Sakila.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.connect('sakila.db')\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of tables\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['actor',\n",
       " 'country',\n",
       " 'city',\n",
       " 'address',\n",
       " 'language',\n",
       " 'category',\n",
       " 'customer',\n",
       " 'film',\n",
       " 'film_actor',\n",
       " 'film_category',\n",
       " 'film_text',\n",
       " 'inventory',\n",
       " 'staff',\n",
       " 'store',\n",
       " 'payment',\n",
       " 'rental']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"\"\"select name from sqlite_master where type = 'table';\"\"\")\n",
    "print(\"List of tables\")\n",
    "table_list = [table[0] for table in cur.fetchall()]\n",
    "table_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tables we need to use to acheive the Goals and Objectives."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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>category_id</th>\n",
       "      <th>name</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Action</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Animation</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Children</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Classics</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Family</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Games</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Horror</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Music</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>New</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Sports</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Travel</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    category_id         name          last_update\n",
       "0             1       Action  2021-03-06 15:52:00\n",
       "1             2    Animation  2021-03-06 15:52:00\n",
       "2             3     Children  2021-03-06 15:52:00\n",
       "3             4     Classics  2021-03-06 15:52:00\n",
       "4             5       Comedy  2021-03-06 15:52:00\n",
       "5             6  Documentary  2021-03-06 15:52:00\n",
       "6             7        Drama  2021-03-06 15:52:00\n",
       "7             8       Family  2021-03-06 15:52:00\n",
       "8             9      Foreign  2021-03-06 15:52:00\n",
       "9            10        Games  2021-03-06 15:52:00\n",
       "10           11       Horror  2021-03-06 15:52:00\n",
       "11           12        Music  2021-03-06 15:52:00\n",
       "12           13          New  2021-03-06 15:52:00\n",
       "13           14       Sci-Fi  2021-03-06 15:52:00\n",
       "14           15       Sports  2021-03-06 15:52:00\n",
       "15           16       Travel  2021-03-06 15:52:00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category = pd.read_sql_query(\"select * from category\", conn)\n",
    "category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>film_id</th>\n",
       "      <th>category_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>6</td>\n",
       "      <td>2021-03-06 15:53:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-03-06 15:53:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-03-06 15:53:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-03-06 15:53:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2021-03-06 15:53:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>12</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>11</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     film_id  category_id          last_update\n",
       "0          1            6  2021-03-06 15:53:28\n",
       "1          2           11  2021-03-06 15:53:28\n",
       "2          3            6  2021-03-06 15:53:28\n",
       "3          4           11  2021-03-06 15:53:28\n",
       "4          5            8  2021-03-06 15:53:28\n",
       "..       ...          ...                  ...\n",
       "995      996            6  2021-03-06 15:53:36\n",
       "996      997           12  2021-03-06 15:53:36\n",
       "997      998           11  2021-03-06 15:53:36\n",
       "998      999            3  2021-03-06 15:53:36\n",
       "999     1000            5  2021-03-06 15:53:36\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "film_category = pd.read_sql_query(\"select * from film_category\", conn)\n",
    "film_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rental_id</th>\n",
       "      <th>rental_date</th>\n",
       "      <th>inventory_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>return_date</th>\n",
       "      <th>staff_id</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2005-05-24 22:53:30.000</td>\n",
       "      <td>367</td>\n",
       "      <td>130</td>\n",
       "      <td>2005-05-26 22:04:30.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2005-05-24 22:54:33.000</td>\n",
       "      <td>1525</td>\n",
       "      <td>459</td>\n",
       "      <td>2005-05-28 19:40:33.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2005-05-24 23:03:39.000</td>\n",
       "      <td>1711</td>\n",
       "      <td>408</td>\n",
       "      <td>2005-06-01 22:12:39.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2005-05-24 23:04:41.000</td>\n",
       "      <td>2452</td>\n",
       "      <td>333</td>\n",
       "      <td>2005-06-03 01:43:41.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2005-05-24 23:05:21.000</td>\n",
       "      <td>2079</td>\n",
       "      <td>222</td>\n",
       "      <td>2005-06-02 04:33:21.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16039</th>\n",
       "      <td>16045</td>\n",
       "      <td>2005-08-23 22:25:26.000</td>\n",
       "      <td>772</td>\n",
       "      <td>14</td>\n",
       "      <td>2005-08-25 23:54:26.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16040</th>\n",
       "      <td>16046</td>\n",
       "      <td>2005-08-23 22:26:47.000</td>\n",
       "      <td>4364</td>\n",
       "      <td>74</td>\n",
       "      <td>2005-08-27 18:02:47.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16041</th>\n",
       "      <td>16047</td>\n",
       "      <td>2005-08-23 22:42:48.000</td>\n",
       "      <td>2088</td>\n",
       "      <td>114</td>\n",
       "      <td>2005-08-25 02:48:48.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16042</th>\n",
       "      <td>16048</td>\n",
       "      <td>2005-08-23 22:43:07.000</td>\n",
       "      <td>2019</td>\n",
       "      <td>103</td>\n",
       "      <td>2005-08-31 21:33:07.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16043</th>\n",
       "      <td>16049</td>\n",
       "      <td>2005-08-23 22:50:12.000</td>\n",
       "      <td>2666</td>\n",
       "      <td>393</td>\n",
       "      <td>2005-08-30 01:01:12.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16044 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rental_id              rental_date  inventory_id  customer_id   \n",
       "0              1  2005-05-24 22:53:30.000           367          130  \\\n",
       "1              2  2005-05-24 22:54:33.000          1525          459   \n",
       "2              3  2005-05-24 23:03:39.000          1711          408   \n",
       "3              4  2005-05-24 23:04:41.000          2452          333   \n",
       "4              5  2005-05-24 23:05:21.000          2079          222   \n",
       "...          ...                      ...           ...          ...   \n",
       "16039      16045  2005-08-23 22:25:26.000           772           14   \n",
       "16040      16046  2005-08-23 22:26:47.000          4364           74   \n",
       "16041      16047  2005-08-23 22:42:48.000          2088          114   \n",
       "16042      16048  2005-08-23 22:43:07.000          2019          103   \n",
       "16043      16049  2005-08-23 22:50:12.000          2666          393   \n",
       "\n",
       "                   return_date  staff_id          last_update  \n",
       "0      2005-05-26 22:04:30.000         1  2021-03-06 15:53:41  \n",
       "1      2005-05-28 19:40:33.000         1  2021-03-06 15:53:41  \n",
       "2      2005-06-01 22:12:39.000         1  2021-03-06 15:53:41  \n",
       "3      2005-06-03 01:43:41.000         2  2021-03-06 15:53:41  \n",
       "4      2005-06-02 04:33:21.000         1  2021-03-06 15:53:41  \n",
       "...                        ...       ...                  ...  \n",
       "16039  2005-08-25 23:54:26.000         1  2021-03-06 15:55:57  \n",
       "16040  2005-08-27 18:02:47.000         2  2021-03-06 15:55:57  \n",
       "16041  2005-08-25 02:48:48.000         2  2021-03-06 15:55:57  \n",
       "16042  2005-08-31 21:33:07.000         1  2021-03-06 15:55:57  \n",
       "16043  2005-08-30 01:01:12.000         2  2021-03-06 15:55:57  \n",
       "\n",
       "[16044 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental = pd.read_sql_query(\"select * from rental\", conn)\n",
    "rental"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>email</th>\n",
       "      <th>address_id</th>\n",
       "      <th>active</th>\n",
       "      <th>create_date</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>MARY</td>\n",
       "      <td>SMITH</td>\n",
       "      <td>MARY.SMITH@sakilacustomer.org</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-02-14 22:04:36.000</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>PATRICIA</td>\n",
