DebraRay_Exercise2_FreeCodeCamp_complete.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![rmotr](https://user-images.githubusercontent.com/7065401/52071918-bda15380-2562-11e9-828c-7f95297e4a82.png)\n",
    "<hr style=\"margin-bottom: 40px;\">\n",
    "\n",
    "# Exercises\n",
    "## The Sakila Database\n",
    "\n",
    "One of the best example databases out there is the <a href=\"https://dev.mysql.com/doc/sakila/en/\">Sakila Database</a>, which was originally created by MySQL and has been open sourced under the terms of the BSD License.\n",
    "\n",
    "The Sakila database is a nicely normalised schema modelling a DVD rental store, featuring things like films, actors, film-actor relationships, and a central inventory table that connects films, stores, and rentals.\n",
    "\n",
    "<img width=\"1200px\" src=\"https://user-images.githubusercontent.com/7065401/58504872-fa243b00-8161-11e9-85ed-4b7d8d7ce9f7.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Hands on! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "DEBRA'S NOTES: we have multiple tables: rental, customer, store, city, film, inventory, address. \n",
    "We conduct multiple inner joins to combine the tables with the information that we want, joining rental with customer on customer id\n",
    "... joining rental and inventory on inventory id ....\n",
    ".... joining inventory and store on store_id ... \n",
    "\n",
    ".... joining address and store on address_id\n",
    ".... joining city and address on city_id\n",
    "... and finally inventory and film on film_id. \n",
    "\n",
    "At least two of the tables have to have a common element to join on with the inner join. \n",
    "\n",
    "https://www.w3schools.com/sql/sql_join_inner.asp\n",
    "'''\n",
    "\n",
    "conn = sqlite3.connect('data/sakila.db')\n",
    "\n",
    "df = pd.read_sql('''\n",
    "    SELECT\n",
    "        rental.rental_id, rental.rental_date, rental.return_date,\n",
    "        customer.last_name AS customer_lastname,\n",
    "        store.store_id,\n",
    "        city.city AS rental_store_city,\n",
    "        film.title AS film_title, film.rental_duration AS film_rental_duration,\n",
    "        film.rental_rate AS film_rental_rate, film.replacement_cost AS film_replacement_cost,\n",
    "        film.rating AS film_rating\n",
    "    FROM rental\n",
    "    INNER JOIN customer ON rental.customer_id == customer.customer_id\n",
    "    INNER JOIN inventory ON rental.inventory_id == inventory.inventory_id\n",
    "    INNER JOIN store ON inventory.store_id == store.store_id\n",
    "    INNER JOIN address ON store.address_id == address.address_id\n",
    "    INNER JOIN city ON address.city_id == city.city_id\n",
    "    INNER JOIN film ON inventory.film_id == film.film_id\n",
    "    ;\n",
    "''', conn, index_col='rental_id', parse_dates=['rental_date', 'return_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_date</th>\n",
       "      <th>return_date</th>\n",
       "      <th>customer_lastname</th>\n",
       "      <th>store_id</th>\n",
       "      <th>rental_store_city</th>\n",
       "      <th>film_title</th>\n",
       "      <th>film_rental_duration</th>\n",
       "      <th>film_rental_rate</th>\n",
       "      <th>film_replacement_cost</th>\n",
       "      <th>film_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rental_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-05-24 22:53:30</td>\n",
       "      <td>2005-05-26 22:04:30</td>\n",
       "      <td>HUNTER</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>BLANKET BEVERLY</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-05-24 22:54:33</td>\n",
       "      <td>2005-05-28 19:40:33</td>\n",
       "      <td>COLLAZO</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>FREAKY POCUS</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>16.99</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-05-24 23:03:39</td>\n",
       "      <td>2005-06-01 22:12:39</td>\n",
       "      <td>MURRELL</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>GRADUATE LORD</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>14.99</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-05-24 23:04:41</td>\n",
       "      <td>2005-06-03 01:43:41</td>\n",
       "      <td>PURDY</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>LOVE SUICIDES</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005-05-24 23:05:21</td>\n",
       "      <td>2005-06-02 04:33:21</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>IDOLS SNATCHERS</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>29.99</td>\n",
