data_wrangling/pandas/data_wrangling_transformations_movie.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mini-Project: Data Wrangling and Transformation with Pandas\n",
    "\n",
    "Working with tabular data is a necessity for anyone with enterprises having a majority of their data in relational databases and flat files. This mini-project is adopted from the excellent tutorial on pandas by Brandon Rhodes which you have watched earlier in the Data Wrangling Unit. In this mini-project, we will be looking at some interesting data based on movie data from the IMDB.\n",
    "\n",
    "This assignment should help you reinforce the concepts you learnt in the curriculum for Data Wrangling and sharpen your skills in using Pandas. Good Luck!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Please make sure you have one of the more recent versions of Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (0.24.2)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from pandas) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from pandas) (2019.1)\n",
      "Requirement already satisfied: numpy>=1.12.0 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from pandas) (1.15.4)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (0.9.0)\n",
      "Requirement already satisfied: matplotlib>=1.4.3 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from seaborn) (3.0.3)\n",
      "Requirement already satisfied: numpy>=1.9.3 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from seaborn) (1.15.4)\n",
      "Requirement already satisfied: scipy>=0.14.0 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from seaborn) (1.1.0)\n",
      "Requirement already satisfied: pandas>=0.15.2 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from seaborn) (0.24.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from matplotlib>=1.4.3->seaborn) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from matplotlib>=1.4.3->seaborn) (2.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from matplotlib>=1.4.3->seaborn) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from pandas>=0.15.2->seaborn) (2019.1)\n",
      "Requirement already satisfied: six in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from cycler>=0.10->matplotlib>=1.4.3->seaborn) (1.12.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\ramand\\appdata\\local\\continuum\\anaconda3\\envs\\tf-gpu\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn) (41.0.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn; seaborn.set()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.24.2'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking a look at the Movies dataset\n",
    "This data shows the movies based on their title and the year of release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244914 entries, 0 to 244913\n",
      "Data columns (total 2 columns):\n",
      "title    244914 non-null object\n",
      "year     244914 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "movies = pd.read_csv('data/titles.csv.bz2', compression='bz2')\n",
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Ticket to the Life</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Parallel Worlds: A New Rock Music Experience</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morita - La hija de Jesus</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gun</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Love or Nothing at All</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          title  year\n",
       "0                        The Ticket to the Life  2009\n",
       "1  Parallel Worlds: A New Rock Music Experience  2016\n",
       "2                     Morita - La hija de Jesus  2008\n",
       "3                                           Gun  2017\n",
       "4                        Love or Nothing at All  2014"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking a look at the Cast dataset\n",
    "\n",
    "This data shows the cast (actors, actresses, supporting roles) for each movie\n",
    "\n",
    "- The attribute `n` basically tells the importance of the cast role, lower the number, more important the role.\n",
    "- Supporting cast usually don't have any value for `n`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3786176 entries, 0 to 3786175\n",
      "Data columns (total 6 columns):\n",
      "title        object\n",
      "year         int64\n",
      "name         object\n",
      "type         object\n",
      "character    object\n",
      "n            float64\n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 173.3+ MB\n"
     ]
    }
   ],
   "source": [
    "cast = pd.read_csv('data/cast.csv.bz2', compression='bz2')\n",
    "cast.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Closet Monster</td>\n",
       "      <td>2015</td>\n",
       "      <td>Buffy #1</td>\n",
       "      <td>actor</td>\n",
       "      <td>Buffy 4</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Suuri illusioni</td>\n",
       "      <td>1985</td>\n",
       "      <td>Homo $</td>\n",
       "      <td>actor</td>\n",
       "      <td>Guests</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Battle of the Sexes</td>\n",
       "      <td>2017</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Bobby Riggs Fan</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Secret in Their Eyes</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>2002 Dodger Fan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>1988 Opera House Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Straight Outta Compton</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Club Patron</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Straight Outta Compton</td>\n",
       "      <td>2015</td>\n",
       "      <td>$hutter</td>\n",
       "      <td>actor</td>\n",
       "      <td>Dopeman</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>For Thy Love 2</td>\n",
       "      <td>2009</td>\n",
       "      <td>Bee Moe $lim</td>\n",
       "      <td>actor</td>\n",
       "      <td>Thug 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lapis, Ballpen at Diploma, a True to Life Journey</td>\n",
       "      <td>2014</td>\n",
       "      <td>Jori ' Danilo' Jurado Jr.</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jaime (young)</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Desire (III)</td>\n",
       "      <td>2014</td>\n",
       "      <td>Syaiful 'Ariffin</td>\n",
       "      <td>actor</td>\n",
       "      <td>Actor Playing Eteocles from 'Antigone'</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  year  \\\n",
       "0                                     Closet Monster  2015   \n",
       "1                                    Suuri illusioni  1985   \n",
       "2                                Battle of the Sexes  2017   \n",
       "3                               Secret in Their Eyes  2015   \n",
       "4                                         Steve Jobs  2015   \n",
       "5                             Straight Outta Compton  2015   \n",
       "6                             Straight Outta Compton  2015   \n",
       "7                                     For Thy Love 2  2009   \n",
       "8  Lapis, Ballpen at Diploma, a True to Life Journey  2014   \n",
       "9                                       Desire (III)  2014   \n",
