Mini_Project_Data_Wrangling_Pandas.ipynb

{
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   "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": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Please make sure you have one of the more recent versions of Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "gradient": {}
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.5'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": {
    "gradient": {}
   },
   "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",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   title   244914 non-null  object\n",
      " 1   year    244914 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "movies = pd.read_csv('titles.csv')\n",
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [
    {
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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": {
    "gradient": {
     "editing": false
    }
   },
   "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": 8,
   "metadata": {
    "gradient": {}
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   "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",
      " #   Column     Dtype  \n",
      "---  ------     -----  \n",
      " 0   title      object \n",
      " 1   year       int64  \n",
      " 2   name       object \n",
      " 3   type       object \n",
      " 4   character  object \n",
      " 5   n          float64\n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 173.3+ MB\n"
     ]
    }
   ],
   "source": [
    "cast = pd.read_csv('cast.csv.zip')\n",
    "cast.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 10,
   "metadata": {
    "gradient": {}
   },
   "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",
      " #   Column   Non-Null Count   Dtype         \n",
      "---  ------   --------------   -----         \n",
      " 0   title    479488 non-null  object        \n",
      " 1   year     479488 non-null  int64         \n",
      " 2   country  479488 non-null  object        \n",
      " 3   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('release_dates.csv', parse_dates=['date'], infer_datetime_format=True)\n",
    "release_dates.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "gradient": {}
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
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       "      <th>country</th>\n",
       "      <th>date</th>\n",
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       "    <tr>\n",
       "      <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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "release_dates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "# Section I - Basic Querying, Filtering and Transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### What is the total number of movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "244914"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### List all Batman movies ever made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Batman Movies: 2\n"
     ]
    },
    {
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       "      <th>52734</th>\n",
       "      <td>Batman</td>\n",
       "      <td>1943</td>\n",
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       "    <tr>\n",
       "      <th>150621</th>\n",
       "      <td>Batman</td>\n",
       "      <td>1989</td>\n",
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       "         title  year\n",
       "52734   Batman  1943\n",
       "150621  Batman  1989"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "batman_df = movies[movies.title == 'Batman']\n",
    "print('Total Batman Movies:', len(batman_df))\n",
    "batman_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### List all Batman movies ever made - the right approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Batman Movies: 35\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
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       "  <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": 19,
     "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": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Display the top 15 Batman movies in the order they were released"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "gradient": {}
   },
   "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>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batman_df.sort_values(by=['year'], ascending=True).iloc[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Section I - Q1 : List all the 'Harry Potter' movies from the most recent to the earliest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Harry Potter Movies: 8\n",
      "                                               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\n"
     ]
    }
   ],
   "source": [
    "hp_df = movies[movies.title.str.contains('Harry Potter', case=False)]\n",
    "hp_df.sort_values(by=['year'], ascending=False).iloc[:15]\n",