       "      <td>JOHNSON</td>\n",
       "      <td>PATRICIA.JOHNSON@sakilacustomer.org</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-02-14 22:04:36.000</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>LINDA</td>\n",
       "      <td>WILLIAMS</td>\n",
       "      <td>LINDA.WILLIAMS@sakilacustomer.org</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-02-14 22:04:36.000</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>BARBARA</td>\n",
       "      <td>JONES</td>\n",
       "      <td>BARBARA.JONES@sakilacustomer.org</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-02-14 22:04:36.000</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>ELIZABETH</td>\n",
       "      <td>BROWN</td>\n",
       "      <td>ELIZABETH.BROWN@sakilacustomer.org</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-02-14 22:04:36.000</td>\n",
       "      <td>2021-03-06 15:53:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  store_id first_name last_name   \n",
       "0            1         1       MARY     SMITH  \\\n",
       "1            2         1   PATRICIA   JOHNSON   \n",
       "2            3         1      LINDA  WILLIAMS   \n",
       "3            4         2    BARBARA     JONES   \n",
       "4            5         1  ELIZABETH     BROWN   \n",
       "\n",
       "                                 email  address_id active   \n",
       "0        MARY.SMITH@sakilacustomer.org           5      1  \\\n",
       "1  PATRICIA.JOHNSON@sakilacustomer.org           6      1   \n",
       "2    LINDA.WILLIAMS@sakilacustomer.org           7      1   \n",
       "3     BARBARA.JONES@sakilacustomer.org           8      1   \n",
       "4   ELIZABETH.BROWN@sakilacustomer.org           9      1   \n",
       "\n",
       "               create_date          last_update  \n",
       "0  2006-02-14 22:04:36.000  2021-03-06 15:53:36  \n",
       "1  2006-02-14 22:04:36.000  2021-03-06 15:53:36  \n",
       "2  2006-02-14 22:04:36.000  2021-03-06 15:53:36  \n",
       "3  2006-02-14 22:04:36.000  2021-03-06 15:53:36  \n",
       "4  2006-02-14 22:04:36.000  2021-03-06 15:53:36  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer = pd.read_sql_query(\"select * from customer\", conn)\n",
    "customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   inventory_id  film_id  store_id          last_update\n",
       "0             1        1         1  2021-03-06 15:52:08\n",
       "1             2        1         1  2021-03-06 15:52:08\n",
       "2             3        1         1  2021-03-06 15:52:08\n",
       "3             4        1         1  2021-03-06 15:52:08\n",
       "4             5        1         2  2021-03-06 15:52:08"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inventory = pd.read_sql_query(\"select * from inventory\", conn)\n",
    "inventory.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1185.0</td>\n",
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       "      <td>2005-06-15 00:54:12.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2005-06-15 18:02:53.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2</td>\n",
       "      <td>1476.0</td>\n",
       "      <td>9.99</td>\n",
       "      <td>2005-06-15 21:08:46.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>16044</th>\n",
       "      <td>16045</td>\n",
       "      <td>599</td>\n",
       "      <td>1</td>\n",
       "      <td>14599.0</td>\n",
       "      <td>4.99</td>\n",
       "      <td>2005-08-21 17:43:42.000</td>\n",
       "      <td>2021-03-06 15:58:08</td>\n",
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       "    <tr>\n",
       "      <th>16045</th>\n",
       "      <td>16046</td>\n",
       "      <td>599</td>\n",
       "      <td>1</td>\n",
       "      <td>14719.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2005-08-21 21:41:57.000</td>\n",
       "      <td>2021-03-06 15:58:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16046</th>\n",
       "      <td>16047</td>\n",
       "      <td>599</td>\n",
       "      <td>2</td>\n",
       "      <td>15590.0</td>\n",
       "      <td>8.99</td>\n",
       "      <td>2005-08-23 06:09:44.000</td>\n",
       "      <td>2021-03-06 15:58:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16047</th>\n",
       "      <td>16048</td>\n",
       "      <td>599</td>\n",
       "      <td>2</td>\n",
       "      <td>15719.0</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2005-08-23 11:08:46.000</td>\n",
       "      <td>2021-03-06 15:58:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16048</th>\n",
       "      <td>16049</td>\n",
       "      <td>599</td>\n",
       "      <td>2</td>\n",
       "      <td>15725.0</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2005-08-23 11:25:00.000</td>\n",
       "      <td>2021-03-06 15:58:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16049 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       payment_id  customer_id  staff_id  rental_id  amount   \n",
       "0               1            1         1       76.0    2.99  \\\n",
       "1               2            1         1      573.0    0.99   \n",
       "2               3            1         1     1185.0    5.99   \n",
       "3               4            1         2     1422.0    0.99   \n",
       "4               5            1         2     1476.0    9.99   \n",
       "...           ...          ...       ...        ...     ...   \n",
       "16044       16045          599         1    14599.0    4.99   \n",
       "16045       16046          599         1    14719.0    1.99   \n",
       "16046       16047          599         2    15590.0    8.99   \n",
       "16047       16048          599         2    15719.0    2.99   \n",
       "16048       16049          599         2    15725.0    2.99   \n",
       "\n",
       "                  payment_date          last_update  \n",
       "0      2005-05-25 11:30:37.000  2021-03-06 15:55:57  \n",
       "1      2005-05-28 10:35:23.000  2021-03-06 15:55:57  \n",
       "2      2005-06-15 00:54:12.000  2021-03-06 15:55:57  \n",
       "3      2005-06-15 18:02:53.000  2021-03-06 15:55:57  \n",
       "4      2005-06-15 21:08:46.000  2021-03-06 15:55:57  \n",
       "...                        ...                  ...  \n",
       "16044  2005-08-21 17:43:42.000  2021-03-06 15:58:08  \n",
       "16045  2005-08-21 21:41:57.000  2021-03-06 15:58:08  \n",
       "16046  2005-08-23 06:09:44.000  2021-03-06 15:58:09  \n",
       "16047  2005-08-23 11:08:46.000  2021-03-06 15:58:09  \n",
       "16048  2005-08-23 11:25:00.000  2021-03-06 15:58:09  \n",
       "\n",
       "[16049 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment = pd.read_sql_query(\"select * from payment\", conn)\n",
    "payment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>A Epic Drama of a Feminist And a Mad Scientist...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
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       "      <td>0.99</td>\n",
       "      <td>86</td>\n",
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       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ACE GOLDFINGER</td>\n",
       "      <td>A Astounding Epistle of a Database Administrat...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>48</td>\n",
       "      <td>12.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Trailers,Deleted Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ADAPTATION HOLES</td>\n",
       "      <td>A Astounding Reflection of a Lumberjack And a ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>50</td>\n",
       "      <td>18.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>Trailers,Deleted Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>AFFAIR PREJUDICE</td>\n",
       "      <td>A Fanciful Documentary of a Frisbee And a Lumb...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>117</td>\n",
       "      <td>26.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Commentaries,Behind the Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>AFRICAN EGG</td>\n",
       "      <td>A Fast-Paced Documentary of a Pastry Chef And ...</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>2.99</td>\n",
       "      <td>130</td>\n",
       "      <td>22.99</td>\n",
       "      <td>G</td>\n",
       "      <td>Deleted Scenes</td>\n",
       "      <td>2021-03-06 15:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   film_id             title   \n",
       "0        1  ACADEMY DINOSAUR  \\\n",
       "1        2    ACE GOLDFINGER   \n",
       "2        3  ADAPTATION HOLES   \n",
       "3        4  AFFAIR PREJUDICE   \n",
       "4        5       AFRICAN EGG   \n",
       "\n",
       "                                         description release_year   \n",
       "0  A Epic Drama of a Feminist And a Mad Scientist...         2006  \\\n",
       "1  A Astounding Epistle of a Database Administrat...         2006   \n",
       "2  A Astounding Reflection of a Lumberjack And a ...         2006   \n",
       "3  A Fanciful Documentary of a Frisbee And a Lumb...         2006   \n",
       "4  A Fast-Paced Documentary of a Pastry Chef And ...         2006   \n",
       "\n",
       "   language_id original_language_id  rental_duration  rental_rate  length   \n",
       "0            1                 None                6         0.99      86  \\\n",
