09 Preparación de los datos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos las librerias\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Esta linea permite que los graficos sean renderizados directamente en nuestro Notebook\n",
    "## Carguemos en un pandas dataframe nuestra base de datos\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Carguemos en un pandas dataframe nuestra base de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "  <code>pd.read_csv</code> es el metodo que nos permite importar los datos desde un CSV y cargarlo en un **DataFrame**, que es la estructura de base de Pandas\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('../vol/datasets/peliculas.csv',encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>color</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>genres</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>director_name</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>1.78</td>\n",
       "      <td>178.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>178.0</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>avatar|future|marine|native|paraplegic</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>CCH Pounder</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>4834</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>760505847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>2.35</td>\n",
       "      <td>169.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>169.0</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>Johnny Depp</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>Jack Davenport</td>\n",
       "      <td>48350</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>309404152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>2.35</td>\n",
       "      <td>148.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>148.0</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>Stephanie Sigman</td>\n",
       "      <td>11700</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>200074175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>164.0</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>106759</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>448130642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         movie_title  title_year  color  \\\n",
       "0                                            Avatar       2009.0  Color   \n",
       "1          Pirates of the Caribbean: At World's End       2007.0  Color   \n",
       "2                                           Spectre       2015.0  Color   \n",
       "3                             The Dark Knight Rises       2012.0  Color   \n",
       "4  Star Wars: Episode VII - The Force Awakens    ...         NaN    NaN   \n",
       "\n",
       "   aspect_ratio  duration language country  duration.1  \\\n",
       "0          1.78     178.0  English     USA       178.0   \n",
       "1          2.35     169.0  English     USA       169.0   \n",
       "2          2.35     148.0  English      UK       148.0   \n",
       "3          2.35     164.0  English     USA       164.0   \n",
       "4           NaN       NaN      NaN     NaN         NaN   \n",
       "\n",
       "                            genres content_rating  \\\n",
       "0  Action|Adventure|Fantasy|Sci-Fi          PG-13   \n",
       "1         Action|Adventure|Fantasy          PG-13   \n",
       "2        Action|Adventure|Thriller          PG-13   \n",
       "3                  Action|Thriller          PG-13   \n",
       "4                      Documentary            NaN   \n",
       "\n",
       "                                       plot_keywords      director_name  \\\n",
       "0             avatar|future|marine|native|paraplegic      James Cameron   \n",
       "1  goddess|marriage ceremony|marriage proposal|pi...     Gore Verbinski   \n",
       "2                bomb|espionage|sequel|spy|terrorist         Sam Mendes   \n",
       "3  deception|imprisonment|lawlessness|police offi...  Christopher Nolan   \n",
       "4                                                NaN        Doug Walker   \n",
       "\n",
       "      actor_1_name      actor_2_name          actor_3_name  \\\n",
       "0    p  CCH Pounder  Joel David Moore             Wes Studi   \n",
       "1      Johnny Depp     Orlando Bloom        Jack Davenport   \n",
       "2  Christoph Waltz      Rory Kinnear      Stephanie Sigman   \n",
       "3        Tom Hardy    Christian Bale  Joseph Gordon-Levitt   \n",
       "4      Doug Walker        Rob Walker                   NaN   \n",
       "\n",
       "   cast_total_facebook_likes       budget  imdb_score        gross  \n",
       "0                       4834  237000000.0         7.9  760505847.0  \n",
       "1                      48350  300000000.0         7.1  309404152.0  \n",
       "2                      11700  245000000.0         6.8  200074175.0  \n",
       "3                     106759  250000000.0         8.5  448130642.0  \n",
       "4                        143          NaN         7.1          NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5043, 19)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cuantas lineas y columnas tiene nuestro dataframe\n",
    "### Un dataframe es una estructura de datos que se compone de los elementos siguientes\n",
    "movies.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "  Un dataframe es una estructura de datos que se compone de los elementos siguientes\n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"img/dataframe_structure.png\" style=\"margin:0 auto;clear:both\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_title', 'title_year', 'color', 'aspect_ratio', 'duration',\n",
       "       'language', 'country', 'duration.1', 'genres', 'content_rating',\n",
       "       'plot_keywords', 'director_name', 'actor_1_name', 'actor_2_name',\n",
       "       'actor_3_name', 'cast_total_facebook_likes', 'budget', 'imdb_score',\n",
       "       'gross'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visualizemos las columnas\n",
    "movies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5043, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                              Avatar \n",
       "1            Pirates of the Caribbean: At World's End \n",
       "2                                             Spectre \n",
       "3                               The Dark Knight Rises \n",
       "4    Star Wars: Episode VII - The Force Awakens    ...\n",
       "Name: movie_title, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columna1 = movies['movie_title']\n",
    "columna1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                                Batman v Superman: Dawn of Justice \n",
       "title_year                                                                2016\n",
       "color                                                                    Color\n",
       "aspect_ratio                                                              2.35\n",
       "duration                                                                   183\n",
       "language                                                               English\n",
       "country                                                                    USA\n",
       "duration.1                                                                 183\n",
       "genres                                                 Action|Adventure|Sci-Fi\n",
       "content_rating                                                           PG-13\n",
       "plot_keywords                based on comic book|batman|sequel to a reboot|...\n",
       "director_name                                                      Zack Snyder\n",
       "actor_1_name                                                      Henry Cavill\n",
       "actor_2_name                                                      Lauren Cohan\n",
       "actor_3_name                                                    Alan D. Purwin\n",
       "cast_total_facebook_likes                                                24450\n",
       "budget                                                                 2.5e+08\n",
       "imdb_score                                                                 6.9\n",
       "gross                                                              3.30249e+08\n",
       "Name: 10, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linea = movies.loc[10,:]\n",
    "linea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                              Avatar \n",
       "1            Pirates of the Caribbean: At World's End \n",
       "2                                             Spectre \n",
       "3                               The Dark Knight Rises \n",
       "4    Star Wars: Episode VII - The Force Awakens    ...\n",
       "Name: movie_title, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.loc[:,'movie_title'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Intentemos inspeccionar nuestros datos y entenderlos mejor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5043 entries, 0 to 5042\n",
      "Data columns (total 19 columns):\n",
      "movie_title                  5043 non-null object\n",
      "title_year                   4935 non-null float64\n",
      "color                        5024 non-null object\n",
      "aspect_ratio                 4714 non-null float64\n",
      "duration                     5028 non-null float64\n",
      "language                     5031 non-null object\n",
      "country                      5038 non-null object\n",
      "duration.1                   5028 non-null float64\n",
      "genres                       5043 non-null object\n",
      "content_rating               4740 non-null object\n",
      "plot_keywords                4890 non-null object\n",
      "director_name                4939 non-null object\n",
      "actor_1_name                 5036 non-null object\n",
      "actor_2_name                 5030 non-null object\n",
      "actor_3_name                 5020 non-null object\n",
      "cast_total_facebook_likes    5043 non-null int64\n",
      "budget                       4551 non-null float64\n",
      "imdb_score                   5043 non-null float64\n",
      "gross                        4159 non-null float64\n",
      "dtypes: float64(7), int64(1), object(11)\n",
      "memory usage: 748.6+ KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "  A diferencia de Numpy, Pandas permite cargar no solo datos numericos pero tambien **datos de texto** que vemos por ejemplo en las columnas de actores y **mezclar distintos tipos de datos**.\n",
    "<ul>\n",
    " <li>int64 y float64 corresponden a los mismos dtypes de Numpy </li>\n",
    " <li> object es el dtype que permite manejar datos de texto </li>\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                  False\n",
       "title_year                    True\n",
       "color                        False\n",
       "aspect_ratio                  True\n",
       "duration                      True\n",
       "language                     False\n",
       "country                      False\n",
       "duration.1                    True\n",
       "genres                       False\n",
       "content_rating               False\n",
       "plot_keywords                False\n",
       "director_name                False\n",
       "actor_1_name                 False\n",
       "actor_2_name                 False\n",
       "actor_3_name                 False\n",
       "cast_total_facebook_likes    False\n",
       "budget                        True\n",
       "imdb_score                    True\n",
       "gross                         True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columnas númericas y columnas de texto\n",
    "movies.dtypes == float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                  False\n",
       "title_year                   False\n",
       "color                        False\n",
       "aspect_ratio                 False\n",
       "duration                     False\n",
       "language                     False\n",
       "country                      False\n",
       "duration.1                   False\n",
       "genres                       False\n",
       "content_rating               False\n",
       "plot_keywords                False\n",
       "director_name                False\n",
       "actor_1_name                 False\n",
       "actor_2_name                 False\n",
       "actor_3_name                 False\n",
       "cast_total_facebook_likes     True\n",
       "budget                       False\n",
       "imdb_score                   False\n",
       "gross                        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.dtypes == int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                   True\n",
       "title_year                   False\n",
       "color                         True\n",
       "aspect_ratio                 False\n",
       "duration                     False\n",
       "language                      True\n",
       "country                       True\n",
       "duration.1                   False\n",
       "genres                        True\n",
       "content_rating                True\n",
       "plot_keywords                 True\n",
       "director_name                 True\n",
       "actor_1_name                  True\n",
       "actor_2_name                  True\n",
       "actor_3_name                  True\n",
       "cast_total_facebook_likes    False\n",
       "budget                       False\n",
       "imdb_score                   False\n",
       "gross                        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.dtypes == object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                  False\n",
       "title_year                    True\n",
       "color                        False\n",
