{
 "metadata": {
  "name": "",
  "signature": "sha256:6b8ae5325b2d44bd77b36718ca2cd17ae6a7cf0fd6b4b86f7c96aef59e2f0029"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Porque Charles Xavier debe cambiar a Cerebro por Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "speakers = [{'name':'Mai Gim\u00e9nez', \n",
      "           'twitter': '@adahopper',\n",
      "           'weapons': ['Python', 'Bash', 'C++'],\n",
      "           'pyladies': True}, \n",
      "            {'name':'Angela Rivera', \n",
      "           'twitter': '@ghilbrae ',\n",
      "           'weapons': ['Python', 'Django', 'C++'],\n",
      "           'pyladies': True}]\n",
      "\n",
      "for speaker in speakers:\n",
      "    for k,v in speaker.items():\n",
      "        print(\"- {}: {}\".format(k,v))\n",
      "    print()\n",
      "#print('\\n'.join([\"- {}: {}\".format(k, v) for speaker in speakers for k,v in speaker.items()]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "- name: Mai Gim\u00e9nez\n",
        "- pyladies: True\n",
        "- weapons: ['Python', 'Bash', 'C++']\n",
        "- twitter: @adahopper\n",
        "\n",
        "- name: Angela Rivera\n",
        "- pyladies: True\n",
        "- weapons: ['Python', 'Django', 'C++']\n",
        "- twitter: @ghilbrae \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='pyladies.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
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8AgBAoTTqkCkHraGMNSR8T73jJEf2BAFQQ8PjavKpMED9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\nJjFTUQBKEXmzxDoNq+uFAAAgCyYiFMzNwMCYTGeLL/Le5isRBoCl859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XWcmJu3AyUAIPP5UKFiwFN01QHgKdUe8tT3cJdd/cOFdUjSOLRTddvJYWP+0rrs7O7t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\nYRMRP+gf9f32XQU7ecbyMfT1vvr2qoKdyRrr1lk2G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       "prompt_number": 2,
       "text": [
        "<IPython.core.display.Image at 0x10f6fd390>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "\u00bfQu\u00e9 es lo que vamos a ver en esta charla?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Explicaci\u00f3n a trav\u00e9s de ejemplos de pandas y numpy.\n",
      "- C\u00f3mo hacer gr\u00e1ficos simples con matplotlib.\n",
      "- Una breve explicaci\u00f3n del aprendizaje autom\u00e1tico (machine learning).\n",
      "- El algoritmo Knn "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename='notebook.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAATsAAAE7CAIAAAAtvrGQAAAHJUlEQVR4nO3d0Y0kNRRAUQaRF0EQ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\nRK5CYII=\n",
       "prompt_number": 3,
       "text": [
        "<IPython.core.display.Image at 0x10f6fd310>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(filename='marvel_logo.jpg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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ma8PZWP0SM04KR714ZtvsnhvTocYIgUn6nn+tbAFRQRiKGOMDhFCj8BU1e3BW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\nR/8AQWn/AE/wp2h5h/tPaP3v/Ip/2Rqv/QOu/wD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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.Image at 0x10f6fd3d0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Marvel**, es una editorial de c\u00f3mics estadounidense fundada por **Martin Goodman** en 1939, como *Marvel Mystery Comics*. Aunque Marvel, tal y como hoy la conocemos (*Marvel Worldwide Inc.*), data de 1961 con la publicaci\u00f3n de *Los Cuatro Fant\u00e1sticos* y otras historias de superh\u00e9roes creadas por autores como **Stan Lee**, **Jack Kirby** o **Steve Ditko**, entre otros.\n",
      "\n",
      "Marvel es madre de archiconocidos personajes o equipos como:\n",
      "*   Spider-Man\n",
      "*   X-Men\n",
      "*   Captain America\n",
      "*   Black Widow\n",
      "*   Fantastic Four\n",
      "*   ...\n",
      "\n",
      "\n",
      "\u00a1Y todos estos datos son **nuestros**!"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "1. Explorando la API de Marvel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Existe una liber\u00eda para acceder directamente a la API de Marvel en Python desarrollada por *Garrett Pennington* [pymarvel](https://github.com/gpennington/PyMarvel \"pymarvel\") en Python 2 y est\u00e1 portada a Python 3 en [pymarvel3](https://github.com/mshopper/PyMarvel \"pymarvel3\")\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from marvel.marvel import Marvel\n",
      "from marveldev import Developer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Para acceder a la API es necesario pedir unas credenciales de desarrolladores en http://developer.marvel.com/\n",
      "\n",
      "\u00a1Ojo con las peticiones! Podemos pedir hasta 100 resultados cada vez."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "developer = Developer()\n",
      "marvel = Marvel(*developer.get_marvel_credentials())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "character_data_wrapper = marvel.get_characters(orderBy=\"-modified\", limit=\"100\")\n",
      "print(character_data_wrapper.status)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Ok\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for character in character_data_wrapper.data.results[:10]:\n",
      "    print(\"* {character.name}: {character.modified_raw}\".format(character=character))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "* Thor (Goddess): 2014-11-05T15:16:57-0500\n",
        "* Spider-Man (Miles Morales): 2014-10-23T12:07:33-0400\n",
        "* Hawkeye (Kate Bishop): 2014-10-23T12:05:03-0400\n",
        "* Black Widow: 2014-09-09T16:09:03-0400\n",
        "* New Mutants: 2014-08-12T12:59:29-0400\n",
        "* Cosmo (dog): 2014-07-24T15:14:21-0400\n",
        "* Rocket Raccoon: 2014-07-17T17:32:43-0400\n",
        "* Ronan: 2014-07-17T16:45:26-0400\n",
        "* Star-Lord (Peter Quill): 2014-07-14T20:45:53-0400\n",
        "* Captain Marvel (Carol Danvers): 2014-07-08T18:17:18-0400\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00bfQu\u00e9 informaci\u00f3n tenemos disponible para cada personaje?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "', '.join([attr for attr in dir(character) if not attr.startswith('_')])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "'comics, description, detail, dict, events, get_comics, get_events, get_related_resource, get_series, get_stories, id, list_to_instance_list, marvel, modified, modified_raw, name, resourceURI, resource_url, series, stories, thumbnail, to_dict, urls, wiki'"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mmmm, no est\u00e1 mal pero \u00bfes eso lo que buscamos? Veamos el wiki:\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "HTML(\"<iframe src={} width=1000 height=800></iframe>\".format(character_data_wrapper.data.results[2].wiki))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://marvel.com/universe/Hawkeye_%28Kate_Bishop%29?utm_campaign=apiRef&utm_source=28af7c7a8dac2c9156b74ac956598479 width=1000 height=800></iframe>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<IPython.core.display.HTML at 0x1127b8610>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extrayendo datos de la web (scrap)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Aqu\u00ed encontramos muchos m\u00e1s datos acerca del personaje: Rasgos f\u00edsicos, ocupaci\u00f3n, educaci\u00f3n... \u00a1Tiene buena pinta! \u00a1Scrappemos la wiki!\n",
      "\n",
      "El problema reside en que Marvel s\u00f3lo nos deja obtener hasta 100 resultados cada vez. \n",
      "\n",
      "Lo primero que deber\u00edamos hacer es recoger informaci\u00f3n de la web y almacenarla. \n",
      "\n",
      "Pero, a alguien m\u00e1s se le ha ocurrido eso, y no vamos a reinventar la rueda. @asamiller ha desarrollado una app en node.js que explora la API de Marvel y almacena los datos usando [Orchestrate](https://orchestrate.io/ \"Orchestrate\"). El c\u00f3digo est\u00e1 disponible en [github]( https://github.com/asamiller/marvelousdb \"Marvelus DB\")."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font color='red'> # TODO </font> \n",
      "\n",
      "*En realidad molar\u00eda scrappear la wiki y no tener una versi\u00f3n est\u00e1tica, que adem\u00e1s puede estar un poco desfasada, pero esto deber\u00edamos incluirlo en la librer\u00eda pyMarvel. Si te animas, b\u00fascanos despu\u00e9s de la charla y hablamos.*"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Cargamos los ficheros json."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from os.path import join\n",
      "from os import listdir\n",
      "import socket\n",
      "\n",
      "MARVELOUSDB_PATH_A = \"../marvelousdb-master/data/characters/\"\n",
      "MARVELOUSDB_PATH_M = \"../marvelousdb/data/characters/\"\n",
      "MARVELOUSDB_PATH = MARVELOUSDB_PATH_M if 'alan' in socket.gethostname() else MARVELOUSDB_PATH_A"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_db = [join(MARVELOUSDB_PATH, json_file) for json_file in listdir(MARVELOUSDB_PATH)]\n",
      "print(\"En MarvelousDB tenemos un backup de {} personajes\".format(len(json_db)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "En MarvelousDB tenemos un backup de 1402 personajes\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Organicemos la informaci\u00f3n que hemos conseguido (\u00a1Pandas time!)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas es una librer\u00eda de c\u00f3digo abierto, con licencia BSD, que permite trabajar eficientemente analizando datos en Python.\n",
      "\n",
      "A pandas se le da bien:\n",
      "- Estructuras de datos eficientes (**DataFrames**) para trabajar con datos indexados.\n",
      "- Herramientas para **leer y escribir datos eficientemente**. Es capaz de trabajar con distintos formatos:\n",
      "    - csv\n",
      "    - Ficheros de texto\n",
      "    - Microsoft Excel\n",
      "    - Bases de datos SQL\n",
      "    - HDF5 format\n",
      "    - ...\n",
      "- **Remodelado** flexible y alternancia entre conjuntos de datos.\n",
      "- Selecci\u00f3n inteligente basada en etiquetas, indexaci\u00f3n compleja, selecci\u00f3n de **subconjuntos** en grandes conjuntos de datos.\n",
      "- Se pueden insertar y borrar columnas: **mutabilidad** de los conjuntos de datos.\n",
      "- **Agrupado y fusionados** sencillo de conjuntos de datos.\n",
      "- **Funciones para series de tiempos**: gestiona eficientemente rangos de fechas.\n",
      "- ..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "DataFrames"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Un DataFrame es una estructura de 2 dimensiones con datos etiquetados en columnas. Los datos que componen un DataFrame pueden ser de distintos tipos. \n",
      "Piensa en un dataframe como si fuera una hoja de c\u00e1culo o una tabla SQL. \n",
      "\n",
      "Se pueden crear a partir de:\n",
      "- Diccionarios 1D de ndarrays, listas, diccionarios o Series (Pandas).\n",
      "- Una matriz 2D ndarray.\n",
      "- Otro DataFrame\n",
      "\n",
      "Al crear un DataFrame, tambi\u00e9n se pueden especificar los \u00edndices (index, etiquetas para las filas) y las columnas. Si no se proporcionan estas etiquetas como argumentos pandas crear\u00e1 un DataFrame usando el sentido com\u00fan."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "En nuestro caso, leeremos todos los ficheros *json* y crearemos un DataFrame. Como tenemos informaci\u00f3n jer\u00e1rquica en los ficheros *json* necesitamos normalizar los datos, pero pandas tiene funciones que lo hacen por nosotros."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_to_dataframe = []\n",
      "for json_file in json_db:\n",
      "    with open(json_file, 'r') as jf:\n",
      "        json_character = json.loads(''.join(jf.readlines()))\n",
      "        json_plain = pd.io.json.json_normalize(json_character)\n",
      "        json_to_dataframe.append(json_plain)\n",
      "        \n",
      "marvel_df = pd.concat(json_to_dataframe)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Podemos hacer esto en una super instrucci\u00f3n. Perdemos en legibilidad pero ganamos en molancia. \u00a1Totalmente desaconsejado!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.concat([pd.io.json.json_normalize(json.loads(''.join(open(json_file,'r').readlines()))) for json_file in json_db])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Podemos realizar operaciones l\u00f3gica sobre todos los elementos de un DataFrame, son operaciones vectoriales. Esto acerlera los c\u00e1lculos."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all(df == marvel_df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00bfY que pinta tiene un DataFrame?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>comics.available</th>\n",
        "      <th>comics.collectionURI</th>\n",
        "      <th>comics.items</th>\n",
        "      <th>comics.returned</th>\n",
        "      <th>description</th>\n",
        "      <th>events.available</th>\n",
        "      <th>events.collectionURI</th>\n",
        "      <th>events.items</th>\n",
        "      <th>events.returned</th>\n",
        "      <th>id</th>\n",
        "      <th>...</th>\n",
        "      <th>wiki.specieshistory</th>\n",
        "      <th>wiki.team_name</th>\n",
        "      <th>wiki.teamicon</th>\n",
        "      <th>wiki.technology</th>\n",
        "      <th>wiki.tie-ins</th>\n",
        "      <th>wiki.title_graphic</th>\n",
        "      <th>wiki.universe</th>\n",
        "      <th>wiki.weapons</th>\n",
        "      <th>wiki.weaponss</th>\n",
        "      <th>wiki.weight</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 36</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Marvel Adventures Super Heroes (201...</td>\n",
        "      <td> 36</td>\n",
        "      <td> AIM is a terrorist organization bent on destro...</td>\n",
        "      <td> 0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1009144</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                        NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 43</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Incredible Hulks (2009) #619', 'res...</td>\n",
        "      <td> 43</td>\n",
        "      <td> Formerly known as Emil Blonsky, a spy of Sovie...</td>\n",
        "      <td> 2</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Chaos War', 'resourceURI': 'http://...</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1009146</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     Marvel Universe</td>\n",
        "      <td>                                              None</td>\n",
        "      <td> NaN</td>\n",
        "      <td> (Abomination) 980 lbs.; (Blonsky) 180 lbs.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 43</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Avengers Academy (2010) #21', 'reso...</td>\n",
        "      <td> 43</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 4</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Fear Itself', 'resourceURI': 'http:...</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1009148</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td> He uses a prison ball-and-chain as a weapon, a...</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                        365 lbs. (variable)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  8</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Uncanny X-Men (1963) #402', 'resour...</td>\n",
        "      <td>  8</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 1</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Age of Apocalypse', 'resourceURI': ...</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1009149</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                 Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 20</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Weapon X: Days of Future Now (Trade...</td>\n",
        "      <td> 20</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1009150</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td> Agent Zero carries a wide array of weapons inc...</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                   230 lbs.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 89 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "   comics.available                               comics.collectionURI  \\\n",
        "0                36  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                43  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                43  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                 8  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                20  http://gateway.marvel.com/v1/public/characters...   \n",
        "\n",
        "                                        comics.items  comics.returned  \\\n",
        "0  [{'name': 'Marvel Adventures Super Heroes (201...               36   \n",
        "0  [{'name': 'Incredible Hulks (2009) #619', 'res...               43   \n",
        "0  [{'name': 'Avengers Academy (2010) #21', 'reso...               43   \n",
        "0  [{'name': 'Uncanny X-Men (1963) #402', 'resour...                8   \n",
        "0  [{'name': 'Weapon X: Days of Future Now (Trade...               20   \n",
        "\n",
        "                                         description  events.available  \\\n",
        "0  AIM is a terrorist organization bent on destro...                 0   \n",
        "0  Formerly known as Emil Blonsky, a spy of Sovie...                 2   \n",
        "0                                                                    4   \n",
        "0                                                                    1   \n",
        "0                                                                    0   \n",
        "\n",
        "                                events.collectionURI  \\\n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "\n",
        "                                        events.items  events.returned  \\\n",
        "0                                                 []                0   \n",
        "0  [{'name': 'Chaos War', 'resourceURI': 'http://...                2   \n",
        "0  [{'name': 'Fear Itself', 'resourceURI': 'http:...                4   \n",
        "0  [{'name': 'Age of Apocalypse', 'resourceURI': ...                1   \n",
        "0                                                 []                0   \n",
        "\n",
        "        id   ...    wiki.specieshistory wiki.team_name wiki.teamicon  \\\n",
        "0  1009144   ...                    NaN            NaN           NaN   \n",
        "0  1009146   ...                    NaN            NaN           NaN   \n",
        "0  1009148   ...                    NaN            NaN           NaN   \n",
        "0  1009149   ...                    NaN            NaN           NaN   \n",
        "0  1009150   ...                    NaN            NaN           NaN   \n",
        "\n",
        "   wiki.technology wiki.tie-ins wiki.title_graphic        wiki.universe  \\\n",
        "0              NaN          NaN                NaN  [[Marvel Universe]]   \n",
        "0              NaN          NaN                NaN      Marvel Universe   \n",
        "0              NaN          NaN                NaN  [[Marvel Universe]]   \n",
        "0              NaN          NaN                NaN  [[Marvel Universe]]   \n",
        "0              NaN          NaN                NaN  [[Marvel Universe]]   \n",
        "\n",
        "                                        wiki.weapons wiki.weaponss  \\\n",
        "0                                                NaN           NaN   \n",
        "0                                               None           NaN   \n",
        "0  He uses a prison ball-and-chain as a weapon, a...           NaN   \n",
        "0                                         Unrevealed           NaN   \n",
        "0  Agent Zero carries a wide array of weapons inc...           NaN   \n",
        "\n",
        "                                  wiki.weight  \n",
        "0                                         NaN  \n",
        "0  (Abomination) 980 lbs.; (Blonsky) 180 lbs.  \n",
        "0                         365 lbs. (variable)  \n",
        "0                                  Unrevealed  \n",
        "0                                    230 lbs.  \n",
        "\n",
        "[5 rows x 89 columns]"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Los DataFrames de pandas est\u00e1n implementados sobre **numpy**, de modo que si queremos saber la longitud que tiene un DataFrame es exactamente igual que en **numpy**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "(1402, 89)"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tenemos 89 columnas, es decir 89 campos que explorar sobre personajes de la Marvel, \u00a1Genial!  <br />"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "', '.join(marvel_df.columns.values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "'comics.available, comics.collectionURI, comics.items, comics.returned, description, events.available, events.collectionURI, events.items, events.returned, id, modified, name, resourceURI, series.available, series.collectionURI, series.items, series.returned, stories.available, stories.collectionURI, stories.items, stories.returned, thumbnail.extension, thumbnail.path, urls, wiki.Date_of_birth, wiki.Place_of_birth, wiki.abilities, wiki.aliases, wiki.appearance, wiki.base_of_operations, wiki.bio, wiki.bio_text, wiki.blurb, wiki.builder, wiki.categories, wiki.categorytext, wiki.citizenship, wiki.creator, wiki.creators, wiki.current_members, wiki.debut, wiki.distinguishing_features, wiki.dstinguishing_features, wiki.education, wiki.event_text, wiki.eyes, wiki.features, wiki.former_members, wiki.govenment, wiki.government, wiki.groups, wiki.hair, wiki.height, wiki.home_world, wiki.identity, wiki.key_characters, wiki.key_issues, wiki.leader, wiki.location, wiki.main_image, wiki.members, wiki.object_text, wiki.occupation, wiki.origin, wiki.other_members, wiki.owner, wiki.paraphernalia, wiki.place_of_birth, wiki.place_of_creation, wiki.place_text, wiki.points_of_interest, wiki.power, wiki.powers, wiki.real_name, wiki.relatives, wiki.significant_citizens, wiki.significant_issues, wiki.skin, wiki.special_limitations, wiki.specieshistory, wiki.team_name, wiki.teamicon, wiki.technology, wiki.tie-ins, wiki.title_graphic, wiki.universe, wiki.weapons, wiki.weaponss, wiki.weight'"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " *En realidad no deber\u00edamos lanzar las campanas al vuelo porque __spoiler__ muchos de los campos est\u00e1n vacios *"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>comics.available</th>\n",
        "      <th>comics.collectionURI</th>\n",
        "      <th>comics.items</th>\n",
        "      <th>comics.returned</th>\n",
        "      <th>description</th>\n",
        "      <th>events.available</th>\n",
        "      <th>events.collectionURI</th>\n",
        "      <th>events.items</th>\n",
        "      <th>events.returned</th>\n",
        "      <th>id</th>\n",
        "      <th>...</th>\n",
        "      <th>wiki.specieshistory</th>\n",
        "      <th>wiki.team_name</th>\n",
        "      <th>wiki.teamicon</th>\n",
        "      <th>wiki.technology</th>\n",
        "      <th>wiki.tie-ins</th>\n",
        "      <th>wiki.title_graphic</th>\n",
        "      <th>wiki.universe</th>\n",
        "      <th>wiki.weapons</th>\n",
        "      <th>wiki.weaponss</th>\n",
        "      <th>wiki.weight</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>0 rows \u00d7 89 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "Empty DataFrame\n",
        "Columns: [comics.available, comics.collectionURI, comics.items, comics.returned, description, events.available, events.collectionURI, events.items, events.returned, id, modified, name, resourceURI, series.available, series.collectionURI, series.items, series.returned, stories.available, stories.collectionURI, stories.items, stories.returned, thumbnail.extension, thumbnail.path, urls, wiki.Date_of_birth, wiki.Place_of_birth, wiki.abilities, wiki.aliases, wiki.appearance, wiki.base_of_operations, wiki.bio, wiki.bio_text, wiki.blurb, wiki.builder, wiki.categories, wiki.categorytext, wiki.citizenship, wiki.creator, wiki.creators, wiki.current_members, wiki.debut, wiki.distinguishing_features, wiki.dstinguishing_features, wiki.education, wiki.event_text, wiki.eyes, wiki.features, wiki.former_members, wiki.govenment, wiki.government, wiki.groups, wiki.hair, wiki.height, wiki.home_world, wiki.identity, wiki.key_characters, wiki.key_issues, wiki.leader, wiki.location, wiki.main_image, wiki.members, wiki.object_text, wiki.occupation, wiki.origin, wiki.other_members, wiki.owner, wiki.paraphernalia, wiki.place_of_birth, wiki.place_of_creation, wiki.place_text, wiki.points_of_interest, wiki.power, wiki.powers, wiki.real_name, wiki.relatives, wiki.significant_citizens, wiki.significant_issues, wiki.skin, wiki.special_limitations, wiki.specieshistory, wiki.team_name, wiki.teamicon, wiki.technology, wiki.tie-ins, wiki.title_graphic, wiki.universe, wiki.weapons, wiki.weaponss, wiki.weight]\n",
        "Index: []\n",
        "\n",
        "[0 rows x 89 columns]"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Series"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Series es un array de 1 dimensi\u00f3n etiquetado. Como una tabla con una \u00fanica columna. Puede almacenar cualquier tipo de datos:\n",
      "- Enteros\n",
      "- Cadenas\n",
      "- N\u00fameros en coma flotante.\n",
      "- Objetos Python.\n",
      "- ...\n",
      "\n",
      "Se etiquetan en funci\u00f3n del \u00edndice, si por ejemplo, el \u00edndice que le pasamos son fechas se crear\u00e1 una instancia de *TimeSerie*.\n",
      "\n",
      "Cuando se hace una selecci\u00f3n de 1 columna en un DataFrame se crea una Serie."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a usar los creadores de comics para jugar un poco con las Series."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sacamos la lista de creadores que hay en nuestros datos\n",
      "creators_serie = marvel_df['wiki.creators'].dropna()\n",
      "creators_serie.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "count                               119\n",
        "unique                               37\n",
        "top       this has not been updated yet\n",
        "freq                                 44\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Renombramos la serie y el \u00edndice\n",
      "creators_serie.name = 'Creadores de personajes'\n",
      "creators_serie.index.name = 'creators'\n",
      "\n",
      "# Podemos usar head o como estamos sobre series tambi\u00e9n podemos coger una porci\u00f3n de la lista\n",
      "# creators_serie.head()\n",
      "creators_serie[:20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "creators\n",
        "0            this has not been updated yet\n",
        "0                                         \n",
        "0                                         \n",
        "0                                         \n",
        "0                                         \n",
        "0                                         \n",
        "0                                         \n",
        "0                  Peter David & Sam Keith\n",
        "0               Bill Mantlo and Ed Hanigan\n",
        "0                                         \n",
        "0                 Stan Lee and Steve Ditko\n",
        "0             Grant Morrison & Igor Kordey\n",
        "0                          Chris Claremont\n",
        "0                                         \n",
        "0           Chris Claremont & Dave Cockrum\n",
        "0            this has not been updated yet\n",
        "0            this has not been updated yet\n",
        "0                                         \n",
        "0                     Stan Lee, Jack Kirby\n",
        "0                           Grant Morrison\n",
        "Name: Creadores de personajes, dtype: object"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Usando m\u00e1scaras para extraer informaci\u00f3n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a eliminar todos aquellas filas en el DataFrame en las que el creador no exista, bien porque encontremos la cadena de error o bien porque el campo est\u00e9 vac\u00edo. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "default_string = creators_serie != \"this has not been updated yet\"\n",
      "default_string.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "creators\n",
        "0           False\n",
        "0            True\n",
        "0            True\n",
        "0            True\n",
        "0            True\n",
        "Name: Creadores de personajes, dtype: bool"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "empty_string = creators_serie != \"\"\n",
      "empty_string[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "creators\n",
        "0            True\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0            True\n",
        "0            True\n",
        "0           False\n",
        "Name: Creadores de personajes, dtype: bool"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ahora simplemente juntamos estas dos m\u00e1scaras."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "default_string and empty_string"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-47-544bf713079b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdefault_string\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mempty_string\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/ada/Dev/.virtualenvs/marvel/lib/python3.3/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    690\u001b[0m         raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m    691\u001b[0m                          \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m                          .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m     \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A pesar de que la palabra reservada *and* exista tambi\u00e9n en pandas y pudi\u00e9ramos pensar que funcionar\u00eda para unir series no es as\u00ed, ya que la operaci\u00f3n no se aplica elemento a elemento. \n",
      "\n",
      "Sin embargo, pandas sabe que esto nos podr\u00eda hacer falta y tenemos operadores que funcionan para elementos (& (and), | (or), ~(not))"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "creators_mask = default_string & empty_string\n",
      "creators_mask[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "creators\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0           False\n",
        "0            True\n",
        "0            True\n",
        "0           False\n",
        "Name: Creadores de personajes, dtype: bool"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "creators_serie[creators_mask].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "creators\n",
        "0                Peter David & Sam Keith\n",
        "0             Bill Mantlo and Ed Hanigan\n",
        "0               Stan Lee and Steve Ditko\n",
        "0           Grant Morrison & Igor Kordey\n",
        "0                        Chris Claremont\n",
        "Name: Creadores de personajes, dtype: object"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Aqu\u00ed ya tenemos buena parte de la informaci\u00f3n que queremos, pero vamos a separar los autores que trabajan juntos para poder contar cuantos personajes ha creado cada uno."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "creators = [re.split('&|and|,', line) for line in creators_serie[creators_mask]]\n",
      "clean_creators =  pd.Series([c.rstrip().lstrip() for creator in creators for c in creator])\n",
      "clean_creators.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "0    Peter David\n",
        "1      Sam Keith\n",
        "2    Bill Mantlo\n",
        "3     Ed Hanigan\n",
        "4       Stan Lee\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_creators.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "Chris Claremont         17\n",
        "Stan Lee                 9\n",
        "John Byrne               7\n",
        "Jack Kirby               4\n",
        "Brian K. Vaughan         4\n",
        "Adrian Alphona           4\n",
        "Steve Ditko              3\n",
        "Grant Morrison           2\n",
        "Christina Weir           2\n",
        "John Buscema             2\n",
        "Scott Lobdell            2\n",
        "Nunzio DeFilippis        2\n",
        "Paul Smith               1\n",
        "Jim Lee                  1\n",
        "Keron Grant              1\n",
        "Marc Silvestri           1\n",
        "John Romita Jr.          1\n",
        "Brian Michael Bendis     1\n",
        "Joe Bennett              1\n",
        "Sam Keith                1\n",
        "John Romita Sr.          1\n",
        "Roger Cruz               1\n",
        "Mark Millar              1\n",
        "Andy Kubert (artist)     1\n",
        "Javier Saltares          1\n",
        "John Cassaday            1\n",
        "Frank Miller             1\n",
        "Bill Everett             1\n",
        "Ed Hanigan               1\n",
        "Len Wein                 1\n",
        "Peter David              1\n",
        "Bill Mantlo              1\n",
        "Marv Wolfman             1\n",
        "Christopher Priest       1\n",
        "Alan Moore               1\n",
        "Keront Grant             1\n",
        "Chris Bachalo            1\n",
        "Howard Mackie            1\n",
        "Mark Millar (writer)     1\n",
        "Salvador Larroca         1\n",
        "Art Adams                1\n",
        "Alan Davis               1\n",
        "Joss Whedon              1\n",
        "Dave Cockrum             1\n",
        "Mark Bagley              1\n",
        "Igor Kordey              1\n",