       "1            1                 None                3         4.99      48   \n",
       "2            1                 None                7         2.99      50   \n",
       "3            1                 None                5         2.99     117   \n",
       "4            1                 None                6         2.99     130   \n",
       "\n",
       "   replacement_cost rating                  special_features   \n",
       "0             20.99     PG  Deleted Scenes,Behind the Scenes  \\\n",
       "1             12.99      G           Trailers,Deleted Scenes   \n",
       "2             18.99  NC-17           Trailers,Deleted Scenes   \n",
       "3             26.99      G    Commentaries,Behind the Scenes   \n",
       "4             22.99      G                    Deleted Scenes   \n",
       "\n",
       "           last_update  \n",
       "0  2021-03-06 15:52:00  \n",
       "1  2021-03-06 15:52:00  \n",
       "2  2021-03-06 15:52:00  \n",
       "3  2021-03-06 15:52:00  \n",
       "4  2021-03-06 15:52:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "film = pd.read_sql_query(\"select * from film\", conn)\n",
    "film.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "film.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "film_id                 0.0\n",
       "title                   0.0\n",
       "description             0.0\n",
       "release_year            0.0\n",
       "language_id             0.0\n",
       "original_language_id    0.0\n",
       "rental_duration         0.0\n",
       "rental_rate             0.0\n",
       "length                  0.0\n",
       "replacement_cost        0.0\n",
       "rating                  0.0\n",
       "special_features        0.0\n",
       "last_update             0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "film.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "table: rental\n",
      "('rental_id', 'INT')\n",
      "('rental_date', 'TIMESTAMP')\n",
      "('inventory_id', 'INT')\n",
      "('customer_id', 'INT')\n",
      "('return_date', 'TIMESTAMP')\n",
      "('staff_id', 'SMALLINT')\n",
      "('last_update', 'TIMESTAMP')\n",
      "____________________________________________________________\n",
      "table: customer\n",
      "('customer_id', 'INT')\n",
      "('store_id', 'INT')\n",
      "('first_name', 'VARCHAR(45)')\n",
      "('last_name', 'VARCHAR(45)')\n",
      "('email', 'VARCHAR(50)')\n",
      "('address_id', 'INT')\n",
      "('active', 'CHAR(1)')\n",
      "('create_date', 'TIMESTAMP')\n",
      "('last_update', 'TIMESTAMP')\n",
      "____________________________________________________________\n",
      "table: inventory\n",
      "('inventory_id', 'INT')\n",
      "('film_id', 'INT')\n",
      "('store_id', 'INT')\n",
      "('last_update', 'TIMESTAMP')\n",
      "____________________________________________________________\n",
      "table: payment\n",
      "('payment_id', 'INT')\n",
      "('customer_id', 'INT')\n",
      "('staff_id', 'SMALLINT')\n",
      "('rental_id', 'INT')\n",
      "('amount', 'DECIMAL(5,2)')\n",
      "('payment_date', 'TIMESTAMP')\n",
      "('last_update', 'TIMESTAMP')\n",
      "____________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for table in ('rental','customer','inventory','payment'):\n",
    "    print('table:', table)\n",
    "    column_info = conn.execute(\"PRAGMA table_info({})\".format(table))\n",
    "    for col in column_info.fetchall():\n",
    "        print(col[1:3])\n",
    "    print ('_'*60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "table: rental\n",
      "rental_id         0\n",
      "rental_date       0\n",
      "inventory_id      0\n",
      "customer_id       0\n",
      "return_date     183\n",
      "staff_id          0\n",
      "last_update       0\n",
      "dtype: int64\n",
      "____________________________________________________________\n",
      "table: customer\n",
      "customer_id    0\n",
      "store_id       0\n",
      "first_name     0\n",
      "last_name      0\n",
      "email          0\n",
      "address_id     0\n",
      "active         0\n",
      "create_date    0\n",
      "last_update    0\n",
      "dtype: int64\n",
      "____________________________________________________________\n",
      "table: inventory\n",
      "inventory_id    0\n",
      "film_id         0\n",
      "store_id        0\n",
      "last_update     0\n",
      "dtype: int64\n",
      "____________________________________________________________\n",
      "table: film\n",
      "film_id                    0\n",
      "title                      0\n",
      "description                0\n",
      "release_year               0\n",
      "language_id                0\n",
      "original_language_id    1000\n",
      "rental_duration            0\n",
      "rental_rate                0\n",
      "length                     0\n",
      "replacement_cost           0\n",
      "rating                     0\n",
      "special_features           0\n",
      "last_update                0\n",
      "dtype: int64\n",
      "____________________________________________________________\n",
      "table: category\n",
      "category_id    0\n",
      "name           0\n",
      "last_update    0\n",
      "dtype: int64\n",
      "____________________________________________________________\n",
      "table: film_category\n",
      "film_id        0\n",
      "category_id    0\n",
      "last_update    0\n",
      "dtype: int64\n",
      "____________________________________________________________\n",
      "table: payment\n",
      "payment_id      0\n",
      "customer_id     0\n",
      "staff_id        0\n",
      "rental_id       5\n",
      "amount          0\n",
      "payment_date    0\n",
      "last_update     0\n",
      "dtype: int64\n",
      "____________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for table in ('rental','customer','inventory','film','category','film_category','payment'):\n",
    "    print('table:', table)\n",
    "    df_table = pd.read_sql_query(f\"select * from {table}\", conn)\n",
    "    print(df_table.isnull().sum())\n",
    "    print ('_'*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2005-05-24 22:53:30.000</td>\n",
       "      <td>367</td>\n",
       "      <td>130</td>\n",
       "      <td>2005-05-26 22:04:30.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2005-05-24 22:54:33.000</td>\n",
       "      <td>1525</td>\n",
       "      <td>459</td>\n",
       "      <td>2005-05-28 19:40:33.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2005-05-24 23:03:39.000</td>\n",
       "      <td>1711</td>\n",
       "      <td>408</td>\n",
       "      <td>2005-06-01 22:12:39.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2005-05-24 23:04:41.000</td>\n",
       "      <td>2452</td>\n",
       "      <td>333</td>\n",
       "      <td>2005-06-03 01:43:41.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2005-05-24 23:05:21.000</td>\n",
       "      <td>2079</td>\n",
       "      <td>222</td>\n",
       "      <td>2005-06-02 04:33:21.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:53:41</td>\n",
       "    </tr>\n",
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       "      <th>15856</th>\n",
       "      <td>16045</td>\n",
       "      <td>2005-08-23 22:25:26.000</td>\n",
       "      <td>772</td>\n",
       "      <td>14</td>\n",
       "      <td>2005-08-25 23:54:26.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15857</th>\n",
       "      <td>16046</td>\n",
       "      <td>2005-08-23 22:26:47.000</td>\n",
       "      <td>4364</td>\n",
       "      <td>74</td>\n",
       "      <td>2005-08-27 18:02:47.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15858</th>\n",
       "      <td>16047</td>\n",
       "      <td>2005-08-23 22:42:48.000</td>\n",
       "      <td>2088</td>\n",
       "      <td>114</td>\n",
       "      <td>2005-08-25 02:48:48.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15859</th>\n",
       "      <td>16048</td>\n",
       "      <td>2005-08-23 22:43:07.000</td>\n",
       "      <td>2019</td>\n",
       "      <td>103</td>\n",
       "      <td>2005-08-31 21:33:07.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15860</th>\n",
       "      <td>16049</td>\n",
       "      <td>2005-08-23 22:50:12.000</td>\n",
       "      <td>2666</td>\n",
       "      <td>393</td>\n",
       "      <td>2005-08-30 01:01:12.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15861 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rental_id              rental_date  inventory_id  customer_id   \n",
       "0              1  2005-05-24 22:53:30.000           367          130  \\\n",
       "1              2  2005-05-24 22:54:33.000          1525          459   \n",
       "2              3  2005-05-24 23:03:39.000          1711          408   \n",
       "3              4  2005-05-24 23:04:41.000          2452          333   \n",
       "4              5  2005-05-24 23:05:21.000          2079          222   \n",
       "...          ...                      ...           ...          ...   \n",
       "15856      16045  2005-08-23 22:25:26.000           772           14   \n",
       "15857      16046  2005-08-23 22:26:47.000          4364           74   \n",
       "15858      16047  2005-08-23 22:42:48.000          2088          114   \n",
