The_Sakila_Database_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "698659b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the open-sourced MySQL Sakila Database(DB) to Python, by importing the sqlite3 package.\n",
    "# Importing as well the other packages we will later need.\n",
    "\n",
    "import sqlite3\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mydb = sqlite3.connect('/Users/davidgomez/Desktop/Python Exploratory Analysis/sakila.db')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18a5f4b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection Successful\n"
     ]
    }
   ],
   "source": [
    "# Writing a function to verify the DB has been succesfully imported\n",
    "\n",
    "if mydb:\n",
    "    print(\"Connection Successful\")\n",
    "else:\n",
    "    print(\"Connection Unsuccessful\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3afbfc00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the desired Pandas dataframe by executing the following SQL query on the `mydb`\n",
    "# SQL database. Setting the index column, and parsing dates for better data handling.\n",
    "\n",
    "df = pd.read_sql_query('''\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",
    "''', mydb, index_col='rental_id', parse_dates=['rental_date', 'return_date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "263297a1",
   "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>store_id</th>\n",
       "      <th>film_rental_duration</th>\n",
       "      <th>film_rental_rate</th>\n",
       "      <th>film_replacement_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16044.000000</td>\n",
       "      <td>16044.00000</td>\n",
       "      <td>16044.000000</td>\n",
       "      <td>16044.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.506171</td>\n",
       "      <td>4.93549</td>\n",
       "      <td>2.942630</td>\n",
       "      <td>20.215443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499978</td>\n",
       "      <td>1.40169</td>\n",
       "      <td>1.649678</td>\n",
       "      <td>6.081771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>9.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>14.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>20.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>25.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>29.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           store_id  film_rental_duration  film_rental_rate  \\\n",
       "count  16044.000000           16044.00000      16044.000000   \n",
       "mean       1.506171               4.93549          2.942630   \n",
       "std        0.499978               1.40169          1.649678   \n",
       "min        1.000000               3.00000          0.990000   \n",
       "25%        1.000000               4.00000          0.990000   \n",
       "50%        2.000000               5.00000          2.990000   \n",
       "75%        2.000000               6.00000          4.990000   \n",
       "max        2.000000               7.00000          4.990000   \n",
       "\n",
       "       film_replacement_cost  \n",
       "count           16044.000000  \n",
       "mean               20.215443  \n",
       "std                 6.081771  \n",
       "min                 9.990000  \n",
       "25%                14.990000  \n",
       "50%                20.990000  \n",
       "75%                25.990000  \n",
       "max                29.990000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Once the Database has been successfully imported and the Dataframe (df) has been created, \n",
    "# let's now explore the Dataframe.\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f81e4c5",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7375dbe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16044, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b46da604",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Dataframe contains 16044 rows and 10 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b39bdea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16044 entries, 1 to 16049\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   rental_date            16044 non-null  datetime64[ns]\n",
      " 1   return_date            15861 non-null  datetime64[ns]\n",
      " 2   customer_lastname      16044 non-null  object        \n",
      " 3   store_id               16044 non-null  int64         \n",
      " 4   rental_store_city      16044 non-null  object        \n",
      " 5   film_title             16044 non-null  object        \n",
      " 6   film_rental_duration   16044 non-null  int64         \n",
      " 7   film_rental_rate       16044 non-null  float64       \n",
      " 8   film_replacement_cost  16044 non-null  float64       \n",
      " 9   film_rating            16044 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), int64(2), object(4)\n",
      "memory usage: 1.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rental_date                0\n",
       "return_date              183\n",
       "customer_lastname          0\n",
       "store_id                   0\n",
       "rental_store_city          0\n",
       "film_title                 0\n",
       "film_rental_duration       0\n",
       "film_rental_rate           0\n",
       "film_replacement_cost      0\n",
       "film_rating                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Cleaning. First, I will check for null values in our DataFrame and will drop them.\n",
    "\n",
    "df.info()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6692db77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have 183 missing records of `return_dates`, which may be due to unreturned movies\n",
    "# at the time the DataBase was imported.\n",
    "# In this case, dropping the NaN values (blank return_date values) does not seem viable, as we need those\n",
    "# records to ensure the rentals are actually returned. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5cd5e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# However, below I will comment out the code for how I would do it,\n",
    "# if this were to be done.\n",
    "'''\n",
    "df = df.dropna() #Changes are mostly inmutable in Pandas, so to make them permanent, I would need to modify the\n",
    "#DataFrame df.\n",
    "df.info() #The records would have been reduced to 15,861, after dropping the 183 missing records. '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "045ee628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.94"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's now do the requested Exploratory Analysis of the df DataFrame.\n",
    "\n",