       "\n",
       "                        name   type                               character  \\\n",
       "0                   Buffy #1  actor                                 Buffy 4   \n",
       "1                     Homo $  actor                                  Guests   \n",
       "2                    $hutter  actor                         Bobby Riggs Fan   \n",
       "3                    $hutter  actor                         2002 Dodger Fan   \n",
       "4                    $hutter  actor                 1988 Opera House Patron   \n",
       "5                    $hutter  actor                             Club Patron   \n",
       "6                    $hutter  actor                                 Dopeman   \n",
       "7               Bee Moe $lim  actor                                  Thug 1   \n",
       "8  Jori ' Danilo' Jurado Jr.  actor                           Jaime (young)   \n",
       "9           Syaiful 'Ariffin  actor  Actor Playing Eteocles from 'Antigone'   \n",
       "\n",
       "      n  \n",
       "0  31.0  \n",
       "1  22.0  \n",
       "2  10.0  \n",
       "3   NaN  \n",
       "4   NaN  \n",
       "5   NaN  \n",
       "6   NaN  \n",
       "7   NaN  \n",
       "8   9.0  \n",
       "9   NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking a look at the Release dataset\n",
    "\n",
    "This data shows details of when each movie was release in each country with the release date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 479488 entries, 0 to 479487\n",
      "Data columns (total 4 columns):\n",
      "title      479488 non-null object\n",
      "year       479488 non-null int64\n",
      "country    479488 non-null object\n",
      "date       479488 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(1), object(2)\n",
      "memory usage: 14.6+ MB\n"
     ]
    }
   ],
   "source": [
    "release_dates = pd.read_csv('data/release_dates.csv.bz2', compression='bz2', parse_dates=['date'], infer_datetime_format=True)\n",
    "release_dates.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>#73, Shaanthi Nivaasa</td>\n",
       "      <td>2007</td>\n",
       "      <td>India</td>\n",
       "      <td>2007-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#BKKY</td>\n",
       "      <td>2016</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>2017-10-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#Beings</td>\n",
       "      <td>2015</td>\n",
       "      <td>Romania</td>\n",
       "      <td>2015-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#Captured</td>\n",
       "      <td>2017</td>\n",
       "      <td>USA</td>\n",
       "      <td>2017-09-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#Ewankosau saranghaeyo</td>\n",
       "      <td>2015</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2015-01-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title  year      country       date\n",
       "0   #73, Shaanthi Nivaasa  2007        India 2007-06-15\n",
       "1                   #BKKY  2016     Cambodia 2017-10-12\n",
       "2                 #Beings  2015      Romania 2015-01-29\n",
       "3               #Captured  2017          USA 2017-09-05\n",
       "4  #Ewankosau saranghaeyo  2015  Philippines 2015-01-21"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "release_dates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section I - Basic Querying, Filtering and Transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the total number of movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "244914"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Batman movies ever made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Batman Movies: 2\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>150621</th>\n",
       "      <td>Batman</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         title  year\n",
       "52734   Batman  1943\n",
       "150621  Batman  1989"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batman_df = movies[movies.title == 'Batman']\n",
    "print('Total Batman Movies:', len(batman_df))\n",
    "batman_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Batman movies ever made - the right approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Batman Movies: 35\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16813</th>\n",
       "      <td>Batman: Anarchy</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30236</th>\n",
       "      <td>Batman Forever</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31674</th>\n",
       "      <td>Batman Untold</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31711</th>\n",
       "      <td>Scooby-Doo &amp; Batman: the Brave and the Bold</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41881</th>\n",
       "      <td>Batman the Rise of Red Hood</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43484</th>\n",
       "      <td>Batman: Return of the Caped Crusaders</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46333</th>\n",
       "      <td>Batman &amp; Robin</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51811</th>\n",
       "      <td>Batman Revealed</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52734</th>\n",
       "      <td>Batman</td>\n",
       "      <td>1943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56029</th>\n",
       "      <td>Batman Beyond: Rising Knight</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             title  year\n",
       "16813                              Batman: Anarchy  2016\n",
       "30236                               Batman Forever  1995\n",
       "31674                                Batman Untold  2010\n",
       "31711  Scooby-Doo & Batman: the Brave and the Bold  2018\n",
       "41881                  Batman the Rise of Red Hood  2018\n",
       "43484        Batman: Return of the Caped Crusaders  2016\n",
       "46333                               Batman & Robin  1997\n",
       "51811                              Batman Revealed  2012\n",
       "52734                                       Batman  1943\n",
       "56029                 Batman Beyond: Rising Knight  2014"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batman_df = movies[movies.title.str.contains('Batman', case=False)]\n",
    "print('Total Batman Movies:', len(batman_df))\n",
    "batman_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the top 15 Batman movies in the order they were released"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52734</th>\n",
       "      <td>Batman</td>\n",
       "      <td>1943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100056</th>\n",
       "      <td>Batman and Robin</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161439</th>\n",
       "      <td>Batman Dracula</td>\n",
       "      <td>1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84327</th>\n",
       "      <td>Alyas Batman at Robin</td>\n",
       "      <td>1965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68364</th>\n",
       "      <td>James Batman</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161527</th>\n",
       "      <td>Batman: The Movie</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56159</th>\n",
       "      <td>Batman Fights Dracula</td>\n",