        "                         1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Esper\u00e1bamos que **Stan Lee** ganara y adem\u00e1s tenemos la impresi\u00f3n de que ha creado m\u00e1s de 9 personajes, sin querer hacer un feo a **Chris Claremont**."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Seg\u00fan nuestras fuentes (Wikipedia & ComicVine):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='stanvschris.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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KfDzGTccEeQ7LGSpdYGbXvPni17z98kukyegnAceHQ0bDLt1uRByHqFBg8ALJ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\n12lpaXFdjaEwDx0+gCpCd3c3P3rheSZvTzI7O0tfXx8DAwPIqkomk2VsdIS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So+cIIKsq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bZ1K0NDQ0yMeVyx1157jc2bN/Oexx9nfGKCmvo6NE0jFAnTsqKFiYkJ+rZsYfXq1YTDYbq6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1PU1rayvpdLrSdtd1nfmFxUpFdXZ2lp6eTSwvL9PS0sKGDRuYm5tj5PJlRq+N0b2xB1EU\nSSaTVFVFKv7AtbU17jp4kL6+PnK5HCdOnGBiYoITJz9i3759DOzYyfXRq1wcPMfK6gofDZ4nUltF\nS/MGOrs6ae/ZSOvmjSRiq+Rm5jjy3b/FPnuZ3XWNyNkUUa+KxygQdCxCgkBYEIhIElWSRFgUCTkO\nXtPCYxmEJZGIRyWsymiCjeqYSFgYegFRkJAEFcEBQy9gFgxkVLx+heXYNFrISyqfp7CUYYck0iCr\n6KKAE/Azm8uTsYpomk0QqNId1hJx9MZahhcXuHp9krGJSSy9yNb+rfRu7eOug/fQuaGJuZlFfvB3\nf0ezX2b4tefY0d6MqkhcOnuc1loJDwapWIKALFPnkwnIsGf3VhpqIvR0djE+dhXbyLHvllt49+23\n8WoaHq+XQr5I04YmTp48SX00is+roWkyyVgMv8/DW28cpnXDBqoiIfw+D6LgppR4NA1wUBUFBIdE\nIo6iqPT29tLS0sLk5CTZbJaGhgauXr3Knlv2kspkyWSyLC0tsba2RiqVpqFhA4VCAUEQKOgW6XSa\nW2+9jfr6erq6uxgYGGD7tm6i9U2Mj0+SzWUpFHLMzcxw/twgjfVRTn5wHNMo0NLSjG0bSKX4stsf\n+NwnAgvgr//yLxAcKOYLFPOFCohTlZWbqOzrp+7WV4NE8Uab73/1Lr0iZkTBze8TXHO7WUJF2EIp\nfNeyMUyr8jySJKGVInc0TUNWFFSvtyLgVlfXyOdz1NTUIEkSyWTSDZjmRpBzeaquYqJ37P/JMYoV\no7RQ4nOVvWflqpZuFLBsG8uxKOSzYDts79/G5o096MX8jRxDIJVMoaoq1VU1BEMhZFlhamqamqo6\nN7MumyWfL3D3vfeSSqc4f/ECyVQKWVHYsWMHumEgiSLZdJZioYBtu59TPp8rvR8qmseDorjixrYd\nDMMkl8thmm7rSlVVCoUCmXSaYrGI1+PFNMzKNJ1b2hXdSSNFJZfP4/X6XI+ZJJaGBKi0Zh1Jpmvr\ndiI1dXg93krryx2esEvVPZtIJIxTaumGgqX2qCxy4dJFVK/C888/z3vvncDvUwkEA3g0vxspJEqs\nrsVob28DHJZXlrEtE49XRVYUDNNCltSbxLumeW7y2pWrULFYjFzOzSV0q48GFjAzM01rm1tpqKur\nI7a2VoE5plIpPJpamQAsVzXLosrr9aFpKrperAgsECkUXYGlKAqO7ZQElljCVpQFlpuhKJcukuXv\nVPk8/frv/ItPVNFPcP3+7/0n7r3nXu5/8EEkWeb8+fO8d+Qo27ZtZeeuXcQSCWzb4YUXfkR7ezv3\n3XUrza0dTM9M4/P5mZ9fpJDPsaO/lx8++ywH9u+ju7uDi+cv8PJLL9GzcSPhcIhNPT10tEaZnVlk\naOgyerHI0sISHo+XpcVFRi5f5qUXXyQRj1NXW0skHEZTZSKRMLZlsmdggA1NjczPzfL5n32Szb29\nXB4e4t1336Oru4ve3l6uXbuGJElUV1ezFotX2ucHDhwgHk/Q09PD9evX0TS3hX/9+nU29/WRTqeI\nxdZobWmuJFSEA366Ojv4zne+Q2trK/feuZdQuIb+/n7m5uZobW1lc087G2oa2Na3lepoLbFUgsFz\ng6yurXHu3AUC1RHqGxupCvo4uH0PR//0L2nL6ERtm0J8GcfMI0rglDJEBUVCUBVsWaIoQh6LvGNR\nFB1sRUL0qNgKmI6F7piYgrsXOBZ4RIWQouG1bZyiQR4LQ7GxMRBlEcOB/FKKW1Qf3dEG1uwcM/kM\nUk0dE3PTJBOr9NRV0VffREbXWRAd7nvySQJ19Tzx2Ufx+UJIkkTRsnj55Vc4/t5JVBEGtvYQKqyR\nHDxGk2LS3dGKrCdIzQ0TVEwiXi+r0xP49DQHtm/EL+t8dOxtzGwKv+zyEM98dBpJFPnaP/tFQqEg\ntmURjoRpqI9y/Pj7+Dwah197lc72NuZnpvF6VCQcNFVGEBx0w+VN6boLxW1oaCAUDCKJIpu3bOHy\n5ctcvHiR3t5e6qJRLl66xC1793P+/HkcBHTdzQi2bZvFpWWWl5cZHBzk8uXLCJLI8PAwExOT1NfX\ns7i0xNtvv00imWVhfhG/6Eg0jQAAIABJREFU309tbR3nBs/S0FDPwvw8xXyW69fHEAWHttYmRNHB\nKbElDx568pMWobszWVimgWkUcWz3bt39QF0RgWOBA7Zzc7uvYsQthdqub3F8HNz4PyKlV0CkIiBK\npUqT2yJcj2koT1zdaAMJOI6AbYFp2oSDPjK5LHrBFTuipGBaDpROQsOw/kFlzXEcbMfEsV3h9PG2\njPv3b25JiqJQ2bQrxn3nZo5WGY1QnpyUEPB5NVZjMSTJDZjOF3Rq66PUqRp+f5Dl2RU8Hi9tbW28\nf+IEUzMzbN7Wj+HYFPNpZpZWaGho4JZbb8fSDbbszHJ8bY1gMIgjSRgFnesToyU6czeNDc3IEhQK\nOo4jkM8VEEQIh8PkcgVM0yIeT+LxqO7UXixGJpnCMd2Wm9cTrAjSQj5PIBBC13UkS0AMCHg8LhIi\nl8+QTTk0NzZhOA7FYh6Px/W5ZXM5HMci4HPN9gG/n3ghj2WaOKaFLIrkszkc02Tw7ClWl+doba4m\nmc6RyWVA0Ght62RsdBzbdhgbG6OpuZH6xgZWV5YRS+b7svdNFGVE8UZL1/UBujy2YDDI4uIitm3j\n8/kwdAsQCAQCxFNJ6upqOXfuHKIosmPHDmRFJRdPUFdXh8/nI51OY9gOtqFXBijceCHn/xWp4ACC\nJLoxP+L/Wnvwf9ZS/2T9f7yTlSTuvvtuLly4wPLqCjV1dTz++OPURmtZXltleHgYXdf54he/WLkB\ny2azBINBent7qalZpbu7m7/4y++wa/cAt+3fzltvn+DokWPcdfedeDSV2OoKR959h1wux56BnTQ1\nNREOBvH5fCTTKd58621uu/VWfvu3fotEIsH09CSSIHLp0qWSV2Yfa8trtLY2s7y4yPPPPoskSbS3\ntvKlL32JYrHIxYsXWVtbw3Ectm7dWrkJ1DSNffv28XdPP0NzczOJRIKhoSFWV1eRZZlUKoWiKEQi\nbvve5/MxNTWFKok89thjtLW1MTQ0xH/9kwuYpskdd9xBJBLh+PHjzE9Os6GqmqqaCF1bu8mL23jg\ngfuYmJji5MkPuXDhAqcunUNOp2m/5Q6Wr4zSiYSdS1NXGyZv5tBNC0sQcESwBAFLEsg7FlnTImca\nOIKE6YBoOSi6iYiDbQuIKIiOhIiJbZp47ByhQIhgOIxfURhOp5mKL5PU8/giATb1dDEyPMtMMs5k\nMEBO0pGCfkbnp2hqa0FKKQjZPEvmLFhFJkcu4xdEzk9MkTIcjr/9Nl6vl87eHhobNnDnY7fTXBvh\n+vAgI+9/wEB7A57CGmErzm3dUV48/A4hsqzkxymaAtlijmmnQENzE1pyDX82RO+uW/nW954m3NjC\nHfv2ITo2ZqGAgM07b73J3gP7URSJk6c+4MtfeIrxsVEkEaI1NQR9XhKJGOBO9P/f7L1nkFzneaZ9\nnXw6x8kZAwxyzkxgELMoirQlUbKSldaWbWktWfba62+9689l71oul9MGy5ZkLUlFJjECBBNIEEQg\nEjFIM0gzmDzTOZ/4/TjdzQHk/aet3aqPp2oK0xh0zwz6nPM+7/Pc93VHwrFmGkEgEPDOBcHTD+bz\nedavX088keD8+fMsX76S06dPs2TJIMNnziHLnmMzEomg+7x784MPPkg4HEb3C+zYsYOLFy/zzDPP\nYFgmwaBnmHJsl7Gr43R3dyMqMgvpDMFgGNsQ6O7uZfjkYUqlEsGAgr3IQf1BBwv49p//2TXOuMVa\nJ9d1cWynOSJrgBkXwz5xr9VHXe/GWzwmvB6oKEoispcsDQjYru2NBoX62MdxcFzxmpGeIsueQFrT\nUVSFcCSK47qkUikKhcIi0KR3k6xW6xlzqtIU4Xs/W2Mh+0XtmFccik2Lvuu69dTwa1lFouSNdKq1\nCka1SjgY4IadO+nr7kEUBTRVxefXKZdKtLa1o6gKU1PTpLIZFFlhyZJBkrFWxi6Pofl0SpUKgVCQ\nnp5eWtvbWTI4SDgS5vix4+g+HwP9fbimRSqV5urVcSyzRjCoU8xnKeQLSJJCwO9HVfT66NYj30uS\njN8foFypAB5kUPfp9QvOi7nx0AUqfn+QYqFApr7TCUc8PZvf740HU+mF+k1boqWji6WrN1CseGy0\noD+ArKpUymUAotEo6cwCogilYhHTqnm79liE0ZERnn7maU6fOcnAQB+p1ALBUIRcroBRs2hrbfdw\nG6JIJpMhHAmhaV7HztOAVZEVBcuweJ/KbzfjkizLplwuN52gDSdfpVytM2OiFEpFDKPWFKf39PRw\nzz33UCqVmJudbb7fruvi1FENDcxIrVZD92lIkohpm95I1LZw8HIwJVVrdj89ojuUKxWs+vidOgrE\n64heq1m0LIs/+P0PSO6/zOOlF19ncHApp8+c5c4772bDphVcnZjh6sQEzz7/PI5j86lP/xqd7TFs\nG37ys6ewbJtEPM7rb7zGAx++k917X0WSRG6+9RZOnx7h6sQYH/3oQ0zPTjEw0M/2HVu46eYbGFgy\nQKlU5tZbb0D3hxgeHubypct883d/k0S8leFTpzh29CjZTIpapcxdd36IHdu3kc9lqFWrXLo0SqlQ\nQFVkUgsLhMIhVq9dQ1u7B4McHBzk1KlTbN26lfGrEywsLCAIAtGox6EbGRnh/vvv5+TJk4yNjbF2\n7VoKxRKWZdW1jzZ+TQfXZWCgnwMHDjC4ZAkfvvdW/IFIEx8RjUZ55JGPE4/EuDB8mrGrYxy/fIEz\nF84y1DNIWzxJb08fO7ZuIBiO4qRzXNzzOkdeeIotLZ1oggkBKJpVJEFHRANJwxZUaoJEBSjZAmXH\nBdWHLUi4roToyki2jGwrqMioyDiugyYI+AQHVXRQfZqn5zQNMCyyikiqXCEp65Sm5gkIMLhsCaJr\nkinkqQQizMzNE7KqrOvtwa/IVByHjCsgxZOkBRFb0/jQLbfR293Nhk0bicc9jtSBt/ZTyac5/cZe\nhmSTZS1RNFUnEtboSvrwaxK5bIGuRJyV3QlKs1dYPdhJRyzApTOnOXr4CG29S0i0dZJOp8ikFpia\nmSUWi1Mzajz55BNUymU+97nP8d6JY4g4tLcksMwqlllFU2R8/gCRcJRcLsfCwgIAuVyOcrVGf/8A\nHe1dhCNRAsEQExOTVCpVxq5OsHLVKmbn5shksgwMDDSnP21t7ezbt4/Z2VmOHTtGPNmGZVlMT8/g\n8/n45Kc+WR8Z3kh3byddvb0sXdrFtq07KZdKFLJZcB3OnTmFgEUyGSYaCeLYFpZlctv9j3zQwfIa\nWBY4ElJ9JNjYejfGaYsBiY3iqLGIeaOtSt1yTlPs3QiPXmxtv3ZyKDaf874mquHgA1EQkCSQHBfq\nI8LG4tNY8DRNIxyJeEWTIjMyMoJpWgQC/nqHScZ23ndnXQ8HbXQ6TMv5V/AP73+fZmfhXxl/iqKI\nUxc7u65LIBAgFo54BVi9mJydniEYCno8qmLBs0cHfExOTTAycoFt63dw3733cub8CC3xFt49cowd\nN20nlSqgKDKhWJK+waVMjV9hoKONdZs2Iwkifz96gbJRJu5q6IoKrkgxlyU1N48m+wj4I4iCiOOA\nqso4loXguN54TpIQ67iAYDBIW2sriqximqZHp696o+JYLIauKYg+DUmWKZc9nVg6ncbn7yYajXpu\nUdNEqp8buvI+SR6gWCx654Xjejsl3Svszp07x/jVKywZHGBk9AItySSpdJb+3h5mFrKMjY3R0dlD\nrlgik80yPj5OLBbB5/d7HC7BG4vYpo2qStc4OxfDOxv5g5bpYFvez9hwzmiahm2bdHR1ks1mOXXq\nFD09PYRCXhcvFAqRy+W888B+f4T3/nhcvCaayHt8bbH+/kj8/ceu1/j9V8fpH3Sw/vcdkzPTfPoz\nn8FyPU3V3ldfwbIs4vE4D/3KQ8zOzqKrCo8//jjJZJJbb70ZVYKNGzfy9LMvYbsWn/zUQ7z82j4k\nSeEjD32EV195neWrllPM5cnk8rTEwywb7EVVVfbtP8To6CgrhpZz220rKJcdDry1n0uXL3DjjTey\nZfNaxsZneP3VV5iammrmga5dvYZkPM7s7AxbNm1kIZ3h4MGDLFu+AoDh4WEOHjzIkiVLSCQSpFIp\nIpEIkUiEG3du5K//9jvMz8+TzWaRZZlSqVTPV+xB0zS2bdvGnhdfolwus2HDOjRN49SpU0xPT2Pb\nXvdqcnKSkZERCoUSkiTwq7/6EVwF3royysTUJE8/+RRi1UQJBNi560aisSAPPfARXjl4imXhLjS/\nRs3JkyqmsF2HmOZDcMASXEzXwrQcTNvyUhnwOleCKyAJApLrOZZd1wtTN3HQJAldlVAlA4sqbk0k\nomgkAwFiwSCXro5RsgX8io9ke5wrM2ku5dL0Sy7xcIjRfJWKXaMlHqaUyTCXT3HT/R/h8/c+wH95\n9kW++A//wLtXxvAHArz0wosE3nsPzadz9swIH73vPm5ctwqunEfPXgR/EKOQw7GKxP0a86k5dm3b\nzMjoJVr0AHOaiFpNE/AHWNoaImzp7Dl2lHW3JJienmFkZATFF+D4yRPMzs8RDAZZu3YtF0bOk0zG\n0RUFw6gSCfjx+zRy6Qy1oklbWxuqqrJs2TISiQSXx65QKnlSlMPvHqG9vZ3x8XG6envoG/DOjfPn\nz1Or1di8eTMLCwvMznrmjYElS3Ech97eXrLZLPv376/LgbzN49zcHHfeeSff+973aG1rI1sosHHj\nRqIB7zzz+QJMp2YBkYmJSYYGu+ntafuFdfb/9x2sf/qvf1fv1NhN7ELDKu5lValNDVKje6VpWjM0\nt1atNcdpi51c17N8rs+K84KDRS8+BnBc6ouTB130QqChZhiIotR8PVGWCPj8xOJxzw2jyGi6zsjI\nSLOl34BOmqaJ3+9rdgkaDjDv60I9KNpBWMT1avwelvU+hqEhvL4+PqeRA1guFbFMm5Zkkp3btxEK\nBjFNA8GFWCyKIsvMzs4gijLJlgTFQh7btujr7WXs0hXOnD3D4OAgybYkp4ZPUSyUWL9+NelUhlql\nRk93L6FAgAujFzAtm96+AVavW8ee3S/hcy2Cmoov4PMoJJKE4vMhaSqWbZNKL6ApCrZp4jouqXQK\n3aeh+jTi8QRGrUYwECQcDpMrFBFEMCwbRZEJh0PIgkStUvU0YPUgZCQBRdUQFI2OwSEsvPfbFwwg\nywqpdJZ8voAsSwQDobqT0yUejyEKNvl8lr17d3Pp0nmCQR/YFrbt4vcHcUWRYqlCJBpnem6OaDyG\nz+cnnUnhuhAMBvDpPgL+ANlsDuoRQUATMKrrepPU3li0ioWSNwL1+SgUCh6awfU2BOl6hIQgCLz9\n1lsUSyVAwLQsRLxup+U49YLVRZalJvpBqBd2iqLVO65Qq1SQ6y5XT7AfQBA856JlGs1NjHcdiHV9\nnwdAdRwX23L4gz/4QIP1yzy+9y8/4lvf+haO4/Do449hmhb9S5YwOTnBTTffzNKBDgRJ5S+//Zc8\n/Cu/wq5bbuDttw8yOzPPi7t3ky/m+OynP8m77w3T09tNKBLmscd+wO133MGS3i4EWcJ2bWzg+Rde\nZPj0GYKhEL/y8AP09Hbx6suvcPTdI2zeuJEP338nHe1tfO+7P+DQwYMM9PXR3dXFiuVD3LprF7ls\nlqNHjjA1dZXp6SmmZ2eRFJWx8fGmBnTnzp2Ypomme2iahuZK0YIYhgf/PHr0qDf2lmVESaajo4O5\nuTlvM2IZuK5DOp1m/fr1fOhDd3D69GmGh4e5cuUKo6OjhMNh5mamOHfqNHOTM+SrBr7uJKuXruLW\nTeuxCwaxWJzp6SmefPIJEo7Im997nECpgmwUcBSbcsBCjQeomFVM0cYUTGpmhWq1jFOrItsWuuui\n2g6abSE7FqJtgltDEG1cyUZwbWKSDxWBmq+GJTsIhoNarRFzXaLBKJYeY2IhS8EtI/hkCpkiq3q6\nSNRqZOYXWND9lGo11sbjtOgq41NzzJfSbP/1f8PBg4cpxaK8tO9NnIpJazJJe3s7N++6le6+PiKx\nBKdOnmF29CLR/BxtikLJdDEFQLYIReOk8xaOaZMM6mTnJhHsGi2JBNFIjJors2AITMxniEQizM/P\nky8WQBQplytIskRrWws+n4quqqiKiFkrMz87TSwawXVs4okkmqqTzWbRNI1iucL8/ILXkXQcIpEo\n0WiUbTt2MjExyejoKFWjRiLZSrlSYWpykpUrV5JIJCgUCnziEx9HEEQOHTpEJBJB03Ue+cSDdHUP\nEIlEePSxx5ifn+fy5cvs2rULG4ehoeXsf2s/w6dOUSoU8OsK69eu4o3XXiYS8tHV1YZr2/9XdbD+\njxdY3/n7v/GcZbqGbZngOmiqQq1Sw7UdgoEAkiDi1LPoNFXF7/Ph2DbZXA5V1RBlEduxMcwaLq5X\nOAkulm2hakqd1u54oc4iOK5nzRc8dRWaImPbLrZl4+ARrS3TRpRkHBdM28R2bGRFQpIlL6uw/pFI\ntjA1NUUqtYDjeE6IRCKBZRsYZg1JlrEdG8s2QQBJ9ooEx3GxDMvrxIkeVJUGUNUFSZSQRBGj3tXx\nFj/HI8BLIoIoeAXMvKeH8Pt0XMfmrrvvpKOtjWw2Q2tLC7VqhWw27UUO+XykF+YxajXCgQACDoLs\nUioXeGP/a6xYvoyVK5fz3LPPsm3zNiQEXMtGVVSC/iDJtjYWMlksQaK1pwdNljl36ABhRaSju5ts\npchcIUNrTxeRZBQHh3BARxIgHPDj9+vUalVmU7MUqxVqplHHSUCtZqBqGqquEwoFcV2H1PwctmER\n0HUUWUUUBPyhIKWqQbZUoq23j4EVK5icmUHXfGRyeXBFOru6yWZzGKaN368zOztDOBxEVlwi4QDz\nqUm++8//A59foZjNI8sauuZnamaO1rYuPnTX3Zw8dYaaYYIg0N7ZRSgQYvzyRUAkGY+TXkgRDoWp\n1QwvLsdxqNYMNF1H1TTyhQIuYJgmlm2jaiouLjWzhqIqSIrUXKzC4SDVSpVgIIgoqxSLJXp7+/jY\nJx7hyJEjVKsG4XDUC8n1+8kXi4RDYURR8hIObBfLshEEydOauDQD0EVRoKOjnVqtRqFQQJZkHMvB\ndVzv/JeUZrHu1vlvjuPyB3/wgYvwl3m8feAYgZCfkfOjDCzpZ+nQED/80ePcesftdHV1EQz7+f73\nH+Xhhx8iFAii6378us7Pf/5zPvHxj3PHbTdxbPgMPp+Prq5u3tn/Nnfc8SFisRj5Yolqpcro6AUO\nHTrM1i1buf32XSxZ0sely1d59ZXXUSSZtpYkW7duIpXK8sQTTzIwMMiqVasZ6BtgaGgZp06d4ic/\n+SnZdIY1a9awetUaurq6SSYSXq6rpKCrCo5l0d6WJJNJMTszycz0BLtu28WxY+/S2tbORx64k72v\n7+fU8FkGl65gfiHDYH8fZ06f4itf+hInjx8nm8163eu2Nvbv38/Q0BDr1q1lw4aNrFixAsMw+OhH\nH6S/tx+fT0cUYGJ+lp/+/GkkRaWaq9LT3c/qdYP09S9h6eAS8hcv8uzf/B07e/rQBRvZJ1B0DUqW\nZ/5wBQkHjx1n1jykjCxKaKKMJoleHJVl4joWguuiygKqqhBQNCKCD8usUJaqiJqEYktQqSLXTETV\nx7wlM5qZpoCBPxFhZj5HfyxEwrSolqukBRm/ptHmuPgch3A8yEK+xA2338W3//bvCXR18bVvfov+\n3j6C4TAXp65y/vJl3ntvmDf2vUVf9wDLujqZOXOCgK7ii4QolQv4NIlyoUQ+X0FwXNJz0xTzWcKx\nCJF4HFeQcGUNSQ8xfmWccjaFW6vS392JXG8u9HV309PbRSjgZ2Z6Ar8q0dfRzrpVQ6we7KctGsGn\nimDWyKfnaImGcK0asaCPW3bdQCToJz0/gyKJHD74DoIA69auo1Qqc+bsaaLRGKYJTzzxFMtXrCKb\nzfPC7pfwBwKs37gOTVV46cUXyGTyzM3Moika27Zv56abdpBMtGFaFhs3bqI17mNoaAV9PQOsWrkS\nVdXYtm0L4XCAo8cO09vXj1PPWL39vk98MCJsjLnMRWOuprZqkTOwQfNudG6aAvXrCNSL/1w81rt+\nZHLNf4AgXuNKFJqdIgl30YhlcV6b5dhYloFheATgxkJZq9WIx+NNIfriHMGGOHlxvInjOl6yuCBc\nMwJsML4Wd+MEhOs0Wu+Tyl28EGsPMulQqVWxLAcbuw78lJuIA6NaIRgMEouEyBby1GoGXX0dFCtl\nvvu97/Bvv/5Ntm/exHPPPM1nPv9ZZibnsGomjiIhKT6QNaZSKfyVCnIgyNDq1YweO4wUjWA6FuFw\nhHg8SiCoUzDy6P4Ac+NTzEyOEwpFEBVP5FgxDTKZDJn5BSLBEMFIGEESyReLHt9J0+jr6SU159nP\nERS0oJ+gHmDVqlWcuTBKS2s7kUiEvj6Zy2PjhMNRZFmmUqnQ3dXDyIUR2lqT+P06hUIOfyBOsZhl\n7949iJJFJBCgUqyQz5WQVZH21nZWrFjBkiVLWb16NcPnRshms1jOFVriCdas38SF0fOMj02QTMSa\ngMjFDrzGCLAxRo7FYiwsLHjMr1DIywKr/46apiGLAtlUmlgi2TxvWltbyeQKiKLM8pWrOXHihDcq\nFERisRiVSoVCoUBHRwe24RX/jg0uLq7lnWdSXVDa0P01rwlncc6mi1IfmTeinxwHPogj/OUf4xNj\nGLbJgw/fz/DZczz6+P9kw+aNbN++hUwuzxe++FV+8N3/Rs2EQqHMgbcOcO7sWX7nt76G3y8jAZfO\nX2bp0qX85LGfMjY2Rmuyg+eefpahoSHGxsYYGBjgC5/+JKYFR4+c5O233yaRSPDA/ffRmgiRy5fZ\n++prvPLKK3zlK7/BsqW95PMmL+/Zw549e/jMZz7Db3z1tykXimiaxtT0BC+99BItLS20t3awde1a\nCqU8hUKWUiFFUHOZr6Zpaw8yOTHCt37/3/Lyy29wbHiUPXvfYNP2XRSrIg5eSkF/Tzsv73mRjRvW\nsvvlveg+jampKTZv3szuPXsol8ts2bSVRDLG+TPn8Gs6/X19dPX1EIkuJ1uosnTpCgqVCq8efJd8\nscimtauIB3Vu3b6J/KHD9PvD+GoGdtVAkhQkW0WVQFECmDUv6cEwwHE1REVEkmQsUcJuokskZEFC\nESVUUUIXVTRBQjYcJAGCUgCjZlErFQloQTR/lKwrEtVl4oi4msdGlIGaJFLR/ZSdHG26RjmdJ9qe\nAMsCx8Kxq0ydPsPf/5f/zJtXxnlv9x5OXp1EaW3lUmqONes28KH7PoxZLNPV3oE7O8nJcJKj+TRr\nAhpmIY1P8OHHpU1SiHZ0MJvSMF3wJVuQQknsWgXJqtLtc9mUVCkUDfKWiS+XRnJgxcoh1GiUsYmr\nLF+3kv4tG9i0YTUL41fY8+xT2IU8Vy6MUrNqKKqKZZoel7BuLvtOMEy0pYVNW2/gkU9/jk1rV3Dk\n3eMcPfwOwUiUm2+8id6+TvI52LHzdl59/VVOnj7Lxx/5VbK5BZ585gk+9cgjfOmLn+fiyAVEx+bU\n8WMMrVrD0LJupmcWeOedd+juOUFbWwuJaCuZTIZ8Lse+1/fy4P33snrLjex+7RWmUkV6upJQ/b8n\nieL/eIEly57ts5G3psgauGJzXCgIErZt4DhOU3fljfoEFNkbH7qLxoDXj9EWuwgXi9WbxZb0fsag\nKwoIbqOAcXAWcbMaRVJjATXq+YipVKr52DAMgsEgc3NzyIq4KFDaQRDcXwh3FkUR27SwF0ndm6iJ\nOl5BpMFb8jpc12u1GoXaYmaW19XwFtrZmVmGhpY208u7u7ub7JloIk4qfRWfz8fNN9+M67rs3r2b\nW2+9nf37DzB+6QqBYBhJErEME13XGRwcpKuri0qlxEJ7B9q2HciKwMmTJ4nFYmiqRWp8imoqx7o1\na5ienKJUK1M1q8i2TkBX6107BZ+mNTVEpmmiSlpTwF0sFjFrBuFwGEkQkWQdS3DJ5XLIpo+FhXST\nFuwVKl6hXi6XCasKkWgYn66SyaQwDINQ0EfPsmWcOvQWhw4eIBmPIwoyPr1CNldBEGVaEi20t3ei\nyBrJZCvpuf109vaTzRfJFQu0tLTQ2dnJ5OQkYSvokaj9fhzHoVwuUykWCQaDdHR0MDMzw/TUVNMJ\nI0ke36ox4pYkr/ukBvx1LZZNLBZjamaWTCaDqvt59tlnKZVK3nNUuYl98DYlTvMaed/44AnUqV8H\nct2Q0Th3F593XPd3i00gH5Dcf/nH8lUruOWWW5iem+eFF1+ku7uL9evXMzUzy2uvvcZf/dVfMXzu\nMiuGBpienubA22/zx3/8+5w6eY5SpczTTz3Dv/9//pipqSn6unv54ucfoVqF/u4eOjuTKBLYLoyN\nz3LgwAGS8QRf+dKXUVUF13XY+8obHDlymG3btvPtv/xz5ueLvPnmESYmpohEIvzpf/ozL4jdMJme\nm+ed/W9hWRZf+9rXEASBmYkpDh8+TK6QI5NN0dXVRtDv45677iadzTA2McHM5BRLBwf5j//hT8hl\nM0RDQQwLVqxYQXrhMitWLGFyYpqX9uwm0dJGMBxBlBXmUmm2bNlKe0srkiTxw8cfZdeuXXS1t/Pi\nCy/Q3t1BorOFoKpz89qtKCp0L1/BQj5LJZvmnb0vMXn+JKd/+jRtoRCJgJ9MzcsPlSSQNJ1KxcQw\nLQzLwRUkxPr92XQdDNtAFgVkUUASBWRBRBUlVEn2QNcuuIKFJAsIjoMiiQiahgvkagYlwcUWRfw+\njYlKhclcFhHIFgvkJR+y7icUCEG+AqLEQiaN4ZrULIt9+/aRUTWefuNN/uS//w/uuHEntLbyodYk\n58+PMnnxAmPnRnluZoatKwdZtmUTp/Y+gzqW4+5NKylOj5OamUMJRHHLFSLhKKLPjxhQqVreuhcJ\naGjUcDuDzC8UuHQ1TWm+SjDawvChN7njwYfY+sDdhNvivPTCszz6T3+DW8wjGxW6YxGWdiTwBXzU\nrBqFXI5yWUSop6InJJMOAAAgAElEQVRYtgWlDE8++h0OvLaHtt4BPv3rX+GRRx7m9HvDHNj3KuWN\n22htHwJEHnjgbsKxAM+/9CL/4d9/k4mrV3j11VcxyhXWr1nLsqVLeeD+e/jN3/kWZ89fpGbZFCtV\nli9fztDSZYxducrMzBytra109QyQyhXQAwluv/M+Lo0Ok8kWCAb0DzRYjeOf/9vfX1MYaZqObdsU\ni8W640BvUrkbuqtGwSTVtS/Uu1wNanojPmZxBuH1xY230HgXlayo2A4Ypo1TR0LYttMMf3Zc5xcK\nNVlqoBcEyuVy046s6zqlUgmfT29qvRrfa3FYdBPdsOh3vwY2KojXEezfXxwbhaNlWdSqNdw6UDQe\ni3DTjTfi92mYhoHfr2ObnuNNUZQmHFBRFDRdJ5VOI0oymWyWSrVKT08v6XQKWfKEqdu376Ba9VhU\n9RKWOigcRZHRVYVEMowrily6dAUJEbdqMjs+QUeihbA/QK1WQ5AEdL9OIBhElETMSg1VUkhEY0Sj\nUUTJKyBVTcGne+6iQi5Pam4eVVY8U4IoYTkuFcMknS8gqgo33HIrjiiSKxTo6uomn81TKBVpa20n\nl88iiiLZXIpqtUxPdwehWIinf/JD3nvvBG2tSSrlKtlsEUn2EW9tpbOrl56+AULhKKru48TwMKri\nw607H3P5PBs3rse2TNw6diGX9/hiqqpi1LuwDf1Vk2VVF9ZXq1UvkLvumPUinGwSiQSZbA5JkvAH\ngh5VulRm9upVipUKsVgM17G9vLRIhHQ6TaBOendt531DRF3nZ9sWsqo2sx09sGnR49fUS3m53gkW\nJeEXGFiO4/BHf/SBi/CXeUzPZchkMjz19FM4rst9992Hz+/nL7/9bf7oj76JpikkEjF++PjPqNVq\nLF82xLFjJ7k6fpUj777Lpz71a3T3tBAKeVqXcFDDdiAa9XP16izHT5zm8uWrnDlzmm3btrF969q6\nFAFeffUVUpkUX/3Nr9Dd3Yttu0xOTvPss8+ybdt2br55O8GAzsmTwzz2+KOEw2Huu/ceHnroPva9\nuZ9jx47x7rGjaD6NVavXcNfdd5NO5xBQuTo2zZUrk4QCcY4ffY+erl5e2/sqSwf76WhNIAkWgmsS\nS8a4Mn6V9s4u4i2tzKdSuIj4/AEESeTE8RNs37mDnt42yqUqrS1JNq5bxrJlK+jt7SKTnmdk+AwX\n3zvHxMQsI2MX2bBpNR2JGBvXrGR5bxfPf/efaCmU8Rs1ZMlCUFwM0caWBArFIlXTwHIsRElA0UQk\nRcByTAyrhiwLCCLIqoCqSEiKhCQLIIErOtiOjS26mK6BIIGqqdi2g1W1sTSdS6U8k2aVnAIVx6S3\nJUKr4qPVAB8Sc9UyRaNGRzRJoZCjbFRwNYkrc3NcmhgnWywytGY1bT1d7Nu3j6tjFzmy7w0SiKzu\n7WHN0CBbVy9l1ZohipfOcvXMcWSzSrWYo629DcNyKZkWpisQCAXw6RqCY2JVKzhGhXK1iKgJyKpI\noVKgVC0jB3TGJseQ/QpvHniDP/+L/8SpE4dZsbSXwe42wpqEbJs4RgXbNKhWK9TKRRzLRFc8WYvg\n2igi9HR14tgWY5cvs/+tt5geu8y2zZvYuX0rZ4dP893v/guVSgVNVdmydR3vnXqPQ4cOYxoGt928\nC0VSKORKHHznMCMXrvAbv/VVbrx5PW1dg8zOTrN+3Wp6elvp6mqlrbUHvz+AUTVob2tlzcrlrFox\nxKF33qaYSxEK+Lnjnoc+KLAaGixZlhEFGfAAi40Ohs/nQ1W1ZryIrusoiorjeNWGXA8Ups7MahRY\njU7XvyZub+zWBUFAFAREQURWZWzboWaYXuQIXoFlux4p/voCSxTFus4LfHqgGQmQTCabzplA0N8U\ntXvPe3+E2XitBlH7eoyE15W6Nni3UWA1xqIeKqBW/4KX0ZeIR7n5ppsJBnzguEiSQDgYJJfL1unz\nviaJWVEVcoU8us9PqVyiXCpjVA1WDC1ndnoW23UplYoMLhmod0w8DpNrO1SrJSzTRNFkymaVeGsS\n1xY4efQYQz0DVLJ5lDq7aWBwAFlTPJaTKKBKCrZpY1dNcAQM00JRvFGWhw/zimkBwcsd1DwwZ7Vm\nYNo2WjBAKpdjxZq1rFq7FmQZw7Jo6e0nt5Amnc0Si0UpFj0XpKYoiCIoksCLP3+KvXt3Yxll/D6d\nSrVGLm/Q3TNAV3cf0XgCPRBCkFX8gSAXLl1ianKGRDKJIAjk83nisSiqpnpdrGgYXK9jaJomsiw3\nOWSVSgW/3zsHPMCn1YQrFuvFjq7rVMpl79/ZNguzsyxdvpx169YxMnqB1o4OEokE8Xic6alJJEmi\ntaWF2akpEomEd37XzRAIgqf3q18HsqoSCoUIBAIAlEueTV6oO3QbnS8vhuXakbPjOPzhH34gcv9l\nHj94/KfEYjHm5ue46667qFarHDt+nC9/5SuMjU3S0RLjpT2vUymXvfd7coqHHnqQbVvWsGnzDvp7\nk9QM8OlwdWKWK2NTnDlzjldeeZ25uTlyuRyhUIj77rubYDCEKgscPXqKH/7whyxfPsQ999zFxOQU\nx48e49ChI1y8eInf+73fIplsY3j4HK+9ug/btvnCF3+doaEhKqUSe/e+xsVLFwhHo9x6x21s37mT\n8YkJvvvdf8E2XULBCN2d/WzesJWwP0I0HGVheobO9laW9HRy9tRxhpZ0k88voOp+km1tXL48Tk9f\nH2fOnOXOe+5m+PRZjzLf3sZb+9/i0uXLTE9PMTMzRc2yyKRTrF25jIG+PtoSSW7ZsoMLFy6QLRU5\nffYME5cvIlZL5GcnefvnP2elL0TQNKlZJSxMym6VmmlQqxkIuCiSV0BpsogougiOjeA66JqCKoko\nsoAiit4GWvDSDsClZpkggekYXgKCqmDYFqIlocbinM6nuFDMQiRAOB5lqH8A8iXaDAFNkClqKgXL\nxKlUKBbzGI6FPxaigoUjCeSLJY6dOMauO3aRSETYsm4NW5YPsaa9g2XtrZx+7yjnjh3k3FsvE3Br\nxBSXcnaBsN+Pi0AwmqBQqVCsVqmZJpVyEcuooGGDYGFhggx+fwDTcShVa8xn0xSNCi/ufpORCyPc\neOM21q9ZRSG9QGZ2BqtUwioVMWtVMtkFBNdBFkUkAUQBRNfGp8pEw0HKxRyuY6GIEPT7mBi7zN49\nLyFYNe56+CEGe/spFAtcvnyB5198Dl3T+OpXv8rwe8O8ue9NOjt6uPvOu/EHghw5/C410ybe0snI\nhVFEWeD5F55nbjbF2JUZLl66zMULF7k0Osr5c2eYuDqGVa3w+KPfw6iV6O7q4I57H/5gREjdAeXz\n+XAlwcuBqndsGjvpRpGxmNJ+jUNQ9NRJ10fNLB6HLLazX2NBFwRsXBxEHEH0ihpEz9IuiYiWg1vv\nGDUWrsZrNAOlTbPZbWvQ2xOJxC/Y9RcDRRd305R6kbi46LoejOoVXNdqrxoFliSIzWJy8RjTC7k2\nvQBmn69OVH8//LpWq9He3u4FNus64XCUQqHA/PwcnV3tzM0u8MYbb+A4DkuXLqW9vZ2aaeDUsxYN\nwyCdM7EUnXBHmO233cHU5XFGjp9gsLOdyasTuLZF35I+FJ+GpkhUajaO4CBJCq4kNItFSVJwcepu\nQY8bFtB9qJJM0B9CVXVMFxayWQrFMoVimXiylVKthj8aJaZqmPkcoXCATD5HOp2mrb2dYimPpino\nvgTHjx7ir//q2wz0dzZDRkVRRtf9dHb34vMHcQWBSqWGK5WRFY2Ojg5GRy82i/tEIsbw8HssX76c\nUqXE+Pg4He2dTV1VPp8nFos1I0ImJiaIRCLNAqzxvsuyjGl6VONQwO8R/3Uf3X19FItFMpkMbW1t\n3hg3GiUQCBCPxymXy3VaO/j9fjKZDMJ17LfGtdF4LNeLrkZx7liei1Vq4Evca6OlPsA0/O/Tmmaz\nWVauXElPTw//+I//yO//4b+jUvOcpn/zD//E4OAgd955JwcOHGDHjh34NLh0eZaLly9x9sw5tm7f\nxqVLl8hms6xatYqhoSFWrVxBf28LAJmcgabAdx/7EYILvb29fO5zn6OrM87E1Czf//6/cMPOm9i8\ndSsrlg9gAxcuXOb1119n7dq17Ny5k0hY4umnd/PeeyeIRCJ84/e+jgv8+KmneewnP6arvZtPf+Zz\ntCTayMylmZuZZ8/ze7AMk3AkwPbtm0llInR2Jdm0qo8D77zJ9nXrOTx8CS0QoKu7g8nJq3R3d/Py\nS7vZuHEzuVyOy5cv87GPfYyxK5dIxuN87MF7ePrZF0in05wfOYMmS9y+8yZkWeT2W3Yx71SJtMb4\n+c9+yoXz5zj11quIjo2sSRiCQc2ooGsaZs0gWygS8UeQRBlZkhBEEcG2cXDRBAFUFVX24thEF0TX\nRXJsGvWVK4AluQiqiFwVsCyTqiVhOw6yLCHrGoZPZXbeRjUtEtEopgv5VBq0BLKuUBVsCqLLdDlP\ndyKGLDnYkku1UiGbL9PbFufsbJrK9Bgtvb0snDnG7OQUYyfOkFlIUZHBp4v47RLB7lZKCzNcPX+a\nhdYWSrkSA8tWoPqDRBKtyIKAXa1iVg1EycEWXdSQSsWoIUoCoaCOrorIooNRLlGswK98+Fb0cJCx\n0YuoskhbNImRzyIj4ZhVj6moe1FxtmlgWQaOZWBbEq7sEtFlHFHBp4hoPhHTr5MtlNj30lO88cpe\nvvA7f8iH77qFC1fG+MIXHuFP/+LbPP7oY+zYugNF1Hh7/0HKpRo9PT18+MGPsv/gOwiHFM6MnGdy\n8ipbtm7m4Ycf5ur4NEcPHyeRSKBLCkalyG233sjLLz5JeiGF7PpZmJv9QIPVOEzbxS8pONhYrgOS\niKQquKKEYTvYuAiyhOAK2C6eGHFRYLIgXhtme70+6XouxrULiIDj4a4RRKcpcpckGdEWEAVwRQlJ\ncBEEszkmA6dZ5OTzeUqlErFYjHQ63XztSqXSHFU2ntcYwSwe9dEQHQsNB6H3IQr1DpzrekTuBjxV\n8vLvLNcLMnYEEUkWmliLYrFILBJEV1UMw+uaTE1NeGOFcJiZmRn8fj/JZJJcsYCqquiiTnohRSAQ\nwKx5RWM6s8D999zN7t27SacX2LZtG6FQhEqp3Bx9+vQA7d39jJw9Q3dbD1/52jf43c9+lrlMnq6W\nViYnJzly8BDLVq0gGI3hSC6yK+CIIrKqoPoDqIpC1ahQrpYwbRtJ9jAdjukZGxYWFgiFo/jCYUqV\nMrmaSVdfPz39/ag+P1XTwuf3UygUCUUi+NNZUql5+gZ6yRccqtUyLS0xDh06SLHoiXeDgQCqLGHU\ncoQjEcLhKJVqDdXvFTKhWBzTcti0aROvvfYG6fQCiuaNAS3XoVgp09bZwcLsHFeuXGFoaIiWlhYu\nXLjQ7CQ0iudGZ8u2vageoenuEz1C/SKdYEdHB6MXLzE5OUl7ZzeBQIBsNksoFCIajVIqlcjn88h1\nzZbjOCii9K+aOBrfp3GeNq4F231fC3j9ZmRxB+uD45d7lEqlJqvuscce49Of/jQnTpxg181beeKZ\n/YTDYe6553Ye/cGP6e/v5/HHH8fv97Nty1ba29uJhKNs2bKSNWtWoyig1m8rY+PznD0/Tq1WY3x8\nnGIxz8DAALfctJP67YK//bvvsGRJP1/+8pdpbWmhWjU5d36Mc+fOMT8zz9e//psoCpw/P8EP/sUT\ntf/xH/87JAleeOFlDh4+xLLVK/nkpz6FrvoI6UGOHDjCmVPDVPJFHrj3XnbcvIWF8SkuXzpDZX6S\nK5kxrk5cJiRWuXr+BF3JHqYnxujt7SedzdDS1s62DRt446036e3tJZmI8eQTP8U2TW666SYWsiW2\nbd9Oa0uScqHAz378Y46+e5yjpSOkMznuf+TjiLbLfXfdjeZUMSfH4NwoVy9cpdtxUGWFqmFSLlfQ\nVRVdkVFEGVn0nISu7f4CtFqkwQ90EFwQ3cadW0TSFARJQpYsXNOhapiAjCjKFKs15o0ypgay7Gmf\nsuWSF7Pm17BdiflSjoJt0BqNEGpJks3OkU7PU3NtVA2iAYWb1w1w9tB+GNaYy6ZxTYsVbV2sWj1A\nxqwxn56BiklqbgK/6LJq/VoE26LmuIxOjBMIhgkUCoQCPqJBPxG/huS6OIJN2J/AQKBqgKp6GqXO\n9g4OHj/LrVsHsKsWaNDb1U80EKBSLBBuCRIPhjh/+gQBv9aUwziWiSqLiAJYtQpFxyLR0kqhmENG\nxCoZFEpV+vv6aeno5ujwCH/9F3/C3R/9GIrPj2WXuev2Wzl9doSf/OQnbNq0lUce+RTDp87w9FPP\n0b+kjy986Yv4wxK33Xkbl8fHOPbuYQQJlizpIByIkppPceLIUXTNjy4ruJZNW0srVi3LzNTUBwUW\n1wnTG92XBkT0eohiQ/zTuCgan4uCeK04/LoFo9ElWczJ+oXQZ1HwyON156C0WCPV+ByhCSRd/Prl\nShHbttF1nYmJCQKBALIsUzM81H/jd7Asoyk8bxQogiBg1rPhbPd/8bNdt2gujtyxXAfHshElL3Ou\nWq1SKpW8KApNb46jBgYGmotzW1sb5XKZK1euEI/HScYTFMslWltb6xEvFrPT02iKQqlc4J577+KF\n519i5Ow5HvjogyxfvZZKocDs7CyuJFKyHNo7+0lNjtMZCfFnf/u3/PHXfgtRcoiEQpw/O+IBQ1Uf\niqygaSrgkK0WSeWzqIIn/naw8fm15vvuhUL7yGcLGGYKn22TyuWxZZWNW7fS2t1DvlzGtE0k20IP\n+PH5gwDkCnlSqRSVSgmxrsk7f/48a9euJZVKoWsSya5OZmZT9PYuIxKNUZiY8kjNPh+qqpLJLqCr\nGoFAgGAwSCgUYiGdoqWlhYsXLzIw0I/m91Gr1JicnCQWixEIBLBtm8nJSXK5XJ1DBYV8HqlObzdN\nk3wmg6LrdHd3MzPlPdcVRM6fP4+keOf+1NQUX//613n++ecZGxtDVz0NV6BOwM9kMl7ntw7CvX7z\n0CjgF7PXFl8fjYXFFZxrrsXFm4APjl/eMTo6yi233MK5c+dYtmwZg4MDJFpb+Po3/4jPfvazLF26\nlB/96Eny+Tz9/f3sunEHmgKVCug+yGSMetEOIyNXKJfLXL58mfPnzrJ+/Xqmp6fZsmULWzffQbnq\nUq1aHD16lLNnz7JmzRrWrl1Na0uEJ558jkqlgmnaVCo1fuern6dWg1OnLvHMM8+wZcsmtmzZguvC\nc8/tZmxsjNtuu42bb72JXLHMyJkRvv/UP7G0t59fe+QhBgb7mR29wE//+b/z3NM/Y3ZmnHIpi6aC\nP6DQ29dNplwj1DZEvL0fuZKnI6iTmRznvdkpHr7nLl7au5e2jg4+et/9vLRnN3MzM/zoRz8hHA5y\nww030Jps4fO//iWCqsDhfUeJlSrs2/cm2VKeZNhHTyJCLV9ldmqBgCsh+v2YpSLp+QwV0WZgoAe3\nXPPu4Y6XYCAiIYhCHYdTny649cxXQHAWSTpEAVeQsC0XzZVwkag4LpKigiEyl0pRElyWb92CUXOZ\nnJxEFiHkC1B1XMr5PNHeFqYnLuD6VabzC+Sy8/iDGp2dLSRbW1jat4SWjk6mc2lcXaG/O4JZLBF2\noJAeA1kk7nfp7OvHrpQwC1l0TQJE1vT1U6ma5AolpicmmZmt0NfRhpyIoPh0QtEwRg0E24cqiFgy\niK6OrATxKUEU0Ycq+WmNdzC3kObc+au0JeIIuk6+JnLzhz7C+bOHwa5QcVwEQULTFSTBxTINRFyK\n+Rxtra2kMllsx6avs5VsapZsap6+9h7UnMX5U0fYecttHNj3KiYqD33811i+fDXf+cfvs2H9Jh58\n8CG6eno5cOgAb729n/aeDqp2laXLljF6aZSf/ewJcukStbJJOV9icmycvq4OXsjO8cJzz5PLpFDE\nGpoif1BgLW6d5/N5otE4mmZiWQ6KojZ306IgeXgoGqMQqZ79Vh+XCU4T3eC5YIzmGAVo0sEbovAG\nuLThWrRdFxuoGRaa34dleeMzb7Tj3dTmygtomobjOJi253g0rBqdnd3MzcyzfGglk5OTRKNRfD4f\ntm3T1dVFOp32iijTxLKMpgC/0bVoJImLkogiSNd0EATx/cLQMAwqRo1kMkk4HGZ2dhbDMLzumOsV\nbbFIlFQq1XSjlQtFfD4Fs1JFFEVCoRDFYhHLsgiFQiiaSqlUQrEcZqamGRoawrZt8rUq8USU6elp\n0gsLZFIpbtl1EzMzM7y6dw9TVyfYvHkz7S2tpHJ5ipUSoUCYWjDCZCrF4NJBfu/P/pT/+I3fJeHX\n6IzGOH7kJPFwkmUrVpIq1mNwZBndp1HKF/FpGlWjco0DMujTmZubIxAKovuDVGwHfyBE99AQazZs\nZCGbxxcKoqoKquYFTKfTKSKxKKVqhVTGG32GgkFeeOEFJiYm6GhP0tvTT0sySjabx3EgFksQCoWa\nkTahUIhiqYxtWvgDPtatW8OBQwdR1B5sy0BVw14ETzpDa2sbRqlKIZ/Hdb1QZW/x8orrRih1pN59\nSs3Po2gaSBJmfWw4MDDA1NQUhUKRYDhMQNdJp9PUShV8Ph/xeJzhU6eoSIKn4Zmbo7u7G8eqo03q\naAi33jVtjL5LpVKzWG1cD41roSG4p35dXa+/+uD45R/lcplqtYphGPT19eE48Oyzz3LjjTeyYsUK\nzp49y7p16/BpOoP97dSqkK2YaIrC7FyBkyfeY25h3jONCAJ9fX0MDg5y7z13EwzKGAaUy957Ojc3\nx09//BM2bNjAJz7xCUIhT8f4xJPPMTMzw5e//GUqFZNoRGF2tshzzz1HrVbjG9/4BoEAXLw0wX/+\ni/+Xe+69m9/+7X+DAxw7dZrXX32NRCTC73z1i/hlmURA583nf8xf/OmfEAv50GRY0R8jFGgnFAoi\niDaVYgHVVckvTDF69SrusuXMLKTp6lsCksTwof3ct+tGRi+NsfvZZ9i0cTMPP3wfIxenOP7eSc6e\nPsf/PPIomzasY8eWbXT09tPXl8Bw4NjxsxjlPLuffQphbppsOouvtZOFVAqxXCEca8Uvu5RKJj5R\nwqnraiUEROl9sLNredmwzSkIXmah67q4uNgIWDa4pk3AElFQEXFQFR9YCql8gdhgF4O37mL07CVO\nvHcK0/CjCCLzpSJGKUc02sP8cBW73cERoXuwj6XL+rDsKoLgUqlmmRwvYSoijiUgKCJBXUY3bOSA\ngFkukEjG8asCkupHCGsoioSoqJiGTUtLkA5ZpaOnG7tSIqhIFBfmuDJ2iUA2zpVsCUEL0NLRhRaK\noGk+RFnlhh07efPwMWQ9yL6Ll+nq6SUcjlI2bPLlHNPT05wePc+mdUvBLoOsYZQLmIBpGTim5enV\ncMlkMoiAL+DHqJbRFBl/MES5mCERTjKfnuLpnz7Gf/3/2HvvKEnP+t7z8+ZQuaqrq3P35NGMBmlG\naJRBAmNEkEyyL1ovDnAdOMZ3jfEFjuH6Yl8b8OKE7XvXd7GNM0GAACGDLARIg5DEjPJoRpNT566q\nrvDWm8P+8VbV9Ayy9x92zdnVc46OdHTqVFd1P+/zfH+/3zf809184Z/u4fc/+nu8/o1v5Z3vfCdf\nu/cb/N3f/wO3vepWrr76atbW1kCBM/NnOX7yGLquc9ddb6OxavPUoacoZku47Q7FvElvfYVPr65Q\nymWRZY1iofASwLq8apZlmWw2O9z0A2XWxo7N4PWX2C68SPX+YuOSy8eHQ/6WlAK1iITQD7BtF98P\nkZWUq2QYBqam4/QBAEAYRyiKNAy9bLfbuK47lNs7jpNekv3DcGP36RKC+4bvc4mvVZJAEl3yuXVd\nR9M0PM+7JGg4CKNhR8/zPJrNJq6bgipFUYi8i4BzAEo9z8MP0vFUq9Vibm6OXq83tA/odTtMTIyx\nupoqI+urq2yem6NWq/HM08/Rbre55ZZXkstmGa1O8tjB75PNZTBqKodPn2T3vn185I//iN/9z+9H\nWm+zaWySxx49iNVzmdm6ldrEKL0oQpZV/K7Dar2O6zpURisIQkIQesiySJiE+GFI4rmYhRJZRK6/\n+Sbq7Ta5kSqJIuE6LnouB7JMuTbO+rFjBHFEFKa2FZIi85V7v0oQR2TzOQrlEn4YYrsOO3ZcQT6f\n749pI1Qp5RdEgYcogSTKTE1OoEoyOTMzBH9CH8Douk6xWGS9H4njed7w7zLgbbUaDapjYxiGgaZp\nrK2tEbgu9DujZ86cQdM0CqXSkEg/MzOD7fp86lOfot1uUyqXWV9ZIp/P01hbpdvtsml2hl6vR3Ot\n/uId3Bfh8l2y9y9bG/fkj1LUxP9Xlu/7LCwssG3bNrLZLJ/4xCd4w513MDE1ySOPPMItt9yEoYDt\nwrGT8xx97jBLS0tsntuEqqdCnxtuuIGJiRrNZpvx8QJxDHYvRBTAcTyOHz/OmTOn8DyPd7zjHYyM\nlFAVWFhs8uf/48/4mZ/5Gd761jvwAlheXuaFF1qsrq6ydcd2rr/+Gtptm4cf/j6PfO8A/9t7fw3P\nc0mA48dP8diBR9gxt4nXvPoWDCmh01jgl971K6wtnGL/3m0UTAXX6iCTIIsJYmITBzGddhshiBgz\nSkzn8yyffo6MZtI5cwTFzGMmAV/6m6d4+zt+FiUKeeKRA/z4q25DCALuvP11hHHMpukZeo7Fl7/2\nVXotmyuvuJIrrthNpTTC9JVXoAchD/7j32MYOeqdHoYXMlGsIkoiYegjCiKu5yJz0YcwDEKEMD2D\nNFlBEbUNvN+Lz0AURQRJQiBIaKJC4DsosoKsgB8mqIJIIissrK6y8Mgj+N2QfLFINqtj11sEmsFI\ncZJeEFCrZRCkhJ07ryAM2qxbaxiGitVpQ9TPrM2a6LqJpKogemimQqmUxwxNzHwB09QRpX6HWpEJ\nY3C9AEFWSMKYTCEDhoopCYyNlBDCTbhegNrxWO306HbaLK01iQIf2TQpFk3mpmuMVEvkCjk6to2i\nKySSRLPdwgSKOBIAACAASURBVJNjztfXeOQvn+CO22/ixv0v58ypE8Rej4wmc/rEMXIZg5FyGVlM\nCwnPD/oZqDmarRbl0RoL9WXGJmc4cuIMv/ef38cHPvIxxqdn+cKX72PXlXt581vfwmOPPs7f/M3f\n8Oa3vZlrrn0Vk9Nlmt1refKZpzl29AW+//1nmJ6cxrUdVro9DEnh0GNPc+A79xP4NoqQoClQLuRf\nAliD5XppJV4JUy7NwHRUVlQEUUoz0ySRJE7ZT1ESIyYxkiin4OgyZd7loOpy49GNl4woikiqTpwI\nBFFMz3boWWm2UsYPUFU1Hd8kCQvLC+moMBHxwwDDSMFOIVdkeWm1H4tjDked3W536EuVjmvkS3gu\nl4/74tQmst+lTj2vkiRBUhU8x2EQDt2zbaI4JulXxbIgDrtiURSxsrKC4ziokjz8TIMOn6qqxHGc\nqtqilJ+VMQxUWUZTJHq9HmHkI8kCrfUGxBHrjRabtmxlcXERz/PZvWsXi4uLfPHuL7B///WUylW2\nb9pC02qyutokUx2hFURMX7Gb933kt/mrP/xj7BjqC0vIus7k1i0Evk+YROQLJeJcgGvbJCKouoYs\ni5SMIoahEUQhpWIVUdVYbrTZvXcfejaHK0p4cULWzGAUSukFZttEUULP9UAU8MIAWVNZX1/nmWee\n4WV7djM5OYlAyMriIj3LpXb1OJKq4HoOkiRSrhTRDZVuz0ISIE4iJkaryIDvuRiqSuB6aLJCJwLP\nDShmM/i+T6fTGXauBiBWkiTEPg9LUZRhxNNgDLe6ukrGNGmtr1MaqTI2NsbRI0fZ9spXsuvKl/EH\nn/gEhf5YESCfzxP3/3sAxl5sz6f7Jxkan24UTwj/CrgagP3B+PCl9cMX80RRRLlcpl6vs2XLFl72\nsp10LI98Ps+pU2dot9ucPH6CarXKzp072b9/P9OTZYII4ghU9WJX/vjxeZ5//nlWlpeYmJig0Wjw\n/PPP8/GP/y6akvrqra51eeihh+h0OrzlLW9h5/ZNdHshf/qnf8r05AwTExOMj49z7ct3sbzS5S/+\n4n9QG6ny4Q//JqqaWjz8t9/7PWanZ/iV//guuut1lMDhiUcP8Cd/8DtIicVtN+4icjvook3PXmKk\nOkqrsU4xVyYWRFxVIm/oKCK0W0sU4oCCmcWLA1bXzrPQbhDGIn/xBx9n7/4b2HfFNr74D3+LYpiI\n0Q1MTk4jxyG3XL+P/dfu5cQLp1EllSce/x6ebXP6zHF2TU1y+vgJrHYHN18gIcGKYyRVI0EklMBJ\nesgiKAPKR/9ZUUSRQErtTuIkSf/ZYO2TAizwvYCMJBE7IQghTcvHl2RMOY8dhrTbbeaffQ5DMhHj\nCKvbIQ5t5EqVSIRdu7eTqxrMmAa1apFeN8KPJXJFk0o1T9nMpedDPouS0YjkCCGOKCCT1TUSRUXR\nNBIBXN8jiCMERUZVVGQztRhSZQXMEKu1nvpVRQEZRUXVdQqyDnpqQtp2POJmCz92cHsdHGsVTRql\nNjtKq2uz2mqBIFGbKCA0Y06fO87Y7Bj33PcIZ+ZX+NmffjvLF85w+vhRtl25l4VzpwlJpzJRIkEi\nIIgyQRRjuz5yp0O1lKexssBIqcDJY0f49Kf+J7/w3g+gGkU+8/kvs1Zvcdttr0aQRZaXlzlz7iSF\nco7ZLXPs2bOHVqvDAw88QMHMsba0xvbNW7DimPFahaPPPY2pS4h4FPMFFFV6CWBtvBQ8z8Pz0oMG\nGGbwDRzcL+dQDcYcsiynaobLukKXd7Euv4Q2voeiqtiei+M4KcAI42G3wTRNisUiSZKwvLZMTIKE\nRBL4iIqM1x8Bdbtd8vn8sINlGAZ+4GIYxsZe3SVAcEha719sURJfqpDsq7uiKOpbLGQoFou0+t2y\njZfggEwviiLr6+vpYY6AZVmIZgZJuliNDb+7IKMoCqqq98el8bDrYllWatiZz6fk7RPHKZYrTE/P\n0u1aVKtVKmV47LHHkASZV7zyVsZnJnBdm26vxfxqnc1jNa699dVossbHPvRf2DQ2wbrj8Oj3D7Lv\numvQNZVOs4GIRL5YpCCJRHHaBSoUc4iiSNvqkgipxUPTcpmam8WNIkrj4whGBj+OefBrX+dNP/FG\nFEnjNbe/hp++6y7e/Na3cuLYMYIw5B8/+xkESWTb9u0ossTi/FnWOx0yfaBjahrWeioxNnQFIY6J\nowBVlgiihHKxwGi1Qn1lmcmZWeprDTK5LBnDIAoCPM9D11Ovto37KnWM9slkUhuPsbEx6vU6vu8P\nVZ2ebSPnspT6CsHz58+jZzI888wznD0/T21sjE2bNvHYd7/Llh07KBQKIIpUKpWUbHpZN3cApjZ2\nsF5MmfqvdbAG73dRmPHS+mGec4uLi4zO13jwW99iy5Yt/Omf/gXTszM0m01mZmaoVqtcf/31bNs2\njQrUmw5BkPKuXjg5z2p9jfn5eer1Ort372Zqaordu67gih0znJ9v8IY3vIEoSvAFgScPPc3dd9/N\nu971LrZv34wow9PPHuPEiVPccssrueXma2iuu2RNnS/dcz+nTp3iV3/1V1FEqR+UrvHZz36G2267\njR2bZpGlmFIly9f/8a/5+O9+iFtvvopaZRw1sTALIoHdpWDEZJUITwrIahJrqy2yuorv+uhiDJLH\nSDlDlDgQJtRMmY7TJUlERjN5nnj4Adr1JWzPZ2pmM3/y4DdIEoH9N1zP04cOMLdlM6qeZc/OKynd\n8HI0VeFv/u481197FZ/6Lx8itprUpQShZ9HrtMkqGl4YEYoRopmQyCANOLtxX0yUQBILBL5/yZk8\nVKwLEiISciTgChKuG6LIAj0pIpAiIjx6oYPdc1AKGjIehqYzUs0xKo8yU52goCiockK1mMUgxnd7\nIESomoimyZiGRjmTplBIhga6jCBJhL5Dz7Fx3B7V0VEUVUYxTDICJIKIoMggSERxRK9rp554SYyZ\nzZDRVBQBlD7VJLK7FHUNUdXI+D6ikdALfBJJYXLMpLF0lChcR9ZNpkYzYOg4SYJsZAmSbZw70yBS\nJR594iSCcg+33/YKtuy+muXzp9HzFWJJxPN6+FGCmAjIgBilGb+e6+PYqyiaSSZjomTyfPUrX+Tm\nV72G0do4r3v9a/js576E7/ts2rSJV73mVTx7+BnOXjjNvV/9KlddfTXF4gjv+Ol3QJBw6tgLTIyM\nsnz+DN+6/z50TURTQEJibGwUVX6JgzVcpmniOM7wgtI0nThOMAyDXq9HHA8K8ovgSlVVNNVAlCCO\nL9ohvJiK8JLqXbhUcaiqKrEo0LNdLNvFCyKyRpZKpUIum8c0zWGQ9OA9BwR1URSJI7AsC0EQhjye\nAeFdEJNhZyk1QfUvAVbDB1iWiIOAJLy00xb1zU5916NSqTA+McXU1BT1RoN2uz3kkymiNBxLDaTg\nA65XFMWsr6+Tz2eHXK4UVKWyZEmSWG+0GBsb48Sp44yOjmJqGo3VVWYmJ+l2u6ysLjE7OwuIrCwv\nI0oySSIgILFz53aeeepZHviX+9l/435GxmpkMgYLns/CapO6AHtuvJnf+pNP8qH3/hqjxTxrR58n\nU8yyb9/VNNrraGoOzdBT085eC9uz6VgWURzgOA6ut8ZyvcErf/x1IImMTIzR9QMKRY0vf+XL/Ic3\n/yTVyij1RpNvfuvb3HTTLTzz3LNcs3cf586f4dATT7L/+huRVY12u8nKyhpxBFu2b6dUqSJKKZ+C\n2CcMHFynSxx6GHoGSQQhl+HKXVdw39e/wfbtO6n3QXAmk6HZbNJLYnK5HIZhYFnW0K1d2GB+GwUB\ne/bs4YEHHqDX66H2WxGyplFfWyNfKKSg3PcpFIqsrq6yuLzKTTfdxPz8PMQx5XKZCxcuDL2urE47\n9TXz/H91rJdcVo0PfNcuKTYuey3wEsD6f2A9c/Agd955J1NTU3zsYx9LeZHFArmMxErDolbJkgBr\ndYteL6IXRhw5coT58xfo9ix6ls0Vu3exf/9+RFFk02wV1weraxMDM1MVmi2Ps2fPcujQIWrVUT74\nwQ8yWk0Vrfd/8wD33XcfH/vYxzF1WFrpUCrl+cd/+CzLy8t86Dd/LT2PgCeefJZ77vkSb3nrm7jy\nyl0IvsOJQ9/luw98jWOHn+RnfvKNxE6Dgi4gJRKVosGF1jK1QoYkcBnJZzHUFCAUy1lCx6JoFmi5\nXfJZlZbloAsJpUIOSYwp9lMa5IpJsL7A6nIdwWlRUHVOnD7Do51lvCQiEMHxQyqlEW7Zdz1bNm8m\n6KxgN5cZLWiIaolsIUOckRAsFydMsEMfWVFwXAdRBlkEUZRRRAldVpFFCTGBXC6P3D9Lh8IoSUSW\n1fR5CEGNYgzHwzA0Chr4gkAkKBQ6beLT6+zdvpXOehvXsdk9O0ktq6H5AfmCQctZx5QF1DhGFAIq\nIzn0rIZuKmR1A8ELCX0XO3DAV4g1kdBz8Zot3E4HBAHT98llAyRNR1JkJEREEYQkwdS1NMFBN5CK\nZZCVNPPKcXBsCyFyUQQZSVXIaCIjco58FJPJqqhSSLPRpt2zcNtdRooz6FmVhmUhZySy2+Zo1GOK\nVQWr3eS7jz9Pp9Ph9T/+KmoTsywvnCWRVJygRxgBUYgpqoQIqFoGUZbQJRFEhayhs3RhGZGYX/7l\n/8in/vZz7L5yJ28L3sITTz3FQw8/yE03X0elXEJRt7J371UkgsR3vnOA+koTKUo4f/oU8/ksgdXm\nwMMP4LltJASMrMJIpYS3gc7z/3uAJctpJ8WyrH4nqHCJ4/nl3JFBvpqmaSREBIEMBJeAl42XxYvy\ntja8T8tOQ3AHMST5fJ5KpYLYz/br9Xp0uxZRlPRl9snwgpQkCdu2yWQy6Lo+9L4adJcGnlSXkNdf\nJCNxMDLamMUY9R26B6BtcDFXq1Vs22Z1dZXA9xmtjAxBXJIkNJvNYayQIEg4UTy0CRi838ZuoOM4\nw8iX9fV1ej0rtWsIUoAjiiKqJkOSihFcy0LTdAw9RxzHXLFrB0uLKzz/3GHMM2fYtnMH5dIoyyuL\n+GHIiZU1Jnbt5CN/8sf8+rt/gZmRMo88cgCv2+G6666n0wtwQ49YAFGSUoJkGKT2CqNj2I7HmK6z\n95prULNZZEXD8y3cKORfvvktjFy27wzdRDcyfPRjv8/H//ff54knnuDAgQPc941/4U133o7nO0zU\nRqmNTxIFKYBqNBqoukGcBEiSiCwIaKqMoalIIjg9Fz8ImJuZgiSi015HkSQCz8fQ9H6XNRjmTg64\nWYqiDFWovu9DX2Sg6/qQXD7gHCZRhmajQaFcwfd9VhYWEBQFSdH47kMPQRQxMjHBhQsXWJ6fZ2xi\nfOjBNthPL4KsLuFg/d+NCC8fXb80Ivzhr1//4Ad55zvfSSaXJZeVWF7tIsvp77nZbA4VxxcuXODC\nhQsQpoXa/v372bp1glY7JJOTEQSo13t9QUjIqVOnOHrUZ35+nm63C8TcfvvtzE2PAnDw0GG+853v\ncO111/NfP/I7aHqqgz74/UOcOXOGO+64g/GJUc5faDAzXeHL995Po7nGT931dq7ctRMROHH0ab5z\n72fRgi5XbhqlWjY4e2ydsYlZVEFnafEcpqiRRAmqYhAnImHgURstE0Yxo6MV8hmdpeVz6EEGw5SR\nI4iiHpHbojo5jeuFJL5LFLhctWWUhATfb7OjZlKsaKw263hRzHrk0jq7xPHQ4cxT3+Xsyjyt089y\nxdYxYqdHpAioYgHNjoltD8cLKedz6GGIkiRpNJkfQZKgiWkUDnFMHCZIgog4IMNHEXESIQgusSyz\nHnskQozsuihBQi/2cOMYwcjQcB12jOrsmRmnmTUIfJtN1RxZJYFmmzARUSUYKRapKBKGDHpORdYE\nkAV0XaVgFhCRiGWZWJUIFJEo9InNEr7d48yZs6maMRFIEFF0jWKxmKqXdYPa5BSIIQQxkW8TxSKi\nJCMbOYxSBbGhEoQObhiQCElq2h1FiHGEMFJm+8QUjcY6x06ewV1YRAkixspFIkXhwrpHLpunZYsk\ntk11zODZ5y/Q6dzDz739pyhXx7G767h+iNwPjUeSiRFAkgn8ENvuMDI6xsL8PDPTcziCSmd+hccf\n/x5rLYttW69kdnaKqalJDhx4mEOHDnHdjTcwp8+y/9rdnHjhNJ22ReJ5yKLA9PgoX7vnIQK/h6FL\naErMWG2EykiRZrP+EsDaCC4URcG2HVqtNoZhXtKp2jgeTCNqVFQllaoHYfQDZPaNY7eN/IeN4GZw\nwamqirXaoNuzEEUJQzfI5XKYponVsYbcmUEeXAqYQNcNgiBA19K4k2wmTxRFw0vP933CKLwkvmdw\ncW3spm3kx2wc3220rUh/NzZW7wJjY2PD9/N9H6XfuYqitBc76GBZlkXWMFFVkWKxiCCkrxmMVaMo\nIkriYS5eo9FgZmaKc+fO0epZTE1NsbK2jCRJbJqYwbJ6IApUq1Vc10cUJOJYZHV1lenpabZsy2B1\nuyzML/HP93yN6268gd0vu4ojJ45ytr6EQ8TYji188tN/xW+95z34gcfpI0eoZouYlXG6foBldUiE\nhGzWBCkNrC6XR7Bcj+tuuBEzm8MoFrAcm1J5BGSZII5xrB7HXjjG3KbNnD17uu+iEXP13r10Oy0A\nvv3QAexOj9GRLBldZ2SkjCjC1NQUncVFFEWh2+nSXK+nXUshIYoCPN/B81PeTG20xtLSEtXRcbq9\nHoaRIZ/N0Wo1sW27b9aap9Pp4Pv+UDAwSCY4cuRIGoXTd3cf7AfP9xit1ag314mDgJktW9N4G0Wj\n2WwOzUbnz5/FzOep1Wp0u10ymcwlxryX86kGz8PlBraXjwo3grBBtuElRr4vrR/K+thHP0yj6RLH\nMcdOLOE4Di8ceBgjY7KwsEAURUxNTZExTObm5rhy53Z0FXw/zRi0LIv1dsjKygrPPfccqpqqgPO5\nLFdffTWKorB9+/ah+MYP4PHHD3HPPffw0Y//HmEIpgFPPXOce++9l5tvvJl3vvOdaJqAqkCtVuED\nH/wt7rzzTu6847UAzC8t8vQTh5DdJjNjeVZPnsEwDSIrYPNEDa/ZQjU1JB+qxTGWlpYYHR1laa1O\n4LuMj09x8vQZxufmWFteQlQVZF0hiGKSKEKRRXRVIPa6RK5HKaMhSypR2Em5ou02U9UqnfV5xlQR\nVI2MlJCYGYTuMrKuM1XWkbEoZiBUZOphDzGTQdFEMATiICRbKhI12xCE+FGEHdhEfoCMkHKvvKg/\nOhQR4mRYjAZxRJJAIIFnKki6goKHGIR07YBAhFI5JXhXyiOohORGCtTGNkFkI0YumYqGJiVIoUje\nVChoOkLs4TpdfMtFUGSSbA7wKJh5FFnHCxN8zwVisnqGSrZIuVKl1e2wslqnXm/Saq6yfGEJSQQJ\ngdFqhUI2R3WsRm1sArVYBEmBEGInRKvU0AIP7C6JZyMqIVHgIcciRkEmsDxmilXUKWh2urhegrBu\nI5o6ihsjIhElqdo+CaE6VmR5pcXnv3QP73j7W+l2WoRRjK6bEIeIskQUJYRxhNfrMVEdgSgmDkKe\nf/55ChMzWL0Wh59/mp//xffw6b/+R0rlKls2b2PfvpfTbrd5/tnn6PW6PH/4BaanNrHvqms59vyz\ntBSBx7/3Pb794P0UsiLlUpYk7FEsZQkCb5j88hLA6l8Cuq5j287Q7XqjF1YQRD8AoAY5gyTiD4z9\nLq3I0xjl5DJzxQFxXpBS8BJ4IYaRkpAVPe1EuH4qoy/mC4RhSngXSTMBJUkicANQ4uE4bmAPkfLC\nwqH83XGcfqZiGg4aRxFRHCOJEmIfcJGIJHHqwZLqg0UgBVy9njP0V+p2u2mI8NISURSxfds2VpeW\nUSQZSRaQFA3XdbGsHh29i65JqMUiXl8iPnCbT6uzGFM3cCQ7Jew7DmPjtTSpvNOhUiqTCODYNmEY\nkCQBqm5iaiqNVpvA9Snks7Q7jX6gdcRtr7yJb3/7Ie7/xj/jhi5XXr2HTE7n7LlT+K7F9q1b+G9/\n+Id89IMf4OiFFfTCCW79sc1ImkbX7tFqtVB1hUKmQMeycYOERNG48qp96PkCkqohiWnosaJqWN0u\nACO1GmfPnqZQLvPud/8STz75JP/y9fvQTQNT11hvttAUWGtYrEQW587Xee7wcUarJW6+cT+6prCw\nsECrlSo/c7kc2XwJXe1z22SNHdu2ceCRR9k0t4V2uz3MxkySSxWFURTRarWG3csBsD969Ci5XG4Y\nSG2aZgq+nNRhP5M1yZhZrE6bCxfOsW/fyyEOyWdNFi6cQxRFdu3cwdraGuvr62TN1K/Ld73UiJYX\nd2EXNvCxxL7VT5IkJC+pCP9fXZ+/+z7W1tZYa9TZd801KIrC+Pg4I6NVbr31VjqdDqOjBaIAbNtD\nFGG13uPs6TMgCnz7W99hbGKcyclJ9uzZw/5rdyMA7U5AJqOgSNDtxeQyIl+599tcOHee6667jne/\n+91oCmgK/OWnP0u9Xue3Pvw+BOD8hSYz02UeP3iYJw4e4m1vexvXvvxluH5aAHzyk5/kzW/+CfzV\nNqur57lq+zQry4vogkKv1WFuapr68hKVzChiJKJJWSQ0FFFFzZkEoYeZ0wkTH8u3KY6OkC0Vqdfr\ndF2LnFJA1URsq3mRV+haSKJIrVxCcEK0uE1J8siZJkHioWVlZFlHdkFWFbpKuq/FVpd8RcNyHBQj\nzRSUszqR5xPlBRxZQxYM4tBEdPNogkhG1REFgdiL6LbaJBEpaYgECVBJp2whCd1mHUUFGRlZVshl\nBURdZXRuEkwTOxaJiRFkgYou02pYCHEPNSdhGjKKkkNMInzXQiIgSjwkRcA0dTKqTl7KYCoGIOGH\nAUKUIMsKhqyjKSqW7SKgUC5WKOYraSGeRCSBT+h7xL6HbVmcOtri/Ll5StVxRsanqIxNIVaq4PRA\nNTFFE0nziEIX33aIoh6qImB5DTRRpTiWI6jBQqPJyYV5ems9AnRGSiMcPrvK1MwcS+dO4boh1WqB\n06cbPH/kBSaqJSRJ6t+FFyc2acEm06yvM1obBxwmamOs9rqYps5DD32bj/7+H3HXXT/J//mpTxP6\nATPT0+zbexX2zu10uz3COOFb3/oOpUIRMQmQYp+7v/A5kiQknytw8sRp9u3djGGqrK2tDPnMPwrr\n3z2L8FN/8X/QarfRNIMoitF1A0GUMMwMltUjIW3byrKComqYmRyGaabhv1GIphupXUE/1Dj0A0ib\nk0OPKEkS8QMfsa9KlBSNcmWEZqvNhfllJFFGkRWq1RFmZmY4c/YUnu+Sy2cJApdmq0m320ZRVFRF\nJYlFJFHBtT1EUUhzCRPhkuzAIIoIoxhF1TDMDL2eTSwImEYGP0z9vWRVI4wjGs319DWOg6rrhHGc\nfl9FJY4S1tbqlIolkjih3WrTqjfYsfMKstkc8wsLjIxUcB0bURCIooDJqUmuv3Y/nucwPjbGwuJC\n2nkzdBRFJgh8Op02uXyOwPMIg4Akimm3muSzWRRJIp8tIMsKdtdKb+kYYDBiilAkAUPXCaOAM6dP\nkc9nQEzIFXKUy3lOnXiBo88/y2tffStSnFBfXsEPArbv3MVV19/A333pS6zbDtVCgWohT7lcRFE1\nPM8nTAT0TAEriNl/0yuZ3rYdKVfERyBIBNqWlYYe1+v8831fo5DPYfXaRKHPH3zi47z/N97Hww89\nxPlzZ3E9m1K5gGV5aKqKrMgomkYQQ6vV48iRU+SzKUhuNuskScT582cxdJ2JiQk810n3VxRy5uw5\ngiBCNwwc1yNfLNBpt1HViyrOy7l+iqIM7ToGYo5qtUqr1cI0zT4wijD7IddxFDExOcF6o0kUhpiG\nzvhYjZFKhW67jee66JqGIIgEQYgkS6lXT9/nbWjUKggUSyXiICQMAqINMU9x399HFNK9OygQBp03\nTdN43/t+/SVU9ENcjZbDW956Ozt27OGqq3ewZcsMsmKyeWYU3wdREMnqEq4Px468wDPPHObZp5+l\n0WgyOTHJFbt3cdNN17Nt6xRBKJDNpRxBxwnxvJDFpTXCMOTLX7mP5eUlfu7n38nmTWNUynm++70n\n+Mznv8ib3vQT3HbbrXQ6acpEuWTwGx/4CBMTE/zU2+9kcqLGyso6sgCf+vM/5MO/8R5OPPUIxw9+\nk80VBVP0EeIEp2exaW6WyA8JwxhEieWlFVTDwPE8VE1htFZjYXGBmekp2u1WGvQui6iaSpLEtNpt\nGo01HNtGlkQmxkaQRfBdG10RyGUNJAKs9jrFnMFouYDv9FAUkVZjhawi49gdVF2iNlKgs7JMqZTF\nxUPRFBQRMhkdSRaQTQV0EclUiVWJWE5IVIFEE/CEGDtyU86TBpGSEGsCkS4T6yKhmhDKMWYpg5JR\nEQ2JYrVIqTZCsTZCtlYmkkUkUWDr5jnEJERMAuTYQ5UgSQJKpRKGYtBpNuiuN4hDHz1jMDJSpVod\nJWdmSYII3w1otls0W+tYjkMYBLhWj2ajge05uG76/1RFxtA1VFlBTBKSOCKXTUVQI+URVFWj0+my\nvLJCu9HC73TJmzkEWUFQFRRJQRQkfD8g8SI0ScUQZMRQwLF6KJKCmcmi6xkEWaXr+lgRjE2MY+gK\n9cYqnhsQhhEJIbl8jpktm+j0uqiagmf3EJMYkghNV9O7R9PxgxA1l2W52WK9ZyMqGn6U0O72uP76\nm6iNTbC6Vue73/0eC4vLtFptZjbN8brbX0m73cN3LeLA4d577ibwLMrFDIHfY9++3WzdupXJyWnc\nIKY6Os5NP/ajEfb87x+VEwRomoZjp2qsTqfD+Ph42k1SFIR+ZMxGb6yLHCaxj5QvOsLLsowoCchC\nWr97SUAcp10nUU67Q6qqgijh2H2fqiR975GREbrdLr7vkxCBEOOF3tCELsVOYp/jkoZFF/Il/MAl\nClP1oCiKaaAySd/iIX25H0ZICViJSxTFqKaOKMrYvRa5bJ52qz2s4kzTpFar0el0iMKU4DzIuguC\nADOXJJy+dgAAIABJREFU8p8ajUY6LoxC4ijtTqQOzQ4xqfXDYMQkianh6SBkOIoi6vU6uiwR+C6u\n06NQKAyNKVut1jBep9PpYGZSj7JGo4GiKJhZk7XGGr4fsnnzXOpQ3mn1x5oCO7Zv5syZM3zkwx/m\nl3/5l7njDW/k4MGDPP7UM9x88838/Ve+yi/cdRf3fv0+fvINr2diaoZ8ociFFxYZUTSWVxaY27Gd\n7bv34IQJWgJaoUziOFQzGSTgK1+6B5KI5ZVFNm2a5Rd/8Rd5//t/g9mZGW697RW0O+s8+eSz9LoW\nU1MTzM8voioq5ZEqK4tLaIaK7/jYtk2lUsE0dbrdDqqqMTExhu+75LJZWl2LcqlELpej27MoZTJ4\nve4weicM/Us4UaIoDnlSkiRRKBTodDrk83nm5uZwHIfI95FkkYxRoNtt0263h8rVOPDRFJmRcil9\nDwGSKEwjPPpjv8s7T5dzFeMoIg7Cf7MbFZNAnAy5gwNe4UscrB/+2rR5OwnQs33UVky5JOLZHkeO\nnKfRaAw7nPl8HlEU2b//emqjJhfmW0xPFfEDCALw/NRT65lnXqDb7XL48GEURcFxHGq1Gjt2XsHe\nvbsB8EL420//E0Hs8Rvv+1W8KMaUIKMZfOWr32RxeYH/9OvvoVobIegT3L/z8AGWjx/l/R94D09+\n9e9ZffRebtk2hef0aHV7KKLE1NwMoiTjuDaenO7FiW2zKZewWCSj6ywvL1PNF8krOkKhjB04BLKI\n5zlMjY2ytrSAljMQRYnR0VE8x0YEioXc0FMuFkREScHI5RA0hUKlSL29TilvousgqRJBaBHZIvmS\nhuf3GNG0dAggJcSBhanKqHKEJgnEcURERCxJ+G6A7/eQBIlcUUPoF8iWZWFZFsTpdERWZTKmQawI\niLKUEuFFAUGUiCUJhwRJkyiU85y7cDLlNkkqruum3KpsgdXFJqG3jNPtYMoy2ewYJCJrS3U6zS7l\nUgnPTtWHWtakIOr4cfo8ypKMKRs06w2yuobSD4wfJDEIukoQmNhWF01Nu+KGDhnVxHJcnPY6C2tL\nLBw/wtz27Yzt3AmGhpxEFMtlQkOns7JK4PUgDBgZL+P6IWuNJh2/y8z2Ocyez1irx/ePnmBpZZFd\nO7Zy4tQ8iqwR+C7G6DRHl1cZGR2FyCFoeZiJTM40aTpNYkQ8X0TRTGLPxQ8DdNUgChOIIx7/3kGu\n2PVy9Eye6256Bdt3t3jiiSc5PX+BWJaYX1xATjw6rQusN+q88Y4fY25qCtNQSEKHJAoRSc/G2S0i\noviSivAiPypM0DWTTtvCNE263S7VahVV1THN7DDmQxRlVFUfcogGF8kAEAyc2SVJQpQE4iT6Ac8p\nURQJgzjtHPR9hMIwRJFVstkspVKJkydPpiAnow/d1getzrRL0QdzXDTtDKOAJBYuybYSJBFd1+n1\n7KHizzRN4vjipTjwMRrwcTqdDplMZmgAGscxtm2jKArtdpskSTBNk9nZWSRJ4ty5c5d0TlJjvZBu\ntzt8X9/3KZeLRGEwdPqWJIlcLsfq6iqqaWCa5hDYdS0LSZKGI8+BG/2Ay6OqqarG931c10VRNCqV\nCu12G8uyUBRlaFkxeM/777+f6elpyuUyhmFw+PBhbrzxRn73Yx/lN3/pP/Hc0RcYn91My+pQrFZY\nbqxRGh3nplfcQrY2CqZJ2O3RWl0lACRNRxRUlpaW0DW9HyFj8vrbX8fB7z/GFz5/Ny9/+TX8xB13\nsPuKK7j/n7/B4vwipXyOkZER1tYabNk8R9ZUcK01pqZm6Hat9ILasZMgCNKxbCKgKDqdnkulUmFi\nYoKnnj5MZTTtUjqO01e7psHbcRwPC4NBRM1g38VxTKFQIJvN0mg00gvQ8yhkc/i+NiS/p+RlF0lS\nhjzEATAejNA35lkOQNdFjuDFsfnAkPbfGs8LwkWRxUaF7Evrh7u+8/ABNm/ezOOPP46u60iSwNra\nGju2bWdqaopbbrmF0dFRclmNtbUuxbJJAoiyyvxSlyOHn+XkyZPk83mWlpa49tpr2bx5M3v27MF1\nXQqFQj9NQEQEPnf3fdTrdV772tcyOl5jdb3DaCnPsVPn+KtP/TWvf/3ruePOn6XrR6ysNRivVvij\nP/nvvOKavfzM63+RJ776GQ498CXedNteFs4eJU4UTNMkn88PFbNdt4flpaCrPFKmQIzrODiOnZ4T\nkoRnO5iqRhD5iCqYhkEcBeSyJmNjYzi2x+TkJO12Jy0yTJMkMej10rMqk88hKjKSImMoImXy/fMt\nRNNFDFFFkwQSQ0tTFNxU1CNLEomYoCoCGUOhZ/vEgoAiiwiCREZTiGMj5ckGMXEcEQYRmqFgZkdS\nMnlfdCSpEl4UkkgCoiD3z3gFQZaQSIsRx7bQNAlJiJHFJKWWRD6ddYs4isnqGqOFSUqFIoVCIc3P\nS1JivaIomEaq9JYUmYyho+s6oiQRRCFREKKqcgreFIVYYHi+OI6HE/pIGROfNM2EKEaWRPL5LDkz\n5Qu7QcjTjz2C8vxz7L3xBsqzcyS2jed56Z0jKzTmF7GsLkGSIEkChVKe8kgFnzqjlTGu2D7L5x98\nmC88cIAdV99Aq+exutbgwYe/h2W3uf1V1zI3kidGRs/kcQOX1rrF5OwsYQBdywUkCsUSgpYl6TjI\nHT815l5f5+j3D6EbObbt3MmuK1/GdbnUG+zJg9+jvnSGUt7gyt07yZo6QgKO56KIKXBLghCEVAAQ\n/QiFUfxIcLA2BuAmSWq5XypVyGQyWJZ1CaDaKDsfXCoDMnmSCCmNKUmIopSofKmviUBE6hPi+yGW\nZQ07OsVikTAMWV9v4Ps+tbHqMEB54DSvKApxnHpfDfIKW+1UATQI9hUEIfXDSlJ/LLn/IOm6Ti6X\nw3X94Wf3fZ9cLku9Xh8qE1Miucv27ds5ePAg1WqVXq/Xd8ZNs/by+fzQBmBgZrqRRN/pdIagUFHS\nn7/Rad6yrGG+3sDHafB5gL4PWPoQW5bF5OQk3Z6N4ziMjIzgOA7r6+v9/MIU/A1UdAOZ88rKCrVa\njU2bNnH+/HmWl5c5ePAgN9xwA67rcujQId7y8z9PUq/z/g/9Dttf9jKMXI4QUDIZJmZmmNv3clqL\nixQnJrFdnzCKMbI5vDjk7NmzPPfssynQQKBULNKo1/mxV70a37aZnJzkhSOHmZqa4iff9mYOHXqS\n5557jtOnuxi6wqte+QpmpieYP/8Cipzw3HOHqdXG2LdvHyvLa0iSgkDKjdM0jRiJmZkZnn7meXzX\nJZvN43j28PtuVKsOXfT7wgLbttNwbMNgZWWF1dVVcsUi7XabseroEHxt5GcNzEsHZHbbtoesqgEY\nSvdjMvy7D7iJsiynY8H+z/+3Ug42KlCHAeQvrR/6uv/++/nzP//DoTfenisnAejZkDFhZS2199A1\nGK3lOHN2mePHj/Nsf4/ffOP1XH311dx0w1U4HoRhgqoKaAoEkU4UgSynHNGHH3mSTqfDz/3cz6Kq\nIEkwv+Dw4DcfRpZjPvCb70u7lgCxhN12+dAf/DYf+PVfYaqi8PXP/SVR4yxXXbUD1wtRtRKqLCHr\nKook4zsunp2aGY+UyqktTd+qpttu49opz4YkwbZtiv38TFVJz5m1tTUkSaJUKhEGjaHqNgxDSqUS\nqqpi2+4GnmPS7wYL/Y5xiG27/SLcJIoicjktPQs8Lz2LJHlYiKTPSzL0RxoULrKUCgXa7Q4jlRLt\ndnfI/3Wd9CzUtJTGMVCQyxIoioyqaqmdTpgWJ77roCkKkR/gA4VcDs8JCREYKZfIZQ2q5RLFfAqE\nhb66WxLS7z0AyIIkImsqoqqm3Mmg35U2VAJJICQd8QuyhGzoFEtFSoJAo7GePs8JiIKAJqZxc07P\nJrBi7HaLamWEtufz2IFHqJw8ze7du8nmCrirqyiyymi1iqgatNbqLDe75EplpjdvY6Ra5fiJw8zN\njHHX7a9GVw0+c//DrLpp0LSuKeDCmedOMXHtVeSK4zQ8B0XKkM1P0m4EyLqMH0UkSYTrOXiWj4dK\npVolXxlh7969IKs8fOBRzpw5w+zsHJVKZXi/FQolxsfKlIsloj7vTFHlNFw68pEl5Ufyuf/3B1iy\nhB/1cwCTGDOXpb7epNRppxJUMYfoyERRApJImGx0bBcvGYuknaq0e5UqB9OHQRRFgjAi6Vf4pmli\nWXVszyVJUs+tQqEwNILUNI2Ryiirq6uEQYyANFTgBUGEIIQgXLyQNsbcDFy3wzhVLuZyeTzPI9pg\nHaEoyvBgcJweURwiJgKbNm2iVCoxNjbGm970JhrNOrlcjiNHjmCYOvlCjoWFBRrNOrVajUzWxHP9\n4c8fRDysr6/juu6w09TrJbiO0yejx5cQ3i3bThWRup6q4Yy0qjOMDGEYUqvV0tGt1RsqJG07BRZj\nY2N0OhaNvrnpoHvTbrdpNpvkcrkhKXv//v2MjIzw1FNPDUGj8uCDvPW97+Xhhx/m05/5Av/1tz/M\nkeMnGB2f4BU/9ioiq02xOsLi+XM4QYSRy4PrkinmiUvp7zEOI7ZunuN/vet/YfHCPIHrse/qvbTb\n6+Qmxui014mDkPe991c5dOgQf/Zn/5O7/sPbeMPrX8uZsyfYtm0bS4vnmZ3ZxNYt21lvtrFtm7Fi\niWarg4CcdlitBpPj4yn3q9lkcnoTjtMbguoB2B8IHQaH+MAaI/079IYWGvl8noUL68zOztLttvE8\nD1VV+7mcRaLIG+7rwbgx3d/SDzi0bwRNG8UeAxXs5a+7XMW60SJkANZeWj/cNTs9g6rC1NQkigJ+\nAKurFq1WC0mSuDB/blgQnTx5Ej8MueWWW7j99lcgiKAA5y406PYSchmBM8tp1uaJEyc4ePBg/5lN\nvQOnp6f5hXfdxTPPneKqPVv4+gOPcvDQM9xw841cf90eTFWg5zskwBc+dzcyIh//rQ+T0TwOfP0f\nOPnsATaP5VEknaXlDhlVoZDP4fouVqeLF6TgI5fLDfd6p5WGmytaCnRkTU2DyPt7bHDu2rZNkiRD\nK5g4jodjuUFxOjiPNypbB914TUsD2Ie0jz4wEwSGQe2pkEncMOHwkCUZWZHSkXgcISQgyWlHSxQF\ncv1EBhGBKEnwPQcjY2LoOpbdRSZBEmQUSUROBGLfIwqT4YRFVVSEJMHQVHRVgThCEiGby1IuFinm\nM5hmChbDIEjzEEURuZ/woRaLg6E/hCG+beP2vSEFVQZRQNQUVM3oA0iR1AQrfdYz/UlNEkaEQUCQ\nOlEgZXQMSUSTROIwIhtGrLZarCzM41o9ZicmKGcyKeCLYlBBTKCYyzM1twVGawRrK1x1zV6Wzp4n\nh8rrbnkFFxo2J+ptnjj2AnEMV27djCmEaIqJoukstdtkdINCpkSnsY5IiGrkEBWNwPbQJY3pyTmU\nbAnL8bnnnnu4at8+3viG12E7KVf11Kk09ml6YpxqrYympvZFgR8iSwqZjAFhTNgLkSQZQUheAliX\nr1QpGAzl7KVSCcuyWFlZIZfLUSwW0XUd3w9/QG4exzGylBKMZUklFkNEEqLg4rgjDVOWCDwPUZQx\ndCM1aOz1+j9XplQqIYqwtraGoigUi8WhnH5wKQ0AlixHQzKxKKZjQMdxiCPIZDKpAtF1kRSZcrl8\nyehncMhcvJTT0VutVsN1XRYXF1lcXCSOYz74wQ9y/Ngxdu3ezcrKyvAgi8OQRqMxDH5espaHhwwC\nl1g1lIpZbKuHLItD3lUcx2QyGURRHCrmbNsedkRUVe17aTFUci4uLqLqBmNjY3S7XQRBoFKp4Dnu\nsOpcX18nl8sNR2flcpn19XVs2yaXy1Gv15mZmSGTyXD+/HmefPJJGkuLFBSFj/3xJ/nuLbfw0KOP\nMj49w9zWrZSnplhZXqHVnkfPZpmcmiISRbo9h0PfP8jxE6dQFIUbbriJ3/nt/0pjdYXHn3ic0POQ\nFZHxWpX5+fPEnsfuK3dhddrsu/oqPv2X/x0zk+HsmRMoiogqGczObCKfK6e2CV5ANpsnDGIkRMI4\nRpJFIj8lq87OzvLEk08RBynnyvOc4QUyAFgb7Q427ul6vc62bduGpHIEgT179iAICQ888ACTk5PD\n8eKg+zTYx4OA8I1cq/TveVFlO6jYB68ZfJaN6QGDfTj4t+9dqt4dPDMvrR/u+uLdX+Dd7343rVaL\nL37xi+QKqeWGZXXSTrCq/F/svXmMZel53vc7+3LPXavura337uluTk8PhzMcDrchxWVo0gxlm5IJ\n2WSEOBFESbAVw5Ag2ZKSwI4hUAmCwEIQSZElR4sNEdQSiaK4U+ZQQ86wh7NPT/f0Xtut5e737Fv+\n+M45XT0kFSSgIALpAzS6qruWe8/yfc/7vM/7PPS3dzh16gT/4Id+CMOUMDVIchgOfXodi8FgwNWr\nV8nznG9961ucOHGCs2fP8tGPfpSNjQ3Onz9bTMv6zD1xfX/5E7/K2x59F7/wL3+CwRQMHRLgS1/5\nKl/5wpf5xZ/9VxzpNZnvbnPhq19hcuNlHji1iq0kKFmKYprUm01cd0wUhUXbW6yHeZYzm82qVnkc\nxzRaTfI0I8lSIMeuO2QSFYM7mQitaZ4JwNRqtSqg3+ncZsM6nRZZljCdTnGcGroqM5vFyLKK49jC\n468IVi+fw9IaJY5jZETXQEK+XZxoCqQZaRSTJRF5qmBoCnXHFsJsCVRJIg4jyBIsQxctttDFqDvV\ncxElifC/S3IkWUJXZdqtJlHg0263MXSV0f4AQ5HptJuocjHs4gfExdS8ZYj3GcYRSZJw6blnCcOQ\nIAgIwxAvDIiLDoVhmYRZgqJpmLZFrVbDqTdpNps0O20Mx6F55LCwqPFD5vsDRqORKPZkBVPXaLRa\nDPb20RSVI4cO40xn7O3tcfXKFQaGyYm1NUxJwbJlIcXxY5IwQTNrNM7cC/MBK0dPEI8zprf2+W9/\n4p/xo//iZzBVk2NHDlEHVjtNFFVj5rlopoWfpsTenMVul4wYP4kYT2ckaCyvLHHqzBlMp816fx/T\ntrlw4QL7gxGmadNut1lZ7rG4uEgS+GRJRJbJ6KqB4RiQi/MvFwSJyvfnmvW3DrBKsJLnUrGpmyws\ndNnc3GRlZYVWqyOSv2URS5KlCJYqy0jSCEmVyCREcHMuk2WJEO8W7Y80y9ALdkFShQ+RYVjM5x5x\nEtNoNGi1RF/f8zwMw6TdXqjc5cuNTZZVFEVDUZJi8xT6lShKCw1LfofmpaxGJ5OpaAsUm55hiOBW\nEUfjMh2PMXWNmmlx8uRJEdi8P+Dyyxc5euwYLz//ApZt02o3SMIIy7SIg1BoGwzzjuDnNM0q3yzP\n8+gutqrWo2EInU/J2CWJyCLUi4e/BJHlYlR+raDQJ7RVrdqALctCVVVGoxG6aaApEoamQCZatY4t\nqPvd3V1aDYe1tTWm0ym7/S0UReFNb3yQI4dWef7Z5/j0F77AY+9+D//853+On/7ZX+Cf/dSP8eh7\n3sOVVy/T7va4Z3WN0WTC40/8FZ/+zF+QZDCdTjlx8h7+51/5n3jbI2/m4osv8OrlV3Asm0iS0XSF\n4f4AdzZneanHkUNr3Lhxi9FwQLezwGCwj6kZKLJMlqnUWjWiKCMIIrrdNo1Gg53dXXHukoygmAKs\n1WqcPn2KS5dfxfM8dEUlTMIKbJdtwtcWEOU1KnVrJegBGI/HvPvd7+Yv/uIvKp8s0VK8reMqQb7Q\nE94O7T5oXlv+7oP6qYPxNwfzNw++toPg66AJ7d3je3tceeUV/o9f/w1+7Md+jI9+9KPCAqbTxjBk\nDFv4XSmKsFMA2Nge47qi0HzyySdZ6S0zmUw4d+4c999/Pw8//DBZltFqiPVmYeEsWSbAVXfB4olv\nPM9Xv/pVPvaxj7G61kYCpAy++sTzvPji87zzHW/nf/ild7BgGRDOkNxtLl/4CrY0Zbm7ymR/QpRl\nWEaD4XhAEnpIeVk4KORpRhhHpLEAAY4j9LK2aQmrkuEIXVWpOQ5RFJHKWXWP1mo18kzYmzQajUqm\nUKZh+AXbXkZQGYaB74rhI1lWsW27AnTleqaqadWWL1NB0jRFVeTK2kdRZHIJskxBykEmQ9VUlHqN\n2WSKJovpPFmWUfIcU9fIswQZMIsc1yhJUKQcS1NRayamVau0UdNZjqbKxHGI781pt1ssdtrkeYqM\nRJJlaLpOvV7HUDVmsxnj8VjoeJOkssiRJAnNNLBME1XT0FQNXdbJJMjDmEkwZNDfJ0Wkheimwfnz\n52l2OtBo46xZOI0mbqGLjb0ANwqp1W0SFOZhiGObdE7dgzeb07+5ztXwBi3b5lStgbnYo5ZJrG9t\ncXpjG44fA92AOGP/+kssLB2iceQUWze3WVhdQUNl6s2IojmymdFq22gq6IZGqmVMEo/ID4TNjmHT\nW1ph7chxdMvGqjkcOWKzvbPH8SNHOHbsGDWnQcOpV9rqXJGoO22yJEGRZXRFhjwmiQLkXEbTDKQ0\nIy90199PDPz3BcASm39ULeyLi4tcunQJr/BvOmjQWfpQ5XlOlkIqpaRp+X8CpKWpoIBlTSYuTB2F\nMF3BsOwqKw4ocv4sNjY2KpGwqLTGtwN7y4DQA3+qSJs0FYLoRACs0hE+jmMR5ltzME2TyUxMneW5\nVL2vUrReLgg//dM/zeOPP86/+df/mmPHj4s2nqpy5swZLMvi8uXLlTu4AIMGeZ5XAKkUoZcbc+kW\n7rozNPW21qZsObVaLSaD/UIbJjb+6XRa6IHSKgex2+3ihxH9fl+YYCoiGLpske7u7tJoNHBdlyiK\nWFhYqATvhiEMM8vKMgxD+v0+kiTxzne+k62NdT7/5S9x3/338/r7z7C0dgir4XBqdZX+9g6/83u/\nx8XLlzB0C8dxOHfveXTLZHFxkbpd48XnnuXKq5eo1Sxs22LHnbHb38Wp2xw9cpiFhTY3r1/n8KFD\n9Pt99ge7oq+vauS5oOlvXL9Fo16n3V5gOBwW2Y5aZYI7mkyFhkRVOHroMAudNrO5i9OoE82j20zp\nAd+rEuCURq6irZFx8+ZN8jyn2+1CnvP444/z4Q///UqHIvQnHo4jzGuDIKgyOUuAdbAtfbAlWH58\nUHf4naKiDn7twVZh+X0lE3b3+B4eeYLrzVhdMxkMdfJcotGSmE4hceHVV69wc/0Wmia0SePRAMg5\nduwYH/7whzlyZBnfE7mElgFBBFevbvLiy5NKOzmZTOh2u3z9ySHr6+v883/xT5EkGI5FvMuXvvhZ\ndgdzPv7xj6EDeQzhvM/m9Wf4s//zVzm2YPP6k8dI3Dk2GqmaEyUuZs1EyVSiQAz0aJJEVtxD9Xod\nx3EIY6EtTbKUPMswLFOED8syGaCpYlJcU42itRZVA0FJkqAoMnEcFbrCrNKMNhoNoQX1PRRNJ5cl\n/KgMUlchzYpAezFJWWbBlutcGosCXlPFWinlObqmIucSeZ6SJSl5LqHKoGgKuiKjOTaWphLEEW4Y\nocoQx0KLiyxhWSaWZWNYFrKqgSRVsgmRaTtDksAwNRGRlmaEsUjF0BQI/Ag/D5lOZ4XEQ6bZbJPn\nwsuwHIgqo9Y0TSN0RWJIu93Gtm2CKGR/OGQ6nRMlMc89cYHO4gKrK2t0ul1watTai9QMm8iYE7uC\naURWcIw6YZKSJjn1ZgPnzGkuPvcc/a1t9gcTjp08RWdpjVkisf3SRfIXL7F6/0OQSdRXTuK86W38\nyr/61wRpxuHFw8zDOX6e0N/fZmHZZMGwSVwPTdHILIn98Zw8Unjg9DmWVldZXjuKWaszGM2KLpLF\nmTNnWFpeZXt7myCOaThiX/R9n06jh2UYuLM5ge8VwFnB0C3IhC3TbZImuctgHTwMy2Y+n2OaJhk5\nXuDTaDVptjrs7Yv4k1arJTRBaU6cBJClqJqKram48+m3MQal1ihJMxRFZTqdYZk14jSl0WgQBAGj\n0QgkiaWlLqPRgDiOURSFVqvFtWvXePjhh1EUjatXr1b2CeUofpIk2LZZvW5NE5VOqbep1WoEkQAS\nZ8++TjijP/MMhmFUegORcyhypEzT4Pr163ziE58oglaFnmBzc5M0SfjxH/9xvv71r3Px4sWqwitF\nz2W7SWhoFOIoJvAFKB2NRkhpRqvVIE3iagEoQVi/3xc2DYWOII5FG6ysHMTUY1ZMInaqSJ1+v0+v\n1yuo+RBVVZnP56iqSr1ex/f9CuwZhSbDdV3abcEO3bp1i/F4jGma6LbN+YceZDZz+W8+/hOsHjrC\nPAj588/+EX/0J3+MYZg8/OZHaNRb1OoOhmExm83YuHVDVMmSQm+xy/r6TWa6ioLEQrvNeDyk1+vS\ncBxMXSPwXNI4IkuE3s/UVKJUsFOKolYgtYwsqdccBqMhrXqTdjtjfWObOHVRVIm3v/2t/M7v/h4L\nCwtYhsFoMqHT6VSMVMlSlhOvpmmi6zqD/X3WDh0iz3N2dnZAUbhy5Qq/9Eu/RLfbrZguy7Kq6Z6y\nBXIbCN1mfUuAdPDzCigVmpVyYvZgxlqpuSoLmnKIo9Sv3BW6f+8PzdB5/C+/ws1rP86tzS0uPP00\nGRKrhw9x+swZRpNxYVgrGI5jRw/TbrdwLImZl5HnoOvwzDMvIUkSGxsb2LYNwIkTJ7h16xaTyYQH\nHniAbrdbMFygqvAH//E/ocgpH/3Yf4XjKLzyyoR7zzaRc/iNX/937F57ikdft8qCkYM7ZrC5TbPd\nIQgjxsGMpaUupmkSe4HwYdJ1TEMnLta7shUuy3IliXAcYesSeD5JkhQT1W61pqRpSqvVwnEcIdmY\nzXBdF8epV5+XEWSTyRjTsGm26pXg/SDLWsobyozWRqOBqqoiVSEIURSpAEmJOCeKApKwuwjipOpQ\n5GlMFElibdb1asBKtPinQI6qGqKYq9dBkpnNPabuXHQMFtrkUoYkQ2dRiPW3tjbw5i6WYVfP9Ww2\nqwZV6rVaVZyapolSgKqyxQ9gGQZLq2sirSODcOYSxRF6LrHU6qBbJuOCrbr44ktkWUaj2WRpaYkx\nnb4gAAAgAElEQVRGoyF8GjUFVTJRNJ00l4m9iCxPIINMlrjvwQfYuHGTV1+5wsXrNzh97+tpLfZY\nWlhCQuWVrz5BffUQa+95H6QZv/K//SqW1sA0bV65fgmplrFydBWr7RAEHqaiMBwN2I89Vo6c4PjK\nWR68/2HCOGUymzNyA4Iwxqo1kBXhKr+5cUu0gC2Lnf42qqrSarWYT6a4k2m1t8t5djubGLHWZWn8\nHYd4/n8PsA6OlpebyMGHthSByrJ6R5TMQT1J+afcOASSPeAZJIs2YslIhHFMlMQVWxVFETs7O5w6\ndYowDHnwwQfp9/sYhkG73WZra4sTJ07g+361aa2v36z0A+XPkYpKZjAYYDs1Dh8+zM7ODp7nceqU\niEC5ePFSxcDV6w2yJKbf77O8vMzOzg4XL17k737wg1y8eFHYDzgOly5d4ubNm4UtgkatViMMw8oO\noTwncRxhahpJ7DObzTh54gg3r14jz1NRtVVfJ8CkaZpo0u1zWG6y4gGPqoWrZD/K32WaZgU0nUa9\nouMVRSEIAmazGbqu0+kIXVMJREu2S1VVer0ew+EQFJn9/pCaXefw0aPcXN/gq197HM8P+dCHPsSx\nY8dBETqw+XRGpIcEUYg39wlDnxSJwHNZXV7CdWfkpEynY9bWVqk5FroqQHMURUL873loikIapcz9\ngLXVQ6RpVgnRg0A4+FtmShonzCYTfN9HU4TDchKFLHZanDx6lM3NDTqLi6iqShAEVSpB2boodXMl\n46jpesVmtdttDh85xM7WNvv7+3ieh+u6rK2tVezgQSbsO5Iir2Gl/jpq/DsxViWDfBBU3W0P/g0t\ntBJcvnSRb154kocefoTTZ87gBT5mzWFxQQEOMZyJjF6nyAucuwmvvLrJK69cZjye0Osu4/s+R48e\n5YMffB+KAnqxgrfbbd7whjewsGAgAeNJyt7ehE9+8pN86IPv5957DgtLjhyatsbLT1/lxQtfYba7\nzptff5aO4mEmLsHEpaZJhPM5sqnRaDjM53NqmNWGX2oDfd8ncUVEmKqLSLFWq0Wv1+Py5ct0u11k\nVWHt8CFUWeXKlSukaVYw7wCS8AwswszH4zFbW5t0u13qdQfPc6u9oNlu4XkelmUx3htgGcLGJ89z\n0lwSE36qjG06RGGAKlusLi+xv79PmqaMRiNkchRFA00kNJiGhlpM0Rq6GPKxLeHtFAYxmiITeC6+\n74rsWtui1V5At0z6e/tEaYJp2fihR2+5i+M4bK5voCsKqqGzv7+Pgmhhri6vkiUJcaGtLJkoYZId\nIykKyCIPtSyE6vU6hqYRhxGKrBEnUdVOdOp14iRjcbFLmufohsny8gpzTzDew/GIS69eESyYrnD6\nzHHMRg1JMcjDGDUFZJUsiZAyhbk7Z3F1GbvZ4vkXL/KZL36eRmeRc+cepNHpsXb0GHMl44uf+zQ/\n9fO/iEvCO970Rqa+j92oozUlhtMBSdJBrmkM9nZAlvnQf/H3MesdGmaX4dQlCmMkRUVWJUzTrq6v\nrmrkEqRJioRCzTTodDrs7+9Tr9VxnDqDwYBz976O4d4u5CkKOf3NDWzbIvZLpl7+vlrDvi+yCFVV\nRZZUJFJkSUVTjQOalYgwjKsNXFHUglWJyTIxpSIrCpKqkOcZWSrGWMufneYZhm6QFP5TjUaD6Xxe\ntdBKz6leb5EoCuj1lquIniNHjnDjxi0WFrrMZrOCthYUdikyn8xElWPoFoZhCIGi52HVhFBve1sA\ntVkR61JqutI05dKlS5w6cRzT0Lh48SKqcoQ0idje2iAKfaLQp7vY4YXnn2V7a4MsjclkKjZJknoV\nhS5JEnmWV+xdOcVXAr+SNi8p+HLzl9Kkej0HWcASsFaTOL5fsVnlQEKapuzt7rC6ukqe59y8eZMo\nilhdXcWyLMESAoosIUvgey5RGFReX2KqLuXwkWNVa8w0dd761jfj+yG2U2M+nzGdTplOp3ieYN9Q\nZNIopNVwCOYeaRwhy0K4P51NBQPlOKiqXIVx24ZNt9Ml9ELcuY/aNFlaWqqEsooioxbicjF9F6Oo\nMqPRAGSVzkKLIIxxA58kiTh6bI3rt27SXuhgGEZVnVuWRRAEVaFQjhmXjOBkMqkMXA8dOsRof1BV\n62XrVIAduQJqfx24+m7Zgq8FVwc/PliMlBOkpVbvtTquu8f3aJhHyZlPhS7p5PEF5l5hDJwkvPBS\nn3qryWQy4ZvffBKnbtN06mxs3KJZeLetrR7hjQ+dQQJmrmgT3rg15LnnnhPtZm4XkPV6na9//esM\nBgN+5md+Ag3Y2ehzeG2Zp59+kS9/8Uu89Y33I4VDzp86Sjy+gdpUCOYzTDLarRa5ajD2fFxf6J4G\nkwGOaWHZNkmaMhyPCOOYZrtFp9MhI6+KM6+YTC4tdIIgwHf9ynJhNptV7a/ZTBQTnU6nYuNLpqe8\nF8v3VR62bdOsC6uaUjsb+lol6tZ1Hd/3GY/HwgKhXsdut4rXc+fmW+4BgR9U652maVUMWwkmnWYD\nSZFRdI0kS9FMA12xiJIU1dDZ3t3hHscR2tP+Nrs72+iywj0njnP08BHyVMy858XzbRiiFViCgXIK\ntJzELgdldFUl1jRUy0A2NJQwwg18UllM4KNrSHnG9s1N6o0GM9ej013knrVlWsMenuex1d/kiQsX\nWF7u0VtawzIdVM1AUXOSSCHGx1BqbG9s4tQavP3dP0B9cZmv/Oevsf6lL7By5Bibn/tjJpLMl791\nlVEMP/hDP4wXpux6A6aRS77rs7RYZzYNkcKEd73jvZw+e4ZplGDWGoSecJs3ujaqqpNkEKcJsqSh\nGsIWw7Qtm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9vt0mg0yLKkek5Lr6uyeCt/Rsm4C90qKCiVzMEoRPWmblRFZqk/y7KMLMmr\ndVRkw6oF36vcYbWT5xQyD1Fs5FkZOSZV7chyylBXhcu5pmi4foArz5GAKAxp1BzOP/wIpw4d5eor\nF3nh6aeJfZ+GVaPTXWR5ZYU0Ten3++wO9oVzvSIjKXJlx+L7vjAGNU3ywlh7OBiQA+tbWxi2xWQy\nQytkMpZhVlmlygsXiZOE7vKSyC/MJdoLHayajW1aBEFGqOrIaY6lmdRbNZQY5CxnNB3zxc99nrPn\nztLpLBL4EbZp0Kq1yUPYurrFz/3XP8Un/tPv8NKTLzAezVEZ8OK3nme4tcNSu8NKvcEbzp3jzL1n\nyImxTBVVzokNkEyFYBxgW0bFPpYspWlY2LaBplqEns8zzzzHcy++wHQ84dqNazh2HUkFsozpzOPk\n8VXe/KY3FtY2CotdYWhd001BJCDf1WAdPMqefcmWlNX/wVZGFEXkGZVfVPl1sizjJWIzkiXxVlJy\ncglIM5I8Q5ME46UZegXCwjBEQkKRNabzGbO58NaYzl3iW7eEjkZSquiSP/3TP2U6m/DII48AGZPJ\nSGhsopRckovXRcVaeZ4nKik1wTJMms0Gm+vrHD96lBdeeIEP/J3H+K3f+nXOnj3PeDLmLW95Mxsb\nG2xublbi73K83rZtRqNR1cLs9Xq8+OKL1Ot1JFllMhmztLTE+myK73l0Wm1yKDZ6h9AXflm+76PI\nUpF/WL+dO3cgyLmk8EtPppIFK40ASx1YaUZaUu+WZVWTOuUggSRJbG5uomkahw4dYm9vj+1+n0Nr\na9i1GtPJhDTLOHbsGKPJmNFgyEJ3kWaziRf42LbDkSPH2NvbE8Gys1kRzaEThjGSpBAEPqoqrlEQ\nBDQaDtPpnDNnzqBpwqai399hcXGR0I+4efMahmEJgJvCjZvXqgo5r/L9DPI8JY5SYlJsy2E+nSGr\nGrqqMHND7AKEHV5bFWPkc1foAFSZ1EvJyaocNdd1WVhYqNz4wzBEM80qkDsKfDzXpeY4AnBGUdUy\nKH3Y/jr29/8LY1wNgKS3zRlfa1h69/jeHsPhPlatjuuF1GoWb3nrI1x45nleeOEF9gYTtrb7fPPp\nb1GvOUhSzkKzRRxHfPrPbnL8+HHqTYfz58+ztlxjME555plnWF5errI9Dx1awFAhiIVZqS9IU25t\nTvEHW/xfv/kfGN14gQfPnSMcD7D0DNmSqBkpk+kGQSohG3Vss4th2KCFhIxIojHzuYthtSp7BLVg\ngDRNCL5LRv6gKD1JUmSgWW8QJTFx7FVFVdkeLE1BSxD12ns6L0KHLctiOByj6cbBsgFZViobGMsQ\nP9uyDeQcJEmsn2ksLCLCOKHUtwsJiYIsSSBryGpKo6UWAfVCCyUhxOtOo4WU57izudBmpTlkGaEf\nkCY5umbgz3ze9MaHWVvqobY7LDbbnDh8FClOGe7ucvPqNY6fuYfQTRhPJuwOxGRjo9WsrDaazWZh\n0izaomkQCRBSFkcZyLKKGuY0nbrw+zJNslTsofv7+6wsLGBLBhEK61sbjDb79JaWuPTcizzyzrdS\nrzXI1JTIDcgzmXarhaEq1Ec10ihkb7vP9sYmjUYLp9ZmtD9lNplx+ugptsMR/+7n/kf+/Sc/g2Y3\niT144YlnsO0ajz32Du697wxnz5xiPtpFM1JWnRo3tq7TWl1lHo6ptxxMSQVkUnKUQu6jaTrIMuPR\niG9euEAuyXzgAx/gp37yn/Jrv/br/M7v/j6eN+Nob5l7z9/D2soqH/nIPyRNE2bzKVmU3EGuHJyQ\nvguwAN8P0XW/aM8p+H6IYcQsLCyIvLrCJiAMQ8IoYLu/xdGjR6tevmlazGZTgihEUYUA2vM8sixF\nVhWiOMG0RVuw2+3izeb4cx9VVomjiDSRcL0A0zCI4pQ88zFME88X2Vif+8LnSJKEH/7hHyZNU557\n7jl6vWWm0ylpmqOqEookk8s5EhmyJNx880wmIyNNIhqtJv3tXSajEQ8+8ABPfO1rvP78g2RJwtEj\nR7ly9brQMWSgGzqWrTMYjjl2/CT7gxGyouEHEXv7Q+EJZjsoql5tyGtrK9X59DwPrXC3H45HYkGU\nJeIspdXqYJkmg8FeAaKE1qrR1ASAqwnQVp7zVquF7/tMp1NeefklVldXUWUJP46QyTF0HS/IIJfI\nM3Bq9SKdPsGwTExLCL33B0MUVaO3skKSZgwnE6QsJ5dlgigmSjIWuj0yYO757A1GLCz2SDK4cWsD\n0zTpLPYYDAZEUUajtchWf79qHQs7jQXCMGR5+RD7w7FwWQ9jslw4tJNLLC+vVnEdpf1HGAYYuo6B\nTuhHeF6AaQqRudCEyNRqdaHbS3x0ScFUNJBTHNvk9D0neerZZ4mTCMsSrKPvuyiKhqar2DWL8WRU\ngas0hwxRObt+QBKHLCz2cF2X+dxjYaHLfO7hOE6lwTpoo1AKcMuN4uCAQtn2SNMUVdOqSc+yaiwt\nNsrJq9LMsCxW7oKrv7lDt0wUTcVp6IwmIh2h0+lw9Pgpbqxv89j73k2WQqMBcQzbW2MA7r//9czn\nMw4f6zIYuDz5ratsbm7SWxYeR8+/9AqmZfDSxYtYhsZ8NsGfz4mjgP7WJu9777t56ckvsXv9ed71\n4Bkm232WW3WkYIbvugTenOZCB1s1UY0GQSyzt7ePnCV0mib15iJpEjIYzZA0vfJqEvYIGVKckOW5\nAACSInRBkkyUhCRZDqrKbDIhi0WgcZqmIoXCttEVtSomQy8glyEMYqHrjFJUNUGSlCoCp+44FYMe\nhiG+69EodJ22bTIej5FNC9MyCp9BmeFwiKobxLmCBiilq7tcTvdmxVBSgqYZGLpU6VOz1CPPIQoi\n8jQnSzLyLMWQdXRJQ5FUmjWHyA3wRlO2w4TDYU4WZViGg25JyJmE02jw9IULGLYlOjOSRL3epNXq\nVOtClmSoKFiqgambhLl026ld16mZNmQ5uqKy1OuRJSmqpDKde7ieT/t0l8XVNYbjCYtODb25iKTI\nrN1zD0/85ed56aWXOH36FPXWAkqUEsx9dNUB02Qiq6ysrLG9u0Pke6i6RZqK7kjNcnjojQ9zffsm\nl/pbfOAH3slnvvoN2gsLdFpd/tGPfJRz5+/Di13iNMF26kSeh67qLHZbKI4mci9zjSAUbWfV0G93\nO8ZTBqMhw+EYwzJ53X3nGAzH3NpY50Mf/nv86Wf+nEZeY2Vlhfe991286Y1vJIli6rYjfONTHNcA\nACAASURBVMFswTBKioyUCaPxuwDrNUcYhkRRdAeYKsXCpXi4ZGEajQae5xVCZoksTpGkA55BWVGF\nI1x7ZVUpNo+soCbFBqVIElB6/0AUF5YEcoYfBoSFYV4cxywuLlaTVp1Op9i4chqNFp43r1iA17Jx\nJUuQxglLvUX29vZYX7/Bx3/843zkIx/hU3/0h/za//4bFLIxtMI7qkTipUNzOUVTTu+VOgTRz48q\nb6680A2hyMRxgqqbuLMJaIJtm3ku2YG8PEmSUFCq8OFyYZlOp5immKorF8XJZML6+vod+rCDIu6y\nXVtqtkr9VXl9Z55bGWLeIVYtNvWsqFi3d/aIooQ4SwtN1VLBRO2KyjhO2ZmJVolpmjTqzUKTcTuz\nT1WF4WiSJISemPCUuD0hWWrbXHcmNFOacscUnaJoWEUERnmu8lwASr1g+OIoQsrBMDShjZuMaTQa\nmKZeBDcLsbwkifsvl8tWXREMXlDZTq1etSxKQ8bSif+1U3//b1isgxXdQX+50r+sbPeWX3sw7ufu\nFOHfgNZUNfGiBAmFRquJpGisLPUgizl98ig3rrzKZCZAdo5UBBebBZjwuXjlMvVGg9XVNU6cOUkG\n7A3nNJfWIIuYzecsLi3Sa9dQ85D77jmOIaU88Z+/RNy/xBsOt4h3rtJUNGI/hCQiS2Mspw2SjpSr\njPf3COMc0xTgJ/BdkjAtMv0UsiQDDXRFDGwEXkCWF8acRZTY3PfwdnZF3qlpsD+fUas30KWQUX8g\ncvgUjSyKkS0bTdFJ4ww38rAdC0PVUSSJyXiA4zhMpkLOkSUpoe8zn4rosTiKOHXiOPP5nCSOmU6T\nwldJJclgMhPFuVWrE0QJjeYCcZpDmhXrlNAeqql4HmqRzHzuIuUymqpRs2tMJyGyZLLQ6bB14wah\nG9FsOcKzbjTDsmuM/Jjh7j7D5iKnHjoBqUT3yHG8iceh1RU2bq0TJSHTDZ+Nm7c4d995sf4ZFnqm\nsH19mze84Q1cufyqWPPlAFXWhO2QphIqEugyjmMQzF0kXWYwHFKvNdBsA6dpkRgBC0eOgmnQWl4j\nUzUOnSwsII6d4P4s53d/63/hoUcfIR1O2NvdYrHRwZ9PBAhGRlYNfuA97+elSxdRZI35bMah48dQ\nc0kEVW+kvOHwEdotl1vXrjPMcn7xl/97vDhndzpAzmUso0MWSWR5TpyLaJ/xYESz3sLdndJpdMSE\npCyjqCob21s8++xzDCdjWs0OXuBzPD2FZus47RpBGGM3LN7wwCP8o4/8A0xVwlAMFMdGyiWRPphT\nTBDK5Egg5Uh3W4TfDrBKAFHaApS99lLrI8ty5Z8yHA5pNptVe6ecPEwPuLmWm+XBicM8z8VIcanf\nek20SFnJl/YQpUZLlmUuXrxIq9Wi2WzeDuQsxPIHHbJfK0Rut9sMBgNUVeXs2bPs7u7y27/923zq\nU59ifzhAVXQowlMty6pGlctzUL62MsqmBFOlY3Ke50yn0wrYlDR+CX58V4CeKkRYvS1yL20Fbgem\n3m73lee+vC62bVd+VPP5vHotpl2rrCNKAKOqKnPPrcBXyZwcFFynqWgtjIZCWzYYDIrfX6uYGeGb\nIzIAx+Nx9X2u61ZC+hwhCE/SuAITul4CLGGcKiuQxglB4KFppVZM5FZK8u3hirJ9YVnWHdNM5XUt\ng2gPgpJ6o8ZSd5G9nT6GoWGadjWxJLQl1p3xNRRgpmQ2dJ0kCiswf5Di/m7RNgcF6982PfgakPXa\ne7vU1pWDJGmafJt5793jb2ChVXSQxPSyiPlKyNKkctTf6W+jagYNx0ZWdRRVQzPMwqNMZbC/Ry5l\nXLl+jae+9Qy6XS8GRXRm0xlLqytsb9xEzXweuvcUiTvmS5//NC9860mOdizUNCGNQuIsvH1v5BKa\nrEGqYhsmppmjSBFkMWmeFFNsdRqOTZreZkBVXUfKZep2oZ01jCr43dItVOP2WhTHMZNkjC3p+GGA\nqmuomgFSRpwKk1KKPNQozcjJibIM3TTIJYkkzYijiDhOIMtQJQUZkat4sPgp16x6TUwrep5XpEmY\nWJZClqbosoKqaFUiQhxHhL4o7g1FIVUVyCQMRUZXVGHCnKVkcUSvt8zN6zeK9XbGUm+ZdnuBq9ev\n4c48pqMpaZyhamKzt2oOQRSj2zZqbtDtLXP15g08L+BNb7qfvd0BcZxy8vgpXn35MrqsoCkatmJi\naxaZJiansXQcp1btg6qqYkgaZBKxn6DaDgvLC7DQBVO0R2VdB6eFrOqgG0znPpKkQBKh1BzazRay\nJNYC148Y7A44/9BDGHaDQytHUHWN9Zu32N3ZIXA9Ljz1TY4udTEaDoEk8w8/9CHyhUUCf04qGfR6\nPSI3QkokpFTFMFRUXUdOcmqKhGPZNFdrmIqBrpmkecaTTz7JjfVbTGdzDh8+zNPPPsejjz5Ko9kk\nSmIuX3mVK9eu8dj7H+NDH/wAUhRiKAL8KorIkjy4LipI5FJ+N4vwOx1p8aCVYscyYuRgCG35eSlQ\nF7EKTgWwBNN1u1r/TpuFaAmFd2xOJUtUVvSl75aUi02pZM5eeukler0ea2trNBoNer2eCDvW9QpY\nlK89iqIK6JQO8Xt7e9y4cYPl5eWKQeku9ZiMZ3iBf0cMTyk0L3VPJSAMgqCiVkuAJHQDXhXxEqV5\n9bUH2SJsmygW+YtlXEW5mZcMYpqmRa6XYLVKwFEKZsspt729vepalWxj6XmVFnFEpRC+1G85ll1d\nx3LhjaII0zRZX1+n0+lUwEbXdVZXV3Fdl8FgxOLiYhU8XY5zdzqdAnTmZFmComjUahaqKvzFNFkB\nKaPeEKamQe6jKFJhXuuR52l1fUs2rcz9K1mcOI4ro9bSdLC0N8jzHE1RqVnC+6teXFPLkqrAbcGG\nlTYLhZ3CATCUJTGBlxOGQQV+yg2jNFd8reD8tff1Qe3UdwJj5TUui4DyPZau/e48qJjjinG9m0X4\nvT+yTOSjSsIYWZZUJFVF0000w+TE0aOYtRqG6ZDmYgKvnFDWdQ21sEZotxZYXg6xa02cZp39vRH9\njQnjnS3uO32ccLLPH/zBH7Bz41W0PMRSYX/iojsGEjYi7kVFkUHKUqIkI0gTdgZbmLqBY5vVwEmW\nREzHQ2aj4e0iNk7IpwLQ5BIiDzCJUXVd3MeaTpLHeJ5XJVwkEsR5AqaCZAnWejqdk/siHqaz0GE0\nEu1/VVUJkhjDsYilDBnw44g4TYSpp6qTZDlJDn4YkSZCoqBrBnEUM8crnmkDVRXB1HEQIsUxqiKh\nFht0nkvkcYSexsh5gq4ayEpCLksYSowmy5hSiJrKpFFEkqssrvVYv7XJ0toqr7v3PsbjKSuHjrKx\nscHWxiaDyRTHNqnLObKuEZNh1Sxcb0a93WFp9TB7ozFf+suvsbOzQ820WV1aZjoaY6oqi50FOp0W\nDacBuoQXugRJTDT3aDU7zMYhRLKwvKg3hVNtJkEYke/sIC2vgqaT7I/xbvWRZQWn2SYcTunaTYL+\nCE0xScKMXErRzRq6USeMMszWArS79FKJYDbl2OoRVhaWGI/HzMcjFM1gafkQI1Rc3+fZq9e4+Y1n\nedcHfpAolDBlk0xO0TUNVZWRJRXHrBGmEb7rs9RaYjKccPnyc9y6dYsbN27wwEMPoukiJi4IAi5c\nuIAfBJx53VlUVeX40aOcP38edyZYy4OpIyXAqoYVku/PNetv3wfrQMCs7xe9+QOGmeVDWiavl47o\n5YSdqpuvAVlJsZHL1YZRbi4lgDoI7KaF6ehBmwLf97FNq9I0lXb+vu9z7do1VlZWOH78OJ1OhySJ\nmEwmVYxNyYCV1YamaWxubqLrOsvLy9UGXWYvlkCpnBgrTVTLHL/SELScKmy32xUjIWwmapVxqaZp\nyIV/UxAETCYTUe0V7z0I3eJnqxWQ29/fr2JzSlBRBjXLskyr1WIwGLC3t0ev16vE18LtXBNVWmEM\nOxqNqvNfgpFy8xctObc6NyVwLqcNLctiZ2eHkyfv4eWXX65GzMt4ojLGBoRlhaZpTKZjFEXEZFiW\nQafTqqwVFEXCQKuuuWFoqFqTJE5JC+bKtAySgvUsW2gls1Maslb5V8V9WgJKSZLQDRVVljB1leVe\nl63+NmkcUrMM9nbnlQfYHUxUnqFIkOWCSi8BftlOLe+D0u7hIKh6rd/bQdbqO7m6H/z3Unx88Pcd\n/J7y44MWHHeP792hKEoVg6Jown5A000000TVDMF0ymoRmQJZwyROIUlEAXHfvfcQhhFekFCzbSRZ\nQ8lyVpfbrC2/lf7GJvPxHvvb2ywu9ljtNIlmA/z5GEfL2dnZQE7Ec2fqErqmossqpqGgaCoNq4Yq\nS0h5jh//3+y92Y9l2Znd99v7jHeMecyh5iJZLJHFbootUmywNXS3LUGCBBk2DNuwYAgQYNkP8r+g\nVwOGn/xoARIMyxD0YgkGLEPz0C2pKTXJJos1ZVVOkTHd+Z5zz7S3H/b5Tpx7M7KqZZMAoc5TSFRG\nZMS9Z9h377XXt761MjAlntIEgVvMnLZFUeYVq5px7XZj4jBq5AyrNCUyBlNo0jqdYNDpUBqLscr5\nPvkevueha1F65GvwNFlZNAtoURR0ep1mjvE8D+uFjXdhUdhG2uEi1KrGQV42qnGt2yqKgiLL6PsQ\nKI2mcr9owFcGL/Ih8p2eVltMZV2iiC1RlGALyqriepZy77U3yKqSb3/7V+Hea1Tf/12yLOfOvVdJ\nlhnX4zG7u2+Q5SXj6Yhet8Ow3yOwMZPxivEyZW9vj/7WFnv7R5iq4smnDzk52GcxmTK6uiJdzuk+\n/JTOsEt/Z8j+6SHh/iEQ0DFTktmE6bJkJwMGQ5etZAyr6ZyIZ5RaY5UmVh66siwePubZJ5/ytTff\nxeQarxOwtbNPVVmq0rJcJqzSgtX5iLgzBC8iIiDe6nN9fU5eFHQGQ3pbO4xmCyp89g9O+Mn//Y/4\n7GrCn/9Pt4m7QyaXEwb9Ht0wYrqYshhf09vuM9hzG4aPPvqIR4+eQGW4+8p9wk7Mp59+ygcffsT2\n9jY7OzscHBxw5/QUX3t8+1f+SFOdGfT7eGWBVvaFG802s/+SwWodwtBIq6oswE2XSivINoqiJtBz\nNpu5NtrdcE1DIjqcdstve0FvAyyLW8i8wF/roJL3kjJPEAQEtaZK9EmHh4d1nuCgYdfau38BV20j\nOYlvkAzDZ8/OsOYmrLdtACqBy9IlI63+jXaqvk4BZBKtoLANULu4uGB/d5uq8gla2ihp0d80mZTI\nFNEeiS5OGDNhD9uGeVXtHyPeXzLQ5f7JzresF3exfmhYuMLpu8S64vz8nMlkwsnJCcYYtra2GI/H\njEYjPM9ruivTNG1E3w7Manr9Dsly5UxgK9WwZXme0YljJ6bNMrIsrZmvm7zKtvu6uNSLQaicqzwT\nGZ+u+7Ck34k5Otzn4uKCIlvR2d5pfq8wkp+pn/vwGwPd7k08Rtt8UUrCm2XCF+mvPu/v8kdYRWk/\nb7u4C9h9Ca5+Pseg173RuWnHYHm+T6AUylRoHJDCWpT2HNjR4HnK5W0ulpRVTifs0OvHVBYWS0tV\nVESRx8HONvv9iINBjFnN2O1GdHwYXz7l+uKMn+QVVZFTFjmLqkBlOYFWDL2Q7aBDvlqiyxKbF1AV\n+FoThT7WU00p01NeHU1Wz0GBAz15XpIkC6gM3SjGj0LKIsfXHkWWk6Qr+ts7KOVRloZeb8Ce1kwm\nE6q84NmTp2iNsywxFltW6NJSpTm6NERBSNQNsEZhKjCmohNF+Frjh25eLvOC0I/wAzeesyQlT50P\nYDfu0PUhaJXM5fNf1QLYosxc96BnMVRUtsLU/1UGDo8PSLOEMI744MMP2Lq45ujoDh+8/zFvf+kd\nVmnJ1cUzhsMtRlfnnF2cc+fkkOFOh27U4+mHH/P1b36bX/7N3wRrWX7yAFNWnB6dks2WnO4fs5xO\nmIxGnI0eoTwY7m2Tzmbs7U3ZHh7g64ijvSNMlmGyHG1nWM8jrSri3W10mRNqDzpdqnnK1eUlDz76\nhI/e/zFb773DxeMR91/fQochV2NXoux3e3zlD73H+cMzOvMP2Lpzgjo8hVUCVhPWRq8fPXrC2+9+\nje3ukB89fsyDzx7ypff+MJ1Oj6uLK4bdbZJkRZkW9Ho9dne3MR6cX17y+OkTxuMpWmvefPMNKmvo\n9Qd88uBTfN/nu9/9Lqenp5Rlya/92h9ntlywWLg82zDwsKq2zDLPewIqmRd95xagzUuAtXZ0Op0m\ntVw0P4vFoolOGI/HTX1d/p+mKZ7nMZvN2B5sPVfSaHdEtfVXogtoqMXag6jdqi5ARwTlEvuySl2W\nlrAST548qUtczkk4CAImk0kDIKTkIwGoAiZff/11AMbjMYPBgMX8hiET1kjOQ0CaAE1xnpfFsdPp\nkKbLteuSwdXr9VqlQoh67hwDfcP2dTodlFENayVgTgCWAMYwDJ8DNmL4GtfeUwJ4xbG8ff/lvASE\nCbuVZRnTyZydnR0mk0mdr+gxGAyYTCaua9H3G9ZSnktbh7VcTqlMgaUiDINae+FCba11ANvYEmsr\nqqqgLHOUcv5ZSbIA9BogbuuwZGfe1sHJrhqcOV7geRzu7zlgVbu+m7JydhNJgrFCY68DH2XdTlrA\nqJQCl8slR0dHTTm0zWLdpsF6EejadGUXbaPo7sIwbLRYbZF924fo5fEzZOql0UBrjDVAhTIVVB5V\nXY7TfohWUFY5aeKYE6U0vqeJA02lA0xlSOcrtB/SjTTGeJjK+e5VxmN7uIXuxaTTa2ajMVu9Ib/8\nrdf56nvfJEsT5rMpl+dnPP70Y548/JSLRxeYIuP0cA/fFkSeph/3CKIAz9dYW2GVIStWGOvh+R7a\nC9GeotQeqzynWGX4QUQYdvDjHkpZPD+iMiXzRUqSrVBBjK8DAj/AVob5dMFkNKXX6xH4Gq00sQ4p\nVgW6hCopsFlBnhVEw4AwjFjlZbMzEVY5iiJsZSiU865TxjodktZOAW3cxjcpDIFWBFHoNoTaQ1tL\nVbkqR2EVNqg3iFpT4VEoD095lMCzp0/Y2tknTTOCqMthpwevv8XrueLxZ494/bW3GF9d8+mnD1nM\nxmit6G/10VqxWC3ZPz7hG3/2L8DuLlxdEfWHjC/O2T88YRlM2Ik72IMD8iQhSWYsljMmywmfffgB\nH/3ox9jMY2e4x+72NoPBgL2DA/TBLiru0M1XZMmCcpUwns9ZLlLOnj4lmScs5wt6vs8Pvv8jnl6e\nc+eNN7j/9hsc3b/P8av3WS2WXC9W9HZ2efrgIbayxOMp1/MJd/7QV9j1Kn7rt/81T0YTvvXqayyf\nnfP//ON/xtn5FX/ha9+g1x3gHcaYUmHLJdpzEUqVLfnkwUc8ePQpi8wFlB8fu7Dyv/W//y2++s67\nfO9738PzPO7evbsmu7m6uuLk5MR1sF+P2d3bBhVgayJjjdVvzW2AA+gvAdbNIUHIspjkec5isWhY\nkuVy2QitpbV8Pp83VLIAoy/KapN/l4VMFhZZTCQDS5ggKfWJ8H5Vm3H2ej2qquL6+prj42MWiwX7\n+/sNIBPNUlEUzfm2PaWEvTHG4Ad+U6qU0poAyV6vx9bWFpeXl/i+T5IkjXu7lM4EYImGRq4ripzw\n8ODggItnTwFDP+42zJeAiV6vx2w8a7RRWZZxdXXV6L+Gw2HDGopoXkqT0izQ7Q8aULy9vU0QODNP\niYGQe7FIk6bcKkacZVlyeHjYmIJqrTk/v2jyCh88eIBSjlXa2tpqrArkuUVRlzwPqIwLa3alPac5\nU/XEGkY+WRaiUTUzmNVAsqQoMqz1GrZUchBdVJMrQ15cXKxp1cQ7ShomosBna3eHy/NnxGFQZy4m\nbO04Azy8urxYM2XN2DTGebnUY1kCcosanIoWUADWFx2bwvfb/shnRQB2kiToGvgJsGrbQLw8foYS\nrCJHqQBFAK7fCa2sK3tYRTqf44euNI3S+NaitCIMI4LAI5k5qw9feRRVBQq0DqmKilXqDDeNNah6\nDETdLp04RJuCp5cjrNZo7dHdPuSNnT3uvvI688k16WyGKVc8evAx6WxEspizmK+w4znKVsRhRK8T\n0Ym6FICq3ObK0w4UVVphAo9KW8LQZ1YYijInrxRaB/g6JOpEFLml1w2gsKyKjGScUCY53a09OmFU\nz2+W1Sxx3k+5IbROB+r1XGmyqjc3ZWXAVJRlAVGIrzVe6LNKwFQVXhQSxBFKWXTdTVwZyPAIrCI0\nGj8K0J6mxFJRkasSooggiEE79rsIYpR2m9n9o2N2dnb4yY8/YtDPUcbC02f47/whik8eEYQdjg6O\n+cmPf0gcKe7dPWZna+DKjVrz1lfeYfbJJ1z81m/x5te+jv+d73Dw/e8ze/zEGQxnOX7cI45ihoMu\nxWrIQbbNMksoigpVBCTJivF4xNnFM3rnZ2yfPyHohuTGEvd7TOZzPnv6GFNBHHZ49e4dsnSF0gE/\n+fBTdGfAOC/513//H3C1mPGVr77LW6++zje+9A5xt8+dN96EqmS+WrG0FuKY9z95n+9/9CFvvfIW\n/+P/9D/z9/7hP4Juj//6L/1lfuM3/2OuJgtGowlb/R32j0+oipyHj5/w4OEDzs6fsnO4y3e/+atu\nvQ18SmPZ2dunO+hzcvcOyjg7oDiKmM9mKKU4PnIZiWEYcuf4hLLKKY3FaoWIWHXtHCBr/+aflwCr\nPqQk1c5cE1db8enZ2dnh6dOna+yHsCRPnriYiDiOGlfbdpaTgCxZxLeGq2aRWdXltdlsxvHxMZPJ\nhDRNGwahLEum02kDrMIwbECCGGlq7UDia6+9xuXl5Vp5R8Ts7UVNrjGKIhbJsmF52mL+IAgawLG9\nvc1yuWz8UkRnsFqtODg4YDy+bn6vrEp8dKND6Ha73L9/nzxf3VgN1IDW8zzOzs7YGe6Q5y7Dr20v\nYYxpugVdN05YZz/euL7PZjMMjj07OTnh0aNHVFXF6ekpo8mYfr/fvOZ8Pufw8JCzs7PmebfLUwJQ\nT05OePr0KUdHR3WUS9yAamMMeZYwGAxcxJHndq37+3vEcdwwoVVVUWRpXfaqGUwMvu+YqNls1nQp\nZllVh86ahnWrqqrRoUmZVDpKJa6pKAp6vS7T5YKLZ48JPMUr9+5yNRozn8/ZOzh0TKCxa9oAZU0D\nuEI/IC8L0jRtJougNrft9/uN1q3dCdtmm9pdVMLCCgjNViu6vd5aKVk2Lt1ul+Vy2QAz+VzJLvCl\nTcPPRwoR+IELGa5tSTAWW3fO9To9Z9diKlDWidBRlHlCuXIMlacU2tcEyqOsLMUqwfMCdurg+U4U\n4Ec+Ve6RlQWerwl0gEETxh3KMsfYCryI3lZMf7iFtoZAWd77pW+xmI1YzmYk8zHnZ095+uQxyXzB\nwsIscXYRVVGgtZu3w9BJJ3zfYzmbMRj08CsFBoK4j/J9jNKuq7cwpFnOYrakP+gy3N2nP9gmjmOu\nri7qru6A+SKj1+1gjE8Y+KRJAdZ35SdT4GmxxKkoioxuHIPn1SXCgO6wSxSHpMuEPF8Rxx7d7gBw\n3nZVZcmsKzVqpUErjNKU2ifwA/yui3KxRQE6oNQaU3dQPv7sEYNujzIrWSUr1NMzhsevcLh/xPzq\nmldfeYUf//DfEvV7HOztkec5/V7M/t09Pn1yzdnImSCDgo8/hldeZdgbMP/xj4mVBk+BdnbxAYZh\n4LksReVR5WCVh7UVq6pgOp8xno/J5wX4HslFwdHxMW+/9bpbB3NDv9/n7PGSJC+h23V+hYMhydUl\naSfm4WLBb/+9v8df/9/+D97YP+FPfuePcrx/wO7+Dks0f+Nv/23+l7/xvxIPehzv/IDxaM43f/XX\n+GN/6k9z7623uRiNSVcFB0fHZGnOPE349JOPOD8/586dE95+9x0qW5JXTpawqOe173znO+zt7fHm\nm2+SzBccHx+zXCyYTqdNhajKC3pDRzB0utEaeLotLqxNMrwEWBvlPFmA2s6+oi3qdrtcXV0RBEHD\nLklZcTgcNoJuaw1h6DqjVpleM2Dc/CNAR0pcWZE3TEK/33ellFqjJGxWmwlra2OUUsxmM8Zj1+02\nn89vROWr1a0lnNvQdls/1nbbFtG/vJfoaPr9fmOyWTUu5E7kLizMYDAgXyV4noLSrAE4AXy+75Om\nacMgip2CMDaz2eyGiq+fkdy3brdLVQMTAQlSVhMQKlovKX822q2qIkkSfC9sAKEruXabsqN0jTa5\nYTXIaMpY1c29awvRBYgIG1gUBatk2YBD8fZ69uwZcXzjmD4YDNZigOTeCrhuZw12Oh0ODw+5uL6i\nE0dgLFdXV9w5OebjBw8ZXV07e4n54rlSnrUVYDG2bMqDcn1N+bEue7c1hG2d4OZEc5v+Sr6Wa5JN\nRxtAyT39PCb45fH//+jGPf7u3/27vPPuV/nKV77CZDJDhaED/Vq5bNPGi6wexzgrmdraDjCuTKIM\nHqC0xhoXlOuaWQBJuhj0UVWJMRVdP6g/EyFY97kqK9d9p62ixGJCj3DrkP7uCYGv+dI3FKZw9jlF\ntmIyvaYoU5bLJZPJhPHVNdfXl1xMJiTJkt3tIYt5gbU5CoO1rlNXWfCsRWUpvSik1+mTJRm67qpV\naYb26gXUKLZO7hIFPoEXgrYM4i4rU7Iq3eYhzQsUmtl8SRQHZEVFLwxBO0NpqRa4daKH1vJ5qdA6\nQPs3a01Rz5vKu9HvWls5LZa2HN9xm+55umS1NNw5ucvkasZkPOOT1Yd85ctfh88eMZtM2R72sXnJ\nsD/g+MCtA92eD6/chVXGq+98Be/hMx589inz5YKdQZ/w4oJ4MGDwxuuMfvAjQlPS9wPoxRAGqGyJ\nVzoBkr+7BVlOWeVEQY+d+we8qmCVZ6zyFJRHkuVo5eGHAVlhWaQLysAymi9IfJ+82+PJaMxnszl/\n8a/8Zd756rtcnZ3z9KMHvP/b3+cf/u7vUixTKiqejC84T8Ycfektvv7L3+TP/safm4QxtQAAIABJ\nREFUwdMhXhhR+QGrCqzn40Ue8zRhe7jD+++/T1bk/PKvfIvjk0Oux2NGkzHKg2S2qDuYDV/+8jsc\nHR008/bDhw/xZb1YOYubbiciTRb0e66CZBRofeP2b+oqgKod/d28ZevuwpcAa727piWyFV1ImqZ1\n/MmQ8XjcAApJWZcAXwFlxlSE4aBZyDcXGVmUxRurXaI0WKbTKdvb2zdsSVmt+QPdBrDcpOWyDS8v\nL3n77bc5Pz9vFscXASxhWeR82qL1dk6WMHwCWNoMy927d3n48GETx1IUBZ1uH1tUVBUNWFoli+Zc\nUKZ2To4bVkrOo63zaYMU6VAUACWgUMqzeenKm5PJpLmeyWSCF/hN15/s4PM8b1g9ed6S1wfUQdNh\nYz3hvLlurBI2n6sTObpWbK09lLppbFB4+F6I9dwk6tdmdHm+anRV7nW9RoMVhmEzfgRg7u3tMZ/P\nWSwWDdiVTsJOHLO347IYbWWoipyTo0OenD1jPB5z994r6KVrV8fW99bWugFj13Rqou8DGrG7gCMB\nwu2NyCYYuo0al2cpzQVyne3n2AZ0VUvj8PL42R5/82/+Tfr9fhPmHkURYb25iaKoiW7RSuHMPLQD\nUcqVEquqdIuHNmB1bU6smsXGfRacttCYEmWV61SF2pSxcsDnZnSAct9VyiMta8NfU+LRsrqxHWzo\nc/zGIdYWzfxT1J+jqshq8G4xlStrZ8mSJElYZQl5WlCVGdVyznIx4Xw0YnpxQZG5Oa0bho1/lVdB\nVDl7El8XaFsnC9iKftyhcleDF/pEfkAcR3hRRKU8lK+olMFahVbgB0GttQRjFPPZjDju0ul08EQY\nb02juTrc3yPNszorVGHKElMW+FqxPRzS9TuUq5JnT5/xwU8/YXu4z53jV9keLkiXc3pRSOdgl6Oj\nA1ZFyun9+yyWY8xojN7ehari3mv32dnbZnR1zWePHjHoxLwaBlBVTBZzjreHUHeJM70mz1aEWwPY\n34N0CSH4ygFPtMKaCovF0z6mAs+4DsJVWTBazJnNlyR5QRIoVnGI6nX4R//yn/Kn/pM/T/fgiKm1\njEvD6+/9Er/xH/1Zpk+fcfXkjPOrc86nV+QBHL/xCq+99Rb5PKe0HrYylCanMppKOXCjfY/JbMqb\nb7/F0dERe3t75PmKZZFh51OWSULse7z66qscHx3Va5qEW7vMSFklta03ibWWShheMbjZ1JXKPNr+\n+iWD1T6BWg8URVGzaBeFK5uIhkkChefzOUVRNIthlmXuISmF73uNUaOAlbWOQdm11CUXWYAEcM3q\ngGLpHtOoNdPHF9V3BXAJc3V6esrHH3/c+BjJObRfRzRXba+v9nu0r0GMQMUawFpLkrgymSuJ2qa8\nc0Oh0jBoTpTugGsU+40wvwl1zspmob1hAx34Eh2SeJR5nteEsoqdRX+41XQYDgaDxq+q2+s1QEyu\nR85bhNZyj9plKXm+0sgg59kuJwo75tWeV21mC0QEfpO1l+QJCpf3tVh4zOezGtQGdVdTr2HN8jxn\ne3u7Yc9OTk4aPy+5jhvn/oy9nW2qImeOqUNlPfb393n46GytvGgqV6ZUtgKl0Rp8FOkqo9PrNgxh\nmqakadowrXI/hHWVMXybvvBFIEvKiXIPBbgJgG97e708fj7Hd777q5ycnHBycsL19XXzOa8qSxR1\n1uYqlz8nmsobfZ2FWmNVgQWLG/cuH0A5QTquOxorf9NYZfDN+r7eqGaAYK1jTm1VQVVRGFc+87w6\n+kZXXC2XKC2fxQAvDoi6Gl2/ahAEKFuHNCMaPoOyGlRJFHuYelFdLpfMZ1NGoxHTqxHz+ZzlYkZZ\ns3ZpvbCWVUllKnRZsqMUgXVl/k7QQQcdMuWRpRXFLGFne1gDS+sMVPHQHm6BLp03n8JQ5AmLuWOi\n0c6FPu52SOZLUmGyt4boOMQjJ9IRno5YzUr+xb/4LXa3D/il936Zi7NLnj19TDeMSJYzotMjUJb9\ng10++uQDsiLH80MKa9HpkiAaOEf24z36+1tcP37C5dNnfPjxB6xmC1aLKf1eRFdb8DzoxGi/BsLJ\nwrFaQQcCH0wBZYEpVgR+jG8tVWVJRlPyvKBQiqSyjPKclTGswi5Xs5z/65//M77zJ36DX/8zf47H\n11eUkwX7915ldHHNv/rJ++x2e+y89gr7X3qDL2tYUTArVlzPUiIvRtua7bZuE6CUwtOOFFiZFb1B\nl62dISUVl+MR4/kM5WkGgwF3j4/Y3toiDpzMJltleNrDD+qxXftYGVln67VRNpJeEFDhnu0ayNLS\nNCTj2vKLNIv9QhiNCoslh3hiCWMli1zb60TcvecGBoNBw9LIIijMT3vhkQVUFnUxHo27HXQNrMSH\nyNvY6cuftmZKGAgBcyJ8f/z4MWmaNgvx5iLYNhRtC5RldyjX4Ps++/v7jbYsCILG6mE6nTa/I2an\nRVFgi4ogcNc/Go0wlfs6iiLi2EVUiE2DtZZAB2usiAA5YYw6nU6j/5ISVpv9kPsp56GUakwGpfQX\nRRERtmG0RH8l7vCOubqxQpBynzNETZr3bGvVtNb4gUccdx1TZW7y+Hw/oChv7vH11ZiyKOoQao3n\nBbUNRUqvN1hjGduWFwCPHj1if3+fbrfLdDpt0gREoxZ4ljh0Gr27d0558PAx+7s7XF07LVbc64NV\nFDZfAz26iXcqm9dKkgRrDGHtwyUAVe5JuyGifT9uKz3TYl7b8U2i35Lu0DYj1w7rfXn8bI8vf+kd\nKlMwnbhN4tZwpy5JLW+ep1UvLPXePOsNdh6FpcJWDtA08wnWaXZUhTauDKmsqhcfvV5GUZqscIDb\nKh88l5dpDJgyo8JpA50cXGGNxSintZKzmdQbTK3qz2C9idBaYz3N07PLRrsV7R5y5/gO97RuAJBS\nDhQKA10VZc2qFnhVyeLpM5ajEZdX50ynU64XUzeHa4XGMFk5gBp4iiDwiWpdoUbhWUNRrIh9Ny92\nBx2CnvscFcZQJLljEQP3ucxLt6gneUGaFlRlwfXZlP2DO7z1xpucnJwyGY15//0P+Kf//J/wta99\njTD2IF+we/eUnfmITx4/5OjokGGnzzJfYdIFFBlRrwvHx+wNh/Q6HT754EM+ePAR77z2OtTmzpQF\nVVXiRwFeHGE90BrwvZrigZIKozRKK5TVzMcT+oNtKuXz5PKK6+WKzItIqXg6W/CPf+dHfOkb3+A/\n/4v/DZNkweHRHUbjMaPxDKU9OoMhGZrzRUpZ5ajAJ+xFaC8i8DWV9VB1w45PuyrjRsDWoM9iseCT\nTz5Bt2QnW1tb7O/u0Y8jTF5wPbtu5nun93UVKGU2ugNb0hmDdf5p3HQJPkd4mM/3A/wDC7Dai7kw\nFtJxJwDrtddea4TNwuZ0u10HtrK8KW9UVc1uReslFXkPY0zTySe7Lnk/0dsII3bbg7rtwRpjm3LX\nxcUFp6enDAYDZrNZswhu/r5oYkQ7pVBrIOs2ANpmsMqy5PLykjAMWSxmTRhqu6TkOgxTAs9lLjpx\nNg2rJm7qR/tHTVRR2y5CrB9EPyUgTtgsYwydToez84vG0V3OoyxLVyKozVeDIMCo9fKU7NjF68ta\nS7fbJU2diaHE86Rpttas0GZhdB0xdNOyu15itZamE5SaoZIYGwHgMo6UUnXguOvUlCzGfr/fPJOD\ng4PGeiPPcw739jl/9pher4sXRMTdPo/PzlGB03ONxnPi3vo4v4l2gNJaPF81wCkMQ2fOOBiwWq24\nurpqAFI7Rqddpr5tE7DWvVaD9nb2oABfsaJog7aXcTk/n2MydyHgLuOyS1ZWUBaNX1zz7J57hqYu\n53m1yKpehJSpQRmNRqUNmpTvxr+1zrjUGutKhXVuJ+33sBpTR035rVzXG/sbhSlKpwGzqraZWJcU\n9HtbMsndbCKNwVZgS8tg65AKB6bSrGKR1GkZgcbXHmVZoa1cokYrB3j8UOFjufv2IZ7W4LtzL5cL\npuMx15cXLJZzRleXaKCqSrIyZzzLyPMZtjJoY9gb9vBrlllKtL4fYZXbXGq/hw2cO/z1NHEl2qCD\n6nawq5zr6Rl3Du/w9rtfB1Nyfn7O8ekRnB7hdzzm+YJBNEAd7vJW511+9IMfsDQlZ6MxW7s7zOcT\n1xU/XRHlCf7WDvErJ7wThySLGZmpKH2FiUJU4FEVBTbQ2MAHT1GucnRVYpDqR+mCq61GlSWdeEgQ\ndJgsUkbTFbmJ0L0uTx8/4h/89u/QP7zPf/dX/we09imnc8IcumEPKugP+04rpxS+go6WR2kwZYk2\n1llz1DYjHsqlElhQ2nX3ac8jKyuMKQn8gJ3dLXbVNr4X0o1jxpfXeNatZf3ugKoqSBZLlHEbP6Vl\nnWZt49iMRdl0eO75a7OxDiPsqX4JsDZLhG2tk/xdWJY8z1kulw3AEjNSWRyX5aIGAhCG/pqAvV0m\nlIVKRMrCwChPsUyStaBpASHtclQbILVFxuKKLhYTo9GI/f19FzGwWNxaxpFFT7RC8h7tiVb0aJeX\nlw3IEtap0+kwmUy4d+9eAw7duWryGgx0u05vsEoWKGUJok4TybJa5c9l3m1qvzbF/MJuAWsAS6wb\n2s+x0+lQ2XUTz00bDGGxpAwqvlZFcSOi3/SAakf/GGNA2Xo3pBsQZirXiGVrNsDzPPr9AXHoStAX\nl+ckSVLrraI1HZwwaG1QPJ/PG6C/t7fX+J2laUroaYZbrlQ7Gs9Ae5yennI1mbO1tcVk6oCYVl7d\nVq9rZc3N/ZT3kjJ5r9dbsyBpNxdsgrTbwP/m5kVep80SC1jv9XrN1wKm200WL4+foci9FUHVlNG7\nXXq9AUmyuLnvt4Bcq1x4vfybNtaF8QHWVE0pUcrjMh5u5hOFVQqsdzNO6h2/dilODuhRf97rf3Pl\nnwCtubWE3B6Ls7rF3tsYo45N81lMV/i++/z6KkCpispUVBkYbfHwHTxULplBKWcD4T4ziqQyqMpi\nCuNKkbpL77DP3v03IAwgSRzArArS5YLFYsEqWbjPs7VMr69YTGecj0akozkwbzXGGMpHI+I4bDaW\nvu83kWKr2YKt/VO+/Wt/Arod5o8+47W33mQ6nfDDH/6QaTIl6IfYCILznO6dU9791je5vDjnpx9+\nxF3PY9ANsFVGZSExOaRLBv0hamubb/7Kr/D+D39EWubkk1HToONpjc1zSlOgPYVXeVQ1wFLK0gl8\nTKUxuaUf9bi+nrPIKqJom+nlE37vJz/kfJWyfXSP/+Iv/bckqwxfV5zsH/PZZ4+4c+ceYRRxNR4R\nhjF5mdcRYArPOuYvDkIGvQHLsnRMkrFQA2+tPXzl4fk+yzRhMBwQxB1Xyqsbo6rCMBtNCH3fxSxt\nrIO+cj9nbLlWEn9OhrOhN1UbOtPP8wb8Aw2wpKOtnZkmDJPstq+urnjllVfWNDij0YjhcIgfBWRl\n7kJrgz6er9ZKeW0GyzE8eb3QWDxfE8Uxo/HoRivT8sUSpqOuhD8HlNz5O38uObfRaMSrr77KYDBo\nxNubx3rL6Q1ib4NLWfSEcRPTUOluWywWa119TeeithjjJszhcMhyNnf4P4KyrIhrxqfT6TX3V8px\n0lwgbIp4Q0n5tSn3RRGr1appv5WFuc2yCQiWrsTVatWwZrLQ+75Pslw0uYfuHJyjdRBIBqCt/yiX\nMxgFRHFY7+KK2gfMW7tvTvNU4XkhQaAZj8fkYcTW1hbb29skScJisWj0W0EQEMdxc7+la3A6dTqR\n+/fv0+l0uLy8ZDqduuzBwYDp6JrjoxOqquLicsQqzTk9PmniTHxPUeUFyr8pm1hHOVBUFUVVEgQR\npVhLFAXT6bTZFMiY2mxRfhHIuu0QprTdPdjOupSQXvmZXzQfmf9QjvFo4nSmuM1YvzcgikMqY0jS\nrO6u1WsLzJrrPxaFh62F6loWE8BSoWWGathc3YAzhQIjz7ZuZFAWbQ3gWLCyyJyFCGBr53aN0wMp\nz4OiXJsDDev61N3hVv139Rybqoylo0OsKSmTmrkHIs9tljxPUdUlSqctM1BJqRMKIEeBp/G8EE/E\n61iqUuNVBqtCPAW+H9Hp9Okc6pr5s479w4O8pKznJdm4z6czknSBVq7yIdWItl2LPx6hrkf8zr/+\nHXrdmJOTI7ztIbv7O3xrK+bHP/4R89WMlcoxS8WdbkTnzikHUcA4y7keXxH5fUyeoMOQ3vY2SZrx\n5OwJh0VBGPf48te/xvWzZzx9fEaSLAiikE6vR9SJ8UKPIs2IYhcbZKxGW4WpPKpSYQrD+eiaymgM\nPtdXC77/u+/z6WTCV7/9bf7or/86915/m88ePuJo/4jZaMxrx3fwvICLs0u6/R5+EGFrNlVb0KVB\nlyU6N+TFCh2FUGdDajx8awiswrcKZXANGZULxi6qEmMgX2WObcxyBtt7rCpX1cjTFZ6n6ITRjYQB\ns1ZxkrFTUQEuCUMEV6oe16h6g+AGmdMe6pci9/UFoKooqwpjrQtBrfUjVVVRlCV+VXH27Bl7+/vc\nvXeP8WTS/GxeFGhfoalzBMuMVe5KUk5/4KG8gEWyIpRORVuyWiUcHR3w+Owpg+HQsTLpisFggLJQ\nFWWTz5dlmfMJqssrUhcWMLRYpvR6Lmh5PJkRd3qMxlPiTo8w6pDlddSP1hirqMqKLC8pSoOpIIxv\n/Ic8pR3163n42sOUFcfHxygLqyQl8HyCrs9iNqfT6TCfztjb2+Ozzz7j9M5xw6y1swjTNMVSYfKc\n/qCLqVyHUrbK6fW7PLu8YHd3l8dnzntqsL3V7LCTZAWebgKClVasipzSmtrITxF3IqjB3Ww2azoT\n4zhe893ylc+br73J+++/T6/Xa1gkT0OZrxj2uySLGZ0oqIGWs+o43N91DKFXB9QGiix1MQpKw/aw\nz/7+rhNOpst6gSjYHg4bzVSv16PIMrI0ZdDtc//OPdI05dGjhzWQ9EiSRe0HtoXnKdJ0ie9rvvrV\nr2CtZTwe0+3GdLsxP/3pTzk6OiLPMtK0wNce/c4Wi/kFofbY7vY5V4qTvT0efPaQ/sERabbCDwKM\n8UhXKb1eD7taYYxbE9vPLo7jZpFtZ1y22bYbXyzwfU1R5PXifFMmFtF8u2kizwuKomwMc0eTMcYq\notjt1i2gveAlIvpZM1i9TuNtZq0B5XzkgjDk6PiANMnqMslmecQNEG0VVhkHpAQ01YBEKYU1bjPi\n/r8JvG+Am9s/OqG8wnOsFe5z6inwtXNvr4oSjGzeXFnegBM6a/XcRlM2pJsaMbmO0A8wlYevA8Cg\ntQ84X6oiK/BoX7N2m6qakas0js3xXKekrbstjTUUxpJTEQURpSnJC5eh6DmrOarKbcSUjkB56KBP\nEHvEnscQOCqrZnMhLLtsRPI8pywy/DJn+eQJFw8f8MOf/oTzZMmbGHa2+xT4LEtDsVgy0HB8esps\nMaV8VDDYO+DtP/JNfvrP/gnnoyu2B318DeVshrWKbqeDrQxXT56yf3TK3uEhe7sHJNmK6/EVl6Nr\nphMXXq1MhdU9wtCnqr39jAVVGorSEA23yZKSf/47/47/8x/8Y6penz/3X/2XfPN73yOxhmfnF9y7\n9wrXF5fEntskX1+PXQxRFLFIE7zQfe5tWRGi6QUBgacoqtKZ2/pOkhH4Gs8asNaVfauSbqfHMk0h\nz+n1+xA6HZa20O/3ub6+Jg7CRprRNgjNsoy4E64rC5XTCWpVVzHwudlD3IxvGTfyer9oVjO/ECVC\n8QJq+wHJjl50UldXV/R6PQ4PD/nggw9cOLEHeV7W3TPibVKivJvdVVu/0/zRFl0LB6MwJI7iNYGz\n7PKlLNPOF5QPX7uc0maRRCPWLi8KIGtbRQhQa7MGsmiK2Wc7H25d92Wa+7O1M2xYNjm3Gz8r5bIS\nTUGgPGdd4CviyHXwFXX0RFmWTblIrk1iadqlAbkmKac6gLl8jlFx7KG3dr9EoC/eZcPhsGa/bNO1\nKKJ5Ka12u9160lMURUaSOCDq8h+Lpqu0Hcgtpdfl0pm4Hh0dOYC0WjVh01LePD09JclWLQ1f1ZQG\npJQjdhHt8HFh5QCur8Yc7O+zu7uHAUbXEybRjJ3hEN/3mU6nTMfXdHoDtFIkK9f8UBhLaWVBs89F\nI8m9vLq6asZXO2VA2MA4Dte85G7o95ufl8DcNHUZjBJmnWX5c9leL9mrn89hlSFZLd3zEBbZj+tu\n1Rm+H9YaJ7tW6gPXCed5urX4OMG6+9A7oGIUoCyK9TK/lvFQVqDMjc4FXQO1G6+0SinKyjnI4wWg\nboyRrbJYZbCNRcTG9VXr2lHdGkaVgtRkqEYTbQCn6cJzYc+2ubp6LDZv4rrCPGVc7JRt/ZwyN7Yn\nZeV4O+1KjXI61vqo2iRsPalgQxNZb+xRikrmuyDA89xz2Xl7yMl77/Hl2R/n/R/8gH/z8U9RRc4g\n9oh6e0SBpigrzp+e0+1F6LIgVBWRWfGlb/0S148e8ulnjygWUw73A/pRlzwtMekMX/lcP35Ct9sn\nrrWnfueEwe42o9mUxXSG5/lcnV+RFRWdXo/+9g6drW38sEuVFTwdz/jrf+fv8E9+61/x3T/56/yl\nv/Lfc3DvPhejMUp59PtDkmRFt+cawibpEq8bus12sXKZf6a27LGWSllSYyi0hxd5+CjHvmMplcGg\nahBUS2i0Ju671y6MRdmy0XwWRUG3ezNnRn60Ns/Efrd5pu61rGMclcFTCqyu12DxOaTFcLVLh9L0\nwUuA1Xz46kWt3XEnAEOcqsuy5OLigr29Pfb39/nwww/pdDpkebo2mYhYV2vd6Ag2Bb/tfDwBSZ1O\npylpCRiSRU1KX+1A4DaTJa30svCladoAw01bAQFGcp1t7VV7kWtrgNoeVe2/y6TY7boW/zzPm1gf\nEbnLvTBWNWHaKrihwcV4M89zBoNBoz+T1xYWatMfS/5dOgfb90a61KhZRXmPKOqQ53ljxnpjWBo1\noNS0AqHb3YJyHuKuL4BCXkPsH8Sc1vedB5cI89udn6LrE0DfaLdaweDSbSqlUqAxoA2CgLt37zqd\nVBRz/uwZUegTRp21Uq/o0o6Ojrgav08sE0KdIZmXFQq95j8lGwwZ9+2dtbCcQYtJ5RbT202dX1uz\n5srNN6kEDnCZWxmHl8fP9vA81WRgGlO2dKfUc4p5YdnXAarWs7Gbz8pDKfMcMGuXXHxN4/nmSoTr\nUgWjbgBN47LQWqzcr3rOBuI2EK43dTOb1+DsIhxA0g2z1vx/8+fX+Ax3Jhqz9v327zQNGvbmvrYq\nplg0SrWzUdc1ZKZ6/twF7BqlybTi0dNndPyQr333e7z51Xf5/r/8F3z20U8JbMG7X3qDKkvIypRy\nkbLKC5arlH6ypDMbsHdyFy/ocv70jKvzETMmnB4e0Q0jlrOlqzrMl6TLhFWRM0+WLFYpRrvxsb27\nTxB2yCpYJis+/PQJl//uJ4yXKeP5kn/1b39Af2+fv/rX/hq/+af/DLM058PPHhF1uvQGXSg27Ib0\nTfedAjxP4zTkN7Ia6g5Qo5Ur2+LsPZSiCWBuNtWqpR+0L55H5HvtNU7Wtud+3ur6WVh8P7y16awt\nhXgZlXPLkSTJc2Ci7fkjD3u5XHJ97dyxT05OmE6nLJaztUzBtnDYU/o58LUZlttmjtraFFnUZDFs\nM1ubYjqxemjrpLLMaSokgqTd/SWv1WbANoXz7e/JOQnobJco22J+ySYUgCW+YmVWlx4qS1kaJ1ys\nS1BVVRHFUQNGFotF07LfBpxyj9vgsLFqqMGMgCR5FgIWBGTK191uF9/3G/AgvyO+WuK0nqYp19fX\nDAYDut1uwyq1x4uIwkUUPxqNsNays7NDmRfEcdxkOcZxvObp5Riuqi516sZ6QUSuAvC3t7cbJkjG\nkJR6VBiyv7+P8jRZnmJMRdyL2dvfYTKfMZvPGfa79HtdisL57gj7Ja8ZRjcWG+3JQr7XLheu7bg9\nj6I1ZjcBFhs6xPbmpb2z3ATQwEsW6+fE1G8uDreZ57af8+eB3s9bvG4TusvGUL4nc0mbnXZf33Qn\nrr2WAJYNjYy8hrdpAvwcwHLSBEFIm1etXjDknAjfuIgfq59jJ+QuGFWz94j2Sj33c25uct3H7Tnc\n3Q+7obFV9eJuKLHkhWVn/4hylfPwyRmx9njvD3+b09O7fPLTH3E2WmCLFYqKuOPTDUKMDTG5z2pR\nYC8m5NMlB/0d7m3tM7m44urRU1ZRh92dnTqHNKOoSkoUnrH0/IhKAX7Ag7NLvDACHfD0csLv/fRj\nZknCztEJJ6+9xX/2tW/yjT/8Rzh95TXm6Yq8MBzfuU9eVo1MQgBVe33BE6azvmfezfNRdSmWjeer\nWv9JiVpK1ornNaJKqzUbhef+fWMDf9s89KIYnHYSykuAdcvR9kBqd4vJ4iAsgzGGy8tLoijijTfe\n4P3332c8UU3enExUDcPh+bc6sLdrtG2QI39vP7gsy5run3aZpv167YmrHfcjgKDtX9R24G7bDbR/\nf5O9cpoN+xxrI78jGp35YtpkLrbLeFVVOf2ZUhhTrr2PLLbiTyW/K4uvMEliLirX0HacF4uHtoDd\n/bvX5P2FYchs5kT5Eu9jjMvKcuURx6oI+Gov/sYYZrNZw6gJ0JTzEGAk9gltsDydTpv7JK8r3l7S\ndZnmWdMg0O6c3HTWty0GSgKvtYXt7WETfO15ik43ZlD26HbjpjPm/t17fPTJAwyKre1dZrM5ZeXC\ncTdjb9rNDnLP2zu+tvFqGEV1YLW9dZKSr6UsKmVfub+bMTztcvjL42d7bC4mt7Wiby5Am4vI54Gs\nzQXrOfa+FrO3GQRnXqrXNrVGdFdmvXTsxMbrzNLnAcXnAZN5IUN129ft7ze6rFuKP+oF9+OLFtnf\nL0vr2D5NQUmWZFCWqCB2wn/P4+DuK2zv7PL40adMr68YXZ9zeeW6FIPIp9OJ6EURB72EbqDpdbpU\ngcfBwRGnR8dcn53ze7/3Q+7evY8fROjAp8hyVllJVhlWZUVSLugeHPHBw0fRnz5OAAAgAElEQVQ8\nOXtGXhoO7r3O9957jy9/9Wts7R5QoiksPDm/ZJnlDIbbJGlBkuXs7R9SZM68WGn1nCRFgFf7vngb\n92dzE3db5eXz/tiWR9vn3f/N19xkFV80x202A70EWA11frOItTs45KEKa6C1ZjqdEgQBb7/9Nltb\nW8zmk8a/qd35UZYuVqK9SKqNTix5PwkklsU6y7LmPSVsd3Oxa7thtyeXtuBTDDjb5c7GmqFV/pKB\n3l7s2qVDKYu1y4Ltsk6aOsH05dV50/XY1kqVNbsWKM9111qzxpY0bEhRNOHaUpYTU9d2iVOuQ7ys\n4trtXa5TwIpSumGK5OfbDvpS2muX8cTBXbovj4+PsdYyGo0Yj8cMh8M15/Z2kLcACK9lGCteZK5p\n4GYsiRBfSm6ep9cijOT7ooUSECoCTQHxlWfJ8hXWVoRxRGAtyzRDK+hEAb1OzGg85/hwnydnz0hW\nOZiaTTVOcFyWufObaWn/2rYl7Ulk09rBidZfPJm02URpmxYLEgGwbVC3yW68PH72IKstGbiNRb8N\nYP1+2KwvAliY2l+91mE1jtgqWCulibjYajcGGvG5UBtqfcXzaj2MqnPiGmC1hnxam1r7XHWP275z\n48zdKiOp2zVeMtal69CRXhuLreTYbbyTtY6F0Y1ut2VT0tgBKMKgw3S+xFOa3a0dqEpGkwmetezt\nHXOvM+AVDIGn8QPFKs+4uHjG06dPWYyvWD655GA4oBjCuCpRVcne1had3S3eePerfPjBxwy3t7Be\nwOV4QroqiXoDwqiD9TV//7e+z0opDg9Pee9LX+H1N95i5+CQVV7xww8/ob+9y/VoQm9rm4OjO4ym\nM4rKMBhuka5yfLRz2Fe3lJzreUdsNm4DMqIBfNGm4PMsPNoM6W1s1KYO9Pb/ey8Ezl+UtvIHGmC1\nwUV7l97WSglwEkDx5MkT+v1+U0q6baDIAJJyyG1xIlLuEQATRVHTdSXu6WmaNg7lAi4EnMnvCTsi\nIEFKbMJgiZ6nXfJsC983RcrtyVZE1nIdm/mFWZY1ZaxNsBaGIZmUT+U9LWtgVH5utVqxtbXV6LAE\naAl4kg+JAJxVLRrXdbeleMa0d+aysDsfs61GH7VcLvF9n/l8Tr/fbe6PlOcE7InIXTrehGFpe5MJ\nu5ZlGcfHxw3rKecjLJZo0NomrHme44U+VWVb2ZBCnxcURUWel86SYTpnOBySZQVZ5oB+Web0OhGm\ndqiuKkuyWhEEHtvbWxwdHzBfJvi+x52TYx49fsZyuXQ/WzovIgc6ny8Ti4ZLSrZtBvXzbBoaRkrf\nsLPtMGzZgNwYi7LGGr8sEf78wJV85tuf99sYzNsYrN8vyHoRwJJok8bhf2NhNKZ87vVuW/g2/y5A\np5mz7K0XX1snmc9lql6owVJebRFgPxdgbbzlmhDa+wKW60Y6csvnSmmsDjg6GlIVGfPZHB/L9v4h\nGsN0Pme4tevc56nAU3hhl/2oT//4PrrMKceXfPLjH/KDTx4S+R69TshVuiTwNBbD1iv3SLOcs8sr\nLsczOt0B+8NtSr/DdJHwrT/268Rb2xwdH9PrDVjlBaMkI4w77J/cIc0qTu+/RmXgcjRGaY+jo2Oq\nyvLkySP2tgbr5TvLray594K5RSn7ueNP5ucXMYjeLdWazwNXz2cA385gtTVcLwHWLYcsbO3yWRtg\nySIj4MVay4MHD3j33Xebckf7oTa6pXowNcaj5sb4sv0wBEi1PYeUcloZcZQXk8Y2EGuDnXZ8jLyG\n6GfaIHFTQ7U5IDcHR1s70975CrjyfZ+izBsRuCyacg+aDDta+htFYxgq7yEWC8Ph0MW/1P5XEvcj\ni7R0n4leqygKrq6u2Nvbo9vtNqCqvSAcHR0xmUzqtuDr2p095d69ey7G5vq6ieOxdfCn5AJOp9OG\nZRT2SEK+pSlCWKokSTg4OMAY414zcvf++PjY/bznNd2JURQ1bN10MVsTprdLjiJqF1DZ6/Uoy7Ip\nyxpTNuJx0aFNpvM6MLbCV4puHFGVOSeHR1yNxlxeTzk4PGKVFY3uzPP0GqMhz22T8ZPPxCZj+nkL\nujzfm9LqDbB2Inj/37ts8vL4/8bUv6iM+yL2qi1S//dhszZBklLOabutfrJKDBtr4F6LrPSGD9cN\nIWVvZRa8TWikbikRvkBrtjZ3b3Qhti9L9FP2c0qJmwL/ptXf2maRa3RkqJq5sk0XpZRKn7cC0PVa\nYsmyFF9rDg4OCD2fqszJ0hVBp8Myq6sC2ifwfIy1FEpTeR5WB8RHEe+dnDKfjfj4w5/w+LMHqCKj\nMjmj0RU7O3vMlymlVRy++ipffufr3L3/OkaHTJcJnf4Oy8zFbZXa+Xr5nsbika5yJzJXilXh9L+D\n/pDxeMxyueTVV+6TLWc31h7tOcI2CLW+2ts1UuYFvXkvZpy+mIX9ovnrNpbq8753G/j6Aw+wRPB8\nwyCsR4DIQipaJAnCffr0Ke989ctMJhNGoyuGwyGz2axhCAQ0SYnPtkCMCLI7nQ7Pnj1jOBxSliWL\nxYLhcEiSJE0H2SpbNQt6HMdNmaxd95Vyi4AypRTb29trXWECatoaCLECmM/nDdPlyj45w+GQOI65\nvr5mOHQ6H2HRZMEMw5B0nqzpsbpRWNsYOICirCWMQjyrWK1SsqpswKQAyTRNnTC8VX4Sm4Rer0eW\nZY24PE1Toiji7OyMO3fu0Ou7DKqLi4tm8XeRM5rFYsHDhw/Z3d1t3NCNMRwcHDCfz9eMSNulOwnO\nFuZQGK72PZcyoJQYhR0TwDe+HjVAtN/vo6FJAhDAFwSBKzXPZk2Q9WKxYGtri6urq0YbKKxekiSN\ns7o8p35/m9liQVE63V0c+izTqgGKg14Hqzwm85Q7J8ckSxfAnRdV04VZVTcid3Fxl1K1vN9muHMb\naEtMkZQy3WYkXNMQCtgqy2otOsf3vefsRtp0/svjZwewmmpda457EVj+Iq3K54Gr20Tut0V2taN5\nmvNRz4fPu3OubmUYxOD0ufNfv3q+AF8913WoN9Vbitqz64Xwav0e6PXNa2XNhl5L38qkPA8KXEdi\nFIR4xvmDLRYzPBR+3aTS7ffIVxmlcZ6OpbHkVeXsCrSP9kNyU5DkKd39U947PuXeV845P3vEYjll\nr8iYzGfcHQx55dW3eevLX6Uz3CfPS5argr09zXKR1OXYWi5QGXzt4/shnTojcJXmDLqdukqwoN/t\nsL+7Q7ZK8LW3hlpty6j288p1N9Fe6lZ7Djna3c23Aiv7+WP688TtbtMZ3jrO2tZF8v8vAvN/4EqE\nbaC1qT8R1kgmKGmhn8/nXFxccOfOHWazyVq+Wp7ndCLnzC1s06bmoe0+/iKRsNYa31sv+YngXRb0\nTdAkC5eU2DYnq02Ba/u929YOwjjM5/OGwWsPJgFvwlY12XpR2IDRMAz59OOPGQx7DDo9PE81LFRV\nVSRJQpLcdM3JPZYuxCAIuL6+bsBqmqYNAyVMj4j5BfxlmcsS7Ha7HB8fMxqNakYsbFgiEc87gFk1\nTI4ACsmFlGxI6WaU+92+twK84jhutFwS+i33uCgKaAnjhS0rigLl67VOz7azuzxbAT/t11VKMRhs\nYYywE3HjN+Upi+dpet2Y3Z1tHj59yrC/ze7BPpfXIx4+ecbJyR3SbEVZFmgdrEU8tSc3uZ+b46YN\nhtqND+1JTO5XuxT1RZT8y/Lgz+eQsbUJdDZZrC+aJzd3/23m/EULZKM1quFK7bRVv6aDSLaqjUJV\nXYiz1Rqo94P1NnndiJfF6NQ8x2jdnKNpSkSb41AYE6vVcwvyzc/VLt/KW2Nh1j4vnpsvjcyxqsX8\nWjE2fb6ULu7gN+ern/s5C+iqwFfgewo83xnCWsBUlLkhjCN0WULdPBJ6PpEw00phbEnQG4ACow0H\n94ccv/I6lpLSlvhhgEFjrM/KapaLlLzSVMZ3uk08vPBms9WRaCVTobFURU7ga0yRU5mKXuz0ozZP\n8axBeWLnsREOX9XSkjqWyGNjHqijKzfH33Ol4tb83N4Myxru+d5zz+zzWKfNis5mCXLzHNpNSr9I\nGlL/F+EkZFET4CCgShZHKZkIEyTO2t5DxTvvvMPh4SGffvrpTXdXq6NQhMptbVJ7IRVw0e4Wkwck\nrIYs/jcJ4DdaFbFoEGAiA23Nk6uFsOXfhVVoA5v2pCg6p8Vi0bBfbR8uGcTCqHW7XQfEel3m83mj\nJTs4OCBJFzUz44zglktneLizs+PMCVsBzJuan/39/cZj6+Lign6/z2w2I45jVqsVaV1CFdZwsXDv\nBU4/trW1xWKxYHt7tykzjsdjer1eLa7P1jpAmqib+lkNBoNGhybX3m6KENav2+02wE0Yp3bskanH\njYAgAeq2UJjKohVUpUErD4Um8J0lRLfjUZWGOHIl06IOp85WOX7gkZcF2vdQlXPTFmYtihSdKCCO\nQzxg0O9ydHqHIq94cnbGcrmgqMo1YN4eezLW2tqrzd2ZA+U3zshtkNQOd27/vrmlXfo2duPl8bPX\nYP1+uwb/fdiqLyrv3nz/RssiGqx1mdGGbMGuA0DL7Xlvz5WTbuWYBMhUz583LygxtfU1dSaKeuHr\n88L7epsNye9X/XXzu6DVjeGqe33nxq9cVhGmLByQrP3GtFZNGdZYQAWIIszgYazFWI1FY61Pkjrg\nW+Jy90qcWWppXAi2trZmsNwfU+dJujKvwfe8OvpI42mDpzxQlTPiVOB5fu1dtQFQtVm7Vl+9AOh7\n+nPZ0naFpt2JLMx426bkNn3fixjc9ubki6RGLwHWLYfYNAjgEe2LsBECktqdY+D8s66vLZPJhFdf\nfZXz8/OG8ej1emSrbE0UrTeQextgydebXjGbJqF5nq89/KIoGiG7LN5trdKmZ1RbSyWvK4yK/KwA\nDOkg3OxibC+60j2Z5zm9Xo/5fN6UHGezGVVVMRwO8XxFnjhR+qoOYe52uzVAXK3p39rgTynVmIIK\niyOlUPng9Hq9tYVaWMHlMmm61VwpbdiUBMV6Q4BcGIaN7kpieaQLchP4yjMQkCQfLgGYUjIU53gB\nxJs5l8KQFa2dUZsJEIAmpdnBYLA2HheLBVrHWKtcaLSqsNZQlg7whn5AJwpRtnIRRTYny1O+9OW3\n+OzpY77/b39Ip44Easc8CFDaZOakTNq+Dne/16Mi2hPUJkBz4/B5cNWezH7Roib+QwNYmwD59+N5\n9aLy3xeBt9uAg1I3rKYzf68XIx02hqRuHGwaQZa36sH0JtCyt7+3i9e56fLjOVPJdcAlcUCNR5NW\nt16RdAsqz79BH23VmNY1qtwogT3XlaZvKT05R02tnHeWslULf9X3T4HRCmOEtdF4wq7X6SKqsijr\nN+/j/b/tnXl0FFX2x29vSXf2TkMCISQhQQHBERAUF44aIhidATJRQaIDoqJH5eAoyCCE3wGRIwoI\nqOAy7oMryhpnHJXFDREQJwt7EkICSQjZk07v9/dHpx7VnU6ns5CE8P2ck5OqrlevXr16dd+t++67\nz+4Mg6EgZ2R8OztI3RjslckZpV/BKlIykVqhImY7KRXOQKnSLSoVisZgsU4vMenDUJrvKRb/VjoD\nJCjVCs9DyErXkRQlNV08mRWNEw28KPfuvoRyq6t7SJLmJm14GzpkVngdTuyuMqvLFSxplp40zCS3\nVkmdTHNfJhaLhU6cOEFjx95EsbGxdOTIERFgU1KamgulIO1bG1f4lh93j8clddpS5y73DZI3RGnY\nUD5DUFJe3Buie9BIyTInDZNJCoenIIFyh2dJ+ZGUHknxkGJClZaUkD48lHSh/lRVVUFWq1UsV1NW\nVkbhBr2LH9wFXx2n9U1aVFryC6urq6OAgAChABsbfeKkkBYXLHNMQUFBwodOUhok5UlSIDUalRga\nlcf7kpQZaZFoSfGT6lKK6VRVVeXiPyQ558tnaLovICpfkkkjm9klX55I8j2TlDT5c5BH6LfbHKRR\na8lOVrLZLI1DhGoxzKhSKchP7QwTYTWbyE8bQMOH/4ny8vPJbmdSqZ2zGOUzUuUhOeQTL9wtTdIE\nC7mVVP6xIF866MIQtdLjjDb3yR+g4xUsb52TV/+VZvJpbpafL5YakY9bVHhPhgIpDpWndtGkM3VT\nAKU2R8KfyXV40z2OlXx2m/g4VXi+RykivUcltcn9ug9JuT8PpUdfLHaQuAYplKRkJdmdA3bkUDiH\nOJ06XOO9KJRi+FSpcAYMdSidQ3BOi5OycYmfxthedoeY4clMpFYoydY429NBkmO+5ODvGkxTCgjr\nXHq7ce0+6RE5nB9TpFCQQlqY0cOzU7oNzSqYPK7cQQqlS5gM9/qWL+/VpA7dXB88KUju7jbu6ex2\n7zMQ3ZfEg4IlU7CkzlHqLORWFHln4j6cQkRUWlpKZWVl1K9fPzp69CiFhITQ2bNnKTgwyMVxWMFN\nh0ZI7gsgu5a7j5TU6UkxkOSdlzSjzFMeksOy++zBZoVTYycvrYcnn5YvXxtRUhJcgl9qLljEJGdn\nlUpFIY2LWUvLtpDNKoaU5A7QcuuJxWIR/kZS3CTJOmQ0GoUCKykBusZYWJI10Km8OK1kdXV1Inq6\nlLekREvllYewkPzXJIVWWs5FrtRI+1KUcyl/uWVHboV0OBzi+cuvJbdsyicv1NfXk8FgEEqrpCRK\nCqKUt83hDONASjXZbXZiB5Fa5ScUMZVKRVo/f7I7rNSndz8KDe9NJecrqVcvA8XF9qc//pdDYXqD\nR2uEpJxLQWDd277Urvz81C5KsaQkypVRT1+aYlq2m0UPMbA6V8mSz/b0ZpHyNKTSkjXLvWNrplCi\nQyZyjTvl0hEqnQqSkJeyeFbyIUeWzmtUGBQOahKLSrYWzYV15RpXWFEpXKOwK2TrJbK7gkV8IXyA\nQuVcRJsvhKO4kMomc3pXupZBZNbMUKGy0ZLVqCRyo88WN66Vx43WNZXygg8lk50UjYqWUqUgUqmc\nQ4x2agwD06g0cWO+7CCSFnBWNKpTCntjmRUidhmRpukwvsNZyexwplM21qeDLtSFSvZclbJAYAqp\ncunCEjnSvFHJkuUcynRa8FRypajJpAYWqwDIjZKiPSqbj9rurhh5UrKUSoVH3yxXK2v3cnDvFgqW\n8IWRTVGXpsbLOxL5endiYV67s6M4ceIEXXXVVTRgwAAqLy8X57pEqpYFNPU0c0duIZDHWZL/Jlmy\nJKVK/icPiCrCI8jK35xzqzzoqFQfch8seegFqX4kBYGZRRwoqY6sVquwPElDhMaGukZFKJA0/n5i\neFMKnSA5yEs+T1LATmmpGaPR6GLBq6mpIYPBQH5+flRdU+MSeVylUpHRaKSGBpOwaknBQwMDA4US\nI0Vyt1rNLoE03X2s5AFA5YFkJcVcCg7rHIYMujD0R1aX2E8OD06SSqWSGsxmcT3pvqXgrvLZdlK8\nNamOJZ8zq9VKGv8Li5dKHwkWs5lsdiv5+WtIq3FOPND4qSk0LJhMNhvFx8dTzuGjjU7sSheB4u5H\n4KmjlA8by62lkgVU7vzu7kwtb9+S9VQeG6u7CamegMFgQCUAwpuF590lSpbUWUnKiKRkyWf/yWeO\n2Ww2slmdnXRxcTGVlpbS4MGDxTR7udLmcDjI5nCQXRoqEh26U6X3FHBN6tjklgBpFqHVdiFSu8lk\nEgpETU0NVVVVUVVVFVVWVlJFRYVL9Pbmvizdl2SR7q+urs7FJ0Ye+NP59dEYvVzd6BTucJCp0Um9\ntnFdwZMnT5LDTqRQKamkpIQqq2uoV69eFBwcTIWFhaTVasnf399laFOuMEVFRVF4eDj16dOHdDod\nabVaqq6uFr5ywcHBpFQqyWg0CsUzODiYgoKCnGELtFrRoQcFBQnlTqo/52xHjVi+RhqWk5TH4OBg\nCgwMJJ1OR4GBgWJfmuUolVtqN5L/ntx3z263k9lqJavkEye+qpRksZrJwXbS6vwpNCyEQkNDSalS\niDZZXV0tgqpKCq5SqRTWP43an/w1GtJo/MUMSJVKQdZGRdwZ2FRLuSeO06m8XDKEBhPZzBQXHUVD\nrxpMdtsFnzLxXCUlU6N2WR7I2rgQtlQOKWyIyyQO2YQNT9YopVJBCrFQq2uUeFivAACgBylYlsZO\nQ+p0pcWS1Wo1hYWFCSuGpJhIfk7S8FBNTQ0FB4dSYeEZKio6S9dcM4LMZivZmUjjryWFSk12dpqn\nlWoN2RzOeMIOUpLZ6hwqCw4JJKvNTA62kdnS4LRry5Q/uZ+KWq2hAF0gKRRKKi+vIK1WK8ovKXNV\nVVVUWlrqMqwlTIaNZZeWp7HazEQKB9nsFrLazKTWKEnjpyKb3ekUrVAyqTVK8d9qM1ODqZ7UGqXw\njSotKyOdTkdKWRiGU6dOkclspsiovs6I7o0z6/y0WjpfUUkVVdXUOzJC+DgFBweLoU4pvcPhoLq6\nOvLz8yOdTiesN9ISNlKnbDQaSafTiYCc0lI3tbXO6OdOS5VVBBoNCwujmpoaKisrJTs5qK6hjmrq\na8nqsJFCrSSL3Uq1RudvRrOJlBo12VlB9Q1msjmI7Kwgi81BSrWfsFZKipTULrQBOtIG6Mhqt5FS\nrSJWENU3GMloMhEriBrMZiouLSaVRkkafzXZ2UbVtVV0vuI8aQP8yWQxkcZfQwFBAeSv8ye1n5oq\nqyvJYrOSLjCAjCann1eATkcKdgZKtDSYKCAggKJj+lNQSDBZbTaqqConrVZL8XH9iexmOn+2iKLC\n9RTdO5T+cnsihQUFUoOxjgIDdVRbU0WsZPIP8CeTqZ5IyVRvqndO4/ZXk1bnVCoVTOSw2clhszv9\nzVSNJnK7nRSNsXGYiex2R2O8HxKWOOf7ZCeHw0aKxhmk8qFPd8suAACAtqFgSFMAAAAAgJ5lwQIA\nAAAAgIIFAAAAAACgYAEAAAAAQMECAAAAAICCBTqKropC252i3wIAAABQsAAUQgAAuETkUFfLLMhM\nKFidTnV1NS1YsICuvPJKCggIoPDwcJo8eTJ9//33l/RL7+mvJwmv9kb5yMnJoYSEBLyJ4LKhJ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       "prompt_number": 53,
       "text": [
        "<IPython.core.display.Image at 0x115d53650>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Obviamente es un problema de falta de datos. Por eso debemos ser muy cuidadosos con la confianza que tenemos en nuestros resultados. Un corpus con errores nos llevar\u00e1 a conclusiones err\u00f3neas, hay que ser conscientes de esto."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Limpiando los datos"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Eliminar grupos "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "En la API Marvel no distingue entre personajes y equipos. Es decir, **Los Vengadores** tiene el mismo status de personaje que **Rachel Grey**, pero existe un campo en la wiki que nos permite diferenciar grupos de personajes: *Former members*. Intentaremos entonces filtrar para quedarnos s\u00f3lo con los personajes.\n",
      "\n",
      "Lo normal es que quisi\u00e9ramos eliminar las filas que contienen nulos, y pandas tiene implementada una funci\u00f3n para ello *dropna*. Pero lo que queremos es quedarnos con aquellas filas en cuya columna *current_members* tengamos un nulo, porque hemos comprobado que si no hay miembros es porque es un personaje."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " marvel_df.dropna(subset=['wiki.current_members'])['name']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "0                         A.I.M.\n",
        "0                       Avengers\n",
        "0    Brotherhood of Evil Mutants\n",
        "0                         Exiles\n",
        "0                 Fantastic Four\n",
        "0                    Force Works\n",
        "0                  Hellfire Club\n",
        "0                          Hydra\n",
        "0                 Imperial Guard\n",
        "0                      Marauders\n",
        "0                        Reavers\n",
        "0                   S.H.I.E.L.D.\n",
        "0                Serpent Society\n",
        "0                        X-Force\n",
        "0                          X-Men\n",
        "...\n",
        "0                         Sinister Six\n",
        "0                          ClanDestine\n",
        "0                            New X-Men\n",
        "0                      Masters of Evil\n",
        "0                         Generation X\n",
        "0              Guardians of the Galaxy\n",
        "0                               U-Foes\n",
        "0                            Sentinels\n",
        "0                          New Mutants\n",
        "0             Lightning Lords of Nepal\n",
        "0           Nine-Fold Daughters of Xao\n",
        "0          Confederates of the Curious\n",
        "0                             X-Babies\n",
        "0                        Lethal Legion\n",
        "0    Brotherhood of Mutants (Ultimate)\n",
        "Name: name, Length: 70, dtype: object"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit (~marvel_df['wiki.current_members'].isnull())\n",
      "\n",
      "import numpy as np\n",
      "%timeit (np.invert(marvel_df['wiki.current_members'].isnull()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 206 \u00b5s per loop\n",
        "1000 loops, best of 3: 226 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "not_groups_mask = marvel_df['wiki.current_members'].isnull()\n",
      "not_groups_mask.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "0    False\n",
        "0     True\n",
        "0     True\n",
        "0     True\n",
        "0     True\n",
        "Name: wiki.current_members, dtype: bool"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df_characters = marvel_df[not_groups_mask]\n",
      "marvel_df_characters.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>comics.available</th>\n",
        "      <th>comics.collectionURI</th>\n",
        "      <th>comics.items</th>\n",
        "      <th>comics.returned</th>\n",
        "      <th>description</th>\n",
        "      <th>events.available</th>\n",
        "      <th>events.collectionURI</th>\n",
        "      <th>events.items</th>\n",
        "      <th>events.returned</th>\n",
        "      <th>id</th>\n",
        "      <th>...</th>\n",
        "      <th>wiki.specieshistory</th>\n",
        "      <th>wiki.team_name</th>\n",
        "      <th>wiki.teamicon</th>\n",
        "      <th>wiki.technology</th>\n",
        "      <th>wiki.tie-ins</th>\n",
        "      <th>wiki.title_graphic</th>\n",
        "      <th>wiki.universe</th>\n",
        "      <th>wiki.weapons</th>\n",
        "      <th>wiki.weaponss</th>\n",
        "      <th>wiki.weight</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 43</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Incredible Hulks (2009) #619', 'res...</td>\n",
        "      <td> 43</td>\n",
        "      <td> Formerly known as Emil Blonsky, a spy of Sovie...</td>\n",
        "      <td> 2</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Chaos War', 'resourceURI': 'http://...</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1009146</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     Marvel Universe</td>\n",
        "      <td>                                              None</td>\n",
        "      <td> NaN</td>\n",
        "      <td> (Abomination) 980 lbs.; (Blonsky) 180 lbs.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 43</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Avengers Academy (2010) #21', 'reso...</td>\n",
        "      <td> 43</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 4</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Fear Itself', 'resourceURI': 'http:...</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1009148</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td> He uses a prison ball-and-chain as a weapon, a...</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                        365 lbs. (variable)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  8</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Uncanny X-Men (1963) #402', 'resour...</td>\n",
        "      <td>  8</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 1</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Age of Apocalypse', 'resourceURI': ...</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1009149</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                 Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 20</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Weapon X: Days of Future Now (Trade...</td>\n",
        "      <td> 20</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1009150</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td> Agent Zero carries a wide array of weapons inc...</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                   230 lbs.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 11</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Uncanny X-Men (1963) #181', 'resour...</td>\n",
        "      <td> 11</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 1</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'name': 'Secret Wars', 'resourceURI': 'http:...</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1009151</td>\n",
        "      <td>...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     Marvel Universe</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                    100 lbs</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 89 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "   comics.available                               comics.collectionURI  \\\n",
        "0                43  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                43  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                 8  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                20  http://gateway.marvel.com/v1/public/characters...   \n",
        "0                11  http://gateway.marvel.com/v1/public/characters...   \n",
        "\n",
        "                                        comics.items  comics.returned  \\\n",
        "0  [{'name': 'Incredible Hulks (2009) #619', 'res...               43   \n",
        "0  [{'name': 'Avengers Academy (2010) #21', 'reso...               43   \n",
        "0  [{'name': 'Uncanny X-Men (1963) #402', 'resour...                8   \n",
        "0  [{'name': 'Weapon X: Days of Future Now (Trade...               20   \n",
        "0  [{'name': 'Uncanny X-Men (1963) #181', 'resour...               11   \n",
        "\n",
        "                                         description  events.available  \\\n",
        "0  Formerly known as Emil Blonsky, a spy of Sovie...                 2   \n",
        "0                                                                    4   \n",
        "0                                                                    1   \n",
        "0                                                                    0   \n",
        "0                                                                    1   \n",
        "\n",
        "                                events.collectionURI  \\\n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "0  http://gateway.marvel.com/v1/public/characters...   \n",
        "\n",
        "                                        events.items  events.returned  \\\n",
        "0  [{'name': 'Chaos War', 'resourceURI': 'http://...                2   \n",
        "0  [{'name': 'Fear Itself', 'resourceURI': 'http:...                4   \n",
        "0  [{'name': 'Age of Apocalypse', 'resourceURI': ...                1   \n",
        "0                                                 []                0   \n",
        "0  [{'name': 'Secret Wars', 'resourceURI': 'http:...                1   \n",
        "\n",
        "        id   ...    wiki.specieshistory wiki.team_name wiki.teamicon  \\\n",
        "0  1009146   ...                    NaN            NaN           NaN   \n",
        "0  1009148   ...                    NaN            NaN           NaN   \n",
        "0  1009149   ...                    NaN            NaN           NaN   \n",
        "0  1009150   ...                    NaN            NaN           NaN   \n",
        "0  1009151   ...                    NaN            NaN           NaN   \n",
        "\n",
        "   wiki.technology wiki.tie-ins wiki.title_graphic        wiki.universe  \\\n",
        "0              NaN          NaN                NaN      Marvel Universe   \n",
        "0              NaN          NaN                NaN  [[Marvel Universe]]   \n",
        "0              NaN          NaN                NaN  [[Marvel Universe]]   \n",
        "0              NaN          NaN                NaN  [[Marvel Universe]]   \n",
        "0              NaN          NaN                NaN      Marvel Universe   \n",
        "\n",
        "                                        wiki.weapons wiki.weaponss  \\\n",
        "0                                               None           NaN   \n",
        "0  He uses a prison ball-and-chain as a weapon, a...           NaN   \n",
        "0                                         Unrevealed           NaN   \n",
        "0  Agent Zero carries a wide array of weapons inc...           NaN   \n",
        "0                                                              NaN   \n",
        "\n",
        "                                  wiki.weight  \n",
        "0  (Abomination) 980 lbs.; (Blonsky) 180 lbs.  \n",
        "0                         365 lbs. (variable)  \n",
        "0                                  Unrevealed  \n",
        "0                                    230 lbs.  \n",
        "0                                     100 lbs  \n",
        "\n",
        "[5 rows x 89 columns]"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Se nos han colado **The Watchers**, \u00e9ste es uno de los problemas del aprendizaje autom\u00e1tico que los datos de entrada pueden contener errores, y el sistema que entrenemos debe ser capaz de generalizar suficiente como para sobreponerse a estos errores."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df_characters.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(1332, 89)"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hemos comenzado con 1402 personajes, eliminando los equipos nos quedamos con 1332. Es decir, hemos perdido el 4.9929 % de los datos. Nada grave por ahora."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Representaci\u00f3n racial, cultural y de g\u00e9nero en los c\u00f3mics de Marvel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Un caso de estudio..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='oracle.jpg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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NnrYbC1OZNbHnHiQ3BlIT3Od3auL1ea6RDmaNR2GeTXQeL7ySLcw68/ka851u5WQrIjfN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\n2Q==\n",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<IPython.core.display.Image at 0x116388390>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Por ejemplo, \u00bfqu\u00e9 encontramos en relaci\u00f3n a la representaci\u00f3n racial?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df_characters['wiki.skin'].dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "0    White (as GAmbit), Black (as Death)\n",
        "Name: wiki.skin, dtype: object"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Otro ejemplo: ser\u00eda muy interesante saber quienes son los l\u00edderes de los grupos de superh\u00e9roes, pero..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_groups =  marvel_df.dropna(subset=['wiki.current_members'])\n",
      "marvel_groups['wiki.leader'].dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "0    Steve Rogers\n",
        "0                \n",
        "Name: wiki.leader, dtype: object"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Eliminar aquellos personajes de los que no tenemos informaci\u00f3n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a eliminar a todos aquellos personajes de los que no tenemos informaci\u00f3n. Sin datos no tenemos nada que analizar. A los cient\u00edficos nos encantar\u00eda tener muchos datos disponibles, porque eso implicar\u00eda que podr\u00edamos hacer muchos experimentos y sacar conclusiones probablemente v\u00e1lidas. Lamentablemente la mayor parte del tiempo no podremos hacer *machine learning* sobre *big data*. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a querer quedarnos con la siguiente informaci\u00f3n:\n",
      "\n",
      "- Id (*id*)\n",
      "- Nombre (*name*)\n",
      "- Descripci\u00f3n\n",
      "- Educaci\u00f3n\n",
      "- Peso\n",
      "- Altura\n",
      "- Bio\n",
      "- Color del pelo\n",
      "- Color de los ojos\n",
      "- Nacionalidad\n",
      "- Lugar de nacimiento"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Agrupamos los datos para tener claro con que queremos trabajar\n",
      "# No hay nadie con 'ocupation' as\u00ed que lo quitamos\n",
      "physical_data = {'wiki.hair':'hair', 'wiki.weight':'weight', 'wiki.height':'height', 'wiki.eyes':'eyes'}\n",
      "cultural_data = {'wiki.education':'education', 'wiki.citizenship':'citizenship', \n",
      "                 'wiki.place_of_birth':'place_of_birth', 'wiki.occupation':'occupation'}\n",
      "personal_data = {'wiki.bio':'bio', 'wiki.bio_text':'bio', 'wiki.categories':'categories'}\n",
      "marvelesque_data = {'wiki.abilities':'abilities', 'wiki.weapons':'weapons', 'wiki.powers': 'powers'}\n",
      "\n",
      "data_keys = (list(physical_data.keys()) + list(cultural_data.keys()) + \n",
      "             list(personal_data.keys()) + ['name','comics.available'])\n",
      "#+ marvelesque_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(data_keys)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['wiki.height', 'wiki.hair', 'wiki.eyes', 'wiki.weight', 'wiki.place_of_birth', 'wiki.citizenship', 'wiki.occupation', 'wiki.education', 'wiki.bio_text', 'wiki.bio', 'wiki.categories', 'name', 'comics.available']\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df = marvel_df_characters.dropna(subset = data_keys)\n",
      "clean_df = clean_df[data_keys].set_index('name')\n",
      "clean_df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "(762, 12)"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Podemos explorar secciones de un dataframe"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Datos f\u00edsicos"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df[list(physical_data.keys())].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>wiki.height</th>\n",
        "      <th>wiki.hair</th>\n",
        "      <th>wiki.eyes</th>\n",
        "      <th>wiki.weight</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Abomination (Emil Blonsky)</th>\n",
        "      <td> (Abomination) 6'8\"; (Blonsky) 5'10\"</td>\n",
        "      <td>   (Abomination) None; (Blonsky) Blond</td>\n",
        "      <td> (Abomination) Green; (Blonsky) Blue</td>\n",
        "      <td> (Abomination) 980 lbs.; (Blonsky) 180 lbs.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Absorbing Man</th>\n",
        "      <td>                     6'4\" (variable)</td>\n",
        "      <td>                                  Bald</td>\n",
        "      <td>                                Blue</td>\n",
        "      <td>                        365 lbs. (variable)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abyss</th>\n",
        "      <td>                          Unrevealed</td>\n",
        "      <td>                            Unrevealed</td>\n",
        "      <td>                          Unrevealed</td>\n",
        "      <td>                                 Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Agent Zero</th>\n",
        "      <td>                                6'3\"</td>\n",
        "      <td> (Originally) Brown; (currently) Black</td>\n",
        "      <td>                                Blue</td>\n",
        "      <td>                                   230 lbs.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Annihilus</th>\n",
        "      <td>                               5'11\"</td>\n",
        "      <td>                                  None</td>\n",
        "      <td>                               Green</td>\n",
        "      <td>                                   200 lbs.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "                                                    wiki.height  \\\n",
        "name                                                              \n",
        "Abomination (Emil Blonsky)  (Abomination) 6'8\"; (Blonsky) 5'10\"   \n",
        "Absorbing Man                                   6'4\" (variable)   \n",
        "Abyss                                                Unrevealed   \n",
        "Agent Zero                                                 6'3\"   \n",
        "Annihilus                                                 5'11\"   \n",
        "\n",
        "                                                        wiki.hair  \\\n",
        "name                                                                \n",
        "Abomination (Emil Blonsky)    (Abomination) None; (Blonsky) Blond   \n",
        "Absorbing Man                                                Bald   \n",
        "Abyss                                                  Unrevealed   \n",
        "Agent Zero                  (Originally) Brown; (currently) Black   \n",
        "Annihilus                                                    None   \n",
        "\n",
        "                                                      wiki.eyes  \\\n",
        "name                                                              \n",
        "Abomination (Emil Blonsky)  (Abomination) Green; (Blonsky) Blue   \n",
        "Absorbing Man                                              Blue   \n",
        "Abyss                                                Unrevealed   \n",
        "Agent Zero                                                 Blue   \n",
        "Annihilus                                                 Green   \n",
        "\n",
        "                                                           wiki.weight  \n",
        "name                                                                    \n",
        "Abomination (Emil Blonsky)  (Abomination) 980 lbs.; (Blonsky) 180 lbs.  \n",
        "Absorbing Man                                      365 lbs. (variable)  \n",
        "Abyss                                                       Unrevealed  \n",
        "Agent Zero                                                    230 lbs.  \n",
        "Annihilus                                                     200 lbs.  "
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df[list(physical_data.keys())].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>wiki.height</th>\n",
        "      <th>wiki.hair</th>\n",
        "      <th>wiki.eyes</th>\n",
        "      <th>wiki.weight</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>        762</td>\n",
        "      <td>   762</td>\n",
        "      <td>  762</td>\n",
        "      <td>        762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>unique</th>\n",
        "      <td>        213</td>\n",
        "      <td>   223</td>\n",
        "      <td>  165</td>\n",
        "      <td>        307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>top</th>\n",
        "      <td> Unrevealed</td>\n",
        "      <td> Black</td>\n",
        "      <td> Blue</td>\n",
        "      <td> Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>freq</th>\n",
        "      <td>         44</td>\n",
        "      <td>   165</td>\n",
        "      <td>  236</td>\n",
        "      <td>         48</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "       wiki.height wiki.hair wiki.eyes wiki.weight\n",
        "count          762       762       762         762\n",
        "unique         213       223       165         307\n",
        "top     Unrevealed     Black      Blue  Unrevealed\n",