       "      <td>NC-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rental_date         return_date customer_lastname  store_id  \\\n",
       "rental_id                                                                       \n",
       "1         2005-05-24 22:53:30 2005-05-26 22:04:30            HUNTER         1   \n",
       "2         2005-05-24 22:54:33 2005-05-28 19:40:33           COLLAZO         2   \n",
       "3         2005-05-24 23:03:39 2005-06-01 22:12:39           MURRELL         2   \n",
       "4         2005-05-24 23:04:41 2005-06-03 01:43:41             PURDY         1   \n",
       "5         2005-05-24 23:05:21 2005-06-02 04:33:21            HANSEN         2   \n",
       "\n",
       "          rental_store_city       film_title  film_rental_duration  \\\n",
       "rental_id                                                            \n",
       "1                Lethbridge  BLANKET BEVERLY                     7   \n",
       "2                 Woodridge     FREAKY POCUS                     7   \n",
       "3                 Woodridge    GRADUATE LORD                     7   \n",
       "4                Lethbridge    LOVE SUICIDES                     6   \n",
       "5                 Woodridge  IDOLS SNATCHERS                     5   \n",
       "\n",
       "           film_rental_rate  film_replacement_cost film_rating  \n",
       "rental_id                                                       \n",
       "1                      2.99                  21.99           G  \n",
       "2                      2.99                  16.99           R  \n",
       "3                      2.99                  14.99           G  \n",
       "4                      0.99                  21.99           R  \n",
       "5                      2.99                  29.99       NC-17  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### What's the mean of `film_rental_duration`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.935489902767389"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['film_rental_duration'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.935489902767389"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['film_rental_duration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### What's the most common rental duration?\n",
    "\n",
    "Show a <b>bar plot</b> with all the durations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c23f77c48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AhiR2AACAIYkdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIYgcAABiS2AEAAIYkdgAAgCGJHQAAYEhiBwAAGJLYAQAAhiR2AACAIYkdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIK8ZOVd1QVY9W1T1LZi+tqn1Vdd/088xpXlX1/qqar6q7q+q1S56za9r/vqradWLeDgAAwKLVHNn5cJKdh8yuSnJbd29Lctt0P0kuTrJtuu1Ocn2yGEdJrk5yfpIdSa4+GEgAAAAnwoqx091/muSxQ8aXJrlx2r4xyVuWzD/Si76Q5IyqOjvJRUn2dfdj3f2dJPvy7IACAABYM8f6nZ2Xd/fDSTL9fNk0PyfJg0v2OzDNDjd/lqraXVVzVTW3sLBwjMsDAABOdWt9gYJaZtZHmD972L2nu2e7e3ZmZmZNFwcAAJw6jjV2HplOT8v089FpfiDJliX7bU7y0BHmAAAAJ8Sxxs7eJAevqLYrySeXzN8xXZXtgiSPT6e53Zrkwqo6c7owwYXTDAAA4ITYtNIOVfXRJK9PclZVHcjiVdWuTXJzVV2e5FtJ3jrtfkuSS5LMJ3kiyTuTpLsfq6r3Jblj2u+93X3oRQ8AAADWzIqx091vO8xDb1xm305yxWF+zw1Jbjiq1QEAAByjtb5AAQAAwHOC2AEAAIYkdgAAgCGJHQAAYEhiBwAAGJLYAQAAhiR2AACAIYkdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIYgcAABiS2AEAAIYkdgAAgCGJHQAAYEhiBwAAGJLYAQAAhiR2AACAIYkdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIYgcAABiS2AEAAIYkdgAAgCGJHQAAYEhiBwAAGJLYAQAAhiR2AACAIYkdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIYgcAABjSccVOVT1QVV+tqruqam6avbSq9lXVfdPPM6d5VdX7q2q+qu6uqteuxRsAAABYzloc2fmF7t7e3bPT/auS3Nbd25LcNt1PkouTbJtuu5NcvwavDQAAsKwTcRrbpUlunLZvTPKWJfOP9KIvJDmjqs4+Aa8PAABw3LHTSf64qu6sqt3T7OXd/XCSTD9fNs3PSfLgkucemGbPUFW7q2ququYWFhaOc3kAAMCpatNxPv913f1QVb0syb6q+rMj7FvLzPpZg+49SfYkyezs7LMeBwAAWI3jOrLT3Q9NPx9N8okkO5I8cvD0tOnno9PuB5JsWfL0zUkeOp7XBwAAOJxjjp2q+omqevHB7SQXJrknyd4ku6bddiX55LS9N8k7pquyXZDk8YOnuwEAAKy14zmN7eVJPlFVB3/Pf+3u/1FVdyS5uaouT/KtJG+d9r8lySVJ5pM8keSdx/HaAAAAR3TMsdPd9yd59TLz/5vkjcvMO8kVx/p6AAAAR+NEXHoaAABgw4kdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIYgcAABiS2AEAAIYkdgAAgCGJHQAAYEhiBwAAGJLYAQAAhiR2AACAIYkdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIYgcAABiS2AEAAIYkdgAAgCGJHQAAYEhiBwAAGJLYAQAAhiR2AACAIYkdAABgSGIHAAAYktgBAACGJHYAAIAhiR0AAGBIYgcAABiS2AEAAIYkdgAAgCGJHQAAYEhiBwAAGJLYAQAAhrTusVNVO6vq61U1X1VXrffrAwAAp4Z1jZ2qOi3JB5JcnOS8JG+rqvPWcw0AAMCpYb2P7OxIMt/d93f3Xye5Kcml67wGAADgFFDdvX4vVvXLSXZ2969M99+e5PzuvnLJPruT7J7u/v0kX1+3BY7lrCTf3uhFcMrweWM9+byxnnzeWG8+c8fm73X3zKHDTeu8iFpm9oza6u49Sfasz3LGVVVz3T270evg1ODzxnryeWM9+byx3nzm1tZ6n8Z2IMmWJfc3J3londcAAACcAtY7du5Isq2qzq2qFyS5LMnedV4DAABwCljX09i6+6mqujLJrUlOS3JDd+9fzzWcQpwKyHryeWM9+byxnnzeWG8+c2toXS9QAAAAsF7W/Y+KAgAArAexAwAADEnsAAAAQxI7AADAkMTOAKpqR1X9