       "aspect_ratio                  True\n",
       "duration                      True\n",
       "language                     False\n",
       "country                      False\n",
       "duration.1                    True\n",
       "genres                       False\n",
       "content_rating               False\n",
       "plot_keywords                False\n",
       "director_name                False\n",
       "actor_1_name                 False\n",
       "actor_2_name                 False\n",
       "actor_3_name                 False\n",
       "cast_total_facebook_likes     True\n",
       "budget                        True\n",
       "imdb_score                    True\n",
       "gross                         True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = (movies.dtypes == float) | (movies.dtypes == int)\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_title', 'title_year', 'color', 'aspect_ratio', 'duration',\n",
       "       'language', 'country', 'duration.1', 'genres', 'content_rating',\n",
       "       'plot_keywords', 'director_name', 'actor_1_name', 'actor_2_name',\n",
       "       'actor_3_name', 'cast_total_facebook_likes', 'budget', 'imdb_score',\n",
       "       'gross'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie_title\n",
      "title_year\n",
      "color\n",
      "aspect_ratio\n",
      "duration\n",
      "language\n",
      "country\n",
      "duration.1\n",
      "genres\n",
      "content_rating\n",
      "plot_keywords\n",
      "director_name\n",
      "actor_1_name\n",
      "actor_2_name\n",
      "actor_3_name\n",
      "cast_total_facebook_likes\n",
      "budget\n",
      "imdb_score\n",
      "gross\n"
     ]
    }
   ],
   "source": [
    "for el in num.index:\n",
    "    print(el)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = [c for c in num.index if num[c]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['title_year',\n",
       " 'aspect_ratio',\n",
       " 'duration',\n",
       " 'duration.1',\n",
       " 'cast_total_facebook_likes',\n",
       " 'budget',\n",
       " 'imdb_score',\n",
       " 'gross']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                   True\n",
       "title_year                   False\n",
       "color                         True\n",
       "aspect_ratio                 False\n",
       "duration                     False\n",
       "language                      True\n",
       "country                       True\n",
       "duration.1                   False\n",
       "genres                        True\n",
       "content_rating                True\n",
       "plot_keywords                 True\n",
       "director_name                 True\n",
       "actor_1_name                  True\n",
       "actor_2_name                  True\n",
       "actor_3_name                  True\n",
       "cast_total_facebook_likes    False\n",
       "budget                       False\n",
       "imdb_score                   False\n",
       "gross                        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.dtypes == object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = (movies.dtypes == object)\n",
    "obj_cols = [c for c in obj.index if obj[c]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['movie_title',\n",
       " 'color',\n",
       " 'language',\n",
       " 'country',\n",
       " 'genres',\n",
       " 'content_rating',\n",
       " 'plot_keywords',\n",
       " 'director_name',\n",
       " 'actor_1_name',\n",
       " 'actor_2_name',\n",
       " 'actor_3_name']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['title_year',\n",
       " 'aspect_ratio',\n",
       " 'duration',\n",
       " 'duration.1',\n",
       " 'cast_total_facebook_likes',\n",
       " 'budget',\n",
       " 'imdb_score',\n",
       " 'gross']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_num = movies[num_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4935.000000</td>\n",
       "      <td>4714.000000</td>\n",
       "      <td>5028.000000</td>\n",
       "      <td>5028.000000</td>\n",
       "      <td>5043.000000</td>\n",
       "      <td>4.551000e+03</td>\n",
       "      <td>5043.000000</td>\n",
       "      <td>4.159000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2002.470517</td>\n",
       "      <td>2.220403</td>\n",
       "      <td>107.201074</td>\n",
       "      <td>107.201074</td>\n",
       "      <td>9699.063851</td>\n",
       "      <td>3.975262e+07</td>\n",
       "      <td>6.442138</td>\n",
       "      <td>4.846841e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.474599</td>\n",
       "      <td>1.385113</td>\n",
       "      <td>25.197441</td>\n",
       "      <td>25.197441</td>\n",
       "      <td>18163.799124</td>\n",
       "      <td>2.061149e+08</td>\n",
       "      <td>1.125116</td>\n",
       "      <td>6.845299e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1916.000000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.180000e+02</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.620000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>5.340988e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>3090.000000</td>\n",
       "      <td>2.000000e+07</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2.551750e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>13756.500000</td>\n",
       "      <td>4.500000e+07</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>6.230944e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>656730.000000</td>\n",
       "      <td>1.221550e+10</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>7.605058e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        title_year  aspect_ratio     duration   duration.1  \\\n",
       "count  4935.000000   4714.000000  5028.000000  5028.000000   \n",
       "mean   2002.470517      2.220403   107.201074   107.201074   \n",
       "std      12.474599      1.385113    25.197441    25.197441   \n",
       "min    1916.000000      1.180000     7.000000     7.000000   \n",
       "25%    1999.000000      1.850000    93.000000    93.000000   \n",
       "50%    2005.000000      2.350000   103.000000   103.000000   \n",
       "75%    2011.000000      2.350000   118.000000   118.000000   \n",
       "max    2016.000000     16.000000   511.000000   511.000000   \n",
       "\n",
       "       cast_total_facebook_likes        budget   imdb_score         gross  \n",
       "count                5043.000000  4.551000e+03  5043.000000  4.159000e+03  \n",
       "mean                 9699.063851  3.975262e+07     6.442138  4.846841e+07  \n",
       "std                 18163.799124  2.061149e+08     1.125116  6.845299e+07  \n",
       "min                     0.000000  2.180000e+02     1.600000  1.620000e+02  \n",
       "25%                  1411.000000  6.000000e+06     5.800000  5.340988e+06  \n",
       "50%                  3090.000000  2.000000e+07     6.600000  2.551750e+07  \n",
       "75%                 13756.500000  4.500000e+07     7.200000  6.230944e+07  \n",
       "max                656730.000000  1.221550e+10     9.500000  7.605058e+08  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estadísticas de las columnas númericas\n",
    "movies_num.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estadísticas de las columnas de texto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para hacer nuestro primer modelo más simple para este trabajaremos solo con las columnas númericas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f22bfda24a8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4BvCVzNxJIxCuLIeVLgfuBsjMR4EfRMT3gD8Hbmpp5pKkWTHjPYbM/AGwrkl9BLjiFNvc\nMtPxJEmd4SefJUkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkV\ng0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEkVg0GSVDEYJEmVhd2e\ngLpn/MQJ9u59Yda+/+hoPyMjY1Vt9erzWbBgwayNKal1BsMb2A+PDHPvtldYNPByR8Z77fBB7v/o\n1axZs7Yj40maGYPhDW7RwDL6l6zs9jQkzSHzKhie/NrfcXD4Xzoy1sGhl4H+jowlSXPJvAqGv/na\nP3Lo7J/uyFhjo0c6Mo4kzTUdD4aIuAq4j8YVUZsy855Oz0GSdGodvVw1Is4CPgW8G/jPwHsj4mc6\nOQdJ0ul1eo/hYuD5zHwBICK2AhuA73Z4HuqC2b48thkvj5XOXKeDYSWwb9Lj/TTCQm8Anb489tVD\nB/if113EueeeNyvf389pqFd1Ohj6mtTGp7vx0dde4cSRH7VxOqd24vAr/NtZb+nIWBN+eGSE5j+i\n3hnvzYvf2rHx/m1slD9+8Kv8RP/SDo03wv/68JWzFkSd1iz4esXpevNzNp0Phv3AuZMerwJeOs36\nfYODi3/8YOsmz1NL0mzrdDA8A1wQEecBLwPXAe/t8BwkSafR0auSMvM4cAuwE/hHYGtmPtfJOUiS\nTq9vfHzah/glSW8A3nZbklQxGCRJFYNBklSZszfR64V7KkXEJuA9wFBmvqPUlgDbgPOAPcC1mXm4\nPPcAsB54FdiYmbu6Me/piIhVwBZgBXAceDAzH+ih/t4E/C1wNo1/J49k5l0RsRrYCiwBvg38XmYe\ni4izafw8fh54BfjtzNzblcmfgXKbmm8B+zPz6l7qLyL2AIeBE8DRzLy4V16fABExAHwW+C80erwB\n+Cfa0N+c3GPooXsqPUSjh8luA57IzACeBG4HiIj1wJrMXAvcCHymkxOdgWPARzLz7cAvATeX31FP\n9JeZPwIuy8yLgHXA+oh4J3APcG/p7xDwwbLJB4GR0t99wJ90YdozcSuwe9LjXurvBHBpZl6UmRN3\nWOiJ12dxP/BoZl4I/CyNWwu1pb85GQxMuqdSZh6l8Q5mQ5fndMYy8+vA6EnlDcDmsryZ1/vaQOMd\nGZn5NDAQEcs7Mc+ZyMwDE+84MnMMeI7GBxZ7oj+AzHytLL6Jxl7DOHAZ8OVS3wz8Zlme3PcjwOUd\nmuaMlb2+X6fxrnPCr9Ij/dH4WP/Jf+N64vUZEYuBX8nMhwAy81jZM2hLf3M1GJrdU6lX/jdjyzJz\nCBp/XIFlpX5yzy8yT3ouhx/WAd8AlvdKfxFxVkQ8CxwAvgr8M3AoM0+UVSa/Ln/cX/m8zqGI6My9\nOGbuk8BHKbeliYi3AqM91N848HhEPBMRHyq1Xnl9ng+8EhEPRcS3I+IvImIRbepvrgZDS/dUmqfm\nZc8R0U/jHeStZc/hVHOed/1l5olyKGkVjb3YC5usNtHDyf31MYf7i4jfoHHuaxevz72P/7+Pedlf\n8cuZ+Qs09opujohfoXdenwuBnwP+LDN/jsZ5g9toU39zNRjO9J5K88nQxC5cRKwADpb6fuCcSevN\n+Z4jYiGNUPhCZm4v5Z7pb0Jm/ivwNeAS4C3lHBjUPfy4v4hYAPzHzDz5MOJc8i7g6oj4PvB/aBxC\nuo/GIYZe6G/iHTOZOQz8NY1w75XX535gX2Z+qzz+Mo2gaEt/czUYfnxPpXI1xHXAji7PaaZOfhe2\nA9hYljcC2yfV3w8QEZfQOGQx1JkpztjngN2Zef+kWk/0FxFvK1d9EBFvBq6gcZL2KeCastr11P1d\nX5avoXHib87KzDsy89zMPJ/Gv68nM/N36ZH+ImJR2ZslIn4S+DXgO/TI67PMbV9ETPy/ji+ncZuh\ntvQ3Z2+JUS5XvZ/XL1e9u8tTOmMR8SXgUuCtwBBwJ413Ln9JI733Atdk5qGy/qeAq2jsFn4gM7/d\nhWlPS0S8i8blnN+hsUs6DtwBfBN4mPnf33+lcfLurPK1LTM/FhH/idcv53wW+N3MPFoub/0CcBHw\nL8B1mbmnK5M/QxHx34D/US5X7Yn+Sh9/ReN1uRD4YmbeXc6LzPvXJ0BE/CyNCwf+A/B94APAAtrQ\n35wNBklSd8zVQ0mSpC4xGCRJFYNBklQxGCRJFYNBklQxGCRJFYNBklQxGCRJlX8H3wNW2KWk3h8A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22bfd92da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies_num['duration'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f22bfd3ea58>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22bfd13940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies_num['imdb_score'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f22bfc60ba8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C3A4cnZkPdLHFMTXpPSLOAK4AdgD/k5mf7G6XY2vwf2cWsBB4QxlzaWb+W9cbHUNELAA+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\nZfoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22bfc6a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies_num['budget'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "Para poder debuggear esta situación utilizaremos una tecnica muy tipica de pandas que se llama **boolean filtering**. Basicamente construimos una serie de booleanos con el mismo indice que nuestro dataframe (la serie clasicamente la llamaremos mask).\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (movies_num['budget'] > 1e9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>color</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>genres</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>director_name</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>Princess Mononoke</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>1.85</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Japan</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Adventure|Animation|Fantasy</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>anime|cult film|forest|princess|studio ghibli</td>\n",
       "      <td>Hayao Miyazaki</td>\n",
       "      <td>Minnie Driver</td>\n",
       "      <td>Jada Pinkett Smith</td>\n",
       "      <td>Billy Crudup</td>\n",
       "      <td>2710</td>\n",
       "      <td>2.400000e+09</td>\n",
       "      <td>8.4</td>\n",
       "      <td>2298191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>Steamboy</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>1.85</td>\n",
       "      <td>103.0</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Japan</td>\n",
       "      <td>103.0</td>\n",
       "      <td>Action|Adventure|Animation|Family|Sci-Fi|Thriller</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>19th century|ball|boy|inventor|steam</td>\n",
       "      <td>Katsuhiro Ôtomo</td>\n",
       "      <td>William Hootkins</td>\n",
       "      <td>Robin Atkin Downes</td>\n",
       "      <td>Rosalind Ayres</td>\n",
       "      <td>991</td>\n",
       "      <td>2.127520e+09</td>\n",
       "      <td>6.9</td>\n",
       "      <td>410388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>The Host</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>1.85</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Korean</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Comedy|Drama|Horror|Sci-Fi</td>\n",
       "      <td>R</td>\n",
       "      <td>daughter|han river|monster|river|seoul</td>\n",
       "      <td>Joon-ho Bong</td>\n",
       "      <td>Doona Bae</td>\n",
       "      <td>Kang-ho Song</td>\n",
       "      <td>Ah-sung Ko</td>\n",
       "      <td>1173</td>\n",
       "      <td>1.221550e+10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2201412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>Fateless</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>2.35</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Hungarian</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Drama|Romance|War</td>\n",
       "      <td>R</td>\n",
       "      <td>bus|death|gay slur|hatred|jewish</td>\n",
       "      <td>Lajos Koltai</td>\n",
       "      <td>Marcell Nagy</td>\n",
       "      <td>Péter Fancsikai</td>\n",
       "      <td>Bálint Péntek</td>\n",
       "      <td>11</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>7.1</td>\n",
       "      <td>195888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423</th>\n",
