Proyecto_Final_Alvaro_Gonz_Rodr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proyecto Final\n",
    "## Recomendador de películas.\n",
    "\n",
    "Realizado por: **Álvaro González Rodríguez**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import altair as alt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abrimos los dos datasets que vamos a analizar en este proyecto. Se han escogidos los dos datasets iniciales que se nos entregaron al inicio del curso, pues hemos adquirido más práctica conforme hemos avanzado y además estos datasets presentan más datos que el que se nos dió más adelante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawmovies = pd.read_csv('../ml-latest-small/movies.csv',sep=\",\")\n",
    "rawratings = pd.read_csv('../ml-latest-small/ratings.csv',sep=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Leyenda de los dataset:**\n",
    "- moviesId = Identificador de película (cada una tiene la suya)\n",
    "- title = Título de la película\n",
    "- genres = Género de la película\n",
    "- rating = Valoración dada por un usuario a una película\n",
    "- timestamp = Fecha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Preprocesamiento de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9742, 3)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(rawmovies.shape)\n",
    "rawmovies.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100836, 4)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(rawratings.shape)\n",
    "rawratings.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definimos una función que nos devuelve el % de valores nulos de un dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Percent_NaN(dataset):\n",
    "    total = dataset.isnull().sum().sort_values(ascending=False)\n",
    "    percent1 = dataset.isnull().sum()/dataset.isnull().count()*100\n",
    "    percent2 = (round(percent1,1)).sort_values(ascending=False)\n",
    "    missing_data = pd.concat([total,percent2],\\\n",
    "                             axis=1,\\\n",
    "                             keys=['Count', '% NaN'])\n",
    "    return missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Count  % NaN\n",
      "genres       0    0.0\n",
      "title        0    0.0\n",
      "movieId      0    0.0 \n",
      "\n",
      "           Count  % NaN\n",
      "timestamp      0    0.0\n",
      "rating         0    0.0\n",
      "movieId        0    0.0\n",
      "userId         0    0.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(Percent_NaN(rawmovies),\"\\n\")\n",
    "print(Percent_NaN(rawratings),\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ninguno de los dos dataset presentan valores nulos.\n",
    "\n",
    "Comprobamos los distintos géneros que presentan las películas en el dataset *rawmovies*. Estos géneros están separados por el carácter '|', por lo que tendremos que usar la función *.split()*. Después, serán guardados en una lista con la función *.extend()*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generos de las películas: \n",
      " ['(no genres listed)', 'Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'IMAX', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']\n"
     ]
    }
   ],
   "source": [
    "new = []\n",
    "for genre in rawmovies['genres']:\n",
    "    new.extend(genre.split('|'))\n",
    "genres = sorted(set(new), reverse=False)\n",
    "print('Generos de las películas: \\n',genres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que existe un valor '(no genres listed)'. Este es equivalente a un NaN, por lo que tendremos que eliminar las películas que presenten este género.\n",
    "\n",
    "A continuación comprobamos la cantidad de *movieId* y *title* distintos del dataset movies. Vemos que existen más *movieId* que *title*, por lo que hay películas repetidas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tamaño de la tabla:  (9742, 3) \n",
      "MovieId distintos:  9742 \n",
      "Peliculas distintas:  9737\n",
      "\n",
      "Peliculas que estan repetidas:\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5931</th>\n",
       "      <td>34048</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6932</th>\n",
       "      <td>64997</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>2851</td>\n",
       "      <td>Saturn 3 (1980)</td>\n",
       "      <td>Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9468</th>\n",
       "      <td>168358</td>\n",
       "      <td>Saturn 3 (1980)</td>\n",
       "      <td>Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5854</th>\n",
       "      <td>32600</td>\n",
       "      <td>Eros (2004)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9135</th>\n",
       "      <td>147002</td>\n",
       "      <td>Eros (2004)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>838</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5601</th>\n",
       "      <td>26958</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4169</th>\n",
       "      <td>6003</td>\n",
       "      <td>Confessions of a Dangerous Mind (2002)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9106</th>\n",
       "      <td>144606</td>\n",
       "      <td>Confessions of a Dangerous Mind (2002)</td>\n",
       "      <td>Comedy|Crime|Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                   title  \\\n",
       "5931    34048                War of the Worlds (2005)   \n",
       "6932    64997                War of the Worlds (2005)   \n",
       "2141     2851                         Saturn 3 (1980)   \n",
       "9468   168358                         Saturn 3 (1980)   \n",
       "5854    32600                             Eros (2004)   \n",
       "9135   147002                             Eros (2004)   \n",
       "650       838                             Emma (1996)   \n",
       "5601    26958                             Emma (1996)   \n",
       "4169     6003  Confessions of a Dangerous Mind (2002)   \n",
       "9106   144606  Confessions of a Dangerous Mind (2002)   \n",
       "\n",
       "                                   genres  \n",
       "5931     Action|Adventure|Sci-Fi|Thriller  \n",
       "6932                        Action|Sci-Fi  \n",
       "2141            Adventure|Sci-Fi|Thriller  \n",
       "9468                      Sci-Fi|Thriller  \n",
       "5854                                Drama  \n",
       "9135                        Drama|Romance  \n",
       "650                  Comedy|Drama|Romance  \n",
       "5601                              Romance  \n",
       "4169          Comedy|Crime|Drama|Thriller  \n",
       "9106  Comedy|Crime|Drama|Romance|Thriller  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Tamaño de la tabla: ',rawmovies.shape,\\\n",
    "      \"\\nMovieId distintos: \", len(rawmovies['movieId'].unique()),\\\n",
    "      \"\\nPeliculas distintas: \", len(rawmovies['title'].unique()))\n",
    "\n",
    "veces_pelicula = rawmovies.groupby('title')['movieId'].agg(repeticiones='count').reset_index()\n",
    "\n",
    "#Peliculas que se repiten:\n",
    "repetidas = veces_pelicula[veces_pelicula['repeticiones'] > 1]\n",
    "\n",
    "#El genero indicado no es el mismo. Nuestro criterio sera quedarnos con la pelicula con genero mas largo.\n",
    "#Nos vale solo con quedarnos el index de esas peliculas y luego eliminarlas del dataset inicial.\n",
    "peliculas_repetidas = rawmovies[rawmovies['title'].isin(repetidas.title)].sort_values('title',ascending=False)\n",
    "print('\\nPeliculas que estan repetidas:')\n",
    "peliculas_repetidas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los objetivos principales en la limpieza de los datos será eliminar las películas repetidas y aquellas con el género: *(no genres listed)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Limpieza del dataset *ratings.csv*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El objetivo de este apartado es eliminar valores erróneos. Del apartado anterior hemos visto que hay películas catalogadas como \"no genres listed\" y un pequeño subconjunto repetidas. \n",
    "Tendremos que guardar el Id de la película, pues tendremos que eliminarlas también en el dataset *ratings.csv*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Películas diferentes: 9724\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>4.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
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       "      <td>4.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>610</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1493846415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100836 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
       "3            1       47     5.0   964983815\n",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
       "100832     610   168248     5.0  1493850091\n",
       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Películas diferentes:', rawratings['movieId'].unique().shape[0])\n",
    "rawratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminamos la columna 'timestamp', pues no aporta información relevante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawratings.drop(labels=['timestamp'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Eliminamos las películas sin género (en el dataset ratings):\n",
    "\n",
    "Como hemos dicho, hay que eliminar el subconjunto de películas que no presentan género. En vez de aparecer como NaN, aparecen como '(no genres listed)'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de peliculas sin genero:  34\n",
      "**No hay peliculas repetidas con este genero\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8517</th>\n",
       "      <td>114335</td>\n",
       "      <td>La cravate (1957)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8684</th>\n",
       "      <td>122888</td>\n",
       "      <td>Ben-hur (2016)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8687</th>\n",
       "      <td>122896</td>\n",
       "      <td>Pirates of the Caribbean: Dead Men Tell No Tal...</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8782</th>\n",
       "      <td>129250</td>\n",
       "      <td>Superfast! (2015)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8836</th>\n",
       "      <td>132084</td>\n",
       "      <td>Let It Be Me (1995)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                              title  \\\n",
       "8517   114335                                  La cravate (1957)   \n",
       "8684   122888                                     Ben-hur (2016)   \n",
       "8687   122896  Pirates of the Caribbean: Dead Men Tell No Tal...   \n",
       "8782   129250                                  Superfast! (2015)   \n",
       "8836   132084                                Let It Be Me (1995)   \n",
       "\n",
       "                  genres  \n",
       "8517  (no genres listed)  \n",
       "8684  (no genres listed)  \n",
       "8687  (no genres listed)  \n",
       "8782  (no genres listed)  \n",
       "8836  (no genres listed)  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peliculas_sin_genero = rawmovies[rawmovies['genres']=='(no genres listed)']\n",
    "print('Numero de peliculas sin genero: ',len(peliculas_sin_genero.movieId.unique()))\n",
    "if(len(peliculas_sin_genero.movieId.unique()) == len(peliculas_sin_genero.title.unique())):\n",
    "    print('**No hay peliculas repetidas con este genero')\n",
    "peliculas_sin_genero.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100831</th>\n",
       "      <td>610</td>\n",
       "      <td>166534</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100832</th>\n",
       "      <td>610</td>\n",
       "      <td>168248</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>610</td>\n",
       "      <td>168250</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100834</th>\n",
       "      <td>610</td>\n",
       "      <td>168252</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100835</th>\n",
       "      <td>610</td>\n",
       "      <td>170875</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100789 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating\n",
       "0            1        1     4.0\n",
       "1            1        3     4.0\n",
       "2            1        6     4.0\n",
       "3            1       47     5.0\n",
       "4            1       50     5.0\n",
       "...        ...      ...     ...\n",
       "100831     610   166534     4.0\n",
       "100832     610   168248     5.0\n",
       "100833     610   168250     5.0\n",
       "100834     610   168252     5.0\n",
       "100835     610   170875     3.0\n",
       "\n",
       "[100789 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminamos las peliculas en rawratings con movieId iguales que esten en peliculas_sin_genero.\n",
    "rawratings = rawratings[rawratings['movieId'].isin(peliculas_sin_genero['movieId'])==False]\n",
    "rawratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Eliminamos las películas repetidas:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordamos cual era el subconjunto de películas repetidas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5931</th>\n",
       "      <td>34048</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6932</th>\n",
       "      <td>64997</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>2851</td>\n",
       "      <td>Saturn 3 (1980)</td>\n",
       "      <td>Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9468</th>\n",
       "      <td>168358</td>\n",
       "      <td>Saturn 3 (1980)</td>\n",
       "      <td>Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5854</th>\n",
       "      <td>32600</td>\n",
       "      <td>Eros (2004)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9135</th>\n",
       "      <td>147002</td>\n",
       "      <td>Eros (2004)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>838</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5601</th>\n",
       "      <td>26958</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4169</th>\n",
       "      <td>6003</td>\n",
       "      <td>Confessions of a Dangerous Mind (2002)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9106</th>\n",
       "      <td>144606</td>\n",
       "      <td>Confessions of a Dangerous Mind (2002)</td>\n",
       "      <td>Comedy|Crime|Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                   title  \\\n",
       "5931    34048                War of the Worlds (2005)   \n",
       "6932    64997                War of the Worlds (2005)   \n",
       "2141     2851                         Saturn 3 (1980)   \n",
       "9468   168358                         Saturn 3 (1980)   \n",
       "5854    32600                             Eros (2004)   \n",
       "9135   147002                             Eros (2004)   \n",
       "650       838                             Emma (1996)   \n",
       "5601    26958                             Emma (1996)   \n",
       "4169     6003  Confessions of a Dangerous Mind (2002)   \n",
       "9106   144606  Confessions of a Dangerous Mind (2002)   \n",
       "\n",
       "                                   genres  \n",
       "5931     Action|Adventure|Sci-Fi|Thriller  \n",
       "6932                        Action|Sci-Fi  \n",
       "2141            Adventure|Sci-Fi|Thriller  \n",
       "9468                      Sci-Fi|Thriller  \n",
       "5854                                Drama  \n",
       "9135                        Drama|Romance  \n",
       "650                  Comedy|Drama|Romance  \n",
       "5601                              Romance  \n",
       "4169          Comedy|Crime|Drama|Thriller  \n",
       "9106  Comedy|Crime|Drama|Romance|Thriller  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peliculas_repetidas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos cuántas veces han sido valoradas las películas repetidas. El **criterio** que seguiremos será **quedarnos con las películas que tengan más valoraciones.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>838</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "      <td>838</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>246</td>\n",
       "      <td>838</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489</td>\n",
       "      <td>838</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>838</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>68</td>\n",
       "      <td>64997</td>\n",
       "      <td>2.5</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>28</td>\n",
       "      <td>64997</td>\n",
       "      <td>3.5</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>111</td>\n",
       "      <td>144606</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Confessions of a Dangerous Mind (2002)</td>\n",
       "      <td>Comedy|Crime|Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>318</td>\n",
       "      <td>147002</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Eros (2004)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>514</td>\n",
       "      <td>168358</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Saturn 3 (1980)</td>\n",
       "      <td>Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId  rating                                   title  \\\n",
       "0       104      838     4.0                             Emma (1996)   \n",
       "1       500      838     3.0                             Emma (1996)   \n",
       "2       246      838     4.0                             Emma (1996)   \n",
       "3       489      838     3.0                             Emma (1996)   \n",
       "4       488      838     5.0                             Emma (1996)   \n",
       "..      ...      ...     ...                                     ...   \n",
       "101      68    64997     2.5                War of the Worlds (2005)   \n",
       "102      28    64997     3.5                War of the Worlds (2005)   \n",
       "103     111   144606     4.0  Confessions of a Dangerous Mind (2002)   \n",
       "104     318   147002     4.0                             Eros (2004)   \n",
       "105     514   168358     2.5                         Saturn 3 (1980)   \n",
       "\n",
       "                                  genres  \n",
       "0                   Comedy|Drama|Romance  \n",
       "1                   Comedy|Drama|Romance  \n",
       "2                   Comedy|Drama|Romance  \n",
       "3                   Comedy|Drama|Romance  \n",
       "4                   Comedy|Drama|Romance  \n",
       "..                                   ...  \n",
       "101                        Action|Sci-Fi  \n",
       "102                        Action|Sci-Fi  \n",
       "103  Comedy|Crime|Drama|Romance|Thriller  \n",
       "104                        Drama|Romance  \n",
       "105                      Sci-Fi|Thriller  \n",
       "\n",
       "[106 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valoraciones_peliculas_repetidas = rawratings[rawratings['movieId'].isin(peliculas_repetidas['movieId'])].sort_values('movieId', ascending=True)\n",
    "\n",
    "# Fusionamos con peliculas_repetidas para tener los titulos\n",
    "valoraciones_peliculas_repetidas = valoraciones_peliculas_repetidas.merge(peliculas_repetidas, on='movieId')\n",
    "valoraciones_peliculas_repetidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>veces_valorada</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
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       "      <th>34048</th>\n",
       "      <td>50</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64997</th>\n",
       "      <td>2</td>\n",
       "      <td>War of the Worlds (2005)</td>\n",
       "      <td>Action|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>4</td>\n",
       "      <td>Saturn 3 (1980)</td>\n",
       "      <td>Adventure|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168358</th>\n",
       "      <td>1</td>\n",
       "      <td>Saturn 3 (1980)</td>\n",
       "      <td>Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32600</th>\n",
       "      <td>1</td>\n",
       "      <td>Eros (2004)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147002</th>\n",
       "      <td>1</td>\n",
       "      <td>Eros (2004)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>30</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26958</th>\n",
       "      <td>1</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>15</td>\n",
       "      <td>Confessions of a Dangerous Mind (2002)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144606</th>\n",
       "      <td>1</td>\n",
       "      <td>Confessions of a Dangerous Mind (2002)</td>\n",
       "      <td>Comedy|Crime|Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         veces_valorada                                   title  \\\n",
       "movieId                                                           \n",
       "34048                50                War of the Worlds (2005)   \n",
       "64997                 2                War of the Worlds (2005)   \n",
       "2851                  4                         Saturn 3 (1980)   \n",
       "168358                1                         Saturn 3 (1980)   \n",
       "32600                 1                             Eros (2004)   \n",
       "147002                1                             Eros (2004)   \n",
       "838                  30                             Emma (1996)   \n",
       "26958                 1                             Emma (1996)   \n",
       "6003                 15  Confessions of a Dangerous Mind (2002)   \n",
       "144606                1  Confessions of a Dangerous Mind (2002)   \n",
       "\n",
       "                                      genres  \n",
       "movieId                                       \n",
       "34048       Action|Adventure|Sci-Fi|Thriller  \n",
       "64997                          Action|Sci-Fi  \n",
       "2851               Adventure|Sci-Fi|Thriller  \n",
       "168358                       Sci-Fi|Thriller  \n",
       "32600                                  Drama  \n",
       "147002                         Drama|Romance  \n",
       "838                     Comedy|Drama|Romance  \n",
       "26958                                Romance  \n",
       "6003             Comedy|Crime|Drama|Thriller  \n",
       "144606   Comedy|Crime|Drama|Romance|Thriller  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veces_valorada_peliculas_repetidas = valoraciones_peliculas_repetidas.groupby('movieId')['movieId'].agg(veces_valorada='count')\n",
    "\n",
    "# Fusionamos con peliculas_repetidas para tener los titulos\n",
    "veces_valoraciones_peliculas_repetidas = veces_valorada_peliculas_repetidas.merge(peliculas_repetidas, on='movieId').sort_values('title', ascending=False)\n",
    "veces_valoraciones_peliculas_repetidas = veces_valoraciones_peliculas_repetidas.set_index('movieId')\n",
    "veces_valoraciones_peliculas_repetidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>100778</th>\n",
       "      <td>610</td>\n",
       "      <td>166534</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>100779</th>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
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       "      <td>610</td>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>100781</th>\n",
       "      <td>610</td>\n",
       "      <td>168252</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100782</th>\n",
       "      <td>610</td>\n",
       "      <td>170875</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100783 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating\n",
       "0            1        1     4.0\n",
       "1            1        3     4.0\n",
       "2            1        6     4.0\n",
       "3            1       47     5.0\n",
       "4            1       50     5.0\n",
       "...        ...      ...     ...\n",
       "100778     610   166534     4.0\n",
       "100779     610   168248     5.0\n",
       "100780     610   168250     5.0\n",
       "100781     610   168252     5.0\n",
       "100782     610   170875     3.0\n",
       "\n",
       "[100783 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Guardamos las peliculas buenas. Las usaremos para conocer las peliculas que queremos eliminar. \n",
    "# Lo hacemos asi para darle mayor generalidad al codigo. Si hubiera alguna pelicula con 3 generos distintos (repetida 3\n",
    "# veces) no nos valdria con eliminar la pelicula con valor minimo de 'veces_valorada'.\n",
    "idxbuenos = []\n",
    "for title in set(veces_valoraciones_peliculas_repetidas['title']):\n",
    "    idxbuenos.append(veces_valoraciones_peliculas_repetidas[veces_valoraciones_peliculas_repetidas['title']==title]['veces_valorada'].idxmax())\n",
    "\n",
    "# Los indices a eliminar son todos aquellos que no sean 'idxbuenos'\n",
    "idxeliminar = veces_valoraciones_peliculas_repetidas[veces_valoraciones_peliculas_repetidas.index.isin(idxbuenos) == False].index\n",
    "\n",
    "# Eliminamos del dataset 'rawratings'\n",
    "ratings = rawratings[rawratings['movieId'].isin(idxeliminar)==False].reset_index(drop=True)\n",
    "ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset *ratings* limpio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardamos estos datos en un archivo .csv nuevo:\n",
    "ratings.to_csv('./cleaned_ratings.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Películas diferentes: 9685\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>5.0</td>\n",
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       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100779</th>\n",
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       "      <td>5.0</td>\n",
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       "      <td>610</td>\n",
       "      <td>168250</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100781</th>\n",
       "      <td>610</td>\n",
       "      <td>168252</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100782</th>\n",
       "      <td>610</td>\n",
       "      <td>170875</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100783 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating\n",
       "0            1        1     4.0\n",
       "1            1        3     4.0\n",
       "2            1        6     4.0\n",
       "3            1       47     5.0\n",
       "4            1       50     5.0\n",
       "...        ...      ...     ...\n",
       "100778     610   166534     4.0\n",
       "100779     610   168248     5.0\n",