/LR9XlX966q6ZKPXxZiq6vyq+jvT9gur6j1V9UdV9VtV9ZKNXh9jq6qPbPQaOHVU1T+Z/k29cKPXwniq6l9V1ZaV9+R4uBrbSa6qrk5ycRYvI74vyflJPpfkTUlu7e5rNm51jKiq9id59XQp+T1JnkjysSRvnOb/bEMXyDCq6tC/w1ZJfiHJZ5Kku39x3RfF0Krq9u7eMW3/iyRXJPlEkguT/FF3X7uR62MsVfV4kr9M8o0kH03yB929sLGrGo/YOclV1VeTbE9yepK/SLK5u79XVS9M8sXu/scbukCGU1X3dvcrp+0vdfdrlzx2V3dv37jVMZKq+lKSryX5z0k6i7Hz0Sz+Qep0959s3OoYUVV9ubtfM23fkeSS7l6oqp9I8oXu/kcbu0JGUlVfTvJzWfwP1P88yS8muTOL/zv38e7+/gYubxhOYzv5PdXdT3f3E0m+0d3fS5Lu/qskP9zYpTGoe6rqndP2V6pqNkmq6hVJ/mbjlsWAZrP4D/+/S/J4d38uyV91958IHU6Q51XVmVX1k1n8D8ILSdLdf5nkqY1dGgPq7v5hd/9xd1+e5KeSfDDJziT3b+zSxrFpoxfAcfvrqvrxKXZ+7uBw+u6E2OFE+JUk/6mq/n2Sbyf5X1X1YJIHp8dgTXT3D5NcV1V/MP18JP7d4sR6SRYDu5J0Vf3d7v6LqnrRNIO19IzPVHf/TZK9SfZOZ+iwBpzGdpKrqtO7+8ll5mclObu7v7oBy+IUUFUvTvLTWfw/nwe6+5ENXhKDq6o3J3ldd797o9fCqaWqfjzJy7v7mxu9FsZRVa/o7v+90esYndgBAACG5Ds7AADAkMQOAAAwJLEDAAAMSewAAABD+v+P4AxS1+QkrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['film_rental_duration'].value_counts().plot(kind='bar', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### What is the most common rental rate?\n",
    "\n",
    "- Show a <b>pie plot</b> with all possible rental rates.\n",
    "- Show a <b>bar plot</b> with all possible rental rates.\n",
    "- Which plot you think fits the best in this case? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c24036888>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It is a little difficult to see the difference in rental rates with the pie chart.\n",
    "df['film_rental_rate'].value_counts().plot(kind='pie', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c2379af88>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It is easier to see the difference in rental rates with the bar chart\n",
    "df['film_rental_rate'].value_counts().plot(kind='bar', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### How is the replacement cost distributed?\n",
    "\n",
    "- Show a <b>box plot</b> of the replacement costs.\n",
    "- Show a <b>density plot</b> of the replacement costs.\n",
    "- Add a red line on the <b>mean</b>.\n",
    "- Add a green line on the median <b>median</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c2397ddc8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['film_replacement_cost'].plot(kind='box', vert=False, figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "ax = df['film_replacement_cost'].plot(kind='density', figsize=(14,6  ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x23c1f446f48>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['film_replacement_cost'].plot(kind='density', figsize=(14,6))\n",
    "ax.axvline(df['film_replacement_cost'].mean(), color='red')\n",
    "ax.axvline(df['film_replacement_cost'].median(), color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### How many films of each rating do we have?\n",
    "\n",
    "- Show the raw count of each film rating.\n",
    "- Show a <b>bar plot</b> with all possible film ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PG-13    3585\n",
       "NC-17    3293\n",
       "PG       3212\n",
       "R        3181\n",
       "G        2773\n",
       "Name: film_rating, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['film_rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c26241548>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ADEnYAQAAhiTsAAAAQxJ2AACAIQk7AADAkIQdAABgSMIOAAAwJGEHAAAYkrADAAAMSdgBAACGJOwAAABDEnYAAIAhCTsAAMCQhB0AAGBIwg4AADAkYQcAABiSsAMAAAxJ2AEAAIYk7AAAAEMSdgAAgCEJOwAAwJCEHQAAYEjCDgAAMCRhBwAAGJKwAwAADGm/YaeqLq+q+6rq5pnaMVV1TVXdPr0ePdWrqt5TVTuq6saqOmXmmPOm8bdX1XmH53QAAAAWLefKzgeTnLlH7aIkn+3uTUk+O20nyVlJNk3L1iTvSxbDUZKLk7w4yalJLt4dkAAAAA6H/Yad7v6zJPfvUd6S5Ipp/YokZ8/UP9SLvpzkqKo6LskZSa7p7vu7+4Ek1+TRAQoAAGDFHOwzO8d29z1JMr0+c6pvSHLXzLidU21v9Uepqq1Vtb2qtu/atesg2wMAANa6lZ6goJao9T7qjy52X9rdm7t788LCwoo2BwAArB0HG3bunW5Py/R631TfmeSEmXHHJ7l7H3UAAIDD4mDDzrYku2dUOy/JJ2bqr51mZTstyfem29w+k+T0qjp6mpjg9KkGAABwWKzf34Cq+kiSlyZ5RlXtzOKsau9MclVVnZ/km0lePQ2/OskrkuxI8qMkr0uS7r6/qt6R5Ppp3Nu7e89JDwAAAFbMfsNOd5+7l10vX2JsJ7lgL+9zeZLLD6g7AACAg7TSExQAAAA8Jgg7AADAkIQdAABgSMIOAAAwJGEHAAAYkrADAAAMSdgBAACGJOwAAABDEnYAAIAhCTsAAMCQhB0AAGBIwg4AADAkYQcAABiSsAMAAAxJ2AEAAIYk7AAAAEMSdgAAgCEJOwAAwJCEHQAAYEjCDgAAMCRhBwAAGJKwAwAADEnYAQAAhiTsAAAAQxJ2AACAIQk7AADAkIQdAABgSMIOAAAwJGEHAAAYkrADAAAMSdgBAACGJOwAAABDEnYAAIAhCTsAAMCQDinsVNWdVXVTVd1QVdun2jFVdU1V3T69Hj3Vq6reU1U7qurGqjplJU4AAABgKStxZeefd/fJ3b152r4oyWe7e1OSz07bSXJWkk3TsjXJ+1bgswEAAJZ0OG5j25Lkimn9iiRnz9Q/1Iu+nOSoqjruMHw+AADAIYedTvLfq+orVbV1qh3b3fckyfT6zKm+IcldM8funGoAAAArbv0hHv+S7r67qp6Z5Jqq+ot9jK0lav2oQYuhaWuSPOtZzzrE9gAAgLXqkK7sdPfd0+t9Sf44yalJ7t19e9r0et80fGeSE2YOPz7J3Uu856Xdvbm7Ny8sLBxKewAAwBp20GGnqp5SVU/bvZ7k9CQ3J9mW5Lxp2HlJPjGtb0vy2mlWttOSfG/37W4AAAAr7VBuYzs2yR9X1e