       "      <td>1967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168504</th>\n",
       "      <td>Fight! Batman, Fight!</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150621</th>\n",
       "      <td>Batman</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156239</th>\n",
       "      <td>Alyas Batman en Robin</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156755</th>\n",
       "      <td>Batman Returns</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63366</th>\n",
       "      <td>Batman: Mask of the Phantasm</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30236</th>\n",
       "      <td>Batman Forever</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46333</th>\n",
       "      <td>Batman &amp; Robin</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208220</th>\n",
       "      <td>Batman Begins</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  year\n",
       "52734                         Batman  1943\n",
       "100056              Batman and Robin  1949\n",
       "161439                Batman Dracula  1964\n",
       "84327          Alyas Batman at Robin  1965\n",
       "68364                   James Batman  1966\n",
       "161527             Batman: The Movie  1966\n",
       "56159          Batman Fights Dracula  1967\n",
       "168504         Fight! Batman, Fight!  1973\n",
       "150621                        Batman  1989\n",
       "156239         Alyas Batman en Robin  1991\n",
       "156755                Batman Returns  1992\n",
       "63366   Batman: Mask of the Phantasm  1993\n",
       "30236                 Batman Forever  1995\n",
       "46333                 Batman & Robin  1997\n",
       "208220                 Batman Begins  2005"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batman_df.sort_values(by=['year'], ascending=True).iloc[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q1 : List all the 'Harry Potter' movies from the most recent to the earliest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143147</th>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 2</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152831</th>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109213</th>\n",
       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50581</th>\n",
       "      <td>Harry Potter and the Order of the Phoenix</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187926</th>\n",
       "      <td>Harry Potter and the Goblet of Fire</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61957</th>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82791</th>\n",
       "      <td>Harry Potter and the Chamber of Secrets</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223087</th>\n",
       "      <td>Harry Potter and the Sorcerer's Stone</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  year\n",
       "143147  Harry Potter and the Deathly Hallows: Part 2  2011\n",
       "152831  Harry Potter and the Deathly Hallows: Part 1  2010\n",
       "109213        Harry Potter and the Half-Blood Prince  2009\n",
       "50581      Harry Potter and the Order of the Phoenix  2007\n",
       "187926           Harry Potter and the Goblet of Fire  2005\n",
       "61957       Harry Potter and the Prisoner of Azkaban  2004\n",
       "82791        Harry Potter and the Chamber of Secrets  2002\n",
       "223087         Harry Potter and the Sorcerer's Stone  2001"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hp_df = movies[movies.title.str.contains('harry potter', case=False)]\n",
    "hp_df.sort_values(by=['year'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many movies were made in the year 2017?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11474"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies[movies.year == 2017])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q2 : How many movies were made in the year 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8702"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies[movies.year == 2015])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q3 : How many movies were made from 2000 till 2018?\n",
    "- You can chain multiple conditions using OR (`|`) as well as AND (`&`) depending on the condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114070"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2018 inclusive\n",
    "len(movies[(movies.year >= 2000) & (movies.year <= 2018)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106029"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2018 exclusive\n",
    "len(movies[(movies.year >= 2000) & (movies.year < 2018)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q4: How many movies are titled \"Hamlet\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies[movies.title == 'Hamlet'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q5: List all movies titled \"Hamlet\" \n",
    "- The movies should only have been released on or after the year 2000\n",
    "- Display the movies based on the year they were released (earliest to most recent)"
   ]
  },
  {
   "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>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55639</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227953</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178290</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186137</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191940</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244747</th>\n",
       "      <td>Hamlet</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         title  year\n",
       "55639   Hamlet  2000\n",
       "1931    Hamlet  2009\n",
       "227953  Hamlet  2011\n",
       "178290  Hamlet  2014\n",
       "186137  Hamlet  2015\n",
       "191940  Hamlet  2016\n",
       "244747  Hamlet  2017"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hamlet_df = movies[(movies.title == 'Hamlet') & (movies.year >= 2000)]\n",
    "hamlet_df.sort_values(by=['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q6: How many roles in the movie \"Inception\" are of the supporting cast (extra credits)\n",
    "- supporting cast are NOT ranked by an \"n\" value (NaN)\n",
    "- check for how to filter based on nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 27 supporting cast roles in Inception\n"
     ]
    }
   ],
   "source": [
    "ic_df = cast[cast.title == 'Inception']\n",
    "ic_df_support = ic_df[ic_df.n.isnull()]\n",
    "print(f\"There are {len(ic_df_support)} supporting cast roles in Inception\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q7: How many roles in the movie \"Inception\" are of the main cast\n",
    "- main cast always have an 'n' value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 51 main cast roles in Inception\n"
     ]
    }
   ],
   "source": [
    "ic_df_main = ic_df[~ic_df.n.isnull()]\n",
    "print(f\"There are {len(ic_df_main)} main cast roles in Inception\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q8: Show the top ten cast (actors\\actresses) in the movie \"Inception\" \n",
    "- support cast always have an 'n' value\n",
    "- remember to sort!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "      <th>3731263</th>\n",
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       "      <td>Shannon Welles</td>\n",
       "      <td>actress</td>\n",
