diff --git a/.ipynb_checkpoints/lab-9-checkpoint.ipynb b/.ipynb_checkpoints/lab-9-checkpoint.ipynb
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+++ b/.ipynb_checkpoints/lab-9-checkpoint.ipynb
@@ -0,0 +1,929 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "timely-vaccine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "import getpass  # To get the password without showing the input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "appropriate-handling",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "express-olympus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:'+password+'@localhost/sakila'\n",
+    "engine = create_engine(connection_string)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "military-migration",
+   "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>inventory_id</th>\n",
+       "      <th>film_id</th>\n",
+       "      <th>store_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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   inventory_id  film_id  store_id         last_update\n",
+       "0             1        1         1 2006-02-15 05:09:17\n",
+       "1             2        1         1 2006-02-15 05:09:17\n",
+       "2             3        1         1 2006-02-15 05:09:17\n",
+       "3             4        1         1 2006-02-15 05:09:17\n",
+       "4             5        1         2 2006-02-15 05:09:17"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query('SELECT * FROM inventory', engine)\n",
+    "data.head()\n",
+    "\n",
+    "#testing the connection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "every-platform",
+   "metadata": {},
+   "source": [
+    "1. How many distinct (different) actors' last names are there?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "synthetic-reasoning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = 'SELECT COUNT(DISTINCT(last_name)) AS distinct_actors FROM sakila.actor'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "current-shelf",
+   "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>distinct_actors</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   distinct_actors\n",
+       "0              121"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query, engine)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outstanding-signature",
+   "metadata": {},
+   "source": [
+    "2. Add an additional column day_type with values 'weekend' and 'workday' depending on the rental day of the week."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "rapid-rotation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = '''SELECT *, WEEKDAY(rental_date) AS 'Weekday',\n",
+    "CASE\n",
+    "WHEN (WEEKDAY(rental_date) >  4 ) then 'weekend'\n",
+    "ELSE 'workday'\n",
+    "END AS day_type\n",
+    "FROM sakila.rental;'''\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "sporting-reporter",
+   "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",
+       "      <th>Weekday</th>\n",
+       "      <th>day_type</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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  Weekday day_type  \n",
+       "0 2005-05-26 22:04:30         1 2006-02-15 21:30:53        1  workday  \n",
+       "1 2005-05-28 19:40:33         1 2006-02-15 21:30:53        1  workday  \n",
+       "2 2005-06-01 22:12:39         1 2006-02-15 21:30:53        1  workday  \n",
+       "3 2005-06-03 01:43:41         2 2006-02-15 21:30:53        1  workday  \n",
+       "4 2005-06-02 04:33:21         1 2006-02-15 21:30:53        1  workday  "
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query1, engine)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ahead-drinking",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "executive-southwest",
+   "metadata": {},
+   "source": [
+    "3. Get all films with ARMAGEDDON in the title."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "cosmetic-boutique",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = '''SELECT * FROM sakila.film WHERE title LIKE \"\\%ARMAGEDDON\\%\";'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "blank-advantage",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "unsupported format character 'A' (0x41) at index 46",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-160-8394e0f59e09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_sql_query\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m    375\u001b[0m     \"\"\"\n\u001b[1;32m    376\u001b[0m     \u001b[0mpandas_sql\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandasSQL_builder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m     return pandas_sql.read_query(\n\u001b[0m\u001b[1;32m    378\u001b[0m         \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m         \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m   1292\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1294\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1295\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1160\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_options\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m     def read_table(\n",
+      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/deprecations.py\u001b[0m in \u001b[0;36mwarned\u001b[0;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0m_warn_with_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   3035\u001b[0m         \"\"\"\n\u001b[1;32m   3036\u001b[0m         \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclose_with_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3037\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3039\u001b[0m     @util.deprecated_20(\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             )\n\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             return self._exec_driver_sql(\n\u001b[0m\u001b[1;32m   1186\u001b[0m                 \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_exec_driver_sql\u001b[0;34m(self, statement, multiparams, params, execution_options, future)\u001b[0m\n\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m         \u001b[0mdialect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1484\u001b[0;31m         ret = self._execute_context(\n\u001b[0m\u001b[1;32m   1485\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_ctx_cls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_statement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1748\u001b[0;31m             self._handle_dbapi_exception(\n\u001b[0m\u001b[1;32m   1749\u001b[0m                 \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1931\u001b[0m                 )\n\u001b[1;32m   1932\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1933\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;31m# credit to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1703\u001b[0m                             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m                     self.dialect.do_execute(\n\u001b[0m\u001b[1;32m   1706\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m                     )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmogrify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mmogrify\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m             \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_escape_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: unsupported format character 'A' (0x41) at index 46"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query2, engine)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "foster-shadow",