    "print('Total Harry Potter Movies:', len(hp_df))\n",
    "print(hp_df.sort_values(by=['year'], ascending=False).iloc[:15])\n",
    "# hp_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### How many movies were made in the year 2017?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11474"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies[movies.year == 2017])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Section I - Q2 : How many movies were made in the year 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8702"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies[movies.year == 2015])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 24,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114070\n"
     ]
    }
   ],
   "source": [
    "# print(len(movies.year.isin(range(2000, 2018))))\n",
    "print(len(movies[(movies['year']<=2018)&(movies['year']>=2000)]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Section I - Q4: How many movies are titled \"Hamlet\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Hamlet Movies: 20\n"
     ]
    }
   ],
   "source": [
    "hamlet_df = movies[movies.title.str.contains('Hamlet', case=False)]\n",
    "hamlet_df = movies[movies.title =='Hamlet']\n",
    "print('Total Hamlet Movies:', len(hamlet_df))\n",
    "# hamlet_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 26,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         title  year\n",
      "244747  Hamlet  2017\n",
      "191940  Hamlet  2016\n",
      "186137  Hamlet  2015\n",
      "178290  Hamlet  2014\n",
      "227953  Hamlet  2011\n",
      "1931    Hamlet  2009\n",
      "55639   Hamlet  2000\n"
     ]
    }
   ],
   "source": [
    "print(hamlet_df[hamlet_df.year>=2000].sort_values(by=['year'], ascending=False).iloc[:15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 27,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case 1:27\n",
      "case 2:27\n"
     ]
    }
   ],
   "source": [
    "print(\"case 1:\"+str(sum(cast[(cast['title']==\"Inception\")]['n'].isnull())))\n",
    "inception_supporting_cast = cast[(cast['title']==\"Inception\") & (cast['n'].isnull())]\n",
    "print(\"case 2:\" + str(len(inception_supporting_cast)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 29,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Inception\" main cast number:51\n"
     ]
    }
   ],
   "source": [
    "inception_main_cast = cast[(cast['title']==\"Inception\") & (cast['n'])]\n",
    "# inception_main_cast\n",
    "print('\"Inception\" main cast number:'+str(len(inception_main_cast)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Section I - Q8: Show the top ten cast (actors\\actresses) in the movie \"Inception\" \n",
    "- main cast always have an 'n' value\n",
    "- remember to sort!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             title  year                  name     type        character     n\n",
      "590576   Inception  2010     Leonardo DiCaprio    actor             Cobb   1.0\n",
      "859993   Inception  2010  Joseph Gordon-Levitt    actor           Arthur   2.0\n",
      "3387147  Inception  2010            Ellen Page  actress          Ariadne   3.0\n",
      "940923   Inception  2010             Tom Hardy    actor            Eames   4.0\n",
      "2406531  Inception  2010          Ken Watanabe    actor            Saito   5.0\n",
      "1876301  Inception  2010            Dileep Rao    actor            Yusuf   6.0\n",
      "1615709  Inception  2010        Cillian Murphy    actor   Robert Fischer   7.0\n",
      "183937   Inception  2010          Tom Berenger    actor         Browning   8.0\n",
      "2765969  Inception  2010      Marion Cotillard  actress              Mal   9.0\n",
      "1826027  Inception  2010    Pete Postlethwaite    actor  Maurice Fischer  10.0\n"
     ]
    }
   ],
   "source": [
    "print(inception_main_cast.sort_values(by=['n'], ascending=True).iloc[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 31,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             title  year                 name  \\\n",
      "704984                                  Epic Movie  2007         Dane Farwell   \n",
      "792421         Harry Potter and the Goblet of Fire  2005       Michael Gambon   \n",
      "792423   Harry Potter and the Order of the Phoenix  2007       Michael Gambon   \n",
      "792424    Harry Potter and the Prisoner of Azkaban  2004       Michael Gambon   \n",
      "947789     Harry Potter and the Chamber of Secrets  2002       Richard Harris   \n",
      "947790       Harry Potter and the Sorcerer's Stone  2001       Richard Harris   \n",
      "1685537                      Ultimate Hero Project  2013  George (X) O'Connor   \n",
      "2248085                                     Potter  2015    Timothy Tedmanson   \n",
      "\n",
      "          type         character     n  \n",
      "704984   actor  Albus Dumbledore  17.0  \n",
      "792421   actor  Albus Dumbledore  37.0  \n",