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       "      <td>2010</td>\n",
       "      <td>Jack Gilroy</td>\n",
       "      <td>actor</td>\n",
       "      <td>Old Cobb</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250605</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jason Tendell</td>\n",
       "      <td>actor</td>\n",
       "      <td>Fischer's Driver</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3473041</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Lisa (II) Reynolds</td>\n",
       "      <td>actress</td>\n",
       "      <td>Private Nurse</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1812091</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Andrew Pleavin</td>\n",
       "      <td>actor</td>\n",
       "      <td>Businessman</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049179</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Felix Scott</td>\n",
       "      <td>actor</td>\n",
       "      <td>Businessman</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807795</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Michael Gaston</td>\n",
       "      <td>actor</td>\n",
       "      <td>Immigration Officer</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149008</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Peter Basham</td>\n",
       "      <td>actor</td>\n",
       "      <td>Fischer's Jet Captain</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444628</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Nicole Pulliam</td>\n",
       "      <td>actress</td>\n",
       "      <td>Lobby Sub Con</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3203564</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>Alex (II) Lombard</td>\n",
       "      <td>actress</td>\n",
       "      <td>Lobby Sub Con</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             title  year                name     type              character  \\\n",
       "3731263  Inception  2010      Shannon Welles  actress                Old Mal   \n",
       "833376   Inception  2010         Jack Gilroy    actor               Old Cobb   \n",
       "2250605  Inception  2010       Jason Tendell    actor       Fischer's Driver   \n",
       "3473041  Inception  2010  Lisa (II) Reynolds  actress          Private Nurse   \n",
       "1812091  Inception  2010      Andrew Pleavin    actor            Businessman   \n",
       "2049179  Inception  2010         Felix Scott    actor            Businessman   \n",
       "807795   Inception  2010      Michael Gaston    actor    Immigration Officer   \n",
       "149008   Inception  2010        Peter Basham    actor  Fischer's Jet Captain   \n",
       "3444628  Inception  2010      Nicole Pulliam  actress          Lobby Sub Con   \n",
       "3203564  Inception  2010   Alex (II) Lombard  actress          Lobby Sub Con   \n",
       "\n",
       "            n  \n",
       "3731263  51.0  \n",
       "833376   50.0  \n",
       "2250605  49.0  \n",
       "3473041  48.0  \n",
       "1812091  47.0  \n",
       "2049179  46.0  \n",
       "807795   45.0  \n",
       "149008   44.0  \n",
       "3444628  43.0  \n",
       "3203564  42.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic_df_main.sort_values(by=['n'], ascending=False).iloc[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q9:\n",
    "\n",
    "(A) List all movies where there was a character 'Albus Dumbledore' \n",
    "\n",
    "(B) Now modify the above to show only the actors who played the character 'Albus Dumbledore'\n",
    "- For Part (B) remember the same actor might play the same role in multiple movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>704984</th>\n",
       "      <td>Epic Movie</td>\n",
       "      <td>2007</td>\n",
       "      <td>Dane Farwell</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792419</th>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Michael Gambon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Professor Albus Dumbledore</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792420</th>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 2</td>\n",
       "      <td>2011</td>\n",
       "      <td>Michael Gambon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Professor Albus Dumbledore</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792421</th>\n",
       "      <td>Harry Potter and the Goblet of Fire</td>\n",
       "      <td>2005</td>\n",
       "      <td>Michael Gambon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792422</th>\n",
       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
       "      <td>2009</td>\n",
       "      <td>Michael Gambon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Professor Albus Dumbledore</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792423</th>\n",
       "      <td>Harry Potter and the Order of the Phoenix</td>\n",
       "      <td>2007</td>\n",
       "      <td>Michael Gambon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792424</th>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban</td>\n",
       "      <td>2004</td>\n",
       "      <td>Michael Gambon</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947789</th>\n",
       "      <td>Harry Potter and the Chamber of Secrets</td>\n",
       "      <td>2002</td>\n",
       "      <td>Richard Harris</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947790</th>\n",
       "      <td>Harry Potter and the Sorcerer's Stone</td>\n",
       "      <td>2001</td>\n",
       "      <td>Richard Harris</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685537</th>\n",
       "      <td>Ultimate Hero Project</td>\n",
       "      <td>2013</td>\n",
       "      <td>George (X) O'Connor</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893421</th>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Toby Regbo</td>\n",
       "      <td>actor</td>\n",
       "      <td>Young Albus Dumbledore</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248085</th>\n",
       "      <td>Potter</td>\n",
       "      <td>2015</td>\n",
       "      <td>Timothy Tedmanson</td>\n",
       "      <td>actor</td>\n",
       "      <td>Albus Dumbledore</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                title  year  \\\n",
       "704984                                     Epic Movie  2007   \n",
       "792419   Harry Potter and the Deathly Hallows: Part 1  2010   \n",
       "792420   Harry Potter and the Deathly Hallows: Part 2  2011   \n",
       "792421            Harry Potter and the Goblet of Fire  2005   \n",
       "792422         Harry Potter and the Half-Blood Prince  2009   \n",
       "792423      Harry Potter and the Order of the Phoenix  2007   \n",
       "792424       Harry Potter and the Prisoner of Azkaban  2004   \n",
       "947789        Harry Potter and the Chamber of Secrets  2002   \n",
       "947790          Harry Potter and the Sorcerer's Stone  2001   \n",
       "1685537                         Ultimate Hero Project  2013   \n",
       "1893421  Harry Potter and the Deathly Hallows: Part 1  2010   \n",
       "2248085                                        Potter  2015   \n",
       "\n",
       "                        name   type                   character     n  \n",
       "704984          Dane Farwell  actor            Albus Dumbledore  17.0  \n",