73+a/d/d+q6vokV1XV+Um+meTV0/irk7wiyY4kP0ryukP4bAAAgH066LDT3XckecES9e8kefkS9U5ywcF+HgAAwIE4HFNPAwAAzJ2wAwAADEnYAQAAhiTsAAAAQxJ2AACAIQk7AADAkIQdAABgSMIOAAAwJGEHAAAYkrADAAAMSdgBAACGJOwAAABDEnYAAIAhCTsAAMCQhB0AAGBIwg4AADAkYQcAABiSsAMAAAxJ2AEAAIYk7AAAAEMSdgAAgCEJOwAAwJCEHQAAYEjCDgAAMCRhBwAAGJKwAwAADEnYAQAAhiTsAAAAQxJ2AACAIQk7AADAkIQdAABgSMIOAAAwJGEHAAAYkrADAAAMadXDTlWdWVW3VdWOqrpotT8fAABYG1Y17FTVuiTvTXJWkpOSnFtVJ61mDwAAwNqw2ld2Tk2yo7vv6O6/SXJlki2r3AMAALAGrHbY2ZDkrpntnVMNAABgRVV3r96HVb06yRnd/cvT9muSnNrdb5wZszXJ1mnzHyS5bdUaHMszknx73k2wZvi+sZp831hNvm+sNt+5g/Oz3b2wZ3H9KjexM8kJM9vHJ7l7dkB3X5rk0tVsakRVtb27N8+7D9YG3zdWk+8bq8n3jdXmO7eyVvs2tuuTbKqqE6vqiCTnJNm2yj0AAABrwKpe2enuh6rqwiSfSbIuyeXdfctq9gAAAKwNq30bW7r76iRXr/bnrkFuBWQ1+b6xmnzfWE2+b6w237kVtKoTFAAAAKyW1X5mBwAAYFUIOwAAwJCEHQAAYEjCziCq6glV9YRp/YiqOqWqjpl3XwDweFdV66rql+bdB2Opqi1VdcHM9rVVdce0vGqevY1E2BlAVZ2d5J4k36qqLUm+mORdSW6sqn851+YYTlV9tar+Q1U9e969MD5/DLCaqurpVfXWqvr9qjq9Fr0xyR1J/vW8+2M4v5FH/t7kkUlelOSlSf7tPBoa0apPPc1hcXGSFyR5cpKvJXlRd99WVT+b5ONJ/nSezTGco5McleTzVfVXST6S5KPdffd822JQv5HFH6DebfcfA09J8oEkH5tHUwzrD5M8kOT/JPnlJL+e5IgkW7r7hnk2xpCO6O67Zra/1N3fSfKdqnrKvJoajbAziO7+qySpqm92921T7S9339oGK+iB7v61JL9WVf80yblJvlpVtyb5SHf7fQBWkj8GWE1/r7ufnyRV9Z+TfDvJs7r7B/Nti0EdPbvR3RfObC6sci/D8ofwIGZCzb+Zqa3L4v9IwWHR3V/s7n+XZEOS307yj+fcEuPxxwCr6ce7V7r74STfEHQ4jK6tql/Zs1hVr09y3Rz6GZIfFR1AVb0oyU3d/dd71Dcm+Sfd/V/m0Rdjqqoru/uc/Y+EQ1dVH07yhe5+/x711yd5aXefO5/OGFFVPZzk/+3ezOLt4T+a1ru7nz6v3hhPVT0zyZ8keTDJV6fyz2fxdt2zu/veefU2EmEHgMcsfwwAo6uqlyV53rR5S3d/bp79jEbYGVxVfbq7z5p3H6wNVfW67v7AvPtgPP4YAOBgCDsDqKpT9rYrySe7+7jV7Ie1a5og41nz7oNxVNWTkrwhyXOS3JTksu5+aL5dAfB4YTa2MVyf5H9mMdzs6ahV7oXBVdWNe9uV5NjV7IU14YosPjT+xSRnJXlukjfPtSMAHjeEnTHcmuT13X37njuq6q4lxsOhODbJGVn8LYpZleR/r347DO6kmamAL4sZigA4AMLOGN6WvU8j/sZV7IO14ZNJnrrUD+xV1RdWvx0GNzsV8ENVS13ABoCleWYHgMcsUwEDcCj8qOigquqT8+6BtaOqts67B8bU3eu6++nT8rTuXj+zLugAsE/Czrg2zLsB1pQ3zLsBAIA9CTvj+vN5N8Ca4kEKAOAxxzM7wCGrquO7e+e8+wAAmOXKzgCqaktVXTCzfW1V3TEtr5pnb4ynqn61qs6frXX3zqp6Y1X5/RMA4DHDlZ0BVNX/SnJOd981bd+Q5OVJnpLkA9398nn2x1iq6uYkp3T33+xRPzLJ9d39j+bTGQDAI/mdnTEcsTvoTL7U3d9J8p2qesq8mmJYvWfQmYoPlh9BAQAeQ9zGNoajZze6+8KZzYVV7oU1oKqOXU4NAGCehJ0xXFtVv7Jnsapen+S6OfTD2H4nyaeq6p9V1dOm5aVJ/jTJu+bbGgDAT3lmZwBV9cwkf5LkwSRfnco/n+TIJGd3973z6o0xVdVZSS5K8nNT6eYk7+zuT8+vKwCARxJ2BlJVL0vyvGnzlu7+3Dz7AQCAeTJBwQCq6klZ/AX75yS5Kcll3f3QfLtiVFX1H/exu7v7HavWDADAPriyM4Cq+miSHyf5YpKzktzZ3X7vhMOiqt6yRPkpSc5P8jPd/dRVbgkAYEnCzgCq6qbufv60vj7Jdd19ypzbYg2oqqcleVMWg85VSX63u++bb1cAAIvcxjaGH+9e6e6H/NQJh1tVHZPkV5P8UpIrsvgjow/MtysAgEcSdsbwgqr6/rReSZ48bVcWn6F4+vxaYzRV9TtJ/lWSS5M8v7t/OOeWAACW5DY24IBU1d9mcZrzh5LM/gMiXAMAjynCDgAAMKQnzLsBAACAw0HYAQAAhiTsAAAAQxJ2AACAIQk7AADAkP4/ENr8wpSUfnEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['film_rating'].value_counts().plot(kind='bar', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Does the film replacement cost vary depending on film rating?\n",
    "\n",
    "In the United States, film classification is a voluntary process with the ratings issued by the Motion Picture Association of America (MPAA) via the Classification and Rating Administration (CARA).\n",
    "\n",
    "- G (General Audiences): All Ages are Admitted.\n",
    "- PG (Parental Guidance Suggested): Some Material May Not Be Suitable for Children.\n",
    "- PG-13 (Parents Strongly Cautioned): Some Material May Be Inappropriate for Children Under 13.\n",
    "- R (Restricted): Under 17 Requires Accompanying Parent or Adult Guardian.\n",
    "- NC-17 (Adults Only): No One 17 and Under Admitted.\n",
    "\n",
    "Show a <b>grouped box plot</b> per film rating with the film replacement costs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c262a99c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df[['film_replacement_cost', \"film_rating\"]].boxplot(by='film_rating', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Add and calculate a new `rental_days` column\n",
    "\n",
    "This numeric column should have the count of days between `rental_date` and `return_date`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rental_id\n",
       "1    1.0\n",
       "2    3.0\n",
       "3    7.0\n",
       "4    9.0\n",
       "5    8.0\n",
       "Name: rental_days, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['rental_days'] = df[['rental_date', 'return_date']].apply(lambda x: (x[1]-x[0]).days, axis=1)\n",
    "df['rental_days'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Analyze the distribution of `rental_days`\n",
    "\n",
    "- Calculate the mean of `rental_days`.\n",
    "- Show a <b>density (KDE)</b> of `rental_days`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.525944139713763"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['rental_days'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x23c267da748>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "ax = df['rental_days'].plot(kind='density', figsize=(14,6))\n",