       "100780     610   168250     5.0\n",
       "100781     610   168252     5.0\n",
       "100782     610   170875     3.0\n",
       "\n",
       "[100783 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Películas diferentes:', len(ratings['movieId'].unique()))\n",
    "ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Limpieza del dataset *movies.csv*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Películas diferentes: 9742\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                     title  \\\n",
       "0        1          Toy Story (1995)   \n",
       "1        2            Jumanji (1995)   \n",
       "2        3   Grumpier Old Men (1995)   \n",
       "3        4  Waiting to Exhale (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Películas diferentes:', len(rawmovies['movieId'].unique()))\n",
    "rawmovies.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Eliminamos las películas sin género**\n",
    "\n",
    "Como hemos dicho, hay que eliminar el subconjunto de películas que no presentan género. En vez de aparecer como NaN, aparece como '(no genres listed)'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9708 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                      Adventure|Children|Fantasy  \n",
       "2                                  Comedy|Romance  \n",
       "3                            Comedy|Drama|Romance  \n",
       "4                                          Comedy  \n",
       "...                                           ...  \n",
       "9737              Action|Animation|Comedy|Fantasy  \n",
       "9738                     Animation|Comedy|Fantasy  \n",
       "9739                                        Drama  \n",
       "9740                             Action|Animation  \n",
       "9741                                       Comedy  \n",
       "\n",
       "[9708 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminamos las peliculas en rawmovies con movieId iguales que esten en peliculas_sin_genero.\n",
    "rawmovies = rawmovies[rawmovies['genres'] != '(no genres listed)']\n",
    "rawmovies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Eliminamos las películas repetidas**\n",
    "\n",
    "Hay películas repetidas con distinto valor de *movieId* y género. Nuestro criterio será quedarnos con las películas con más valoraciones. (Como hicimos con el dataset *ratings*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminamos las peliculas repetidas siguiendo el criterio establecido.\n",
    "# Usaremos los indices 'idxeliminar' que ya conociamos.\n",
    "movies = rawmovies[rawmovies['movieId'].isin(idxeliminar)==False].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset *movies* limpio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardamos estos datos en un archivo .csv nuevo:\n",
    "movies.to_csv('./cleaned_movies.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Películas diferentes: 9703\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9698</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9699</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9700</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9701</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9702</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9703 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9698   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9699   193583               No Game No Life: Zero (2017)   \n",
       "9700   193585                               Flint (2017)   \n",
       "9701   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9702   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                      Adventure|Children|Fantasy  \n",
       "2                                  Comedy|Romance  \n",
       "3                            Comedy|Drama|Romance  \n",
       "4                                          Comedy  \n",
       "...                                           ...  \n",
       "9698              Action|Animation|Comedy|Fantasy  \n",
       "9699                     Animation|Comedy|Fantasy  \n",
       "9700                                        Drama  \n",
       "9701                             Action|Animation  \n",
       "9702                                       Comedy  \n",
       "\n",
       "[9703 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Películas diferentes:', movies['movieId'].unique().shape[0])\n",
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es curioso ver que el dataset ***movies*** tiene **9703 películas diferentes** y el dataset ***ratings*** tiene **9685 películas diferentes**. En principio ambos datasets deberían ser del mismo tamaño, ¿es posible que los datos que se nos han dado ya venían con un pequeño preprocesamiento? porque no teníamos ningún NaN por ejemplo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Las siguientes peliculas no han sido valoradas por los usuarios: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>1076</td>\n",
       "      <td>Innocents, The (1961)</td>\n",
       "      <td>Drama|Horror|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>2939</td>\n",
       "      <td>Niagara (1953)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>3338</td>\n",
       "      <td>For All Mankind (1989)</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2587</th>\n",
       "      <td>3456</td>\n",
       "      <td>Color of Paradise, The (Rang-e khoda) (1999)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>4194</td>\n",
       "      <td>I Know Where I'm Going! (1945)</td>\n",
       "      <td>Drama|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4037</th>\n",
       "      <td>5721</td>\n",
       "      <td>Chosen, The (1981)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4506</th>\n",
       "      <td>6668</td>\n",
       "      <td>Road Home, The (Wo de fu qin mu qin) (1999)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4598</th>\n",
       "      <td>6849</td>\n",
       "      <td>Scrooge (1970)</td>\n",
       "      <td>Drama|Fantasy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4704</th>\n",
       "      <td>7020</td>\n",
       "      <td>Proof (1991)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5020</th>\n",
       "      <td>7792</td>\n",
       "      <td>Parallax View, The (1974)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5293</th>\n",
       "      <td>8765</td>\n",
       "      <td>This Gun for Hire (1942)</td>\n",
       "      <td>Crime|Film-Noir|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5421</th>\n",
       "      <td>25855</td>\n",
       "      <td>Roaring Twenties, The (1939)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5452</th>\n",
       "      <td>26085</td>\n",
       "      <td>Mutiny on the Bounty (1962)</td>\n",
       "      <td>Adventure|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5748</th>\n",
       "      <td>30892</td>\n",
       "      <td>In the Realms of the Unreal (2004)</td>\n",
       "      <td>Animation|Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5823</th>\n",
       "      <td>32160</td>\n",
       "      <td>Twentieth Century (1934)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5836</th>\n",
       "      <td>32371</td>\n",
       "      <td>Call Northside 777 (1948)</td>\n",
       "      <td>Crime|Drama|Film-Noir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5956</th>\n",
       "      <td>34482</td>\n",
       "      <td>Browning Version, The (1951)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7563</th>\n",
       "      <td>85565</td>\n",
       "      <td>Chalet Girl (2011)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                         title  \\\n",
       "816      1076                         Innocents, The (1961)   \n",
       "2211     2939                                Niagara (1953)   \n",
       "2499     3338                        For All Mankind (1989)   \n",
       "2587     3456  Color of Paradise, The (Rang-e khoda) (1999)   \n",
       "3118     4194                I Know Where I'm Going! (1945)   \n",
       "4037     5721                            Chosen, The (1981)   \n",
       "4506     6668   Road Home, The (Wo de fu qin mu qin) (1999)   \n",
       "4598     6849                                Scrooge (1970)   \n",
       "4704     7020                                  Proof (1991)   \n",
       "5020     7792                     Parallax View, The (1974)   \n",
       "5293     8765                      This Gun for Hire (1942)   \n",
       "5421    25855                  Roaring Twenties, The (1939)   \n",
       "5452    26085                   Mutiny on the Bounty (1962)   \n",
       "5748    30892            In the Realms of the Unreal (2004)   \n",
       "5823    32160                      Twentieth Century (1934)   \n",
       "5836    32371                     Call Northside 777 (1948)   \n",
       "5956    34482                  Browning Version, The (1951)   \n",
       "7563    85565                            Chalet Girl (2011)   \n",
       "\n",
       "                        genres  \n",
       "816      Drama|Horror|Thriller  \n",
       "2211            Drama|Thriller  \n",
       "2499               Documentary  \n",
       "2587                     Drama  \n",
       "3118         Drama|Romance|War  \n",
       "4037                     Drama  \n",
       "4506             Drama|Romance  \n",
       "4598     Drama|Fantasy|Musical  \n",
       "4704      Comedy|Drama|Romance  \n",
       "5020                  Thriller  \n",
       "5293  Crime|Film-Noir|Thriller  \n",
       "5421      Crime|Drama|Thriller  \n",
       "5452   Adventure|Drama|Romance  \n",
       "5748     Animation|Documentary  \n",
       "5823                    Comedy  \n",
       "5836     Crime|Drama|Film-Noir  \n",
       "5956                     Drama  \n",
       "7563            Comedy|Romance  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ¿Que peliculas no han sido evaluadas?\n",
    "print('Las siguientes peliculas no han sido valoradas por los usuarios: ')\n",
    "movies[movies['movieId'].isin(ratings['movieId'])==False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fusión de ambos datasets:\n",
    "\n",
    "Llegado a este punto, podemos fusionar ambos datasets: *movies* y *ratings*. La tabla resultante sería aquella donde las películas formarían las columnas, los usuarios las filas y los valores de cada elemento de la matriz las puntuaciones aportadas por los usuarios. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
       "      <th>Zulu (2013)</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 9685 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "1              0.0                                      0.0   \n",
       "2              0.0                                      0.0   \n",
       "3              0.0                                      0.0   \n",
       "4              0.0                                      0.0   \n",
       "5              0.0                                      0.0   \n",
       "...            ...                                      ...   \n",
       "606            0.0                                      0.0   \n",
       "607            0.0                                      0.0   \n",
       "608            0.0                                      0.0   \n",
       "609            0.0                                      0.0   \n",
       "610            4.0                                      0.0   \n",
       "\n",
       "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "userId                                                \n",
       "1                          0.0                  0.0   \n",
       "2                          0.0                  0.0   \n",
       "3                          0.0                  0.0   \n",
       "4                          0.0                  0.0   \n",
       "5                          0.0                  0.0   \n",
       "...                        ...                  ...   \n",
       "606                        0.0                  0.0   \n",
       "607                        0.0                  0.0   \n",
       "608                        0.0                  0.0   \n",
       "609                        0.0                  0.0   \n",
       "610                        0.0                  0.0   \n",
       "\n",
       "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "userId                                                               \n",
       "1                             0.0                              0.0   \n",
       "2                             0.0                              0.0   \n",
       "3                             0.0                              0.0   \n",
       "4                             0.0                              0.0   \n",
       "5                             0.0                              0.0   \n",
       "...                           ...                              ...   \n",
       "606                           0.0                              0.0   \n",
       "607                           0.0                              0.0   \n",
       "608                           0.0                              0.0   \n",
       "609                           0.0                              0.0   \n",
       "610                           0.0                              0.0   \n",
       "\n",
       "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                                          \n",
       "1                      0.0                   0.0                          0.0   \n",
       "2                      0.0                   0.0                          0.0   \n",
       "3                      0.0                   0.0                          0.0   \n",
       "4                      0.0                   0.0                          0.0   \n",
       "5                      0.0                   0.0                          0.0   \n",
       "...                    ...                   ...                          ...   \n",
       "606                    0.0                   0.0                          0.0   \n",
       "607                    0.0                   0.0                          0.0   \n",
       "608                    0.0                   0.0                          0.0   \n",
       "609                    0.0                   0.0                          0.0   \n",
       "610                    0.0                   0.0                          3.5   \n",
       "\n",
       "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
       "userId                                  ...                              \n",
       "1                                  0.0  ...          0.0           0.0   \n",
       "2                                  0.0  ...          0.0           0.0   \n",
       "3                                  0.0  ...          0.0           0.0   \n",
       "4                                  0.0  ...          0.0           0.0   \n",
       "5                                  0.0  ...          0.0           0.0   \n",
       "...                                ...  ...          ...           ...   \n",
       "606                                0.0  ...          0.0           0.0   \n",
       "607                                0.0  ...          0.0           0.0   \n",
       "608                                0.0  ...          0.0           0.0   \n",
       "609                                0.0  ...          0.0           0.0   \n",
       "610                                0.0  ...          0.0           4.0   \n",
       "\n",
       "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
       "userId                                           \n",
       "1                 0.0                      0.0   \n",
       "2                 0.0                      0.0   \n",
       "3                 0.0                      0.0   \n",
       "4                 0.0                      0.0   \n",
       "5                 0.0                      0.0   \n",
       "...               ...                      ...   \n",
       "606               0.0                      0.0   \n",
       "607               0.0                      0.0   \n",
       "608               0.0                      0.0   \n",
       "609               0.0                      0.0   \n",
       "610               3.5                      3.0   \n",
       "\n",
       "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "userId                                                           \n",
       "1                                                     0.0        \n",
       "2                                                     0.0        \n",
       "3                                                     0.0        \n",
       "4                                                     0.0        \n",
       "5                                                     0.0        \n",
       "...                                                   ...        \n",
       "606                                                   0.0        \n",
       "607                                                   0.0        \n",
       "608                                                   0.0        \n",
       "609                                                   0.0        \n",
       "610                                                   0.0        \n",
       "\n",
       "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "userId                                                                \n",
       "1                   0.0         0.0                             0.0   \n",
       "2                   0.0         0.0                             0.0   \n",
       "3                   0.0         0.0                             0.0   \n",
       "4                   0.0         0.0                             0.0   \n",
       "5                   0.0         0.0                             0.0   \n",
       "...                 ...         ...                             ...   \n",
       "606                 0.0         0.0                             0.0   \n",
       "607                 0.0         0.0                             0.0   \n",
       "608                 4.5         3.5                             0.0   \n",
       "609                 0.0         0.0                             0.0   \n",
       "610                 0.0         2.0                             1.5   \n",
       "\n",
       "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
       "userId                                                                    \n",
       "1                         4.0                                        0.0  \n",
       "2                         0.0                                        0.0  \n",
       "3                         0.0                                        0.0  \n",
       "4                         0.0                                        0.0  \n",
       "5                         0.0                                        0.0  \n",
       "...                       ...                                        ...  \n",
       "606                       0.0                                        0.0  \n",
       "607                       0.0                                        0.0  \n",
       "608                       0.0                                        0.0  \n",
       "609                       0.0                                        0.0  \n",
       "610                       0.0                                        0.0  \n",
       "\n",
       "[610 rows x 9685 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(\"./cleaned_ratings.csv\", sep=',', usecols=['userId', 'movieId', 'rating'])\n",
    "ratings\n",
    "\n",
    "movies = pd.read_csv('./cleaned_movies.csv', usecols=['movieId', 'title'])\n",
    "ratings\n",
    "\n",
    "# Combinamos ambos datasets\n",
    "movieratings = pd.merge(movies, ratings, on=['movieId'])\n",
    "movieratings\n",
    "\n",
    "# Pivotamos la tabla para que la matriz tenga : fila por usuario y columna por pelicula\n",
    "movieratings = movieratings.pivot_table(index=['userId'], columns=['title'], values='rating')  \n",
    "\n",
    "# Sustituimos los valores NaN por ceros:\n",
    "movieratings = movieratings.fillna(0)\n",
    "movieratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guardaremos esta tabla en un archivo .csv nuevo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "movieratings.to_csv('cleaned_movieratings.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Visualización de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este apartado realizaremos un par de gráficas para visualizar los datos y llegar a comprenderlos mejor.\n",
    "\n",
    "Añadimos un gráfico con el número de evaluaciones que ha realizado cada usuario, con el objetivo de demostrar que si juntamos las tablas *movies* y *ratings* existirán muchos NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>count_rating</th>\n",
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       "  <tbody>\n",
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       "      <th>413</th>\n",
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       "      <td>2696</td>\n",
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       "      <th>598</th>\n",
       "      <td>599</td>\n",
       "      <td>2474</td>\n",
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       "      <th>473</th>\n",
       "      <td>474</td>\n",
       "      <td>2108</td>\n",
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       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>448</td>\n",
       "      <td>1862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>274</td>\n",
       "      <td>1346</td>\n",
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       "      <th>441</th>\n",
       "      <td>442</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>569</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>320</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>576</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  count_rating\n",
       "413     414          2696\n",
       "598     599          2474\n",
       "473     474          2108\n",
       "447     448          1862\n",
       "273     274          1346\n",
       "..      ...           ...\n",
       "441     442            20\n",
       "568     569            20\n",
       "319     320            20\n",
       "575     576            20\n",
       "52       53            20\n",
       "\n",
       "[610 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos cuántas valoraciones ha hecho cada usuario:\n",
    "count_ratings_user = ratings.groupby(by='userId')['movieId']\\\n",
    "                            .agg(count_rating='count')\\\n",
    "                            .reset_index()\\\n",
    "                            .sort_values(by='count_rating',ascending=False)\n",
    "count_ratings_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
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471, \"count_rating\": 28}, {\"userId\": 277, \"count_rating\": 28}, {\"userId\": 396, \"count_rating\": 28}, {\"userId\": 340, \"count_rating\": 28}, {\"userId\": 311, \"count_rating\": 28}, {\"userId\": 335, \"count_rating\": 28}, {\"userId\": 130, \"count_rating\": 28}, {\"userId\": 550, \"count_rating\": 28}, {\"userId\": 205, \"count_rating\": 27}, {\"userId\": 250, \"count_rating\": 27}, {\"userId\": 578, \"count_rating\": 27}, {\"userId\": 530, \"count_rating\": 27}, {\"userId\": 499, \"count_rating\": 27}, {\"userId\": 557, \"count_rating\": 27}, {\"userId\": 461, \"count_rating\": 27}, {\"userId\": 81, \"count_rating\": 26}, {\"userId\": 158, \"count_rating\": 26}, {\"userId\": 218, \"count_rating\": 26}, {\"userId\": 548, \"count_rating\": 26}, {\"userId\": 355, \"count_rating\": 26}, {\"userId\": 208, \"count_rating\": 26}, {\"userId\": 459, \"count_rating\": 26}, {\"userId\": 296, \"count_rating\": 26}, {\"userId\": 515, \"count_rating\": 26}, {\"userId\": 172, \"count_rating\": 26}, {\"userId\": 516, \"count_rating\": 26}, {\"userId\": 25, \"count_rating\": 26}, {\"userId\": 519, \"count_rating\": 26}, {\"userId\": 150, \"count_rating\": 26}, {\"userId\": 529, \"count_rating\": 25}, {\"userId\": 360, \"count_rating\": 25}, {\"userId\": 333, \"count_rating\": 25}, {\"userId\": 228, \"count_rating\": 25}, {\"userId\": 258, \"count_rating\": 25}, {\"userId\": 55, \"count_rating\": 25}, {\"userId\": 392, \"count_rating\": 25}, {\"userId\": 173, \"count_rating\": 25}, {\"userId\": 206, \"count_rating\": 25}, {\"userId\": 289, \"count_rating\": 24}, {\"userId\": 92, \"count_rating\": 24}, {\"userId\": 231, \"count_rating\": 24}, {\"userId\": 518, \"count_rating\": 24}, {\"userId\": 180, \"count_rating\": 24}, {\"userId\": 508, \"count_rating\": 24}, {\"userId\": 175, \"count_rating\": 24}, {\"userId\": 485, \"count_rating\": 23}, {\"userId\": 329, \"count_rating\": 23}, {\"userId\": 299, \"count_rating\": 23}, {\"userId\": 574, \"count_rating\": 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281, \"count_rating\": 21}, {\"userId\": 245, \"count_rating\": 21}, {\"userId\": 364, \"count_rating\": 21}, {\"userId\": 293, \"count_rating\": 21}, {\"userId\": 547, \"count_rating\": 21}, {\"userId\": 157, \"count_rating\": 21}, {\"userId\": 507, \"count_rating\": 21}, {\"userId\": 37, \"count_rating\": 21}, {\"userId\": 26, \"count_rating\": 21}, {\"userId\": 439, \"count_rating\": 21}, {\"userId\": 49, \"count_rating\": 21}, {\"userId\": 87, \"count_rating\": 21}, {\"userId\": 549, \"count_rating\": 21}, {\"userId\": 324, \"count_rating\": 21}, {\"userId\": 595, \"count_rating\": 20}, {\"userId\": 189, \"count_rating\": 20}, {\"userId\": 207, \"count_rating\": 20}, {\"userId\": 194, \"count_rating\": 20}, {\"userId\": 147, \"count_rating\": 20}, {\"userId\": 257, \"count_rating\": 20}, {\"userId\": 406, \"count_rating\": 20}, {\"userId\": 431, \"count_rating\": 20}, {\"userId\": 278, \"count_rating\": 20}, {\"userId\": 442, \"count_rating\": 20}, {\"userId\": 569, \"count_rating\": 20}, {\"userId\": 320, \"count_rating\": 20}, {\"userId\": 576, \"count_rating\": 20}, {\"userId\": 53, \"count_rating\": 20}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ymax = count_ratings_user['count_rating'].max()\n",
    "xmax = count_ratings_user[count_ratings_user['count_rating']==ymax]['userId']\n",
    "\n",
    "grafico = alt.Chart(count_ratings_user).mark_bar().encode(x='userId:O',\\\n",
    "                                                          y='count_rating',\\\n",