       "792419        Michael Gambon  actor  Professor Albus Dumbledore  39.0  \n",
       "792420        Michael Gambon  actor  Professor Albus Dumbledore   2.0  \n",
       "792421        Michael Gambon  actor            Albus Dumbledore  37.0  \n",
       "792422        Michael Gambon  actor  Professor Albus Dumbledore   2.0  \n",
       "792423        Michael Gambon  actor            Albus Dumbledore  36.0  \n",
       "792424        Michael Gambon  actor            Albus Dumbledore  27.0  \n",
       "947789        Richard Harris  actor            Albus Dumbledore  32.0  \n",
       "947790        Richard Harris  actor            Albus Dumbledore   1.0  \n",
       "1685537  George (X) O'Connor  actor            Albus Dumbledore   NaN  \n",
       "1893421           Toby Regbo  actor      Young Albus Dumbledore   NaN  \n",
       "2248085    Timothy Tedmanson  actor            Albus Dumbledore   NaN  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A\n",
    "AD_df = cast[cast.character.str.contains('Albus Dumbledore', case=False)]\n",
    "AD_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dane Farwell',\n",
       " \"George (X) O'Connor\",\n",
       " 'Michael Gambon',\n",
       " 'Richard Harris',\n",
       " 'Timothy Tedmanson',\n",
       " 'Toby Regbo'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#B\n",
    "set(AD_df['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q10:\n",
    "\n",
    "(A) How many roles has 'Keanu Reeves' played throughout his career?\n",
    "\n",
    "(B) List the leading roles that 'Keanu Reeves' played on or after 1999 in order by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A\n",
    "keanu_df = cast[cast.name.str.contains('Keanu Reeves', case=False)]\n",
    "len(keanu_df.character.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1892390</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>1999</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Neo</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892397</th>\n",
       "      <td>The Replacements</td>\n",
       "      <td>2000</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Shane Falco</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892358</th>\n",
       "      <td>Hard Ball</td>\n",
       "      <td>2001</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Conor O'Neill</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892383</th>\n",
       "      <td>Sweet November</td>\n",
       "      <td>2001</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Nelson Moss</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892348</th>\n",
       "      <td>Constantine</td>\n",
       "      <td>2005</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>John Constantine</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892388</th>\n",
       "      <td>The Lake House</td>\n",
       "      <td>2006</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Alex Wyler</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892382</th>\n",
       "      <td>Street Kings</td>\n",
       "      <td>2008</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Detective Tom Ludlow</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892385</th>\n",
       "      <td>The Day the Earth Stood Still</td>\n",
       "      <td>2008</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Klaatu</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892359</th>\n",
       "      <td>Henry's Crime</td>\n",
       "      <td>2010</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Henry Torne</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892342</th>\n",
       "      <td>47 Ronin</td>\n",
       "      <td>2013</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Kai</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892361</th>\n",
       "      <td>John Wick</td>\n",
       "      <td>2014</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>John Wick</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892366</th>\n",
       "      <td>Knock Knock</td>\n",
       "      <td>2015</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Evan</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892399</th>\n",
       "      <td>The Whole Truth</td>\n",
       "      <td>2016</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Ramsey</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892362</th>\n",
       "      <td>John Wick: Chapter 2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>John Wick</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892378</th>\n",
       "      <td>Siberia</td>\n",
       "      <td>2018</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Lucas Hill</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 title  year          name   type  \\\n",
       "1892390                     The Matrix  1999  Keanu Reeves  actor   \n",
       "1892397               The Replacements  2000  Keanu Reeves  actor   \n",
       "1892358                      Hard Ball  2001  Keanu Reeves  actor   \n",
       "1892383                 Sweet November  2001  Keanu Reeves  actor   \n",
       "1892348                    Constantine  2005  Keanu Reeves  actor   \n",
       "1892388                 The Lake House  2006  Keanu Reeves  actor   \n",
       "1892382                   Street Kings  2008  Keanu Reeves  actor   \n",
       "1892385  The Day the Earth Stood Still  2008  Keanu Reeves  actor   \n",
       "1892359                  Henry's Crime  2010  Keanu Reeves  actor   \n",
       "1892342                       47 Ronin  2013  Keanu Reeves  actor   \n",
       "1892361                      John Wick  2014  Keanu Reeves  actor   \n",
       "1892366                    Knock Knock  2015  Keanu Reeves  actor   \n",
       "1892399                The Whole Truth  2016  Keanu Reeves  actor   \n",
       "1892362           John Wick: Chapter 2  2017  Keanu Reeves  actor   \n",
       "1892378                        Siberia  2018  Keanu Reeves  actor   \n",
       "\n",
       "                    character    n  \n",
       "1892390                   Neo  1.0  \n",
       "1892397           Shane Falco  1.0  \n",
       "1892358         Conor O'Neill  1.0  \n",
       "1892383           Nelson Moss  1.0  \n",
       "1892348      John Constantine  1.0  \n",
       "1892388            Alex Wyler  1.0  \n",
       "1892382  Detective Tom Ludlow  1.0  \n",
       "1892385                Klaatu  1.0  \n",
       "1892359           Henry Torne  1.0  \n",
       "1892342                   Kai  1.0  \n",
       "1892361             John Wick  1.0  \n",
       "1892366                  Evan  1.0  \n",
       "1892399                Ramsey  1.0  \n",
       "1892362             John Wick  1.0  \n",
       "1892378            Lucas Hill  1.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keanu_df[(keanu_df.n == 1) & (keanu_df.year >= 1999)].sort_values(by=['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q11: \n",
    "\n",
    "(A) List the total number of actor and actress roles available from 1950 - 1960\n",
    "\n",
    "(B) List the total number of actor and actress roles available from 2007 - 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139020"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#11 A\n",