       "      <td>Akira</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>1.85</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Japan</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Action|Animation|Sci-Fi</td>\n",
       "      <td>R</td>\n",
       "      <td>based on manga|biker gang|gifted child|post th...</td>\n",
       "      <td>Katsuhiro Ôtomo</td>\n",
       "      <td>Mitsuo Iwata</td>\n",
       "      <td>Takeshi Kusao</td>\n",
       "      <td>Tesshô Genda</td>\n",
       "      <td>28</td>\n",
       "      <td>1.100000e+09</td>\n",
       "      <td>8.1</td>\n",
       "      <td>439162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>Lady Vengeance</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Color</td>\n",
       "      <td>2.35</td>\n",
       "      <td>112.0</td>\n",
       "      <td>Korean</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>112.0</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>R</td>\n",
       "      <td>cake|christian|lesbian sex|oral sex|pregnant s...</td>\n",
       "      <td>Chan-wook Park</td>\n",
       "      <td>Min-sik Choi</td>\n",
       "      <td>Yeong-ae Lee</td>\n",
       "      <td>Hye-jeong Kang</td>\n",
       "      <td>907</td>\n",
       "      <td>4.200000e+09</td>\n",
       "      <td>7.7</td>\n",
       "      <td>211667.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             movie_title  title_year  color  aspect_ratio  duration  \\\n",
       "2323  Princess Mononoke       1997.0  Color          1.85     134.0   \n",
       "2334           Steamboy       2004.0  Color          1.85     103.0   \n",
       "2988           The Host       2006.0  Color          1.85     110.0   \n",
       "3005           Fateless       2005.0  Color          2.35     134.0   \n",
       "3423              Akira       1988.0  Color          1.85     124.0   \n",
       "3859     Lady Vengeance       2005.0  Color          2.35     112.0   \n",
       "\n",
       "       language      country  duration.1  \\\n",
       "2323   Japanese        Japan       134.0   \n",
       "2334   Japanese        Japan       103.0   \n",
       "2988     Korean  South Korea       110.0   \n",
       "3005  Hungarian      Hungary       134.0   \n",
       "3423   Japanese        Japan       124.0   \n",
       "3859     Korean  South Korea       112.0   \n",
       "\n",
       "                                                 genres content_rating  \\\n",
       "2323                        Adventure|Animation|Fantasy          PG-13   \n",
       "2334  Action|Adventure|Animation|Family|Sci-Fi|Thriller          PG-13   \n",
       "2988                         Comedy|Drama|Horror|Sci-Fi              R   \n",
       "3005                                  Drama|Romance|War              R   \n",
       "3423                            Action|Animation|Sci-Fi              R   \n",
       "3859                                        Crime|Drama              R   \n",
       "\n",
       "                                          plot_keywords    director_name  \\\n",
       "2323      anime|cult film|forest|princess|studio ghibli   Hayao Miyazaki   \n",
       "2334               19th century|ball|boy|inventor|steam  Katsuhiro Ôtomo   \n",
       "2988             daughter|han river|monster|river|seoul     Joon-ho Bong   \n",
       "3005                   bus|death|gay slur|hatred|jewish     Lajos Koltai   \n",
       "3423  based on manga|biker gang|gifted child|post th...  Katsuhiro Ôtomo   \n",
       "3859  cake|christian|lesbian sex|oral sex|pregnant s...   Chan-wook Park   \n",
       "\n",
       "          actor_1_name        actor_2_name    actor_3_name  \\\n",
       "2323     Minnie Driver  Jada Pinkett Smith    Billy Crudup   \n",
       "2334  William Hootkins  Robin Atkin Downes  Rosalind Ayres   \n",
       "2988         Doona Bae        Kang-ho Song      Ah-sung Ko   \n",
       "3005      Marcell Nagy     Péter Fancsikai   Bálint Péntek   \n",
       "3423      Mitsuo Iwata       Takeshi Kusao    Tesshô Genda   \n",
       "3859      Min-sik Choi        Yeong-ae Lee  Hye-jeong Kang   \n",
       "\n",
       "      cast_total_facebook_likes        budget  imdb_score      gross  \n",
       "2323                       2710  2.400000e+09         8.4  2298191.0  \n",
       "2334                        991  2.127520e+09         6.9   410388.0  \n",
       "2988                       1173  1.221550e+10         7.0  2201412.0  \n",
       "3005                         11  2.500000e+09         7.1   195888.0  \n",
       "3423                         28  1.100000e+09         8.1   439162.0  \n",
       "3859                        907  4.200000e+09         7.7   211667.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"img/akira.png\" width=\"1000\" style=\"margin:0 auto;clear:both\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Tenemos un problema de limpieza de los datos. La BDD fue creada sin diferenciar:\n",
    "- La moneda en la que se ingresaba el presupuesto y el ingreso. \n",
    "- La zona (país/mundial) en la que se registro el ingreso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importar BBDD thenumbers.com## Ahora manejaremos los datos faltantes (nulos o NaN). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>movie_name</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>movie_link</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>release_date</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>760507625</td>\n",
       "      <td>http://www.the-numbers.com/movie/Avatar#tab=su...</td>\n",
       "      <td>425000000</td>\n",
       "      <td>12/18/2009</td>\n",
       "      <td>2783918982</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Star Wars Ep. VII: The Force Awakens</td>\n",
       "      <td>936662225</td>\n",
       "      <td>http://www.the-numbers.com/movie/Star-Wars-Ep-...</td>\n",
       "      <td>306000000</td>\n",
       "      <td>12/18/2015</td>\n",
       "      <td>2058662225</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>309420425</td>\n",
       "      <td>http://www.the-numbers.com/movie/Pirates-of-th...</td>\n",
       "      <td>300000000</td>\n",
       "      <td>5/24/2007</td>\n",
       "      <td>963420425</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>200074175</td>\n",
       "      <td>http://www.the-numbers.com/movie/Spectre#tab=s...</td>\n",
       "      <td>300000000</td>\n",
       "      <td>11/6/2015</td>\n",
       "      <td>879620923</td>\n",
       "      <td>Spectre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>448139099</td>\n",
       "      <td>http://www.the-numbers.com/movie/Dark-Knight-R...</td>\n",
       "      <td>275000000</td>\n",
       "      <td>7/20/2012</td>\n",
       "      <td>1084439099</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>The Lone Ranger</td>\n",
       "      <td>89302115</td>\n",
       "      <td>http://www.the-numbers.com/movie/Lone-Ranger-T...</td>\n",
       "      <td>275000000</td>\n",
       "      <td>7/2/2013</td>\n",
       "      <td>260002115</td>\n",
       "      <td>The Lone Ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>73058679</td>\n",
       "      <td>http://www.the-numbers.com/movie/John-Carter-o...</td>\n",
       "      <td>275000000</td>\n",
       "      <td>3/9/2012</td>\n",
       "      <td>282778100</td>\n",
       "      <td>John Carter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Tangled</td>\n",
       "      <td>200821936</td>\n",
       "      <td>http://www.the-numbers.com/movie/Tangled#tab=s...</td>\n",
       "      <td>260000000</td>\n",
       "      <td>11/24/2010</td>\n",
       "      <td>586581936</td>\n",
       "      <td>Tangled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Spider-Man 3</td>\n",
       "      <td>336530303</td>\n",
       "      <td>http://www.the-numbers.com/movie/Spider-Man-3#...</td>\n",
       "      <td>258000000</td>\n",
       "      <td>5/4/2007</td>\n",
       "      <td>890875303</td>\n",
       "      <td>Spider-Man 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>459005868</td>\n",
       "      <td>http://www.the-numbers.com/movie/Avengers-Age-...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>5/1/2015</td>\n",
       "      <td>1404705868</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>407264745</td>\n",
       "      <td>http://www.the-numbers.com/movie/Captain-Ameri...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>5/6/2016</td>\n",
       "      <td>1150864745</td>\n",
       "      <td>Captain America: Civil War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Batman v Superman: Dawn of Justice</td>\n",
       "      <td>330360194</td>\n",
       "      <td>http://www.the-numbers.com/movie/Batman-v-Supe...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>3/25/2016</td>\n",
       "      <td>868160194</td>\n",
       "      <td>Batman v Superman: Dawn of Justice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>The Hobbit: An Unexpected Journey</td>\n",
       "      <td>303003568</td>\n",
       "      <td>http://www.the-numbers.com/movie/Hobbit-An-Une...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>12/14/2012</td>\n",
       "      <td>1017003568</td>\n",
       "      <td>The Hobbit: An Unexpected Journey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
       "      <td>301959197</td>\n",
       "      <td>http://www.the-numbers.com/movie/Harry-Potter-...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>7/15/2009</td>\n",
       "      <td>935083686</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>The Hobbit: The Desolation of Smaug</td>\n",
       "      <td>258366855</td>\n",
       "      <td>http://www.the-numbers.com/movie/Hobbit-The-De...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>12/13/2013</td>\n",
       "      <td>960366855</td>\n",
       "      <td>The Hobbit: The Desolation of Smaug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>The Hobbit: The Battle of the Five Armies</td>\n",
       "      <td>255119788</td>\n",
       "      <td>http://www.the-numbers.com/movie/Hobbit-The-Ba...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>12/17/2014</td>\n",
       "      <td>955119788</td>\n",
       "      <td>The Hobbit: The Battle of the Five Armies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>241063875</td>\n",
       "      <td>http://www.the-numbers.com/movie/Pirates-of-th...</td>\n",
       "      <td>250000000</td>\n",
       "      <td>5/20/2011</td>\n",
       "      <td>1045663875</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Superman Returns</td>\n",
       "      <td>200120000</td>\n",
       "      <td>http://www.the-numbers.com/movie/Superman-Retu...</td>\n",
       "      <td>232000000</td>\n",
       "      <td>6/28/2006</td>\n",
       "      <td>374085065</td>\n",
       "      <td>Superman Returns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Quantum of Solace</td>\n",
       "      <td>169368427</td>\n",
       "      <td>http://www.the-numbers.com/movie/Quantum-of-So...</td>\n",
       "      <td>230000000</td>\n",
       "      <td>11/14/2008</td>\n",
       "      <td>591692078</td>\n",
       "      <td>Quantum of Solace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Pirates of the Caribbean: Dead Man's Chest</td>\n",
       "      <td>423315812</td>\n",
       "      <td>http://www.the-numbers.com/movie/Pirates-of-th...</td>\n",
       "      <td>225000000</td>\n",
       "      <td>7/7/2006</td>\n",
       "      <td>1066215812</td>\n",
       "      <td>Pirates of the Caribbean: Dead Man's Chest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Man of Steel</td>\n",
       "      <td>291045518</td>\n",
       "      <td>http://www.the-numbers.com/movie/Man-of-Steel#...</td>\n",
       "      <td>225000000</td>\n",
       "      <td>6/14/2013</td>\n",
       "      <td>667999518</td>\n",
       "      <td>Man of Steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>The Chronicles of Narnia: Prince Caspian</td>\n",
       "      <td>141621490</td>\n",
       "      <td>http://www.the-numbers.com/movie/Chronicles-of...</td>\n",
       "      <td>225000000</td>\n",
       "      <td>5/16/2008</td>\n",
       "      <td>417341288</td>\n",
       "      <td>The Chronicles of Narnia: Prince Caspian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>The Amazing Spider-Man</td>\n",
       "      <td>262030663</td>\n",
       "      <td>http://www.the-numbers.com/movie/Amazing-Spide...</td>\n",
       "      <td>220000000</td>\n",
       "      <td>7/3/2012</td>\n",
       "      <td>757890267</td>\n",
       "      <td>The Amazing Spider-Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>652198010</td>\n",
       "      <td>http://www.the-numbers.com/movie/Jurassic-Worl...</td>\n",
       "      <td>215000000</td>\n",
       "      <td>6/12/2015</td>\n",
       "      <td>1670328025</td>\n",
       "      <td>Jurassic World</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>Transformers: Revenge of the Fallen</td>\n",
       "      <td>402111870</td>\n",
       "      <td>http://www.the-numbers.com/movie/Transformers-...</td>\n",
       "      <td>210000000</td>\n",
       "      <td>6/24/2009</td>\n",
       "      <td>836519699</td>\n",
       "      <td>Transformers: Revenge of the Fallen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27</td>\n",
       "      <td>Transformers: Age of Extinction</td>\n",
       "      <td>245439076</td>\n",
       "      <td>http://www.the-numbers.com/movie/Transformers-...</td>\n",
       "      <td>210000000</td>\n",
       "      <td>6/27/2014</td>\n",
       "      <td>1104039076</td>\n",
       "      <td>Transformers: Age of Extinction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28</td>\n",
       "      <td>X-Men: The Last Stand</td>\n",
       "      <td>234362462</td>\n",
       "      <td>http://www.the-numbers.com/movie/X-Men-The-Las...</td>\n",