    "                                                          color=alt.condition(alt.datum.userId == int(xmax),\\\n",
    "                                                                              alt.value('red'),\\\n",
    "                                                                              alt.value('steelblue')\\\n",
    "                                                                              ),\\\n",
    "                                                          tooltip=['userId','count_rating'])\\\n",
    "                                                  .interactive()\\\n",
    "                                                  .properties(width=len(count_ratings_user)*1.45)\\\n",
    "                                                  .configure_axis(titleFontSize=15)\n",
    "grafico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>mean_rating</th>\n",
       "      <th>count_rating</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.920930</td>\n",
       "      <td>215</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.431818</td>\n",
       "      <td>110</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.259615</td>\n",
       "      <td>52</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.357143</td>\n",
       "      <td>7</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>49</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>193581</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9681</th>\n",
       "      <td>193583</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9682</th>\n",
       "      <td>193585</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>Flint (2017)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9683</th>\n",
       "      <td>193587</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9684</th>\n",
       "      <td>193609</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9685 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId  mean_rating  count_rating  \\\n",
       "0           1     3.920930           215   \n",
       "1           2     3.431818           110   \n",
       "2           3     3.259615            52   \n",
       "3           4     2.357143             7   \n",
       "4           5     3.071429            49   \n",
       "...       ...          ...           ...   \n",
       "9680   193581     4.000000             1   \n",
       "9681   193583     3.500000             1   \n",
       "9682   193585     3.500000             1   \n",
       "9683   193587     3.500000             1   \n",
       "9684   193609     4.000000             1   \n",
       "\n",
       "                                          title  \n",
       "0                              Toy Story (1995)  \n",
       "1                                Jumanji (1995)  \n",
       "2                       Grumpier Old Men (1995)  \n",
       "3                      Waiting to Exhale (1995)  \n",
       "4            Father of the Bride Part II (1995)  \n",
       "...                                         ...  \n",
       "9680  Black Butler: Book of the Atlantic (2017)  \n",
       "9681               No Game No Life: Zero (2017)  \n",
       "9682                               Flint (2017)  \n",
       "9683        Bungo Stray Dogs: Dead Apple (2018)  \n",
       "9684        Andrew Dice Clay: Dice Rules (1991)  \n",
       "\n",
       "[9685 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos cuántas valoraciones tiene cada película:\n",
    "count_ratings_film = ratings.groupby(by='movieId')['movieId'].agg(count_rating='count').reset_index()\n",
    "mean_ratings_film = ratings.groupby(by='movieId')['rating'].agg(mean_rating='mean').reset_index()\n",
    "\n",
    "# Les añadimos los títulos a las películas:\n",
    "count_ratings_film = pd.merge(count_ratings_film, movies[['title', 'movieId']], on='movieId')\n",
    "count_mean_ratings_film = pd.merge(mean_ratings_film, count_ratings_film, on='movieId')\n",
    "count_mean_ratings_film"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos planteamos representar los datos de *mean rating* frente a *count rating*. Para ello, vamos a realizar un *JointGrid* de la biblioteca *Seaborn*. Con *JointGrid* podemos representar tres gráficas en una. En nuestro caso, realizaremos un histograma en cada eje y en el centro presentaremos un *scatterplot*.\n",
    "\n",
    "Para tener más flexibilidad a la hora de fijar los parámetros hemos fijado primero los parámetros del JointGrid y luego hemos especificado con *plot_joint*, *ax_marg_{x,y}* los tipos de gráficos que deseabamos en cada parte. (El primero para el centro y los dos últimos para los ejes X e Y).\n",
    "\n",
    "Por último se fijan los tamaños de las etiquetas y números de los ejes.\n",
    "\n",
    "Los colores se han obtenido de esta referencia: https://xkcd.com/color/rgb/ (hay que usar *sns.xkcd_rgb[]*)\n",
    "\n",
    "Se ha añadido la opción de que la gráfica sea interactiva mediante: *%matplotlib nbagg*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B+DrwAPC5ib6o1voh4CGAdevW6Ym+nhBCzEa5f0svuOACPVyHU0lmyucw5UGA1np39j9fVEr1AD9VSt2HdSdfrZRyFYwG1APR7CgA2eNKLcytzz4nhBBCiBKmPCeggB0QnIk1z+8ClhccU5gDcIiCuX+l1GKs0YW8XAEhhBBCDJluQcBl2X/fA14GBoDftZ9USgWx6gVszXnNVuAjSqmanMd+D4gBz09oa4UQQogZbMqmA5RSv8Eq8rMfaxXAZVhFfv5Ja30ke8xfA19TSoWw7uq/iBW4/DDnVD/CWlXwb0qp7wBnAZuB70uNACGEEKK8qcwJaAE+DSwD0sC7wN1Ynbrtr7E6/buxKgy+BmzSWjt7dWqtQ0qpD2MlFD6OtVLgB1iBgBBCCCHKmMqKgV/DKgk83DEauDf7NdxxB4Brxq91QgghxOw33XIChBBCCDFJJAgQQggxLqJxc+SDxLQiQYAQQohxEU1I3bWZRoIAIYQQokJJECCEEEJUKAkChBBCiAolQYAQQghRoSQIEEIIISqUBAFCCCFEhZryrYSFEGKmOdbayeYHH6Uj3E9zdR33fP5mli5unupmCTFmEgQIIcQYHGvt5MY776XjbBPTmyGx923+/Q9a2HTheWy5648kGBAzikwHCCHEGGx+8FErAIhniO87SeDiBtxXN/JMdRs33nkvx1o7Rz7JLOWSHmXGkR+ZEEKMQUe4H+UxSBwaILi+EeWx/owqj0HH2SabH3x0ils4dTJSNXjGkSBACCHGoLm6Dp2yejs7ALApj0FHuH8qmiXEKZEgQAghxuCez9/M/CMGmNoJBmw6ZTK/pm6KWibE2EkQIIQQY7B0cTNPbPkKHz3nPMxXQ04goFMm848YbP7czVPcQiFGT1YHCCHEGC1d3My//PDLQ0sFe/uZX1PH5i2yVFDMLBIECCHEKVq6uJlHvv2FqW6GEKdMpgOEEEKICiVBgBBCCFGhJAgQQggxLqRY0MwjPzIhhBDjQooFzTwSBAghhBAVSoIAIYQQokJJECCEEEJUKAkChBBCiAolQYAQQghRoSQIEEIIISqUBAFCCCFEhZIgQAghhKhQEgQIIYQYF1IxcOaRH5kQQohxIRUDZx4JAoQQQogKJUGAEEIIUaGmLAhQSv2uUuoxpVSbUmpQKfW6UurmgmOeU0rpEl/+guMWKaV+oZQKK6V6lFIPKKWCk/uOhBBCiJnFPYXX/iLwHvDfgR7gRuAflFJNWusf5hz3LPDlgtcm7P9QSnmAJ4Ek8PvAHOD72X//YILaLoQQeY61drL5wUfpCPfTXF3HPZ+/maWLm6e6WUIMayqDgI9rrXtyvn9GKbUQKzjIDQL6tNavDnOeTwErgeVa6/cAlFIp4B+VUvdord8Z74YLIUSuY62d3HjnvXScbaIaDXSqm5Y77+WJLV+RQEBMa1M2HVAQANj2AAvHeKobgBY7AMj6d6yRgetPrXVCCDF6mx981AoAPNafVOUx6DjbZPODj05xy4QY3nRLDPwg8NuCx65TSkWzX08qpS4oeH4FcCj3Aa11EjiSfU4IISZUR7jfCQBsymPQEe6fohYJMTpTOR2QRyn1YeA/AH+U8/DzwE+Bw8BS4CvAC0qpC7XWR7PH1AMnS5wylH2u3PVuA24DWLJkyek1XghR0Zqr69Cp7rxAQKdM5tfUTWGrJkfu39KGpvkcPXp0ahs0DfT29k51E/IsW7as7HNKaz15LSnXCKWWATuBl7XWvzPMcfOx7vr/Tmv9hexj7wC/tr/POfZF4H2t9X8e6frr1q3Tr7322im3XwhR2fJyAjyGFQAcMWZLToAa7YELl67WJ47tn8i2zAhHjx4dtuOdAmV/hlM+HaCUagC2AseA/zLcsVrrDuAl4OKch0NAqXC7PvucEEJMqKWLm3liy1e4SS9nfW8jN+nlsyUAELPclE4HZNfy/wrwAh/TWkdH8TKd/bIdomDuXynlBc4CfjROTRVCiGEtXdzMI9/+wlQ3Q4gxmcpiQW7gX4BzgOu11l2jeM184HLg9ZyHtwLrlVJLcx67CfABvxm/FgshhBCzy1SOBDyIVSDoz4FGpVRjznN7gHOBb2MFCseAJcDdgAncn3Psz7ESBv9NKfU1rKmBHwD/IDUChBBCiPKmMgi4Lvvv/yzx3JlAL1Yyw7eBRiAMPAf8B631+/aBWuuUUup64AHgn7GqCf4jcNeEtVwIIYSYBaYsCNBaLxvFYTeO8lzHsZYXCiGEEGKUpk2dACGEOF1Sv1+IsZEgQAgxK4xn/X4JJk6Na8oXnYuxkh+ZEGJWGK/6/XYw8Zg6TEtjL48bh7nxzns51to5Ec2eVTLmVLdAjJUEAUKIWWG86vfLZkCikkgQIISYFaz6/fm3oqdSv182AxKVRIIAIcSscM/nb2b+EcMJBOz6/Zs/d/OYzjNewYQQM4EEAUKIWWG86vePVzAhxEwwLXYRnGqyi6AQIlfu6oD5NXVs/lxFrw6QXQTHaCbtIihLBIUQooBsBiQqhUwHCCGEEBVKggAhhBCiQkkQIIQQYlxIxcCZR35kQgghxoVUDJx5Rp0YqJT62xEO0UAcOA48o7XeeToNE0IIIcTEGsvqgGuAADA3+30o+2999t9urJGFRkArpZ4AfldrHR+PhgohxESRDYNEpRrLdMD1QAL4K2Cu1rpRa92IFRTcC8SAS4EG4NvAR4F7xre5QggxvmTDIFHJxhIE/BB4Umv9da11r/2g1rpXa/01YDvwQ631Sa31V4GfA787vs0VQojxJRsGiUo2lumADwL/OszzrwO/l/P9c8BNp9AmIYSYNB3hflRjiQ2Deke/YZBMJ4iZaiwjARHgqmGevxqI5nzvB8Kn0CYhhJg0p7thkEwniJlsLEHA/wP+k1LqR0qp85RSvuzXeUqpHwOfyh5j+zAgRaSFENPa6W4YVDidYMYzvNffwxWf+TKf/tL9EgyIaW0sQcDdwD8BtwFvYN31R7P//Rngn4EvASil/MBLwDfHs7FCCDHeTnf3wY5wvxMAZMIp4vtOEri4gejGqoobFZBiQTPPqHMCtNYJ4D8rpb4L3AAszT51DNiqtd6bc2wc+NY4tlMIISbM6WwYZE0ndKM8BolDAwTXN5ZMMqyEDYmkWNDMM+ZdBLOd/d5xb4kQQkyi8Urmu+fzN9Ny5710nG31gHYAYBtrkqEQk0kGb4QQFWc8k/lypxMa497TSjIUYrKNKQhQSn1GKbVTKdWtlMqU+EpPVEOFEGK8jHdtAHs64cWf/vVpJRkKMdnGsnfAt4H/AbyJtQogNPwrhBBiehqP2gCl2KMCmx98lI7efubX1LF5i9QMENPXWHIC/gj4d631f5yoxgghxGTITeaznc6wvRQLEjPVWKYDqoAnJ6ohQggxWU63NkAuKRYkZrKxBAEvARdOVEOEEGKynG5tgFyy94CYycYyHfA5YLtS6jPAT7TWsiJUCDFjnU5tgFwTlV8gxGQYSxCwFQgAPwJ+qJRqAzIFx2it9bnj1TghhJjuxju/YCaTioEzz1h+ZCeAg8AO4BXgfaCt4OvEeDdQCCGms/HML5jppGLgzDOWssFXT2A7hBBiWhltxr+dX3DXlkd49e13IGWycuU5U9BiIcZuzGWDhRBitins8D/zyU3cdt+PrYS/RgOd6qblznuHTR7c33GCyMUBlMfg2dQJbhzheCGmg7LTAUqpJUqpJYXfj/Q12gsrpX5XKfWYUqpNKTWolHpdKVU0fpatUviOUiqePebDJY5ZpJT6hVIqrJTqUUo9oJQKjrYtQoipcay1k1vuvp8bbr9nyrbdLbXE76b//q0xZfzLCgExUw03EnAU0EqpgNY6aX8/inO6RnntLwLvAf8d6AFuBP5BKdWktf4hQDYo+BGwGXgRuAX4lVJqvdb6rewxHqz6BUng94E5wPez//7BKNsihJhkduc7lrvt07lWuaH9Uh14tEYTHMNGQLJCQMxUwwUBf4TV6acKvh8vH9da9+R8/4xSaiFWcPDD7GObgZ9qrf8SQCn1PHAR8CWGOvhPASuB5Vrr97LHpYB/VErdo7V+ZxzbLIQYJ8PdPY/ntrsjBRulOnAMhU6Zo8r4P9bayZF3ThA9HAZD4VtRi6vGU7ErBMTMUjYI0Fr/3XDfn66CAMC2B/iPAEqps4APAH+e8xpTKfUvuY8BNwAtdgCQ9e9YIwPXAxIECDENTdbd80jBRqklft7l1ahdA+gNtSiPMZTxvyV/xtIOMHrXegh6mtApk2hLL76VtSzq9BUdL8R0M+olgkqpv1VKbRzm+Q1Kqb89zfZ8EPht9r9XZP89VHDMQaBBKTU357i8Y7LTF0dyziGEmGasznfit93tCPfndfCQDQTCVrBRaonfok4fv/zWX+RVFHzojs+y+cFH8/IXSgUYwfWNzHsbSQoUM8JYVgd8GtgO7Czz/JnAf8OaNhizbMLff8h5fX3235MFh4Zynu/O/lt4jH1cfYnH7evdBtwGsGTJqPMZhRDj5J7P30yLPUw/zN326RqpmM9wO/9dtmE1UH5KoSlYhVpSHGCcfc7CigkAcv+WNjTN5+jRo1PboGmgt7d3qpuQZ9myZWWfG88lgguA2Km8UCm1DPgH4JfjPe1Qjtb6IeAhgHXr1o1nroMQYhQma9vd0QQbI5UQLjelcGLbe+iaOtz1PufYSssFyP1bunDpaj1ch1NJZsrnMGwQoJT6OPDxnIf+WCl1dYlD5wDXAbvH2gClVANWSeJjwH/Jecq+468j/06/vuD5UPaYQvXAG2NtjxBi8oxX/f6RrnG6wUa5/IVEo4HrjQG4sBZ3vW/CRjOEmCgjjQRcDNya/W8NfCj7VSgCvA7cPpaLZ9fy/wrwAh/TWkdznrbn+VdgBQjkfN+nte7OOS5v7l8p5QXOwlpeKISocKcbbJSbUsBQeC9roPH1FGef0zhhoxlCTJRhEwO11vdorQ2ttQEo4A/s7wu+arTWV9tr90dDKeUG/gU4B7hea91VcO13sZIEfzfnNUb2+605h24F1iulluY8dhPgA34z2vYIIUQ5pZIHoy29+FbUOjkAWx/4Bo98+wsSAIgZZSw5AWdiJeKNlwexCgT9OdColGrMeW6P1jqBVSfg75VSR4GXsBIPzwH+c86xPwe+AvybUuprWFMDPwD+QWoECCHGgz2lsOnWr3NcWfUA/OfPKVsPYLT7Dggx1Ua9RFBrfaxguP50XZf9939i7UqY+7Uge81HgT/BWpnwG+ACrGkDZ8RBa53CqgfQCvwz8ADwr2SzVYUQYjwsXdzMtoe/yZl1TQTW1A8FAAU7BpYqQ3zjnfdOSUlkIUaitB59YrxSahXWnftarGTAwiBCa63PHrfWTZJ169bp1157baqbIYSYQqO9e889bn5NHZs/l3/cLXffz2PqcFH+wE16+YQnQU4QNdoDFy5drU8c2z+RbZkRjh49Ot1WB5T9GY56OkAp9UHgaSAM7MJKGnwGCACXAG9xCqsDhBCVaToNmY9lH4ORkgxlHwExk4x6OgD4JtAGnIu1kQ/At7TWlwFXA8uA/zeejRNCzE7Tbcg8tw5AJpwitjfEe/09bLr160VtGmnnw8mqhCjEeBhLELAB+InW+iRg/4YbAFrrF4CfAH85rq0TQsxK023rXbu0cCacIr7vJIE19QQ3NtG71pMXnIwmeCm1kqAwb2C2co2lRxHTwlh+ZC6sLX8B7ATB3LK8B4Dzx6NRQojZbaR6/pPNvntPHBoguL6xZHByrLWTTbd+nff6e4jtDZEJp0oGL/ZKgtx9ByplH4GMOfIxYnoZyxLB94GlAFrruFKqFbgUa60/wBpAJr2EECMaqZ7/ZLNLC79n6pLBybvvl94t0F4mWDjfPxmVEIUYD2MZCXgGa4Mf298DtyulHlZKPQJ8FvjFOLZNCDFLTfSQ+Ujz9oXsu/czdE3J+fz2E30ldwtMHBqQ+X4xo40lCPgusFkpZe+UsRl4GPgk1v4CPwO+NK6tE0LMShM5ZH6qSYd2HYBSwcnCxU0lRwgwdcXM94vZaUx1AmYrqRMgxOxxuuv0S9UB+MbfPMrjRvE5G19Pse3hb872+X6pEzBGs7JOwIhXUOoC4Mta698fr3MKIcRYne46/VLz+Z/55Cb+9a6deC9rdLYjTr7UyyPf++psDwDELDeqIEAp1QScjbV73zsFz20Avgp8FMiMewuFEGIMRko6PJUiRQ//YhuuC2uJ7Q05j3kvrOXhX2zjsg2rJ+aNCDEJhs0JUEp5lVI/BTqAl4FDSqnXlVJLlFKNSqmfY9X6/zDWtr3nTniLhRCz0liT+coZLunwVPMFOsL9uOt9BNc3Ol/uet+ULWkUYryMlBj4F8B/xSoTfB/wb1hLAf8WeBb4CPAtYKnW+vNa6/cmrqlCiNlqPCsIDpd0OFyRouGCEKkCODpSLGjmGTYxUCl1AGjVWn8k57G7gO8AvwWu1Vofn/BWTjBJDBRiak3Wpjs33H4PLY29RY+f31ZDVzjsBAj26IEdPOTtLVDi+VlOEgPHaCYlBo4Ut50JPFbw2L9l//3ObAgAhBBTb7IqCJa7o29r7Rm2jHElVwEUs9tIiYE+IFTw2Mnsv++Pe2uEEBVpsioI2pUBC+/omxY20OeJ5B1buKJAqgCK2Wg0Mzjl5gukSrQQYlxM1qY75e7oz2xuljl/UZFGygkwsVYGDOY8bABnYW0rHCt4idZaz7gVApITIMTUK1WkZ6KH2+1rvtfdzb6DR8msrcZd76u0Of+RSE7AGM2knICRgoDnKD8SUJLW+kNjOX46kCBAiOnjVNbxn+p1CpP91K4BVi9exLzqGpRhEM4kJrQNM4QEAWM0k4KAYXMCtNZXj3tThBCijLyOudFAp7ppufPecbkjLwwuIpEobc0JknuHBjq9q6qZp2s40NWRExyMXxuEmG5kVacQYtoYbh3/6ShVh2DbvreIvxEisKae4PpGAmvqSRwc4MW9hyakDUJMRxIECCGmjYlaKlgquHBd2oARcBdtDxyJJyZluaIQ04EEAUKIaWOiKvOVCy4wVNFjNXOCslLgFEnFwJlHfmRCiGnhWGsn0ViczEshojt7yIRT47ZUsFxwgamLHrts1bmTslxxNsrIwvEZZ9y2EhZCiFOVmxDovroRV8rEfDXENatXs2XLH59WQt6x1k4ikSjm/hDGJfXOSoCmt01MXx19qfzCQd/bcguQnULozS5X3FLRqwPELCZBgBBiypWaszcuqadaB087ALCDC9bUEtvdhzemuG7N+Xzv/uE7e6kOKCrBqIMApdS7wBe01oV7CdjPfwz4X1rrs8arcUKIytAR7kc1Fs/Zb31pD5/+0v2nvE4/N7hweQyCG5vQKZMq7ZfOXgjGlhOwDKge5vlqYOlptUYIMS0Mt63uRCg3Zz/gTZ/WtsKTtTGREDPVWBMDh6seuJahzYWEEDNUqTX1p9oJj1apvQOiLb34VtSe1jr9iVptIMRsMWwQoJT6U6XUb5VSv80+dJ/9fcFXF/BF4KkJb7EQYkJNVMGe4eRu7ON+qZ/Y3hD+8+fgqvEMteEU7t7He2OiyR4hEWKijTQSMIC1UVBb9vuTOd/bX8eBncBfAp+dkFYKISbNVA2h21v1Xr/uIgJr6p0AAE797r3croGnkl8wFSMkQky0kfYO+CnwUwCl1HvAl8olBgohZgdrCL07LxAo1QlP1EY/93z+ZloKNvaZf8Rg85ZTu3u3g4vTNdwIiSQXWqRY0Mwz6h+Z1vpMCQCEmP1GM4Q+kXfF43n3Pp4kyXBkUixo5jmlOgFKqSqggRLbE2qt3z/dRgkhpo7dCQ9XLGei74rH6+59PI12hESImWQsdQJ8wNeBW4GmYQ51nW6jhBBTa6ROuNy6/o7e2XtXPN7TFEJMB2MZCXgA+CPgMeB5IHS6F1dKLQfuAj4IrAZe0FpfXXDMUYrrD3RqrecXHLcK+GH2XCeBh4F7tNaZ022nECJfJd4Vj2aERIiZZixBwH8EHtFa3zqO118N3Ai8CniGOe4fsDp4WzL3SaVUPbAdOAB8AjgbuA8r5+Gr49heIQSVe1c8HacphDgdYwkCXEDLOF//ca31LwGUUj+n/DRDu9b61WHO8ydAAPik1noA2KaUqgU2K6W+m31MCDFO5K5YiNlhLEHAk8DlwI/H6+Ja6/HKJb0BeLKgs/9H4DvAVcDj43QdIUTWdL0rnqili9P1ukKcjrGs6rwdWK2UulcpdcZENaiMP1ZKJZVS/UqpnyulCnMEVgCHch/IrlKIZp8TQlSAqSroI4WExEw1lpEAu2rghcCXlFImxXsJaK21b1xaNuSXWDkDx4GVwDeAF5RS52ut7VTkekrvWxDKPldEKXUbcBvAkiVLxrnJQoipMFUFfSq5kFDu39KGpvkcPXp0ahs0DfT29k51E/IsW7as7HNjCQL+H8NvIDQhtNZ/nvPtC0qpl4G9wC3A/adx3oeAhwDWrVs36e9LCDH+TnXp4ukO5Vfikklb7t/ShUtX6+E6nEoyUz6HUQcBWutPT2A7Rk1r/ZZS6m3g4pyHQ0CptUn1jMNSRiHEzFBteNEps2jpYo3LV7ajt4fyO842UY0GOtVNy533jqlKYSUumRSzw0yt9KzJH5U4RMHcv1JqMRCkIFdACDF1xrIL3ynt2Kc10V09+VsS7+phMBwpO2c/HrsmjvduhUJMlrFUDPzD0Ryntf7ZqTdnVO04D6vDfyjn4a3AXUqpGq11OPvY7wExrMJGQogpNpY77lO9Ox/UKfwX1BPbOzQA6L+gnrfeOE5kY1VRR3/nd3/CzgPvEPNZpUd8K2px1XicofzRThPIkkkxU40lJ+Dvhnku96581EGAUiqIVSwIYBFQq5T6VPb7J4APAX8A/Ao4gdX5fxV4v6A9PwL+DPg3pdR3gLOAzcD3pUaAENPDWJLnTjXRrrm6DsPoJri+0XlMp0zwGEWb/5jxDNv378e4pJ6gp8YaNWjpxX/+HAy/i2rlOeVpAi1ZRmKGGMt0wJklvpYDm7Ay+F/DqgA4FvOAf8l+XQKsyvl+HtCa/fd+4CmslQHbgMtzO3etdQj4MFZBo8eBe4AfZI8XQkwDY9mF71R37Cs3LL/xA8udx2yJA/0Yl9RjxjNEW3qJ7Q2hXIrYnj7mHzFQhlEyELlryyNF15UlgmKmGkti4LEyT70LPK2UehKrct8XxnDOo5TYibDAh0d5rgPANaO9thBi9MajEE5u0l4mnCJxaABMzRGd4FhrZ975TjXRrtywPDB0V58tc+yNKcx4hvi+kwTXNzqPx1/o5qE7PstXHvoH1JziQOSpvfuK2lvJSwTFzDaeiYGPAf95HM8nhJgGxuMu91hrJ3uPvU90Vw/pUIL4vpME1tQT3NhE71pP0flOJ9HOrmS49YFv8Mi3v8DSxc1OcHCTXs763kZu0svZdOF5JA70OwEAWJ23/4q5/M//9zj7Dh4tGj3QKZNkQBclDZ7qyIUQU208g4BmrGx8IcQsMh7Z85sffJS+1S78F9QTfbWnqOMtPF+pTnssS/ZKKQwOttz1R3hjqmTn/erb75BZW03kha78lQYtvfhW1RV17tbIRXHAIEsExXQ3ltUB5crqzcFK4LsDeO70mySEmGhjGd4fj0I49jlcHgN3k7/0XXPB+SZ6b4Kli5vZdOF5PJNqK5p2IGXirveB1yC2uw8Ma9bSThqc78vv3Ct1V8VCrpm66LyCjeVHdhR4r8TXHqwkvN1YOQFCiGlsrMP743GXW3iO6XLXvOWuPyo57bBh5TnolEngwnp0RltTF+sbrQDgiMGtv7Mpr4YBMO4jFzNRZry2hBOTRulRrmVRSn2aEnsFYFXkO6y1Pji+TZs869at06+99tpUN0OISXHL3ffzmDpcdPd7k15e8s47b81+zl3uWDq53HOY8QzxN0MENzTlne+hOz7Lw7/YNqW7/82vqXPyDnLbmzjQjzemuG7N+fz5f/k4t93349P6PGaYkZK3HQuXrtYnju2fyLbMCEePHp1uZYPL/gxHHQTMZhIEiEpyw+330NJYvMHJ+t5Gtj4wtKo2t3OswoPhMghnEk5HOdYOL/d81cqDMobOd+vvbDqljnUitu+1z/luRyftJ/pYtLiJZXPnOu+5MIjKhFMkDvTTRIBr1pw/G7cQliBgjGZSEDCWYkFDZ1OqCatOAMB7WuueUzmPEGLyjWb5XXHFvtO/2x1ujv+Wu+8f8xK7sVYVHE3AkHfOJQZ6gQfPkTB/940/c47NzZHIhFPOEsOox+Dx1OEx7zsgxFQaUxqHUuqDSqlXgU6s7X1fBTqVUi8rpS6ZiAYKIcbXaJbfjceKgLF4r7u7ZLLg0e7usq8ZSxtL5UGs+W9f5FO3f4uXdu135vc33fp12poTw56zubqOdChBtKWX6M4elMuqNzBSG4SYjkYdBGQ7+WewSvf+b+BPs1//G6vS37NKqY0T0UghxPgZzfK7yV73fqK1p2SyYFtr+UHGsbSxVMBgXFLPr/a/wfV3/ZUTHPSu9ZA4OEAmnCp7zs98chOZNwYIrKmn5toFBC5uIL7vpPOakYIXIaaTsUwH/BXQDlyqte7IfUIp9VfAy9ljNo1f84QQE2Gk5XeTvTXugoUNtLYczavcF23p5bwzlo1LG8stczQjabzXLsgLDoLrG4ntDTn7DxSe8+FfbMN7WWPRa8Lb23HN8ZIJJXlNufj0l+6fjfkBYpYZy3TARuDHhQEAQPaxh7LHCCFmuMneGvfM5mZ8K2uJ7Q05dfx9K2s5a37puf1b7r6f9zo7UTsHRtXGcsscVYmNhZTHAFOXPWe5EQh3g4+qD86lZtMC0nUu/j16UPYPENPeWEs7DLeUQJYZCDFLTETFvuHc8/mbWdTpc9bjB9bUs6jTV9Sh587t718SIb06AM+HOL+tZtg2lgpqoi29GAF3yeDgDF1T9n2XCyjsgkL2yEDy8GDF5QdIsaCZZyx1Ap4GlgEbC1cDZFcL7MRaKXDteDdyoskSQSGmXqn1+oUd+lhrHBSe/64tj/DU3n0kAxrfqjp02iS9tx/f5UM1C5reNnnq/m+UDXhK1U2wtyB21Xic46ItvQTXNxYtvZyBZIngGM3WJYJfB54G3lZK/Qx4O/v4CuC/AlXZf4UQYsxGUyZ4rCWMj7V2cud3f8LOA++Ax9pS+Nff+7JTlKjG5WNX8AhddmlgU2P6xrZT4dsHW8msrs0LAHJHHGpcvlG8eyGmxli2En5JKXUd8H3gzwuefg24Q2v98ng2Tgghco0lGfBYayfX3r6Z45EQwY3Wnf4zqTbe/M7fOHf6t9x9P+GLfQQ9Aed1fani+gSlagzYzzsjA9X5IwO+lbVEd/WgVy6csM9DiNM1phkcrfUOrfU6YAHwwezXAq31Bq31CxPRQCHEzGUn8dk19k83SW4sCYubH3yUtkS/U54YrFGDnnMNZ56+MMkvE04R2xtia8sep70j7bVgjwxU7YwQeaWb8PZ2tKlJHh7Ef0E9gzpV1DYhpotTqhiote7EKhgkhBAljbWiX+FrS1X3KxyKn19Tx+Yt+bkD9mu3tuwBb+mtgu3pg9yRhdzqf+mc6n+r5s0fsZrh0sXNXLP+Qh43ivMVCnccFGI6GXYkQCm1XCkVV0ptGeG4+5RSMaXUsnFtnRBiWhvuTv9Uqw6OZZfDwrzm3NcOeNNg6mF3LMwdWUgcGnDqFOS2d+eBd0ZVlGiyl1UKMR5Gmg74U6AH+MoIx30F6M4eL4SoACN11qdadXC44OGlXfu5+Pe/yD/t2cXz7/62aC3+5gcfpa05QWxvCJ02SXXEiL7andcxN71tOh1z7lLI6liZmgEeo2wgkRsEfeNvHuWhOz5b8dsJi5llpOmA64B/0lonhjtIax1XSv0TcCNwx3g1TggxfQ3XWT/y7S+cctXBcisA3n2/k098+TtwVT3BnAS8tpW1zjXf7egkcXwgr/Lg4DMdRJ9oxxv0EnC5Sbi8/OHX7+fM5mZnmmHz527msR0tkDKL2nvJueew/8iJoh0Ob71jU/F0x33vSscvZpSRRgKWAgdGea5DDO0sKISY5Ua60z/V4fFyxXjaT/ShN9QWletNHh50rtl+oq9oSD+woRFXtRv31Y2kr6knfGmAXceP5o0ibH7wUTJrq4m29Oa1V+0a4Ht33lKycNLDv9g2qZssCTERRhoJSAOeEY6xeYDM6TVHCDFTjHSnP5okvlLu+fzNtBQU45l/xGDu4ib6POG8Y+0Sv/Y1F5Y4Jnl4kMCVc0vuD9CxxuMkILobfajz5xDbG3Jeu37xEqe9hTUMxlqzoBJIxcCZZ6Qg4F3gEuBHozjXxuzxQohprlT2PVAyI7+ccp315i1Dd/rDFQAa6wqAb/zNo+xL9RcFHYGwYvPnbuZYa6e1G+E8T/4Ihamd7P/EoQHnYWc5YHIPtS4/aQPc9b68jYPO0uXf/2RvsjQTZMyRjxHTy7Blg5VSfwncCazTWpetBamUWg28DnxPa/21cW/lBJOywaKSlCp727A/g+FW9Jxr5HXoI81vj6bU72jbMNL1Sr1G7Rrgl9/6C85Y0MSNd95LW3OCxMH8nACeD5FZW130eOT5TvxrG3DX+9Apk+RLvbgurHW+P5X2jOYzm4GkbPAYzaSywSMFAfXAfsCFlfD3T1oPVb5QSnmA/wTcB5jAeVrrvnFq9KSRIEBUklL196M7ewhc3HBKNfnHqw2juV65oCP3fKmOGLE9fdawf9LF//nG5/nst38EV9UXXa9wy+DG11Ocfc7CUQc0pxoEzTASBIzRTAoChp0O0FqHlFI3AI8BPwUeUkq9DQwANcC5gB9oAz4xEwMAISpNqblsjNJFdba+ZFXOG2lqYDzaMJr59HLTC/b5MuEUyXcHqbl2AcpjkA4l+Oy3f+Qs9Su8XuH3Z5+zcEyb/YxmvwMhprMR0zi01m8A5wF/gTXkvwS4FGvlwB7gS1gjAHsmsJ1CiHFSKvu+XFGdAW962GI949mG05lPt8+XW/AnE06RODgAV9UTDZqlt/89heuPdylkIabSqHI5tdZhrfUWrfUVWutGrbUn++/lWuvvaa0HRj6LEGI6KLV0b5Gvjqa3zbzHoi29+FbUTsjSt3s+f3PR9XKL+BQaqeO135OdBAjkBQS+FbVEd/bkXS/xYg/e5dXO96NZvjiWaoZCzASyoEOICpNbJc9e9779gc08df83uEkvx/1SP7G9Ifznz3G2xx1Npb+xMtOa2O4+a9Odbe20n+jjzu/9bVGHOpqO135PZ+iavDv8vByAtElsdx/Rll5iu/sw0po5b6Y5v62Gm/RyHrrjs2x+8NGiQCM3ANl069elNoCYVYZNDKwUkhgoxJBPf+n+khvhjFeS4LHWTjbd+nWOqzA6rdGpDFWXzSubYZ+b9Ocs8zM1Z+gatj38zaLNg+yM/djeEIE1VjJgtKXX+e/c9xTb3ceZdU08dMdnue2+Hxdl+hc+Ht3ZQ3BjU9F7Wt/bOKZcghlGEgPHaCYlBspIgBAiz0RuhGN30r1rPQQ3NhHcYA3Xm3GrzlipO2u7MqG9y19gTT3BjU30rvWUHIpfPX8hVbtj1A64iDzX6byPkvsCGIqOs01u+dr/KnmHX/g4hiqZS1Dj8nHL3fdz1S1384GPfJarb/1KReYLSLGgmUd+ZEKIPKWmC8Zr7Xup/QaCG5qIvREi2tJrDdXvDfFux1DnWSrpz35tbsBgBxhPVx0nurGK1JW1kLGWP6Z74iU773RPnNjeECcGTpYMEgYy8bzHfStqi0oLN71tsufoMX4ROciu40fpXevhrUXhiswXkGJBM89IFQOFEBVoopa+lVoaaMYzkDQJbGxyhuL372rjWGsnSxc3O5UJ38tJ+rPlLmOMxuJFAUbVh5sJP3WC4KVzibb05hcL2tFF8JIm3PU+J2nQjGeGqgqamnnpAOGcTYVcNR58K2vz6gkMLozyTM0JknvLBymyjFBMVzISIIQYd+Wy+UstDUwc6Kfqinl5nafeUOvc4ZdL+oP8ZYxP7d1XMkjwB3wkfxtGm5rwtnbCW08QfvIE/ovrcdf7APCtqiPyYhfxN608guD6RgIXN9Cv4qSe7SG6s4dMOGVVKXxrkAvOWcqP/uJPeOTbX2BQp/LaXnj98U6oFGI8SRAghBhXw2Xzf+aTm+D5ENGdPURbekmHEniiZe7wW/Y4AcTSxc1se/ibwy5jTAZK1zq4YuUKAmGF4TFwz/XjO78Ot8flBABg3eErn4vghqa8Dt34YD3peheBixuI7+wl8ko3rKnl2doTznvKDWzGs/aBEJNhSoMApdRypdSPlVJvKqUySqnnShyjlFJfVkq1KqViSqkdSqk1JY5bpZR6WikVVUqdUEp9Uynlmoz3IYQYUmrev+Nskzu/+xNuu+/HcJWV2BdYU4/7QIzLzjlnxEJFL+3az11bHqG9K0R4WzsDW9uKljH6VtVhvhoqSmgM+jzONYPrG/GeUQVzPEXXVK7SVRPtf4NXzcOMpEkcGsCMZ5yhfjuR0ru8eCvi8UqoFGKiTPVIwGrgRuBt4LdljvkS8DXgO8DHgUFgu1Jqvn1Ado+D7YAGPgF8E2uvg3smrOVCzGKnUxXPzubPpTwGOw+8UxQc6A217D18FLVzIP8Of2cPZiJDbG+II12d3PSlb/N01XG818ylZtMCDJ8L7/JqJwAAMPwurl29uiihMXe43uZbVUfmlfyAoVzyYO57cDf5CaypJ77vpBUIhPud6YrfqVrJhjOW0fh6yqk9MAs3ExKzzFQnBj6utf4lgFLq50DeAlyllB8rCPi21vqB7GOvAEeB24GvZg/9EyAAfDJbvXCbUqoW2KyU+q5UNBRi9PJ2x2s00KluWu68d9QdWrktdvEYJYODk1UZq0N/PoThcxF2pTCjaVx1XgDMwTRsmpuf8HfFPMJPtRO8tInk4UEwNYGw4gs/+P+4bMPqsu3JrTPg7k8Tf6UbI2D9GQysbSS6q8eZErCnG/znzxl6D9nrB9c3Etvdx/yLzgWsvIXNn7uZzQ8+SrDKT3P1rN1MSMwyUzoSoLUeaUHJpUAt8M85r4kAjwM35Bx3A/BkQWf/j1iBwVXj01ohKkO54fzRVsUrV2dg4weWl73Tdtf74Kp6qpUXnTYxqtwELm4guL4Rd6OvZPDgqvMQ3xNy6gZwVT233ffjsiWF06FEXp0Bz/XzUG4Dz+IgAKnWKFpD7DcdVhXDp9rxrazFVePJyz+wr++NKWeoX8oJi5lqqqcDRrICyADvFDx+MPtc7nGHcg/QWr8PRAuOE0KMoNxw/miz3MvVGdhy1x8VBQd2x5oJp6z1+v0hzFDSuRvPhFOk+xKkQwmnjoCdUJgZSBWtKigVrNjtadifKVrCF1zfSOz1PmdFQNXGJoygC9+KWqqunEfi4AADW9sIb2tHm0PVVXXK5Lo15zt3+uUCp023fl0CATGtTfV0wEjqgUGtdabg8RAQVEp5tdbJ7HEnS7w+lH2uiFLqNuA2gCVLloxbg4WY6coN548ly72wzsCx1k42P/goc2tqSL3eQzgZp9+Xcoba4/tOOh10eNuJvAqB/vPnEN8dourKodLC0R1d1HkD6FLBSnY7YvuaHeF+qvDQm4rg8QSLjlc+g9jeEL4V1l2/78q58HyIzFprcyF7W2InaFlZy6JOH9/bcotznnJbIx9XYW4cw1TKTJT7t7ShaT5Hjx6d2gZNA729vVPdhDzDlTCe7kHAhNFaPwQ8BNbeAVPcHCGmDbs4T2Ed/c1bTi3LPS/HYJGBnueh6W0X0c4+DL+L2N5Q3h26EfTkVQiM7R0KACB7B3/lPGp2xunPKeQDQyV8C/Maojt7IKhwlzjeVeclsKbemf931Xg4f+Uy2t7uobfEyEHj6ymeeDi/Uy+bB5EtSzybCwbl/i1duHS1nmY186fMTPkcpvt0QAioLrHUrx6IZkcB7ONK3abUZ58TQozSeJcNLjVU3nOuQaOvmtjuPjL9ybzO03/BHKvTzqkQWGp6IqEyRHflbw8c3dWDNq1Ot605QWRnDwNb20j3JTDjGSIvdZWtMxBc30jikLVKYdncuZx9zkLnuplwyilpHEslKVQqDyJvK2YpGCSmqek+EnAIcAHLsZYR2gpzAA5RMPevlFoMBAuOE0KMwniWDS43VL70zGYC4TDvYXXkeaV5V9ehX8pfwld0B6/Bf2E9sb1Dcb7/gnoGkym6OjqJvxtCeYz84fxXuons7EFH0+iMxqjxkDg04EwFYGpn1OMbf/MoOtWNGc/kTVdEUybX3r6Zi5YtJZxJ0Fxdxz2fv5kntnzF2R0RQzmjClIwSExn030k4GVgAPhd+wGlVBCrXsDWnOO2Ah9RStXkPPZ7QAx4fhLaKcSsU65WQKnHh6srUKpUsH23/cSWr/DRc87LK/KTDiVQuwYwPTD4TAdmLE3khfw7+PlHDDasPAfD7yK4vtH5MvwuqpWHN996DyPgLqoAGPzgXJShUG4rOKi+fJ6z7j8dSnCGrnFGPey7+8SB/rzpCjOe4XgkxNNVx/NWAgBse/ibnFlnFUJyAgApGCSmMaX11E2HZzv0G7Pf3oG1HNDelPsJrXVUKXU3VrGgu7Du6r8IbARWa607s+epBw4Ab2EVFToL+D5wv9bariVQ1rp16/Rrr702bu9LiJkub049Jy/goTs+y233/Tjv8aa3Tcy0pm+1K+9YuzMtda6mt00uWHgGgzpFc3UdH7t8LV/+4d/TF4sQN1P4rpxr3YHv7SN4ifXfiQP90J/iYxvW8r07raQ8+7z28+6IxpWGePXQHH6hgV8fp+a6hcXz98+H+PHdf8KXf/j3DGTi1Lr8fOtP/4AvPvB3RDdWOcdGW3oJrKkvev1NejmPfPsLeQmJ82tmRb2AsnvRF1q4dLU+cWz/RLZlRjh69Oh0ywko+zOc6iBgGfBemafP1FofVUop4MvA/wc0Aq8Bf6a13lNwrlXAA8AHsVYKPAxsLrGyoIgEAULku+Xu+3lMHS7q6BpfT9G71lP0eGx3n7VWP+cxu1OE/Ez9GpePPUeP5QUNyZd6cV1YS/LwoNPBjtTZ2ue987s/Yfv+/RiX1DvnCz/VjrvJR+DihqLXD2xto+6mxUXveckhF2+/34auc4OhrJyE/jRXn7eKV+f2OOexdyMstL63ka0PfKPo8VlAgoAxmklBwJTmBGitjzLCL5i2opR7s1/DHXcAuGbcGidEBSs3jz+QiaM8vqLHMVTRY/ZSPcjPMbjl7vudAMA+1ntZozO3n9tpl6xXUHDe6uqgEwDYxwQvbSK2q7eoAmDk+U5cc7wlcwzeebsds8lFMBs42ImGe/e/x/zFdUPJjaYu+XqZ9xcz0XRPDBRCjELunbadqHY6Q9DllrzVuvz0lugAMfNHFEt1inYbt7bsQV2W/1zh+ezvS3W2NS4ft9x9v/Ne3+vsRC3JDxbc9T5UlRutIbytnYDHSzwcJ/Ahq0qgfTef29lnzBQ1G5rzg4kNTZx8qpOXt3zX+nx7+4k3BNj55BGY6wVD4V1ezaJO3ykvoRRiKk33xEAhxAgmomRtqSVvDfsznLN4ftFOfU1vmyzy5W+nW5gMl9vGAW+6bPlg34paZyc+34raoiWATW+b7Dl6LO+97n+/jXQoUXQ+w+eiamMT55y1iCfu/yoen4fk4UEShwbwnlVNbG+IaEsvA1vb8F9QD35XyZEHt9ftjGR89Q8/xWtt7xP4yHxnJ8R4Sx+fv+G6mT7vPy5c0qPMOPIjE2KGO91a/6Xk1go4v62GmpdjdPf38+q8XlhTS2x3H+nnevlw5Ayeuv8bbH9g87B1BXLbmNvRA04FQHtXQN/KWsJPtRPfdxLdneCSnrnOeS9YeEbRVILeWIvr9cH85YS7BljfvISb9HInmdF33TyC6xsJrKkn+e4gvhW1BNbUYwTdRF7sQifMksHJ5ecNrT6+5Wv/C+9l+QWEqj7UzJ0P/FTKAwOZkXaDEdOOTAcIMcOVm7/PnTs/FfbOeDfeeS9d/piTZOfyGAQ3NqFTJlXa73T2w9UVyG2jq8aD//w5xPaGqE26uWH9Rbx7RoCXn3sHz8IgGIqqK+c5S+zm6Tpnh74XDr6dN5Vg7wpY7XMx5/UUCxY2cNb8ZjY/MjQdcsvd9xcFSfYugGY4hQKqr12AGc8U5RDMOZDmwR/+iXO9sjkRTZ5ZXRVQzF4SBAgxw41Hrf9cufkFR945Qe9aD+xVIybpjaWNrhoPgTX13JDN9P9Pf/7XeE625a0wsK9xtK3bWQoY86YJZPME7L0FgusbMT0GvSkTz5EIP8suySvMQXC2Ec5Kd8bQQO0Ni5zgxn9BPbHdfXgGNJ+8aiObf5ifW1E2J8JQTlXA8c7PEGIiyXSAEDNcua17T6VATWF+wXEVzkvSyzVSoJFbQGhwMErT22bZNoYzCTBUyWu0tfaUnEqw9xYoNQ1SmIOQDiWIvxFydgu0pwEM8oMbV42H4MYmfH4PAH/ynR/lFT965C//jOjzxaWHvcurmV9TJ1sKixlHggAhZrili5t56I7P0vh6CvdL/TS+nuKhOz57SnefhfkFdsdcah5f7RooG2gUdobP1p7ATGs+HDmjZN5Ac3Ud3uXVJa+xYGFD3giC96xqwtvbMXsTZbc8LsxBiOzoIrgxv3pg1VXNmMnSeQCDoUjJjvyyDav5v3f/KbGnOonu7LF2H8zuKmhPWYx3foYQE0mmA4SY4Y61dnLbfT/OFvHx0Zsyue2+H5/Spj+F+QV2B1p15TxrHj+74Y9R7WHD4mUAecv17KHvUp1h32qo0n7++dtfKrquvXNh20or2DCjaYyYyYcuWk1VVYC3UmFnCiD57iA11y4gtjdEOpQgeXjQOY99R26/D3sKQHnztwu224QXwlvbcDcHnOV+id0n8Vw8p2RH/si3v8CnPnYF6y/8wFBVwKo6Nm+x3vdE5WcIMVEkCBBihhvu7nOsiWol8wvSVkXAvIJApubw8Q4u/v0vkllbjbvRh05103LnvTyx5St5nWHuXPz2+Bsca+0sCk7s1Qh3fvcnbM/sx7jKWq+/M9VHw/4MTW5Fz7lYm/2srLUCgL4EmZYk1R9qHlrvv6OLj35pLb968XWSx98n+dswVVfMG3o+Z7vgdCiBEfBQfXXO65/r4gPz5tF2xvBtL7fB0njnZwgx0WQ6QIgZriPcX3ZYfKwK8wsSB/qpvsZaEx9c34hvVR1GlZvgxiYSl9bAVfUkDg6QCafygg970yA7ec+ei49eUuUMrRduOgTkVf/LhFPE9oZoS/STOpngmvBCgoOKxMEBvMurIa2dAMB+z8Er5/GZv3qQj12+luSek04A4Dyfs11w9OVuJwBwnr96HrFYasS2j/bzkw2ExHQnQYAQM1wVHqI7e4i29BJt6SUTTp3y3WdufYD1vY00EcgLMBKHBop35st2rPb3HeH+sjvw2YHCVf/1y1z3hXuK5t3f7ejMy/wPrKknuLGJ/o0B3jxxnFQ4SXB9I8nDg7ibfCWDn6hK8Zl7/gZjXunn0z1xBp/vBEqveIiaSTIvhYi80IVvZe2Y5vcLP79SNROEmE5kOkCIGexYayf72o87a/jtErhnVNWz+YFTu/vMHer+9Jfu5/FU/kZCpTpOmx182J3hFZ/5MtESx5+InKTm0gVFHWzq9T70Ao+z9C/3+Z5zIXokhWdviEx/Ep0ySYcSuOt9edc3w2niCzyobFJjYfvcDT6CG5uI7uzJez4TTpE40E84msJV5yV4YROJgwOo7PSB086c+f1yywErtV6AVAyceeRHJsQMtvnBR+k51yiqd3/RsqXjcvdZOLxtb56Tq9zQ99LFzVyz5vySxyuvUTKYWLS4iYb9GcxIquh5M57BCLoJrKmn5toF1Fy7gPjukFMyWKdMIi90UXVNM+nuOGYsTeSFguV8u3rwrbJGSHyr6pyyxJlwivibIQIXN1Bz7QICa6xpDt/K2rzaAjplUq083HL3/Vx961e4+Pe/yC8iB2U5YJZUDJx5ZCRAiBmsXDZ6OJMo84qRFd7dPnTHZ3n4F9vo6O2n+pz5vPn2cXrOxRl5ULsGWL94CWfpZidL3mZn/duJi3ZynhFwl7xLn1tVw7GjnbhqvUWZ/zqRKZrDr7pyHuHt7bgbfKR7EgTWNqDcBu4GL8FL5mLGM9aKhoEkZjRD9TXznbt6V40H/wX1VO2MAGBsLJ7miO0NkQklyIRTGH4XDfszvOk+bgVeiwyYV0+ipdcZLTjVhEwhpooEAULMYBNRLdCuzqcaDSvj/7538+a1nSCht5/5NXV5JXoL2dMCm279OsdVGAyF//w5AEUletnZz2+iPcTTKXwraonvDlF15VBm/+AzHSVHD5RSeaWGoy29BC+ZW1TiOPxUO4bflfd6w+/iwxsupCPcz6vxDhLZ7YzBWh6JqXHVeWHvANesXo1aZvB01fGSwYI9fSHLAcVMIkGAEDPYPZ+/mZdu30xbIruEz9Qs8tWdcj7AaJYbjnXOe+niZrY9/M2h4CLbqZ9RVc9FkTPojoR58+BRMmurcbkN3Af6Sbw9gLvBhxnP4PJYUweuOd6SowdaawJr6kfMW1A+g/BT7bibfE5NAPeBGJsfuZk7vvMT4m+G8oKS6K4e0j0Jqq+Zj+F3Ua2DZVdi5LZHlgOKmUSCACFmOMOtCKweSgw03j71idnxLnaTO7WwqnkBq8KaQZ2yRhAesEYQbrn7ft6c14+KZ/ISAgvX9ftW1Vlz/gXr/gMXNeSNKth5C2b2fGY0jY5lUIai5roFzmsjz3Wyom4uV3z6S/T1RwheV7CccEMTkVe6nemDjt5+qo3SgYj97/wjBpu3yHJAMXNIYqAQM1ipxMCec41TLlNrr+/PNdzdbeFa/9ykuMLSwc9Ut/HswUNEI3G0HjqHfXddai+A3OWHht8FbkX0N+0MPtlObHcf/vPnWIWKkiaRrSc4510/Hz3nPGrfSBLf20dwfaOVRHj9Qow5Hsx4xjm3f10DB/u7iWyswpznKXmHbwTc+Z+B1k4yof145IUu6mJuWQ4oZiQZCRBiBhvvO/dSiXy5d7e5d/ZVeNjXnk2Ss/MHshUDy5UO1htqadn7Pm8tCDvH2nkN9jGF7wWyS/9eDfHx8y5ky//4Y1re+C3/+cvfJ92XAA1GrQf/FXPpen+Af/zWHdy15ZGiuXvfqjoiL3bhbvI758xNNBzuDl/tHODWb2/ir372c2unwZzcgcD6RtYk50syoJiRZCRAiBms3J37/oNHi+7MR2O4YjfHWju59vbN/NOeXTz/7m95bOfrRaMQucV0hps/t4+94KY/5djxTsxXQ2WXH6a74zS+nmLvT7/Pv/zwyyxd3MyXf/j3GI1e3A0+3HP9GD4Xym1wcpWbzQ8+SjiTv7lQJpwicXCAmmsXOLsI5i5DLLVBUuS5TsyIVbUwvTrAbff9mGrDi+F3EVzf6HwZfpfkAYgZS4IAIWawUmVqo7t6CF/oP+V163bi39YHvsEj3/6CM7x953d/wvGItZY+uL4Rd2Ppinx2ueJyAYrNjGeIBzVvLgnDmlrMRIbI851F2/RWXTGPs89ZmDfM3heLQFo7bQmsqbfm/+MZjnZ3c+SdE3nXKjXV4Kr1Ose4ajzOBknhbe2Et7eDR5E5mcRMZEgeHqStOQFaS1ngYWQymlu/d2KqmyHGQIIAIWYw+879w5EziP4mO09+QT2uGs+4b2O788A7eSWD7W2Gc+XmD5QMUFp68SwOWiWOd/bgqrHm6V01HqqvbMa/toHwtnaiLb3E9obwnz8Hw+/iyDsneGnXfif/INIfLb0vwIF+3jx4lK5zcSoCAmDqooDFTjS0jzH8LnQig3Ipaq5dQPXV86n96Bkoe3fBgwN0DYalLPBwFLR1n3qNCjH5JCdAiBlu6eJmqoJ+qPcQ3NiU95zyGLz7fmfJ7X5Hy84DCMWj+Dw1zuP2EHpuNn9u/oAdoGx+8FEOvnOcNw++h653E3u9j+ClTbjrfUUrANz11uiCveTPfj61JMh1f34Pus4NhsKsdpUchdD9KRLLgiRf7UG5FP3/3ooRdFlLCQvm/A2/C7JbDANgatJ9CavjL1MHoP31voouCyxmHwkChJgFOsL9zp25vQFP4tAAZizN6yl4a0G4ZPLecI61dnLXlkd4au8+Eu4M6WgSb3bpnVNK19QMPHGcBfMb+fBF5xdVDFy6uJnNn7uZG++8l8ANC/I6drvKXm4nq1MmRpXb6ZjTPXGqLp9H7I0QqsogmN0jobDuP1gjDYGUi2hrjJprc5YCPt+J+5yaooAl8kIXKDABw+cCQ6G8pYML+99Fi/ODLCFmOpkOEGIKDLe07lQ0V9fhXV5NtKWXdCjh7MBnBNz4rpw7pp3w7PbdeOe9PF11HPfVjQQ3NuGu9zL4bAfxN0PWVr7Z8xnK4Pu3fzovfyDXXVseKVolULjzIJA3KmDP87ub/NaUwUAqbyoit+6//dr5RwwyaKfKoH3uqquaSRzoR5ua8JMnCP/mBLHdfQTWN1J1+TyUskY1AmvqIW0OuzfCsrlzx/7DEWIak5EAISZZydK8o7w7zz1Hbn3/z3xyEy33vUvbylqir/Y4d8JQetldqZ3w3u3opP1EHwsXN3GitYeuc8Ht8TmvCV4yl/BT7fhW1ZE4OJB3V/3ZHzzEPz35At2xCCdae1iwsIEzm5v5zCc38dTefbivbixqAwzt3JfqjJE8OogRcDH4dDtG0A0aVJWbwRe7iub07br/kSdOMG9+PaRMVq48h87OEJRa71/tofryeVZxoYLqgsENTc5IhP/iBgaf7aD6Q/PzRi18K2ulEJCYlSQIEGKSjaY073DK1fe3N/rZWruHdMEweeGw+ZF3TjijDzfeeS9tzQkSx62Ovc8TRs/z5G2MY7dTuRXJw4NFmfZ6Yx2/3v2WFSCoMMfe7mPX++/x78+8SrLBwFWq3G/KJP5mCN+qOnRG51cK3NWDOZjGt7yG5DsDuOf6S87pK5cicnEA5THYHjpGtCdMTaqu5LVs5Yb7M+EUqfejBLLTE5iadFcMj3JxnTqT7225RRIAxawjQYAQk+x0C/wUBhFmPMN7/X3c/PXvc82a87li1QqeSbVZBXJKJO9FW3rJrKzlxjvvZfX8hXScbZLcW9yx587VQ7YzNXXJTHvlMdCmJr7vJL6Vtdbuf6YmmorhmzunqA3xHd3UmB4yV8/J23zHufaGJmK7+4jt6aPm2gWY8QyRl7qsCn7ZPRLSvQmClzUN5UAcHMC/sZHIjq78jYee78SzpIpMOEW6J050Zw8YCt+KWlw1HidAyF1G6F7vIxNOWZ9vKIXOLXGYVWoEZVnT3DEnXgoxlSQIEGKSne7Of7lBRCaccurtRz0Gj6cO03AsQ6a/H9elDVbN/ZW1hH/ThqvRb5XLtbe9rTYJ734HtbEKKHOHbGqnfXad/tjrfSVHFzInkwQvaSqaKoi80IVvVV1eFj4DaWJV4BtmysLuhJXHgHgG5TYIXDy0R8Lgc50ot/W63A481Rln4PHjGLVWB+8/fw7xPSFS7w1aIwppjc5kiL7agxGwVg4E1lnLC3NHBeJvhAhmtxd+JtXGdV+4h7/9i8/z8C+28V53N/sOHiW1wk/qeNQZQdmX6h/z1I4QU0kSA4WYZKXWz4+l4ExuEZ5SRXD6VruYQ4DY7j6iLb0kDw9i1PuounQuwfWNecP7OmnmtSOXTpmk+xKEt7c7a/Y98wME1jbkra+3h++NGk/JqYKqK+aROhZxkv10RuO9uolEMjXstV01HpTbIB1KWO9zY1PeeauvthL+bHbgkH5/EM+iIK46L+4mP8ptYATd1HxkIb5VdSi3QicyGEE3vtV1YEJsTx+pE1GnHfF9J4uu13Ouwcf+7K94TB3mrUVhuKqe+J6+ovfb1pxg061fH7ekz5kkk9G0dmemuhliDCQIEGKSDVeadzQKg4hSd9FnLp/PmXVNBNbUW50UpTvaC85awvwjhrOyoKha3+XzcNV5Caypd4IHz/wA3g/UEHuqk+jOHut1SRMjO3JQqj3pnjjh7VYlPu9Z1bjrffjX1DOwtQ0zli4OKlp68a2qo/rqZqIvFCcG2ufNnEzmvS72RghXnS+vkmDs9T6qrpzn7CoYuLiBmk0LCW5otKYQLq7HDKcIfnCu8xmY0XTJ60Vd6bwO3z0vULI8ce9aDy2NvadctXGmcrkUi+e6proZYgxkOkCIKXCqBWfseei5NTWkXu9hIOIiVWJoftncufzdN/7Myh/o7edgJkJnzna79t17cOVCnviGVdDn3TM62fvUuyTrjbxpA+/y6qJ5ds+hOJ7rmvOGz2N7+siEkiWnCtxNfoIbm5wOHiD1fpTaGxZZQUIowcATbVYpYp/LuTZYmwOljkfQ2amA3POqgIvY7j7c/SbmKyHMwRQ1m/JXRribrAJEiVK5B9m8B1eDD8/8gFOjwIykS74PM5p22u9bUZtXmwFKj8yMJelTiMkmQYAQM0TeqoBFBnqeh6a3XZj7M/StpmTVPlvGMPGeXZO3+53/gnoGk6m8gORYayfXfeEeZ2MgnTJJ7O/Ht7qO2O4+XL1p5tbVMuhzMbg35CTX2WV/47/uIPJCl1PS1w42/BfUA0Mdb3h7O8FLmoi29GJGrQ7XVe9Fm5qq9UPLCXXKxBxIEdjYRHRnjzNEb58XU+NbVYfhd1HzcoyIihfdwZspk+jOHitQybYZcOoUpLvjGNXWn0K7eNHgjrQVJBUETa45XqeoUbSlF8+SYN77LTdicaq7Ogox0SQIEOI0FK7Xn8jM8FJLCzsXpUi/2Ierx02V28tlF5zLli1/DMC1t2+mLWFVEsSrMQ9GCKwbyglIhxIceecEN9x+T17bn7r/G06lwGRA47+wHsPvInGoH12j6N/gR3mCBApK/uqUyVUXrybo87Bz5zvgMUjH05gXD00l2O1GQ2J/f1GnnulPkQ4l8koKB6+YR/LwIBqI7e6z3g9WEGP4XYS3t+Nu8hMfTGJUu0mHEtbqBMBMZFCmzksojO7sQadNqi7LX0EwuKOTwEUNVjnhjCYTTg9dz9TopJVAaL+H4PpGwk+1479wDgO/Po67OVB2JER2GRTT1bTPCVBKfVoppUt8/UnOMUop9WWlVKtSKqaU2qGUWjOFzRYVwL4zf0wdnpT538Ktee35Z9/1zXiunUvishrePHEcKN7xL3BxA8prENvTB1gBQOaN0nPXSxc388//80u88fff5wxdQ+JAP7G9IZTXRfCSuUXD6YlDAzl35ibV1UFWr1zGNWvO57ILVmD4XWTCKWvToOzGQTqaLkq8C25owt3oI/pqT94GQu56q2CR4XMR3NiUdycf2xsaqjjY5CHdGSP2ao+zqsEcsOb6866zsQkj4M5PMryqGcPnItbSS2RnD4F1jRjVLifgSPcm8gIo+3Wuei+pYxGqP7yA4LpGli6cR9Pb+cmWssugmM5m0kjANUAs5/t3c/77S8DXgLuAQ8AXge1KqfO01h2T10RRSU636M9YFS4tLDX/3HOu1a6dB94p2cmmtnWzvreRI++coPey4eeuly5u5uxzFtLXaM2BR17pLjnUnelPEtsbwnt2Dc+0vIWa54Pszntzjxlk+vpJ+My8ofXMyWTpJYmGwt3gc2oTQH5CYzqUKF6C+Hwn3uVWsqGZ3QUwkLPHQLnrlHqs6op5xPaGMPwuzEgGI+gBUxN0ea0Rghw6ZbJAV7F25VkMJlPM99Wx+QGrs7dzMebX1BXtpyDEdDKTgoAWrfVg4YNKKT9WEPBtrfUD2cdeAY4CtwNfncxGispxukV/xuqez99Mi50TMNL8s8co+dycxmq2PvANrrrlbo7vfd95zp7bLywnfOSdE0QPh9FpTbrLWkJXuIGQ8rvwragl/maI4PX5mwR1r6wl1ZoieEVzXsDhmuMtOWyOqUmdiDoFfbzLq4m91kdgXQPKbRDZ0UnNdQvzlyBe1Uxsbwj3eh9mJJ1XMrkwcS/3OrlyV1qYsTThrW24mvwAeD9QQ/0Rjfdtk55z83Mvnnjk3pIdvCQBipliJgUB5VwK1AL/bD+gtY4opR4HbkCCADFBTrfoz1jlbs379M59DPQm8jo4uw7/fiK4Uzhz67ltu+TcczjW2sn+99sIbMzfrte3spb5NXUca+3kzu/+hO3792NcUk/QY2X1D25PMvBEG0bQhavW6yTkRXf2ENvTR1XhsPv6RgZf6EIbJoGCgMS3qo7BZzup/lBzXk6AOZCi6upmJydg8NkOMuEk8X0n0UkTUKXv7HP+O/f7khUTX+nGjGacz06nTCI7uvCtriMdSqDjGWqyqxbszya0spZN+gyqdVDu8MWsMpOCgCNKqUbgCPB9rfWPs4+vADLAOwXHHwR+bxLbJypM4Z15ucz88WRn8h9r7eTa2zdzPJvBbsYz1p34hiai2bakX+yBNXVOh6pfPcmf//X/x+YHH0VvrC3qsHk+xK0/2MSNd97Le/09BC4ZWpJnxjMYtR6qc7Pl7R3/NjYR2XrCyby3583NeAazP4FyGSXr/mNA+Ddt1t16RoOGqivnOYGL8hhUf2g+4SdPgKmpzt7xl7qzT/fErTX+8Uze807FxO3tuBt8pE5EcTX6CGxszF8pcXE9yd+GSXVE8cwPOs/5VtQ6ywgHz0rxLwV3+JOZGCrERFClamJPJ0qpjwDrgV2AC/h94A+BL2qtf6CU+gpwl9Z6TsHrbgX+D+DTWidLnPc24DaAJUuWrD127NiEvg8xO+V2AvNr6tj8ucnrBOw79p0H3uHkQBTPprlFnWNsazs0D83RL+r0Mbemxqp4V+Ccd/30dvVzXIXJhFPUXLvAea7U7ns6ZTp1/8Pb260tfyNpAtmqhNGdPaQ6Y7jmeCGl8+v5P9uBcht5SwkHn+kgeOncoakGsnfyO3vQSZOqK+ZhRtIk9vcX7Q0QWNuAu95HOpQg9lof1Vc3O/UHoi/34GrwWtUHu2IonyvvvdkGfn0c5XNRfVVzUaCTONBPU8rHNesvdDr6vCWbuVMEs69ksBr2yZy/pbX1zWs/9rlfcu4SH3943ZzJaNu01NvbS2Nj48gHTpJly5aV/RlO+yCgFKXUPwHXAnOBuzmFICDXunXr9GuvvTZBrRVi7MZ6h3nD7ffQkk3gy2WvrbfplEnj6yl613qKC+E824vxoUYnoS6QU5zHHlIvOn82OIjtDRFYU090Vw86ZS2/i2zrQHsV1Vc1W3kEB/rJDKRQPoN0R4y631mS14bBHZ0olypam681VG1sIvJSF5lwCsPnsir6+VxgQvDSprxpj8EdnZjhFEaNB9I6P9B4tgPld+VNXdjvf+BXx6n92BnFgc7uPtI9cQJrG0kdi+CNKTZdeB5ozTM1J4qOv0kvn205AcMGAbmaF6/SX7/vh/zqtbPY+t0zJ7JN09rRo0dZtmzZVDcjV9mf4UyaDsj1c+A/AcuAEFCtlHJprXOLVtcD0ZECACGmm5JbBRdsSlMYJFQbZRLtSmTBL1jYQOZAiJOr3ENz4i904V8/dKfvmutj8LlO544aU5dNsLM3CLJXIIR/3Ubj6ymqqqqJXFptJQJml+bplEn4qRN4l1YXz+27jZK7CUZbejHjGZTHoPb6RXkBQqY/mRcAAGAojCo3ZjhVlERY/aH5DDzRVlQIKPJCF+7mQHGbPFZpYt/KOpKHw3kbCsV2dOPb0ICrICfBTq6UqQIxE8zUIEDn/HsIa5pgOfB2zjErss8JMaOMtPSwVJDQcCxDk1vlZa+rnQN4V1fnnVunTOZV17B33xFScWtbXjORwYylie/vt+6gg27M/iT+ixusuXFTk+qIoV/pdtbc2x0nXgP/RfUkDg5gVLmtTX+q3LyfPIl5Mol7r7UToJ0roDwG7ia/VX43uwLAfl65Sif9mdG0taHPhpxtgw8NoNwGOmES3t7uBCjKY5DujhG8ZC7xN05aIxA5c/++FbUotyLdE2fgsVY8Z1RZOQmA2V+60I9R4yHdES9achm4ci6x3X0ENzY5bcLUHNEJXtq1n9vu+/GwgZwQ08FMDQI+BfQAx4B2YAD4XeCvAJRSQeDjwENT1UBROUZzxzeWu8KRlh6WChL6VsM14YV52eu3fnsTt933Y9rIVtAzNYGwoufMGlJNbqfziu87ac3FHxxwltflzofb1QAjO3ucoXF3k9+Z+wdwZZPnAmvqMQeTuIMBam5cVHQuw+/CjKUhU1DFb1cP6c4Y0Z3kBQaG34WrxkNmIIUZzxB9oQsdSeGeFwBDEVjfSPLQANU57Q5vbyd5aACjxu0kS+Zex8iWOLanSjLhFLGWXgLZMsa5KwniO7rxXTyH2Ot9pUcJQsmi2gW9KZNPfPk7pFcFcHuGkhxlDwExHU37IEAp9a9YSYFvYt3x/17268+01iYQV0r9NfA1pVSIoWJBBvDDqWm1qBSjHbovPOal2zdz0bKlhDOJoqBgpKWH5YKEQV2cvf7QHZ/lE1/+Dq4NQ8sBd73wLqbXWj9vFxyKDbO5jv244XNZSYBPnsjLM7CPx9REdnThnhtwOt68c2UDCB3L4FlclbeawLeqDnMwXRQY6JRJYG0jsT19xF7rRQHVOVMCkRe68K9tyLuWu8HaRTC3Q3fasaGJgcePE36mA53IOJ+BnTegzp8zNPpxIsrl551LV2eMd7JbLhcux9SmJvJ0B1Ufnp93Hb2hlmS2dkHuZyR7CIjpZtqXDcYa4v8j4F+BfwFWAX+otc7t4P8auBcrSfBXWHUDNmmtK2P/TjFlhhu6L3eMGc9wPBLi6arjJcsNF24VXFh61goSircFLlWf4OFfbENvyF8O6LloDmZ/ksHnOkl3xzHjGee5XEXz/wztpFfq+qm2KGbSmlooda5UexTldVH7icUEN1rbHMdaekl1xEgeHqT6mvlFHbbyWiMBym1gBN1DG/Vkj6nK7iuQx1DO1ECpdngWBVEafOfWOpsQ5S4pDK5vJLixCff8AAe7OlhaPQdv2mDwuQ5rxUNHjPibVknm2hsXUfuJxSQODpAJp/I/uxIFiWQPATHdTPsgQGv9Za31uVrroNY6oLVeq7X+vwXHaK31vVrrM7LHXKG13jNVbRaVo7CeP1gdwLsdndxy9/3ccPs9bN/5htPRQrbcb8Gdcm7gYBcFukkvZ31vIzfp5XkjCyMFCYXtM+MZp25/5KVu4m+EqLluITWbFlCzaQHxN0PopFmyY7f/jbb0OlsKA0Rf7c5/flcPriYftTcsQqd00bmSxyOQAVeth9jeEJlwyunEY6/3Fc3F25+L8xm5lNO5Fx5T2NnaSYxl35OhqLpyHom3B/BfUE86W3Sp8DhlKPpVkmeP/RbfDc3UbFpojTDkJBXabbD3UMh9fSCsZA8BMe1N++kAIaazUkP36VCC/a0neWtB2Br+v6SKeHY7XXsOvVRnljtUnLu9b6HcyoEjVa+rwlNyXtyMZ3BlO9nghiYir3QXzYcPPtuJGU2Ren8Q5XUReb4TI+jGqHKj0zp/h714hsAGa35dp621+/Z6e3vOvPamM0rmG7ibfKRaIyWT8uyvdGcMd3Og5DGpzpjzeDqUINUeJfNs0gp+CrcDzl7XDjBcNR6qrpiXtxJCp0wiL3ZhDqZQfqs6Yu7n5SmzisAORnTKRO0a4Jc/+DIP/2KbVBgU05oEAUKchlJVA12vD6Kvqs+/U9zQRGx3H75VdaS74yU7s7EMFQ8XJOQyXEbxXeuGJmeu335MJ60754HHWjGq3Pgzbn5w+3/jjh88QvCmBUVBTmZHH6nBBKYbdCJD9Yet4jvxfSepvWERZjxDbHcfmf4kZiSdt/4+N98gsKbeCiRcFHfYu6xiQ7HdCt95c4jvC2G+kM5b9x/d1YP/vDmEt7WDwioqdJVVdji6swffqjoGfn0cz4IgGCov0dEctHY2xNRgagZ+3YZR7caMpnEF3dRcn5/Y6D2rmlRr1NoRMXtuO6jTKZN0b8IqmlTnZf3iJVy2YTWXbVg96p/pbJDJaL7z84W4XSlu/d4JHr5r4VQ3SYxAggAhTkOpu/L3Vnbzlie/Ip/yGNSnfUT3DhD8YHEW+umWG7ZXH7zb0Un7iT4WLm5iWdNcuiJh1Jzyc/2QvXP1u1CGyquWd9dP/i+pTNrJoncy5veEqPJ78V5jBToDW9sw/K68JMLcugCDz3eWvXO29yxId8XxX1CfX8r3gnr0Xo1vVZ0VXNx4hhNc2CsUvGfXkHx3kJpN+asa1PlzrHoBfmv748Iqg5HnOwmsa8R7RpWzd0BgXR3p9jgARo0nf7RkfSPhp9qpuW5BfgByQb21f8KuHoygi8DaRgy/i7N0Zd7xu1yKv/jUCQB+9dpZU9waMRoSBAhxmgrvyj/9pfvZl+ovutOvcnmJb6gqykKfnw6yauVZ/Ml3fnRKRWXs1QdtzQkSx61s/z5PmH2pftJv9eCe11TUFp3IOP8dbelFGaook95/xVz07j7MwRTh37Q5y/L8F9UTeaUH1dJrHZ+xCgYZVe6Snb09pF/YhnRfguAlTcT3hjCjaRIH+sFQzooBnTLJdMWtJYwFwYUdmJRcAZAdZcjdPIjVdYS3taMMhRmzSht7z6gaeq8X11vBTe4oQ86UhfIYuJt8RSMq4V+3oQ1wzfE6AUDT2yab75e5fzEzSBAgxDgrt7FQ08IG+jwRYCgLHaDvuV6n/OxYisrYd/9P79lHDzF0ny7qENWGOiIvdBUNoddG3PQ/2Y4xx+PUxi/swM14hnRfAneDDzOawUyZGD4X8f39qGp3fv7A850k3x/MKzUMQ8V2ivINnuvECFrnwlDU/YfFzpx+ZEcXrnovmd4EWllFfMx4hnhLr7Mywf6y32eu3Ex/38paBh47jntBAPdcP74VtSQODTgBgC15eLBo5UHuEsly1RfnNFQT1F6SOo37QJJLzj2H791/i8z9ixlDggBR0SaitGu5xL1v/M2jvJU6XNRJJgOaYImVAps/d3PZtuXVHthYhTfkJvpqT1GH6K73gdcoGmaPvdKHf30DycODJA4NkO4r3pY4/mbIKR5kbcTTjWrwYfYni8vxXtVsJReWmNcPXFgPkLf+vjq7gU9kR6dzrkw4ZRUsyh1yf9Xa9je+t4/gJXPzHg8/1Y67yVe6nHH239jrfahqN2g9tAFSiRLIucsEbfb3Tj2Ci+rzntcpk5P9gyQvn8eiziqpBihmJAkCRMUaTaGf3GPHEiyUStwrNUJgvhrCt8ZKCHRKzwJbB3bz8m9/S8+5htO2X/6XLxJQLtwBD+6Uom+1C7fH53Se7obSHaKRvavNfSwSS1Jb73OK2WTCqby79cSB/vwyvQcHnM46urM42FAeq4Sv7/w5TkKgTmQwqj0YfhfKYxBYU8/gc51oba0sMKNp57WAU7jI/t6MZ9AaMgNJzIQrO3rgxnt2NcrrApUm1RnD3NFVtEOhTmvC29rRaRP/eXOI7ztpTQd4remL8NY23M0BZ3fFVHus9JRFd9yaWlhVR+LgAK6c0Yzorh6MOi/x3SGOeBR3fvcn/MsPvzzyL54Q04gEAaJijVSj3zaWYGE4pUYIwquiPOs/MVS+N9vJDOzsIXWukdchxr0ZjA31JLOdUCKbAGd3nnY9gNxh98iOLnQy43RwTiU+8u+G7aHz8FMnrB05cur4F3bOGKpkh+mq9ZBqjRLc2MTA1jZqrs+uEtgbsvYnGEihExlc1R6qLp3rBBS558odiYi93gsaPAuDeaMLg892ovwG7rl+vMurie3sZeCx47jm+TAH0qA07kY/vlV1GH4XETtIcBvE9vShkyY1NyzKDxoyGaKvdueNNkSe76TqinnOCgCjym3VOOhP4qrz4r+gnsShAQIfrCe2u4/t+/dzrLVTRgPEjCJBgKhYI9Xot402WBiNwhECO8B4r7+vqKPN7WRLFRiy56zt710eA7+dcAgYXSm8F9YQ3dUztKafbNZ92ixaGx97rQ8zlsZd78Oo9pTsnIG8hLvCJXSxPX1Wx540rez6Gg++FbXE952kZtMCZ1mgfT7fqjoiL3ZhBN15S+8ShwYwAtafp9w8A+UxqP5Q89DWxS29+M6rI7a7D50w86cSsol9/ovrrakMrwudMp2VBEPnm094axuZ/lRe7QMzkUGnh4oIuWo8zrbJTp6A/dkYCuOSeu7a8ghVQb/sHChmDAkCRMUaqUa/bbTBwqlaPX8h7x8vXkZXeLddtkBNzp25nXCoUybhX7XBzl5rmVxBrX8AnTaHcgVMTaY/gXdxNYGLG6w179k593RfgnQo4WzZ64wabGvHPdcPgPesapJHwnkbENm7DJZaeVD4Por2DNBg+Fx5x+dOl6R7rHLH9tI9T3OgKFgIrm9kYGsb7gYfwUvnkjw8SKY/WfL67uYAQN7npFMm4SdPUPORhUXLAnODjNxg4Km9+3BdVu+MGD3+3+7AmzLwVHnY+IHlbLnrjyQoENPKtC8bLMREGW353bHU6h8LexTg6arjZOrcedfwrah1NtABnGS2wjak+xKYsbQ1115Qxteo9xC8phnS1mvtef9oSy/RnT0YARe+FbXWyQyF8rjQpsaMZ6y199ctILixiZprFxB7vY90KOGcP7G/31oXv6ae4PpGUsciRSMVVVfMw/AYmJF0UaBlSxwaKNqiN7ihiUxP3CniY7c9vu+kc72aaxcQ32dtFexu8pUtKazcBr7V1nx+YE09rjpv2VLCOlu3wP4y4xlcyiD2mw4iT5wg/Nhx0l0xIi90OsWJEgf6ibzUhW9FrZPkmRfsXDKH/qo00Y1VPFPdxnVfuMfZI0KI6UBprUc+apZbt26dfu2116a6GWISFRbXWbS4iWVz57L5c6W3Ab6xxJK/sSzj6wj3U4UHw2U4OwdGIlFnaaC9nW3uUr7IS10ot0FmIIWRAtXgzs+Qz1nHPrij07pzLtiGN7Y3hHd5NdGXuvM24NEpk8jL3WROJvHMH0qQi77cbW0TXGKp38CvjmNUuXHVefGtqkOnTaIvduGq92FG09RcV1wdLrytHZ0y8V8wh3R7HJ02yfQlCV7a5FT1KxylyIRTDD7djvK5ILvE0Ai6S7bJTvYzo2k82US/3Pc/+HwnrjqvMwWRCaeI7eql6sp5mPEMiQP91ucbcJHqjFF30+K8u/5UexT3/CD+1XUk9vdbex64FNUfmj903M4efKvrUG8NwppaJ4fAFnmlG+XOttvUfPSc82ZaAqEa+RBL08KVOnj5vwHgdrk4d7GHRXN9FVc58OjRoyxbtmyqm5Gr7M9QpgNExcnr1JcY6AUePEfC/N03/qxkpz5crf7hVg3kXsf0Zgpq+HcT39NNxqcwfNYduW9VnTMEj6HwX1hP4uAAfreHrX/zNe7/v79kx46DhOIRaw66xkPi0IDV4QXceSsAcumEic7o4h34Lp1LbHefU9kv2tJrXfOtkyXvqo0aDzqjSfcmSD3TDimNCrhJd8TQunj6QqdMq4jOmnqrnG6NB+Vz4W7yEX25J7/WfsHSxNqPDu0zMPhMB6n+KEFP8fbFrjle/OfPIb63r+Q2xEbAXbRLoFYw8OvjuGq9eUHR4LMdhLe1Z5P+5lh7KrzcjdmfJNbSixG0zlV9bX5OQXBjE3W74ly8ajXP+k847yNxaABMTfpElMDGJqc64fZXZ28CYW7FQJtUDpzeJAgQFedUEv1KLfkbadVA7nUSe0NFw+X+K+cOJbjt7MFMmqA16Z44oDATGfyr67hOncllG1bT3tXH4zt3YwTcRSMG5qBV3hdD4VkcJNUatTqgngSpExHcc0tveqOzHXFuomFmMF16qaHfhY5n8G9oJHFwIC8xcOCJtqJEQ3ukwoxncNV6ijcximUwo+m82gK5SxPtdlVfM98ZUShsUyaUtFZVZEdInPeyocm5fmRHZ9FKCB1LFwVF1R+an/fz8F9Yb60kuG5hXkBS6nPsjAzw1Mt7SXo0/o0N+Z/PxQ1EdlijOp75AYxL6k8pqVSIiSA5AaLilNv+tyM8tkS/4YKJUtcpmdiX/de3ug4F1HxkIbUfPcPKcjc1c48ZfO/OWzjW2skffu1+jAZvXudlxjMoj0HNRxYS3NhEYE09if39eJdXW/P51y3AVeMlE0qWnAvPnEwC2SV52eVv9i6AuTkGkR1dmNnNeZKHB4vW87ubfATWNRDZ2UP/Y60MPt9pLTWkzMqGDU3omDWEb+8ZEG3pdbYYLvycXPVeoi29+XkPLb0EL23CjKZLvkZ5DAy/CzQMPtdpFTxq6bWSA93GsD+P4MYmEgf6cdV68h53zSmdU2BWu/BcPw9V7SLybGdRImTVlfOI7ekb+j0Z4++aEBNFRgJExRntqoCRFK4asIeAtyb38Knbv8XbB1uJeqJW0llyqH5+3lBxX4JMOFW6bO0H5/KBnrksXdzMLXffTyZo4BrF0sGqK+cR2xvCvd7nJOiFt7eXXNZn1HiKahREd/bg/UBNfpXBi+vzKhKa8QyJ7PPpnjg11y6wAhJDUZuzBj/yQhdmIlO6kw5YywITB/qdHfnsjr7wZ6PcBp7FQcLb21EeA3MwRfCSubiz+Qj2KAgM5QOk2iKkWgfxr2kgeTSStzfAwBNtZSsN2u3LhJKoaquN9jy/b1Vd3jbJuasZ7ODBbmPR+/UMVSA83aRSIcaLBAGi4pSr7X/rHZu45e77i+b3y8375wYTuR1pIp7h12++SXBjE0FP0Bmyj77Sje/8OUVD6dGWXnTaLNlx7Nh3gE9/6X6O9nRbdfOTptPh2Zn9ua+zA4xMf9JaQ29vxpMw8a2szevYfStrSf42XFQMSJsad041wdz26JSJmcgQey07R54NSpwpj8I74CvmMfDr42WnF5TPBaYmsqOTwNpGvMuriezopOrK/E7W2S3w2gVFn5urylOUD2AOpgmsayT17iDJoxHMgaSTZwBW7YFIQaVBe/rAbp8KujA8BtFXezCCbvwXzMHwuzCj6aIyzPbSReUx8gK+3Pdrf53ujpFCjCcJAkTFKZXod+sdm7jtvh878/vp0Ake//0vsnzpAo50dqE31hbN++cGE7kdaWxnD4ZnqF6/b0UtVZfNI/LrE0Rf6aFm04K8O2nlUqTaSpetjceT/PPeXZjdCYygO/8OdGcPOqcOfuEdvd2x+VbWAjpvvt15PaDjQ3fqmXCKTJ81dWDGM07nhqlRhrK2BTYUyje0tt+u+gelpzzczYHiPQVe7UZndN4WxYPPdZLpT4CG8JPWqgmjyo1OmtYSxBK7BQ48fpzaj5+R//gGa5fB1PtRfOfPIfJsJ0aNJ69tnvlWXYCBx49jBF1WWy6d6+xeOPhsJ2Y8jWrwEbykydkuWCetZMfCMsy50xSAVW0w52cVeb4Tr+niw5Ez+N6W4g2GJmIPCyFGQ4IAMe0V/oH8zCc38fAvtp3WH8zCRL9b7r7fGRmwa+UHr2pk3952Ahvr8zqZ3CRCO5jYmtxDOvtakiaBnM7NvsM0FvgxDGWVAC7orDMDKSIvdlF1eU62+vZ25y63sNKePfTc/+/vOx1O4R293VGGf9OGTpt4z65hYGsbngVBAPwX1hcNwScODeC/cA7h37Sh/C5ctV6n/O7gsx14llWRODRA7RWLnOv4VtUR3dWDchtlh/J9K2qtPQXCKWsUoMZD1WX5yXzVVzcz8Ovjzh27tWlRD2YshZHwlAwwVMBV+nGfy6nu51kULFnq2N3oQ/lcqKCbTGeM6Evd1kIqUxO8fB7uep8TLPkvrHe2DjbqPCSPR4jvs1ZRmOEUKuh2pj8CaxtI7DvJwGOtGFUePCnFNWtW8L/vub3k7+l4laUW4lRIYqCY1uw/kI+pw7Q09vLv0YNcf9dfOd8/bhzmxjvvPe0CLLlJfIlDA87QebkKc3Zilx1MXL/uIquIzqGBklvSJg70O7kBiQP9RZ119dXNmJE04e3thLe3M/BEG2Y84wxX28cVtsNV58V33hzC29rJ9MRLH9Pkp+4TS0geCaMM5RTcsee5vcurGXy2g+TxCKnWCIn9/bjnBQheMpfAxQ1OUZ7qD813chlyE/lcNR78F9STbI0QeaGrKHnPnpIIbmzCVefF3eSHbLVCuzBPqiNGbG8Io9pjfe7hFO56H8FLm3DV+ZzNkXLplAk6fy7fLj2c6U8S2dlDujNmBR5Ja0omt23WngEm5mCaqiuacTcHcDf5qbl+kVMd0UkSPDTgTHu45vlJHBqg5toF1Fy7gNqPnWGNkrzQhfcDNcTfOIlR50V5Xaigi5q6IIfbuvhv9/wvPv2l+4t+V0dKMBViIslIgJjWCv9AJg8PErisseQfzLEuucodYTjyzgnShrX1rpnIOPP2sb2hkne31crDLXffz3vd3Zxo7aG+sQaOhdDB0nP7mVAS33l1JA+HMaOlj8FQuBusGgGBtQ3EdvUW31UXfG9GrKQ4z/xAUXlf+5jMySTR13rJ9KfIRFMMPH4cFXBZm/k0+sj0Ja1qgIZyhtadYkVeF8qliLzYReCiBmvznyuK59ENvwvv4io8S6sYeOy4tcFPk98pZmS3BcDVaK1WCFxW79zxx3eHiubn7b0I7KTDosTGXT0ELm5wCg6Z8aFaDPZ/55b8jezosgKBtMYcSFH1oWbnbj/yovW4a4637KoBnTJxNflIvjOQl1+QO4oRf+OkUwjJngYIESX5wRr6PGH2pfp55Qv38NT933Du8ie6LPVkymQ03/l5fmEgtyvFDf/jvZLHV2IhoelGggAxrZX7A1n4/dM7942pAEvREGyth8xLvXBhLWY45SSgldosJ/VcN8/4T8Ilc1CLDPQ8D60tbfjW1pLZ0VXUWadDCXTaJL7vJO4GH6YuvQ7f3ejLr12fGZrvL7lpz6tWFcDqa+YPDZ+/0IWryY/yGHiXVxPfHSJ4SX6n5F1Ra82Xr6wleXgQY74L0iaBtUMV+eylh3k7Er7Qhf+igimJ9Y3WMP9Aytq8aE8f7gUBUieiZPoS1hK97PuLvtKNNhSpt046KwjACuwKRzzsPQHcTdYKB3tzpMgr3ehYxil+ZEbSZEIJBp/vxBxMOfPwpeoyVF05j/CTJzBjaTyLqkgeHkStyD7vNTCChlOmuGjVQLaksP/8OWWXMRrVHqqvaiaa3d3RVeOh6qpmYrv78trRcy7cteUR/vl/fgkYv9Uq00GpYkHDkUJCU0+CADGtlfsDWfh9DzFuHMM8aqkhWO9ljTS+nuJkVRDTM1RYxt6ZL9Mbt+4WTYXn6rnFneHeEP61DXlJYelQgvjrffmb0OzsIfJSF1WXzcvrYH2r6oi29DptNPwup+PP3erX1eBDGYp0X5LaGxfl5THkbZH7XCe+lbXOyIAZz2DUeIjvO4lR5Saxv38oMe/ihrzOq+TSwyuGlh7mbuaTao9iJq0SvLnZ+4PPdRJ5pRsj4EYnMpiRNNXXWjsJ5v38yqyMsCsn5lUUDFlljjPhFPE3QpiRNDXXD71n+z3Y5yg8p/K7cNdaWxmnQwkiO7pA4SRrxl7vLUpijOzoAp9BIJtDoWOlgzgdS+f9LjhTPoYqaserb78DWMFoNBYnszdEMqCd/AtZQSAmiwQBYlorXM7nXV5N8qVevJflZ8D7z59Dh99k061f5+xzFpZNGLSnALa27CHmTTvz1WD9cbZf+3jqcHF2fLUH1zDz8wDp9jj+tQ3OygB7DX1hQl+0pdc6xtQkWyPWEHUiY1Xku7DeKo7zQhfeMwIM/Oo4yq3QCSuhLt0ZQ/lcGEF3Xh5DqTyDyCvdVgKbS2HGrByDYL2VPV9q1z278yr3HnXKLF6FsKaewec6i0YJqq9uds4XbemlOmejoNwVDfZqhMJONd2XIHBRgxMIxd4IWdv55iwHHHyuk/D2dgACFzXkba9csoxxjQczkibVEXM2SbKDEpfHILDWuo59TjKa4GVWPQJ7AyYzlile7bCrBzOacYI4M5FxrmmXR85tx8neQa665W72v9+G3liL++pGXCkT89UQ16xezZYtfyxJgWJSSBAgprWSy/m+t4mb795Cj8+qdpc773xchelr7C2ZYZ1Xy/+CIBzoL1oDPr+mjs2fu5mXbt/M8Uio6I4wHU4BlFxCZ2b/4CcPD1oPJTJlK9Mpj0FgTT2RF7pwz/EWLSeLxzMYVW5S70VwNVp3/r7VdSQPD+IyvaR7EqDyO7qSnXY0k7+2fmcPcXDuWguPdzqsMsPi6Z448TdCRTv/2R1+bm0BM54h3RMn2tLrbP3ryk5tDD7XSWBdA9FXe3DVewk/1Z43lz74TAeuGg/xN0PoeIaBrW2Q0tTeVDwXb6+ciOzogtV1YGpnxUJeR52TvxDe3p4XnOW+V8Nn1QdItUbAYxB9qRv/mnpS70cJrm8kczLhVDm0+S+oJ9OfcgoVRV7oIh1KENvZi05lnNoO3uXVJA70o6uhpfP9opUnxiX1VOugBABi0kgQIKa1wl34ItE4f/Wzn+N3e/Eu9xYlwdlDr3bC4J3f/QnV1UEn+a/rXFBxo3g9/a4ezqiqZ/MD1ujBRcuW0lflylvPb1S7IWXgX13H4NMdGDXuvE5m4FetYCp0nYkZTeOe48Wo9ZTsTFPtUatjjKSp+8TivI7Av7aBWEsv1dmCOXYFv8IiQ4PPdRLedgJ3ox+dLt1pu+q9eecObmwitruPVF+mdJW94xEGUyaZnjiZ51J5ewFEXurC3eAjM2CVGC4cRcm94011xIqmB6K7evBfYA2pm7E08T2hvOcjL3SBN9tWt5FXQ2C4gkr2v1VXzrP2GIhbOQNaY+UVzPMDOIWAYntDKEM578HOt/CtrC2q+W8/Hn2xa2jaIa0x/K7iWgFJa+mn//w5VF0xj5M/P4ar2o173tAujfE9VqJp8NK5zoqD3KkVgHebO4t+96V2gJgoEgSIaWvYXfhyEvmc9dwtvbjn+xnY2mYdE8/wpK/bmr/PJv8lssO1RevpNzSRfDniXDucSTjr+e0EOoBMb4LBZzusee6MlSymPIaVBFfjdYr52MPtpbLaI893Yg6mcTf4rGH67B2yLXl40NmqFgBDFdXrzx3uD1zcQHhbe1GBmsHnO4u26VUeAzNVusqeTplUXTOf+J6QVQnQpZy7XXtUo7BjtkdhrFGChDNCEnu919l4J/czDm9rxz3XD6V2NczmHGBqp86C89r1jYS3l95EKPe9KQVGdmdBuwZC7jbCpYop+c+fYyUdvtBFzaYFJa+rAkNTL4GLiqsNDj7fifecWvwfqHWCC3eTv3hDpYusnSHt4CkdShQFd/t3tfHSrv18+t4HaEtYS0sxNb/8Ly1ccPYSzmxuloBAjBsJAsSEO9U7mpF24bMT+c4+p5Ej75wg0eQl9V6k6O7Tle1k7T/qg893lryj7PYnnOTCKjxEXugi+MGmoj/Skec70YaCtCb4QevxgSfaqLkmv+O255nd8/2Ef9Pm3BH61zagDvTjv6AenTaJ7OjE1eADE7TW6Him6E67sPOz26xjGdK9CWo2LWDg18etIj8el1VJMJ52svNtOnuXX7jEzd51z13vszrj3X2ku2JOR53bmTqvyc6/29Mayg39v3wfo8rjvP/C9robfQTXNxIp935SplV8p8RzrhpPUecb2dGFb3Wd8950RlN9xTxniaE9YuKqtbZBLlVMyQnSXKXb7JrjReUkKNrVBsPb2lGGwoylMYJu0sej9L/dj9JWLogRcDGwtQ0jW0jIf/4ckocHMaNpMmFr6iCyo8vaLCqnTXpDLf/lf3yfTnekKO9g5/vv8daCsBQTEuNGggAxoV7atZ+b/vu3iNVoZ0jU/gMGDBscFC4PLPUH+uxzFrL1gW9wrLWTlTd9jqobFhR1buHt7VaBGsCzOIiZrVpXdEdpKGcKYV/7cdyNvpJ34FVXNRPe1k517l2jt3ju38kbODjgDCXbXNlOF3DumK0Ked3ZgEATe72XwNpGPIuDxLJ36kXD/XO8RF7sxLOwCqPagxlO4TkrSOpYBOV1EX7qBMFL5+aPlswrva1w7ntM9yZQHoPIC11UXTGv7Oef6YkT291HYH2jU1rXf0G9Ne+dk/znbJjUFaP/sVanyE/h+8n0xItyHZznwimClzQVbWyUPDyIu9FnbdfrNTDjGXwra/M2DLLzDEq9B3MgZS09TJWu+Z85maTq8nl5OQbuRh+uOV68Z1WTPBLOq01Q2HF7z64hdcwqwpQZSKE8aijwg6KRIOUx6B4cIHhdc8nfZXuqS7YjFuNBggBxyka6w39p134+8sVv4rtqLsGcIdG2lbXcteUR9necGLZUauHywFJ/oO211EsXN1NdX4VZYo4VINOXsO7AeuLW3ffL3VRdOjdvPtqzrIrY3hC/bGuj5qOLMHeamNn5b8C5M1cew6qfn21LJpzCHEw5c+yexcHsHWgHOqUxatylO59o2pk+yIRTxHb1glKYEWujIGVAeNsJlKFwNfgYfK6zaHjZe1a11Tnm5go830nwg00ot0HiQD+xbG1/o8YDbqN8EJTz3+5Ga3e+zEDKGoaPZtA5IwH2ca6G/NoGdkdl1HiIvtqN77wSGyZlO0Y7wMh9P8Er5hHZ0VU8hZKtRKgTBVUDEyap1gjprhhoMIJuq7P3GkWrMuxtgAvfgzY1OpxCpzWDz3bgqvM6Q/BmLI1R43GqIg480YZrjodMKIlR5Sb6ajeBdY0kWyPE9/Rh1HkJb2/Hd24tmd4kOqOJ7zuZ93MbfLYTnUiDCcFLm4i/GXJyJVIdMauugCv/98v+fdZJ06nSWKqYkOQRiLFSWuuRj5rl1q1bp1977bWpbsaMkldsxzO0E5/diR9r7eTi3/8iXFXcccT2hvCcNEnVKmc3PHte+Sa93Lm7OdbaycX/8YsMRAYxqtyYkTSec2oIXthg3WW/GuLDq1Zz319Yy6nOvOaPCZ3nzut0om/0kXonbL0+msa7og7/B2oZeLwVtMKos67rPbuadGvM2YbXt6KW+BshJyMfU5PuSRBY24C70Uf4qRO4m/zWioCk6XRmparfDT7XifIZGAF33nsNP3WC2o+eAUD4qRMol8rbPS+yo5NUVxzfWTVgKNLtUYw5XozsFrxW0Rwzr/CO/RlHC3If7HaYsTT+8+aQPh4l+MG5RR1z6ljEult1WzkB9jHpUILY6315GxgNPtuJ8imMoCdv6sKeOoi81EWmN0HtTYtL/g6YiQyGb2i6wj7HwK+OE7xsLvF9J9HRDEa1Gw0YHoN0Zwz/xQ0ot0F0Vw9GwJ0fGGU3+cFjUH35vLzfWbukcO57iOzown9x/dCqhOc7rcJBAbcVBAymwOei+vJ56JRJ/7+/j7vBX/TzJW1SnQ067NoQVVcNrVwo9f4Da+qJ7uyxfsd+G8aztIr43hCuei/pvoRTKbHcplCb9FIn6dXeU8PZBKvE/5OnQY18iKVp4UodvPzfRn1it8vFuYs9p9So6Wxlcy8HOxtHPnAClKnCWPZnKEEAszMImOg7glvuvp/H1OGiP252J/67f/otfvnq61YSGOR1EvYabFed11oydXDAydyueSPO6pXLaK6uo9EX5G+2b8O/tmGoI+6IQTyDe1m1U1hF7Rrg7KYm3j7eTjQad4beY/tPkmmPFS2/U/VedH8q7y40kq37nm6Pk+lPopOmkw/gJAaa2lmjbyeyRbI7BtpZ9jplElzfWHL5YFW2PoA9VJ3uiVP3O0tQHoP+fz1WsrMc2NpG7Q2LrPMd6CdzMmndnfrdVH94ft66/lwDW9ucEsT252/4XVZ1v74EmXAKo8aDTprWfHYogbvWi6veugtOd8cJfrApb/VFOpQg+koPyqXQaTNvmsHO7Pdng6bg+kZrk509IWo/fkZe2zLhlJVD4LXm+X2r6vIS5SLPdVpTG4MpAusbnaV59s8q/OQJjIAbo9qdV+vA/sxiu/tI9yUIXtLkJHSCtUdCfN9JMqEkym2AtsoE2xsp5b4+uLEpL2HSf2E90Zd70MmMtVdAzjXtz8X+XTcTGaqyiY32iEYh+3GdMhn49XFnesRV50VntDVSNJjCVefNS6C025je1s38JY30nGs4vxu6K0Hw+gVl/5/MNca/D6MOApoXr9Jfv++Hoz181qoyUkTMqQlufvXaWWz97pmFD5f9Gcp0wCxRuJRuX/txes41Rr0r2Wj+KPzl9/+Bv/zbf0YFXZiRNP6LG6y71Czlscr3XnXL3bS8866TaV24RlsnTdxz/U4A4FtZS+yNEKRMjMvn0eKx1vkP/LKNqivnFS+Ne7aDZOsgrrk+fGfVoDfUsmf7MYKXNKFfiDvD96n3B/M6VrtiXrojho5liLT0UH3pPKdDP/mL9zFcytq+NpYmtidEIJvNXXhHne6NE//tQNGOgYPPdZa8e4u80EWyNWLV8X+lBzOaBq8xNM8ccOctRwSr4/Y0B4pWKVh1AuKkexNAfuGdxKGB7HI1E+8HavJyAcxkhkx3Ale1NT3hO7cW7+IqIju6cNd5Ud6hzsPd6CP+RojA2qGNhtz1vqE/JUo5Q/P25xfb3UfstT6UVzH4bAdkNLgKKv6FU1ZN/03FSwd12iT+ep/TydqfW2ERIgzljNiUmmZJ91nbEdtJhE6gsqMLz5lV1ghHifoBrhqPVdugL+GMpPhW1RF5rtNaznjdAgaf68zLSfAsDlpFhzblL3XMnecvt0R0YGucwEUNeBYEyYRTJE9EcdUrJ8E09/epMGcgrtK8+84JvKoWsz/lJGmW+jwKpw1k10KRS0YCmPkjAYVD87nV4FIdMWJ7rNrluj/F/Xfeyuc+/bGi11/yh39B72AY5TXQSZPG6hpe/dl3nD8Kf/n9f+Def/0FVVc159+VxjMENzbhmR9w7qLI7lRXeH0znMIIugleOtcpiWt3bKnjETxnVZM6HHaG/o0aD656X9FwajqUIPJ0B0aVm8xgmkw6g7vKg7vJn99Zb2+n5oZFACWXh0We74Sgi+pL55HqiBF/M+QszXOGu73KKe9rs//QZ0JJqw5A2rT+mM8PWHkBUPz5Z7cQzuuUsqMSKq1JHh3Ec0awKKlMa6t4jR0w2e23S94qt0KnNYG1DUV3zHbnBhB9tQcU+UPhz3fiOasa7+Iq+n/xPp5Fpa9vD6sX3iXbmfl2trw9DeAs87u4gcjOHnQ0jU5rK2kvbN3de8+oyvs8w9vbQZO3RM+5ZsFoR3h7O8GNTURe7HJGO3KnWfLu5LPLRhNvW2vyzZNJaj9RenrCt6KW2Gu9GEH3UE5AJE36ZIK6jy8m/tsB0m0xqj809BmGn2rPy+4v/JxKLkvMBj2G30VkRxdaQfUV8+j/RSt1v1OibdlzFbbXu7yayAtduGo9GFUezGjayXPJPXbZfhfheJyBTJxal5/li+axc36o6LhSIwZZMhIwRjISMAWUUquAHwIfBE4CDwP3aK0zU9mu0Xpp135u+dr/cv5HfeQv/4zLNqwe1Ws3P/goR/q7yfwy6sx9e5Zaf2QT+/MLtnzx//wUIC8Q+L0vfIe+RCTvbqbvuU5+7wvf4dV//T4Af/m3/0zNTWeUvsvd0YVOm4Sf6cAddGNUuRn4ZStGkw+VIb8gzI4uzEja2lrWLvFqajLRNPqdAWuYVlm5ApmBJEZ1/j7ydo18+w+53Zkl22PgUdZ1s0FEJp5x7sJKldWtuqqZgV+2AhB9tbt4Z7gPNTPweGvR3ZUZz6DNoep19vsaeOx9XPV+XNXW+zeVxjPHn79ksWBzmYHHWqn7j0tJZDPMi7LBn+nA8LnyVik4n8GNQzXzB55oc+6Obb6VtcT3nQTAVespKhNcdZW1853vrBrnekXX32ZN3eR2Xs7rr5xHeHs7nhsWWR3TwZOkjg3lb5gZa0RCuVRecGUFL0NL7ZTHwN3gc6oKFo6G5CYtApjRtDWaUPC5+lZaS+50yrQSQSNpqHKho2nn2OjOnpJ3y4AVrHmN4voJCWt4PnmoP29kSXkMZ4MjW2z/SZKH+jGCbvr/9RjeFXXOng/KY239bCcBAtZn+JSV8a/8BvHfDlivz36G3hV1pLvjzu9xbkJo4uCAkw9i/w5EXuzKD2Kiad4MJaj9+GKUx0dvyqR1x9t4PUPBm/1eOnr7+cwXf8DfPfmcc/1Pf+Rq/s/3/zti9poVQYBSqh7YDhwAPgGcDdwHGMBXp7Bpo/LSrv1cfv0XcS/wY1S56Y2c5PLrv8iLv/n+qAKB7S/sBZ3O7xh3dJGJpqjNWZpm/+G/4wd/mxcEvLb/MJn+BAP9Sed//nRfnNf6DjvHqKA1D57I3RiFoc6g7x/ew7somJ849nwnxlxf0bF2x6E8VpW5QFgRS5lk+lK4F/itynMZTaY9gU7lr+0u15mn/uUYLo+Lqk8MbdQz+FynU0DHPjaX8hjW0H/KRLkM+h5+B/dc/9Bn0B3Hs6iqaDg3caDfeZ/2eUxt4j2rtuj9e8+tye9YCzaXsdeQG1VuK8P8zRBGwG0l8F1Qj45nnEp8w30GrnpvcRZ+Sy+Zk0lqNhVv2uO8f3/2/butZYHOqFHKGt0gown/5gRmMkP1h+Y7nZf9evvY0L8cxbswWDTSkO6NM+eTS8v+DkB2eLwtipnIcPLf38cdcDs/g9ihkxheF8HU0Psio4sDlvX/f3tnHiVXVefxz7e7k3Q6nT2QhRNIIHDcwBkVFWTVg0TAdSIwDAiDyjiiYQhHZhiCBhGODLIKuA4goKKsIRgSiWAGEVwAZTGKRkL2ztKddHrv6vrNH/dVp5ZX3Z10Uq9T9fuc805V3Xfv+/3ue6/e/b17f/d3J7Lt/jeo2b+2wHmxNmt1xPwFiXrlr22FNKSa2kltaN95D2zroGb8yN5rlH8Os4+X8UHJGAqZe6DtxUbGfXR673XLvwYaEfKmtnaSWt8WprMaYND2dAOMiHw5mjqpnhiWZ467ByAYKzlGzG+25BjSGlZFXRRZcdgpB+Scg5WvrmXJH1eH/2DE9+9awvfvWXKLbV46F6csKQsjAPgcMBL4hJk1A09IGgMskPQ/UdqQ5ZjZ8xjx5jEFDmzHzJ6HNS7tt/zGpqacLs7eB2309pGNhlVhtblpPZs6Y+V3rth52qytp/eNLO6YNaNqChrG+uhNl7dPyMmbyWPdadjSzaO3Lyh6DjpXNNPy1MadEfTSFiu/esyw3rK98k+YTPOitbT/oYnuda2xU9zSrSlSSzfTvaq1D/kNOV3APdu6CnSwbd2MzrsG9cdPZseyDTld3wXy21K0LG8gtaWD7r+39PZGZDei6bYequqqcxqvgt6J1lThQkVHTqR50dqc810gvyPI797aQVVer1Hr/20K6e1pqvYbERt4KL29i/YXGqkaVl1w/TM9HXHXK1unlqc2klrXDjUw4tCYa/DXZpoXrgkrAO5XS82U+DgHVSMLdag/MXdNg7glmVuWNwT5RvF7YHlDmLqZdw4zXfKjjt0/tqegPuptKhb0KeO/0bq8AVp6isqfOXYSr61fTX1ewKYcuq1gTYe690zq7e3KPlfV1VU5vQtTVlbxpz+uLib/C4AbAWVKVf9Z9gk+BCzNa+zvIxgGxyej0sCpmVpb0ICNOn5yjkXeF3FvKBoWxvbzu1KtO406ctMGIv+K808PD6poYZmCYxbRoaqupiBvZtNvm3ni9gW8791v7VOH1356G29fM4YdS9bTvaY1Vn6xuPJVdTXUHTmR2iPGh0h/UdnMA+7wadPY/vxPqJ4SL796Si0fnPUmtj24mh1L1tP+QmOv/gO5BgXGQpb8luUNpJo6eef4qVSPGR4vf/Rwel57lPT6dlqe2phTPpvqscPj5Q8PD/NM41cov4sRG3uoHjWsd9pbr/zj9qd61DB6Ni0htaGDlpjzVz01REGsqi9S/5E1sdcrva2LHY+vY/vDq2F1B9a4lJr9itwD+9WixhTdmzro2dHdG70v/5hxAZsyhmPveYqWZG5euIYdyzbQvGgtXSuasa1L+7wHDkzXk9qRKjgHnSuaGfGWsWFuf12R/8Co8B8odg16tnRyw2fP7fM/sPgbl/POQ2YU3MM5skYWuQYx/8Gp9WP5iM3iyK0T+YjNYvE3Lh/0c8jZNymXnoA3AU9mJ5jZaklt0b5FiWg1QIo1oBo1sMsT94Zi3SGwSn48+dblDdxw8fm7LP+KeWcBcOV37qOnOW9hmeWbSLfE65CtW+ahd8Do8Zxgs1hw584ZCH3pcND0yTx5zzU88NjTnHHh12lZ3lDQ7ZzuiI/2lm4JgXFGHDwaS6XDG+WoGqw1xZEzD+LXj90CULQRq6qvYdE9VwHwjpP+nZfXrCedtgIdil2D9Pbu3Po/tZF0Rw/db7RQ3ZzG1i0BYPi7/6mofICeTUuY/LYzaVy4Bqqhp6krp3cie9w4R35bT+89UHv4uKhbuSvEAmjqxjYuHpB8a1xK7fQP55y/6ul11L9nvzAevXBN0esft6ZBamsHbCCnp6uve6Dr1YUADJtyCqnR1TH3YAPp1p5YHbo3tudcg/bnG0m1p8LDb2t3rw593QN/ffZOAOpnfoztD62memyYTpmZ8dL5yrZ+/wO9BsjidWGaZUcPs99xBIueCffXf9x+Z5//gecevonb73qMS268g54qaGnqzPG1sPYiz4G23P9g1zNbuee6+QVDjUXPf125NBNOHOVydccTnAHzaYr2FSDpAuACgAMPPHCvKTYQij48WlIDKj93zqnctuyJgsb+ojNP45CZ07jkxjuw2irUkeaGi88vmB0wUPlXzDuLK+adxQOPPc1nr7yNrmFphndXce9XvshlV9/BmhiDY2RHdY6z3tw5p3L9lRfs1jmYc9qx9Jx2LIce9a+syjrmjDETeKO5LdbgSW3oyJF/+LRpvPDEt3ZLfna54VNPpXnbzuOmNnTEy1/fniM/taEjdohnIPIbXrmv97smnExzc1e/8nsaOsK2ratPHQYiv2PNohz5ADsa1/Utf2NH2PqRP1AduiOjRRNOzvVh2dABEH8NVrftsWvQ8vojvfJrptb2OiD2KT/vHqxuTuecy12R//nzTuv9/2ZP2bW2HtKbO2PlqzHFxOe7dzodxxgAfcpv7fs5lP0sHT9xMivXjO0zfyUwdUwrG5pH9Z9xL3DUrO2sWrUqJ23GjBlF85fFFEFJ3cCXzOymvPS1wN1m9t99lU96iqAmnMzwNxc6lXWtaB6QTwDAJV/5Lrc88LN+G9u9JR8IjXNzY07jnHmDKoUOw6eeik3Y6VSmxhRdG35WMvmZhqG/xsblD00dykF+7fQP0zOmql+DYxfld1rj0gGNCRxxxBH20ksvDUheObNq1ao+G94EKO+IgZI2AbeZ2ZV56a3AAjO7rq/ySRsBMPgH6L4ufyjo4PL9HnD5sfIHHCfAjYDAvmQElMtwwJ8JY/+9SJoO1EX7hjylftgONflDQQeX7/eAy0/+HnBKS7nMDngcOFnS6Ky0M4B2YHkyKjmO4zjO0KZchgPGEwIFvQJcCxwM3ADcZGb9BguStBl4I/o5Cdiyl1TdF6j0+oOfg0qvP/g5yK7/FjObPZBCkpYMNK8zNCgLIwDIhA2+ldywwQt2NWywpN+b2bv2vIb7BpVef/BzUOn1Bz8HlV7/SqJcfAIwsz8B709aD8dxHMfZVygXnwDHcRzHcXYRNwIK+W7SCiRMpdcf/BxUev3Bz0Gl179iKBufAMdxHMdxdg3vCXAcx3GcCsWNAMdxHMepUNwIIEwvlPQLSW2S1kv6qqTq/kuWB5JmSfqOpJck9Uj6ZdI6lRJJn5T0qKR1klokPS/pn5PWq5RImiPp15K2SuqQ9BdJ8yUNT1q3JJB0QHQvmKT6pPUpBZLOi+qbv30uad2cvUfZTBHcXaJAQ8sIwYY+ChwCXE8wkPoNNFQmvBU4BXgOGJawLkkwD3gduJgQIOUU4EeSJpnZNxPVrHRMJCzHfR0hzsa7gQXAFOALiWmVHNcBLUAyS8Ely/sJ0VYz/D0pRZy9T8U7Bkq6DLgUOMjMmqO0S4kegJm0ckZSlZmlo+8PAJPM7IRktSodUWO/JS/tR8BRZjYzIbUSR9LVwIXAeKugB4Wk44BHgGsIxsBoM2tJVKkSIOk84E4qpL5OwIcD4EPA0rzG/j5gJHB8MiqVlowBUKnkGwARLwLTSq3LEGMrUFHDAdEw4DeBr1LZYYOdCsGNgLD6YM5Kg2a2Gmgjb2VCp6I4CngtaSVKjaRqSXWSjgHmAt+qpF4A4HPACOC2pBVJkJWSUpFfyL8lrYyzd6l4nwBgPGEMNJ+maJ9TYUj6APAx4PyEVUmCVkIjCHA38KUEdSkpkiYCVwFnm1m3VHQJ9nJlA3AF8FugGjgT+LakOjO7MVHNnL2GGwGOk4WkGcCPgIVmdley2iTC0UAdwTHwy4RFuT6fqEal42rgOTNbnLQiSWBmS4GlWUmPS6oF5ku6udKHDcsVNwLCG//YmPTx0T6nQpA0AXicsKz0vySsTiKY2QvR119J2gL8QNL1ZrYySb32NpLeSuj5OU7SuCi5LvocK6nHzNpjC5c3DwCnAzPwWQJliRsBwR8gZ+xf0nTCA+DPsSWcskNSHfAYwRHuNDNrS1iloUDGIJgJlLURABxKmB77bMy+tcD/Ap8pqUZDA8v7dMoMNwLCm9+XJI02sx1R2hmEebLLk1PLKRWSaoD7CQ3B0Wa2KWGVhgrviz5fT1SL0vAr4MS8tNnAfxLiRlTqW/AcwiyJN5JWxNk7uBEA3yZ4QT8k6VrgYEKMgBsqIUYA9L4FnxL9PAAYI2lO9HtxBbwV306o/0XAxMhBLMOLZtaZjFqlQ9ISQtCsV4EeggFwCfCTch8KgN5por/MTov8QwCeroR585IeJDgFvkRwDDwj2ua6P0D5UvFGgJk1Rd7gtwKLCDMFbiQYApXC/oQ34Wwyv2cCq0qqTen5YPR5c8y+Sqg/wO+A8whjvynCm+9lBCPZqQz+QvCLmA6IEEX1U2Z2T6JaOXuVio8Y6DiO4ziVigcLchzHcZwKxY0Ax3Ecx6lQ3AhwHMdxnArFjQDHcRzHqVDcCHAcx3GcCsWNAMdxHMepUNwIcJwKR9ICST5X2HEqEDcCnH0eSedJsmg7pUieh6L9qVLrt6tIWpVVH5PUJulFSV+UtFv/WUnjosb+uD2tr+M4+y5uBDjlRAdwdn5itDrgqdH+fYVXgXOi7XJCFL9bgCt383jjgK8AcUbA14CRu3lcx3H2YdwIcMqJx4CPSqrPSz89+vx5ifUZDBvN7N5ouxE4lrCIy1xJ1XtSkJmlzGxfMpAcx9lDuBHglBM/BkYAn8hLPwf4GdAUV0jSByQ9KWmHpFZJyyUdm5fnIEm3SloR5WmWtEzS0Xn5ZkRd+PMlfUrSnyV1SnpZ0km7W7Gokf4dMIaw1kNG3uGS7pD0N0ntkholLZT0lqw8J7BzJcCrsoYZFkT7C3wCoiGJZZKOlPRMdOw1kubFnL/xku6UtC06Lw9LOiBbhuM4QxM3ApxyYhPhbf+cTIKkg4GjgdhFUCSdzs4egiuA+YSu81/kjZ8fSVhq9iHgYuDrhMWFnpT0tphDfxy4CvgBYTna0cDD0dDE7jKDsK77tqy0DwJvAe4Fvgh8E3gP8LSkKVGeFZHOAA+wc5jhoX7kHUhYVOvXwDxgJXC9pJMzGSIfhUeBc4GfAv8FpAm9Mo7jDHXMzDff9umNsPqdAccAZxGWwp0a7fsy0AgMB+4CUlnlRgFbgR/mHW8k8Dfgmey0GLkTCIbH97LSZkS6bAcmZ6X/Q5R+4QDqswpYDkyKtjcD34jKP5KXty6m/CyC/8PlMXrNj8m/IDwKCnQwYHZW2gigAbg/K+3jUb7L88rfHaUvSPr+8M0334pv3hPglBuPAG0EYwCCo+D9ZtYVk/ckQkN+r6RJmY1gHCwD3iupDsDM2jOFJI2UNJHQk/Zb4J0xx37QzBoyP8zsD0AzcPAA63EcsDna/gRcAjxIWOq1FzNry9JrVKTXNuC1InrtCq+b2ZIsWZ3Ac+TW4UOExv7WvLJxyzI7jjPEqElaAcfZk5hZm6SHgLMlPQMcSl7DmcVh0efiPg45EWiTNJzgXX8OYb31bF4vKBWc+PJpIhgdA+FF4FKCoXEYYYbAJKA9O5OkscA1wCeB/fKOsWWAsopRrA5HZP0+CNhsZtvz8v11kLIdxykBbgQ45ci9hHH+awgN9DNF8mV6wj4NrC6SZ3P0eTNwAXBbdLwmwtj3ZcAhMeV6ihxPfSmeRaOZLYu+/1zSc8BvgKsJ4/MZ7gNOAG4AXgB2RHrdxOB9fgZbB8dxhjhuBDjlyC+ADQRHvqvNrFg0vL9Fn1uyGtxinAncbWZzsxMlfXVQmg4QM/u9pB8DF0q6ycxWSxoHzCaMu+fED5A0ntyegL0VEfAN4CRJY/N6Aw4rVsBxnKGD+wQ4ZYeZpYELCYF1vtdH1qWE8fP5kkbk75SU3b2eJu//Ek0jfO9g9d0FriU4OF6apRMxep0NTMsr2xp9jtvDOj1O6Bn4Ql763Ji8juMMMbwnwClLzOxh4OF+8uyQdAEhvsDLkn4IrAcOAI6Psp0YfS4EzpXUAvyB4LH/GUJkv9F7vALx+r4s6XHg05K+ZmYbJT0FXCqpljCF713AHODveWW3SFoNnCVpJWE44xUze2WQai0EniXEHzgQ+CPwAcL0Sdh7PRCO4+wBvCfAqWjM7H6CJ/5rwEWEefbnEXwBrs3KehHwHUIgopuB9xEa2+dLqC6RTrXs9As4i2DsfJrgFzCLMOthTUzZc4GNBH+BHxP0HxRRr8tphDgMZxLiJxhwRpTFIxE6zhBGxYdLHcdxdg9J/0hwVDzbzH6YtD6O48TjPQGO4wwKSXGLD80j+CwsL7E6juPsAu4T4DjOYLkxmqnwbPT7VMKQxLfMbG1iWjmO0y8+HOA4zqCQdAYhouFhQB0h5PAPgK+bWbFYA47jDAHcCHAcx3GcCsV9AhzHcRynQnEjwHEcx3EqFDcCHMdxHKdCcSPAcRzHcSoUNwIcx3Ecp0L5f3YHg60pCovyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = sns.JointGrid(data=count_mean_ratings_film, \\\n",
