diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..5008ddf
Binary files /dev/null and b/.DS_Store differ
diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..efed0b5
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "752d422d-b865-40b5-a41f-ee5d3c4a3505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0.39\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sqlalchemy\n",
+    "print(sqlalchemy.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8599e393-0a2e-44e0-ad64-42504e8e29d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine, text\n",
+    "import getpass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a31784fe-2018-42f2-bbad-43c376e770ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23c3299c-0e05-4cd1-8760-c20852e382b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Engine(mysql+pymysql://root:***@localhost/sakila)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bd = \"sakila\"\n",
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/'+ bd\n",
+    "engine = create_engine(connection_string)\n",
+    "engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c2c7fe7-9029-4067-b93f-e62ee9304bd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with engine.connect() as connection:\n",
+    "    result = connection.execute(text(\"SELECT DATABASE();\"))\n",
+    "    print(result.scalar())"
+   ]
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/python_connection_lab-checkpoint.ipynb b/.ipynb_checkpoints/python_connection_lab-checkpoint.ipynb
new file mode 100644
index 0000000..edc267d
--- /dev/null
+++ b/.ipynb_checkpoints/python_connection_lab-checkpoint.ipynb
@@ -0,0 +1,815 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "752d422d-b865-40b5-a41f-ee5d3c4a3505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0.39\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sqlalchemy\n",
+    "print(sqlalchemy.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8599e393-0a2e-44e0-ad64-42504e8e29d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine, text\n",
+    "import getpass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a31784fe-2018-42f2-bbad-43c376e770ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "23c3299c-0e05-4cd1-8760-c20852e382b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Engine(mysql+pymysql://root:***@localhost/sakila)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bd = \"sakila\"\n",
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/'+ bd\n",
+    "engine = create_engine(connection_string)\n",
+    "engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2c2c7fe7-9029-4067-b93f-e62ee9304bd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sakila\n"
+     ]
+    }
+   ],
+   "source": [
+    "with engine.connect() as connection:\n",
+    "    result = connection.execute(text(\"SELECT DATABASE();\"))\n",
+    "    print(result.scalar())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a6e0413f-9277-4722-a43c-8bd76297c7a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('actor',), ('actor_info',), ('address',), ('category',), ('city',), ('country',), ('customer',), ('customer_list',), ('film',), ('film_actor',), ('film_category',), ('film_list',), ('film_text',), ('inventory',), ('language',), ('nicer_but_slower_film_list',), ('payment',), ('rental',), ('sales_by_film_category',), ('sales_by_store',), ('staff',), ('staff_list',), ('store',), ('v_rental_info_customer',)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with engine.connect() as connection:\n",
+    "    \n",
+    "    result = connection.execute(text(\"SHOW TABLES;\"))\n",
+    "    print(result.fetchall())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "92aa2451-ae29-4f37-80b9-a17ff954327a",
+   "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",
+       "  </tbody>\n",
+       "</table>\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",
+       "          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  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Write a Python function called rentals_month that retrieves rental data for a given month and year (passed as parameters) from the \n",
+    "# Sakila database as a Pandas DataFrame.\n",
+    "\n",
+    "with engine.connect() as connection:\n",
+    "    query = text(\"SELECT * FROM rental LIMIT 5;\")\n",
+    "    result = connection.execute(query)\n",
+    "    df = pd.DataFrame(result.all(), columns=result.keys())\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "039c1489-f49d-4ce9-a333-336eb5d53816",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year):\n",
+    "    query = text(\"\"\"\n",
+    "    SELECT *\n",
+    "    FROM rental\n",
+    "    WHERE MONTH(rental_date) = :month\n",
+    "    AND YEAR(rental_date) = :year;\n",
+    "    \"\"\")\n",
+    "\n",
+    "    with engine.connect() as connection:\n",
+    "        result = connection.execute(query, {\n",
+    "            \"month\": month,\n",
+    "            \"year\": year})\n",
+    "        rentals_df = pd.DataFrame(\n",
+    "            result.all(), \n",
+    "            columns=result.keys())\n",
+    "\n",
+    "    return rentals_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b2469d5a-3586-421d-a2de-83a143444014",