       "      <td>210000000</td>\n",
       "      <td>5/26/2006</td>\n",
       "      <td>459359555</td>\n",
       "      <td>X-Men: The Last Stand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29</td>\n",
       "      <td>Robin Hood</td>\n",
       "      <td>105269730</td>\n",
       "      <td>http://www.the-numbers.com/movie/Robin-Hood-(2...</td>\n",
       "      <td>210000000</td>\n",
       "      <td>5/14/2010</td>\n",
       "      <td>322241588</td>\n",
       "      <td>Robin Hood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>Battleship</td>\n",
       "      <td>65233400</td>\n",
       "      <td>http://www.the-numbers.com/movie/Battleship#ta...</td>\n",
       "      <td>209000000</td>\n",
       "      <td>5/18/2012</td>\n",
       "      <td>305218228</td>\n",
       "      <td>Battleship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>33</td>\n",
       "      <td>Titanic</td>\n",
       "      <td>658672302</td>\n",
       "      <td>http://www.the-numbers.com/movie/Titanic#tab=s...</td>\n",
       "      <td>200000000</td>\n",
       "      <td>12/19/1997</td>\n",
       "      <td>2207615668</td>\n",
       "      <td>Titanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4311</th>\n",
       "      <td>5120</td>\n",
       "      <td>The Legend of God's Gun</td>\n",
       "      <td>243768</td>\n",
       "      <td>http://www.the-numbers.com/movie/Legend-of-God...</td>\n",
       "      <td>30000</td>\n",
       "      <td>7/18/2008</td>\n",
       "      <td>243768</td>\n",
       "      <td>The Legend of God's Gun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4312</th>\n",
       "      <td>5121</td>\n",
       "      <td>Mutual Appreciation</td>\n",
       "      <td>103509</td>\n",
       "      <td>http://www.the-numbers.com/movie/Mutual-Apprec...</td>\n",
       "      <td>30000</td>\n",
       "      <td>9/1/2006</td>\n",
       "      <td>103509</td>\n",
       "      <td>Mutual Appreciation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4313</th>\n",
       "      <td>5122</td>\n",
       "      <td>Funny Ha Ha</td>\n",
       "      <td>77070</td>\n",
       "      <td>http://www.the-numbers.com/movie/Funny-Ha-Ha#t...</td>\n",
       "      <td>30000</td>\n",
       "      <td>4/29/2005</td>\n",
       "      <td>82698</td>\n",
       "      <td>Funny Ha Ha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4314</th>\n",
       "      <td>5123</td>\n",
       "      <td>Down Terrace</td>\n",
       "      <td>9812</td>\n",
       "      <td>http://www.the-numbers.com/movie/Down-Terrace#...</td>\n",
       "      <td>30000</td>\n",
       "      <td>10/15/2010</td>\n",
       "      <td>9812</td>\n",
       "      <td>Down Terrace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4315</th>\n",
       "      <td>5125</td>\n",
       "      <td>Clerks</td>\n",
       "      <td>3073428</td>\n",
       "      <td>http://www.the-numbers.com/movie/Clerks#tab=su...</td>\n",
       "      <td>27000</td>\n",
       "      <td>10/19/1994</td>\n",
       "      <td>3894240</td>\n",
       "      <td>Clerks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4316</th>\n",
       "      <td>5126</td>\n",
       "      <td>Pink Narcissus</td>\n",
       "      <td>8231</td>\n",
       "      <td>http://www.the-numbers.com/movie/Pink-Narcissu...</td>\n",
       "      <td>27000</td>\n",
       "      <td>12/24/1999</td>\n",
       "      <td>8231</td>\n",
       "      <td>Pink Narcissus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4317</th>\n",
       "      <td>5128</td>\n",
       "      <td>In the Company of Men</td>\n",
       "      <td>2883661</td>\n",
       "      <td>http://www.the-numbers.com/movie/In-the-Compan...</td>\n",
       "      <td>25000</td>\n",
       "      <td>8/1/1997</td>\n",
       "      <td>2883661</td>\n",
       "      <td>In the Company of Men</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4318</th>\n",
       "      <td>5131</td>\n",
       "      <td>Manito</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Manito#tab=su...</td>\n",
       "      <td>25000</td>\n",
       "      <td>6/13/2003</td>\n",
       "      <td>0</td>\n",
       "      <td>Manito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4319</th>\n",
       "      <td>5133</td>\n",
       "      <td>Dutch Kills</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Dutch-Kills#t...</td>\n",
       "      <td>25000</td>\n",
       "      <td>12/1/2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Dutch Kills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4320</th>\n",
       "      <td>5134</td>\n",
       "      <td>Slacker</td>\n",
       "      <td>1227508</td>\n",
       "      <td>http://www.the-numbers.com/movie/Slacker#tab=s...</td>\n",
       "      <td>23000</td>\n",
       "      <td>8/1/1991</td>\n",
       "      <td>1227508</td>\n",
       "      <td>Slacker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4321</th>\n",
       "      <td>5135</td>\n",
       "      <td>Dry Spell</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Dry-Spell#tab...</td>\n",
       "      <td>22000</td>\n",
       "      <td>12/31/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>Dry Spell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322</th>\n",
       "      <td>5136</td>\n",
       "      <td>Flywheel</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Flywheel#tab=...</td>\n",
       "      <td>20000</td>\n",
       "      <td>12/31/2003</td>\n",
       "      <td>0</td>\n",
       "      <td>Flywheel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4323</th>\n",
       "      <td>5137</td>\n",
       "      <td>All Superheroes Must Die</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/All-Superhero...</td>\n",
       "      <td>20000</td>\n",
       "      <td>1/4/2013</td>\n",
       "      <td>0</td>\n",
       "      <td>All Superheroes Must Die</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4324</th>\n",
       "      <td>5139</td>\n",
       "      <td>The Ridges</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Ridges-The#ta...</td>\n",
       "      <td>17300</td>\n",
       "      <td>11/25/2011</td>\n",
       "      <td>0</td>\n",
       "      <td>The Ridges</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4325</th>\n",
       "      <td>5140</td>\n",
       "      <td>The Puffy Chair</td>\n",
       "      <td>194523</td>\n",
       "      <td>http://www.the-numbers.com/movie/Puffy-Chair-T...</td>\n",
       "      <td>15000</td>\n",
       "      <td>6/2/2006</td>\n",
       "      <td>195254</td>\n",
       "      <td>The Puffy Chair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4326</th>\n",
       "      <td>5141</td>\n",
       "      <td>Breaking Upwards</td>\n",
       "      <td>115592</td>\n",
       "      <td>http://www.the-numbers.com/movie/Breaking-Upwa...</td>\n",
       "      <td>15000</td>\n",
       "      <td>4/2/2010</td>\n",
       "      <td>115592</td>\n",
       "      <td>Breaking Upwards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4327</th>\n",
       "      <td>5142</td>\n",
       "      <td>Stories of Our Lives</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Stories-of-Ou...</td>\n",
       "      <td>15000</td>\n",
       "      <td>12/31/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>Stories of Our Lives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4328</th>\n",
       "      <td>5143</td>\n",
       "      <td>Pink Flamingos</td>\n",
       "      <td>413802</td>\n",
       "      <td>http://www.the-numbers.com/movie/Pink-Flamingo...</td>\n",
       "      <td>12000</td>\n",
       "      <td>4/11/1997</td>\n",
       "      <td>413802</td>\n",
       "      <td>Pink Flamingos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4329</th>\n",
       "      <td>5147</td>\n",
       "      <td>Clean</td>\n",
       "      <td>138711</td>\n",
       "      <td>http://www.the-numbers.com/movie/Clean#tab=sum...</td>\n",
       "      <td>10000</td>\n",
       "      <td>4/28/2006</td>\n",
       "      <td>138711</td>\n",
       "      <td>Clean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4330</th>\n",
       "      <td>5149</td>\n",
       "      <td>On the Down Low</td>\n",
       "      <td>1987</td>\n",
       "      <td>http://www.the-numbers.com/movie/On-the-Down-L...</td>\n",
       "      <td>10000</td>\n",
       "      <td>5/28/2004</td>\n",
       "      <td>1987</td>\n",
       "      <td>On the Downlow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4331</th>\n",
       "      <td>5150</td>\n",
       "      <td>Bang</td>\n",
       "      <td>527</td>\n",
       "      <td>http://www.the-numbers.com/movie/Bang#tab=summary</td>\n",
       "      <td>10000</td>\n",
       "      <td>4/1/1996</td>\n",
       "      <td>527</td>\n",
       "      <td>Bang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4332</th>\n",
       "      <td>5153</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>4584</td>\n",
       "      <td>http://www.the-numbers.com/movie/Newlyweds#tab...</td>\n",
       "      <td>9000</td>\n",
       "      <td>1/13/2012</td>\n",
       "      <td>4584</td>\n",
       "      <td>Newlyweds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>5154</td>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>2040920</td>\n",
       "      <td>http://www.the-numbers.com/movie/El-Mariachi#t...</td>\n",
       "      <td>7000</td>\n",
       "      <td>2/26/1993</td>\n",
       "      <td>2041928</td>\n",
       "      <td>El Mariachi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4334</th>\n",
       "      <td>5155</td>\n",
       "      <td>Primer</td>\n",
       "      <td>424760</td>\n",
       "      <td>http://www.the-numbers.com/movie/Primer#tab=su...</td>\n",
       "      <td>7000</td>\n",
       "      <td>10/8/2004</td>\n",
       "      <td>841926</td>\n",
       "      <td>Primer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4335</th>\n",
       "      <td>5156</td>\n",
       "      <td>Cavite</td>\n",
       "      <td>70071</td>\n",
       "      <td>http://www.the-numbers.com/movie/Cavite#tab=su...</td>\n",
       "      <td>7000</td>\n",
       "      <td>5/26/2006</td>\n",
       "      <td>71644</td>\n",
       "      <td>Cavite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4336</th>\n",
       "      <td>5157</td>\n",
       "      <td>The Mongol King</td>\n",
       "      <td>900</td>\n",
       "      <td>http://www.the-numbers.com/movie/Mongol-King-T...</td>\n",
       "      <td>7000</td>\n",
       "      <td>1/1/2004</td>\n",
       "      <td>900</td>\n",
       "      <td>The Mongol King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>5160</td>\n",
       "      <td>Signed Sealed Delivered</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Signed-Sealed...</td>\n",
       "      <td>5000</td>\n",
       "      <td>9/29/2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Signed Sealed Delivered</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4338</th>\n",
       "      <td>5161</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>10443</td>\n",
       "      <td>http://www.the-numbers.com/movie/Shanghai-Call...</td>\n",
       "      <td>3967</td>\n",
       "      <td>2/15/2013</td>\n",
       "      <td>10443</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4339</th>\n",
       "      <td>5162</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.the-numbers.com/movie/Plague-So-Ple...</td>\n",
       "      <td>1400</td>\n",
       "      <td>9/29/2015</td>\n",
       "      <td>0</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4340</th>\n",
       "      <td>5163</td>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>181041</td>\n",
       "      <td>http://www.the-numbers.com/movie/My-Date-With-...</td>\n",
       "      <td>1100</td>\n",
       "      <td>8/5/2005</td>\n",
       "      <td>181041</td>\n",
       "      <td>My Date with Drew</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4341 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                   movie_name  domestic_gross  \\\n",
       "0              0                                       Avatar       760507625   \n",
       "1              1         Star Wars Ep. VII: The Force Awakens       936662225   \n",
       "2              2     Pirates of the Caribbean: At World's End       309420425   \n",
       "3              3                                      Spectre       200074175   \n",
       "4              4                        The Dark Knight Rises       448139099   \n",
       "5              5                              The Lone Ranger        89302115   \n",
       "6              6                                  John Carter        73058679   \n",
       "7              7                                      Tangled       200821936   \n",
       "8              8                                 Spider-Man 3       336530303   \n",
       "9              9                      Avengers: Age of Ultron       459005868   \n",
       "10            10                   Captain America: Civil War       407264745   \n",
       "11            11           Batman v Superman: Dawn of Justice       330360194   \n",
       "12            12            The Hobbit: An Unexpected Journey       303003568   \n",
       "13            13       Harry Potter and the Half-Blood Prince       301959197   \n",
       "14            14          The Hobbit: The Desolation of Smaug       258366855   \n",
       "15            15    The Hobbit: The Battle of the Five Armies       255119788   \n",
       "16            16  Pirates of the Caribbean: On Stranger Tides       241063875   \n",
       "17            17                             Superman Returns       200120000   \n",
       "18            18                            Quantum of Solace       169368427   \n",