+   "metadata": {},
+   "source": [
+    "4. Get 10 the longest films."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "ambient-guitar",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = 'SELECT DISTINCT length, title FROM sakila.film ORDER BY length DESC LIMIT 10;'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "corresponding-preserve",
+   "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>length</th>\n",
+       "      <th>title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185</td>\n",
+       "      <td>CHICAGO NORTH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185</td>\n",
+       "      <td>CONTROL ANTHEM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>185</td>\n",
+       "      <td>DARN FORRESTER</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>185</td>\n",
+       "      <td>GANGS PRIDE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>185</td>\n",
+       "      <td>HOME PITY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>185</td>\n",
+       "      <td>MUSCLE BRIGHT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>185</td>\n",
+       "      <td>POND SEATTLE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>185</td>\n",
+       "      <td>SOLDIERS EVOLUTION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>185</td>\n",
+       "      <td>SWEET BROTHERHOOD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>185</td>\n",
+       "      <td>WORST BANGER</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   length               title\n",
+       "0     185       CHICAGO NORTH\n",
+       "1     185      CONTROL ANTHEM\n",
+       "2     185      DARN FORRESTER\n",
+       "3     185         GANGS PRIDE\n",
+       "4     185           HOME PITY\n",
+       "5     185       MUSCLE BRIGHT\n",
+       "6     185        POND SEATTLE\n",
+       "7     185  SOLDIERS EVOLUTION\n",
+       "8     185   SWEET BROTHERHOOD\n",
+       "9     185        WORST BANGER"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query3, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "middle-arbitration",
+   "metadata": {},
+   "source": [
+    "5. How many films include Behind the Scenes content?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "superior-sharp",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query4 = '''SELECT COUNT(film_id)\n",
+    "FROM sakila.film\n",
+    "WHERE special_features \n",
+    "LIKE '\\%Behind_the_Scenes\\%';'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "smooth-mother",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "unsupported format character 'B' (0x42) at index 71",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-162-9fdf79e36314>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_sql_query\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m    375\u001b[0m     \"\"\"\n\u001b[1;32m    376\u001b[0m     \u001b[0mpandas_sql\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandasSQL_builder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m     return pandas_sql.read_query(\n\u001b[0m\u001b[1;32m    378\u001b[0m         \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m         \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m   1292\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1294\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1295\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1160\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_options\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m     def read_table(\n",
+      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/deprecations.py\u001b[0m in \u001b[0;36mwarned\u001b[0;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0m_warn_with_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   3035\u001b[0m         \"\"\"\n\u001b[1;32m   3036\u001b[0m         \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclose_with_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3037\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3039\u001b[0m     @util.deprecated_20(\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             )\n\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             return self._exec_driver_sql(\n\u001b[0m\u001b[1;32m   1186\u001b[0m                 \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_exec_driver_sql\u001b[0;34m(self, statement, multiparams, params, execution_options, future)\u001b[0m\n\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m         \u001b[0mdialect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1484\u001b[0;31m         ret = self._execute_context(\n\u001b[0m\u001b[1;32m   1485\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_ctx_cls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_statement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1748\u001b[0;31m             self._handle_dbapi_exception(\n\u001b[0m\u001b[1;32m   1749\u001b[0m                 \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1931\u001b[0m                 )\n\u001b[1;32m   1932\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1933\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;31m# credit to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1703\u001b[0m                             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m                     self.dialect.do_execute(\n\u001b[0m\u001b[1;32m   1706\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m                     )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmogrify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mmogrify\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m             \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_escape_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: unsupported format character 'B' (0x42) at index 71"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query4, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "labeled-nirvana",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reflected-qatar",
+   "metadata": {},
+   "source": [
+    "6. Which kind of movies (rating) have a mean duration of more than two hours?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "median-integer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query5 = '''SELECT rating, (AVG(length)) as \"avg_length\" \n",
+    "FROM sakila.film \n",
+    "GROUP BY rating \n",
+    "HAVING avg_length>=120;'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "sealed-rocket",
+   "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",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rating</th>\n",
+       "      <th>avg_length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>120.4439</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  rating  avg_length\n",
+       "0  PG-13    120.4439"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query5, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "designed-delivery",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unnecessary-humanitarian",