       "15859      16048  2005-08-23 22:43:07.000          2019          103   \n",
       "15860      16049  2005-08-23 22:50:12.000          2666          393   \n",
       "\n",
       "                   return_date  staff_id          last_update  \n",
       "0      2005-05-26 22:04:30.000         1  2021-03-06 15:53:41  \n",
       "1      2005-05-28 19:40:33.000         1  2021-03-06 15:53:41  \n",
       "2      2005-06-01 22:12:39.000         1  2021-03-06 15:53:41  \n",
       "3      2005-06-03 01:43:41.000         2  2021-03-06 15:53:41  \n",
       "4      2005-06-02 04:33:21.000         1  2021-03-06 15:53:41  \n",
       "...                        ...       ...                  ...  \n",
       "15856  2005-08-25 23:54:26.000         1  2021-03-06 15:55:57  \n",
       "15857  2005-08-27 18:02:47.000         2  2021-03-06 15:55:57  \n",
       "15858  2005-08-25 02:48:48.000         2  2021-03-06 15:55:57  \n",
       "15859  2005-08-31 21:33:07.000         1  2021-03-06 15:55:57  \n",
       "15860  2005-08-30 01:01:12.000         2  2021-03-06 15:55:57  \n",
       "\n",
       "[15861 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental = pd.read_sql_query(\"select * from rental where return_date is not null\", conn)\n",
    "rental"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2005-05-25 11:30:37.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1185</td>\n",
       "      <td>5.99</td>\n",
       "      <td>2005-06-15 00:54:12.000</td>\n",
       "      <td>2021-03-06 15:55:57</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.99</td>\n",
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       "      <td>2021-03-06 15:55:57</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2021-03-06 15:55:57</td>\n",
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       "      <td>2021-03-06 15:58:08</td>\n",
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       "      <th>16040</th>\n",
       "      <td>16046</td>\n",
       "      <td>599</td>\n",
       "      <td>1</td>\n",
       "      <td>14719</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2005-08-21 21:41:57.000</td>\n",
       "      <td>2021-03-06 15:58:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16041</th>\n",
       "      <td>16047</td>\n",
       "      <td>599</td>\n",
       "      <td>2</td>\n",
       "      <td>15590</td>\n",
       "      <td>8.99</td>\n",
       "      <td>2005-08-23 06:09:44.000</td>\n",
       "      <td>2021-03-06 15:58:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16042</th>\n",
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       "      <td>599</td>\n",
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       "      <td>15719</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2005-08-23 11:08:46.000</td>\n",
       "      <td>2021-03-06 15:58:09</td>\n",
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       "    <tr>\n",
       "      <th>16043</th>\n",
       "      <td>16049</td>\n",
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       "      <td>15725</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2005-08-23 11:25:00.000</td>\n",
       "      <td>2021-03-06 15:58:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16044 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       payment_id  customer_id  staff_id  rental_id  amount   \n",
       "0               1            1         1         76    2.99  \\\n",
       "1               2            1         1        573    0.99   \n",
       "2               3            1         1       1185    5.99   \n",
       "3               4            1         2       1422    0.99   \n",
       "4               5            1         2       1476    9.99   \n",
       "...           ...          ...       ...        ...     ...   \n",
       "16039       16045          599         1      14599    4.99   \n",
       "16040       16046          599         1      14719    1.99   \n",
       "16041       16047          599         2      15590    8.99   \n",
       "16042       16048          599         2      15719    2.99   \n",
       "16043       16049          599         2      15725    2.99   \n",
       "\n",
       "                  payment_date          last_update  \n",
       "0      2005-05-25 11:30:37.000  2021-03-06 15:55:57  \n",
       "1      2005-05-28 10:35:23.000  2021-03-06 15:55:57  \n",
       "2      2005-06-15 00:54:12.000  2021-03-06 15:55:57  \n",
       "3      2005-06-15 18:02:53.000  2021-03-06 15:55:57  \n",
       "4      2005-06-15 21:08:46.000  2021-03-06 15:55:57  \n",
       "...                        ...                  ...  \n",
       "16039  2005-08-21 17:43:42.000  2021-03-06 15:58:08  \n",
       "16040  2005-08-21 21:41:57.000  2021-03-06 15:58:08  \n",
       "16041  2005-08-23 06:09:44.000  2021-03-06 15:58:09  \n",
       "16042  2005-08-23 11:08:46.000  2021-03-06 15:58:09  \n",
       "16043  2005-08-23 11:25:00.000  2021-03-06 15:58:09  \n",
       "\n",
       "[16044 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payment = pd.read_sql_query(\"select * from payment where rental_id is not null\", conn)\n",
    "payment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Analysis:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What are the top 10 most rented movies and how often are they rented?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "query1 = '''\n",
    "SELECT f.title, COUNT(*) as rental_count\n",
    "FROM rental r\n",
    "JOIN inventory i ON r.inventory_id = i.inventory_id\n",
    "JOIN film f ON i.film_id = f.film_id\n",
    "GROUP BY f.film_id\n",
    "ORDER BY rental_count DESC\n",
    "LIMIT 10\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>rental_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUCKET BROTHERHOOD</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ROCKETEER MOTHER</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SCALAWAG DUCK</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIDGEMONT SUBMARINE</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JUGGLER HARDLY</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GRIT CLOCKWORK</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FORWARD TEMPLE</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ZORRO ARK</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WIFE TURN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TIMBERLAND SKY</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 title  rental_count\n",
       "0   BUCKET BROTHERHOOD            34\n",
       "1     ROCKETEER MOTHER            33\n",
       "2        SCALAWAG DUCK            32\n",
       "3  RIDGEMONT SUBMARINE            32\n",
       "4       JUGGLER HARDLY            32\n",
       "5       GRIT CLOCKWORK            32\n",
       "6       FORWARD TEMPLE            32\n",
       "7            ZORRO ARK            31\n",
       "8            WIFE TURN            31\n",
       "9       TIMBERLAND SKY            31"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_df = pd.read_sql_query(query1,conn)\n",
    "top10_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.barplot(data=top10_df, x='rental_count', y='title', palette='viridis')\n",
    "plt.xlabel('Rental Count')\n",
    "plt.ylabel('Film Title')\n",
    "plt.title('Top 10 Most Rented Movies')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which categories of movies are the most popular among customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "query2 = '''\n",
    "SELECT c.name as category_name, COUNT(*) as rental_count\n",
    "FROM rental r\n",
    "JOIN inventory i ON r.inventory_id = i.inventory_id\n",
    "JOIN film f ON i.film_id = f.film_id\n",
    "JOIN film_category fc ON f.film_id = fc.film_id\n",
    "JOIN category c ON fc.category_id = c.category_id\n",
    "GROUP BY c.category_id\n",
    "ORDER BY rental_count DESC\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>category_name</th>\n",
       "      <th>rental_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sports</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Animation</td>\n",
       "      <td>1166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Action</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Family</td>\n",
       "      <td>1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Drama</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Foreign</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Games</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Children</td>\n",
       "      <td>945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>New</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Classics</td>\n",
       "      <td>939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Horror</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Travel</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Music</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category_name  rental_count\n",
       "0         Sports          1179\n",
       "1      Animation          1166\n",
       "2         Action          1112\n",
       "3         Sci-Fi          1101\n",
       "4         Family          1096\n",
       "5          Drama          1060\n",
       "6    Documentary          1050\n",
       "7        Foreign          1033\n",
       "8          Games           969\n",
       "9       Children           945\n",