    "len(cast[(cast.year >= 1950) & (cast.year <= 1960)].character.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "619945"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#11 B\n",
    "len(cast[(cast.year >= 2007) & (cast.year <= 2017)].character.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section I - Q12: \n",
    "\n",
    "(A) List the total number of leading roles available from 2000 to present\n",
    "\n",
    "(B) List the total number of non-leading roles available from 2000 - present (exclude support cast)\n",
    "\n",
    "(C) List the total number of support\\extra-credit roles available from 2000 - present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38919"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A\n",
    "len(cast[(cast.n == 1) & (cast.year >= 2000)].character.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "473347"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B\n",
    "len(cast[(cast.n != 1) & (~cast.n.isnull()) & (cast.year >= 2000)].character.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "382872"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#C\n",
    "len(cast[(cast.n.isnull()) & (cast.year >= 2000)].character.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section II - Aggregations, Transformations and Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the top ten most common movie names of all time?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hamlet                  20\n",
       "Carmen                  17\n",
       "Macbeth                 16\n",
       "Maya                    12\n",
       "Temptation              12\n",
       "The Outsider            12\n",
       "Freedom                 11\n",
       "The Three Musketeers    11\n",
       "Othello                 11\n",
       "Honeymoon               11\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_ten = movies.title.value_counts()[:10]\n",
    "top_ten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the top ten common movie names of all time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f08dd75f8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_ten.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q1:  Which years in the 2000s saw the most movies released? (Show top 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017    11474\n",
       "2016     9440\n",
       "2015     8702\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[(movies.year >= 2000)].year.value_counts()[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q2: # Plot the total number of films released per-decade (1890, 1900, 1910,....)\n",
    "- Hint: Dividing the year and multiplying with a number might give you the decade the year falls into!\n",
    "- You might need to sort before plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of films released in each decade')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies['decade'] = (movies.year // 10 ) * 10\n",
    "movies = movies.sort_values('decade')\n",
    "movies.decade.plot(kind='hist', figsize=(10, 5))\n",
    "plt.xlabel('decades')\n",
    "plt.ylabel('number of films')\n",
    "plt.xlim(1890, 2020)\n",
    "plt.title('Number of films released in each decade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q3: \n",
    "\n",
    "(A) What are the top 10 most common character names in movie history?\n",
    "\n",
    "(B) Who are the top 10 people most often credited as \"Herself\" in movie history?\n",
    "\n",
    "(C) Who are the top 10 people most often credited as \"Himself\" in movie history?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Himself        20746\n",
       "Dancer         12477\n",
       "Extra          11948\n",
       "Reporter        8434\n",
       "Student         7773\n",
       "Doctor          7669\n",
       "Party Guest     7245\n",
       "Policeman       7029\n",
       "Nurse           6999\n",
       "Bartender       6802\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_chars = cast.character.value_counts()[:10]\n",
    "top_chars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Queen Elizabeth II        12\n",
       "Mar?a Luisa (V) Mart?n     9\n",
       "Joyce Brothers             9\n",
       "Luisa Horga                9\n",
       "Hillary Clinton            8\n",
       "Margaret Thatcher          8\n",
       "Rekha                      6\n",
       "Oprah Winfrey              6\n",
       "Sumie Sakai                6\n",
       "In?s J. Southern           6\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character == 'Herself'].name.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adolf Hitler             99\n",
       "Richard Nixon            44\n",
       "Ronald Reagan            41\n",
       "John F. Kennedy          37\n",
       "George W. Bush           25\n",
       "Winston Churchill        24\n",
       "Martin Luther King       23\n",
       "Ron Jeremy               22\n",
       "Bill Clinton             22\n",
       "Franklin D. Roosevelt    21\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character == 'Himself'].name.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q4: \n",
    "\n",
    "(A) What are the top 10 most frequent roles that start with the word \"Zombie\"?\n",
    "\n",
    "(B) What are the top 10 most frequent roles that start with the word \"Police\"?\n",
    "\n",
    "- Hint: The `startswith()` function might be useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Zombie                         6264\n",
       "Zombie Horde                    206\n",
       "Zombie - Protestor - Victim      78\n",
       "Zombie Extra                     70\n",
       "Zombie Dancer                    43\n",
       "Zombie Girl                      36\n",
       "Zombie #1                        36\n",
       "Zombie #2                        31\n",
       "Zombie Vampire                   25\n",
       "Zombie Victim                    22\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character.str.startswith('Zombie')].character.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Policeman              7029\n",
       "Police Officer         4808\n",
       "Police Inspector        742\n",
       "Police Sergeant         674\n",
       "Police officer          539\n",
       "Police                  456\n",
       "Policewoman             415\n",
       "Police Chief            410\n",
       "Police Captain          387\n",
       "Police Commissioner     337\n",
       "Name: character, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast[cast.character.str.startswith('Police')].character.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q5: Plot how many roles 'Keanu Reeves' has played in each year of his career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f000e6358>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cast[cast.name == 'Keanu Reeves'].groupby('year').size().plot(kind='bar', figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q6: Plot the cast positions (n-values) of Keanu Reeve's roles through his career over the years.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f0549fe80>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cast[cast.name == 'Keanu Reeves'].groupby('year').n.mean().plot(figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q7: Plot the number of \"Hamlet\" films made by each decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f001472b0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies[movies.title == 'Hamlet'].decade.plot \\\n",