+   "metadata": {},
+   "source": [
+    "7. Rank films by length (filter out the rows that have nulls or 0s in length column). In your output, only select the columns title, length, and the rank."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "complimentary-fashion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query6 = '''select title, length, rental_rate \n",
+    "from sakila.film \n",
+    "where (length <> '0') order by length;'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "formed-hobby",
+   "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>title</th>\n",
+       "      <th>length</th>\n",
+       "      <th>rental_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ALIEN CENTER</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>IRON MOON</td>\n",
+       "      <td>46</td>\n",
+       "      <td>4.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KWAI HOMEWARD</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LABYRINTH LEAGUE</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>RIDGEMONT SUBMARINE</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>DIVORCE SHINING</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DOWNHILL ENOUGH</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>HALLOWEEN NUTS</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>HANOVER GALAXY</td>\n",
+       "      <td>47</td>\n",
+       "      <td>4.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>HAWK CHILL</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 title  length  rental_rate\n",
+       "0         ALIEN CENTER      46         2.99\n",
+       "1            IRON MOON      46         4.99\n",
+       "2        KWAI HOMEWARD      46         0.99\n",
+       "3     LABYRINTH LEAGUE      46         2.99\n",
+       "4  RIDGEMONT SUBMARINE      46         0.99\n",
+       "5      DIVORCE SHINING      47         2.99\n",
+       "6      DOWNHILL ENOUGH      47         0.99\n",
+       "7       HALLOWEEN NUTS      47         2.99\n",
+       "8       HANOVER GALAXY      47         4.99\n",
+       "9           HAWK CHILL      47         0.99"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query6, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "opposite-northwest",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "exposed-thousand",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "secret-contents",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "unknown-mobile",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "private-delta",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "exclusive-aerospace",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "optimum-wings",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-9.ipynb b/lab-9.ipynb
new file mode 100644
index 0000000..8a0f157
--- /dev/null
+++ b/lab-9.ipynb
@@ -0,0 +1,995 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "basic-somalia",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "athletic-accent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import pymysql\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "import getpass  # To get the password without showing the input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "creative-factor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "········\n"
+     ]
+    }
+   ],
+   "source": [
+    "password = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "elect-investor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection_string = 'mysql+pymysql://root:'+password+'@localhost/sakila'\n",
+    "engine = create_engine(connection_string)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "graphic-industry",
+   "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>inventory_id</th>\n",
+       "      <th>film_id</th>\n",
+       "      <th>store_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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2006-02-15 05:09:17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   inventory_id  film_id  store_id         last_update\n",
+       "0             1        1         1 2006-02-15 05:09:17\n",
+       "1             2        1         1 2006-02-15 05:09:17\n",
+       "2             3        1         1 2006-02-15 05:09:17\n",
+       "3             4        1         1 2006-02-15 05:09:17\n",
+       "4             5        1         2 2006-02-15 05:09:17"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query('SELECT * FROM inventory', engine)\n",
+    "data.head()\n",
+    "\n",
+    "#testing the connection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "warming-blend",
+   "metadata": {},
+   "source": [
+    "1. How many distinct (different) actors' last names are there?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "industrial-anger",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = 'SELECT COUNT(DISTINCT(last_name)) AS distinct_actors FROM sakila.actor'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "opposed-prompt",
+   "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>distinct_actors</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   distinct_actors\n",
+       "0              121"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query, engine)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dedicated-sound",
+   "metadata": {},
+   "source": [
+    "2. Add an additional column day_type with values 'weekend' and 'workday' depending on the rental day of the week."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "controversial-rendering",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query1 = '''SELECT *, WEEKDAY(rental_date) AS 'Weekday',\n",
+    "CASE\n",
+    "WHEN (WEEKDAY(rental_date) >  4 ) then 'weekend'\n",
+    "ELSE 'workday'\n",
+    "END AS day_type\n",
+    "FROM sakila.rental;'''\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "early-consolidation",
+   "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",
+       "      <th>Weekday</th>\n",
+       "      <th>day_type</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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",
+       "      <td>1</td>\n",
+       "      <td>workday</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  Weekday day_type  \n",
+       "0 2005-05-26 22:04:30         1 2006-02-15 21:30:53        1  workday  \n",
+       "1 2005-05-28 19:40:33         1 2006-02-15 21:30:53        1  workday  \n",
+       "2 2005-06-01 22:12:39         1 2006-02-15 21:30:53        1  workday  \n",
+       "3 2005-06-03 01:43:41         2 2006-02-15 21:30:53        1  workday  \n",
+       "4 2005-06-02 04:33:21         1 2006-02-15 21:30:53        1  workday  "
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query1, engine)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "meaning-protein",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "foreign-magnitude",
+   "metadata": {},
+   "source": [