       "10        Comedy           941\n",
       "11           New           940\n",
       "12      Classics           939\n",
       "13        Horror           846\n",
       "14        Travel           837\n",
       "15         Music           830"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_category_df = pd.read_sql_query(query2, conn)\n",
    "popular_category_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "sns.barplot(data=popular_category_df, x='category_name', y='rental_count', palette='rocket')\n",
    "plt.xlabel('Rental Count')\n",
    "plt.ylabel('Category')\n",
    "plt.title('Most Popular Categories of Movies')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many rentals were made in a month and what is the average rental duration?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "query3 = '''\n",
    "SELECT strftime('%Y-%m', rental_date) AS month,\n",
    "       AVG(julianday(return_date) - julianday(rental_date)) AS avg_rental_duration,\n",
    "       COUNT(*) AS num_rentals\n",
    "FROM rental\n",
    "WHERE return_date IS NOT NULL\n",
    "GROUP BY month;\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>month</th>\n",
       "      <th>avg_rental_duration</th>\n",
       "      <th>num_rentals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-05</td>\n",
       "      <td>4.900972</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-06</td>\n",
       "      <td>5.047450</td>\n",
       "      <td>2311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-07</td>\n",
       "      <td>5.020364</td>\n",
       "      <td>6709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-08</td>\n",
       "      <td>5.047487</td>\n",
       "      <td>5685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month  avg_rental_duration  num_rentals\n",
       "0  2005-05             4.900972         1156\n",
       "1  2005-06             5.047450         2311\n",
       "2  2005-07             5.020364         6709\n",
       "3  2005-08             5.047487         5685"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ard_df = pd.read_sql_query(query3,conn)\n",
    "ard_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (10,5))\n",
    "plt.plot(ard_df['month'], ard_df['avg_rental_duration'])\n",
    "plt.title('Average Rental Duration by Month')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Average Rental Duration (Days)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Analysis I wanted to check on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "query4 = '''\n",
    "SELECT film.rental_rate, COUNT(rental.customer_id) AS rental_count\n",
    "FROM film\n",
    "JOIN inventory ON film.film_id = inventory.film_id\n",
    "JOIN rental ON inventory.inventory_id = rental.inventory_id\n",
    "GROUP BY film.rental_rate\n",
    "ORDER BY film.rental_rate\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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_rate</th>\n",
       "      <th>rental_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.99</td>\n",
       "      <td>5652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.99</td>\n",
       "      <td>5120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.99</td>\n",
       "      <td>5272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rental_rate  rental_count\n",
       "0         0.99          5652\n",
       "1         2.99          5120\n",
       "2         4.99          5272"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.read_sql_query(query4,conn)\n",
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "query5 = '''\n",
    "SELECT c.name AS category_name, f.title AS most_rented_film, COUNT(*) AS rental_count\n",
    "FROM rental r\n",
    "JOIN inventory i ON r.inventory_id = i.inventory_id\n",
    "JOIN film f ON i.film_id = f.film_id\n",
    "JOIN film_category fc ON f.film_id = fc.film_id\n",
    "JOIN category c ON fc.category_id = c.category_id\n",
    "GROUP BY c.name\n",
    "HAVING rental_count = (\n",
    "    SELECT COUNT(*)\n",
    "    FROM rental r2\n",
    "    JOIN inventory i2 ON r2.inventory_id = i2.inventory_id\n",
    "    JOIN film f2 ON i2.film_id = f2.film_id\n",
    "    JOIN film_category fc2 ON f2.film_id = fc2.film_id\n",
    "    WHERE fc2.category_id = c.category_id\n",
    "    GROUP BY fc2.category_id\n",
    "    ORDER BY COUNT(*) DESC\n",
    "    LIMIT 1\n",
    ")\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>category_name</th>\n",
       "      <th>most_rented_film</th>\n",
       "      <th>rental_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>AMADEUS HOLY</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Animation</td>\n",
       "      <td>ALTER VICTORY</td>\n",
       "      <td>1166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Children</td>\n",
       "      <td>BACKLASH UNDEFEATED</td>\n",
       "      <td>945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Classics</td>\n",
       "      <td>ARIZONA BANG</td>\n",
       "      <td>939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>AIRPLANE SIERRA</td>\n",
       "      <td>941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>BEAUTY GREASE</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Family</td>\n",
       "      <td>AFRICAN EGG</td>\n",
       "      <td>1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Foreign</td>\n",
       "      <td>AGENT TRUMAN</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Games</td>\n",
       "      <td>AUTUMN CROW</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Horror</td>\n",
       "      <td>ACE GOLDFINGER</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Music</td>\n",
       "      <td>ALASKA PHANTOM</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>New</td>\n",
       "      <td>AMISTAD MIDSUMMER</td>\n",
       "      <td>940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>ANNIE IDENTITY</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sports</td>\n",
       "      <td>ALADDIN CALENDAR</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Travel</td>\n",
       "      <td>BASIC EASY</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category_name     most_rented_film  rental_count\n",
       "0         Action         AMADEUS HOLY          1112\n",
       "1      Animation        ALTER VICTORY          1166\n",
       "2       Children  BACKLASH UNDEFEATED           945\n",
       "3       Classics         ARIZONA BANG           939\n",
       "4         Comedy      AIRPLANE SIERRA           941\n",
       "5    Documentary     ACADEMY DINOSAUR          1050\n",
       "6          Drama        BEAUTY GREASE          1060\n",
       "7         Family          AFRICAN EGG          1096\n",
       "8        Foreign         AGENT TRUMAN          1033\n",
       "9          Games          AUTUMN CROW           969\n",
       "10        Horror       ACE GOLDFINGER           846\n",
       "11         Music       ALASKA PHANTOM           830\n",
       "12           New    AMISTAD MIDSUMMER           940\n",
       "13        Sci-Fi       ANNIE IDENTITY          1101\n",
       "14        Sports     ALADDIN CALENDAR          1179\n",
       "15        Travel           BASIC EASY           837"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.read_sql_query(query5,conn)\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "query6 = '''\n",
    "SELECT film.replacement_cost, COUNT(rental.customer_id) AS rental_count\n",
    "FROM film\n",
    "JOIN inventory ON film.film_id = inventory.film_id\n",
    "JOIN rental ON inventory.inventory_id = rental.inventory_id\n",
    "GROUP BY film.replacement_cost\n",
    "ORDER BY film.replacement_cost DESC;\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>replacement_cost</th>\n",
       "      <th>rental_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.99</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.99</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.99</td>\n",
       "      <td>958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.99</td>\n",
       "      <td>737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.99</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24.99</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.99</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.99</td>\n",
       "      <td>1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.99</td>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.99</td>\n",
       "      <td>863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.99</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18.99</td>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17.99</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.99</td>\n",
       "      <td>639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.99</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.99</td>\n",
       "      <td>821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.99</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12.99</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.99</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.99</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9.99</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    replacement_cost  rental_count\n",
       "0              29.99           868\n",
       "1              28.99           716\n",