    "    .hist(alpha=0.5, figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q8: \n",
    "\n",
    "(A) How many leading roles were available to both actors and actresses, in the 1960s (1960-1969)?\n",
    "\n",
    "(B) How many leading roles were available to both actors and actresses, in the 2000s (2000-2009)?\n",
    "\n",
    "- Hint: A specific value of n might indicate a leading role"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "actor      2532681\n",
       "actress    1253495\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11823"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A\n",
    "len(cast[(cast.n == 1) & (cast.year >= 1960) & (cast.year <= 1969)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26344"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B\n",
    "len(cast[(cast.n == 1) & (cast.year >= 2000) & (cast.year <= 2009)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q9: List, in order by year, each of the films in which Frank Oz has played more than 1 role."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                                 year\n",
       "The Muppet Movie                      1979    8\n",
       "An American Werewolf in London        1981    2\n",
       "The Great Muppet Caper                1981    6\n",
       "The Dark Crystal                      1982    2\n",
       "The Muppets Take Manhattan            1984    7\n",
       "Follow That Bird                      1985    3\n",
       "The Muppet Christmas Carol            1992    7\n",
       "Muppet Treasure Island                1996    4\n",
       "Muppets from Space                    1999    4\n",
       "The Adventures of Elmo in Grouchland  1999    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frank_cast = cast[cast.name == 'Frank Oz'].groupby(['title','year']).size()\n",
    "frank_cast[frank_cast > 1].sort_index(level=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q10: List each of the characters that Frank Oz has portrayed at least twice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "character\n",
       "Animal            6\n",
       "Bert              3\n",
       "Cookie Monster    5\n",
       "Fozzie Bear       4\n",
       "Grover            2\n",
       "Miss Piggy        6\n",
       "Sam the Eagle     5\n",
       "Yoda              6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frank_chars = cast[cast.name == 'Frank Oz'].groupby('character').size()\n",
    "frank_chars[frank_chars > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section III - Advanced Merging, Querying and Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a bar plot with the following conditions\n",
    "- Frequency of the number of movies with \"Christmas\" in their title \n",
    "- Movies should be such that they are released in the USA.\n",
    "- Show the frequency plot by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f00c97160>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "christmas = release_dates[(release_dates.title.str.contains('Christmas')) & (release_dates.country == 'USA')]\n",
    "christmas.date.dt.month.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section III - Q1: Make a bar plot with the following conditions\n",
    "- Frequency of the number of movies with \"Summer\" in their title \n",
    "- Movies should be such that they are released in the USA.\n",
    "- Show the frequency plot by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f00f427b8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summer = release_dates[(release_dates.title.str.contains('Summer')) & \\\n",
    "                      (release_dates.country == 'USA')]\n",
    "summer.date.dt.month.value_counts().sort_index().plot.bar(figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section III - Q2: Make a bar plot with the following conditions\n",
    "- Frequency of the number of movies with \"Action\" in their title \n",
    "- Movies should be such that they are released in the USA.\n",
    "- Show the frequency plot by week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f00f90f28>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "action = release_dates[(release_dates.title.str.contains('Action')) & (release_dates.country == 'USA')]\n",
    "action.date.dt.week.value_counts().sort_index().plot.bar(figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section III - Q3: Show all the movies in which Keanu Reeves has played the lead role along with their   release date in the USA sorted by the date of release\n",
    "- Hint: You might need to join or merge two datasets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>year</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>character</th>\n",
       "      <th>n</th>\n",
       "      <th>country</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>The Night Before</td>\n",
       "      <td>1988</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Winston Connelly</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1988-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bill &amp; Ted's Excellent Adventure</td>\n",
       "      <td>1989</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Ted</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1989-02-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bill &amp; Ted's Bogus Journey</td>\n",
       "      <td>1991</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Ted</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1991-07-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Little Buddha</td>\n",
       "      <td>1993</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Siddhartha</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1994-05-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Speed</td>\n",
       "      <td>1994</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jack Traven</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1994-06-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Johnny Mnemonic</td>\n",
       "      <td>1995</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Johnny Mnemonic</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1995-05-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Walk in the Clouds</td>\n",
       "      <td>1995</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Paul Sutton</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1995-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chain Reaction</td>\n",
       "      <td>1996</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Eddie Kasalivich</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1996-08-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Feeling Minnesota</td>\n",
       "      <td>1996</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Jjaks Clayton</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1996-09-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The Devil's Advocate</td>\n",
       "      <td>1997</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Kevin Lomax</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1997-10-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>1999</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Neo</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>1999-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The Replacements</td>\n",