       "19            20   Pirates of the Caribbean: Dead Man's Chest       423315812   \n",
       "20            21                                 Man of Steel       291045518   \n",
       "21            22     The Chronicles of Narnia: Prince Caspian       141621490   \n",
       "22            23                       The Amazing Spider-Man       262030663   \n",
       "23            24                               Jurassic World       652198010   \n",
       "24            26          Transformers: Revenge of the Fallen       402111870   \n",
       "25            27              Transformers: Age of Extinction       245439076   \n",
       "26            28                        X-Men: The Last Stand       234362462   \n",
       "27            29                                   Robin Hood       105269730   \n",
       "28            30                                   Battleship        65233400   \n",
       "29            33                                      Titanic       658672302   \n",
       "...          ...                                          ...             ...   \n",
       "4311        5120                      The Legend of God's Gun          243768   \n",
       "4312        5121                          Mutual Appreciation          103509   \n",
       "4313        5122                                  Funny Ha Ha           77070   \n",
       "4314        5123                                 Down Terrace            9812   \n",
       "4315        5125                                       Clerks         3073428   \n",
       "4316        5126                               Pink Narcissus            8231   \n",
       "4317        5128                        In the Company of Men         2883661   \n",
       "4318        5131                                       Manito               0   \n",
       "4319        5133                                  Dutch Kills               0   \n",
       "4320        5134                                      Slacker         1227508   \n",
       "4321        5135                                    Dry Spell               0   \n",
       "4322        5136                                     Flywheel               0   \n",
       "4323        5137                     All Superheroes Must Die               0   \n",
       "4324        5139                                   The Ridges               0   \n",
       "4325        5140                              The Puffy Chair          194523   \n",
       "4326        5141                             Breaking Upwards          115592   \n",
       "4327        5142                         Stories of Our Lives               0   \n",
       "4328        5143                               Pink Flamingos          413802   \n",
       "4329        5147                                        Clean          138711   \n",
       "4330        5149                              On the Down Low            1987   \n",
       "4331        5150                                         Bang             527   \n",
       "4332        5153                                    Newlyweds            4584   \n",
       "4333        5154                                  El Mariachi         2040920   \n",
       "4334        5155                                       Primer          424760   \n",
       "4335        5156                                       Cavite           70071   \n",
       "4336        5157                              The Mongol King             900   \n",
       "4337        5160                      Signed Sealed Delivered               0   \n",
       "4338        5161                             Shanghai Calling           10443   \n",
       "4339        5162                         A Plague So Pleasant               0   \n",
       "4340        5163                            My Date With Drew          181041   \n",
       "\n",
       "                                             movie_link  production_budget  \\\n",
       "0     http://www.the-numbers.com/movie/Avatar#tab=su...          425000000   \n",
       "1     http://www.the-numbers.com/movie/Star-Wars-Ep-...          306000000   \n",
       "2     http://www.the-numbers.com/movie/Pirates-of-th...          300000000   \n",
       "3     http://www.the-numbers.com/movie/Spectre#tab=s...          300000000   \n",
       "4     http://www.the-numbers.com/movie/Dark-Knight-R...          275000000   \n",
       "5     http://www.the-numbers.com/movie/Lone-Ranger-T...          275000000   \n",
       "6     http://www.the-numbers.com/movie/John-Carter-o...          275000000   \n",
       "7     http://www.the-numbers.com/movie/Tangled#tab=s...          260000000   \n",
       "8     http://www.the-numbers.com/movie/Spider-Man-3#...          258000000   \n",
       "9     http://www.the-numbers.com/movie/Avengers-Age-...          250000000   \n",
       "10    http://www.the-numbers.com/movie/Captain-Ameri...          250000000   \n",
       "11    http://www.the-numbers.com/movie/Batman-v-Supe...          250000000   \n",
       "12    http://www.the-numbers.com/movie/Hobbit-An-Une...          250000000   \n",
       "13    http://www.the-numbers.com/movie/Harry-Potter-...          250000000   \n",
       "14    http://www.the-numbers.com/movie/Hobbit-The-De...          250000000   \n",
       "15    http://www.the-numbers.com/movie/Hobbit-The-Ba...          250000000   \n",
       "16    http://www.the-numbers.com/movie/Pirates-of-th...          250000000   \n",
       "17    http://www.the-numbers.com/movie/Superman-Retu...          232000000   \n",
       "18    http://www.the-numbers.com/movie/Quantum-of-So...          230000000   \n",
       "19    http://www.the-numbers.com/movie/Pirates-of-th...          225000000   \n",
       "20    http://www.the-numbers.com/movie/Man-of-Steel#...          225000000   \n",
       "21    http://www.the-numbers.com/movie/Chronicles-of...          225000000   \n",
       "22    http://www.the-numbers.com/movie/Amazing-Spide...          220000000   \n",
       "23    http://www.the-numbers.com/movie/Jurassic-Worl...          215000000   \n",
       "24    http://www.the-numbers.com/movie/Transformers-...          210000000   \n",
       "25    http://www.the-numbers.com/movie/Transformers-...          210000000   \n",
       "26    http://www.the-numbers.com/movie/X-Men-The-Las...          210000000   \n",
       "27    http://www.the-numbers.com/movie/Robin-Hood-(2...          210000000   \n",
       "28    http://www.the-numbers.com/movie/Battleship#ta...          209000000   \n",
       "29    http://www.the-numbers.com/movie/Titanic#tab=s...          200000000   \n",
       "...                                                 ...                ...   \n",
       "4311  http://www.the-numbers.com/movie/Legend-of-God...              30000   \n",
       "4312  http://www.the-numbers.com/movie/Mutual-Apprec...              30000   \n",
       "4313  http://www.the-numbers.com/movie/Funny-Ha-Ha#t...              30000   \n",
       "4314  http://www.the-numbers.com/movie/Down-Terrace#...              30000   \n",
       "4315  http://www.the-numbers.com/movie/Clerks#tab=su...              27000   \n",
       "4316  http://www.the-numbers.com/movie/Pink-Narcissu...              27000   \n",
       "4317  http://www.the-numbers.com/movie/In-the-Compan...              25000   \n",
       "4318  http://www.the-numbers.com/movie/Manito#tab=su...              25000   \n",
       "4319  http://www.the-numbers.com/movie/Dutch-Kills#t...              25000   \n",
       "4320  http://www.the-numbers.com/movie/Slacker#tab=s...              23000   \n",
       "4321  http://www.the-numbers.com/movie/Dry-Spell#tab...              22000   \n",
       "4322  http://www.the-numbers.com/movie/Flywheel#tab=...              20000   \n",
       "4323  http://www.the-numbers.com/movie/All-Superhero...              20000   \n",
       "4324  http://www.the-numbers.com/movie/Ridges-The#ta...              17300   \n",
       "4325  http://www.the-numbers.com/movie/Puffy-Chair-T...              15000   \n",
       "4326  http://www.the-numbers.com/movie/Breaking-Upwa...              15000   \n",
       "4327  http://www.the-numbers.com/movie/Stories-of-Ou...              15000   \n",
       "4328  http://www.the-numbers.com/movie/Pink-Flamingo...              12000   \n",
       "4329  http://www.the-numbers.com/movie/Clean#tab=sum...              10000   \n",
       "4330  http://www.the-numbers.com/movie/On-the-Down-L...              10000   \n",
       "4331  http://www.the-numbers.com/movie/Bang#tab=summary              10000   \n",
       "4332  http://www.the-numbers.com/movie/Newlyweds#tab...               9000   \n",
       "4333  http://www.the-numbers.com/movie/El-Mariachi#t...               7000   \n",
       "4334  http://www.the-numbers.com/movie/Primer#tab=su...               7000   \n",
       "4335  http://www.the-numbers.com/movie/Cavite#tab=su...               7000   \n",
       "4336  http://www.the-numbers.com/movie/Mongol-King-T...               7000   \n",
       "4337  http://www.the-numbers.com/movie/Signed-Sealed...               5000   \n",
       "4338  http://www.the-numbers.com/movie/Shanghai-Call...               3967   \n",
       "4339  http://www.the-numbers.com/movie/Plague-So-Ple...               1400   \n",
       "4340  http://www.the-numbers.com/movie/My-Date-With-...               1100   \n",
       "\n",
       "     release_date  worldwide_gross  \\\n",
       "0      12/18/2009       2783918982   \n",
       "1      12/18/2015       2058662225   \n",
       "2       5/24/2007        963420425   \n",
       "3       11/6/2015        879620923   \n",
       "4       7/20/2012       1084439099   \n",
       "5        7/2/2013        260002115   \n",
       "6        3/9/2012        282778100   \n",
       "7      11/24/2010        586581936   \n",
       "8        5/4/2007        890875303   \n",
       "9        5/1/2015       1404705868   \n",
       "10       5/6/2016       1150864745   \n",
       "11      3/25/2016        868160194   \n",
       "12     12/14/2012       1017003568   \n",
       "13      7/15/2009        935083686   \n",
       "14     12/13/2013        960366855   \n",
       "15     12/17/2014        955119788   \n",
       "16      5/20/2011       1045663875   \n",
       "17      6/28/2006        374085065   \n",
       "18     11/14/2008        591692078   \n",
       "19       7/7/2006       1066215812   \n",
       "20      6/14/2013        667999518   \n",
       "21      5/16/2008        417341288   \n",
       "22       7/3/2012        757890267   \n",
       "23      6/12/2015       1670328025   \n",
       "24      6/24/2009        836519699   \n",
       "25      6/27/2014       1104039076   \n",
       "26      5/26/2006        459359555   \n",
       "27      5/14/2010        322241588   \n",
       "28      5/18/2012        305218228   \n",
       "29     12/19/1997       2207615668   \n",
       "...           ...              ...   \n",
       "4311    7/18/2008           243768   \n",
       "4312     9/1/2006           103509   \n",
       "4313    4/29/2005            82698   \n",
       "4314   10/15/2010             9812   \n",
       "4315   10/19/1994          3894240   \n",
       "4316   12/24/1999             8231   \n",
       "4317     8/1/1997          2883661   \n",
       "4318    6/13/2003                0   \n",
       "4319    12/1/2015                0   \n",
       "4320     8/1/1991          1227508   \n",
       "4321   12/31/2014                0   \n",
       "4322   12/31/2003                0   \n",
       "4323     1/4/2013                0   \n",
       "4324   11/25/2011                0   \n",
       "4325     6/2/2006           195254   \n",
       "4326     4/2/2010           115592   \n",
       "4327   12/31/2014                0   \n",
       "4328    4/11/1997           413802   \n",
       "4329    4/28/2006           138711   \n",
       "4330    5/28/2004             1987   \n",
       "4331     4/1/1996              527   \n",
       "4332    1/13/2012             4584   \n",
       "4333    2/26/1993          2041928   \n",
       "4334    10/8/2004           841926   \n",
       "4335    5/26/2006            71644   \n",
       "4336     1/1/2004              900   \n",
       "4337    9/29/2015                0   \n",
       "4338    2/15/2013            10443   \n",
       "4339    9/29/2015                0   \n",
       "4340     8/5/2005           181041   \n",
       "\n",
       "                                            movie_title  \n",
       "0                                               Avatar   \n",
       "1     Star Wars: Episode VII - The Force Awakens    ...  \n",
       "2             Pirates of the Caribbean: At World's End   \n",
       "3                                              Spectre   \n",
       "4                                The Dark Knight Rises   \n",
       "5                                      The Lone Ranger   \n",
       "6                                          John Carter   \n",
       "7                                              Tangled   \n",
       "8                                         Spider-Man 3   \n",
       "9                              Avengers: Age of Ultron   \n",
       "10                          Captain America: Civil War   \n",
       "11                  Batman v Superman: Dawn of Justice   \n",
       "12                   The Hobbit: An Unexpected Journey   \n",
       "13              Harry Potter and the Half-Blood Prince   \n",
       "14                 The Hobbit: The Desolation of Smaug   \n",
       "15           The Hobbit: The Battle of the Five Armies   \n",
       "16         Pirates of the Caribbean: On Stranger Tides   \n",
       "17                                    Superman Returns   \n",
       "18                                   Quantum of Solace   \n",
       "19          Pirates of the Caribbean: Dead Man's Chest   \n",