    "                      x='mean_rating',\\\n",
    "                      y='count_rating',\\\n",
    "                      space=0.1,\\\n",
    "                      height=7,\\\n",
    "                      ratio=3,\\\n",
    "                      xlim=(-0.05,5.05),\\\n",
    "                      ylim=(-5,350))\n",
    "chart.plot_joint(sns.scatterplot, color=sns.xkcd_rgb['blue green'], edgecolor=sns.xkcd_rgb['navy blue'])\n",
    "\n",
    "chart.ax_marg_x.hist(count_mean_ratings_film['mean_rating'],\\\n",
    "                     color=sns.xkcd_rgb['bright blue'],\\\n",
    "                     edgecolor='blue',\\\n",
    "                     bins=np.arange(0,5,0.1),\\\n",
    "                     alpha=0.65)\n",
    "\n",
    "chart.ax_marg_y.hist(count_mean_ratings_film['count_rating'],\\\n",
    "                     color=sns.xkcd_rgb['slate blue'],\\\n",
    "                     edgecolor=sns.xkcd_rgb['blue'],\\\n",
    "                     bins=np.arange(0,300,10),\\\n",
    "                     orientation='horizontal',\\\n",
    "                     alpha=0.65)\n",
    "\n",
    "chart.set_axis_labels(xlabel='Mean Rating',\\\n",
    "                      ylabel='Count Rating',\\\n",
    "                      fontsize=17)\n",
    "\n",
    "plt.tick_params(axis='both', labelsize=15)\n",
    "chart.ax_marg_x.grid(True, alpha=0.5)\n",
    "chart.ax_marg_y.grid(True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La intención de representar los datos del promedio de la puntuación con el número de veces que se ha puntuado esa película es demostrar que el promedio de una película solo es válido si en esta hay muchas puntuaciones. Es decir, solo aquellas películas que tengan un *count rating* grande (>50 o >100) tendrán un *mean rating* válido.\n",
    "\n",
    "Si nos fijamos en los datos, las películas que han sido valoradas muchas veces presentan un valor promedio en su puntuación entre 2.5 y 4.5. (Aunque es verdad que habría que fijarse en la distribución que siguen las valoraciones de una misma película y ver si presentan outliers. En ese caso podríamos guiarnos por la mediana en vez de por la media)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Filtrado colaborativo de contenido"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez tenemos ambos datasets limpios, podemos proceder al análisis de estos.\n",
    "\n",
    "El objetivo en este apartado es crear la matriz de similitud entre las películas para ver cómo se relacionan estas.\n",
    "\n",
    "Usaremos la lista *genres* como condición para crear una matriz de 1 y 0 donde las filas serán las películas y las columnas los géneros posibles. Habrá un 1 si la película presenta ese género y un 0 en caso contrario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9698</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9699</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9700</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9701</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9702</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9703 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9698   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9699   193583               No Game No Life: Zero (2017)   \n",
       "9700   193585                               Flint (2017)   \n",
       "9701   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9702   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                      Adventure|Children|Fantasy  \n",
       "2                                  Comedy|Romance  \n",
       "3                            Comedy|Drama|Romance  \n",
       "4                                          Comedy  \n",
       "...                                           ...  \n",
       "9698              Action|Animation|Comedy|Fantasy  \n",
       "9699                     Animation|Comedy|Fantasy  \n",
       "9700                                        Drama  \n",
       "9701                             Action|Animation  \n",
       "9702                                       Comedy  \n",
       "\n",
       "[9703 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('./cleaned_movies.csv', sep=',')\n",
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crearemos un DataFrame con los nombres de las películas en las columnas y los géneros en las filas. Los valores serán 1 o 0 si la película presenta ese género o no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aprovechamos la variable lista de generos de las peliculas, le eliminamos el elemento '(no genres listed)'\n",
    "# puesto que hemos eliminado esas peliculas.\n",
    "genres.remove('(no genres listed)')\n",
    "\n",
    "# Definimos un  dataframe lleno de ceros. Luego cambiaremos por 1 en donde se cumpla la condición.\n",
    "zeros = np.zeros(shape=(len(genres),len(movies)))\n",
    "df_movies_genres = pd.DataFrame(zeros, index=genres, columns=movies['title'])\n",
    "\n",
    "# Nos movemos por toda la columna de géneros:\n",
    "for genre_film, title_film in zip(movies['genres'], movies['title']):\n",
    "    \n",
    "    # Separamos los géneros de la película. Asignamos un 1 en esas columnas.\n",
    "    genre_film = genre_film.split('|')\n",
    "    for genre in genre_film:\n",
    "        df_movies_genres.loc[(genre,title_film)]= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <th>Jumanji (1995)</th>\n",
       "      <th>Grumpier Old Men (1995)</th>\n",
       "      <th>Waiting to Exhale (1995)</th>\n",
       "      <th>Father of the Bride Part II (1995)</th>\n",
       "      <th>Heat (1995)</th>\n",
       "      <th>Sabrina (1995)</th>\n",
       "      <th>Tom and Huck (1995)</th>\n",
       "      <th>Sudden Death (1995)</th>\n",
       "      <th>GoldenEye (1995)</th>\n",
       "      <th>...</th>\n",
       "      <th>Gintama: The Movie (2010)</th>\n",
       "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
       "      <th>Silver Spoon (2014)</th>\n",
       "      <th>Love Live! The School Idol Movie (2015)</th>\n",
       "      <th>Jon Stewart Has Left the Building (2015)</th>\n",
       "      <th>Black Butler: Book of the Atlantic (2017)</th>\n",
       "      <th>No Game No Life: Zero (2017)</th>\n",
       "      <th>Flint (2017)</th>\n",
       "      <th>Bungo Stray Dogs: Dead Apple (2018)</th>\n",
       "      <th>Andrew Dice Clay: Dice Rules (1991)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documentary</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMAX</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Musical</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romance</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sci-Fi</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>War</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 9703 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title        Toy Story (1995)  Jumanji (1995)  Grumpier Old Men (1995)  \\\n",
       "Action                    0.0             0.0                      0.0   \n",
       "Adventure                 1.0             1.0                      0.0   \n",
       "Animation                 1.0             0.0                      0.0   \n",
       "Children                  1.0             1.0                      0.0   \n",
       "Comedy                    1.0             0.0                      1.0   \n",
       "Crime                     0.0             0.0                      0.0   \n",
       "Documentary               0.0             0.0                      0.0   \n",
       "Drama                     0.0             0.0                      0.0   \n",
       "Fantasy                   1.0             1.0                      0.0   \n",
       "Film-Noir                 0.0             0.0                      0.0   \n",
       "Horror                    0.0             0.0                      0.0   \n",
       "IMAX                      0.0             0.0                      0.0   \n",
       "Musical                   0.0             0.0                      0.0   \n",
       "Mystery                   0.0             0.0                      0.0   \n",
       "Romance                   0.0             0.0                      1.0   \n",
       "Sci-Fi                    0.0             0.0                      0.0   \n",
       "Thriller                  0.0             0.0                      0.0   \n",
       "War                       0.0             0.0                      0.0   \n",
       "Western                   0.0             0.0                      0.0   \n",
       "\n",
       "title        Waiting to Exhale (1995)  Father of the Bride Part II (1995)  \\\n",
       "Action                            0.0                                 0.0   \n",
       "Adventure                         0.0                                 0.0   \n",
       "Animation                         0.0                                 0.0   \n",
       "Children                          0.0                                 0.0   \n",
       "Comedy                            1.0                                 1.0   \n",
       "Crime                             0.0                                 0.0   \n",
       "Documentary                       0.0                                 0.0   \n",
       "Drama                             1.0                                 0.0   \n",
       "Fantasy                           0.0                                 0.0   \n",
       "Film-Noir                         0.0                                 0.0   \n",
       "Horror                            0.0                                 0.0   \n",
       "IMAX                              0.0                                 0.0   \n",
       "Musical                           0.0                                 0.0   \n",
       "Mystery                           0.0                                 0.0   \n",
       "Romance                           1.0                                 0.0   \n",
       "Sci-Fi                            0.0                                 0.0   \n",
       "Thriller                          0.0                                 0.0   \n",
       "War                               0.0                                 0.0   \n",
       "Western                           0.0                                 0.0   \n",
       "\n",
       "title        Heat (1995)  Sabrina (1995)  Tom and Huck (1995)  \\\n",
       "Action               1.0             0.0                  0.0   \n",
       "Adventure            0.0             0.0                  1.0   \n",
       "Animation            0.0             0.0                  0.0   \n",
       "Children             0.0             0.0                  1.0   \n",
       "Comedy               0.0             1.0                  0.0   \n",
       "Crime                1.0             0.0                  0.0   \n",
       "Documentary          0.0             0.0                  0.0   \n",
       "Drama                0.0             0.0                  0.0   \n",
       "Fantasy              0.0             0.0                  0.0   \n",
       "Film-Noir            0.0             0.0                  0.0   \n",
       "Horror               0.0             0.0                  0.0   \n",
       "IMAX                 0.0             0.0                  0.0   \n",
       "Musical              0.0             0.0                  0.0   \n",
       "Mystery              0.0             0.0                  0.0   \n",
       "Romance              0.0             1.0                  0.0   \n",
       "Sci-Fi               0.0             0.0                  0.0   \n",
       "Thriller             1.0             0.0                  0.0   \n",
       "War                  0.0             0.0                  0.0   \n",
       "Western              0.0             0.0                  0.0   \n",
       "\n",
       "title        Sudden Death (1995)  GoldenEye (1995)  ...  \\\n",
       "Action                       1.0               1.0  ...   \n",
       "Adventure                    0.0               1.0  ...   \n",
       "Animation                    0.0               0.0  ...   \n",
       "Children                     0.0               0.0  ...   \n",
       "Comedy                       0.0               0.0  ...   \n",
       "Crime                        0.0               0.0  ...   \n",
       "Documentary                  0.0               0.0  ...   \n",
       "Drama                        0.0               0.0  ...   \n",
       "Fantasy                      0.0               0.0  ...   \n",
       "Film-Noir                    0.0               0.0  ...   \n",
       "Horror                       0.0               0.0  ...   \n",
       "IMAX                         0.0               0.0  ...   \n",
       "Musical                      0.0               0.0  ...   \n",
       "Mystery                      0.0               0.0  ...   \n",
       "Romance                      0.0               0.0  ...   \n",
       "Sci-Fi                       0.0               0.0  ...   \n",
       "Thriller                     0.0               1.0  ...   \n",
       "War                          0.0               0.0  ...   \n",
       "Western                      0.0               0.0  ...   \n",
       "\n",
       "title        Gintama: The Movie (2010)  \\\n",
       "Action                             1.0   \n",
       "Adventure                          0.0   \n",
       "Animation                          1.0   \n",
       "Children                           0.0   \n",
       "Comedy                             1.0   \n",
       "Crime                              0.0   \n",
       "Documentary                        0.0   \n",
       "Drama                              0.0   \n",
       "Fantasy                            0.0   \n",
       "Film-Noir                          0.0   \n",
       "Horror                             0.0   \n",
       "IMAX                               0.0   \n",
       "Musical                            0.0   \n",
       "Mystery                            0.0   \n",
       "Romance                            0.0   \n",
       "Sci-Fi                             1.0   \n",
       "Thriller                           0.0   \n",
       "War                                0.0   \n",
       "Western                            0.0   \n",
       "\n",
       "title        anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "Action                                                     0.0        \n",
       "Adventure                                                  0.0        \n",
       "Animation                                                  1.0        \n",
       "Children                                                   0.0        \n",
       "Comedy                                                     0.0        \n",
       "Crime                                                      0.0        \n",
       "Documentary                                                0.0        \n",
       "Drama                                                      1.0        \n",
       "Fantasy                                                    0.0        \n",
       "Film-Noir                                                  0.0        \n",
       "Horror                                                     0.0        \n",
       "IMAX                                                       0.0        \n",
       "Musical                                                    0.0        \n",
       "Mystery                                                    0.0        \n",
       "Romance                                                    0.0        \n",
       "Sci-Fi                                                     0.0        \n",
       "Thriller                                                   0.0        \n",
       "War                                                        0.0        \n",
       "Western                                                    0.0        \n",
       "\n",
       "title        Silver Spoon (2014)  Love Live! The School Idol Movie (2015)  \\\n",
       "Action                       0.0                                      0.0   \n",
       "Adventure                    0.0                                      0.0   \n",
       "Animation                    0.0                                      1.0   \n",
       "Children                     0.0                                      0.0   \n",
       "Comedy                       1.0                                      0.0   \n",
       "Crime                        0.0                                      0.0   \n",
       "Documentary                  0.0                                      0.0   \n",
       "Drama                        1.0                                      0.0   \n",
       "Fantasy                      0.0                                      0.0   \n",
       "Film-Noir                    0.0                                      0.0   \n",
       "Horror                       0.0                                      0.0   \n",
       "IMAX                         0.0                                      0.0   \n",
       "Musical                      0.0                                      0.0   \n",
       "Mystery                      0.0                                      0.0   \n",
       "Romance                      0.0                                      0.0   \n",
       "Sci-Fi                       0.0                                      0.0   \n",
       "Thriller                     0.0                                      0.0   \n",
       "War                          0.0                                      0.0   \n",
       "Western                      0.0                                      0.0   \n",
       "\n",
       "title        Jon Stewart Has Left the Building (2015)  \\\n",
       "Action                                            0.0   \n",
       "Adventure                                         0.0   \n",
       "Animation                                         0.0   \n",
       "Children                                          0.0   \n",
       "Comedy                                            0.0   \n",
       "Crime                                             0.0   \n",
       "Documentary                                       1.0   \n",
       "Drama                                             0.0   \n",
       "Fantasy                                           0.0   \n",
       "Film-Noir                                         0.0   \n",
       "Horror                                            0.0   \n",
       "IMAX                                              0.0   \n",
       "Musical                                           0.0   \n",
       "Mystery                                           0.0   \n",
       "Romance                                           0.0   \n",
       "Sci-Fi                                            0.0   \n",
       "Thriller                                          0.0   \n",
       "War                                               0.0   \n",
       "Western                                           0.0   \n",
       "\n",
       "title        Black Butler: Book of the Atlantic (2017)  \\\n",
       "Action                                             1.0   \n",
       "Adventure                                          0.0   \n",
       "Animation                                          1.0   \n",
       "Children                                           0.0   \n",
       "Comedy                                             1.0   \n",
       "Crime                                              0.0   \n",
       "Documentary                                        0.0   \n",
       "Drama                                              0.0   \n",
       "Fantasy                                            1.0   \n",
       "Film-Noir                                          0.0   \n",
       "Horror                                             0.0   \n",
       "IMAX                                               0.0   \n",
       "Musical                                            0.0   \n",
       "Mystery                                            0.0   \n",
       "Romance                                            0.0   \n",
       "Sci-Fi                                             0.0   \n",
       "Thriller                                           0.0   \n",
       "War                                                0.0   \n",
       "Western                                            0.0   \n",
       "\n",
       "title        No Game No Life: Zero (2017)  Flint (2017)  \\\n",
       "Action                                0.0           0.0   \n",
       "Adventure                             0.0           0.0   \n",
       "Animation                             1.0           0.0   \n",
       "Children                              0.0           0.0   \n",
       "Comedy                                1.0           0.0   \n",
       "Crime                                 0.0           0.0   \n",
       "Documentary                           0.0           0.0   \n",
       "Drama                                 0.0           1.0   \n",
       "Fantasy                               1.0           0.0   \n",
       "Film-Noir                             0.0           0.0   \n",
       "Horror                                0.0           0.0   \n",
       "IMAX                                  0.0           0.0   \n",
       "Musical                               0.0           0.0   \n",
       "Mystery                               0.0           0.0   \n",
       "Romance                               0.0           0.0   \n",
       "Sci-Fi                                0.0           0.0   \n",
       "Thriller                              0.0           0.0   \n",
       "War                                   0.0           0.0   \n",
       "Western                               0.0           0.0   \n",
       "\n",
       "title        Bungo Stray Dogs: Dead Apple (2018)  \\\n",
       "Action                                       1.0   \n",
       "Adventure                                    0.0   \n",
       "Animation                                    1.0   \n",
       "Children                                     0.0   \n",
       "Comedy                                       0.0   \n",
       "Crime                                        0.0   \n",
       "Documentary                                  0.0   \n",
       "Drama                                        0.0   \n",
       "Fantasy                                      0.0   \n",
       "Film-Noir                                    0.0   \n",
       "Horror                                       0.0   \n",
       "IMAX                                         0.0   \n",
       "Musical                                      0.0   \n",
       "Mystery                                      0.0   \n",
       "Romance                                      0.0   \n",
       "Sci-Fi                                       0.0   \n",
       "Thriller                                     0.0   \n",
       "War                                          0.0   \n",
       "Western                                      0.0   \n",
       "\n",
       "title        Andrew Dice Clay: Dice Rules (1991)  \n",
       "Action                                       0.0  \n",
       "Adventure                                    0.0  \n",
       "Animation                                    0.0  \n",
       "Children                                     0.0  \n",
       "Comedy                                       1.0  \n",
       "Crime                                        0.0  \n",
       "Documentary                                  0.0  \n",
       "Drama                                        0.0  \n",
       "Fantasy                                      0.0  \n",
       "Film-Noir                                    0.0  \n",
       "Horror                                       0.0  \n",
       "IMAX                                         0.0  \n",
       "Musical                                      0.0  \n",
       "Mystery                                      0.0  \n",
       "Romance                                      0.0  \n",