      "792423   actor  Albus Dumbledore  36.0  \n",
      "792424   actor  Albus Dumbledore  27.0  \n",
      "947789   actor  Albus Dumbledore  32.0  \n",
      "947790   actor  Albus Dumbledore   1.0  \n",
      "1685537  actor  Albus Dumbledore   NaN  \n",
      "2248085  actor  Albus Dumbledore   NaN  \n",
      "704984                                    Epic Movie\n",
      "792421           Harry Potter and the Goblet of Fire\n",
      "792423     Harry Potter and the Order of the Phoenix\n",
      "792424      Harry Potter and the Prisoner of Azkaban\n",
      "947789       Harry Potter and the Chamber of Secrets\n",
      "947790         Harry Potter and the Sorcerer's Stone\n",
      "1685537                        Ultimate Hero Project\n",
      "2248085                                       Potter\n",
      "Name: title, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# prints evrything\n",
    "print(cast[cast['character']== 'Albus Dumbledore'])\n",
    "# only the movie title\n",
    "print(cast[cast['character']== 'Albus Dumbledore']['title'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "704984            Dane Farwell\n",
      "792421          Michael Gambon\n",
      "792423          Michael Gambon\n",
      "792424          Michael Gambon\n",
      "947789          Richard Harris\n",
      "947790          Richard Harris\n",
      "1685537    George (X) O'Connor\n",
      "2248085      Timothy Tedmanson\n",
      "Name: name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(cast[cast['character']== 'Albus Dumbledore']['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 33,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cast[cast['name']=='Keanu Reeves'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 title  year          name   type  \\\n",
      "1892378                        Siberia  2018  Keanu Reeves  actor   \n",
      "1892362           John Wick: Chapter 2  2017  Keanu Reeves  actor   \n",
      "1892399                The Whole Truth  2016  Keanu Reeves  actor   \n",
      "1892366                    Knock Knock  2015  Keanu Reeves  actor   \n",
      "1892361                      John Wick  2014  Keanu Reeves  actor   \n",
      "1892342                       47 Ronin  2013  Keanu Reeves  actor   \n",
      "1892359                  Henry's Crime  2010  Keanu Reeves  actor   \n",
      "1892382                   Street Kings  2008  Keanu Reeves  actor   \n",
      "1892385  The Day the Earth Stood Still  2008  Keanu Reeves  actor   \n",
      "1892388                 The Lake House  2006  Keanu Reeves  actor   \n",
      "1892348                    Constantine  2005  Keanu Reeves  actor   \n",
      "1892358                      Hard Ball  2001  Keanu Reeves  actor   \n",
      "1892383                 Sweet November  2001  Keanu Reeves  actor   \n",
      "1892397               The Replacements  2000  Keanu Reeves  actor   \n",
      "\n",
      "                    character    n  \n",
      "1892378            Lucas Hill  1.0  \n",
      "1892362             John Wick  1.0  \n",
      "1892399                Ramsey  1.0  \n",
      "1892366                  Evan  1.0  \n",
      "1892361             John Wick  1.0  \n",
      "1892342                   Kai  1.0  \n",
      "1892359           Henry Torne  1.0  \n",
      "1892382  Detective Tom Ludlow  1.0  \n",
      "1892385                Klaatu  1.0  \n",
      "1892388            Alex Wyler  1.0  \n",
      "1892348      John Constantine  1.0  \n",
      "1892358         Conor O'Neill  1.0  \n",
      "1892383           Nelson Moss  1.0  \n",
      "1892397           Shane Falco  1.0  \n"
     ]
    }
   ],
   "source": [
    "print(cast[(cast['name']=='Keanu Reeves') & (cast['n']== 1) & (cast['year']>1999)].sort_values(by=['year'], ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 35,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "234635"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast.head()\n",
    "len(cast[(cast['year']>=1950) & (cast['year']<=1960)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1452413"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cast[(cast['year']>=2007) & (cast['year']<=2017)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 37,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60568"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cast[(cast['n']==1) & (cast['year']>=2000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1001710"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cast[(cast['n']!=1) & (cast['year']>=2000) & (cast['n']) ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "887484"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cast[(cast['year']>=2000) & (cast['n']).isnull() ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "# Section II - Aggregations, Transformations and Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "## What are the top ten most common movie names of all time?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "gradient": {}