        "freq            44       165       236          48"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Caracter\u00edsticas culturales"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df[list(cultural_data.keys())].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>wiki.place_of_birth</th>\n",
        "      <th>wiki.citizenship</th>\n",
        "      <th>wiki.occupation</th>\n",
        "      <th>wiki.education</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Abomination (Emil Blonsky)</th>\n",
        "      <td>                                Zagreb, Yugoslavia</td>\n",
        "      <td> Citizen of Croatia; former citizen of Yugoslavia</td>\n",
        "      <td>                 Professional Criminal, Former Spy</td>\n",
        "      <td>          Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Absorbing Man</th>\n",
        "      <td>                           New York City, New York</td>\n",
        "      <td>                    U.S.A. with a criminal record</td>\n",
        "      <td>               Professional criminal; former boxer</td>\n",
        "      <td> High school dropout</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abyss</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td>                                       Unrevealed</td>\n",
        "      <td>                                   Cosmic sorcerer</td>\n",
        "      <td>          Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Agent Zero</th>\n",
        "      <td>        Unrevealed location in former East Germany</td>\n",
        "      <td>                                           German</td>\n",
        "      <td> Mercenary, former government operative, freedo...</td>\n",
        "      <td>          Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Annihilus</th>\n",
        "      <td> Planet of [[Arthros]], Sector 17A, [[Negative ...</td>\n",
        "      <td>                                          Arthros</td>\n",
        "      <td>                              Conqueror, scavenger</td>\n",
        "      <td>          Unrevealed</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "                                                          wiki.place_of_birth  \\\n",
        "name                                                                            \n",
        "Abomination (Emil Blonsky)                                 Zagreb, Yugoslavia   \n",
        "Absorbing Man                                         New York City, New York   \n",
        "Abyss                                                              Unrevealed   \n",
        "Agent Zero                         Unrevealed location in former East Germany   \n",
        "Annihilus                   Planet of [[Arthros]], Sector 17A, [[Negative ...   \n",
        "\n",
        "                                                            wiki.citizenship  \\\n",
        "name                                                                           \n",
        "Abomination (Emil Blonsky)  Citizen of Croatia; former citizen of Yugoslavia   \n",
        "Absorbing Man                                  U.S.A. with a criminal record   \n",
        "Abyss                                                             Unrevealed   \n",
        "Agent Zero                                                            German   \n",
        "Annihilus                                                            Arthros   \n",
        "\n",
        "                                                              wiki.occupation  \\\n",
        "name                                                                            \n",
        "Abomination (Emil Blonsky)                  Professional Criminal, Former Spy   \n",
        "Absorbing Man                             Professional criminal; former boxer   \n",
        "Abyss                                                         Cosmic sorcerer   \n",
        "Agent Zero                  Mercenary, former government operative, freedo...   \n",
        "Annihilus                                                Conqueror, scavenger   \n",
        "\n",
        "                                 wiki.education  \n",
        "name                                             \n",
        "Abomination (Emil Blonsky)           Unrevealed  \n",
        "Absorbing Man               High school dropout  \n",
        "Abyss                                Unrevealed  \n",
        "Agent Zero                           Unrevealed  \n",
        "Annihilus                            Unrevealed  "
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00bfC\u00f3mo diri\u00e1is que es f\u00edsicamente el personaje t\u00edpico de la marvel?\n",
      "(*pandas lo sabe*)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df[list(cultural_data.keys())].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>wiki.place_of_birth</th>\n",
        "      <th>wiki.citizenship</th>\n",
        "      <th>wiki.occupation</th>\n",
        "      <th>wiki.education</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>        762</td>\n",
        "      <td>    762</td>\n",
        "      <td>        762</td>\n",
        "      <td>        762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>unique</th>\n",
        "      <td>        412</td>\n",
        "      <td>    262</td>\n",
        "      <td>        636</td>\n",
        "      <td>        357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>top</th>\n",
        "      <td> Unrevealed</td>\n",
        "      <td> U.S.A.</td>\n",
        "      <td> Adventurer</td>\n",
        "      <td> Unrevealed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>freq</th>\n",
        "      <td>        156</td>\n",
        "      <td>    230</td>\n",
        "      <td>         31</td>\n",
        "      <td>        236</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "       wiki.place_of_birth wiki.citizenship wiki.occupation wiki.education\n",
        "count                  762              762             762            762\n",
        "unique                 412              262             636            357\n",
        "top             Unrevealed           U.S.A.      Adventurer     Unrevealed\n",
        "freq                   156              230              31            236"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "De modo que el personaje arquet\u00edpico de la Marvel tiene el pelo negro y los ojos azules, es de EE.UU. se dedica a ser aventurero."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00a1Los datos son caros!  Ten\u00edamos 1402, pero en realidad solo tenemos 762 personajes con datos para poder trabajar. Hemos perdido el 45.6491 % de los datos.\n",
      "\n",
      "Exploremos el dataframe que nos ha quedado:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "wiki.height            object\n",
        "wiki.hair              object\n",
        "wiki.eyes              object\n",
        "wiki.weight            object\n",
        "wiki.place_of_birth    object\n",
        "wiki.citizenship       object\n",
        "wiki.occupation        object\n",
        "wiki.education         object\n",
        "wiki.bio_text          object\n",
        "wiki.bio               object\n",
        "wiki.categories        object\n",
        "comics.available        int64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>comics.available</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>  762.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>   53.292651</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  179.820372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>    2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>   10.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>   33.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 2575.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "       comics.available\n",
        "count        762.000000\n",
        "mean          53.292651\n",
        "std          179.820372\n",
        "min            0.000000\n",
        "25%            2.000000\n",
        "50%           10.000000\n",
        "75%           33.750000\n",
        "max         2575.000000"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df[clean_df['comics.available'] == 2575.000000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>wiki.height</th>\n",
        "      <th>wiki.hair</th>\n",
        "      <th>wiki.eyes</th>\n",
        "      <th>wiki.weight</th>\n",
        "      <th>wiki.place_of_birth</th>\n",
        "      <th>wiki.citizenship</th>\n",
        "      <th>wiki.occupation</th>\n",
        "      <th>wiki.education</th>\n",
        "      <th>wiki.bio_text</th>\n",
        "      <th>wiki.bio</th>\n",
        "      <th>wiki.categories</th>\n",
        "      <th>comics.available</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</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>Spider-Man</th>\n",
        "      <td> 5'10\"</td>\n",
        "      <td> Brown</td>\n",
        "      <td> Hazel</td>\n",
        "      <td> 167 lbs.</td>\n",
        "      <td> Forest Hills, New York</td>\n",
        "      <td> U.S.A.</td>\n",
        "      <td> Scientist and inventor; former freelance photo...</td>\n",
        "      <td> College graduate (biophysics major), doctorate...</td>\n",
        "      <td> The bite of an irradiated spider granted high-...</td>\n",
        "      <td> The bite of an irradiated spider granted high-...</td>\n",
        "      <td> [Avengers, Civil War, Heroes, Marvel Knights, ...</td>\n",
        "      <td> 2575</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "           wiki.height wiki.hair wiki.eyes wiki.weight  \\\n",
        "name                                                     \n",
        "Spider-Man       5'10\"     Brown     Hazel    167 lbs.   \n",
        "\n",
        "               wiki.place_of_birth wiki.citizenship  \\\n",
        "name                                                  \n",
        "Spider-Man  Forest Hills, New York           U.S.A.   \n",
        "\n",
        "                                              wiki.occupation  \\\n",
        "name                                                            \n",
        "Spider-Man  Scientist and inventor; former freelance photo...   \n",
        "\n",
        "                                               wiki.education  \\\n",
        "name                                                            \n",
        "Spider-Man  College graduate (biophysics major), doctorate...   \n",
        "\n",
        "                                                wiki.bio_text  \\\n",
        "name                                                            \n",
        "Spider-Man  The bite of an irradiated spider granted high-...   \n",
        "\n",
        "                                                     wiki.bio  \\\n",
        "name                                                            \n",
        "Spider-Man  The bite of an irradiated spider granted high-...   \n",
        "\n",
        "                                              wiki.categories  \\\n",
        "name                                                            \n",
        "Spider-Man  [Avengers, Civil War, Heroes, Marvel Knights, ...   \n",
        "\n",
        "            comics.available  \n",
        "name                          \n",
        "Spider-Man              2575  "
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00a1**Spiderman** es el rey del c\u00f3mic!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Antes de ponernos a jugar con los datos (m\u00e1s), tenemos una columna de la que se pude sacar mucho partido \"wiki.categories\""
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df['wiki.categories']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "name\n",
        "Abomination (Emil Blonsky)    [Avengers, Deceased, Hulk, International, Vill...\n",
        "Absorbing Man                                   [Avengers, Civil War, Villains]\n",
        "Abyss                                                 [Cosmic, Magic, Villains]\n",
        "Agent Zero                    [Heroes, X-Men, Villains, International, Mutants]\n",
        "Annihilus                      [Annihilation, Cosmic, Fantastic Four, Villains]\n",
        "Apocalypse                            [Mutants, Villains, International, X-Men]\n",
        "Spider-Girl (Anya Corazon)    [Women, Heroes, Spider-Man, Civil War, Initiat...\n",
        "Arcade                                            [Spider-Man, Villains, X-Men]\n",
        "Archangel                           [X-Men, Heroes, Reformed Villains, Mutants]\n",
        "Arclight                      [X-Men, Women, Villains, Mutants, People who u...\n",
        "Aurora                        [Heroes, Women, X-Men, International, Canadian...\n",
        "Avalanche                             [X-Men, International, Villains, Mutants]\n",
        "Banshee                       [X-Men, People who used to be dead but aren't ...\n",
        "Baron Strucker                [Villains, International, Thunderbolts, People...\n",
        "Baron Zemo (Heinrich Zemo)        [Villains, Avengers, Deceased, International]\n",
        "...\n",
        "Contessa (Vera Vidal)                                             [Heroes, Women]\n",
        "Chores MacGillicudy                                            [Deceased, Heroes]\n",
        "Iron Fist (Wu Ao-Shi)                                   [Heroes, Women, Deceased]\n",
        "Loa                                               [X-Men, Women, Heroes, Mutants]\n",
        "Grey Gargoyle                                 [Avengers, International, Villains]\n",
        "Nekra                                        [Avengers, Mutants, Villains, Women]\n",
        "Miss America                                                    [Women, Deceased]\n",
        "Whizzer (Stanley Stewart)                    [Heroes, Avengers, Squadron Supreme]\n",
        "Scarlet Spider (Kaine)          [Villains, Spider-Man, people who used to be d...\n",
        "Hope Summers                                              [Mutants, Women, X-Men]\n",
        "Enchantress (Sylvie Lushton)                             [Avengers, Magic, Women]\n",
        "Hank Pym                                [Heroes, Avengers, Civil War, Initiative]\n",
        "Azazel (Mutant)                                 [Magic, Mutants, Villains, X-Men]\n",
        "Spider-Man (House of M)                                              [House of M]\n",
        "Gargoyle (Yuri Topolov)                                          [Hulk, Villains]\n",
        "Name: wiki.categories, Length: 762, dtype: object"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A priori no tenemos informaci\u00f3n de que personajes son hombres, mujeres o alien\u00edgenas. Pero Marvel debi\u00f3 intuir que nos podr\u00eda interesar el papel de las mujeres en los c\u00f3mics y nos incluy\u00f3 una categor\u00eda: \"Mujeres\", que nos va a facilitar la vida un mont\u00f3n. Vamos a crear dos nuevas columnas en el DataFrame:\n",
      "\n",
      "- *woman*: que simplemente contendr\u00e1 True o False si el personaje es femenino o no respectivamente.\n",
      "- *villain*: \u00eddem T/F si el personaje es villano o no."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "women = clean_df['wiki.categories'].map(lambda x: 'Women' in x)\n",
      "clean_df['Women'] = women \n",
      "women[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "name\n",
        "Abomination (Emil Blonsky)    False\n",
        "Absorbing Man                 False\n",
        "Abyss                         False\n",
        "Agent Zero                    False\n",
        "Annihilus                     False\n",
        "Name: wiki.categories, dtype: bool"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ~ Esto es una negaci\u00f3n element-wise\n",
      "print(\"Women: #{}, men #{}\".format(clean_df[women].shape[0],clean_df[~women].shape[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Women: #199, men #563\n"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Es decir, tenemos 199 personajes femeninos y 563 masculinos. Es decir solo el 26% de los personajes son femeninos. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "villain = clean_df['wiki.categories'].map(lambda x: 'Villains' in x)\n",
      "clean_df['Villain'] = villain "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "men = ~women\n",
      "gender_data = {'Women':{'Heroes':0,'Villains':0},'Men':{'Heroes':0,'Villains':0}}\n",
      "# Women and villains\n",
      "gender_data['Women']['Villains'] = clean_df[villain & women].shape[0]\n",
      "# Women and heroes\n",
      "gender_data['Women']['Heroes'] = clean_df[~villain & women].shape[0]\n",
      "\n",
      "# Men and villains\n",
      "gender_data['Men']['Villains'] = clean_df[villain & men].shape[0]\n",
      "# Men and heroes\n",
      "gender_data['Men']['Heroes'] = clean_df[~villain & men].shape[0]\n",
      "gender_data\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "{'Women': {'Villains': 30, 'Heroes': 169},\n",
        " 'Men': {'Villains': 201, 'Heroes': 362}}"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_groups = 2\n",
      "\n",
      "men_data = (gender_data['Men']['Villains'], gender_data['Men']['Heroes'])\n",
      "women_data = (gender_data['Women']['Villains'], gender_data['Women']['Heroes'])\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "\n",
      "index = np.arange(n_groups)\n",
      "bar_width = 0.4\n",
      "\n",
      "opacity = 0.5\n",
      "rects1 = plt.bar(index, men_data, bar_width,\n",
      "                 alpha=opacity,\n",
      "                 color='b',\n",
      "                 label='Hombres')\n",
      "\n",
      "rects2 = plt.bar(index + bar_width, women_data, bar_width,\n",
      "                 alpha=opacity,\n",
      "                 color='r',\n",
      "                 label='Mujeres')\n",
      "\n",
      "plt.xlabel('Rol')\n",
      "plt.ylabel('N\u00famero de personajes')\n",
      "plt.title('Distribuci\u00f3n por g\u00e9nero y roles')\n",
      "plt.xticks(index + bar_width, ('Villanos', 'H\u00e9roes'))\n",
      "plt.legend(loc=0, borderaxespad=1.)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cCmxKNoT0jjR9aKmCMjOztpN3+OgbwEkljsXMzMrAj4E0M6twbfJgGjOzUvnn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\nIQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117b68610>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Otro caso de adem\u00e1s de contrastar el n\u00famero de villanos, es el de salir de dudas con respecto a una sospecha que tenemos. La cantidad de pelirrojas que hay en los c\u00f3mics!\n",
      "\n",
      "La ocurrencia del cabello rojo en la poblaci\u00f3n es del 1-2% globalmente y del 2-6% en poblaciones con ascendencia del norte u oeste de Europa. Irlanda y Escocia destacan con un 10% y 13% de ocurrencia, respectivamente.\n",
      "\n",
      "Veamos qu\u00e9 ocurre en Marvel."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "red_heads = clean_df['wiki.hair'].map(lambda x: 'Red' in x)\n",
      "clean_df['red_heads'] = red_heads\n",
      "red_heads[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "name\n",
        "Abomination (Emil Blonsky)    False\n",
        "Absorbing Man                 False\n",
        "Abyss                         False\n",
        "Agent Zero                    False\n",
        "Annihilus                     False\n",
        "Name: wiki.hair, dtype: bool"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"Red heads: #{}, Non-red heads #{}\".format(clean_df[red_heads].shape[0],clean_df[~red_heads].shape[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Red heads: #76, Non-red heads #686\n"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "non_red = ~red_heads\n",
      "hair_data = {'Women':{'Red heads':0,'Non-red heads':0},'Men':{'Red heads':0,'Non-red heads':0}}\n",
      "# Red haired women\n",
      "hair_data['Women']['Red heads'] = clean_df[red_heads & women].shape[0]\n",
      "# Non-red haired women\n",
      "hair_data['Women']['Non-red heads'] = clean_df[~red_heads & women].shape[0]\n",
      "\n",
      "# Red haired men\n",
      "hair_data['Men']['Red heads'] = clean_df[red_heads & men].shape[0]\n",
      "# Non-red haired women\n",
      "hair_data['Men']['Non-red heads'] = clean_df[~red_heads & men].shape[0]\n",
      "hair_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "{'Women': {'Non-red heads': 169, 'Red heads': 30},\n",
        " 'Men': {'Non-red heads': 517, 'Red heads': 46}}"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\u00bfQu\u00e9 es esto?\n",
      "redwomen = 30 / 199.\n",
      "redmen = 46 / 563.\n",
      "print('Women: {0:5.2f}%, Men: {1:5.2f}%'.format(redwomen * 100, redmen * 100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Women: 15.08%, Men:  8.17%\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Machine Learning time"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "\u00bfQu\u00e9 es el aprendizaje autom\u00e1tico?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Es \"simplemente\" una serie de algoritmos que permiten que una m\u00e1quina aprenda a partir de datos. Por lo tanto los ingredientes b\u00e1sicos para nuestra receta son datos + algoritmos. \n",
      "\n",
      "Para hacer este c\u00f3cktel necesitamos las habilidades de ingenier\u00eda inform\u00e1tica, estad\u00edstica y conociemiento del problema. \n",
      "\n",
      "Y a partir del aprendizaje autom\u00e1tico podremos:\n",
      "- Podemos predecir eventos futros. \n",
      "- Clasificar datos.\n",
      "- **Descubrir patrones en los datos.**\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Armas "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import matplotlib\n",
      "%matplotlib inline\n",
      "import sklearn\n",
      "\n",
      "print(\"Versi\u00f3n de Python:     \", sys.version)\n",
      "print(\"Versi\u00f3n de Pandas:     \", pd.version.short_version)\n",
      "print(\"Versi\u00f3n de Numpy:      \", np.version.short_version)\n",
      "print(\"Versi\u00f3n de Matplotlib: \", matplotlib.__version__)\n",
      "print(\"Versi\u00f3n de Pandas:     \", pd.version.short_version)\n",
      "print(\"Versi\u00f3n de scikit-learn:     \", sklearn.__version__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Versi\u00f3n de Python:      3.3.4 (default, Jul 25 2014, 00:04:27) \n",
        "[GCC 4.2.1 Compatible Apple LLVM 5.1 (clang-503.0.40)]\n",
        "Versi\u00f3n de Pandas:      0.14.1\n",
        "Versi\u00f3n de Numpy:       1.8.2\n",
        "Versi\u00f3n de Matplotlib:  1.4.0\n",
        "Versi\u00f3n de Pandas:      0.14.1\n",
        "Versi\u00f3n de scikit-learn:      0.15.2\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Scikit-learn mola."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Herramientas simples y eficientes para hacer miner\u00eda de datos y an\u00e1lisis de datos.\n",
      "- Accesible y reusable en distintos contextos.\n",
      "- Construido sobre NumPy, SciPy, and matplotlib\n",
      "- Open source, permite uso commercial - BSD license"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![scikit-learn algorithms](http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Ciclo de trabajo t\u00edpico"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1. Recolectar datos.\n",
      "    1. \u00bfHay suficientes datos? \u00bfNo? Volver a 1.\n",
      "2. Preprocesar datos.\n",
      "3. Dividir el corpus en *train*, *test* y *development*.\n",
      "4. Seleccionar y entrenar el algoritmo.\n",
      "5. Ajustar los par\u00e1metros.\n",
      "6. Verificar los resultados.\n",
      "7. Celebrar."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Algoritmos de los N-Vecinos (Knn)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Algoritmo simple para clasificar muestras.\n",
      "- Necesita muestras etiquetadas.\n",
      "- Calcula la distancia a los vecinos de la muestra a etiquetar. Cada vecino (hasta N o K) vota.\n",
      "\n",
      "- A\u00f1adir nuevos datos no es gratis. \n",
      "- Es computacionalmente complejo. \n",
      "- La mayor\u00eda no siempre tiene la raz\u00f3n."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Clasificaci\u00f3n utilizando el peso y altura "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font color='red'> *Hip\u00f3tesis*: Las caracter\u00edsticas f\u00edsicas diferencian a los personajes femeninos de los masculinos </font> "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "2. Preprocesado de los datos (data munging)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_df['wiki.weight'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "count            762\n",
        "unique           307\n",
        "top       Unrevealed\n",
        "freq              48\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical = clean_df[clean_df['wiki.weight'] != \"Unrevealed\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "any(physical['wiki.height'] == \"Unrevealed\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00a1Genial! Al menos los que no tienen peso son los mismo que no tienen altura"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn = physical[['wiki.weight', 'wiki.height', 'Women', 'Villain']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "wiki.weight    object\n",
        "wiki.height    object\n",
        "Women            bool\n",
        "Villain          bool\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Queremos que sean enteros"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>wiki.weight</th>\n",
        "      <th>wiki.height</th>\n",
        "      <th>Women</th>\n",
        "      <th>Villain</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Abomination (Emil Blonsky)</th>\n",
        "      <td>    (Abomination) 980 lbs.; (Blonsky) 180 lbs.</td>\n",
        "      <td>       (Abomination) 6'8\"; (Blonsky) 5'10\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Absorbing Man</th>\n",
        "      <td>                           365 lbs. (variable)</td>\n",
        "      <td>                           6'4\" (variable)</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Agent Zero</th>\n",
        "      <td>                                      230 lbs.</td>\n",
        "      <td>                                      6'3\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Annihilus</th>\n",
        "      <td>                                      200 lbs.</td>\n",
        "      <td>                                     5'11\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Apocalypse</th>\n",
        "      <td>                           300 lbs. (variable)</td>\n",
        "      <td>              Variable (usually around 7')</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spider-Girl (Anya Corazon)</th>\n",
        "      <td>                                      115 lbs.</td>\n",
        "      <td>                                      5'3\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arcade</th>\n",
        "      <td>                                      140 lbs.</td>\n",
        "      <td>                                      5'6\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Archangel</th>\n",
        "      <td>                                      150 lbs.</td>\n",
        "      <td>                                        6'</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arclight</th>\n",
        "      <td>                                      126 lbs.</td>\n",
        "      <td>                                      5'8\"</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aurora</th>\n",
        "      <td>                                      140 lbs.</td>\n",
        "      <td>                                     5'11\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Avalanche</th>\n",
        "      <td>                                      195 lbs.</td>\n",
        "      <td>                                      5'7\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Banshee</th>\n",
        "      <td>                                      170 lbs.</td>\n",
        "      <td>                                        6'</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Baron Strucker</th>\n",
        "      <td>                                      225 lbs.</td>\n",
        "      <td>                                      6'2\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Baron Zemo (Heinrich Zemo)</th>\n",
        "      <td>                                       180 lbs</td>\n",
        "      <td>                                      5'9\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bastion</th>\n",
        "      <td>                                      375 lbs.</td>\n",
        "      <td>                                      6'3\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Batroc the Leaper</th>\n",
        "      <td>                                      225 lbs.</td>\n",
        "      <td>                                        6\u2019</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Battering Ram</th>\n",
        "      <td>                                      380 lbs.</td>\n",
        "      <td>                                      7'4\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beak</th>\n",
        "      <td>                                           140</td>\n",
        "      <td>                                      5'9\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beast</th>\n",
        "      <td>                                      402 lbs.</td>\n",
        "      <td>                                     5'11\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beef</th>\n",
        "      <td>                                      250 lbs.</td>\n",
        "      <td>                                      6'6\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beta-Ray Bill</th>\n",
        "      <td>     (As Bill) 480 lbs.; (as Walters) 132 lbs.</td>\n",
        "      <td>         (As Bill) 6'7\"; (as Walters) 5'9\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Big Wheel</th>\n",
        "      <td>                                      140 lbs.</td>\n",
        "      <td>                                      5'5\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bishop</th>\n",
        "      <td>                                      275 lbs.</td>\n",
        "      <td>                                      6'6\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Bolt</th>\n",
        "      <td>                                       210 lbs</td>\n",
        "      <td>                                     6' 2\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Cat</th>\n",
        "      <td>                                      120 lbs.</td>\n",
        "      <td>                                     5'10\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Knight</th>\n",
        "      <td>                                      180 lbs.</td>\n",
        "      <td>                                    5\u2019 11\u201d</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Panther</th>\n",
        "      <td>                                      200 lbs.</td>\n",
        "      <td>                                        6'</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Tom</th>\n",
        "      <td>   (originally) 200 lbs.; (currently) Variable</td>\n",
        "      <td>   (originally) 6'0\"; (currently) Variable</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Widow</th>\n",
        "      <td>                                      131 lbs.</td>\n",
        "      <td>                                      5'7\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Blackheart</th>\n",
        "      <td>                            679 lbs (Variable)</td>\n",
        "      <td>                          6'10\" (Variable)</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</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>Starhawk (Stakar Ogord)</th>\n",
        "      <td>                                      450 lbs.</td>\n",
        "      <td>                                      6'4\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Vance Astro</th>\n",
        "      <td>  (as an adult, with protective gear) 250 lbs.</td>\n",
        "      <td>                        (as an adult) 6'1\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jamie Braddock</th>\n",
        "      <td>                                      151 lbs.</td>\n",
        "      <td>                                      6'1\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jazinda</th>\n",
        "      <td>                           135 lbs. (variable)</td>\n",
        "      <td>                           5'6\" (variable)</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tinkerer</th>\n",
        "      <td>                                      120 lbs.</td>\n",
        "      <td>                                      5'4\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cosmo</th>\n",
        "      <td>                                       70 lbs.</td>\n",
        "      <td>                          23\" (at withers)</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Red Hulk</th>\n",
        "      <td>         245 lbs. (Ross); 1200 lbs. (Red Hulk)</td>\n",
        "      <td>                6'1\" (Ross); 7' (Red Hulk)</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>American Eagle (Jason Strongbow)</th>\n",
        "      <td>                                       200 lbs</td>\n",
        "      <td>                                        6'</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cottonmouth</th>\n",
        "      <td>                                      200 lbs.</td>\n",
        "      <td>                                        6'</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Vanisher (Telford Porter)</th>\n",
        "      <td>                                      175 lbs.</td>\n",
        "      <td>                                      5'5\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sphinx (Anath-Na Mut)</th>\n",
        "      <td>                                       450 lbs</td>\n",
        "      <td>                                      7'2\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Molten Man</th>\n",
        "      <td>                                      550 lbs.</td>\n",
        "      <td>                                      6\u20195\u201d</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Henry Peter Gyrich</th>\n",
        "      <td>                                      205 lbs.</td>\n",
        "      <td>                                     6\u2019 1\u201d</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cypher</th>\n",
        "      <td>                                      150 lbs.</td>\n",
        "      <td>                                      5'9\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Karma</th>\n",
        "      <td>                                      119 lbs.</td>\n",
        "      <td>                                      5'4\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>She-Hulk (Lyra)</th>\n",