+    "3. Get all films with ARMAGEDDON in the title."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "nutritional-helen",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query2 = '''SELECT * FROM sakila.film WHERE title LIKE \"\\%ARMAGEDDON\\%\";'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "retired-transport",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "unsupported format character 'A' (0x41) at index 46",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-160-8394e0f59e09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_sql_query\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m    375\u001b[0m     \"\"\"\n\u001b[1;32m    376\u001b[0m     \u001b[0mpandas_sql\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandasSQL_builder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m     return pandas_sql.read_query(\n\u001b[0m\u001b[1;32m    378\u001b[0m         \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m         \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m   1292\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1294\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1295\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1160\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_options\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m     def read_table(\n",
+      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/deprecations.py\u001b[0m in \u001b[0;36mwarned\u001b[0;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0m_warn_with_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   3035\u001b[0m         \"\"\"\n\u001b[1;32m   3036\u001b[0m         \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclose_with_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3037\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3039\u001b[0m     @util.deprecated_20(\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             )\n\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             return self._exec_driver_sql(\n\u001b[0m\u001b[1;32m   1186\u001b[0m                 \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_exec_driver_sql\u001b[0;34m(self, statement, multiparams, params, execution_options, future)\u001b[0m\n\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m         \u001b[0mdialect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1484\u001b[0;31m         ret = self._execute_context(\n\u001b[0m\u001b[1;32m   1485\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_ctx_cls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_statement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1748\u001b[0;31m             self._handle_dbapi_exception(\n\u001b[0m\u001b[1;32m   1749\u001b[0m                 \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1931\u001b[0m                 )\n\u001b[1;32m   1932\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1933\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;31m# credit to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1703\u001b[0m                             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m                     self.dialect.do_execute(\n\u001b[0m\u001b[1;32m   1706\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m                     )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmogrify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mmogrify\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m             \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_escape_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: unsupported format character 'A' (0x41) at index 46"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query2, engine)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pressing-means",
+   "metadata": {},
+   "source": [
+    "4. Get 10 the longest films."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "mental-weapon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query3 = 'SELECT DISTINCT length, title FROM sakila.film ORDER BY length DESC LIMIT 10;'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "relative-offset",
+   "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>length</th>\n",
+       "      <th>title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>185</td>\n",
+       "      <td>CHICAGO NORTH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>185</td>\n",
+       "      <td>CONTROL ANTHEM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>185</td>\n",
+       "      <td>DARN FORRESTER</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>185</td>\n",
+       "      <td>GANGS PRIDE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>185</td>\n",
+       "      <td>HOME PITY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>185</td>\n",
+       "      <td>MUSCLE BRIGHT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>185</td>\n",
+       "      <td>POND SEATTLE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>185</td>\n",
+       "      <td>SOLDIERS EVOLUTION</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>185</td>\n",
+       "      <td>SWEET BROTHERHOOD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>185</td>\n",
+       "      <td>WORST BANGER</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   length               title\n",
+       "0     185       CHICAGO NORTH\n",
+       "1     185      CONTROL ANTHEM\n",
+       "2     185      DARN FORRESTER\n",
+       "3     185         GANGS PRIDE\n",
+       "4     185           HOME PITY\n",
+       "5     185       MUSCLE BRIGHT\n",
+       "6     185        POND SEATTLE\n",
+       "7     185  SOLDIERS EVOLUTION\n",
+       "8     185   SWEET BROTHERHOOD\n",
+       "9     185        WORST BANGER"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query3, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "broke-share",
+   "metadata": {},
+   "source": [
+    "5. How many films include Behind the Scenes content?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "double-diesel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query4 = '''SELECT COUNT(film_id)\n",
+    "FROM sakila.film\n",
+    "WHERE special_features \n",
+    "LIKE '\\%Behind_the_Scenes\\%';'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "foreign-atlanta",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "unsupported format character 'B' (0x42) at index 71",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-162-9fdf79e36314>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_sql_query\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, chunksize)\u001b[0m\n\u001b[1;32m    375\u001b[0m     \"\"\"\n\u001b[1;32m    376\u001b[0m     \u001b[0mpandas_sql\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandasSQL_builder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m     return pandas_sql.read_query(\n\u001b[0m\u001b[1;32m    378\u001b[0m         \u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m         \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex_col\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mread_query\u001b[0;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize)\u001b[0m\n\u001b[1;32m   1292\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1294\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1295\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pandas/io/sql.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1160\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_options\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m     def read_table(\n",