   },
   "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",
       "Honeymoon               11\n",
       "Othello                 11\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_ten = movies.title.value_counts()[:10]\n",
    "top_ten\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Plot the top ten common movie names of all time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_ten.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Section II - Q1:  Which years in the 2000s saw the most movies released? (Show top 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017    11474\n",
      "2016     9440\n",
      "2015     8702\n",
      "Name: year, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_3_2000s = movies[movies['year']>=2000].year.value_counts()[:3]\n",
    "print(top_3_2000s)\n",
    "top_3_2000s.plot(kind='barh')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 55,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def get_the_decade(i):\n",
    "    return i//10*10\n",
    "\n",
    "movies_with_decade=movies.copy(deep = True)\n",
    "movies_with_decade['decade'] = movies_with_decade['year'].map(get_the_decade)\n",
    "hist = movies_with_decade.hist(column='decade', figsize=(12,8), sharex=True, sharey=True, color='#45243c', zorder=2,grid=False, bins=12, range=(1910, 2020), rwidth=.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 58,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "character\n",
       "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",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A\n",
    "#A) What are the top 10 most common character names in movie history?\n",
    "res = cast.groupby(by=['character']).size()\n",
    "res.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Queen Elizabeth II        12\n",
       "Joyce Brothers             9\n",
       "Luisa Horga                9\n",
       "Mar?a Luisa (V) Mart?n     9\n",
       "Hillary Clinton            8\n",
       "Margaret Thatcher          8\n",
       "In?s J. Southern           6\n",
       "Marta Berrocal             6\n",
       "Oprah Winfrey              6\n",
       "Marilyn Monroe             6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#B\n",
    "herself_char = cast[cast.character == 'Herself']\n",
    "herself_char.groupby(by=['name']).size().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "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",
       "Bill Clinton             22\n",
       "Ron Jeremy               22\n",
       "Franklin D. Roosevelt    21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#C\n",
    "himself_char = cast[cast.character == 'Himself']\n",
    "himself_char.groupby(by=['name']).size().sort_values(ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "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": 61,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "character\n",
       "Zombie                         6269\n",
       "Zombie Horde                    206\n",
       "Zombie - Protestor - Victim      78\n",
       "Zombie Extra                     70\n",
       "Zombie Dancer                    43\n",
       "Zombie #1                        36\n",
       "Zombie Girl                      36\n",
       "Zombie #2                        31\n",
       "Zombie Vampire                   25\n",
       "Zombie Victim                    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A\n",
    "top_zombie = cast[cast.character.str.contains('^Zombie')]\n",
    "res = top_zombie.groupby(top_zombie.character.str.strip())\n",
    "res = res.size()\n",
    "res.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "character\n",
       "Policeman              7031\n",
       "Police Officer         4810\n",
       "Police Inspector        742\n",
       "Police Sergeant         674\n",
       "Police officer          539\n",
       "Police                  457\n",
       "Policewoman             415\n",
       "Police Chief            410\n",
       "Police Captain          387\n",
       "Police Commissioner     337\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_police = cast[cast.character.str.contains('^Police')]\n",
    "res = top_police.groupby(top_police.character.str.strip())\n",
    "res = res.size()\n",
    "res.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gradient": {
     "editing": false
    }
   },
   "source": [
    "### Section II - Q5: Plot how many roles 'Keanu Reeves' has played in each year of his career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Keanu_roles = cast[cast.name == 'Keanu Reeves']\n",
    "roles_group = Keanu_roles.groupby(by=['year']).size()\n",
    "roles_group = roles_group.sort_index(ascending=True)\n",
    "plt.xlabel('Year', fontsize=20)\n",
    "plt.ylabel('Total Roles', fontsize=20)\n",
    "plt.title('Roles Keanu Reeves Has Played Per Year',fontsize=20)\n",
    "roles_group.plot(kind='bar', figsize=(30,10), fontsize=20);\n",