        "      <td>                                      220 lbs.</td>\n",
        "      <td>                                      6'6\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>She-Hulk (Ultimate)</th>\n",
        "      <td> 110 lbs. (as Betty); Unrevealed (as She-Hulk)</td>\n",
        "      <td> 5'6\" (as Betty); Unrevealed (as She-Hulk)</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Talon (Fraternity of Raptors)</th>\n",
        "      <td>                                      180 lbs.</td>\n",
        "      <td>                                      6'1\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Angel (Golden Age)</th>\n",
        "      <td>                                              </td>\n",
        "      <td>                                          </td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Meggan</th>\n",
        "      <td>       Usually 120 lbs., 130 lbs. in true form</td>\n",
        "      <td>          Usually 5'7\", 5'10\" in true form</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Loa</th>\n",
        "      <td>                                      139 lbs.</td>\n",
        "      <td>                                      5'8\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Grey Gargoyle</th>\n",
        "      <td>           (normal) 175 lbs.; (stone) 750 lbs.</td>\n",
        "      <td>                                     5\u201911\u201d</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nekra</th>\n",
        "      <td>                                      145 lbs.</td>\n",
        "      <td>                                    5' 11\"</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Miss America</th>\n",
        "      <td>                                       130 lbs</td>\n",
        "      <td>                                      5'8\"</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Whizzer (Stanley Stewart)</th>\n",
        "      <td>                                      180 lbs.</td>\n",
        "      <td>                                     5'11\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scarlet Spider (Kaine)</th>\n",
        "      <td>                                      250 lbs.</td>\n",
        "      <td>                                      6'4\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hank Pym</th>\n",
        "      <td>                     varies, normally 185 lbs.</td>\n",
        "      <td>                       varies, normally 6'</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Azazel (Mutant)</th>\n",
        "      <td>                                      149 lbs.</td>\n",
        "      <td>                                        6'</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spider-Man (House of M)</th>\n",
        "      <td>                                      165 lbs.</td>\n",
        "      <td>                                     5'10\"</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gargoyle (Yuri Topolov)</th>\n",
        "      <td>                                      215 lbs.</td>\n",
        "      <td>                                      4'6\"</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>714 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "                                                                    wiki.weight  \\\n",
        "name                                                                              \n",
        "Abomination (Emil Blonsky)           (Abomination) 980 lbs.; (Blonsky) 180 lbs.   \n",
        "Absorbing Man                                               365 lbs. (variable)   \n",
        "Agent Zero                                                             230 lbs.   \n",
        "Annihilus                                                              200 lbs.   \n",
        "Apocalypse                                                  300 lbs. (variable)   \n",
        "Spider-Girl (Anya Corazon)                                             115 lbs.   \n",
        "Arcade                                                                 140 lbs.   \n",
        "Archangel                                                              150 lbs.   \n",
        "Arclight                                                               126 lbs.   \n",
        "Aurora                                                                 140 lbs.   \n",
        "Avalanche                                                              195 lbs.   \n",
        "Banshee                                                                170 lbs.   \n",
        "Baron Strucker                                                         225 lbs.   \n",
        "Baron Zemo (Heinrich Zemo)                                              180 lbs   \n",
        "Bastion                                                                375 lbs.   \n",
        "Batroc the Leaper                                                      225 lbs.   \n",
        "Battering Ram                                                          380 lbs.   \n",
        "Beak                                                                        140   \n",
        "Beast                                                                  402 lbs.   \n",
        "Beef                                                                   250 lbs.   \n",
        "Beta-Ray Bill                         (As Bill) 480 lbs.; (as Walters) 132 lbs.   \n",
        "Big Wheel                                                              140 lbs.   \n",
        "Bishop                                                                 275 lbs.   \n",
        "Black Bolt                                                              210 lbs   \n",
        "Black Cat                                                              120 lbs.   \n",
        "Black Knight                                                           180 lbs.   \n",
        "Black Panther                                                          200 lbs.   \n",
        "Black Tom                           (originally) 200 lbs.; (currently) Variable   \n",
        "Black Widow                                                            131 lbs.   \n",
        "Blackheart                                                   679 lbs (Variable)   \n",
        "...                                                                         ...   \n",
        "Starhawk (Stakar Ogord)                                                450 lbs.   \n",
        "Vance Astro                        (as an adult, with protective gear) 250 lbs.   \n",
        "Jamie Braddock                                                         151 lbs.   \n",
        "Jazinda                                                     135 lbs. (variable)   \n",
        "Tinkerer                                                               120 lbs.   \n",
        "Cosmo                                                                   70 lbs.   \n",
        "Red Hulk                                  245 lbs. (Ross); 1200 lbs. (Red Hulk)   \n",
        "American Eagle (Jason Strongbow)                                        200 lbs   \n",
        "Cottonmouth                                                            200 lbs.   \n",
        "Vanisher (Telford Porter)                                              175 lbs.   \n",
        "Sphinx (Anath-Na Mut)                                                   450 lbs   \n",
        "Molten Man                                                             550 lbs.   \n",
        "Henry Peter Gyrich                                                     205 lbs.   \n",
        "Cypher                                                                 150 lbs.   \n",
        "Karma                                                                  119 lbs.   \n",
        "She-Hulk (Lyra)                                                        220 lbs.   \n",
        "She-Hulk (Ultimate)               110 lbs. (as Betty); Unrevealed (as She-Hulk)   \n",
        "Talon (Fraternity of Raptors)                                          180 lbs.   \n",
        "Angel (Golden Age)                                                                \n",
        "Meggan                                  Usually 120 lbs., 130 lbs. in true form   \n",
        "Loa                                                                    139 lbs.   \n",
        "Grey Gargoyle                               (normal) 175 lbs.; (stone) 750 lbs.   \n",
        "Nekra                                                                  145 lbs.   \n",
        "Miss America                                                            130 lbs   \n",
        "Whizzer (Stanley Stewart)                                              180 lbs.   \n",
        "Scarlet Spider (Kaine)                                                 250 lbs.   \n",
        "Hank Pym                                              varies, normally 185 lbs.   \n",
        "Azazel (Mutant)                                                        149 lbs.   \n",
        "Spider-Man (House of M)                                                165 lbs.   \n",
        "Gargoyle (Yuri Topolov)                                                215 lbs.   \n",
        "\n",
        "                                                                wiki.height  \\\n",
        "name                                                                          \n",
        "Abomination (Emil Blonsky)              (Abomination) 6'8\"; (Blonsky) 5'10\"   \n",
        "Absorbing Man                                               6'4\" (variable)   \n",
        "Agent Zero                                                             6'3\"   \n",
        "Annihilus                                                             5'11\"   \n",
        "Apocalypse                                     Variable (usually around 7')   \n",
        "Spider-Girl (Anya Corazon)                                             5'3\"   \n",
        "Arcade                                                                 5'6\"   \n",
        "Archangel                                                                6'   \n",
        "Arclight                                                               5'8\"   \n",
        "Aurora                                                                5'11\"   \n",
        "Avalanche                                                              5'7\"   \n",
        "Banshee                                                                  6'   \n",
        "Baron Strucker                                                         6'2\"   \n",
        "Baron Zemo (Heinrich Zemo)                                             5'9\"   \n",
        "Bastion                                                                6'3\"   \n",
        "Batroc the Leaper                                                        6\u2019   \n",
        "Battering Ram                                                          7'4\"   \n",
        "Beak                                                                   5'9\"   \n",
        "Beast                                                                 5'11\"   \n",
        "Beef                                                                   6'6\"   \n",
        "Beta-Ray Bill                             (As Bill) 6'7\"; (as Walters) 5'9\"   \n",
        "Big Wheel                                                              5'5\"   \n",
        "Bishop                                                                 6'6\"   \n",
        "Black Bolt                                                            6' 2\"   \n",
        "Black Cat                                                             5'10\"   \n",
        "Black Knight                                                         5\u2019 11\u201d   \n",
        "Black Panther                                                            6'   \n",
        "Black Tom                           (originally) 6'0\"; (currently) Variable   \n",
        "Black Widow                                                            5'7\"   \n",
        "Blackheart                                                 6'10\" (Variable)   \n",
        "...                                                                     ...   \n",
        "Starhawk (Stakar Ogord)                                                6'4\"   \n",
        "Vance Astro                                              (as an adult) 6'1\"   \n",
        "Jamie Braddock                                                         6'1\"   \n",
        "Jazinda                                                     5'6\" (variable)   \n",
        "Tinkerer                                                               5'4\"   \n",
        "Cosmo                                                      23\" (at withers)   \n",
        "Red Hulk                                         6'1\" (Ross); 7' (Red Hulk)   \n",
        "American Eagle (Jason Strongbow)                                         6'   \n",
        "Cottonmouth                                                              6'   \n",
        "Vanisher (Telford Porter)                                              5'5\"   \n",
        "Sphinx (Anath-Na Mut)                                                  7'2\"   \n",
        "Molten Man                                                             6\u20195\u201d   \n",
        "Henry Peter Gyrich                                                    6\u2019 1\u201d   \n",
        "Cypher                                                                 5'9\"   \n",
        "Karma                                                                  5'4\"   \n",
        "She-Hulk (Lyra)                                                        6'6\"   \n",
        "She-Hulk (Ultimate)               5'6\" (as Betty); Unrevealed (as She-Hulk)   \n",
        "Talon (Fraternity of Raptors)                                          6'1\"   \n",
        "Angel (Golden Age)                                                            \n",
        "Meggan                                     Usually 5'7\", 5'10\" in true form   \n",
        "Loa                                                                    5'8\"   \n",
        "Grey Gargoyle                                                         5\u201911\u201d   \n",
        "Nekra                                                                5' 11\"   \n",
        "Miss America                                                           5'8\"   \n",
        "Whizzer (Stanley Stewart)                                             5'11\"   \n",
        "Scarlet Spider (Kaine)                                                 6'4\"   \n",
        "Hank Pym                                                varies, normally 6'   \n",
        "Azazel (Mutant)                                                          6'   \n",
        "Spider-Man (House of M)                                               5'10\"   \n",
        "Gargoyle (Yuri Topolov)                                                4'6\"   \n",
        "\n",
        "                                  Women Villain  \n",
        "name                                             \n",
        "Abomination (Emil Blonsky)        False    True  \n",
        "Absorbing Man                     False    True  \n",
        "Agent Zero                        False    True  \n",
        "Annihilus                         False    True  \n",
        "Apocalypse                        False    True  \n",
        "Spider-Girl (Anya Corazon)         True   False  \n",
        "Arcade                            False    True  \n",
        "Archangel                         False   False  \n",
        "Arclight                           True    True  \n",
        "Aurora                             True   False  \n",
        "Avalanche                         False    True  \n",
        "Banshee                           False   False  \n",
        "Baron Strucker                    False    True  \n",
        "Baron Zemo (Heinrich Zemo)        False    True  \n",
        "Bastion                           False    True  \n",
        "Batroc the Leaper                 False    True  \n",
        "Battering Ram                     False   False  \n",
        "Beak                              False   False  \n",
        "Beast                             False   False  \n",
        "Beef                              False    True  \n",
        "Beta-Ray Bill                     False   False  \n",
        "Big Wheel                         False   False  \n",
        "Bishop                            False   False  \n",
        "Black Bolt                        False   False  \n",
        "Black Cat                          True   False  \n",
        "Black Knight                      False   False  \n",
        "Black Panther                     False   False  \n",
        "Black Tom                         False    True  \n",
        "Black Widow                        True   False  \n",
        "Blackheart                        False    True  \n",
        "...                                 ...     ...  \n",
        "Starhawk (Stakar Ogord)           False   False  \n",
        "Vance Astro                       False   False  \n",
        "Jamie Braddock                    False    True  \n",
        "Jazinda                            True   False  \n",
        "Tinkerer                          False    True  \n",
        "Cosmo                             False   False  \n",
        "Red Hulk                          False   False  \n",
        "American Eagle (Jason Strongbow)  False   False  \n",
        "Cottonmouth                       False    True  \n",
        "Vanisher (Telford Porter)         False    True  \n",
        "Sphinx (Anath-Na Mut)             False    True  \n",
        "Molten Man                        False    True  \n",
        "Henry Peter Gyrich                False   False  \n",
        "Cypher                            False   False  \n",
        "Karma                              True   False  \n",
        "She-Hulk (Lyra)                    True   False  \n",
        "She-Hulk (Ultimate)               False   False  \n",
        "Talon (Fraternity of Raptors)     False   False  \n",
        "Angel (Golden Age)                False   False  \n",
        "Meggan                             True   False  \n",
        "Loa                                True   False  \n",
        "Grey Gargoyle                     False    True  \n",
        "Nekra                              True    True  \n",
        "Miss America                       True   False  \n",
        "Whizzer (Stanley Stewart)         False   False  \n",
        "Scarlet Spider (Kaine)            False    True  \n",
        "Hank Pym                          False   False  \n",
        "Azazel (Mutant)                   False    True  \n",
        "Spider-Man (House of M)           False   False  \n",
        "Gargoyle (Yuri Topolov)           False    True  \n",
        "\n",
        "[714 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn.applymap(str)\n",
      "physical_knn = physical_knn[physical_knn['wiki.weight'].str.contains(\"lbs.\")]\n",
      "physical_knn = physical_knn[physical_knn['wiki.height'].str.contains('\u2019|\\'')]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_weight(pandas_weight):\n",
      "    \"\"\" Return first int parameter in a string \"\"\"\n",
      "    for p in pandas_weight.split():\n",
      "        try:\n",
      "            return int(p)\n",
      "        except ValueError:\n",
      "            pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn['wiki.weight'] = physical_knn['wiki.weight'].map(get_weight)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "FOOT = 30.48\n",
      "INCH = 2.54\n",
      "def get_height(pandas_height):\n",
      "    \"\"\" Return first int parameter in a string \"\"\"\n",
      "    \n",
      "    height = None \n",
      "    for p in pandas_height.split():\n",
      "        colon_split = p.split('\\'')\n",
      "        strange_colon_split = p.split('\u2019')\n",
      "        if len(colon_split) == 2 :\n",
      "            height = colon_split\n",
      "        elif len(colon_split) == 4 :\n",
      "            height = colon_split[:2]\n",
      "            height[1] += \"\\'\" \n",
      "        elif len(strange_colon_split) == 2 :\n",
      "            height = strange_colon_split\n",
      "        elif len((pandas_height.split()[-1]).split('\\'')) == 2:\n",
      "            height = pandas_height.split()[-1].split('\\'')\n",
      "        elif len((pandas_height.split()[-1]).split('\u2019')) == 2:\n",
      "            height = pandas_height.split()[-1].split('\u2019')\n",
      "                \n",
      "        else:\n",
      "            universe_split = ((pandas_height.split(';')[0]).split()[-1]).split('\\'')\n",
      "            if len(universe_split) == 2:\n",
      "                height = universe_split\n",
      "            else:\n",
      "                space_split = (pandas_height.split(';')[0].split()[-2:])\n",
      "                if space_split[0][-1] == '\\'' or space_split[0][-1] == '\u2019':\n",
      "                    height = [space_split[0][:-1], space_split[1]]\n",
      "                else:\n",
      "                    return None\n",
      "        if height:\n",
      "            try:\n",
      "                foot_part = int(height[0])\n",
      "                inch_part = int(height[1][:-1]) if height[1][:-1].strip() else 0\n",
      "                return (foot_part*FOOT + inch_part*INCH)\n",
      "            except ValueError:\n",
      "                pass\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn['wiki.height'] = physical_knn['wiki.height'].map(get_height)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn = physical_knn.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "wiki.weight    float64\n",
        "wiki.height    float64\n",
        "Women             bool\n",
        "Villain           bool\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "(578, 4)"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00a1Ahora ya podemos empezar a trabajar!"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "3. Separar el corpus en train y test"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import floor"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TRAIN_PERCENTAGE = 0.8\n",
      "train_section = floor(physical_knn.shape[0]*TRAIN_PERCENTAGE)\n",
      "test_section = physical_knn.shape[0]-train_section\n",
      "print(\"Usaremos {} personajes para entrenar el clasificador y\"\\\n",
      "      \" {} para probar el clasificador entrenado.\".format(train_section, test_section))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Usaremos 462 personajes para entrenar el clasificador y 116 para probar el clasificador entrenado.\n"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_rows = np.random.choice(physical_knn.index.values, train_section)\n",
      "test_rows = np.setdiff1d(physical_knn.index.values,train_rows)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "physical_knn.loc[train_rows[0]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "wiki.weight      190\n",
        "wiki.height    190.5\n",
        "Women           True\n",
        "Villain        False\n",
        "Name: Cerise, dtype: object"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Separamos datos y etiquetas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train = physical_knn.loc[train_rows][['wiki.weight','wiki.height']]\n",
      "y_train = physical_knn.loc[train_rows]['Women']\n",
      "\n",
      "X_test = physical_knn.loc[test_rows][['wiki.weight','wiki.height']]\n",
      "y_test = physical_knn.loc[test_rows]['Women']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "3.1. Visualizar los datos"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a echarle un vistazo a los datos para comprobar la complejidad de la tarea:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i, group in physical_knn.groupby(women):\n",
      "    if not i:\n",
      "        ax = group.plot(kind='scatter', x='wiki.height', y='wiki.weight', \n",
      "                        color='DarkBlue', label='Men');\n",
      "    else:\n",
      "        print(i)\n",
      "        group.plot(kind='scatter', x='wiki.height', y='wiki.weight', \n",
      "                          color='DarkGreen', label='Women', ax=ax)\n",
      "\n",
      "print(physical_knn.groupby(women).aggregate(np.mean))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n",
        "                 wiki.weight  wiki.height  Women   Villain\n",
        "wiki.categories                                           \n",
        "False             243.191943   183.439763      0  0.355450\n",
        "True              149.628205   170.701026      1  0.134615"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NacciXXEu2XtURLoBZcC/A1cDB1X1YRG5B+ilqve4g+1P4xSbQcArwNmx/VhB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\nBmNA2QueoQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110e38850>"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4. Entrenar el clasificador"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Creamos una instancia del clasificador"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import neighbors\n",
      "classifier = neighbors.KNeighborsClassifier()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "classifier.fit(X_train, y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           metric_params=None, n_neighbors=5, p=2, weights='uniform')"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict = classifier.predict(X_test)\n",
      "predict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "array([False, False, False, False, False, False,  True,  True, False,\n",
        "       False, False, False, False, False,  True, False, False, False,\n",
        "       False, False,  True, False,  True, False, False, False, False,\n",
        "       False, False, False, False, False,  True, False, False, False,\n",
        "       False, False, False, False,  True, False, False, False, False,\n",
        "       False,  True,  True,  True, False,  True, False, False, False,\n",
        "       False, False, False,  True, False, False, False,  True,  True,\n",
        "       False, False, False, False, False, False, False, False, False,\n",
        "        True,  True, False, False, False, False, False, False,  True,\n",
        "       False,  True,  True, False,  True, False, False, False, False,\n",
        "       False, False, False, False, False, False, False, False, False,\n",
        "       False, False, False, False, False, False, False, False, False,\n",
        "       False,  True,  True, False, False,  True,  True, False, False,\n",
        "       False, False,  True, False, False, False, False, False, False,\n",
        "       False,  True, False, False, False, False,  True,  True,  True,\n",
        "        True, False,  True,  True, False,  True, False, False,  True,\n",
        "        True, False, False, False, False,  True, False, False,  True,\n",
        "       False, False, False, False, False, False, False, False, False,\n",
        "       False, False, False, False,  True, False, False, False, False,\n",
        "       False,  True, False, False, False, False, False, False,  True,\n",
        "       False,  True, False, False, False, False, False, False, False,\n",
        "       False, False, False, False, False, False, False,  True, False,\n",
        "       False, False, False, False,  True,  True, False,  True, False,\n",
        "       False,  True, False,  True, False,  True,  True, False, False,\n",
        "        True,  True, False, False, False, False, False,  True, False,\n",
        "       False,  True,  True, False, False, False, False, False, False,\n",
        "       False, False, False, False, False, False, False,  True,  True,\n",
        "       False,  True, False, False, False, False,  True,  True, False,\n",
        "       False, False, False, False, False, False,  True, False, False,\n",
        "       False, False], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "6. Comprobar los resultados."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "accuracy = metrics.accuracy_score(y_test, predict)\n",
      "precision, recall, f1, _ = metrics.precision_recall_fscore_support(y_test, predict)\n",
      "print(\"* Acierto: {:.2f}%\".format(accuracy*100))\n",
      "print(\"* Precisi\u00f3n: {}\\n* Exhaustividad: {}.\\n* F1-Score: {}\".format(accuracy*100, precision, recall, f1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "* Acierto: 82.13%\n",
        "* Precisi\u00f3n: 82.12927756653993\n",
        "* Exhaustividad: [ 0.89162562  0.58333333].\n",
        "* F1-Score: [ 0.87864078  0.61403509]\n"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "La ciencia tras la bestia"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- *True positive*: el elemento es miembro de la clase y decimos que lo es. \n",
      "- *True negative*: el elemento no es miembro de la clase y decimos que no lo es.\n",
      "- *False positive*: falsa alarma, el elemento es miembro de la clase y decimos que no lo es. \n",
      "- *False negative*: el elemento no es miembro de la clase y decimos que lo es."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%latex\n",
      "\\begin{align}\n",
      "accuray = \\frac{\\text{# True Positives}+\\text{# True Negatives}}\n",
      "{\\text{# True Positives}+\\text{False Positives} + \\text{False Negatives} + \\text{True Negatives}}\n",
      "\\end{align}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "\\begin{align}\n",
        "accuray = \\frac{\\text{# True Positives}+\\text{# True Negatives}}\n",
        "{\\text{# True Positives}+\\text{False Positives} + \\text{False Negatives} + \\text{True Negatives}}\n",
        "\\end{align}"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Latex at 0x116224850>"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%latex\n",
      "\\begin{align}\n",
      "precision = \\frac{\\text{# True Positives}} {\\text{# True Positives}+\\text{False Positives}}\n",
      "\\end{align}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "\\begin{align}\n",
        "precision = \\frac{\\text{# True Positives}} {\\text{# True Positives}+\\text{False Positives}}\n",
        "\\end{align}"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Latex at 0x115e94e90>"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "7. \u00a1Celebrar!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.colors import ListedColormap\n",
      "\n",
      "cmap_light = ListedColormap(['#AAAAFF', '#AAFFAA'])\n",
      "cmap_bold = ListedColormap(['#0000FF', '#00FF00'])\n",
      "\n",
      "step = 2\n",
      "\n",
      "x_min, x_max = X_test['wiki.height'].min() - 1, X_test['wiki.height'].max() + 1\n",
      "y_min, y_max = X_test['wiki.weight'].min() - 1, X_test['wiki.weight'].max() + 1\n",
      "\n",
      "xx, yy = np.meshgrid(np.arange(x_min, x_max, step),\n",
      "                     np.arange(y_min, y_max, step))    \n",
      "prediction = classifier.predict(X_test)\n",
      "Z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])\n",
      "Z = Z.reshape(xx.shape)\n",
      "plt.figure()\n",
      "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
      "plt.scatter( X_test['wiki.height'], X_test['wiki.weight'], c=y_test, cmap=cmap_bold)\n",
      "plt.xlim(xx.min(), xx.max())\n",
      "plt.ylim(yy.min(), 400)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "(19.0, 400)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IMRgMBAWF5TiZA95W/Y/APkADBgI2Zs3axuzZfwO+CDEe/RbRDqRcQc2at+ZB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\nAV0ppYKEBnSllAoS/w9DFeFRbmoyHgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117a1add0>"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "\u00bfY si... ?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "\n",
      "for n in range(1,20, 2):\n",
      "    classifier = neighbors.KNeighborsClassifier(n_neighbors=n)\n",
      "    classifier.fit(X_train, y_train)\n",
      "    predict = classifier.predict(X_test)\n",
      "    accuracy = metrics.accuracy_score(y_test, predict)\n",
      "    print(\"({}) Acierto: {:.2f}%\".format(n, accuracy*100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1) Acierto: 74.52%\n",
        "(3) Acierto: 79.09%\n",
        "(5) Acierto: 82.13%\n",
        "(7) Acierto: 84.03%\n",
        "(9) Acierto: 85.93%\n",
        "(11) Acierto: 86.69%\n",
        "(13) Acierto: 86.31%\n",
        "(15) Acierto: 85.93%\n",
        "(17) Acierto: 86.31%\n",
        "(19) Acierto: 85.93%\n"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Educaci\u00f3n superior y ciudadan\u00eda"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font color='red'> *Hip\u00f3tesis*: Las caracter\u00edsticas sociales (nacionalidad y educaci\u00f3n) diferencian a los personajes femeninos de los masculinos </font> "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "2. Preprocesado de los datos (data munging)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cultural_knn = clean_df[['wiki.education', 'wiki.citizenship', 'Women', 'Villain']]\n",
      "cultural_knn.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "wiki.education      object\n",
        "wiki.citizenship    object\n",
        "Women                 bool\n",
        "Villain               bool\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "usa = cultural_knn['wiki.citizenship'].map(lambda x: 'U.S.A.' in x)\n",
      "cultural_knn['USA'] = usa\n",
      "cultural_knn = cultural_knn.drop('wiki.citizenship',1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cultural_knn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>wiki.education</th>\n",
        "      <th>Women</th>\n",
        "      <th>Villain</th>\n",
        "      <th>USA</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Abomination (Emil Blonsky)</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Absorbing Man</th>\n",
        "      <td>                               High school dropout</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abyss</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Agent Zero</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Annihilus</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Apocalypse</th>\n",
        "      <td>                 Centuries of study and experience</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spider-Girl (Anya Corazon)</th>\n",
        "      <td>                               High school student</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arcade</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Archangel</th>\n",
        "      <td> College degree from Xavier's School for Gifted...</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arclight</th>\n",
        "      <td>                Unrevealed; some military training</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aurora</th>\n",