    "ax.axvline(df['rental_days'].mean(), color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Add and calculate a new `film_daily_rental_rate` column\n",
    "\n",
    "This value should be the division of `film_rental_rate` by `film_rental_duration`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rental_id\n",
       "1    0.427143\n",
       "2    0.427143\n",
       "3    0.427143\n",
       "4    0.165000\n",
       "5    0.598000\n",
       "Name: film_daily_rental_rate, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['film_daily_rental_rate'] = df['film_rental_rate']/df['film_rental_duration']\n",
    "df['film_daily_rental_rate'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### Analyze the distribution of `film_daily_rental_rate`\n",
    "\n",
    "- Calculate the mean of `film_daily_rental_rate`.\n",
    "- Show a <b>density (KDE)</b> of `film_daily_rental_rate`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6458262471655172"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df['film_daily_rental_rate'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x23c26b90cc8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code goes here\n",
    "ax = df['film_daily_rental_rate'].plot(kind='density', figsize=(14,6))\n",
    "ax.axvline(df['film_daily_rental_rate'].mean(), color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### List 10 films with the lowest daily rental rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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_date</th>\n",
       "      <th>return_date</th>\n",
       "      <th>customer_lastname</th>\n",
       "      <th>store_id</th>\n",
       "      <th>rental_store_city</th>\n",
       "      <th>film_title</th>\n",
       "      <th>film_rental_duration</th>\n",
       "      <th>film_rental_rate</th>\n",
       "      <th>film_replacement_cost</th>\n",
       "      <th>film_rating</th>\n",
       "      <th>rental_days</th>\n",
       "      <th>film_daily_rental_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rental_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2005-05-25 01:10:47</td>\n",
       "      <td>2005-05-31 06:35:47</td>\n",
       "      <td>MARTINEZ</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>ROMAN PUNK</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>28.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2005-05-25 04:44:31</td>\n",
       "      <td>2005-05-29 01:03:31</td>\n",
       "      <td>ELROD</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>BORROWERS BEDAZZLED</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>22.99</td>\n",
       "      <td>G</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2005-05-25 06:20:46</td>\n",
       "      <td>2005-06-02 05:42:46</td>\n",
       "      <td>CASTRO</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>GUN BONNIE</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>27.99</td>\n",
       "      <td>G</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2005-05-25 11:09:48</td>\n",
       "      <td>2005-05-26 12:23:48</td>\n",
       "      <td>TERRY</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>GREEDY ROOTS</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>14.99</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2005-05-25 11:30:37</td>\n",
       "      <td>2005-06-03 12:00:37</td>\n",
       "      <td>SMITH</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>PATIENT SISTER</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>29.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2005-05-25 13:52:43</td>\n",
       "      <td>2005-05-29 11:12:43</td>\n",
       "      <td>ROBERT</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>GANDHI KWAI</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>9.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2005-05-25 19:30:46</td>\n",
       "      <td>2005-05-31 23:59:46</td>\n",
       "      <td>MILLER</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>VALENTINE VANISHING</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>9.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2005-05-25 21:48:30</td>\n",
       "      <td>2005-05-30 00:26:30</td>\n",
       "      <td>GILBERT</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>BORROWERS BEDAZZLED</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>22.99</td>\n",
       "      <td>G</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2005-05-26 00:25:23</td>\n",
       "      <td>2005-06-01 19:29:23</td>\n",