    "#roles_group.sort_values(ascending=False).shape"
   ]
  },
  {
   "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": 64,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kr = cast[cast['name'].str.contains('Keanu Reeves', case=False)]\n",
    "simeple_scatter_plot = kr.plot.scatter(x='year', y='n',c='DarkBlue', title=\"Keanu Reeve's roles through his career over the years\", ylabel='position')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section II - Q7: Plot the number of \"Hamlet\" films made by each decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies['decade'] = movies['year'].map(get_the_decade)\n",
    "movies[movies['title']=='Hamlet'].hist(column='decade', range=(1900,2020), grid=False,rwidth=0.5, color='grey', figsize=(12,10))\n",
    "plt.show()\n"
   ]
  },
  {
   "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": 68,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "actor      8320\n",
       "actress    3503\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (A) How many leading roles were available to both actors and actresses, in the 1960s (1960-1969)?\n",
    "\n",
    "cast_n_1_1960s = cast[(cast.n == 1) & ( (cast.year >= 1960) & (cast.year <= 1969) )]\n",
    "cast_n_1_1960s.groupby(by=['type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "actor      18557\n",
       "actress     7787\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (B) How many leading roles were available to both actors and actresses, in the 2000s (2000-2009)?\n",
    "cast_n_1_1960s = cast[(cast.n == 1) & ( (cast.year >= 2000) & (cast.year <= 2009) )]\n",
    "cast_n_1_1960s.groupby(by=['type']).size()"
   ]
  },
  {
   "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": 70,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  title                               \n",
       "1979  The Muppet Movie                        8\n",
       "1981  An American Werewolf in London          2\n",
       "      The Great Muppet Caper                  6\n",
       "1982  The Dark Crystal                        2\n",
       "1984  The Muppets Take Manhattan              7\n",
       "1985  Follow That Bird                        3\n",
       "1992  The Muppet Christmas Carol              7\n",
       "1996  Muppet Treasure Island                  4\n",
       "1999  Muppets from Space                      4\n",
       "      The Adventures of Elmo in Grouchland    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_o = cast[cast.name == 'Frank Oz'].groupby(['year','title']).size()\n",
    "f_o[f_o>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": 71,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "character\n",
       "Grover            2\n",
       "Bert              3\n",
       "Fozzie Bear       4\n",
       "Cookie Monster    5\n",
       "Sam the Eagle     5\n",
       "Animal            6\n",
       "Miss Piggy        6\n",
       "Yoda              6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_o_2 = cast[cast.name == 'Frank Oz'].groupby(['character']).size()\n",
    "type(f_o_2)\n",
    "f_o_2[f_o_2>1].sort_values()"
   ]
  },
  {
   "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": 72,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 73,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "christmas = release_dates[(release_dates.title.str.contains('Summer')) & (release_dates.country == 'USA')]\n",
    "christmas.date.dt.month.value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "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": 76,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "christmas = release_dates[(release_dates.title.str.contains('Action')) & (release_dates.country == 'USA')]\n",
    "christmas.date.dt.isocalendar().week.value_counts().sort_index().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "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": 78,
   "metadata": {
    "gradient": {}
   },
   "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>16837127</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>8395767</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>2709676</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>10232894</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>8629940</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>1803683</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>10458033</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>13350949</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>14609249</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>7805546</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>12657786</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>31232</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>6070901</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>815853</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>7858007</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>2270941</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>10560879</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>3622167</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>6320117</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>2705538</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>15147049</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>13522810</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",