+      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/deprecations.py\u001b[0m in \u001b[0;36mwarned\u001b[0;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0m_warn_with_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   3035\u001b[0m         \"\"\"\n\u001b[1;32m   3036\u001b[0m         \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclose_with_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3037\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3039\u001b[0m     @util.deprecated_20(\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             )\n\u001b[1;32m   1184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             return self._exec_driver_sql(\n\u001b[0m\u001b[1;32m   1186\u001b[0m                 \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0mmultiparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_exec_driver_sql\u001b[0;34m(self, statement, multiparams, params, execution_options, future)\u001b[0m\n\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m         \u001b[0mdialect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1484\u001b[0;31m         ret = self._execute_context(\n\u001b[0m\u001b[1;32m   1485\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1486\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_ctx_cls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_statement\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1748\u001b[0;31m             self._handle_dbapi_exception(\n\u001b[0m\u001b[1;32m   1749\u001b[0m                 \u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m             )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1931\u001b[0m                 )\n\u001b[1;32m   1932\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1933\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;31m# credit to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1703\u001b[0m                             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m                     self.dialect.do_execute(\n\u001b[0m\u001b[1;32m   1706\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m                     )\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m         \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmogrify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/Cellar/jupyterlab/3.0.12/libexec/lib/python3.9/site-packages/pymysql/cursors.py\u001b[0m in \u001b[0;36mmogrify\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m             \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_escape_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: unsupported format character 'B' (0x42) at index 71"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query4, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "seventh-grass",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.0.2'"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pymysql\n",
+    "pymysql.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "raised-affiliate",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.4.4'"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sqlalchemy\n",
+    "sqlalchemy.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "varied-biodiversity",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.2.3'"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas\n",
+    "pandas.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "absolute-cuisine",
+   "metadata": {},
+   "source": [
+    "6. Which kind of movies (rating) have a mean duration of more than two hours?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "comfortable-counter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query5 = '''SELECT rating, (AVG(length)) as \"avg_length\" \n",
+    "FROM sakila.film \n",
+    "GROUP BY rating \n",
+    "HAVING avg_length>=120;'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "friendly-dictionary",
+   "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>rating</th>\n",
+       "      <th>avg_length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>120.4439</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  rating  avg_length\n",
+       "0  PG-13    120.4439"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query5, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tamil-portal",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "behavioral-kitty",
+   "metadata": {},
+   "source": [
+    "7. Rank films by length (filter out the rows that have nulls or 0s in length column). In your output, only select the columns title, length, and the rank."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "classified-audio",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query6 = '''select title, length, rental_rate \n",
+    "from sakila.film \n",
+    "where (length <> '0') order by length;'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "becoming-cornell",
+   "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>title</th>\n",
+       "      <th>length</th>\n",
+       "      <th>rental_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ALIEN CENTER</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>IRON MOON</td>\n",
+       "      <td>46</td>\n",
+       "      <td>4.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KWAI HOMEWARD</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>LABYRINTH LEAGUE</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>RIDGEMONT SUBMARINE</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>DIVORCE SHINING</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>DOWNHILL ENOUGH</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>HALLOWEEN NUTS</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>HANOVER GALAXY</td>\n",
+       "      <td>47</td>\n",
+       "      <td>4.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>HAWK CHILL</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 title  length  rental_rate\n",
+       "0         ALIEN CENTER      46         2.99\n",
+       "1            IRON MOON      46         4.99\n",
+       "2        KWAI HOMEWARD      46         0.99\n",
+       "3     LABYRINTH LEAGUE      46         2.99\n",
+       "4  RIDGEMONT SUBMARINE      46         0.99\n",
+       "5      DIVORCE SHINING      47         2.99\n",
+       "6      DOWNHILL ENOUGH      47         0.99\n",
+       "7       HALLOWEEN NUTS      47         2.99\n",
+       "8       HANOVER GALAXY      47         4.99\n",
+       "9           HAWK CHILL      47         0.99"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_sql_query(query6, engine)\n",
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "determined-breast",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "rural-walter",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "false-aquarium",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "asian-distributor",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adopted-equation",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "electrical-vermont",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "attractive-gravity",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}