       "Sci-Fi                                       0.0  \n",
       "Thriller                                     0.0  \n",
       "War                                          0.0  \n",
       "Western                                      0.0  \n",
       "\n",
       "[19 rows x 9703 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies_genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guardaremos en un archivo .csv estos datos, para así evitarnos en un futuro tener que volver a hacer este preprocesamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies_genres.to_csv('movies_genres.csv', sep=',', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora podemos crear la matriz de correlación. Esta matríz correlacionará las películas según sus géneros. Devolverá valores altos para películas con géneros parecidos y valores cercanos a cero o negativos en aquellas películas que no presenten géneros en común.\n",
    "\n",
    "Puesto que tenemos 9703 películas diferentes, las dimensiones de la matriz serán 9703x9703."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "films_correlation = pd.DataFrame(np.corrcoef(df_movies_genres, \\\n",
    "                                             rowvar=False),\\\n",
    "                                 columns = df_movies_genres.columns,\\\n",
    "                                 index = df_movies_genres.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <th>Jumanji (1995)</th>\n",
       "      <th>Grumpier Old Men (1995)</th>\n",
       "      <th>Waiting to Exhale (1995)</th>\n",
       "      <th>Father of the Bride Part II (1995)</th>\n",
       "      <th>Heat (1995)</th>\n",
       "      <th>Sabrina (1995)</th>\n",
       "      <th>Tom and Huck (1995)</th>\n",
       "      <th>Sudden Death (1995)</th>\n",
       "      <th>GoldenEye (1995)</th>\n",
       "      <th>...</th>\n",
       "      <th>Gintama: The Movie (2010)</th>\n",
       "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
       "      <th>Silver Spoon (2014)</th>\n",
       "      <th>Love Live! The School Idol Movie (2015)</th>\n",
       "      <th>Jon Stewart Has Left the Building (2015)</th>\n",
       "      <th>Black Butler: Book of the Atlantic (2017)</th>\n",
       "      <th>No Game No Life: Zero (2017)</th>\n",
       "      <th>Flint (2017)</th>\n",
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       "      <th>Toy Story (1995)</th>\n",
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       "      <td>0.724569</td>\n",
       "      <td>0.184482</td>\n",
       "      <td>0.069007</td>\n",
       "      <td>0.394405</td>\n",
       "      <td>-0.258775</td>\n",
       "      <td>0.184482</td>\n",
       "      <td>0.573944</td>\n",
       "      <td>-0.140859</td>\n",
       "      <td>0.069007</td>\n",
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       "      <td>0.277746</td>\n",
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       "      <th>Jumanji (1995)</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>0.792118</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.223607</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>0.130437</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>-0.102062</td>\n",
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       "      <th>Grumpier Old Men (1995)</th>\n",
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       "      <td>-0.148522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792118</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243544</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>0.243544</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>0.687184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting to Exhale (1995)</th>\n",
       "      <td>0.069007</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>0.792118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.544331</td>\n",
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       "      <td>-0.148522</td>\n",
       "      <td>-0.102062</td>\n",
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       "      <td>0.130437</td>\n",
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       "      <td>-0.102062</td>\n",
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       "      <td>0.544331</td>\n",
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       "      <th>Father of the Bride Part II (1995)</th>\n",
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       "      <td>-0.102062</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>0.544331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.102062</td>\n",
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       "      <td>0.456435</td>\n",
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       "      <td>-0.055556</td>\n",
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       "      <td>0.544331</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.080845</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\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",
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       "      <th>Black Butler: Book of the Atlantic (2017)</th>\n",
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       "      <td>0.130437</td>\n",
       "      <td>0.243544</td>\n",
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       "      <td>-0.177123</td>\n",
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       "      <td>-0.121716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.838525</td>\n",
       "      <td>-0.121716</td>\n",
       "      <td>0.664211</td>\n",
       "      <td>0.456435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Game No Life: Zero (2017)</th>\n",
       "      <td>0.724569</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.544331</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.484481</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>0.544331</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>0.838525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>0.544331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flint (2017)</th>\n",
       "      <td>-0.140859</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>0.544331</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.121716</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.121716</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bungo Stray Dogs: Dead Apple (2018)</th>\n",
       "      <td>0.184482</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>-0.148522</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.664211</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>-0.117647</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>0.664211</td>\n",
       "      <td>0.321798</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrew Dice Clay: Dice Rules (1991)</th>\n",
       "      <td>0.394405</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>0.544331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.102062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456435</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>0.687184</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>0.456435</td>\n",
       "      <td>0.544331</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.080845</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9703 rows × 9703 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title                                      Toy Story (1995)  Jumanji (1995)  \\\n",
       "title                                                                         \n",
       "Toy Story (1995)                                   1.000000        0.724569   \n",
       "Jumanji (1995)                                     0.724569        1.000000   \n",
       "Grumpier Old Men (1995)                            0.184482       -0.148522   \n",
       "Waiting to Exhale (1995)                           0.069007       -0.187500   \n",
       "Father of the Bride Part II (1995)                 0.394405       -0.102062   \n",
       "...                                                     ...             ...   \n",
       "Black Butler: Book of the Atlantic (2017)          0.570922        0.130437   \n",
       "No Game No Life: Zero (2017)                       0.724569        0.208333   \n",
       "Flint (2017)                                      -0.140859       -0.102062   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                0.184482       -0.148522   \n",
       "Andrew Dice Clay: Dice Rules (1991)                0.394405       -0.102062   \n",
       "\n",
       "title                                      Grumpier Old Men (1995)  \\\n",
       "title                                                                \n",
       "Toy Story (1995)                                          0.184482   \n",
       "Jumanji (1995)                                           -0.148522   \n",
       "Grumpier Old Men (1995)                                   1.000000   \n",
       "Waiting to Exhale (1995)                                  0.792118   \n",
       "Father of the Bride Part II (1995)                        0.687184   \n",
       "...                                                            ...   \n",
       "Black Butler: Book of the Atlantic (2017)                 0.243544   \n",
       "No Game No Life: Zero (2017)                              0.321798   \n",
       "Flint (2017)                                             -0.080845   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                      -0.117647   \n",
       "Andrew Dice Clay: Dice Rules (1991)                       0.687184   \n",
       "\n",
       "title                                      Waiting to Exhale (1995)  \\\n",
       "title                                                                 \n",
       "Toy Story (1995)                                           0.069007   \n",
       "Jumanji (1995)                                            -0.187500   \n",
       "Grumpier Old Men (1995)                                    0.792118   \n",
       "Waiting to Exhale (1995)                                   1.000000   \n",
       "Father of the Bride Part II (1995)                         0.544331   \n",
       "...                                                             ...   \n",
       "Black Butler: Book of the Atlantic (2017)                  0.130437   \n",
       "No Game No Life: Zero (2017)                               0.208333   \n",
       "Flint (2017)                                               0.544331   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                       -0.148522   \n",
       "Andrew Dice Clay: Dice Rules (1991)                        0.544331   \n",
       "\n",
       "title                                      Father of the Bride Part II (1995)  \\\n",
       "title                                                                           \n",
       "Toy Story (1995)                                                     0.394405   \n",
       "Jumanji (1995)                                                      -0.102062   \n",
       "Grumpier Old Men (1995)                                              0.687184   \n",
       "Waiting to Exhale (1995)                                             0.544331   \n",
       "Father of the Bride Part II (1995)                                   1.000000   \n",
       "...                                                                       ...   \n",
       "Black Butler: Book of the Atlantic (2017)                            0.456435   \n",
       "No Game No Life: Zero (2017)                                         0.544331   \n",
       "Flint (2017)                                                        -0.055556   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                                 -0.080845   \n",
       "Andrew Dice Clay: Dice Rules (1991)                                  1.000000   \n",
       "\n",
       "title                                      Heat (1995)  Sabrina (1995)  \\\n",
       "title                                                                    \n",
       "Toy Story (1995)                             -0.258775        0.184482   \n",
       "Jumanji (1995)                               -0.187500       -0.148522   \n",
       "Grumpier Old Men (1995)                      -0.148522        1.000000   \n",
       "Waiting to Exhale (1995)                     -0.187500        0.792118   \n",
       "Father of the Bride Part II (1995)           -0.102062        0.687184   \n",
       "...                                                ...             ...   \n",
       "Black Butler: Book of the Atlantic (2017)     0.130437        0.243544   \n",
       "No Game No Life: Zero (2017)                 -0.187500        0.321798   \n",
       "Flint (2017)                                 -0.102062       -0.080845   \n",
       "Bungo Stray Dogs: Dead Apple (2018)           0.321798       -0.117647   \n",
       "Andrew Dice Clay: Dice Rules (1991)          -0.102062        0.687184   \n",
       "\n",
       "title                                      Tom and Huck (1995)  \\\n",
       "title                                                            \n",
       "Toy Story (1995)                                      0.573944   \n",
       "Jumanji (1995)                                        0.792118   \n",
       "Grumpier Old Men (1995)                              -0.117647   \n",
       "Waiting to Exhale (1995)                             -0.148522   \n",
       "Father of the Bride Part II (1995)                   -0.080845   \n",
       "...                                                        ...   \n",
       "Black Butler: Book of the Atlantic (2017)            -0.177123   \n",
       "No Game No Life: Zero (2017)                         -0.148522   \n",
       "Flint (2017)                                         -0.080845   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                  -0.117647   \n",
       "Andrew Dice Clay: Dice Rules (1991)                  -0.080845   \n",
       "\n",
       "title                                      Sudden Death (1995)  \\\n",
       "title                                                            \n",
       "Toy Story (1995)                                     -0.140859   \n",
       "Jumanji (1995)                                       -0.102062   \n",
       "Grumpier Old Men (1995)                              -0.080845   \n",
       "Waiting to Exhale (1995)                             -0.102062   \n",
       "Father of the Bride Part II (1995)                   -0.055556   \n",
       "...                                                        ...   \n",
       "Black Butler: Book of the Atlantic (2017)             0.456435   \n",
       "No Game No Life: Zero (2017)                         -0.102062   \n",
       "Flint (2017)                                         -0.055556   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                   0.687184   \n",
       "Andrew Dice Clay: Dice Rules (1991)                  -0.055556   \n",
       "\n",
       "title                                      GoldenEye (1995)  ...  \\\n",
       "title                                                        ...   \n",
       "Toy Story (1995)                                   0.069007  ...   \n",
       "Jumanji (1995)                                     0.208333  ...   \n",
       "Grumpier Old Men (1995)                           -0.148522  ...   \n",
       "Waiting to Exhale (1995)                          -0.187500  ...   \n",
       "Father of the Bride Part II (1995)                -0.102062  ...   \n",
       "...                                                     ...  ...   \n",
       "Black Butler: Book of the Atlantic (2017)          0.130437  ...   \n",
       "No Game No Life: Zero (2017)                      -0.187500  ...   \n",
       "Flint (2017)                                      -0.102062  ...   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                0.321798  ...   \n",
       "Andrew Dice Clay: Dice Rules (1991)               -0.102062  ...   \n",
       "\n",
       "title                                      Gintama: The Movie (2010)  \\\n",
       "title                                                                  \n",
       "Toy Story (1995)                                            0.277746   \n",
       "Jumanji (1995)                                             -0.223607   \n",
       "Grumpier Old Men (1995)                                     0.243544   \n",
       "Waiting to Exhale (1995)                                    0.130437   \n",
       "Father of the Bride Part II (1995)                          0.456435   \n",
       "...                                                              ...   \n",
       "Black Butler: Book of the Atlantic (2017)                   0.683333   \n",
       "No Game No Life: Zero (2017)                                0.484481   \n",
       "Flint (2017)                                               -0.121716   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                         0.664211   \n",
       "Andrew Dice Clay: Dice Rules (1991)                         0.456435   \n",
       "\n",
       "title                                      anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "title                                                                                               \n",
       "Toy Story (1995)                                                                    0.184482        \n",
       "Jumanji (1995)                                                                     -0.148522        \n",
       "Grumpier Old Men (1995)                                                            -0.117647        \n",
       "Waiting to Exhale (1995)                                                            0.321798        \n",
       "Father of the Bride Part II (1995)                                                 -0.080845        \n",
       "...                                                                                      ...        \n",
       "Black Butler: Book of the Atlantic (2017)                                           0.243544        \n",
       "No Game No Life: Zero (2017)                                                        0.321798        \n",
       "Flint (2017)                                                                        0.687184        \n",
       "Bungo Stray Dogs: Dead Apple (2018)                                                 0.441176        \n",
       "Andrew Dice Clay: Dice Rules (1991)                                                -0.080845        \n",
       "\n",
       "title                                      Silver Spoon (2014)  \\\n",
       "title                                                            \n",
       "Toy Story (1995)                                      0.184482   \n",
       "Jumanji (1995)                                       -0.148522   \n",
       "Grumpier Old Men (1995)                               0.441176   \n",
       "Waiting to Exhale (1995)                              0.792118   \n",
       "Father of the Bride Part II (1995)                    0.687184   \n",
       "...                                                        ...   \n",
       "Black Butler: Book of the Atlantic (2017)             0.243544   \n",
       "No Game No Life: Zero (2017)                          0.321798   \n",
       "Flint (2017)                                          0.687184   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                  -0.117647   \n",
       "Andrew Dice Clay: Dice Rules (1991)                   0.687184   \n",
       "\n",
       "title                                      Love Live! The School Idol Movie (2015)  \\\n",
       "title                                                                                \n",
       "Toy Story (1995)                                                          0.394405   \n",
       "Jumanji (1995)                                                           -0.102062   \n",
       "Grumpier Old Men (1995)                                                  -0.080845   \n",
       "Waiting to Exhale (1995)                                                 -0.102062   \n",
       "Father of the Bride Part II (1995)                                       -0.055556   \n",
       "...                                                                            ...   \n",
       "Black Butler: Book of the Atlantic (2017)                                 0.456435   \n",
       "No Game No Life: Zero (2017)                                              0.544331   \n",
       "Flint (2017)                                                             -0.055556   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                                       0.687184   \n",
       "Andrew Dice Clay: Dice Rules (1991)                                      -0.055556   \n",
       "\n",
       "title                                      Jon Stewart Has Left the Building (2015)  \\\n",
       "title                                                                                 \n",
       "Toy Story (1995)                                                          -0.140859   \n",
       "Jumanji (1995)                                                            -0.102062   \n",
       "Grumpier Old Men (1995)                                                   -0.080845   \n",
       "Waiting to Exhale (1995)                                                  -0.102062   \n",
       "Father of the Bride Part II (1995)                                        -0.055556   \n",
       "...                                                                             ...   \n",
       "Black Butler: Book of the Atlantic (2017)                                 -0.121716   \n",
       "No Game No Life: Zero (2017)                                              -0.102062   \n",
       "Flint (2017)                                                              -0.055556   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                                       -0.080845   \n",
       "Andrew Dice Clay: Dice Rules (1991)                                       -0.055556   \n",
       "\n",
       "title                                      Black Butler: Book of the Atlantic (2017)  \\\n",
       "title                                                                                  \n",
       "Toy Story (1995)                                                            0.570922   \n",
       "Jumanji (1995)                                                              0.130437   \n",
       "Grumpier Old Men (1995)                                                     0.243544   \n",
       "Waiting to Exhale (1995)                                                    0.130437   \n",
       "Father of the Bride Part II (1995)                                          0.456435   \n",
       "...                                                                              ...   \n",
       "Black Butler: Book of the Atlantic (2017)                                   1.000000   \n",
       "No Game No Life: Zero (2017)                                                0.838525   \n",
       "Flint (2017)                                                               -0.121716   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                                         0.664211   \n",
       "Andrew Dice Clay: Dice Rules (1991)                                         0.456435   \n",
       "\n",
       "title                                      No Game No Life: Zero (2017)  \\\n",
       "title                                                                     \n",
       "Toy Story (1995)                                               0.724569   \n",
       "Jumanji (1995)                                                 0.208333   \n",
       "Grumpier Old Men (1995)                                        0.321798   \n",
       "Waiting to Exhale (1995)                                       0.208333   \n",
       "Father of the Bride Part II (1995)                             0.544331   \n",
       "...                                                                 ...   \n",
       "Black Butler: Book of the Atlantic (2017)                      0.838525   \n",
       "No Game No Life: Zero (2017)                                   1.000000   \n",
       "Flint (2017)                                                  -0.102062   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                            0.321798   \n",
       "Andrew Dice Clay: Dice Rules (1991)                            0.544331   \n",
       "\n",
       "title                                      Flint (2017)  \\\n",
       "title                                                     \n",
       "Toy Story (1995)                              -0.140859   \n",
       "Jumanji (1995)                                -0.102062   \n",
       "Grumpier Old Men (1995)                       -0.080845   \n",
       "Waiting to Exhale (1995)                       0.544331   \n",
       "Father of the Bride Part II (1995)            -0.055556   \n",
       "...                                                 ...   \n",
       "Black Butler: Book of the Atlantic (2017)     -0.121716   \n",
       "No Game No Life: Zero (2017)                  -0.102062   \n",
       "Flint (2017)                                   1.000000   \n",
       "Bungo Stray Dogs: Dead Apple (2018)           -0.080845   \n",
       "Andrew Dice Clay: Dice Rules (1991)           -0.055556   \n",
       "\n",
       "title                                      Bungo Stray Dogs: Dead Apple (2018)  \\\n",
       "title                                                                            \n",
       "Toy Story (1995)                                                      0.184482   \n",
       "Jumanji (1995)                                                       -0.148522   \n",
       "Grumpier Old Men (1995)                                              -0.117647   \n",
       "Waiting to Exhale (1995)                                             -0.148522   \n",
       "Father of the Bride Part II (1995)                                   -0.080845   \n",
       "...                                                                        ...   \n",
       "Black Butler: Book of the Atlantic (2017)                             0.664211   \n",