       "16837127                  The Night Before  1988  Keanu Reeves  actor   \n",
       "8395767   Bill & Ted's Excellent Adventure  1989  Keanu Reeves  actor   \n",
       "2709676         Bill & Ted's Bogus Journey  1991  Keanu Reeves  actor   \n",
       "10232894                     Little Buddha  1993  Keanu Reeves  actor   \n",
       "8629940                              Speed  1994  Keanu Reeves  actor   \n",
       "1803683                    Johnny Mnemonic  1995  Keanu Reeves  actor   \n",
       "10458033              A Walk in the Clouds  1995  Keanu Reeves  actor   \n",
       "13350949                    Chain Reaction  1996  Keanu Reeves  actor   \n",
       "14609249                 Feeling Minnesota  1996  Keanu Reeves  actor   \n",
       "7805546               The Devil's Advocate  1997  Keanu Reeves  actor   \n",
       "12657786                        The Matrix  1999  Keanu Reeves  actor   \n",
       "31232                     The Replacements  2000  Keanu Reeves  actor   \n",
       "6070901                     Sweet November  2001  Keanu Reeves  actor   \n",
       "815853                           Hard Ball  2001  Keanu Reeves  actor   \n",
       "7858007                        Constantine  2005  Keanu Reeves  actor   \n",
       "2270941                     The Lake House  2006  Keanu Reeves  actor   \n",
       "10560879                      Street Kings  2008  Keanu Reeves  actor   \n",
       "3622167      The Day the Earth Stood Still  2008  Keanu Reeves  actor   \n",
       "6320117                           47 Ronin  2013  Keanu Reeves  actor   \n",
       "2705538                          John Wick  2014  Keanu Reeves  actor   \n",
       "15147049                       Knock Knock  2015  Keanu Reeves  actor   \n",
       "13522810              John Wick: Chapter 2  2017  Keanu Reeves  actor   \n",
       "\n",
       "                     character    n country       date  \n",
       "16837127      Winston Connelly  1.0     USA 1988-04-15  \n",
       "8395767                    Ted  1.0     USA 1989-02-17  \n",
       "2709676                    Ted  1.0     USA 1991-07-19  \n",
       "10232894            Siddhartha  1.0     USA 1994-05-25  \n",
       "8629940            Jack Traven  1.0     USA 1994-06-10  \n",
       "1803683        Johnny Mnemonic  1.0     USA 1995-05-26  \n",
       "10458033           Paul Sutton  1.0     USA 1995-08-11  \n",
       "13350949      Eddie Kasalivich  1.0     USA 1996-08-02  \n",
       "14609249         Jjaks Clayton  1.0     USA 1996-09-13  \n",
       "7805546            Kevin Lomax  1.0     USA 1997-10-17  \n",
       "12657786                   Neo  1.0     USA 1999-03-31  \n",
       "31232              Shane Falco  1.0     USA 2000-08-11  \n",
       "6070901            Nelson Moss  1.0     USA 2001-02-16  \n",
       "815853           Conor O'Neill  1.0     USA 2001-09-14  \n",
       "7858007       John Constantine  1.0     USA 2005-02-18  \n",
       "2270941             Alex Wyler  1.0     USA 2006-06-16  \n",
       "10560879  Detective Tom Ludlow  1.0     USA 2008-04-11  \n",
       "3622167                 Klaatu  1.0     USA 2008-12-12  \n",
       "6320117                    Kai  1.0     USA 2013-12-25  \n",
       "2705538              John Wick  1.0     USA 2014-10-24  \n",
       "15147049                  Evan  1.0     USA 2015-10-09  \n",
       "13522810             John Wick  1.0     USA 2017-02-10  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we assume leading role is when n=1\n",
    "cast_release = cast.merge(release_dates, on=['title', 'year'])\n",
    "Keanu_res = cast_release[(cast_release.name == 'Keanu Reeves') & (cast_release.n == 1) & (cast_release.country == 'USA')].sort_values(['date'], ascending=True)\n",
    "Keanu_res"
   ]
  },
  {
   "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": 80,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Keanu_res = cast_release[(cast_release.name == 'Keanu Reeves') & (cast_release.country == 'USA')].sort_values(['date'], ascending=True)\n",
    "\n",
    "Keanu_res.date.dt.month_name().value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "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": 81,
   "metadata": {
    "gradient": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "McKellen_res = cast_release[(cast_release.name.str.contains('McKellen')) & (cast_release.country == 'USA')].sort_values(['date'], ascending=True)\n",
    "\n",
    "McKellen_res.date.dt.month_name().value_counts().sort_index().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}