       "20                                        Man of Steel   \n",
       "21            The Chronicles of Narnia: Prince Caspian   \n",
       "22                              The Amazing Spider-Man   \n",
       "23                                      Jurassic World   \n",
       "24                 Transformers: Revenge of the Fallen   \n",
       "25                     Transformers: Age of Extinction   \n",
       "26                               X-Men: The Last Stand   \n",
       "27                                          Robin Hood   \n",
       "28                                          Battleship   \n",
       "29                                             Titanic   \n",
       "...                                                 ...  \n",
       "4311                           The Legend of God's Gun   \n",
       "4312                               Mutual Appreciation   \n",
       "4313                                       Funny Ha Ha   \n",
       "4314                                      Down Terrace   \n",
       "4315                                            Clerks   \n",
       "4316                                    Pink Narcissus   \n",
       "4317                             In the Company of Men   \n",
       "4318                                            Manito   \n",
       "4319                                       Dutch Kills   \n",
       "4320                                           Slacker   \n",
       "4321                                         Dry Spell   \n",
       "4322                                          Flywheel   \n",
       "4323                          All Superheroes Must Die   \n",
       "4324                                        The Ridges   \n",
       "4325                                   The Puffy Chair   \n",
       "4326                                  Breaking Upwards   \n",
       "4327                              Stories of Our Lives   \n",
       "4328                                    Pink Flamingos   \n",
       "4329                                             Clean   \n",
       "4330                                    On the Downlow   \n",
       "4331                                              Bang   \n",
       "4332                                         Newlyweds   \n",
       "4333                                       El Mariachi   \n",
       "4334                                            Primer   \n",
       "4335                                            Cavite   \n",
       "4336                                   The Mongol King   \n",
       "4337                           Signed Sealed Delivered   \n",
       "4338                                  Shanghai Calling   \n",
       "4339                              A Plague So Pleasant   \n",
       "4340                                 My Date with Drew   \n",
       "\n",
       "[4341 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('../vol/datasets/thenumbers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "financials = pd.read_csv('../vol/datasets/thenumbers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "financials = financials[['movie_title','production_budget','worldwide_gross']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "gross_opening = pd.read_csv('../vol/datasets/opening_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4341, 3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "financials.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5043, 19)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                 Avatar \n",
       "1               Pirates of the Caribbean: At World's End \n",
       "2                                                Spectre \n",
       "3                                  The Dark Knight Rises \n",
       "4       Star Wars: Episode VII - The Force Awakens    ...\n",
       "5                                            John Carter \n",
       "6                                           Spider-Man 3 \n",
       "7                                                Tangled \n",
       "8                                Avengers: Age of Ultron \n",
       "9                 Harry Potter and the Half-Blood Prince \n",
       "10                    Batman v Superman: Dawn of Justice \n",
       "11                                      Superman Returns \n",
       "12                                     Quantum of Solace \n",
       "13            Pirates of the Caribbean: Dead Man's Chest \n",
       "14                                       The Lone Ranger \n",
       "15                                          Man of Steel \n",
       "16              The Chronicles of Narnia: Prince Caspian \n",
       "17                                          The Avengers \n",
       "18           Pirates of the Caribbean: On Stranger Tides \n",
       "19                                        Men in Black 3 \n",
       "20             The Hobbit: The Battle of the Five Armies \n",
       "21                                The Amazing Spider-Man \n",
       "22                                            Robin Hood \n",
       "23                   The Hobbit: The Desolation of Smaug \n",
       "24                                    The Golden Compass \n",
       "25                                             King Kong \n",
       "26                                               Titanic \n",
       "27                            Captain America: Civil War \n",
       "28                                            Battleship \n",
       "29                                        Jurassic World \n",
       "                              ...                        \n",
       "5013                                              Manito \n",
       "5014                                             Rampage \n",
       "5015                                             Slacker \n",
       "5016                                         Dutch Kills \n",
       "5017                                           Dry Spell \n",
       "5018                                            Flywheel \n",
       "5019                                              Exeter \n",
       "5020                                          The Ridges \n",
       "5021                                     The Puffy Chair \n",
       "5022                                Stories of Our Lives \n",
       "5023                                    Breaking Upwards \n",
       "5024                            All Superheroes Must Die \n",
       "5025                                      Pink Flamingos \n",
       "5026                                               Clean \n",
       "5027                                          The Circle \n",
       "5028                                         Tin Can Man \n",
       "5029                                            The Cure \n",
       "5030                                      On the Downlow \n",
       "5031                        Sanctuary; Quite a Conundrum \n",
       "5032                                                Bang \n",
       "5033                                              Primer \n",
       "5034                                              Cavite \n",
       "5035                                         El Mariachi \n",
       "5036                                     The Mongol King \n",
       "5037                                           Newlyweds \n",
       "5038                             Signed Sealed Delivered \n",
       "5039                           The Following             \n",
       "5040                                A Plague So Pleasant \n",
       "5041                                    Shanghai Calling \n",
       "5042                                   My Date with Drew \n",
       "Name: movie_title, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['movie_title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>178.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>4834</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>760505847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>169.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>48350</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>309404152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>148.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>11700</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>200074175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>106759</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>448130642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>132.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1873</td>\n",
       "      <td>263700000.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>73058679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>156.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>46055</td>\n",
       "      <td>258000000.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>336530303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2036</td>\n",
       "      <td>260000000.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>200807262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>92000</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>458991599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>153.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>58753</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>301956980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>183.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>24450</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>330249062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>169.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>29991</td>\n",
       "      <td>209000000.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>200069408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>106.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>168368427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>151.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>48486</td>\n",
       "      <td>225000000.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>423032628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>45757</td>\n",
       "      <td>215000000.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>89289910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>143.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>20495</td>\n",
       "      <td>225000000.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>291021565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>22697</td>\n",
       "      <td>225000000.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>141614023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>173.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>87697</td>\n",
       "      <td>220000000.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>623279547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>136.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>54083</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>241063875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>106.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>12572</td>\n",
       "      <td>225000000.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>179020854.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>9152</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>255108370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>153.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>28489</td>\n",
       "      <td>230000000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>262030663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>156.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>3244</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>105219735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>186.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>9152</td>\n",
       "      <td>225000000.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>258355354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>113.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>24106</td>\n",
       "      <td>180000000.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>70083519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>201.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>7123</td>\n",
       "      <td>207000000.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>218051260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1997.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>194.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>45223</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>658672302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>147.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>64798</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>407197282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>131.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>26679</td>\n",
       "      <td>209000000.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>65173160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>124.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>8458</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>652177271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5013</th>\n",
       "      <td>2002.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>243</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5015</th>\n",
       "      <td>1991.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1227508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5016</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>366</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5017</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>841</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5018</th>\n",