        "      <td>                  Madame DuPont's School for Girls</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Avalanche</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Banshee</th>\n",
        "      <td> Bachelor of Science degree from Trinity Colleg...</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Baron Strucker</th>\n",
        "      <td>                               University graduate</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Baron Zemo (Heinrich Zemo)</th>\n",
        "      <td>                                  Doctorate Degree</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bastion</th>\n",
        "      <td>                                      Inapplicable</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Batroc the Leaper</th>\n",
        "      <td>                                 Military training</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Battering Ram</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beak</th>\n",
        "      <td>                        Some college-level courses</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beast</th>\n",
        "      <td>                                  Ph.D. Biophysics</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beef</th>\n",
        "      <td>                                                  </td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beta-Ray Bill</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Big Wheel</th>\n",
        "      <td>                                  College educated</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bishop</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Bolt</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Cat</th>\n",
        "      <td>                     College graduate (arts major)</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Knight</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Panther</th>\n",
        "      <td>                                   Ph.D in physics</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Tom</th>\n",
        "      <td>                                 Oxford University</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Black Widow</th>\n",
        "      <td> Unrevealed; intensive espionage training throu...</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</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>Vanisher (Telford Porter)</th>\n",
        "      <td>                                                  </td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sphinx (Anath-Na Mut)</th>\n",
        "      <td> studied under caretakers of Arcturus, absorbed...</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Molten Man</th>\n",
        "      <td>                                  College graduate</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Henry Peter Gyrich</th>\n",
        "      <td>                               University graduate</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Reptil</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cypher</th>\n",
        "      <td> High school, university level courses in langu...</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Karma</th>\n",
        "      <td>                                        unrevealed</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>She-Hulk (Lyra)</th>\n",
        "      <td>                       Tutored by the [[Gynosure]]</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>She-Hulk (Ultimate)</th>\n",
        "      <td> Communications degree from Berkeley, studies a...</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Talon (Fraternity of Raptors)</th>\n",
        "      <td> Unrevealed, but the Datasong of Talon's armor ...</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Angel (Golden Age)</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Romulus</th>\n",
        "      <td>       Unrevealed, possible knowledge of genetics.</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Meggan</th>\n",
        "      <td> No formal schooling; self-taught from watching...</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lucky Pierre</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Shadu the Shady</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Contessa (Vera Vidal)</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chores MacGillicudy</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Iron Fist (Wu Ao-Shi)</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Loa</th>\n",
        "      <td>            Currently in high school level courses</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Grey Gargoyle</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nekra</th>\n",
        "      <td>                                 Elementary school</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Miss America</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Whizzer (Stanley Stewart)</th>\n",
        "      <td>                              High school Graduate</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scarlet Spider (Kaine)</th>\n",
        "      <td> Possesses memories of Peter Parker's college e...</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hope Summers</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Enchantress (Sylvie Lushton)</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hank Pym</th>\n",
        "      <td> extensive knowledge in various fields of scien...</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Azazel (Mutant)</th>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spider-Man (House of M)</th>\n",
        "      <td>                              Ph.D in biochemistry</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gargoyle (Yuri Topolov)</th>\n",
        "      <td>                                                  </td>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>762 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "                                                                  wiki.education  \\\n",
        "name                                                                               \n",
        "Abomination (Emil Blonsky)                                            Unrevealed   \n",
        "Absorbing Man                                                High school dropout   \n",
        "Abyss                                                                 Unrevealed   \n",
        "Agent Zero                                                            Unrevealed   \n",
        "Annihilus                                                             Unrevealed   \n",
        "Apocalypse                                     Centuries of study and experience   \n",
        "Spider-Girl (Anya Corazon)                                   High school student   \n",
        "Arcade                                                                Unrevealed   \n",
        "Archangel                      College degree from Xavier's School for Gifted...   \n",
        "Arclight                                      Unrevealed; some military training   \n",
        "Aurora                                          Madame DuPont's School for Girls   \n",
        "Avalanche                                                             Unrevealed   \n",
        "Banshee                        Bachelor of Science degree from Trinity Colleg...   \n",
        "Baron Strucker                                               University graduate   \n",
        "Baron Zemo (Heinrich Zemo)                                      Doctorate Degree   \n",
        "Bastion                                                             Inapplicable   \n",
        "Batroc the Leaper                                              Military training   \n",
        "Battering Ram                                                         Unrevealed   \n",
        "Beak                                                  Some college-level courses   \n",
        "Beast                                                           Ph.D. Biophysics   \n",
        "Beef                                                                               \n",
        "Beta-Ray Bill                                                         Unrevealed   \n",
        "Big Wheel                                                       College educated   \n",
        "Bishop                                                                Unrevealed   \n",
        "Black Bolt                                                            Unrevealed   \n",
        "Black Cat                                          College graduate (arts major)   \n",
        "Black Knight                                                          Unrevealed   \n",
        "Black Panther                                                    Ph.D in physics   \n",
        "Black Tom                                                      Oxford University   \n",
        "Black Widow                    Unrevealed; intensive espionage training throu...   \n",
        "...                                                                          ...   \n",
        "Vanisher (Telford Porter)                                                          \n",
        "Sphinx (Anath-Na Mut)          studied under caretakers of Arcturus, absorbed...   \n",
        "Molten Man                                                      College graduate   \n",
        "Henry Peter Gyrich                                           University graduate   \n",
        "Reptil                                                                Unrevealed   \n",
        "Cypher                         High school, university level courses in langu...   \n",
        "Karma                                                                 unrevealed   \n",
        "She-Hulk (Lyra)                                      Tutored by the [[Gynosure]]   \n",
        "She-Hulk (Ultimate)            Communications degree from Berkeley, studies a...   \n",
        "Talon (Fraternity of Raptors)  Unrevealed, but the Datasong of Talon's armor ...   \n",
        "Angel (Golden Age)                                                    Unrevealed   \n",
        "Romulus                              Unrevealed, possible knowledge of genetics.   \n",
        "Meggan                         No formal schooling; self-taught from watching...   \n",
        "Lucky Pierre                                                          Unrevealed   \n",
        "Shadu the Shady                                                       Unrevealed   \n",
        "Contessa (Vera Vidal)                                                 Unrevealed   \n",
        "Chores MacGillicudy                                                   Unrevealed   \n",
        "Iron Fist (Wu Ao-Shi)                                                 Unrevealed   \n",
        "Loa                                       Currently in high school level courses   \n",
        "Grey Gargoyle                                                         Unrevealed   \n",
        "Nekra                                                          Elementary school   \n",
        "Miss America                                                          Unrevealed   \n",
        "Whizzer (Stanley Stewart)                                   High school Graduate   \n",
        "Scarlet Spider (Kaine)         Possesses memories of Peter Parker's college e...   \n",
        "Hope Summers                                                          Unrevealed   \n",
        "Enchantress (Sylvie Lushton)                                          Unrevealed   \n",
        "Hank Pym                       extensive knowledge in various fields of scien...   \n",
        "Azazel (Mutant)                                                       Unrevealed   \n",
        "Spider-Man (House of M)                                     Ph.D in biochemistry   \n",
        "Gargoyle (Yuri Topolov)                                                            \n",
        "\n",
        "                               Women Villain    USA  \n",
        "name                                                 \n",
        "Abomination (Emil Blonsky)     False    True  False  \n",
        "Absorbing Man                  False    True   True  \n",
        "Abyss                          False    True  False  \n",
        "Agent Zero                     False    True  False  \n",
        "Annihilus                      False    True  False  \n",
        "Apocalypse                     False    True  False  \n",
        "Spider-Girl (Anya Corazon)      True   False   True  \n",
        "Arcade                         False    True   True  \n",
        "Archangel                      False   False   True  \n",
        "Arclight                        True    True   True  \n",
        "Aurora                          True   False  False  \n",
        "Avalanche                      False    True  False  \n",
        "Banshee                        False   False  False  \n",
        "Baron Strucker                 False    True  False  \n",
        "Baron Zemo (Heinrich Zemo)     False    True  False  \n",
        "Bastion                        False    True  False  \n",
        "Batroc the Leaper              False    True  False  \n",
        "Battering Ram                  False   False   True  \n",
        "Beak                           False   False   True  \n",
        "Beast                          False   False   True  \n",
        "Beef                           False    True   True  \n",
        "Beta-Ray Bill                  False   False  False  \n",
        "Big Wheel                      False   False   True  \n",
        "Bishop                         False   False   True  \n",
        "Black Bolt                     False   False  False  \n",
        "Black Cat                       True   False   True  \n",
        "Black Knight                   False   False  False  \n",
        "Black Panther                  False   False  False  \n",
        "Black Tom                      False    True  False  \n",
        "Black Widow                     True   False  False  \n",
        "...                              ...     ...    ...  \n",
        "Vanisher (Telford Porter)      False    True  False  \n",
        "Sphinx (Anath-Na Mut)          False    True  False  \n",
        "Molten Man                     False    True   True  \n",
        "Henry Peter Gyrich             False   False  False  \n",
        "Reptil                         False   False  False  \n",
        "Cypher                         False   False   True  \n",
        "Karma                           True   False  False  \n",
        "She-Hulk (Lyra)                 True   False  False  \n",
        "She-Hulk (Ultimate)            False   False   True  \n",
        "Talon (Fraternity of Raptors)  False   False  False  \n",
        "Angel (Golden Age)             False   False  False  \n",
        "Romulus                        False    True  False  \n",
        "Meggan                          True   False  False  \n",
        "Lucky Pierre                   False   False  False  \n",
        "Shadu the Shady                False   False  False  \n",
        "Contessa (Vera Vidal)           True   False  False  \n",
        "Chores MacGillicudy            False   False  False  \n",
        "Iron Fist (Wu Ao-Shi)           True   False  False  \n",
        "Loa                             True   False  False  \n",
        "Grey Gargoyle                  False    True  False  \n",
        "Nekra                           True    True  False  \n",
        "Miss America                    True   False  False  \n",
        "Whizzer (Stanley Stewart)      False   False  False  \n",
        "Scarlet Spider (Kaine)         False    True  False  \n",
        "Hope Summers                    True   False  False  \n",
        "Enchantress (Sylvie Lushton)    True   False  False  \n",
        "Hank Pym                       False   False  False  \n",
        "Azazel (Mutant)                False    True  False  \n",
        "Spider-Man (House of M)        False   False   True  \n",
        "Gargoyle (Yuri Topolov)        False    True  False  \n",
        "\n",
        "[762 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def delete_without_education(cultural_knn, not_education):\n",
      "    for word in not_education:\n",
      "        cultural_knn = cultural_knn[~cultural_knn['wiki.education'].str.contains(word)]\n",
      "    return cultural_knn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Eliminar todo los que sean \"Unreveal\"\n",
      "cultural_knn = delete_without_education(cultural_knn, [\"Unrevealed\", \"unrevealed\", 'None', 'none', 'Not applicable',\n",
      "                                                       'Unknown', 'unknown', 'Inapplicable', 'Limited'])\n",
      "cultural_knn = cultural_knn[cultural_knn['wiki.education'] != '']\n",
      "\n",
      "# Crear los grupos de niveles educativos\n",
      "education = cultural_knn['wiki.education']\n",
      "\n",
      "unfinished = education.map(lambda x: 'unfinished' in x or 'dropout' in x or 'incomplete' in x\n",
      "                           or 'drop-out' in x or 'No official schooling' in x \n",
      "                           or 'No formal education' in x or 'Unfinished' in x\n",
      "                           or 'Incomplete' in x)\n",
      "education[unfinished].tolist()\n",
      "education = education[~unfinished]\n",
      "\n",
      "phd = education.map(lambda x: 'Ph.D' in x or 'master' in x or 'Masters' in x or 'PhD' in x  \n",
      "                    or 'doctorate' in x or 'Doctorate' in x or 'Ph.d.' in x or 'Doctoral' in x \n",
      "                    or 'NASA' in x or 'Journalism graduate' in x or 'scientist' in x\n",
      "                    or 'Geneticist' in x or 'residency' in x)\n",
      "education[phd].tolist()\n",
      "education = education[~phd]\n",
      "\n",
      "college = education.map(lambda x: 'College' in x or 'college' in x or 'University' in x \n",
      "                        or 'post-graduate' in x or 'B.A' in x or 'B.S.' in x or 'university' in x  \n",
      "                        or 'Master' in x or 'Collage' in x or 'Degree' in x or 'degree' in x\n",
      "                        or 'Engineering' in x or 'engineer' in x or 'programming' in x\n",
      "                        or 'Programming' in x or 'Doctor' in x or 'Medical school' in x\n",
      "                        or 'higher education' in x) \n",
      "education[college].tolist()\n",
      "education = education[~college]\n",
      "\n",
      "militar = education.map(lambda x: 'Military' in x or 'Xandarian Nova Corps' in x  or 'FBI' in x\n",
      "                        or 'S.H.I.E.L.D.' in x or 'military' in x or 'Nicholas Fury' in x \n",
      "                        or 'Warrior' in x or 'combat' in x or 'Combat' in x or 'Soldier' in x\n",
      "                        or 'spy academy' in x or 'Police' in x or 'warfare' in x or 'Public Eye' in x)\n",
      "education[militar].tolist()\n",
      "education = education[~militar]\n",
      "\n",
      "hs = education.map(lambda x: 'High school' in x or 'high school' in x or 'High-school' in x \n",
      "                   or 'High School' in x or 'high School' in x)\n",
      "education[hs].tolist()\n",
      "education = education[~hs]\n",
      "\n",
      "tutored = education.map(lambda x: 'Tutored' in x or 'tutors' in x or 'tutored' in x \n",
      "                        or 'Mentored' in x or 'Home schooled' in x  or 'Private education' in x)\n",
      "education[tutored].tolist()\n",
      "education = education[~tutored]\n",
      "\n",
      "autodidacta = education.map(lambda x: 'Self-taught' in x or 'self-taught' in x \n",
      "                            or 'Little or no formal schooling' in x or 'Little formal schooling' in x\n",
      "                            or 'Some acting school' in x or 'through observation' in x)\n",
      "education[autodidacta].tolist()\n",
      "education = education[~autodidacta]\n",
      "\n",
      "special = education.map(lambda x: 'Sorcery' in x or 'cosmic experience' in x or 'magic' in x \n",
      "                        or 'Priests of Pama' in x or 'Xavier Institute' in x or 'Carlos Javier\u2019s' in x\n",
      "                        or 'Self educated' in x or 'Shao-Lom' in x or 'Centuries of study and experience' in x\n",
      "                        or 'Askani' in x or 'Madame DuPont' in x or 'Titanian' in x or 'arcane arts' in x\n",
      "                        or 'Muir-MacTaggert' in x or 'Uploaded data' in x or 'Programmed' in x\n",
      "                        or 'Accelerated' in x or 'Inhumans' in x or 'Able to access knowledge' in x\n",
      "                        or 'lifetime' in x or 'Watchers\\' homeworld' in x or 'Uranian Eternals' in x\n",
      "                        or 'Arcturus' in x or 'Oatridge School for Boys' in x)\n",
      "education[special].tolist()\n",
      "education = education[~special] \n",
      "\n",
      "basic = education.map(lambda x: 'Self-taught' in x or 'Homed schooled' in x or 'graduate school' in x\n",
      "                      or 'Elementary school' in x or 'Secondary school' in x or 'school graduate' in x\n",
      "                      or 'Boarding school' in x or 'Massachusetts Academy' in x\n",
      "                      or 'school graduate' in x)\n",
      "education[basic].tolist()\n",
      "education = education[~basic] \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "educational_dict = {'autodidacta': autodidacta, 'unfinished': unfinished, 'superior': phd, 'college':college, \n",
      "                    'militar': militar, 'high school':hs, 'tutored': tutored, 'special':special, 'basic': basic}\n",
      "\n",
      "numeric = {'autodidacta': 1, 'unfinished': 2, 'superior': 3, 'college':4, \n",
      "           'militar': 5, 'high school':6, 'tutored': 7, 'special':8, 'basic': 9}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def clean_education_levels(educational_dict, cultural_knn):\n",
      "    \"\"\" It will use our new categories in the wiki.education column\"\"\"\n",
      "    for k, education in educational_dict.items():\n",
      "        index = education[education.loc[:]].index\n",
      "        for character in index:\n",
      "            cultural_knn.loc[character, 'wiki.education'] = numeric[k]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_education_levels(educational_dict, cultural_knn)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "3. Separar el corpus en train y test"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TRAIN_PERCENTAGE = 0.8\n",
      "train_section = floor(cultural_knn.shape[0]*TRAIN_PERCENTAGE)\n",
      "test_section = cultural_knn.shape[0]-train_section\n",
      "print(\"Usaremos {} personajes para entrenar el clasificador y\"\\\n",
      "      \" {} para probar el clasificador entrenado.\\n\".format(train_section, test_section))\n",
      "\n",
      "train_rows = np.random.choice(cultural_knn.index.values, train_section)\n",
      "test_rows = np.setdiff1d(cultural_knn.index.values,train_rows)\n",
      "\n",
      "print(cultural_knn.loc[train_rows[0]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Usaremos 344 personajes para entrenar el clasificador y 87 para probar el clasificador entrenado.\n",
        "\n",
        "wiki.education        4\n",
        "Women             False\n",
        "Villain            True\n",
        "USA                True\n",
        "Name: Toxin (Eddie Brock), dtype: object\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "3.1. Visualizar los datos"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i, group in cultural_knn.groupby(women):\n",
      "    print(group)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                           wiki.education  Women Villain    USA\n",
        "name                                                           \n",
        "Absorbing Man                           2  False    True   True\n",
        "Apocalypse                              8  False    True  False\n",
        "Archangel                               4  False   False   True\n",
        "Banshee                                 4  False   False  False\n",
        "Baron Strucker                          4  False    True  False\n",
        "Baron Zemo (Heinrich Zemo)              3  False    True  False\n",
        "Batroc the Leaper                       5  False    True  False\n",
        "Beak                                    4  False   False   True\n",
        "Beast                                   3  False   False   True\n",
        "Big Wheel                               4  False   False   True\n",
        "Black Panther                           3  False   False  False\n",
        "Black Tom                               4  False    True  False\n",
        "Blackheart                              7  False    True  False\n",
        "Blade                                   6  False   False   True\n",
        "Blizzard                                6  False   False   True\n",
        "Cable                                   8  False   False   True\n",
        "Luke Cage                               2  False   False   True\n",
        "Cannonball                              6  False   False   True\n",
        "Captain America                         5  False   False   True\n",
        "Captain Britain                         3  False   False  False\n",
        "Captain Marvel (Mar-Vell)               5  False   False  False\n",
        "Captain Stacy                           4  False   False   True\n",
        "Carnage                                 6  False    True   True\n",
        "Chamber                                 8  False   False  False\n",
        "Cloak                                   2  False   False   True\n",
        "Malcolm Colcord                         5  False    True  False\n",
        "Colossus                                4  False   False   True\n",
        "Constrictor                             4  False   False   True\n",
        "Count Nefaria                           4  False    True   True\n",
        "Crimson Dynamo                          3  False    True  False\n",
        "...                                   ...    ...     ...    ...\n",
        "Mindworm                                6  False   False   True\n",
        "Calamity                                4  False   False  False\n",
        "Skaar                                   2  False   False  False\n",
        "Supernaut                               5  False   False  False\n",
        "Hypno-Hustler                           2  False    True  False\n",
        "Vin Gonzales                            5  False   False  False\n",
        "Blue Shield                             6  False   False  False\n",
        "Crimson Crusader                        9  False   False  False\n",
        "Cobalt Man                              3  False    True  False\n",
        "Jackal                                  3  False    True   True\n",
        "High Evolutionary                       3  False    True  False\n",
        "Anole                                   6  False   False   True\n",
        "Justin Hammer                           4  False    True  False\n",
        "Junta                                   5  False   False   True\n",
        "Omega Sentinel                          5  False   False  False\n",
        "3-D Man                                 5  False   False   True\n",
        "Nightcrawler (Ultimate)                 6  False   False  False\n",
        "Angel (Ultimate)                        4  False   False  False\n",
        "Vance Astro                             4  False   False  False\n",
        "Jamie Braddock                          4  False    True  False\n",
        "Tinkerer                                4  False    True  False\n",
        "Sphinx (Anath-Na Mut)                   8  False    True  False\n",
        "Molten Man                              4  False    True   True\n",
        "Henry Peter Gyrich                      4  False   False  False\n",
        "Cypher                                  4  False   False   True\n",
        "She-Hulk (Ultimate)                     4  False   False   True\n",
        "Whizzer (Stanley Stewart)               6  False   False  False\n",
        "Scarlet Spider (Kaine)                  4  False    True  False\n",
        "Hank Pym                                3  False   False  False\n",
        "Spider-Man (House of M)                 3  False   False   True\n",
        "\n",
        "[305 rows x 4 columns]\n",
        "                           wiki.education Women Villain    USA\n",
        "name                                                          \n",
        "Spider-Girl (Anya Corazon)              6  True   False   True\n",
        "Aurora                                  8  True   False  False\n",
        "Black Cat                               4  True   False   True\n",
        "Catseye                                 9  True    True   True\n",
        "Clea                                    4  True   False  False\n",
        "Crystal                                 7  True   False  False\n",
        "Dagger                                  2  True   False   True\n",
        "Darkstar                                5  True   False  False\n",
        "Dazzler                                 4  True   False   True\n",
        "Dust                                    6  True   False  False\n",
        "Elektra                                 4  True   False  False\n",
        "Expediter                               7  True   False  False\n",
        "Firestar                                4  True   False   True\n",
        "Emma Frost                              4  True   False   True\n",
        "Husk                                    8  True   False  False\n",
        "Invisible Woman                         2  True   False   True\n",
        "Jocasta                                 2  True   False  False\n",
        "Jessica Jones                           6  True   False  False\n",
        "Jubilee                                 6  True   False  False\n",
        "Lady Deathstrike                        7  True    True  False\n",
        "Magik (Illyana Rasputin)                6  True   False  False\n",
        "Magma (Amara Aquilla)                   4  True   False  False\n",
        "Marrow                                  5  True   False   True\n",
        "Rachel Grey                             4  True   False   True\n",
        "Alicia Masters                          4  True   False  False\n",
        "Medusa                                  7  True   False  False\n",
        "Meltdown                                2  True   False   True\n",
        "Moondragon                              8  True   False   True\n",
        "Namorita                                2  True   False  False\n",
        "Nocturne                                4  True   False   True\n",
        "...                                   ...   ...     ...    ...\n",
        "Hobgoblin (Robin Borne)                 3  True    True   True\n",
        "Nova (Frankie Raye)                     4  True   False  False\n",
        "Puck (Zuzha Yu)                         2  True   False  False\n",
        "Wind Dancer                             6  True   False  False\n",
        "Sway                                    8  True   False   True\n",
        "Mantis                                  8  True   False  False\n",
        "Joystick                                2  True   False  False\n",
        "Satana                                  7  True   False   True\n",
        "Turbo                                   4  True   False   True\n",
        "M (Monet St. Croix)                     9  True   False  False\n",
        "Bloodaxe                                3  True    True   True\n",
        "Layla Miller                            9  True   False   True\n",
        "Beyonder                                7  True   False  False\n",
        "Cammi                                   2  True   False   True\n",
        "Tana Nile                               9  True   False  False\n",
        "Praxagora                               8  True   False  False\n",
        "Skreet                                  8  True   False  False\n",
        "Thena                                   8  True   False  False\n",
        "Mockingbird                             3  True   False  False\n",
        "Menace                                  4  True    True   True\n",
        "Geiger                                  4  True   False  False\n",
        "Carlie Cooper                           4  True   False   True\n",
        "Imp                                     9  True   False  False\n",
        "Armor (Hisako Ichiki)                   8  True   False   True\n",
        "Thundra                                 5  True   False  False\n",
        "Vapor                                   4  True    True  False\n",
        "She-Hulk (Lyra)                         7  True   False  False\n",
        "Meggan                                  1  True   False  False\n",
        "Loa                                     6  True   False  False\n",
        "Nekra                                   9  True    True  False\n",
        "\n",
        "[126 rows x 4 columns]\n"
       ]
      }
     ],
     "prompt_number": 150
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i, group in cultural_knn.groupby(women):\n",
      "    if not i:\n",
      "        area = (np.pi * (group.shape[0])**2)*.002\n",
      "        ax = group.plot(kind='scatter', x='wiki.education', y='USA', s=area, \n",
      "                        color='Cornflowerblue', label='Men', alpha=0.5);\n",
      "    else:\n",
      "        area = (np.pi * (group.shape[0])**2)*.002\n",
      "        group.plot(kind='scatter', x='wiki.education', y='USA', \n",
      "                   color='LightGreen', label='Women', ax=ax, s=area, alpha=0.5)\n",
      "        \n",
      "        \n",
      "\n",
      "ax.set_xticks(range(1,10))\n",
      "ax.set_xticklabels(list(numeric.keys()), rotation='vertical')\n",
      "ax.set_yticks(range(0,2))\n",
      "ax.set_yticklabels(['USA', 'non USA'], rotation='horizontal')\n",
      "\n",
      "ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=2, fancybox=True, shadow=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 168,
       "text": [
        "<matplotlib.legend.Legend at 0x117198a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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c8geY7ewefHgdtwQc23Z02Pb+DfuItp8hwJs901u/vibn9Tm+t9tT//5n92Ep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\nGsA+Xkn0XMoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x116fc0090>"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4 y 6. Entrenar el clasificador y comprobar los resultados"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train = cultural_knn.loc[train_rows][['wiki.education','USA']]\n",
      "y_train = cultural_knn.loc[train_rows]['Women']\n",
      "\n",
      "X_test = cultural_knn.loc[test_rows][['wiki.education','USA']]\n",
      "y_test = cultural_knn.loc[test_rows]['Women']\n",
      "\n",