       "No Game No Life: Zero (2017)                                          0.321798   \n",
       "Flint (2017)                                                         -0.080845   \n",
       "Bungo Stray Dogs: Dead Apple (2018)                                   1.000000   \n",
       "Andrew Dice Clay: Dice Rules (1991)                                  -0.080845   \n",
       "\n",
       "title                                      Andrew Dice Clay: Dice Rules (1991)  \n",
       "title                                                                           \n",
       "Toy Story (1995)                                                      0.394405  \n",
       "Jumanji (1995)                                                       -0.102062  \n",
       "Grumpier Old Men (1995)                                               0.687184  \n",
       "Waiting to Exhale (1995)                                              0.544331  \n",
       "Father of the Bride Part II (1995)                                    1.000000  \n",
       "...                                                                        ...  \n",
       "Black Butler: Book of the Atlantic (2017)                             0.456435  \n",
       "No Game No Life: Zero (2017)                                          0.544331  \n",
       "Flint (2017)                                                         -0.055556  \n",
       "Bungo Stray Dogs: Dead Apple (2018)                                  -0.080845  \n",
       "Andrew Dice Clay: Dice Rules (1991)                                   1.000000  \n",
       "\n",
       "[9703 rows x 9703 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "films_correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20.0, 5.0)\n",
    "plt.imshow(films_correlation);\n",
    "plt.xlabel('userId')\n",
    "plt.ylabel('userId')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos las películas más correlacionadas con la película ejemplo *Toy Story*:\n",
    "\n",
    "Definimos una función que nos relacione una película ejemplo.\n",
    "\n",
    "Tenemos que tener en cuenta las veces que se han valorados las películas, pues solo serán válidas las películas que hayan sido valoradas >100 veces. (El promedio de una película valorada muchas veces tiene más sentido estadístico que el promedio de una película evaluada pocas veces). Añadimos la condición de que $count\\_rating>100$ y mostramos por pantalla las películas más relacionadas con la película ejemplo: Toy Story."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PeliculasRelacionadas(pelicula,n):\n",
    "    \n",
    "    # Cogemos los datos de la películas ejemplo:\n",
    "    pelicula_ejemplo = df_movies_genres[pelicula]\n",
    "    \n",
    "    # Hacemos uso del método corrwith() para ver\n",
    "    # las películas correlacionadas con la pelicula ejemplo.\n",
    "    peliculas_similares = df_movies_genres.corrwith(pelicula_ejemplo)\n",
    "    \n",
    "    # Creamos un DataFrame con los datos. Eliminamos la película ejemplo.\n",
    "    # No tiene sentido guardarla.\n",
    "    df_peliculas_similares = pd.DataFrame(peliculas_similares, columns=['Correlation']).drop(pelicula, axis=0)\n",
    "    \n",
    "    # Añadimos las veces que se han puntuado las películas\n",
    "    df_peliculas_similares = df_peliculas_similares.join(count_mean_ratings_film[['count_rating', 'title']].set_index('title'), on='title')\n",
    "    \n",
    "    # Hacemos una purga con la condicion count_rating>100:\n",
    "    df_peliculas_similares = df_peliculas_similares.sort_values(['Correlation', 'count_rating'], ascending=False).reset_index()\n",
    "    \n",
    "    # Devolvemos las n películas más similares:\n",
    "    return df_peliculas_similares[df_peliculas_similares['count_rating']>100][['title', 'Correlation']].sort_values('Correlation',ascending=False).reset_index(drop=True).head(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monsters, Inc. (2001)</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shrek (2001)</td>\n",
       "      <td>0.879664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Finding Nemo (2003)</td>\n",
       "      <td>0.864099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>0.728571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Incredibles, The (2004)</td>\n",
       "      <td>0.728571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Monty Python and the Holy Grail (1975)</td>\n",
       "      <td>0.724569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>0.724569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (a.k.a. ...</td>\n",
       "      <td>0.724569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  Correlation\n",
       "0                              Monsters, Inc. (2001)     1.000000\n",
       "1                                       Shrek (2001)     0.879664\n",
       "2                                Finding Nemo (2003)     0.864099\n",
       "3                                     Aladdin (1992)     0.728571\n",
       "4                            Incredibles, The (2004)     0.728571\n",
       "5             Monty Python and the Holy Grail (1975)     0.724569\n",
       "6                                     Jumanji (1995)     0.724569\n",
       "7  Harry Potter and the Sorcerer's Stone (a.k.a. ...     0.724569"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PeliculasRelacionadas(pelicula='Toy Story (1995)', n=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las películas que nos recomienda nuestro recomendador teniendo como ejemplo Toy Story son:\n",
    "- Monster, Inc.\n",
    "- Shrek\n",
    "- Finding Nemo\n",
    "- ...\n",
    "\n",
    "De dibujos animados, orientadas a niños, de fantasía... justo los géneros que presenta la película ejemplo Toy Story."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Filtrado colaborativo de popularidad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro objetivo en este apartado es crear la matriz que nos muestre la relación/similitud entre usuarios. Para ello haremos uso de la similitud por el coseno, el cual proyecta un usuario sobre otro. Si la proyección es máxima (=1) la similitud es perfecta, en cambio si los vectores son perpendiculares (=0) no existe relación entre los dos usuarios estudiados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recordamos la tabla de las puntuaciones a cada película: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>'71 (2014)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'Tis the Season for Love (2015)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <th>...</th>\n",
       "      <th>Zulu (2013)</th>\n",
       "      <th>[REC] (2007)</th>\n",
       "      <th>[REC]² (2009)</th>\n",
       "      <th>[REC]³ 3 Génesis (2012)</th>\n",
       "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <th>xXx (2002)</th>\n",
       "      <th>xXx: State of the Union (2005)</th>\n",
       "      <th>¡Three Amigos! (1986)</th>\n",
       "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
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       "    <tr>\n",
       "      <th>610</th>\n",
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       "<p>610 rows × 9685 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "1              0.0                                      0.0   \n",
       "2              0.0                                      0.0   \n",
       "3              0.0                                      0.0   \n",
       "4              0.0                                      0.0   \n",
       "5              0.0                                      0.0   \n",
       "...            ...                                      ...   \n",
       "606            0.0                                      0.0   \n",
       "607            0.0                                      0.0   \n",
       "608            0.0                                      0.0   \n",
       "609            0.0                                      0.0   \n",
       "610            4.0                                      0.0   \n",
       "\n",
       "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "userId                                                \n",
       "1                          0.0                  0.0   \n",
       "2                          0.0                  0.0   \n",
       "3                          0.0                  0.0   \n",
       "4                          0.0                  0.0   \n",
       "5                          0.0                  0.0   \n",
       "...                        ...                  ...   \n",
       "606                        0.0                  0.0   \n",
       "607                        0.0                  0.0   \n",
       "608                        0.0                  0.0   \n",
       "609                        0.0                  0.0   \n",
       "610                        0.0                  0.0   \n",
       "\n",
       "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "userId                                                               \n",
       "1                             0.0                              0.0   \n",
       "2                             0.0                              0.0   \n",
       "3                             0.0                              0.0   \n",
       "4                             0.0                              0.0   \n",
       "5                             0.0                              0.0   \n",
       "...                           ...                              ...   \n",
       "606                           0.0                              0.0   \n",
       "607                           0.0                              0.0   \n",
       "608                           0.0                              0.0   \n",
       "609                           0.0                              0.0   \n",
       "610                           0.0                              0.0   \n",
       "\n",
       "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                                          \n",
       "1                      0.0                   0.0                          0.0   \n",
       "2                      0.0                   0.0                          0.0   \n",
       "3                      0.0                   0.0                          0.0   \n",
       "4                      0.0                   0.0                          0.0   \n",
       "5                      0.0                   0.0                          0.0   \n",
       "...                    ...                   ...                          ...   \n",
       "606                    0.0                   0.0                          0.0   \n",
       "607                    0.0                   0.0                          0.0   \n",
       "608                    0.0                   0.0                          0.0   \n",
       "609                    0.0                   0.0                          0.0   \n",
       "610                    0.0                   0.0                          3.5   \n",
       "\n",
       "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
       "userId                                  ...                              \n",
       "1                                  0.0  ...          0.0           0.0   \n",
       "2                                  0.0  ...          0.0           0.0   \n",
       "3                                  0.0  ...          0.0           0.0   \n",
       "4                                  0.0  ...          0.0           0.0   \n",
       "5                                  0.0  ...          0.0           0.0   \n",
       "...                                ...  ...          ...           ...   \n",
       "606                                0.0  ...          0.0           0.0   \n",
       "607                                0.0  ...          0.0           0.0   \n",
       "608                                0.0  ...          0.0           0.0   \n",
       "609                                0.0  ...          0.0           0.0   \n",
       "610                                0.0  ...          0.0           4.0   \n",
       "\n",
       "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
       "userId                                           \n",
       "1                 0.0                      0.0   \n",
       "2                 0.0                      0.0   \n",
       "3                 0.0                      0.0   \n",
       "4                 0.0                      0.0   \n",
       "5                 0.0                      0.0   \n",
       "...               ...                      ...   \n",
       "606               0.0                      0.0   \n",
       "607               0.0                      0.0   \n",
       "608               0.0                      0.0   \n",
       "609               0.0                      0.0   \n",
       "610               3.5                      3.0   \n",
       "\n",
       "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "userId                                                           \n",
       "1                                                     0.0        \n",
       "2                                                     0.0        \n",
       "3                                                     0.0        \n",
       "4                                                     0.0        \n",
       "5                                                     0.0        \n",
       "...                                                   ...        \n",
       "606                                                   0.0        \n",
       "607                                                   0.0        \n",
       "608                                                   0.0        \n",
       "609                                                   0.0        \n",
       "610                                                   0.0        \n",
       "\n",
       "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "userId                                                                \n",
       "1                   0.0         0.0                             0.0   \n",
       "2                   0.0         0.0                             0.0   \n",
       "3                   0.0         0.0                             0.0   \n",
       "4                   0.0         0.0                             0.0   \n",
       "5                   0.0         0.0                             0.0   \n",
       "...                 ...         ...                             ...   \n",
       "606                 0.0         0.0                             0.0   \n",
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       "608                 4.5         3.5                             0.0   \n",
       "609                 0.0         0.0                             0.0   \n",
       "610                 0.0         2.0                             1.5   \n",
       "\n",
       "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
       "userId                                                                    \n",
       "1                         4.0                                        0.0  \n",
       "2                         0.0                                        0.0  \n",
       "3                         0.0                                        0.0  \n",
       "4                         0.0                                        0.0  \n",
       "5                         0.0                                        0.0  \n",
       "...                       ...                                        ...  \n",
       "606                       0.0                                        0.0  \n",
       "607                       0.0                                        0.0  \n",
       "608                       0.0                                        0.0  \n",
       "609                       0.0                                        0.0  \n",
       "610                       0.0                                        0.0  \n",
       "\n",
       "[610 rows x 9685 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movieratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si quisiéramos ver la relación de un usuario ejemplo (en este caso el usuario 1) con los demás usuarios, se debe proceder como hicimos con las películas:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definiremos otra función que relacione los usuarios. (Realmente es el método es muy parecido al de las películas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def UsuariosRelacionados(user,n):\n",
    "    \n",
    "    # Cogemos los datos del usuario ejemplo:\n",
    "    user_ejemplo = movieratings.loc[user]\n",
    "    \n",
    "    # En este caso tenemos que especificar axis=1, \n",
    "    # debido a la distribución de la tabla.\n",
    "    users_similares = movieratings.corrwith(user_ejemplo, axis=1)\n",
    "    \n",
    "    # Creamos un DataFrame con los datos. Eliminamos el usuario ejemplo.\n",
    "    # No tiene sentido guardarlo.\n",
    "    df_users_similares = pd.DataFrame(users_similares, columns=['Correlation']).drop(user, axis=0)\n",
    "    \n",
    "    # Devolvemos los n usuarios más similares:\n",
    "    return df_users_similares.sort_values('Correlation',ascending=False).reset_index().head(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>266</td>\n",
       "      <td>0.344932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>313</td>\n",
       "      <td>0.333802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>368</td>\n",
       "      <td>0.323954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>0.323860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>0.320081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  Correlation\n",
       "0     266     0.344932\n",
       "1     313     0.333802\n",
       "2     368     0.323954\n",
       "3      57     0.323860\n",
       "4      39     0.320081"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UsuariosRelacionados(user=1,n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Recomendador de películas según la valoración de los usuarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partimos del mismo dataset que en el apartado 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos la cantidad de ceros que tenemos en el dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity: 1.71%\n"
     ]
    }
   ],
   "source": [
    "values_rating = movieratings.values\n",
    "sparsity = float(len(values_rating.nonzero()[0]))\n",
    "sparsity /= (values_rating.shape[0] * values_rating.shape[1])\n",
    "sparsity *= 100\n",
    "print('Sparsity: {:4.2f}%'.format(sparsity))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.71% de ceros en nuestro dataset. (No parecen muchos).\n",
    "\n",
    "Dividimos nuestros datos en train y test para poder saber cómo de preciso es nuestro recomendador."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(488, 9685) \n",
      " (122, 9685)\n"
     ]
    }
   ],
   "source": [
    "# Dividimos en train y test:\n",
    "ratings_train , ratings_test = train_test_split(values_rating,\\\n",
    "                                test_size = 0.2,\n",
    "                                random_state=123,\n",
    "                                shuffle=False)\n",
    "\n",
    "print(ratings_train.shape,'\\n',ratings_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos la **matriz de similitud entre usuarios** mediante el coeficiente de correlación de Pearson. (En la siguiente celda está comentadas las líneas donde se calcula la matriz de similitud mediante la distancia del coseno. Al final se decidió por el método de Pearson porque en la literatura se dice, resumidamente, que a este método no le afecta el comportamiento de los usuarios. Además al evaluar el recomendador, el método de Pearson conlleva una mejoría de unas décimas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019368</td>\n",
       "      <td>0.053028</td>\n",
       "      <td>0.176848</td>\n",
       "      <td>0.120833</td>\n",
       "      <td>0.104320</td>\n",
       "      <td>0.143731</td>\n",
       "      <td>0.128512</td>\n",
       "      <td>0.055231</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067291</td>\n",
       "      <td>0.149882</td>\n",
       "      <td>0.186831</td>\n",
       "      <td>0.056472</td>\n",
       "      <td>0.134333</td>\n",
       "      <td>0.121800</td>\n",
       "      <td>0.254137</td>\n",
       "      <td>0.262119</td>\n",
       "      <td>0.085401</td>\n",
       "      <td>0.098517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.019368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002605</td>\n",
       "      <td>-0.003835</td>\n",
       "      <td>0.013169</td>\n",
       "      <td>0.016219</td>\n",
       "      <td>0.021543</td>\n",
       "      <td>0.023736</td>\n",
       "      <td>-0.003462</td>\n",
       "      <td>0.061857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200915</td>\n",
       "      <td>0.010864</td>\n",
       "      <td>-0.004097</td>\n",
       "      <td>-0.005366</td>\n",
       "      <td>-0.007951</td>\n",
       "      <td>0.011226</td>\n",
       "      <td>0.005784</td>\n",
       "      <td>0.032674</td>\n",
       "      <td>0.024360</td>\n",
       "      <td>0.089273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.053028</td>\n",
       "      <td>-0.002605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004583</td>\n",
       "      <td>0.001875</td>\n",
       "      <td>-0.004611</td>\n",
       "      <td>-0.005656</td>\n",
       "      <td>0.001690</td>\n",
       "      <td>-0.003123</td>\n",
       "      <td>-0.005524</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.011153</td>\n",
       "      <td>-0.004842</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>-0.003315</td>\n",
       "      <td>0.012859</td>\n",
       "      <td>0.008041</td>\n",
       "      <td>-0.002975</td>\n",
       "      <td>0.015892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176848</td>\n",
       "      <td>-0.003835</td>\n",
       "      <td>-0.004583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.120987</td>\n",
       "      <td>0.065625</td>\n",
       "      <td>0.100543</td>\n",
       "      <td>0.054200</td>\n",
       "      <td>0.002380</td>\n",
       "      <td>0.015552</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073936</td>\n",
       "      <td>0.114230</td>\n",
       "      <td>0.281756</td>\n",
       "      <td>0.039645</td>\n",
       "      <td>0.065415</td>\n",
       "      <td>0.164679</td>\n",
       "      <td>0.115050</td>\n",
       "      <td>0.116722</td>\n",
       "      <td>0.023896</td>\n",
       "      <td>0.062327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.120833</td>\n",
       "      <td>0.013169</td>\n",
       "      <td>0.001875</td>\n",
       "      <td>0.120987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.294112</td>\n",
       "      <td>0.101698</td>\n",
       "      <td>0.426566</td>\n",
       "      <td>-0.004202</td>\n",
       "      <td>0.023442</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062736</td>\n",
       "      <td>0.414916</td>\n",
       "      <td>0.095332</td>\n",
       "      <td>0.254099</td>\n",
       "      <td>0.141045</td>\n",
       "      <td>0.090089</td>\n",
       "      <td>0.145735</td>\n",
       "      <td>0.122553</td>\n",
       "      <td>0.258277</td>\n",
       "      <td>0.040284</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>0.121800</td>\n",
       "      <td>0.011226</td>\n",
       "      <td>-0.003315</td>\n",
       "      <td>0.164679</td>\n",
       "      <td>0.090089</td>\n",
       "      <td>0.047270</td>\n",
       "      <td>0.172381</td>\n",
       "      <td>0.081839</td>\n",
       "      <td>0.057912</td>\n",
       "      <td>0.054731</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156319</td>\n",
       "      <td>0.084069</td>\n",
       "      <td>0.224297</td>\n",
       "      <td>0.035119</td>\n",
       "      <td>0.106573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115840</td>\n",
       "      <td>0.188024</td>\n",
       "      <td>0.052311</td>\n",
       "      <td>0.093360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>0.254137</td>\n",
       "      <td>0.005784</td>\n",
       "      <td>0.012859</td>\n",
       "      <td>0.115050</td>\n",
       "      <td>0.145735</td>\n",
       "      <td>0.142086</td>\n",
       "      <td>0.173241</td>\n",
       "      <td>0.178105</td>\n",
       "      <td>0.003222</td>\n",
       "      <td>-0.004871</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081194</td>\n",
       "      <td>0.187537</td>\n",
       "      <td>0.172895</td>\n",
       "      <td>0.126220</td>\n",
       "      <td>0.101066</td>\n",
       "      <td>0.115840</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258141</td>\n",
       "      <td>0.142506</td>\n",
       "      <td>0.098342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>0.262119</td>\n",
       "      <td>0.032674</td>\n",
       "      <td>0.008041</td>\n",
       "      <td>0.116722</td>\n",
       "      <td>0.122553</td>\n",
       "      <td>0.137781</td>\n",
       "      <td>0.305363</td>\n",
       "      <td>0.175865</td>\n",
       "      <td>0.086169</td>\n",
       "      <td>0.048250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138411</td>\n",
       "      <td>0.173970</td>\n",
       "      <td>0.164179</td>\n",
       "      <td>0.133643</td>\n",
       "      <td>0.144756</td>\n",
       "      <td>0.188024</td>\n",
       "      <td>0.258141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109511</td>\n",
       "      <td>0.248617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>0.085401</td>\n",
       "      <td>0.024360</td>\n",
       "      <td>-0.002975</td>\n",
       "      <td>0.023896</td>\n",
       "      <td>0.258277</td>\n",
       "      <td>0.207095</td>\n",
       "      <td>0.084469</td>\n",
       "      <td>0.421617</td>\n",
       "      <td>-0.003953</td>\n",
       "      <td>0.014956</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030112</td>\n",
       "      <td>0.331036</td>\n",
       "      <td>0.045932</td>\n",
       "      <td>0.232097</td>\n",
       "      <td>0.089778</td>\n",
       "      <td>0.052311</td>\n",
       "      <td>0.142506</td>\n",
       "      <td>0.109511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>0.098517</td>\n",