       "2              27.99           958\n",
       "3              26.99           737\n",
       "4              25.99           740\n",
       "5              24.99           633\n",
       "6              23.99           690\n",
       "7              22.99          1044\n",
       "8              21.99           912\n",
       "9              20.99           863\n",
       "10             19.99           657\n",
       "11             18.99           696\n",
       "12             17.99           691\n",
       "13             16.99           639\n",
       "14             15.99           559\n",
       "15             14.99           821\n",
       "16             13.99           803\n",
       "17             12.99           864\n",
       "18             11.99           749\n",
       "19             10.99           720\n",
       "20              9.99           684"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = pd.read_sql_query(query6,conn)\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.barplot(data=df6, x='replacement_cost', y='rental_count', palette='mako')\n",
    "plt.xlabel('Replacement Cost')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('How Replacement Cost affects Customers')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How can we optimize rental prices to maximize profitability while still retaining customers?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Customer Renting Behavior Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "query8 = '''\n",
    "SELECT i.film_id, f.title, f.rental_rate, COUNT(DISTINCT r.customer_id) AS rental_count, SUM(p.amount) AS revenue,\n",
    "       AVG(f.rental_rate) AS avg_rental_rate\n",
    "FROM inventory AS i\n",
    "JOIN film AS f ON i.film_id = f.film_id\n",
    "JOIN rental AS r ON i.inventory_id = r.inventory_id\n",
    "JOIN payment AS p ON r.rental_id = p.rental_id\n",
    "GROUP BY i.film_id\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>film_id</th>\n",
       "      <th>title</th>\n",
       "      <th>rental_rate</th>\n",
       "      <th>rental_count</th>\n",
       "      <th>revenue</th>\n",
       "      <th>avg_rental_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ACADEMY DINOSAUR</td>\n",
       "      <td>0.99</td>\n",
       "      <td>23</td>\n",
       "      <td>36.77</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ACE GOLDFINGER</td>\n",
       "      <td>4.99</td>\n",
       "      <td>7</td>\n",
       "      <td>52.93</td>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ADAPTATION HOLES</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12</td>\n",
       "      <td>37.88</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>AFFAIR PREJUDICE</td>\n",
       "      <td>2.99</td>\n",
       "      <td>22</td>\n",
       "      <td>91.77</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>AFRICAN EGG</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12</td>\n",
       "      <td>51.88</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>996</td>\n",
       "      <td>YOUNG LANGUAGE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>7</td>\n",
       "      <td>6.93</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>997</td>\n",
       "      <td>YOUTH KICK</td>\n",
       "      <td>0.99</td>\n",
       "      <td>6</td>\n",
       "      <td>16.94</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>998</td>\n",
       "      <td>ZHIVAGO CORE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>9</td>\n",
       "      <td>14.91</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>999</td>\n",
       "      <td>ZOOLANDER FICTION</td>\n",
       "      <td>2.99</td>\n",
       "      <td>17</td>\n",
       "      <td>73.83</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>1000</td>\n",
       "      <td>ZORRO ARK</td>\n",
       "      <td>4.99</td>\n",
       "      <td>30</td>\n",
       "      <td>214.69</td>\n",
       "      <td>4.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>958 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     film_id              title  rental_rate  rental_count  revenue   \n",
       "0          1   ACADEMY DINOSAUR         0.99            23    36.77  \\\n",
       "1          2     ACE GOLDFINGER         4.99             7    52.93   \n",
       "2          3   ADAPTATION HOLES         2.99            12    37.88   \n",
       "3          4   AFFAIR PREJUDICE         2.99            22    91.77   \n",
       "4          5        AFRICAN EGG         2.99            12    51.88   \n",
       "..       ...                ...          ...           ...      ...   \n",
       "953      996     YOUNG LANGUAGE         0.99             7     6.93   \n",
       "954      997         YOUTH KICK         0.99             6    16.94   \n",
       "955      998       ZHIVAGO CORE         0.99             9    14.91   \n",
       "956      999  ZOOLANDER FICTION         2.99            17    73.83   \n",
       "957     1000          ZORRO ARK         4.99            30   214.69   \n",
       "\n",
       "     avg_rental_rate  \n",
       "0               0.99  \n",
       "1               4.99  \n",
       "2               2.99  \n",
       "3               2.99  \n",
       "4               2.99  \n",
       "..               ...  \n",
       "953             0.99  \n",
       "954             0.99  \n",
       "955             0.99  \n",
       "956             2.99  \n",
       "957             4.99  \n",
       "\n",
       "[958 rows x 6 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = pd.read_sql_query(query8, conn)\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8['revenue_per_rental'] = df8['revenue'] / df8['rental_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8['revenue_per_rental_rate'] = df8['revenue'] / df8['avg_rental_rate'] / df8['rental_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8 = df8.sort_values(by='revenue_per_rental_rate', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>revenue_per_rental_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>MOTHER OLEANDER</td>\n",
       "      <td>5.040404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>RUN PACIFIC</td>\n",
       "      <td>4.535354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>WAIT CIDER</td>\n",
       "      <td>4.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>EGYPT TENENBAUMS</td>\n",
       "      <td>4.327392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>BUTTERFLY CHOCOLAT</td>\n",
       "      <td>4.308603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>RIDGEMONT SUBMARINE</td>\n",
       "      <td>4.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>NEIGHBORS CHARADE</td>\n",
       "      <td>4.085859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>DESTINATION JERK</td>\n",
       "      <td>4.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>TIMBERLAND SKY</td>\n",
       "      <td>4.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>CADDYSHACK JEDI</td>\n",
       "      <td>4.027972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title  revenue_per_rental_rate\n",
       "573      MOTHER OLEANDER                 5.040404\n",
       "716          RUN PACIFIC                 4.535354\n",
       "912           WAIT CIDER                 4.454545\n",
       "258     EGYPT TENENBAUMS                 4.327392\n",
       "101   BUTTERFLY CHOCOLAT                 4.308603\n",
       "697  RIDGEMONT SUBMARINE                 4.125000\n",
       "592    NEIGHBORS CHARADE                 4.085859\n",
       "208     DESTINATION JERK                 4.030303\n",
       "853       TIMBERLAND SKY                 4.030303\n",
       "103      CADDYSHACK JEDI                 4.027972"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[['title', 'revenue_per_rental_rate']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>film_id</th>\n",
       "      <th>title</th>\n",
       "      <th>rental_rate</th>\n",
       "      <th>rental_count</th>\n",
       "      <th>revenue</th>\n",
       "      <th>avg_rental_rate</th>\n",
       "      <th>revenue_per_rental</th>\n",
       "      <th>revenue_per_rental_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>599</td>\n",
       "      <td>MOTHER OLEANDER</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14</td>\n",
       "      <td>69.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>5.040404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>750</td>\n",
       "      <td>RUN PACIFIC</td>\n",
       "      <td>0.99</td>\n",
       "      <td>10</td>\n",
       "      <td>44.90</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>4.535354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>953</td>\n",
       "      <td>WAIT CIDER</td>\n",
       "      <td>0.99</td>\n",
       "      <td>19</td>\n",
       "      <td>83.79</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.410000</td>\n",
       "      <td>4.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>275</td>\n",
       "      <td>EGYPT TENENBAUMS</td>\n",
       "      <td>0.99</td>\n",
       "      <td>17</td>\n",
       "      <td>72.83</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.284118</td>\n",
       "      <td>4.327392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>109</td>\n",
       "      <td>BUTTERFLY CHOCOLAT</td>\n",
       "      <td>0.99</td>\n",
       "      <td>29</td>\n",