       "      <td>BURNS</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>UNFORGIVEN ZOOLANDER</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>15.99</td>\n",
       "      <td>PG</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2005-05-26 01:27:11</td>\n",
       "      <td>2005-06-03 00:30:11</td>\n",
       "      <td>NGO</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>LIGHTS DEER</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>R</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.141429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rental_date         return_date customer_lastname  store_id  \\\n",
       "rental_id                                                                       \n",
       "18        2005-05-25 01:10:47 2005-05-31 06:35:47          MARTINEZ         1   \n",
       "37        2005-05-25 04:44:31 2005-05-29 01:03:31             ELROD         2   \n",
       "48        2005-05-25 06:20:46 2005-06-02 05:42:46            CASTRO         1   \n",
       "74        2005-05-25 11:09:48 2005-05-26 12:23:48             TERRY         2   \n",
       "76        2005-05-25 11:30:37 2005-06-03 12:00:37             SMITH         2   \n",
       "87        2005-05-25 13:52:43 2005-05-29 11:12:43            ROBERT         2   \n",
       "117       2005-05-25 19:30:46 2005-05-31 23:59:46            MILLER         2   \n",
       "133       2005-05-25 21:48:30 2005-05-30 00:26:30           GILBERT         1   \n",
       "148       2005-05-26 00:25:23 2005-06-01 19:29:23             BURNS         2   \n",
       "158       2005-05-26 01:27:11 2005-06-03 00:30:11               NGO         1   \n",
       "\n",
       "          rental_store_city            film_title  film_rental_duration  \\\n",
       "rental_id                                                                 \n",
       "18               Lethbridge            ROMAN PUNK                     7   \n",
       "37                Woodridge   BORROWERS BEDAZZLED                     7   \n",
       "48               Lethbridge            GUN BONNIE                     7   \n",
       "74                Woodridge          GREEDY ROOTS                     7   \n",
       "76                Woodridge        PATIENT SISTER                     7   \n",
       "87                Woodridge           GANDHI KWAI                     7   \n",
       "117               Woodridge   VALENTINE VANISHING                     7   \n",
       "133              Lethbridge   BORROWERS BEDAZZLED                     7   \n",
       "148               Woodridge  UNFORGIVEN ZOOLANDER                     7   \n",
       "158              Lethbridge           LIGHTS DEER                     7   \n",
       "\n",
       "           film_rental_rate  film_replacement_cost film_rating  rental_days  \\\n",
       "rental_id                                                                     \n",
       "18                     0.99                  28.99       NC-17          6.0   \n",
       "37                     0.99                  22.99           G          3.0   \n",
       "48                     0.99                  27.99           G          7.0   \n",
       "74                     0.99                  14.99           R          1.0   \n",
       "76                     0.99                  29.99       NC-17          9.0   \n",
       "87                     0.99                   9.99       PG-13          3.0   \n",
       "117                    0.99                   9.99       PG-13          6.0   \n",
       "133                    0.99                  22.99           G          4.0   \n",
       "148                    0.99                  15.99          PG          6.0   \n",
       "158                    0.99                  21.99           R          7.0   \n",
       "\n",
       "           film_daily_rental_rate  \n",
       "rental_id                          \n",
       "18                       0.141429  \n",
       "37                       0.141429  \n",
       "48                       0.141429  \n",
       "74                       0.141429  \n",
       "76                       0.141429  \n",
       "87                       0.141429  \n",
       "117                      0.141429  \n",
       "133                      0.141429  \n",
       "148                      0.141429  \n",
       "158                      0.141429  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.loc[df['film_daily_rental_rate']==df['film_daily_rental_rate'].min()].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### List 10 films with the highest daily rental rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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_date</th>\n",