+   "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>3470</td>\n",
+       "      <td>2005-07-05 22:49:24</td>\n",
+       "      <td>883</td>\n",
+       "      <td>565</td>\n",
+       "      <td>2005-07-07 19:36:24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3471</td>\n",
+       "      <td>2005-07-05 22:51:44</td>\n",
+       "      <td>1724</td>\n",
+       "      <td>242</td>\n",
+       "      <td>2005-07-13 01:38:44</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3472</td>\n",
+       "      <td>2005-07-05 22:56:33</td>\n",
+       "      <td>841</td>\n",
+       "      <td>37</td>\n",
+       "      <td>2005-07-13 17:18:33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3473</td>\n",
+       "      <td>2005-07-05 22:57:34</td>\n",
+       "      <td>2735</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2005-07-12 23:53:34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3474</td>\n",
+       "      <td>2005-07-05 22:59:53</td>\n",
+       "      <td>97</td>\n",
+       "      <td>594</td>\n",
+       "      <td>2005-07-08 20:32:53</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>6704</th>\n",
+       "      <td>10176</td>\n",
+       "      <td>2005-07-31 23:40:35</td>\n",
+       "      <td>1181</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2005-08-09 00:46:35</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6705</th>\n",
+       "      <td>10177</td>\n",
+       "      <td>2005-07-31 23:42:33</td>\n",
+       "      <td>2242</td>\n",
+       "      <td>279</td>\n",
+       "      <td>2005-08-03 01:30:33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6706</th>\n",
+       "      <td>10178</td>\n",
+       "      <td>2005-07-31 23:43:04</td>\n",
+       "      <td>1582</td>\n",
+       "      <td>491</td>\n",
+       "      <td>2005-08-03 00:43:04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6707</th>\n",
+       "      <td>10179</td>\n",
+       "      <td>2005-07-31 23:49:54</td>\n",
+       "      <td>2136</td>\n",
+       "      <td>131</td>\n",
+       "      <td>2005-08-01 20:46:54</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6708</th>\n",
+       "      <td>10180</td>\n",
+       "      <td>2005-07-31 23:57:43</td>\n",
+       "      <td>757</td>\n",
+       "      <td>50</td>\n",
+       "      <td>2005-08-09 04:04:43</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6709 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      rental_id         rental_date  inventory_id  customer_id  \\\n",
+       "0          3470 2005-07-05 22:49:24           883          565   \n",
+       "1          3471 2005-07-05 22:51:44          1724          242   \n",
+       "2          3472 2005-07-05 22:56:33           841           37   \n",
+       "3          3473 2005-07-05 22:57:34          2735           60   \n",
+       "4          3474 2005-07-05 22:59:53            97          594   \n",
+       "...         ...                 ...           ...          ...   \n",
+       "6704      10176 2005-07-31 23:40:35          1181           19   \n",
+       "6705      10177 2005-07-31 23:42:33          2242          279   \n",
+       "6706      10178 2005-07-31 23:43:04          1582          491   \n",
+       "6707      10179 2005-07-31 23:49:54          2136          131   \n",
+       "6708      10180 2005-07-31 23:57:43           757           50   \n",
+       "\n",
+       "             return_date  staff_id         last_update  \n",
+       "0    2005-07-07 19:36:24         1 2006-02-15 21:30:53  \n",
+       "1    2005-07-13 01:38:44         2 2006-02-15 21:30:53  \n",
+       "2    2005-07-13 17:18:33         2 2006-02-15 21:30:53  \n",
+       "3    2005-07-12 23:53:34         1 2006-02-15 21:30:53  \n",
+       "4    2005-07-08 20:32:53         1 2006-02-15 21:30:53  \n",
+       "...                  ...       ...                 ...  \n",
+       "6704 2005-08-09 00:46:35         2 2006-02-15 21:30:53  \n",
+       "6705 2005-08-03 01:30:33         2 2006-02-15 21:30:53  \n",
+       "6706 2005-08-03 00:43:04         1 2006-02-15 21:30:53  \n",
+       "6707 2005-08-01 20:46:54         2 2006-02-15 21:30:53  \n",
+       "6708 2005-08-09 04:04:43         2 2006-02-15 21:30:53  \n",
+       "\n",
+       "[6709 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# testing the function\n",
+    "rentals_july_2005 = rentals_month(engine, 7, 2005)\n",
+    "rentals_july_2005"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "1eeee65b-8ef5-4e53-8566-5e5cefb9749c",
+   "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>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year\n",
+       "0  2005\n",
+       "1  2006"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the unique values for month\n",
+    "with engine.connect() as connection:\n",
+    "    query = text(\"\"\"\n",
+    "    SELECT DISTINCT YEAR(rental_date) AS year\n",
+    "    FROM rental\n",
+    "    ORDER BY year;\n",
+    "    \"\"\")\n",
+    "    result = connection.execute(query)\n",
+    "    df_months = pd.DataFrame(result.all(), columns=result.keys())\n",
+    "\n",