       "      <td>2003.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>108</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5019</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>91.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5020</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>770</td>\n",
       "      <td>17350.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5021</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1064</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>192467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5023</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>88.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1546</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>76382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5024</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>674</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5025</th>\n",
       "      <td>1972.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>108.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>760</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>180483.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5026</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>776</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>136007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5027</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>5</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>673780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5028</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>15</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>1997.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>115</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>94596.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5030</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5031</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1111</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5032</th>\n",
       "      <td>1995.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>368</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>424760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5034</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>70071.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5035</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>147</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2040920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5036</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5037</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>690</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.3</td>\n",
       "      <td>10443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>1.85</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>163</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>85222.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      title_year  aspect_ratio  duration  duration.1  \\\n",
       "0         2009.0          1.78     178.0       178.0   \n",
       "1         2007.0          2.35     169.0       169.0   \n",
       "2         2015.0          2.35     148.0       148.0   \n",
       "3         2012.0          2.35     164.0       164.0   \n",
       "4            NaN           NaN       NaN         NaN   \n",
       "5         2012.0          2.35     132.0       132.0   \n",
       "6         2007.0          2.35     156.0       156.0   \n",
       "7         2010.0          1.85     100.0       100.0   \n",
       "8         2015.0          2.35     141.0       141.0   \n",
       "9         2009.0          2.35     153.0       153.0   \n",
       "10        2016.0          2.35     183.0       183.0   \n",
       "11        2006.0          2.35     169.0       169.0   \n",
       "12        2008.0          2.35     106.0       106.0   \n",
       "13        2006.0          2.35     151.0       151.0   \n",
       "14        2013.0          2.35     150.0       150.0   \n",
       "15        2013.0          2.35     143.0       143.0   \n",
       "16        2008.0          2.35     150.0       150.0   \n",
       "17        2012.0          1.85     173.0       173.0   \n",
       "18        2011.0          2.35     136.0       136.0   \n",
       "19        2012.0          1.85     106.0       106.0   \n",
       "20        2014.0          2.35     164.0       164.0   \n",
       "21        2012.0          2.35     153.0       153.0   \n",
       "22        2010.0          2.35     156.0       156.0   \n",
       "23        2013.0          2.35     186.0       186.0   \n",
       "24        2007.0          2.35     113.0       113.0   \n",
       "25        2005.0          2.35     201.0       201.0   \n",
       "26        1997.0          2.35     194.0       194.0   \n",
       "27        2016.0          2.35     147.0       147.0   \n",
       "28        2012.0          2.35     131.0       131.0   \n",
       "29        2015.0          2.00     124.0       124.0   \n",
       "...          ...           ...       ...         ...   \n",
       "5013      2002.0          1.78      79.0        79.0   \n",
       "5014      2009.0          2.35      80.0        80.0   \n",
       "5015      1991.0          1.37     100.0       100.0   \n",
       "5016      2015.0           NaN      90.0        90.0   \n",
       "5017      2013.0          1.78      90.0        90.0   \n",
       "5018      2003.0          1.85     120.0       120.0   \n",
       "5019      2015.0          1.85      91.0        91.0   \n",
       "5020      2011.0           NaN     143.0       143.0   \n",
       "5021      2005.0           NaN      85.0        85.0   \n",
       "5022      2014.0           NaN      60.0        60.0   \n",
       "5023      2009.0          2.35      88.0        88.0   \n",
       "5024      2011.0          2.35      78.0        78.0   \n",
       "5025      1972.0          1.37     108.0       108.0   \n",
       "5026      2004.0          2.35     110.0       110.0   \n",
       "5027      2000.0          1.85      90.0        90.0   \n",
       "5028      2007.0          1.33      83.0        83.0   \n",
       "5029      1997.0          1.85     111.0       111.0   \n",
       "5030      2004.0           NaN      84.0        84.0   \n",
       "5031      2012.0         16.00      82.0        82.0   \n",
       "5032      1995.0           NaN      98.0        98.0   \n",
       "5033      2004.0          1.85      77.0        77.0   \n",
       "5034      2005.0           NaN      80.0        80.0   \n",
       "5035      1992.0          1.37      81.0        81.0   \n",
       "5036      2005.0           NaN      84.0        84.0   \n",
       "5037      2011.0           NaN      95.0        95.0   \n",
       "5038      2013.0           NaN      87.0        87.0   \n",
       "5039         NaN         16.00      43.0        43.0   \n",
       "5040      2013.0           NaN      76.0        76.0   \n",
       "5041      2012.0          2.35     100.0       100.0   \n",
       "5042      2004.0          1.85      90.0        90.0   \n",
       "\n",
       "      cast_total_facebook_likes       budget  imdb_score        gross  \n",
       "0                          4834  237000000.0         7.9  760505847.0  \n",
       "1                         48350  300000000.0         7.1  309404152.0  \n",
       "2                         11700  245000000.0         6.8  200074175.0  \n",
       "3                        106759  250000000.0         8.5  448130642.0  \n",
       "4                           143          NaN         7.1          NaN  \n",
       "5                          1873  263700000.0         6.6   73058679.0  \n",
       "6                         46055  258000000.0         6.2  336530303.0  \n",
       "7                          2036  260000000.0         7.8  200807262.0  \n",
       "8                         92000  250000000.0         7.5  458991599.0  \n",
       "9                         58753  250000000.0         7.5  301956980.0  \n",
       "10                        24450  250000000.0         6.9  330249062.0  \n",
       "11                        29991  209000000.0         6.1  200069408.0  \n",
       "12                         2023  200000000.0         6.7  168368427.0  \n",
       "13                        48486  225000000.0         7.3  423032628.0  \n",
       "14                        45757  215000000.0         6.5   89289910.0  \n",
       "15                        20495  225000000.0         7.2  291021565.0  \n",
       "16                        22697  225000000.0         6.6  141614023.0  \n",
       "17                        87697  220000000.0         8.1  623279547.0  \n",
       "18                        54083  250000000.0         6.7  241063875.0  \n",
       "19                        12572  225000000.0         6.8  179020854.0  \n",
       "20                         9152  250000000.0         7.5  255108370.0  \n",
       "21                        28489  230000000.0         7.0  262030663.0  \n",
       "22                         3244  200000000.0         6.7  105219735.0  \n",
       "23                         9152  225000000.0         7.9  258355354.0  \n",
       "24                        24106  180000000.0         6.1   70083519.0  \n",
       "25                         7123  207000000.0         7.2  218051260.0  \n",
       "26                        45223  200000000.0         7.7  658672302.0  \n",
       "27                        64798  250000000.0         8.2  407197282.0  \n",
       "28                        26679  209000000.0         5.9   65173160.0  \n",
       "29                         8458  150000000.0         7.0  652177271.0  \n",
       "...                         ...          ...         ...          ...  \n",
       "5013                        243      24000.0         7.0          NaN  \n",
       "5014                       3197          NaN         6.3          NaN  \n",
       "5015                          5      23000.0         7.1    1227508.0  \n",
       "5016                        366      25000.0         4.8          NaN  \n",
       "5017                        841      22000.0         3.3          NaN  \n",
       "5018                        108      20000.0         6.9          NaN  \n",
       "5019                       2679          NaN         4.6          NaN  \n",
       "5020                        770      17350.0         3.0          NaN  \n",
       "5021                       1064      15000.0         6.6     192467.0  \n",
       "5022                        170      15000.0         7.4          NaN  \n",
       "5023                       1546      15000.0         6.2      76382.0  \n",
       "5024                        674      20000.0         4.0          NaN  \n",
       "5025                        760      10000.0         6.1     180483.0  \n",
       "5026                        776       4500.0         6.9     136007.0  \n",
       "5027                          5      10000.0         7.5     673780.0  \n",
       "5028                         15      10000.0         6.7          NaN  \n",
       "5029                        115    1000000.0         7.4      94596.0  \n",
       "5030                         62          NaN         6.1          NaN  \n",
       "5031                       1111     200000.0         5.4          NaN  \n",
       "5032                       1186          NaN         6.4          NaN  \n",
       "5033                        368       7000.0         7.0     424760.0  \n",
       "5034                          0       7000.0         6.3      70071.0  \n",
       "5035                        147       7000.0         6.9    2040920.0  \n",
       "5036                         93       3250.0         7.8          NaN  \n",
       "5037                        690       9000.0         6.4       4584.0  \n",
       "5038                       2283          NaN         7.7          NaN  \n",
       "5039                       1753          NaN         7.5          NaN  \n",
       "5040                          0       1400.0         6.3          NaN  \n",
       "5041                       2386          NaN         6.3      10443.0  \n",
       "5042                        163       1100.0         6.6      85222.0  \n",
       "\n",
       "[5043 rows x 8 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_num = pd.concat([movies_num, movies['movie_title']],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "gross_opening = gross_opening.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_v2 = pd.merge(financials,movies_num,on='movie_title',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_v2 = pd.merge(movies_v2,gross_opening,on='movie_title',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4385, 13)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_v2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ahora solucionaremos el problema de los datos faltantes (nulos o NaN). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "  Los datos faltantes generan problemas con muchos algoritmos de ML. Es por esto que existen distintas estrategias para lidiar con ellos.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function value_counts in module pandas.core.base:\n",
      "\n",
      "value_counts(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)\n",
      "    Returns object containing counts of unique values.\n",
      "    \n",
      "    The resulting object will be in descending order so that the\n",
      "    first element is the most frequently-occurring element.\n",
      "    Excludes NA values by default.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    normalize : boolean, default False\n",
      "        If True then the object returned will contain the relative\n",
      "        frequencies of the unique values.\n",
      "    sort : boolean, default True\n",
      "        Sort by values\n",
      "    ascending : boolean, default False\n",
      "        Sort in ascending order\n",
      "    bins : integer, optional\n",
