id)
+from sakila.rentals_june
+group by customer_id
+order by count(rental_id) desc;
+
+-- Join temporary tables for May and June.
+
+CREATE TEMPORARY TABLE sakila.joined_rentals as
+SELECT m.customer_id, m.rental_id as rental_id_may, j.rental_id as rental_id_june
+FROM sakila.rentals_may m
+LEFT JOIN sakila.rentals_june j
+  ON m.customer_id = j.customer_id;
+  
+select customer_id,count(rental_id_may) as rentals_may , count(rental_id_may) as rentals_june, 
+case
+	when count(rental_id_june) > count(rental_id_may) then 'borrowed_more_in_june'
+    when count(rental_id_june) < count(rental_id_may) then 'borrowed_less_in_june'
+    when count(rental_id_june) = count(rental_id_may) then 'borrowed_same_in_june'
+end as rentals_difference 
+from sakila.joined_rentals
+group by customer_id;