+    "df_months"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "235dd0c5-0516-42aa-9aa0-a6ed7d1f0675",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(rentals_df, month, year):\n",
+    "    \n",
+    "    rental_count = (\n",
+    "        rentals_df\n",
+    "        .groupby(\"customer_id\")\n",
+    "        .size()\n",
+    "        .reset_index(name=f\"rentals_{month:02d}_{year}\")\n",
+    "    )\n",
+    "    \n",
+    "    return rental_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "def6f1ac-3d4d-48dd-a8c2-e82af0901e2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_may = rentals_month(engine, 5, 2005)\n",
+    "\n",
+    "rental_count_may = rental_count_month(\n",
+    "    rentals_may,\n",
+    "    5,\n",
+    "    2005\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "6bab7e65-91b0-412b-824d-c7a591d9c43a",
+   "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>rentals_05_2005</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  rentals_05_2005\n",
+       "0            1                2\n",
+       "1            2                1\n",
+       "2            3                2\n",
+       "3            5                3\n",
+       "4            6                3"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rental_count_may.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1629ba9e-00f0-4207-9882-70f95ef3db13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_june = rentals_month(engine, 6, 2005)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ce8ed30f-7f33-4fb3-ba51-6d5898794fa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rental_count_june = rental_count_month(\n",
+    "    rentals_june,\n",
+    "    6,\n",
+    "    2005\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "7a55fd28-644f-488b-bbcd-6b304c480865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    comparison = pd.merge(\n",
+    "        df1,\n",
+    "        df2,\n",
+    "        on=\"customer_id\",\n",
+    "        how=\"inner\"\n",
+    "    )\n",
+    "\n",
+    "    comparison[\"difference\"] = (\n",
+    "        comparison.iloc[:, 2] - comparison.iloc[:, 1]\n",
+    "    )\n",
+    "\n",
+    "    return comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a77ebd69-1852-4658-8bfb-53579fd5321a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparison = compare_rentals(\n",
+    "    rental_count_may,\n",
+    "    rental_count_june\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "7184c304-1a11-442d-9461-037e5d1cb5af",
+   "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>rentals_05_2005</th>\n",
+       "      <th>rentals_06_2005</th>\n",
+       "      <th>difference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  rentals_05_2005  rentals_06_2005  difference\n",
+       "0            1                2                7           5\n",
+       "1            2                1                1           0\n",
+       "2            3                2                4           2\n",
+       "3            5                3                5           2\n",
+       "4            6                3                4           1"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "comparison.head()"
+   ]
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python_connection_lab.ipynb b/python_connection_lab.ipynb
new file mode 100644
index 0000000..edc267d
--- /dev/null
+++ b/python_connection_lab.ipynb
@@ -0,0 +1,815 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "752d422d-b865-40b5-a41f-ee5d3c4a3505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0.39\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sqlalchemy\n",
+    "print(sqlalchemy.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8599e393-0a2e-44e0-ad64-42504e8e29d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine, text\n",
+    "import getpass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a31784fe-2018-42f2-bbad-43c376e770ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "23c3299c-0e05-4cd1-8760-c20852e382b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Engine(mysql+pymysql://root:***@localhost/sakila)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bd = \"sakila\"\n",
+    "connection_string = 'mysql+pymysql://root:' + password + '@localhost/'+ bd\n",
+    "engine = create_engine(connection_string)\n",
+    "engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2c2c7fe7-9029-4067-b93f-e62ee9304bd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sakila\n"
+     ]
+    }
+   ],
+   "source": [
+    "with engine.connect() as connection:\n",
+    "    result = connection.execute(text(\"SELECT DATABASE();\"))\n",
+    "    print(result.scalar())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a6e0413f-9277-4722-a43c-8bd76297c7a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('actor',), ('actor_info',), ('address',), ('category',), ('city',), ('country',), ('customer',), ('customer_list',), ('film',), ('film_actor',), ('film_category',), ('film_list',), ('film_text',), ('inventory',), ('language',), ('nicer_but_slower_film_list',), ('payment',), ('rental',), ('sales_by_film_category',), ('sales_by_store',), ('staff',), ('staff_list',), ('store',), ('v_rental_info_customer',)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with engine.connect() as connection:\n",