       "      <td>123.70</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.265517</td>\n",
       "      <td>4.308603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>730</td>\n",
       "      <td>RIDGEMONT SUBMARINE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>32</td>\n",
       "      <td>130.68</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.083750</td>\n",
       "      <td>4.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>619</td>\n",
       "      <td>NEIGHBORS CHARADE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>18</td>\n",
       "      <td>72.81</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4.045000</td>\n",
       "      <td>4.085859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>225</td>\n",
       "      <td>DESTINATION JERK</td>\n",
       "      <td>0.99</td>\n",
       "      <td>8</td>\n",
       "      <td>31.92</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.990000</td>\n",
       "      <td>4.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>891</td>\n",
       "      <td>TIMBERLAND SKY</td>\n",
       "      <td>0.99</td>\n",
       "      <td>31</td>\n",
       "      <td>123.69</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.990000</td>\n",
       "      <td>4.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>111</td>\n",
       "      <td>CADDYSHACK JEDI</td>\n",
       "      <td>0.99</td>\n",
       "      <td>13</td>\n",
       "      <td>51.84</td>\n",
       "      <td>0.99</td>\n",
       "      <td>3.987692</td>\n",
       "      <td>4.027972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     film_id                title  rental_rate  rental_count  revenue   \n",
       "573      599      MOTHER OLEANDER         0.99            14    69.86  \\\n",
       "716      750          RUN PACIFIC         0.99            10    44.90   \n",
       "912      953           WAIT CIDER         0.99            19    83.79   \n",
       "258      275     EGYPT TENENBAUMS         0.99            17    72.83   \n",
       "101      109   BUTTERFLY CHOCOLAT         0.99            29   123.70   \n",
       "697      730  RIDGEMONT SUBMARINE         0.99            32   130.68   \n",
       "592      619    NEIGHBORS CHARADE         0.99            18    72.81   \n",
       "208      225     DESTINATION JERK         0.99             8    31.92   \n",
       "853      891       TIMBERLAND SKY         0.99            31   123.69   \n",
       "103      111      CADDYSHACK JEDI         0.99            13    51.84   \n",
       "\n",
       "     avg_rental_rate  revenue_per_rental  revenue_per_rental_rate  \n",
       "573             0.99            4.990000                 5.040404  \n",
       "716             0.99            4.490000                 4.535354  \n",
       "912             0.99            4.410000                 4.454545  \n",
       "258             0.99            4.284118                 4.327392  \n",
       "101             0.99            4.265517                 4.308603  \n",
       "697             0.99            4.083750                 4.125000  \n",
       "592             0.99            4.045000                 4.085859  \n",
       "208             0.99            3.990000                 4.030303  \n",
       "853             0.99            3.990000                 4.030303  \n",
       "103             0.99            3.987692                 4.027972  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### These films may be good candidates for rental price increases to maximize profitability while still retaining customers."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profitability Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>film_id</th>\n",
       "      <th>title</th>\n",
       "      <th>rental_rate</th>\n",
       "      <th>rental_id</th>\n",
       "      <th>rental_count</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>879</td>\n",
       "      <td>TELEGRAPH VOYAGE</td>\n",
       "      <td>4.99</td>\n",
       "      <td>8705</td>\n",
       "      <td>27</td>\n",
       "      <td>231.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>973</td>\n",
       "      <td>WIFE TURN</td>\n",
       "      <td>4.99</td>\n",
       "      <td>8615</td>\n",
       "      <td>28</td>\n",
       "      <td>223.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>ZORRO ARK</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1084</td>\n",
       "      <td>30</td>\n",
       "      <td>214.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>369</td>\n",
       "      <td>GOODFELLAS SALUTE</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5107</td>\n",
       "      <td>31</td>\n",
       "      <td>209.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>764</td>\n",
       "      <td>SATURDAY LAMBS</td>\n",
       "      <td>4.99</td>\n",
       "      <td>4611</td>\n",
       "      <td>28</td>\n",
       "      <td>204.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>996</td>\n",
       "      <td>YOUNG LANGUAGE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>12377</td>\n",
       "      <td>7</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>261</td>\n",
       "      <td>DUFFEL APOCALYPSE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14093</td>\n",
       "      <td>7</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>335</td>\n",
       "      <td>FREEDOM CLEOPATRA</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14459</td>\n",
       "      <td>5</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>885</td>\n",
       "      <td>TEXAS WATCH</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14028</td>\n",
       "      <td>6</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>635</td>\n",
       "      <td>OKLAHOMA JUMANJI</td>\n",
       "      <td>0.99</td>\n",
       "      <td>10995</td>\n",
       "      <td>6</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>958 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     film_id              title  rental_rate  rental_id  rental_count  revenue\n",
       "0        879   TELEGRAPH VOYAGE         4.99       8705            27   231.73\n",
       "1        973          WIFE TURN         4.99       8615            28   223.69\n",
       "2       1000          ZORRO ARK         4.99       1084            30   214.69\n",
       "3        369  GOODFELLAS SALUTE         4.99       5107            31   209.69\n",
       "4        764     SATURDAY LAMBS         4.99       4611            28   204.72\n",
       "..       ...                ...          ...        ...           ...      ...\n",
       "953      996     YOUNG LANGUAGE         0.99      12377             7     6.93\n",
       "954      261  DUFFEL APOCALYPSE         0.99      14093             7     6.93\n",
       "955      335  FREEDOM CLEOPATRA         0.99      14459             5     5.95\n",
       "956      885        TEXAS WATCH         0.99      14028             6     5.94\n",
       "957      635   OKLAHOMA JUMANJI         0.99      10995             6     5.94\n",
       "\n",
       "[958 rows x 6 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rentals = pd.read_sql_query('''\n",
    "    SELECT i.film_id, f.title, f.rental_rate, r.rental_id, COUNT(DISTINCT r.customer_id) AS rental_count, SUM(p.amount) AS revenue\n",
    "    FROM inventory AS i\n",
    "    JOIN film AS f ON i.film_id = f.film_id\n",
    "    JOIN rental AS r ON i.inventory_id = r.inventory_id\n",
    "    JOIN payment AS p ON r.rental_id = p.rental_id\n",
    "    GROUP BY i.film_id\n",
    "    ORDER BY revenue DESC\n",
    "''', conn)\n",
    "df_rentals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>film_id</th>\n",
       "      <th>title</th>\n",
       "      <th>rental_rate</th>\n",
       "      <th>rental_id</th>\n",
       "      <th>rental_count</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profitability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>879</td>\n",
       "      <td>TELEGRAPH VOYAGE</td>\n",
       "      <td>4.99</td>\n",
       "      <td>8705</td>\n",
       "      <td>27</td>\n",
       "      <td>231.73</td>\n",
       "      <td>8.582593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>973</td>\n",
       "      <td>WIFE TURN</td>\n",
       "      <td>4.99</td>\n",
       "      <td>8615</td>\n",
       "      <td>28</td>\n",
       "      <td>223.69</td>\n",
       "      <td>7.988929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>ZORRO ARK</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1084</td>\n",
       "      <td>30</td>\n",
       "      <td>214.69</td>\n",
       "      <td>7.156333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>369</td>\n",
       "      <td>GOODFELLAS SALUTE</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5107</td>\n",
       "      <td>31</td>\n",
       "      <td>209.69</td>\n",
       "      <td>6.764194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>764</td>\n",
       "      <td>SATURDAY LAMBS</td>\n",
       "      <td>4.99</td>\n",
       "      <td>4611</td>\n",
       "      <td>28</td>\n",
       "      <td>204.72</td>\n",
       "      <td>7.311429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>996</td>\n",
       "      <td>YOUNG LANGUAGE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>12377</td>\n",
       "      <td>7</td>\n",