       "      <th>return_date</th>\n",
       "      <th>customer_lastname</th>\n",
       "      <th>store_id</th>\n",
       "      <th>rental_store_city</th>\n",
       "      <th>film_title</th>\n",
       "      <th>film_rental_duration</th>\n",
       "      <th>film_rental_rate</th>\n",
       "      <th>film_replacement_cost</th>\n",
       "      <th>film_rating</th>\n",
       "      <th>rental_days</th>\n",
       "      <th>film_daily_rental_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rental_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2005-05-25 00:22:55</td>\n",
       "      <td>2005-05-30 04:28:55</td>\n",
       "      <td>MCWHORTER</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>KING EVOLUTION</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>24.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2005-05-25 05:09:04</td>\n",
       "      <td>2005-05-27 23:12:04</td>\n",
       "      <td>YEE</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>MINDS TRUMAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>22.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2005-05-25 09:47:31</td>\n",
       "      <td>2005-05-31 10:20:31</td>\n",
       "      <td>ORTIZ</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>TEEN APOLLO</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>25.99</td>\n",
       "      <td>G</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2005-05-25 18:18:19</td>\n",
       "      <td>2005-06-04 00:01:19</td>\n",
       "      <td>AUSTIN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>SHOW LORD</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>24.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2005-05-25 19:27:51</td>\n",
       "      <td>2005-05-26 16:23:51</td>\n",
       "      <td>GARCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>WIFE TURN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>27.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2005-05-25 20:46:11</td>\n",
       "      <td>2005-05-30 00:47:11</td>\n",
       "      <td>MENDOZA</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>BACKLASH UNDEFEATED</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>24.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2005-05-25 21:58:58</td>\n",
       "      <td>2005-06-03 17:50:58</td>\n",
       "      <td>ROYAL</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>AMERICAN CIRCUS</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>17.99</td>\n",
       "      <td>R</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2005-05-26 00:41:10</td>\n",
       "      <td>2005-06-03 06:05:10</td>\n",
       "      <td>MORGAN</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>MIDSUMMER GROUNDHOG</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>27.99</td>\n",
       "      <td>G</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2005-05-26 01:15:05</td>\n",
       "      <td>2005-06-01 00:03:05</td>\n",
       "      <td>BARBEE</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>BEHAVIOR RUNAWAY</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>20.99</td>\n",
       "      <td>PG</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2005-05-26 02:26:23</td>\n",
       "      <td>2005-06-04 06:36:23</td>\n",
       "      <td>GRAHAM</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>KISSING DOLLS</td>\n",
       "      <td>3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>9.99</td>\n",
       "      <td>R</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.663333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rental_date         return_date customer_lastname  store_id  \\\n",
       "rental_id                                                                       \n",
       "13        2005-05-25 00:22:55 2005-05-30 04:28:55         MCWHORTER         1   \n",
       "40        2005-05-25 05:09:04 2005-05-27 23:12:04               YEE         1   \n",
       "68        2005-05-25 09:47:31 2005-05-31 10:20:31             ORTIZ         2   \n",
       "106       2005-05-25 18:18:19 2005-06-04 00:01:19            AUSTIN         2   \n",
       "116       2005-05-25 19:27:51 2005-05-26 16:23:51            GARCIA         1   \n",
       "124       2005-05-25 20:46:11 2005-05-30 00:47:11           MENDOZA         1   \n",
       "135       2005-05-25 21:58:58 2005-06-03 17:50:58             ROYAL         1   \n",
       "152       2005-05-26 00:41:10 2005-06-03 06:05:10            MORGAN         1   \n",
       "155       2005-05-26 01:15:05 2005-06-01 00:03:05            BARBEE         2   \n",