       "      <td>2000</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Shane Falco</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2000-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sweet November</td>\n",
       "      <td>2001</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Nelson Moss</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2001-02-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hard Ball</td>\n",
       "      <td>2001</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Conor O'Neill</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2001-09-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Constantine</td>\n",
       "      <td>2005</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>John Constantine</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2005-02-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Lake House</td>\n",
       "      <td>2006</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Alex Wyler</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2006-06-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Street Kings</td>\n",
       "      <td>2008</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Detective Tom Ludlow</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2008-04-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>The Day the Earth Stood Still</td>\n",
       "      <td>2008</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Klaatu</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2008-12-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47 Ronin</td>\n",
       "      <td>2013</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Kai</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2013-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>John Wick</td>\n",
       "      <td>2014</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>John Wick</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2014-10-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Knock Knock</td>\n",
       "      <td>2015</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>Evan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2015-10-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>John Wick: Chapter 2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Keanu Reeves</td>\n",
       "      <td>actor</td>\n",
       "      <td>John Wick</td>\n",
       "      <td>1.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>2017-02-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  year          name   type  \\\n",
       "20                  The Night Before  1988  Keanu Reeves  actor   \n",
       "3   Bill & Ted's Excellent Adventure  1989  Keanu Reeves  actor   \n",
       "2         Bill & Ted's Bogus Journey  1991  Keanu Reeves  actor   \n",
       "12                     Little Buddha  1993  Keanu Reeves  actor   \n",
       "13                             Speed  1994  Keanu Reeves  actor   \n",
       "10                   Johnny Mnemonic  1995  Keanu Reeves  actor   \n",
       "1               A Walk in the Clouds  1995  Keanu Reeves  actor   \n",
       "4                     Chain Reaction  1996  Keanu Reeves  actor   \n",
       "6                  Feeling Minnesota  1996  Keanu Reeves  actor   \n",
       "17              The Devil's Advocate  1997  Keanu Reeves  actor   \n",
       "19                        The Matrix  1999  Keanu Reeves  actor   \n",
       "21                  The Replacements  2000  Keanu Reeves  actor   \n",
       "15                    Sweet November  2001  Keanu Reeves  actor   \n",
       "7                          Hard Ball  2001  Keanu Reeves  actor   \n",
       "5                        Constantine  2005  Keanu Reeves  actor   \n",
       "18                    The Lake House  2006  Keanu Reeves  actor   \n",
       "14                      Street Kings  2008  Keanu Reeves  actor   \n",
       "16     The Day the Earth Stood Still  2008  Keanu Reeves  actor   \n",
       "0                           47 Ronin  2013  Keanu Reeves  actor   \n",
       "8                          John Wick  2014  Keanu Reeves  actor   \n",
       "11                       Knock Knock  2015  Keanu Reeves  actor   \n",
       "9               John Wick: Chapter 2  2017  Keanu Reeves  actor   \n",
       "\n",
       "               character    n country       date  \n",
       "20      Winston Connelly  1.0     USA 1988-04-15  \n",
       "3                    Ted  1.0     USA 1989-02-17  \n",
       "2                    Ted  1.0     USA 1991-07-19  \n",
       "12            Siddhartha  1.0     USA 1994-05-25  \n",
       "13           Jack Traven  1.0     USA 1994-06-10  \n",
       "10       Johnny Mnemonic  1.0     USA 1995-05-26  \n",
       "1            Paul Sutton  1.0     USA 1995-08-11  \n",
       "4       Eddie Kasalivich  1.0     USA 1996-08-02  \n",
       "6          Jjaks Clayton  1.0     USA 1996-09-13  \n",
       "17           Kevin Lomax  1.0     USA 1997-10-17  \n",
       "19                   Neo  1.0     USA 1999-03-31  \n",
       "21           Shane Falco  1.0     USA 2000-08-11  \n",
       "15           Nelson Moss  1.0     USA 2001-02-16  \n",
       "7          Conor O'Neill  1.0     USA 2001-09-14  \n",
       "5       John Constantine  1.0     USA 2005-02-18  \n",
       "18            Alex Wyler  1.0     USA 2006-06-16  \n",
       "14  Detective Tom Ludlow  1.0     USA 2008-04-11  \n",
       "16                Klaatu  1.0     USA 2008-12-12  \n",
       "0                    Kai  1.0     USA 2013-12-25  \n",
       "8              John Wick  1.0     USA 2014-10-24  \n",
       "11                  Evan  1.0     USA 2015-10-09  \n",
       "9              John Wick  1.0     USA 2017-02-10  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keanu_df = cast[(cast.name == 'Keanu Reeves') &\n",
    "               (cast.n == 1)]\n",
    "usa_release = release_dates[release_dates.country == 'USA']\n",
    "pd.merge(left=keanu_df, right=usa_release, on=['title', 'year']).sort_values('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section III - Q4: Make a bar plot showing the months in which movies with Keanu Reeves tend to be released in the USA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f019e5438>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "keanu_df = cast[(cast.name == 'Keanu Reeves')]\n",
    "pd.merge(left=keanu_df, right=usa_release, on=['title', 'year']).sort_values('date') \\\n",
    "    .date.dt.month.value_counts().sort_index().plot.bar(figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section III - Q5: Make a bar plot showing the years in which movies with Ian McKellen tend to be released in the USA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22f02c5cf98>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ian_df = cast[(cast.name == 'Ian McKellen')]\n",
    "pd.merge(left=ian_df, right=usa_release, on=['title', 'year']).sort_values('date') \\\n",
    "    .date.dt.year.value_counts().sort_index().plot.bar(figsize=(10, 5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow-GPU-1.13.1",
   "language": "python",
   "name": "tf-gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}