       "      <td>6.93</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>261</td>\n",
       "      <td>DUFFEL APOCALYPSE</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14093</td>\n",
       "      <td>7</td>\n",
       "      <td>6.93</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>335</td>\n",
       "      <td>FREEDOM CLEOPATRA</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14459</td>\n",
       "      <td>5</td>\n",
       "      <td>5.95</td>\n",
       "      <td>1.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>885</td>\n",
       "      <td>TEXAS WATCH</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14028</td>\n",
       "      <td>6</td>\n",
       "      <td>5.94</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>635</td>\n",
       "      <td>OKLAHOMA JUMANJI</td>\n",
       "      <td>0.99</td>\n",
       "      <td>10995</td>\n",
       "      <td>6</td>\n",
       "      <td>5.94</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>958 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     film_id              title  rental_rate  rental_id  rental_count   \n",
       "0        879   TELEGRAPH VOYAGE         4.99       8705            27  \\\n",
       "1        973          WIFE TURN         4.99       8615            28   \n",
       "2       1000          ZORRO ARK         4.99       1084            30   \n",
       "3        369  GOODFELLAS SALUTE         4.99       5107            31   \n",
       "4        764     SATURDAY LAMBS         4.99       4611            28   \n",
       "..       ...                ...          ...        ...           ...   \n",
       "953      996     YOUNG LANGUAGE         0.99      12377             7   \n",
       "954      261  DUFFEL APOCALYPSE         0.99      14093             7   \n",
       "955      335  FREEDOM CLEOPATRA         0.99      14459             5   \n",
       "956      885        TEXAS WATCH         0.99      14028             6   \n",
       "957      635   OKLAHOMA JUMANJI         0.99      10995             6   \n",
       "\n",
       "     revenue  profitability  \n",
       "0     231.73       8.582593  \n",
       "1     223.69       7.988929  \n",
       "2     214.69       7.156333  \n",
       "3     209.69       6.764194  \n",
       "4     204.72       7.311429  \n",
       "..       ...            ...  \n",
       "953     6.93       0.990000  \n",
       "954     6.93       0.990000  \n",
       "955     5.95       1.190000  \n",
       "956     5.94       0.990000  \n",
       "957     5.94       0.990000  \n",
       "\n",
       "[958 rows x 7 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rentals['profitability'] = df_rentals['revenue'] / df_rentals['rental_count']\n",
    "df_rentals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>category_id</th>\n",
       "      <th>category_name</th>\n",
       "      <th>rental_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>Sports</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Action</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>Family</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Drama</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Animation</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>Children</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Games</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>New</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>Classics</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>Horror</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12</td>\n",
       "      <td>Music</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Travel</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    category_id category_name  rental_count\n",
       "0            15        Sports           519\n",
       "1             1        Action           510\n",
       "2            14        Sci-Fi           507\n",
       "3             8        Family           501\n",
       "4             7         Drama           501\n",
       "5             2     Animation           500\n",
       "6             5        Comedy           495\n",
       "7             9       Foreign           493\n",
       "8             6   Documentary           483\n",
       "9             3      Children           482\n",
       "10           10         Games           474\n",
       "11           13           New           468\n",
       "12            4      Classics           468\n",
       "13           11        Horror           451\n",
       "14           12         Music           447\n",
       "15           16        Travel           442"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categories = pd.read_sql_query('''\n",
    "    SELECT c.category_id, c.name AS category_name, COUNT(DISTINCT r.customer_id) AS rental_count\n",
    "    FROM rental AS r\n",
    "    JOIN inventory AS i ON r.inventory_id = i.inventory_id\n",
    "    JOIN film_category AS fc ON i.film_id = fc.film_id\n",
    "    JOIN category AS c ON fc.category_id = c.category_id\n",
    "    GROUP BY c.category_id\n",
    "    ORDER BY rental_count DESC\n",
    "''', conn)\n",
    "df_categories"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Customer Behavior Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>rental_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>MARY</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>PATRICIA</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LINDA</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>BARBARA</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ELIZABETH</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>595</td>\n",
       "      <td>TERRENCE</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>596</td>\n",
       "      <td>ENRIQUE</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>597</td>\n",
       "      <td>FREDDIE</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>598</td>\n",
       "      <td>WADE</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>599</td>\n",
       "      <td>AUSTIN</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>599 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer_id first_name  rental_count\n",
       "0              1       MARY            32\n",
       "1              2   PATRICIA            27\n",
       "2              3      LINDA            26\n",
       "3              4    BARBARA            22\n",
       "4              5  ELIZABETH            38\n",
       "..           ...        ...           ...\n",
       "594          595   TERRENCE            30\n",
       "595          596    ENRIQUE            28\n",
       "596          597    FREDDIE            25\n",
       "597          598       WADE            22\n",
       "598          599     AUSTIN            19\n",
       "\n",
       "[599 rows x 3 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers = pd.read_sql_query('''\n",
    "    SELECT r.customer_id, c.first_name , COUNT(r.rental_id) AS rental_count\n",
    "    FROM rental r\n",
    "    JOIN customer c ON r.customer_id = c.customer_id\n",
    "    GROUP BY r.customer_id\n",
    "''', conn)\n",
    "df_customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23., 26., 30.])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rental_count_percentiles = df_customers['rental_count'].quantile([0.25, 0.5, 0.75]).values\n",
    "rental_count_percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorize_customers(row):\n",
    "    if row['rental_count'] <= rental_count_percentiles[0]:\n",
    "        return 'Low frequency'\n",
    "    elif row['rental_count'] <= rental_count_percentiles[1]:\n",
    "        return 'Medium frequency'\n",
    "    elif row['rental_count'] <= rental_count_percentiles[2]:\n",
    "        return 'High frequency'\n",
    "    else:\n",
    "        return 'Very high frequency'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>rental_count</th>\n",
       "      <th>frequency_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>MARY</td>\n",
       "      <td>32</td>\n",
       "      <td>Very high frequency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>PATRICIA</td>\n",
       "      <td>27</td>\n",
       "      <td>High frequency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LINDA</td>\n",
       "      <td>26</td>\n",
       "      <td>Medium frequency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>BARBARA</td>\n",
       "      <td>22</td>\n",
       "      <td>Low frequency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ELIZABETH</td>\n",
       "      <td>38</td>\n",
       "      <td>Very high frequency</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id first_name  rental_count   frequency_category\n",
       "0            1       MARY            32  Very high frequency\n",
       "1            2   PATRICIA            27       High frequency\n",
       "2            3      LINDA            26     Medium frequency\n",
       "3            4    BARBARA            22        Low frequency\n",
       "4            5  ELIZABETH            38  Very high frequency"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers['frequency_category'] = df_customers.apply(categorize_customers, axis=1)\n",
    "df_customers.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## THANK YOU FOR CHECKING UP!"
   ]
  }
 ],
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