       "163       2005-05-26 02:26:23 2005-06-04 06:36:23            GRAHAM         1   \n",
       "\n",
       "          rental_store_city           film_title  film_rental_duration  \\\n",
       "rental_id                                                                \n",
       "13               Lethbridge       KING EVOLUTION                     3   \n",
       "40               Lethbridge         MINDS TRUMAN                     3   \n",
       "68                Woodridge          TEEN APOLLO                     3   \n",
       "106               Woodridge            SHOW LORD                     3   \n",
       "116              Lethbridge            WIFE TURN                     3   \n",
       "124              Lethbridge  BACKLASH UNDEFEATED                     3   \n",
       "135              Lethbridge      AMERICAN CIRCUS                     3   \n",
       "152              Lethbridge  MIDSUMMER GROUNDHOG                     3   \n",
       "155               Woodridge     BEHAVIOR RUNAWAY                     3   \n",
       "163              Lethbridge        KISSING DOLLS                     3   \n",
       "\n",
       "           film_rental_rate  film_replacement_cost film_rating  rental_days  \\\n",
       "rental_id                                                                     \n",
       "13                     4.99                  24.99       NC-17          5.0   \n",
       "40                     4.99                  22.99       PG-13          2.0   \n",
       "68                     4.99                  25.99           G          6.0   \n",
       "106                    4.99                  24.99       PG-13          9.0   \n",
       "116                    4.99                  27.99       NC-17          0.0   \n",
       "124                    4.99                  24.99       PG-13          4.0   \n",
       "135                    4.99                  17.99           R          8.0   \n",
       "152                    4.99                  27.99           G          8.0   \n",
       "155                    4.99                  20.99          PG          5.0   \n",
       "163                    4.99                   9.99           R          9.0   \n",
       "\n",
       "           film_daily_rental_rate  \n",
       "rental_id                          \n",
       "13                       1.663333  \n",
       "40                       1.663333  \n",
       "68                       1.663333  \n",
       "106                      1.663333  \n",
       "116                      1.663333  \n",
       "124                      1.663333  \n",
       "135                      1.663333  \n",
       "152                      1.663333  \n",
       "155                      1.663333  \n",
       "163                      1.663333  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.loc[df['film_daily_rental_rate']==df['film_daily_rental_rate'].max()].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### How many rentals were made in Lethbridge city?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7923"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.loc[df['rental_store_city'] == 'Lethbridge'].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### How many rentals of each film rating were made in Lethbridge city?\n",
    "\n",
    "Show a <b>bar plot</b> with each film rating count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PG-13    1849\n",
       "NC-17    1625\n",
       "R        1537\n",
       "PG       1535\n",
       "G        1377\n",
       "Name: film_rating, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.loc[df['rental_store_city']=='Lethbridge', 'film_rating'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "cell_type": "solution"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23c26c173c8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['rental_store_city'] =='Lethbridge', 'film_rating'].value_counts().plot(kind='bar', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### How many rentals were made in Woodridge city with rental duration higher than 5 days?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3186"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.loc[(df['rental_store_city']=='Woodridge') & (df['film_rental_duration']>5)].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "### How many rentals were made at the store with id 2 or with replacement cost lower than 10.99 USD?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16044"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# your code goes here\n",
    "df.loc[(df['store_id']==2) | df['film_replacement_cost']<10.99].shape[0]"
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