    "# Question #1. What is the mean of rental duration for all films?\n",
    "\n",
    "round(df['film_rental_duration'].mean(),2)\n",
    "\n",
    "# Answer: The films are rented for an average of 4.94 days before they are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef783288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "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": [
    "# Question #2. What is the most common rental duration?\n",
    "\n",
    "df['film_rental_duration'].value_counts()\n",
    "df['film_rental_duration'].value_counts().plot(kind= 'bar', figsize=(14,6))\n",
    "\n",
    "# Answer: The most common rental duration is 3 days, with 3,412 rentals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5e2c85f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='film_rental_rate'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "# Question #3. What is the most common rental rate?\n",
    "\n",
    "df['film_rental_rate'].value_counts()\n",
    "df['film_rental_rate'].value_counts().plot(kind= 'pie', figsize=(6,6))\n",
    "\n",
    "# Answer: The most common rental rate is 0.99 USD, with 5,652 titles renting at that price.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "916bbcf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f800b1e8a00>"
      ]
     },
     "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": [
    "# Question #4. How is the replacement cost distributed?\n",
    "\n",
    "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')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6b425971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Question #5. How many films of each rating does the store have?\n",
    "\n",
    "df['film_rating'].value_counts()\n",
    "df['film_rating'].value_counts().plot(kind= 'bar', figsize=(14,6))\n",
    "\n",
    "# Answer: The DVD rental store has 3,585 PG-13 titles, 3,293 NC-17 films, 3,212 PG, 3,181 R, and 2,773 G-rated movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35a1a7b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'film_replacement_cost'}, xlabel='[film_rating]'>"
      ]
     },
     "execution_count": 18,
     "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": [
    "# Question #6. Does the film replacement cost vary depending on film rating?\n",
    "\n",
    "df[['film_replacement_cost', 'film_rating']].boxplot(by='film_rating', figsize=(14,6))\n",
    "\n",
    "# Answer: By analyzing the box chart, we can conclude that yes, film\n",
    "# replacement cost varies across film ratings. In fact, this is highest for PG-13 movies\n",
    "# and lowest for PG-rated titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3fab998",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question #7. Give us the rental period in days.\n",
    "\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": "code",
   "execution_count": 20,
   "id": "c1d6183d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f800cc72580>"
      ]
     },
     "execution_count": 20,
     "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": [
    "# Question #8. How are the rental days distributed?\n",
    "\n",
    "ax = df['rental_days'].plot(kind= 'density', figsize=(14,6))\n",
    "ax.axvline(df['rental_days'].mean(), color = 'red')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f732213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rental_id\n",
       "14763    1.663333\n",
       "Name: daily_rental_rate, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question #9. What is the highest daily rental rate for the films?\n",
    "\n",
    "df['daily_rental_rate'] = df['film_rental_rate'] / df['film_rental_duration']\n",
    "\n",
    "df ['daily_rental_rate'].sort_values(ascending=False).head(1)\n",
    "\n",
    "# The highest daily rental rate is 1.66 USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1c6c62fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rental_id\n",
      "18             ROMAN PUNK\n",
      "37    BORROWERS BEDAZZLED\n",
      "48             GUN BONNIE\n",
      "74           GREEDY ROOTS\n",
      "76         PATIENT SISTER\n",
      "Name: film_title, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Question #10. What are the titles of the 5 films with the lowest daily rental rate?\n",
    "\n",
    "lowest = df.loc[df['daily_rental_rate'] == df['daily_rental_rate'].min()].head(5)\n",
    "print(lowest['film_title'])\n",
    "\n",
    "# Answer: The top 5 titles with the lowest daily rental are  as follows: Roman Punk,\n",
    "# Borrowers Bedazzled, Gun Bonnie, Greedy Roots, and Patient Sister.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8663d357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rental_id\n",
      "13    KING EVOLUTION\n",
      "40      MINDS TRUMAN\n",
      "68       TEEN APOLLO\n",
      "Name: film_title, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Question #11. What are the titles of the 3 films with the highest daily rental rate?\n",
    "\n",
    "highest = df.loc[df['daily_rental_rate'] == df['daily_rental_rate'].max()].head(3)\n",
    "print(highest['film_title'])\n",
    "\n",
    "# Answer: The top 3 titles with the highest daily rental are as follows: King Evolution,\n",
    "# Minds Truman, Teen Apollo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d5d9367a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7923"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question #12. How many rentals were made in Lethbridge city?\n",
    "\n",
    "df.loc[df['rental_store_city'] == 'Lethbridge'].shape[0]\n",
    "\n",
    "# Answer: 7,923 rentals were made in Lethbridge city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad1b19b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3186"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question #13. How many rentals were made in Woodridge city with a rental duration higher than 5 days?\n",
    "\n",
    "df.loc[(df['rental_store_city'] == 'Woodridge') & (df['film_rental_duration'] > 5)].shape[0]\n",
    "\n",
    "# Answer: 3,186 films with a rental duration of more than 5 days were rented in Woodridge.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7352920b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8444"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question #14. How many rentals were made at the store with id 2 or with a replacement cost lower than 10.99 USD?\n",
    "\n",
    "df.loc[(df['store_id'] == 2) | (df['film_replacement_cost'] < 10.99)].shape[0]\n",
    "\n",
    "# Answer: 8,444 rentals that either have a replacement cost lower than 10.99 USD or were rented from store_id 2."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}