+    "    \n",
+    "    result = connection.execute(text(\"SHOW TABLES;\"))\n",
+    "    print(result.fetchall())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "92aa2451-ae29-4f37-80b9-a17ff954327a",
+   "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",
+       "  </tbody>\n",
+       "</table>\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",
+       "          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  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Write a Python function called rentals_month that retrieves rental data for a given month and year (passed as parameters) from the \n",
+    "# Sakila database as a Pandas DataFrame.\n",
+    "\n",
+    "with engine.connect() as connection:\n",
+    "    query = text(\"SELECT * FROM rental LIMIT 5;\")\n",
+    "    result = connection.execute(query)\n",
+    "    df = pd.DataFrame(result.all(), columns=result.keys())\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "039c1489-f49d-4ce9-a333-336eb5d53816",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rentals_month(engine, month, year):\n",
+    "    query = text(\"\"\"\n",
+    "    SELECT *\n",
+    "    FROM rental\n",
+    "    WHERE MONTH(rental_date) = :month\n",
+    "    AND YEAR(rental_date) = :year;\n",
+    "    \"\"\")\n",
+    "\n",
+    "    with engine.connect() as connection:\n",
+    "        result = connection.execute(query, {\n",
+    "            \"month\": month,\n",
+    "            \"year\": year})\n",
+    "        rentals_df = pd.DataFrame(\n",
+    "            result.all(), \n",
+    "            columns=result.keys())\n",
+    "\n",
+    "    return rentals_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b2469d5a-3586-421d-a2de-83a143444014",
+   "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>3470</td>\n",
+       "      <td>2005-07-05 22:49:24</td>\n",
+       "      <td>883</td>\n",
+       "      <td>565</td>\n",
+       "      <td>2005-07-07 19:36:24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3471</td>\n",
+       "      <td>2005-07-05 22:51:44</td>\n",
+       "      <td>1724</td>\n",
+       "      <td>242</td>\n",
+       "      <td>2005-07-13 01:38:44</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3472</td>\n",
+       "      <td>2005-07-05 22:56:33</td>\n",
+       "      <td>841</td>\n",
+       "      <td>37</td>\n",
+       "      <td>2005-07-13 17:18:33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3473</td>\n",
+       "      <td>2005-07-05 22:57:34</td>\n",
+       "      <td>2735</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2005-07-12 23:53:34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3474</td>\n",
+       "      <td>2005-07-05 22:59:53</td>\n",
+       "      <td>97</td>\n",
+       "      <td>594</td>\n",
+       "      <td>2005-07-08 20:32:53</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>6704</th>\n",
+       "      <td>10176</td>\n",
+       "      <td>2005-07-31 23:40:35</td>\n",
+       "      <td>1181</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2005-08-09 00:46:35</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6705</th>\n",
+       "      <td>10177</td>\n",
+       "      <td>2005-07-31 23:42:33</td>\n",
+       "      <td>2242</td>\n",
+       "      <td>279</td>\n",
+       "      <td>2005-08-03 01:30:33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6706</th>\n",
+       "      <td>10178</td>\n",
+       "      <td>2005-07-31 23:43:04</td>\n",
+       "      <td>1582</td>\n",
+       "      <td>491</td>\n",
+       "      <td>2005-08-03 00:43:04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6707</th>\n",
+       "      <td>10179</td>\n",
+       "      <td>2005-07-31 23:49:54</td>\n",
+       "      <td>2136</td>\n",
+       "      <td>131</td>\n",
+       "      <td>2005-08-01 20:46:54</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6708</th>\n",
+       "      <td>10180</td>\n",
+       "      <td>2005-07-31 23:57:43</td>\n",
+       "      <td>757</td>\n",
+       "      <td>50</td>\n",
+       "      <td>2005-08-09 04:04:43</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 21:30:53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6709 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      rental_id         rental_date  inventory_id  customer_id  \\\n",
+       "0          3470 2005-07-05 22:49:24           883          565   \n",
+       "1          3471 2005-07-05 22:51:44          1724          242   \n",
+       "2          3472 2005-07-05 22:56:33           841           37   \n",
+       "3          3473 2005-07-05 22:57:34          2735           60   \n",
+       "4          3474 2005-07-05 22:59:53            97          594   \n",
+       "...         ...                 ...           ...          ...   \n",
+       "6704      10176 2005-07-31 23:40:35          1181           19   \n",
+       "6705      10177 2005-07-31 23:42:33          2242          279   \n",
+       "6706      10178 2005-07-31 23:43:04          1582          491   \n",