       "      <td>0.089273</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0.062327</td>\n",
       "      <td>0.040284</td>\n",
       "      <td>-0.010676</td>\n",
       "      <td>0.163649</td>\n",
       "      <td>0.058082</td>\n",
       "      <td>0.055183</td>\n",
       "      <td>0.086889</td>\n",
       "      <td>...</td>\n",
       "      <td>0.203519</td>\n",
       "      <td>0.050647</td>\n",
       "      <td>0.064149</td>\n",
       "      <td>0.018354</td>\n",
       "      <td>0.072524</td>\n",
       "      <td>0.093360</td>\n",
       "      <td>0.098342</td>\n",
       "      <td>0.248617</td>\n",
       "      <td>0.033629</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId       1         2         3         4         5         6         7    \\\n",
       "userId                                                                         \n",
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       "2       0.019368  1.000000 -0.002605 -0.003835  0.013169  0.016219  0.021543   \n",
       "3       0.053028 -0.002605  1.000000 -0.004583  0.001875 -0.004611 -0.005656   \n",
       "4       0.176848 -0.003835 -0.004583  1.000000  0.120987  0.065625  0.100543   \n",
       "5       0.120833  0.013169  0.001875  0.120987  1.000000  0.294112  0.101698   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "606     0.121800  0.011226 -0.003315  0.164679  0.090089  0.047270  0.172381   \n",
       "607     0.254137  0.005784  0.012859  0.115050  0.145735  0.142086  0.173241   \n",
       "608     0.262119  0.032674  0.008041  0.116722  0.122553  0.137781  0.305363   \n",
       "609     0.085401  0.024360 -0.002975  0.023896  0.258277  0.207095  0.084469   \n",
       "610     0.098517  0.089273  0.015892  0.062327  0.040284 -0.010676  0.163649   \n",
       "\n",
       "userId       8         9         10   ...       601       602       603  \\\n",
       "userId                                ...                                 \n",
       "1       0.128512  0.055231 -0.000369  ...  0.067291  0.149882  0.186831   \n",
       "2       0.023736 -0.003462  0.061857  ...  0.200915  0.010864 -0.004097   \n",
       "3       0.001690 -0.003123 -0.005524  ...  0.000238 -0.000607  0.011153   \n",
       "4       0.054200  0.002380  0.015552  ...  0.073936  0.114230  0.281756   \n",
       "5       0.426566 -0.004202  0.023442  ...  0.062736  0.414916  0.095332   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "606     0.081839  0.057912  0.054731  ...  0.156319  0.084069  0.224297   \n",
       "607     0.178105  0.003222 -0.004871  ...  0.081194  0.187537  0.172895   \n",
       "608     0.175865  0.086169  0.048250  ...  0.138411  0.173970  0.164179   \n",
       "609     0.421617 -0.003953  0.014956  ...  0.030112  0.331036  0.045932   \n",
       "610     0.058082  0.055183  0.086889  ...  0.203519  0.050647  0.064149   \n",
       "\n",
       "userId       604       605       606       607       608       609       610  \n",
       "userId                                                                        \n",
       "1       0.056472  0.134333  0.121800  0.254137  0.262119  0.085401  0.098517  \n",
       "2      -0.005366 -0.007951  0.011226  0.005784  0.032674  0.024360  0.089273  \n",
       "3      -0.004842  0.003650 -0.003315  0.012859  0.008041 -0.002975  0.015892  \n",
       "4       0.039645  0.065415  0.164679  0.115050  0.116722  0.023896  0.062327  \n",
       "5       0.254099  0.141045  0.090089  0.145735  0.122553  0.258277  0.040284  \n",
       "...          ...       ...       ...       ...       ...       ...       ...  \n",
       "606     0.035119  0.106573  1.000000  0.115840  0.188024  0.052311  0.093360  \n",
       "607     0.126220  0.101066  0.115840  1.000000  0.258141  0.142506  0.098342  \n",
       "608     0.133643  0.144756  0.188024  0.258141  1.000000  0.109511  0.248617  \n",
       "609     0.232097  0.089778  0.052311  0.142506  0.109511  1.000000  0.033629  \n",
       "610     0.018354  0.072524  0.093360  0.098342  0.248617  0.033629  1.000000  \n",
       "\n",
       "[610 rows x 610 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La función cosine_distances() calcula la distancia, \n",
    "# para conseguir la similaridad del coseno hay que restar 1.\n",
    "\n",
    "# similarity_matrix = pd.DataFrame(1 - cosine_distances(movieratings),\\\n",
    "#                                  index = movieratings.index,\\\n",
    "#                                  columns=movieratings.index)\n",
    "\n",
    "# similarity_matrix\n",
    "\n",
    "# Método de Pearson:\n",
    "similarity_matrix = pd.DataFrame(np.corrcoef(movieratings, \\\n",
    "                                             rowvar=True),\\\n",
    "                                 columns = movieratings.index,\\\n",
    "                                 index = movieratings.index)\n",
    "similarity_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20.0, 5.0)\n",
    "plt.imshow(similarity_matrix);\n",
    "plt.xlabel('userId')\n",
    "plt.ylabel('userId')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(488, 488) \n",
      " (122, 122)\n"
     ]
    }
   ],
   "source": [
    "# Separamos las filas de train y test de la matriz de similitud:\n",
    "similarity_matrix_train = similarity_matrix.loc[0:ratings_train.shape[0]\\\n",
    "                                               ,0:ratings_train.shape[0]]\n",
    "\n",
    "similarity_matrix_test = similarity_matrix.loc[ratings_train.shape[0]+1:similarity_matrix.shape[0]\\\n",
    "                                              ,ratings_train.shape[0]+1:similarity_matrix.shape[0]]\n",
    "\n",
    "print(similarity_matrix_train.shape,'\\n',similarity_matrix_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para calcular el rating $R_U$ que un usuario $U$ daría a un item $I$ (no valorado por él mismo), lo podemos calcular mediante la media ponderada de las puntuaciones que han dado los demás usuarios (a ese mismo item) multiplicado por la similitud que tiene el usuario $U$ con los demás usuarios ($S_u$).\n",
    "\n",
    "Básicamente es coger una fila o columna de la matriz de similitudes (es simétrica, así que da igual fila o columna) y multiplicarla por la columna , que representa al item $I$, de la matriz de las puntuaciones. A continuación, normalizamos por la fila de la matriz de similitudes.\n",
    "\n",
    "(Con la media ponderada, estamos dando más importancia a las puntuaciones que hagan los usuarios con mayor similitud)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation}\n",
    "    R_U = \\frac{\\sum_{u=1}^{n} R_u * S_u}{\\sum_{u=1}^{n} S_u}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos una matriz con las predicciones de la valoración de los usuarios a cada película."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>'71 (2014)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'Tis the Season for Love (2015)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <th>...</th>\n",
       "      <th>Zulu (2013)</th>\n",
       "      <th>[REC] (2007)</th>\n",
       "      <th>[REC]² (2009)</th>\n",
       "      <th>[REC]³ 3 Génesis (2012)</th>\n",
       "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <th>xXx (2002)</th>\n",
       "      <th>xXx: State of the Union (2005)</th>\n",
       "      <th>¡Three Amigos! (1986)</th>\n",
       "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015230</td>\n",
       "      <td>0.015806</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>0.006470</td>\n",
       "      <td>0.003159</td>\n",
       "      <td>0.121954</td>\n",
       "      <td>0.008536</td>\n",
       "      <td>0.212046</td>\n",
       "      <td>0.041705</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005389</td>\n",
       "      <td>0.057518</td>\n",
       "      <td>0.022842</td>\n",
       "      <td>0.011429</td>\n",
       "      <td>-0.000576</td>\n",
       "      <td>0.204237</td>\n",
       "      <td>0.159771</td>\n",
       "      <td>0.019905</td>\n",
       "      <td>0.297789</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.008637</td>\n",
       "      <td>-0.000876</td>\n",
       "      <td>-0.001667</td>\n",
       "      <td>0.005073</td>\n",
       "      <td>0.031173</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.441999</td>\n",
       "      <td>0.027354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.054963</td>\n",
       "      <td>0.024017</td>\n",
       "      <td>0.005455</td>\n",
       "      <td>0.014093</td>\n",
       "      <td>0.112392</td>\n",
       "      <td>0.151620</td>\n",
       "      <td>0.030890</td>\n",
       "      <td>0.147292</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019219</td>\n",
       "      <td>0.070595</td>\n",
       "      <td>-0.001624</td>\n",
       "      <td>-0.002637</td>\n",
       "      <td>-0.002072</td>\n",
       "      <td>0.152658</td>\n",
       "      <td>-0.008124</td>\n",
       "      <td>-0.041504</td>\n",
       "      <td>0.008233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.031783</td>\n",
       "      <td>0.016523</td>\n",
       "      <td>0.017891</td>\n",
       "      <td>-0.003796</td>\n",
       "      <td>0.148355</td>\n",
       "      <td>0.034729</td>\n",
       "      <td>0.018083</td>\n",
       "      <td>0.144397</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013313</td>\n",
       "      <td>0.015274</td>\n",
       "      <td>0.007125</td>\n",
       "      <td>0.017718</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>0.107857</td>\n",
       "      <td>0.015721</td>\n",
       "      <td>0.213333</td>\n",
       "      <td>0.035203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003660</td>\n",
       "      <td>0.038493</td>\n",
       "      <td>0.008726</td>\n",
       "      <td>0.005208</td>\n",
       "      <td>-0.000468</td>\n",
       "      <td>0.221191</td>\n",
       "      <td>0.132263</td>\n",
       "      <td>0.013950</td>\n",
       "      <td>0.241118</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>0.008739</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.058141</td>\n",
       "      <td>0.006006</td>\n",
       "      <td>0.125601</td>\n",
       "      <td>0.017959</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.032979</td>\n",
       "      <td>0.009661</td>\n",
       "      <td>0.004195</td>\n",
       "      <td>-0.000375</td>\n",
       "      <td>0.086230</td>\n",
       "      <td>0.083355</td>\n",
       "      <td>0.013219</td>\n",
       "      <td>0.118759</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012518</td>\n",
       "      <td>0.013619</td>\n",
       "      <td>0.003852</td>\n",
       "      <td>0.006920</td>\n",
       "      <td>0.003529</td>\n",
       "      <td>0.108330</td>\n",
       "      <td>0.008741</td>\n",
       "      <td>0.412802</td>\n",
       "      <td>0.050508</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005804</td>\n",
       "      <td>0.076985</td>\n",
       "      <td>0.027484</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.163006</td>\n",
       "      <td>0.223797</td>\n",
       "      <td>0.038620</td>\n",
       "      <td>0.235063</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>0.006707</td>\n",
       "      <td>-0.000327</td>\n",
       "      <td>0.001725</td>\n",
       "      <td>0.001432</td>\n",
       "      <td>0.047437</td>\n",
       "      <td>0.003615</td>\n",
       "      <td>0.094868</td>\n",
       "      <td>0.019811</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002486</td>\n",
       "      <td>0.024466</td>\n",
       "      <td>0.011395</td>\n",
       "      <td>0.004343</td>\n",
       "      <td>-0.000281</td>\n",
       "      <td>0.078376</td>\n",
       "      <td>0.077490</td>\n",
       "      <td>0.011108</td>\n",
       "      <td>0.103008</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006652</td>\n",
       "      <td>0.007392</td>\n",
       "      <td>0.001655</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>0.052687</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.114456</td>\n",
       "      <td>0.021416</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>0.032309</td>\n",
       "      <td>0.014836</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>-0.000358</td>\n",
       "      <td>0.094716</td>\n",
       "      <td>0.093129</td>\n",
       "      <td>0.011430</td>\n",
       "      <td>0.114448</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015271</td>\n",
       "      <td>0.012804</td>\n",
       "      <td>-0.000553</td>\n",
       "      <td>0.001257</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.059791</td>\n",
       "      <td>0.005977</td>\n",
       "      <td>0.362081</td>\n",
       "      <td>0.031008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.059947</td>\n",
       "      <td>0.013539</td>\n",
       "      <td>0.007129</td>\n",
       "      <td>0.004620</td>\n",
       "      <td>0.139662</td>\n",
       "      <td>0.185134</td>\n",
       "      <td>0.024870</td>\n",
       "      <td>0.164528</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012709</td>\n",
       "      <td>0.012522</td>\n",
       "      <td>0.004945</td>\n",
       "      <td>0.008084</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.078368</td>\n",
       "      <td>0.012619</td>\n",
       "      <td>0.277912</td>\n",
       "      <td>0.032652</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002931</td>\n",
       "      <td>0.053203</td>\n",
       "      <td>0.010991</td>\n",
       "      <td>0.007233</td>\n",
       "      <td>-0.000054</td>\n",
       "      <td>0.145059</td>\n",
       "      <td>0.155077</td>\n",
       "      <td>0.018143</td>\n",
       "      <td>0.184572</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>488 rows × 9685 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "1              0.0                                 0.015230   \n",
       "2              0.0                                 0.010882   \n",
       "3              0.0                                 0.019219   \n",
       "4              0.0                                 0.013313   \n",
       "5              0.0                                 0.007400   \n",
       "...            ...                                      ...   \n",
       "484            0.0                                 0.012518   \n",
       "485            0.0                                 0.006041   \n",
       "486            0.0                                 0.006652   \n",
       "487            0.0                                 0.015271   \n",
       "488            0.0                                 0.012709   \n",
       "\n",
       "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "userId                                                \n",
       "1                     0.015806             0.003692   \n",
       "2                     0.008637            -0.000876   \n",
       "3                     0.070595            -0.001624   \n",
       "4                     0.015274             0.007125   \n",
       "5                     0.008739             0.000709   \n",
       "...                        ...                  ...   \n",
       "484                   0.013619             0.003852   \n",
       "485                   0.006707            -0.000327   \n",
       "486                   0.007392             0.001655   \n",
       "487                   0.012804            -0.000553   \n",
       "488                   0.012522             0.004945   \n",
       "\n",
       "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "userId                                                               \n",
       "1                        0.006470                         0.003159   \n",
       "2                       -0.001667                         0.005073   \n",
       "3                       -0.002637                        -0.002072   \n",
       "4                        0.017718                         0.000957   \n",
       "5                        0.002232                         0.001171   \n",
       "...                           ...                              ...   \n",
       "484                      0.006920                         0.003529   \n",
       "485                      0.001725                         0.001432   \n",
       "486                      0.003229                         0.001742   \n",
       "487                      0.001257                         0.003983   \n",
       "488                      0.008084                         0.001799   \n",
       "\n",
       "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                                          \n",
       "1                 0.121954              0.008536                     0.212046   \n",
       "2                 0.031173              0.000092                     0.441999   \n",
       "3                 0.152658             -0.008124                    -0.041504   \n",
       "4                 0.107857              0.015721                     0.213333   \n",
       "5                 0.058141              0.006006                     0.125601   \n",
       "...                    ...                   ...                          ...   \n",
       "484               0.108330              0.008741                     0.412802   \n",
       "485               0.047437              0.003615                     0.094868   \n",
       "486               0.052687              0.003202                     0.114456   \n",
       "487               0.059791              0.005977                     0.362081   \n",
       "488               0.078368              0.012619                     0.277912   \n",
       "\n",
       "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
       "userId                                  ...                              \n",
       "1                             0.041705  ...     0.005389      0.057518   \n",
       "2                             0.027354  ...     0.005862      0.054963   \n",
       "3                             0.008233  ...     0.001707      0.031783   \n",
       "4                             0.035203  ...     0.003660      0.038493   \n",
       "5                             0.017959  ...     0.001701      0.032979   \n",
       "...                                ...  ...          ...           ...   \n",
       "484                           0.050508  ...     0.005804      0.076985   \n",
       "485                           0.019811  ...     0.002486      0.024466   \n",
       "486                           0.021416  ...     0.002031      0.032309   \n",
       "487                           0.031008  ...     0.003099      0.059947   \n",
       "488                           0.032652  ...     0.002931      0.053203   \n",
       "\n",
       "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
       "userId                                           \n",
       "1            0.022842                 0.011429   \n",
       "2            0.024017                 0.005455   \n",
       "3            0.016523                 0.017891   \n",
       "4            0.008726                 0.005208   \n",
       "5            0.009661                 0.004195   \n",
       "...               ...                      ...   \n",
       "484          0.027484                 0.010013   \n",
       "485          0.011395                 0.004343   \n",
       "486          0.014836                 0.006351   \n",
       "487          0.013539                 0.007129   \n",
       "488          0.010991                 0.007233   \n",
       "\n",
       "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "userId                                                           \n",
       "1                                               -0.000576        \n",
       "2                                                0.014093        \n",
       "3                                               -0.003796        \n",
       "4                                               -0.000468        \n",
       "5                                               -0.000375        \n",
       "...                                                   ...        \n",
       "484                                              0.000820        \n",
       "485                                             -0.000281        \n",
       "486                                             -0.000358        \n",
       "487                                              0.004620        \n",
       "488                                             -0.000054        \n",
       "\n",
       "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "userId                                                                \n",
       "1              0.204237    0.159771                        0.019905   \n",
       "2              0.112392    0.151620                        0.030890   \n",
       "3              0.148355    0.034729                        0.018083   \n",
       "4              0.221191    0.132263                        0.013950   \n",
       "5              0.086230    0.083355                        0.013219   \n",
       "...                 ...         ...                             ...   \n",
       "484            0.163006    0.223797                        0.038620   \n",
       "485            0.078376    0.077490                        0.011108   \n",
       "486            0.094716    0.093129                        0.011430   \n",
       "487            0.139662    0.185134                        0.024870   \n",
       "488            0.145059    0.155077                        0.018143   \n",
       "\n",
       "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
       "userId                                                                    \n",
       "1                    0.297789                                        0.0  \n",
       "2                    0.147292                                        0.0  \n",
       "3                    0.144397                                        0.0  \n",
       "4                    0.241118                                        0.0  \n",
       "5                    0.118759                                        0.0  \n",
       "...                       ...                                        ...  \n",
       "484                  0.235063                                        0.0  \n",
       "485                  0.103008                                        0.0  \n",
       "486                  0.114448                                        0.0  \n",
       "487                  0.164528                                        0.0  \n",
       "488                  0.184572                                        0.0  \n",
       "\n",
       "[488 rows x 9685 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# En este caso no estamos consiguiendo un solo valor de rating,\n",
    "# sino todas las puntuaciones que haría el usuario.\n",
    "# Estamos rellenando toda la matriz del train.\n",
    "users_predictions = similarity_matrix_train.dot(ratings_train) / np.array([np.abs(similarity_matrix_train).sum(axis=1)]).T\n",
    "\n",
    "# Le añadimos el nombre a las columnas:\n",
    "users_predictions.columns = movieratings.columns\n",
    "users_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (12.0, 5.0)\n",
    "plt.imshow(users_predictions);\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ponemos a prueba las predicciones:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buscamos cuales son las películas que más le gustarán a un usuario ejemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>Predicted Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>3.104421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>3.079185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>2.882621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Braveheart (1995)</td>\n",
       "      <td>2.534023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>2.472076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>2.311805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              title  Predicted Rating\n",
       "0               Pulp Fiction (1994)          3.104421\n",
       "1  Shawshank Redemption, The (1994)          3.079185\n",
       "2               Forrest Gump (1994)          2.882621\n",
       "3                 Braveheart (1995)          2.534023\n",
       "4  Silence of the Lambs, The (1991)          2.472076\n",
       "5                  Apollo 13 (1995)          2.311805"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionamos ejemplo:\n",
    "usuario_ejemplo = 5\n",
    "\n",
    "# Cogemos las predicciones del usuario ejemplo:\n",
    "# Lo ordenamos para ver los valores más altos.\n",
    "predicciones = pd.DataFrame(users_predictions.loc[usuario_ejemplo].sort_values(ascending=False).head(6)).reset_index()\n",
    "predicciones.columns = ['title', 'Predicted Rating']\n",
    "predicciones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medimos el error del recomendador:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mediremos el error mediante el error por mínimos cuadrados: (mean_squared_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mse(preds, actuals):\n",
    "    if preds.shape[1] != actuals.shape[1]:\n",
    "        actuals = actuals.T\n",
    "    preds = preds[actuals.nonzero()].flatten()\n",
    "    actuals = actuals[actuals.nonzero()].flatten()\n",
    "    return mean_squared_error(preds, actuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training error:  9.428676157905187\n"
     ]
    }
   ],
   "source": [
    "print('Training error: ',get_mse(users_predictions.values, ratings_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing error:  9.214272688834477\n"
     ]
    }
   ],
   "source": [
    "# Realizo las predicciones para el test set\n",
    "users_predictions_test = similarity_matrix.dot(values_rating) / np.array([np.abs(similarity_matrix).sum(axis=1)]).T\n",
    "users_predictions_test = users_predictions_test.loc[ratings_train.shape[0]+1:similarity_matrix.shape[0],:]\n",
    "\n",
    "print('Testing error: ',get_mse(users_predictions_test.values, ratings_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método empleado nos arroja un error en los datos de entrenamiento de 9.43 y un error en los datos de testeo de 9.21. Ambos resultados son muy cercanos lo que puede indicar que no está habiendo overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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