      "        Rather than count values, group them into half-open bins,\n",
      "        a convenience for pd.cut, only works with numeric data\n",
      "    dropna : boolean, default True\n",
      "        Don't include counts of NaN.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    counts : Series\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.Series.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>title_year</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>gross</th>\n",
       "      <th>opening_gross</th>\n",
       "      <th>screens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>274</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>671</td>\n",
       "      <td>2106</td>\n",
       "      <td>2164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4358</td>\n",
       "      <td>4111</td>\n",
       "      <td>4372</td>\n",
       "      <td>4372</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4050</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>3714</td>\n",
       "      <td>2279</td>\n",
       "      <td>2221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_title  production_budget  worldwide_gross  title_year  \\\n",
       "False          NaN                NaN              NaN          27   \n",
       "True        4385.0             4385.0           4385.0        4358   \n",
       "\n",
       "       aspect_ratio  duration  duration.1  cast_total_facebook_likes  budget  \\\n",
       "False           274        13          13                        NaN     335   \n",
       "True           4111      4372        4372                     4385.0    4050   \n",
       "\n",
       "       imdb_score  gross  opening_gross  screens  \n",
       "False         NaN    671           2106     2164  \n",
       "True       4385.0   3714           2279     2221  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_v2.notnull().apply(pd.Series.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>title_year</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>gross</th>\n",
       "      <th>opening_gross</th>\n",
       "      <th>screens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4104</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4358</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "      <td>4385.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_title  production_budget  worldwide_gross  title_year  \\\n",
       "False          NaN                NaN              281         NaN   \n",
       "True        4385.0             4385.0             4104      4385.0   \n",
       "\n",
       "       aspect_ratio  duration  duration.1  cast_total_facebook_likes  budget  \\\n",
       "False           NaN       NaN         NaN                         27     NaN   \n",
       "True         4385.0    4385.0      4385.0                       4358  4385.0   \n",
       "\n",
       "       imdb_score   gross  opening_gross  screens  \n",
       "False         NaN     NaN            NaN      NaN  \n",
       "True       4385.0  4385.0         4385.0   4385.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(movies_v2 != 0).apply(pd.Series.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "available = ((movies_v2 != 0) & (movies_v2.notnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2252\n",
       "True     2133\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available.all(axis=1).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No podemos entrenar nuestro algoritmo con datos cuya variable objetivo no esta definida o sea nula (valor falso). Eliminemos esas líneas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = available['worldwide_gross']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_v2 = movies_v2[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    4104\n",
       "Name: worldwide_gross, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((movies_v2 != 0) & (movies_v2.notnull())).worldwide_gross.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "  En el caso de las features que no son la variable objetivo una mejor solución para lidiar con los datos faltantes es <strong>remplazar estos datos por otros que sean manejables</strong> y no afecten la calidad de las predicciones. La estrategia más comun es utilizar la media de todos los ejemplos para la feature dada.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_v2 = movies_v2.drop('movie_title',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_v2 = movies_v2.drop('duration',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_v2 = movies_v2.drop('gross',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>title_year</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>opening_gross</th>\n",
       "      <th>screens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>425000000</td>\n",
       "      <td>2783918982</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>178.0</td>\n",
       "      <td>4834</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>77025481.0</td>\n",
       "      <td>3452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>306000000</td>\n",
       "      <td>2058662225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300000000</td>\n",
       "      <td>963420425</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>169.0</td>\n",
       "      <td>48350</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>139802190.0</td>\n",
       "      <td>4362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300000000</td>\n",
       "      <td>879620923</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>148.0</td>\n",
       "      <td>11700</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>70403148.0</td>\n",
       "      <td>3929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>275000000</td>\n",
       "      <td>1084439099</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164.0</td>\n",
       "      <td>106759</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>160887295.0</td>\n",
       "      <td>4404.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   production_budget  worldwide_gross  title_year  aspect_ratio  duration.1  \\\n",
       "0          425000000       2783918982      2009.0          1.78       178.0   \n",
       "1          306000000       2058662225         NaN           NaN         NaN   \n",
       "2          300000000        963420425      2007.0          2.35       169.0   \n",
       "3          300000000        879620923      2015.0          2.35       148.0   \n",
       "4          275000000       1084439099      2012.0          2.35       164.0   \n",
       "\n",
       "   cast_total_facebook_likes       budget  imdb_score  opening_gross  screens  \n",
       "0                       4834  237000000.0         7.9     77025481.0   3452.0  \n",
       "1                        143          NaN         7.1            NaN      NaN  \n",
       "2                      48350  300000000.0         7.1    139802190.0   4362.0  \n",
       "3                      11700  245000000.0         6.8     70403148.0   3929.0  \n",
       "4                     106759  250000000.0         8.5    160887295.0   4404.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_v2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.5/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "movies_v2 = movies_v2[available.screens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2221"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies_v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(missing_values=np.nan, strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>production_budget</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>title_year</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>duration.1</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>budget</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>opening_gross</th>\n",
       "      <th>screens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>425000000.0</td>\n",
       "      <td>2.783919e+09</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>178.0</td>\n",
       "      <td>4834.0</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>77025481.0</td>\n",
       "      <td>3452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300000000.0</td>\n",
       "      <td>9.634204e+08</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>169.0</td>\n",
       "      <td>48350.0</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>139802190.0</td>\n",
       "      <td>4362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300000000.0</td>\n",
       "      <td>8.796209e+08</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>148.0</td>\n",
       "      <td>11700.0</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>70403148.0</td>\n",
       "      <td>3929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>275000000.0</td>\n",
       "      <td>1.084439e+09</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164.0</td>\n",
       "      <td>106759.0</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>160887295.0</td>\n",
       "      <td>4404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>275000000.0</td>\n",
       "      <td>2.600021e+08</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>150.0</td>\n",
       "      <td>45757.0</td>\n",
       "      <td>215000000.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>29210849.0</td>\n",
       "      <td>3904.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   production_budget  worldwide_gross  title_year  aspect_ratio  duration.1  \\\n",
       "0        425000000.0     2.783919e+09      2009.0          1.78       178.0   \n",
       "2        300000000.0     9.634204e+08      2007.0          2.35       169.0   \n",
       "3        300000000.0     8.796209e+08      2015.0          2.35       148.0   \n",
       "4        275000000.0     1.084439e+09      2012.0          2.35       164.0   \n",
       "5        275000000.0     2.600021e+08      2013.0          2.35       150.0   \n",
       "\n",
       "   cast_total_facebook_likes       budget  imdb_score  opening_gross  screens  \n",
       "0                     4834.0  237000000.0         7.9     77025481.0   3452.0  \n",
       "2                    48350.0  300000000.0         7.1    139802190.0   4362.0  \n",
       "3                    11700.0  245000000.0         6.8     70403148.0   3929.0  \n",
       "4                   106759.0  250000000.0         8.5    160887295.0   4404.0  \n",
       "5                    45757.0  215000000.0         6.5     29210849.0   3904.0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = imputer.fit_transform(movies_v2)\n",
    "X = pd.DataFrame(values)\n",
    "X.columns = movies_v2.columns\n",
    "X.index = movies_v2.index\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2221"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.25000000e+08,   2.78391898e+09,   2.00900000e+03, ...,\n",
       "          7.90000000e+00,   7.70254810e+07,   3.45200000e+03],\n",
       "       [  3.06000000e+08,   2.05866222e+09,              nan, ...,\n",
       "          7.10000000e+00,              nan,              nan],\n",
       "       [  3.00000000e+08,   9.63420425e+08,   2.00700000e+03, ...,\n",
       "          7.10000000e+00,   1.39802190e+08,   4.36200000e+03],\n",
       "       ..., \n",
       "       [  7.00000000e+03,   9.00000000e+02,   2.00500000e+03, ...,\n",
       "          7.80000000e+00,              nan,              nan],\n",
       "       [  3.96700000e+03,   1.04430000e+04,   2.01200000e+03, ...,\n",
       "          6.30000000e+00,              nan,              nan],\n",
       "       [  1.10000000e+03,   1.81041000e+05,   2.00400000e+03, ...,\n",
       "          6.60000000e+00,              nan,              nan]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_v2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.25000000e+08,   2.78391898e+09,   2.00900000e+03, ...,\n",
       "          7.90000000e+00,   7.70254810e+07,   3.45200000e+03],\n",
       "       [  3.06000000e+08,   2.05866222e+09,   5.91165594e+08, ...,\n",
       "          7.10000000e+00,   5.91165594e+08,   5.91165594e+08],\n",
       "       [  3.00000000e+08,   9.63420425e+08,   2.00700000e+03, ...,\n",
       "          7.10000000e+00,   1.39802190e+08,   4.36200000e+03],\n",
       "       ..., \n",
       "       [  7.00000000e+03,   9.00000000e+02,   2.00500000e+03, ...,\n",
       "          7.80000000e+00,   1.90568571e+03,   1.90568571e+03],\n",
       "       [  3.96700000e+03,   1.04430000e+04,   2.01200000e+03, ...,\n",
       "          6.30000000e+00,   2.70237857e+03,   2.70237857e+03],\n",
       "       [  1.10000000e+03,   1.81041000e+05,   2.00400000e+03, ...,\n",
       "          6.60000000e+00,   2.31883063e+04,   2.31883063e+04]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.to_csv('../vol/intermediate_results/X_opening.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inv_map' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-68-27d06730cdf2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minv_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   2292\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2293\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2294\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2296\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/src/inference.pyx\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:66124)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-68-27d06730cdf2>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0minv_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'inv_map' is not defined"
     ]
    }
   ],
   "source": [
    "pd.Series(X.index).apply(lambda x: inv_map.loc[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.to_csv('../vol/intermediate_results/X.csv',index=False)"
   ]
  }
 ],
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