+       "6707      10179 2005-07-31 23:49:54          2136          131   \n",
+       "6708      10180 2005-07-31 23:57:43           757           50   \n",
+       "\n",
+       "             return_date  staff_id         last_update  \n",
+       "0    2005-07-07 19:36:24         1 2006-02-15 21:30:53  \n",
+       "1    2005-07-13 01:38:44         2 2006-02-15 21:30:53  \n",
+       "2    2005-07-13 17:18:33         2 2006-02-15 21:30:53  \n",
+       "3    2005-07-12 23:53:34         1 2006-02-15 21:30:53  \n",
+       "4    2005-07-08 20:32:53         1 2006-02-15 21:30:53  \n",
+       "...                  ...       ...                 ...  \n",
+       "6704 2005-08-09 00:46:35         2 2006-02-15 21:30:53  \n",
+       "6705 2005-08-03 01:30:33         2 2006-02-15 21:30:53  \n",
+       "6706 2005-08-03 00:43:04         1 2006-02-15 21:30:53  \n",
+       "6707 2005-08-01 20:46:54         2 2006-02-15 21:30:53  \n",
+       "6708 2005-08-09 04:04:43         2 2006-02-15 21:30:53  \n",
+       "\n",
+       "[6709 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# testing the function\n",
+    "rentals_july_2005 = rentals_month(engine, 7, 2005)\n",
+    "rentals_july_2005"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "1eeee65b-8ef5-4e53-8566-5e5cefb9749c",
+   "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>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year\n",
+       "0  2005\n",
+       "1  2006"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking the unique values for month\n",
+    "with engine.connect() as connection:\n",
+    "    query = text(\"\"\"\n",
+    "    SELECT DISTINCT YEAR(rental_date) AS year\n",
+    "    FROM rental\n",
+    "    ORDER BY year;\n",
+    "    \"\"\")\n",
+    "    result = connection.execute(query)\n",
+    "    df_months = pd.DataFrame(result.all(), columns=result.keys())\n",
+    "\n",
+    "df_months"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "235dd0c5-0516-42aa-9aa0-a6ed7d1f0675",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rental_count_month(rentals_df, month, year):\n",
+    "    \n",
+    "    rental_count = (\n",
+    "        rentals_df\n",
+    "        .groupby(\"customer_id\")\n",
+    "        .size()\n",
+    "        .reset_index(name=f\"rentals_{month:02d}_{year}\")\n",
+    "    )\n",
+    "    \n",
+    "    return rental_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "def6f1ac-3d4d-48dd-a8c2-e82af0901e2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_may = rentals_month(engine, 5, 2005)\n",
+    "\n",
+    "rental_count_may = rental_count_month(\n",
+    "    rentals_may,\n",
+    "    5,\n",
+    "    2005\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "6bab7e65-91b0-412b-824d-c7a591d9c43a",
+   "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>rentals_05_2005</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  rentals_05_2005\n",
+       "0            1                2\n",
+       "1            2                1\n",
+       "2            3                2\n",
+       "3            5                3\n",
+       "4            6                3"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rental_count_may.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1629ba9e-00f0-4207-9882-70f95ef3db13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rentals_june = rentals_month(engine, 6, 2005)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ce8ed30f-7f33-4fb3-ba51-6d5898794fa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rental_count_june = rental_count_month(\n",
+    "    rentals_june,\n",
+    "    6,\n",
+    "    2005\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "7a55fd28-644f-488b-bbcd-6b304c480865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_rentals(df1, df2):\n",
+    "    comparison = pd.merge(\n",
+    "        df1,\n",
+    "        df2,\n",
+    "        on=\"customer_id\",\n",
+    "        how=\"inner\"\n",
+    "    )\n",
+    "\n",
+    "    comparison[\"difference\"] = (\n",
+    "        comparison.iloc[:, 2] - comparison.iloc[:, 1]\n",
+    "    )\n",
+    "\n",
+    "    return comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a77ebd69-1852-4658-8bfb-53579fd5321a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparison = compare_rentals(\n",
+    "    rental_count_may,\n",
+    "    rental_count_june\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "7184c304-1a11-442d-9461-037e5d1cb5af",
+   "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>rentals_05_2005</th>\n",
+       "      <th>rentals_06_2005</th>\n",
+       "      <th>difference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  rentals_05_2005  rentals_06_2005  difference\n",
+       "0            1                2                7           5\n",
+       "1            2                1                1           0\n",
+       "2            3                2                4           2\n",
+       "3            5                3                5           2\n",
+       "4            6                3                4           1"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "comparison.head()"
+   ]
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}