      "classifier = neighbors.KNeighborsClassifier()\n",
      "\n",
      "classifier.fit(X_train, y_train)\n",
      "\n",
      "predict = classifier.predict(X_test)\n",
      "\n",
      "accuracy = metrics.accuracy_score(y_test, predict)\n",
      "precision, recall, f1, _ = metrics.precision_recall_fscore_support(y_test, predict)\n",
      "print(\"* Acierto: {:.2f}%\".format(accuracy*100))\n",
      "print(\"* Precisi\u00f3n: {}\\n* Exhaustividad: {}.\\n* F1-Score: {}\".format(accuracy*100, precision, recall, f1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "* Acierto: 68.04%\n",
        "* Precisi\u00f3n: 68.04123711340206\n",
        "* Exhaustividad: [ 0.72159091  0.27777778].\n",
        "* F1-Score: [ 0.90714286  0.09259259]\n"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "7. \u00a1Celebrar!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cmap_light = ListedColormap(['#AAAAFF', '#AAFFAA'])\n",
      "cmap_bold = ListedColormap(['#0000FF', '#00FF00'])\n",
      "\n",
      "step = 1\n",
      "\n",
      "xx, yy = np.meshgrid(np.arange(1, 10, step),\n",
      "                     np.arange(0, 1, step))    \n",
      "prediction = classifier.predict(X_test)\n",
      "Z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])\n",
      "Z = Z.reshape(xx.shape)\n",
      "\n",
      "plt.figure()\n",
      "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
      "plt.scatter( X_test['wiki.education'], X_test['USA'], c=y_test, cmap=cmap_bold)\n",
      "plt.xlim(xx.min(), xx.max())\n",
      "plt.yticks(range(0,2),['USA', 'non USA'], rotation='horizontal')\n",
      "plt.ylim(-0.5, 1.5)\n",
      "plt.xticks(range(1,11), list(numeric.keys()), rotation='vertical')\n",
      "plt.xlabel(\"Education\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "<matplotlib.text.Text at 0x7f8220813e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DkUWJjY1FXl4eBg4cyAX7iCwIy4KIiAxZqQ5ARESWj2VBRESGWBZE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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f822171b850>"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "\u00a1Gracias! "
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Si tienes una pregunta es el momento. * si no nos hemos quedado sin tiempo, claro :)*"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      " "
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      " "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "# Ten\u00edamos m\u00e1s..."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "\u00bfEn que momentos se han modificado personajes?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline --no-import-all\n",
      "pd.set_option('display.mpl_style', 'default')\n",
      "figsize(15, 6)\n",
      "pd.set_option('display.line_width', 4000)\n",
      "pd.set_option('display.max_columns', 100)\n",
      "from matplotlib.pyplot import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "line_width has been deprecated, use display.width instead (currently both are\n",
        "identical)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df['modified'] = pd.to_datetime(marvel_df['modified'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(marvel_df['modified'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f8221388350>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Zl91QtDtCCbfUhdLt4qXvxdmwyqZz3nJ9Bf7ovkNKyZZKtLlCsBTgukvfy/BU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\nor1do8T5BGTXjciNOQNb8zQz+TxsAGy2qxRV1H+C+T5L9Gj2b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rnnblFmHSL515VBZ7Zd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07ty5/vvihYt0+pVXemOdOHFCPMsrr7wqDupeOH+evve97wmaSP7xjAsMh7jSz3/+P/3+3pU9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9Mj9XMEA8zrcRmZF4WjZmyxQt54Nnkhy/ZU/Ajbq+WUyxzLvpnzWAYco0PZnNc94LKQqP3M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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8221b44910>"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start = marvel_df.modified.min()\n",
      "end =  marvel_df.modified.max()\n",
      "\n",
      "yearly_range = pd.date_range(start, end, freq='365D6H')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df[['modified']].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>modified</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1970-01-01 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1970-01-01 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1970-01-01 00:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2011-05-17 21:26:18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1970-01-01 00:00:00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 137,
       "text": [
        "             modified\n",
        "0 1970-01-01 00:00:00\n",
        "0 1970-01-01 00:00:00\n",
        "0 1970-01-01 00:00:00\n",
        "0 2011-05-17 21:26:18\n",
        "0 1970-01-01 00:00:00"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "characters_per_year = marvel_df.groupby(marvel_df['modified'].map(lambda x: x.year)).size()\n",
      "characters_per_year"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 139,
       "text": [
        "modified\n",
        "1970        802\n",
        "2004          8\n",
        "2010         54\n",
        "2011        119\n",
        "2012         53\n",
        "2013        292\n",
        "2014         74\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "characters_per_year.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f821f8ca990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uuyRJu3fv1urVq/WVr3xF7e3tWrhwofx+v1auXKmkpCT5fL5TzvelvLxcM2bM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\nERGNIqYWKz4oqsORc1dxT0IwlqVFYlywSuppEd2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z5kwUFxdj37590Gq12Lp1K15++WXk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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f82211db510>"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "marvel_df.sort('modified',ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>comics.available</th>\n",
        "      <th>comics.collectionURI</th>\n",
        "      <th>comics.items</th>\n",
        "      <th>comics.returned</th>\n",
        "      <th>description</th>\n",
        "      <th>events.available</th>\n",
        "      <th>events.collectionURI</th>\n",
        "      <th>events.items</th>\n",
        "      <th>events.returned</th>\n",
        "      <th>id</th>\n",
        "      <th>modified</th>\n",
        "      <th>name</th>\n",
        "      <th>resourceURI</th>\n",
        "      <th>series.available</th>\n",
        "      <th>series.collectionURI</th>\n",
        "      <th>series.items</th>\n",
        "      <th>series.returned</th>\n",
        "      <th>stories.available</th>\n",
        "      <th>stories.collectionURI</th>\n",
        "      <th>stories.items</th>\n",
        "      <th>stories.returned</th>\n",
        "      <th>thumbnail.extension</th>\n",
        "      <th>thumbnail.path</th>\n",
        "      <th>urls</th>\n",
        "      <th>wiki.Date_of_birth</th>\n",
        "      <th>wiki.Place_of_birth</th>\n",
        "      <th>wiki.abilities</th>\n",
        "      <th>wiki.aliases</th>\n",
        "      <th>wiki.appearance</th>\n",
        "      <th>wiki.base_of_operations</th>\n",
        "      <th>wiki.bio</th>\n",
        "      <th>wiki.bio_text</th>\n",
        "      <th>wiki.blurb</th>\n",
        "      <th>wiki.builder</th>\n",
        "      <th>wiki.categories</th>\n",
        "      <th>wiki.categorytext</th>\n",
        "      <th>wiki.citizenship</th>\n",
        "      <th>wiki.creator</th>\n",
        "      <th>wiki.creators</th>\n",
        "      <th>wiki.current_members</th>\n",
        "      <th>wiki.debut</th>\n",
        "      <th>wiki.distinguishing_features</th>\n",
        "      <th>wiki.dstinguishing_features</th>\n",
        "      <th>wiki.education</th>\n",
        "      <th>wiki.event_text</th>\n",
        "      <th>wiki.eyes</th>\n",
        "      <th>wiki.features</th>\n",
        "      <th>wiki.former_members</th>\n",
        "      <th>wiki.govenment</th>\n",
        "      <th>wiki.government</th>\n",
        "      <th>wiki.groups</th>\n",
        "      <th>wiki.hair</th>\n",
        "      <th>wiki.height</th>\n",
        "      <th>wiki.home_world</th>\n",
        "      <th>wiki.identity</th>\n",
        "      <th>wiki.key_characters</th>\n",
        "      <th>wiki.key_issues</th>\n",
        "      <th>wiki.leader</th>\n",
        "      <th>wiki.location</th>\n",
        "      <th>wiki.main_image</th>\n",
        "      <th>wiki.members</th>\n",
        "      <th>wiki.object_text</th>\n",
        "      <th>wiki.occupation</th>\n",
        "      <th>wiki.origin</th>\n",
        "      <th>wiki.other_members</th>\n",
        "      <th>wiki.owner</th>\n",
        "      <th>wiki.paraphernalia</th>\n",
        "      <th>wiki.place_of_birth</th>\n",
        "      <th>wiki.place_of_creation</th>\n",
        "      <th>wiki.place_text</th>\n",
        "      <th>wiki.points_of_interest</th>\n",
        "      <th>wiki.power</th>\n",
        "      <th>wiki.powers</th>\n",
        "      <th>wiki.real_name</th>\n",
        "      <th>wiki.relatives</th>\n",
        "      <th>wiki.significant_citizens</th>\n",
        "      <th>wiki.significant_issues</th>\n",
        "      <th>wiki.skin</th>\n",
        "      <th>wiki.special_limitations</th>\n",
        "      <th>wiki.specieshistory</th>\n",
        "      <th>wiki.team_name</th>\n",
        "      <th>wiki.teamicon</th>\n",
        "      <th>wiki.technology</th>\n",
        "      <th>wiki.tie-ins</th>\n",
        "      <th>wiki.title_graphic</th>\n",
        "      <th>wiki.universe</th>\n",
        "      <th>wiki.weapons</th>\n",
        "      <th>wiki.weaponss</th>\n",
        "      <th>wiki.weight</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 26</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'id': 36834, 'resourceURI': 'http://gateway....</td>\n",
        "      <td> 26</td>\n",
        "      <td> Decades after participating in military airdro...</td>\n",
        "      <td> 0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1011006</td>\n",
        "      <td>2014-03-05 18:58:52</td>\n",
        "      <td> Wolverine (Ultimate)</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> 17</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td> 17</td>\n",
        "      <td> 21</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td> 20</td>\n",
        "      <td> jpg</td>\n",
        "      <td> http://i.annihil.us/u/prod/marvel/i/mg/9/03/53...</td>\n",
        "      <td> [{'url': 'http://marvel.com/comics/characters/...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>    Logan, Weapon X, Lucky Jim</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Howlett's past is mostly unknown, but during W...</td>\n",
        "      <td> Howlett's past is mostly unknown, but during W...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                       [Ultimate Marvel, Deceased]</td>\n",
        "      <td> NaN</td>\n",
        "      <td>              Presumably Canada</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                           NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                          Ultimate X-Men #1 (2001)</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                        Blue</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[X-Men (Ultimate)|X-Men]]; formerly [[Brother...</td>\n",
        "      <td>                       Black</td>\n",
        "      <td>  5'9\"</td>\n",
        "      <td> NaN</td>\n",
        "      <td> (\"Logan\") publicly known; (James Howlett) Know...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>               Ultwolv.jpg</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Student, adventurer; formerly mercenary, gover...</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> Unrevealed, probably somewhere in Canada</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Wolverine's mutant healing factor enables him ...</td>\n",
        "      <td>            James Howlett</td>\n",
        "      <td> Wife (name and status unrevealed); son (allege...</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>        [[Ultimate]]</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     292 lbs. (including adamantium)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  6</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td>  6</td>\n",
        "      <td> While Eddie Brock\u2019s academic career seemed to ...</td>\n",
        "      <td> 0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1011128</td>\n",
        "      <td>2014-03-05 18:58:42</td>\n",
        "      <td>     Venom (Ultimate)</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>  5</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td>  5</td>\n",
        "      <td>  3</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td>  3</td>\n",
        "      <td> jpg</td>\n",
        "      <td> http://i.annihil.us/u/prod/marvel/i/mg/e/10/53...</td>\n",
        "      <td> [{'url': 'http://marvel.com/comics/characters/...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Eddie has a natural aptitude for bioengineerin...</td>\n",
        "      <td>                      The Suit</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Eddie Brock was the son of a brilliant scienti...</td>\n",
        "      <td> Eddie Brock was the son of a brilliant scienti...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                 [Ultimate_Marvel]</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                  United States</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                           NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                    Ultimate Spider-Man #33 (2003)</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> College student, extensive Bioengineering studies</td>\n",
        "      <td> NaN</td>\n",
        "      <td> (Eddie) Blue; (Venom) White</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                              none</td>\n",
        "      <td> (Eddie) Blond; (Venom) None</td>\n",
        "      <td> 5'11\"</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                            Secret</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>         Ultimatevenom.jpg</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                           Student</td>\n",
        "      <td>                 Ultimate Spider-Man #36-37 (2003)</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                       New York, New York</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> The symbiotic suit bonded to Brock was designe...</td>\n",
        "      <td> Edward \"Eddie\" Brock Jr.</td>\n",
        "      <td> Edward Brock Sr. (father, deceased), unidentif...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Reunited with Peter, shared their fathers\u2019 wor...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>        [[Ultimate]]</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                             175 lbs</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 22</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'id': 35528, 'resourceURI': 'http://gateway....</td>\n",
        "      <td> 22</td>\n",
        "      <td> One of Spider-Man's oldest enemies, Mac Gargan...</td>\n",
        "      <td> 5</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1010788</td>\n",
        "      <td>2014-03-05 18:58:37</td>\n",
        "      <td>   Venom (Mac Gargan)</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> 10</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td> 10</td>\n",
        "      <td> 21</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td> 20</td>\n",
        "      <td> jpg</td>\n",
        "      <td> http://i.annihil.us/u/prod/marvel/i/mg/5/50/53...</td>\n",
        "      <td> [{'url': 'http://marvel.com/comics/characters/...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Mac Gargan has the intellectual skills of an a...</td>\n",
        "      <td> Spider-Man; formerly Scorpion</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> One of Spider-Man's oldest enemies, MacGargan ...</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [Avengers, Civil War, Spider-Man, Spider-Man V...</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  U.S.A. with a criminal record</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                           NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> (As Gargan) Amazing Spider-Man #19 (1964); (as...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                              High school graduate</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                       Brown</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Formerly [[Avengers (Osborn's team)]], [[Thund...</td>\n",
        "      <td>         Brown (shaves head)</td>\n",
        "      <td>  6'3\"</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                    Publicly known</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Venom(MacGargan)_Head.jpg</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> U.S. government agent; former professional cri...</td>\n",
        "      <td> (As Scorpion) Amazing Spider-Man #20 (1965); (...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> As Scorpion: Mac Gargan wore a costume that wa...</td>\n",
        "      <td>                        Yonkers, New York</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> As Scorpion: enhanced strength, enabling him t...</td>\n",
        "      <td>   MacDonald \"Mac\" Gargan</td>\n",
        "      <td>                                              None</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Confronted by Venom symbiote (Marvel Knights: ...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> [[Marvel Universe]]</td>\n",
        "      <td> As Scorpion: The tail on his Scorpion costume ...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 220 lbs. / 245 lbs. (with symbiote)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>  0</td>\n",
        "      <td>                                                  </td>\n",
        "      <td> 0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1011239</td>\n",
        "      <td>2014-03-05 18:58:33</td>\n",
        "      <td>  Valkyrie (Ultimate)</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>  0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td>  0</td>\n",
        "      <td> jpg</td>\n",
        "      <td> http://i.annihil.us/u/prod/marvel/i/mg/4/20/53...</td>\n",
        "      <td> [{'url': 'http://marvel.com/comics/characters/...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                           NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                            NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                           NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                         NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                         NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                       NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                                      NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                      NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                 NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                 NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 30</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'id': 41845, 'resourceURI': 'http://gateway....</td>\n",
        "      <td> 30</td>\n",
        "      <td> He claims he is the legendary Norse thunder de...</td>\n",
        "      <td> 0</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td>                                                []</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1011025</td>\n",
        "      <td>2014-03-05 18:58:19</td>\n",
        "      <td>      Thor (Ultimate)</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> 19</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td> 19</td>\n",
        "      <td> 24</td>\n",
        "      <td> http://gateway.marvel.com/v1/public/characters...</td>\n",
        "      <td> [{'resourceURI': 'http://gateway.marvel.com/v1...</td>\n",
        "      <td> 20</td>\n",
        "      <td> jpg</td>\n",
        "      <td> http://i.annihil.us/u/prod/marvel/i/mg/3/80/53...</td>\n",
        "      <td> [{'url': 'http://marvel.com/comics/characters/...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>                          None</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> He claims he is the legendary Norse thunder de...</td>\n",
        "      <td> He claims he is the legendary Norse thunder de...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                 [Ultimate Marvel]</td>\n",
        "      <td> NaN</td>\n",
        "      <td> (Thor) Asgard; (Golmen) Norway</td>\n",
        "      <td> NaN</td>\n",
        "      <td> this has not been updated yet</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                           The Ultimates #4 (2002)</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                        Unrevealed</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                        Blue</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                Formerly Ultimates</td>\n",
        "      <td>                       Blond</td>\n",
        "      <td>  6'5\"</td>\n",
        "      <td> NaN</td>\n",
        "      <td> (Thor) Publicly known; (Golmen) known to autho...</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>          Thorult head.jpg</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>        Guardian deity; formerly psychiatric nurse</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td>           (Thor) Asgard; (Golmen) Norway</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Thor possesses immense superhuman strength, en...</td>\n",
        "      <td>  Thor or Thorlief Golmen</td>\n",
        "      <td> (Thor) Odin (father), Loki (half-brother); (Go...</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                                               NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>        [[Ultimate]]</td>\n",
        "      <td>                   Enchanted hammer named Mjolnir.</td>\n",
        "      <td> NaN</td>\n",
        "      <td>                            285 lbs.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "   comics.available                               comics.collectionURI                                       comics.items  comics.returned                                        description  events.available                               events.collectionURI                                       events.items  events.returned       id            modified                  name                                        resourceURI  series.available                               series.collectionURI                                       series.items  series.returned  stories.available                              stories.collectionURI                                      stories.items  stories.returned thumbnail.extension                                     thumbnail.path                                               urls wiki.Date_of_birth wiki.Place_of_birth                                     wiki.abilities                   wiki.aliases wiki.appearance wiki.base_of_operations                                           wiki.bio                                      wiki.bio_text wiki.blurb wiki.builder                                    wiki.categories wiki.categorytext                wiki.citizenship wiki.creator                  wiki.creators wiki.current_members                                         wiki.debut wiki.distinguishing_features wiki.dstinguishing_features                                     wiki.education wiki.event_text                    wiki.eyes wiki.features wiki.former_members wiki.govenment wiki.government                                        wiki.groups                    wiki.hair wiki.height wiki.home_world                                      wiki.identity wiki.key_characters wiki.key_issues wiki.leader wiki.location            wiki.main_image wiki.members wiki.object_text                                    wiki.occupation                                        wiki.origin wiki.other_members wiki.owner                                 wiki.paraphernalia                       wiki.place_of_birth wiki.place_of_creation wiki.place_text wiki.points_of_interest wiki.power                                        wiki.powers            wiki.real_name                                     wiki.relatives wiki.significant_citizens                            wiki.significant_issues wiki.skin wiki.special_limitations wiki.specieshistory wiki.team_name wiki.teamicon wiki.technology wiki.tie-ins wiki.title_graphic        wiki.universe                                       wiki.weapons wiki.weaponss                          wiki.weight\n",
        "0                26  http://gateway.marvel.com/v1/public/characters...  [{'id': 36834, 'resourceURI': 'http://gateway....               26  Decades after participating in military airdro...                 0  http://gateway.marvel.com/v1/public/characters...                                                 []                0  1011006 2014-03-05 18:58:52  Wolverine (Ultimate)  http://gateway.marvel.com/v1/public/characters...                17  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...               17                 21  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...                20                 jpg  http://i.annihil.us/u/prod/marvel/i/mg/9/03/53...  [{'url': 'http://marvel.com/comics/characters/...                NaN                 NaN                                                NaN     Logan, Weapon X, Lucky Jim             NaN                     NaN  Howlett's past is mostly unknown, but during W...  Howlett's past is mostly unknown, but during W...        NaN          NaN                        [Ultimate Marvel, Deceased]               NaN               Presumably Canada          NaN                            NaN                  NaN                           Ultimate X-Men #1 (2001)                          NaN                         NaN                                         Unrevealed             NaN                         Blue           NaN                 NaN            NaN             NaN  [[X-Men (Ultimate)|X-Men]]; formerly [[Brother...                        Black        5'9\"             NaN  (\"Logan\") publicly known; (James Howlett) Know...                 NaN             NaN         NaN           NaN                Ultwolv.jpg          NaN              NaN  Student, adventurer; formerly mercenary, gover...                                                                   NaN        NaN                                                NaN  Unrevealed, probably somewhere in Canada                    NaN             NaN                     NaN        NaN  Wolverine's mutant healing factor enables him ...             James Howlett  Wife (name and status unrevealed); son (allege...                       NaN                                                NaN       NaN                      NaN                 NaN            NaN           NaN             NaN          NaN                NaN         [[Ultimate]]                                                NaN           NaN      292 lbs. (including adamantium)\n",
        "0                 6  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...                6  While Eddie Brock\u2019s academic career seemed to ...                 0  http://gateway.marvel.com/v1/public/characters...                                                 []                0  1011128 2014-03-05 18:58:42      Venom (Ultimate)  http://gateway.marvel.com/v1/public/characters...                 5  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...                5                  3  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...                 3                 jpg  http://i.annihil.us/u/prod/marvel/i/mg/e/10/53...  [{'url': 'http://marvel.com/comics/characters/...                NaN                 NaN  Eddie has a natural aptitude for bioengineerin...                       The Suit             NaN                     NaN  Eddie Brock was the son of a brilliant scienti...  Eddie Brock was the son of a brilliant scienti...        NaN          NaN                                  [Ultimate_Marvel]               NaN                   United States          NaN                            NaN                  NaN                     Ultimate Spider-Man #33 (2003)                          NaN                         NaN  College student, extensive Bioengineering studies             NaN  (Eddie) Blue; (Venom) White           NaN                 NaN            NaN             NaN                                               none  (Eddie) Blond; (Venom) None       5'11\"             NaN                                             Secret                 NaN             NaN         NaN           NaN          Ultimatevenom.jpg          NaN              NaN                                            Student                  Ultimate Spider-Man #36-37 (2003)                NaN        NaN                                                NaN                        New York, New York                    NaN             NaN                     NaN        NaN  The symbiotic suit bonded to Brock was designe...  Edward \"Eddie\" Brock Jr.  Edward Brock Sr. (father, deceased), unidentif...                       NaN  Reunited with Peter, shared their fathers\u2019 wor...       NaN                      NaN                 NaN            NaN           NaN             NaN          NaN                NaN         [[Ultimate]]                                                NaN           NaN                              175 lbs\n",
        "0                22  http://gateway.marvel.com/v1/public/characters...  [{'id': 35528, 'resourceURI': 'http://gateway....               22  One of Spider-Man's oldest enemies, Mac Gargan...                 5  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...                5  1010788 2014-03-05 18:58:37    Venom (Mac Gargan)  http://gateway.marvel.com/v1/public/characters...                10  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...               10                 21  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...                20                 jpg  http://i.annihil.us/u/prod/marvel/i/mg/5/50/53...  [{'url': 'http://marvel.com/comics/characters/...                NaN                 NaN  Mac Gargan has the intellectual skills of an a...  Spider-Man; formerly Scorpion             NaN                     NaN  One of Spider-Man's oldest enemies, MacGargan ...                                                           NaN          NaN  [Avengers, Civil War, Spider-Man, Spider-Man V...               NaN   U.S.A. with a criminal record          NaN                            NaN                  NaN  (As Gargan) Amazing Spider-Man #19 (1964); (as...                          NaN                         NaN                               High school graduate             NaN                        Brown           NaN                 NaN            NaN             NaN  Formerly [[Avengers (Osborn's team)]], [[Thund...          Brown (shaves head)        6'3\"             NaN                                     Publicly known                 NaN             NaN         NaN           NaN  Venom(MacGargan)_Head.jpg          NaN              NaN  U.S. government agent; former professional cri...  (As Scorpion) Amazing Spider-Man #20 (1965); (...                NaN        NaN  As Scorpion: Mac Gargan wore a costume that wa...                         Yonkers, New York                    NaN             NaN                     NaN        NaN  As Scorpion: enhanced strength, enabling him t...    MacDonald \"Mac\" Gargan                                               None                       NaN  Confronted by Venom symbiote (Marvel Knights: ...       NaN                      NaN                 NaN            NaN           NaN             NaN          NaN                NaN  [[Marvel Universe]]  As Scorpion: The tail on his Scorpion costume ...           NaN  220 lbs. / 245 lbs. (with symbiote)\n",
        "0                 0  http://gateway.marvel.com/v1/public/characters...                                                 []                0                                                                    0  http://gateway.marvel.com/v1/public/characters...                                                 []                0  1011239 2014-03-05 18:58:33   Valkyrie (Ultimate)  http://gateway.marvel.com/v1/public/characters...                 0  http://gateway.marvel.com/v1/public/characters...                                                 []                0                  0  http://gateway.marvel.com/v1/public/characters...                                                 []                 0                 jpg  http://i.annihil.us/u/prod/marvel/i/mg/4/20/53...  [{'url': 'http://marvel.com/comics/characters/...                NaN                 NaN                                                NaN                            NaN             NaN                     NaN                                                NaN                                                NaN        NaN          NaN                                                NaN               NaN                             NaN          NaN                            NaN                  NaN                                                NaN                          NaN                         NaN                                                NaN             NaN                          NaN           NaN                 NaN            NaN             NaN                                                NaN                          NaN         NaN             NaN                                                NaN                 NaN             NaN         NaN           NaN                        NaN          NaN              NaN                                                NaN                                                NaN                NaN        NaN                                                NaN                                       NaN                    NaN             NaN                     NaN        NaN                                                NaN                       NaN                                                NaN                       NaN                                                NaN       NaN                      NaN                 NaN            NaN           NaN             NaN          NaN                NaN                  NaN                                                NaN           NaN                                  NaN\n",
        "0                30  http://gateway.marvel.com/v1/public/characters...  [{'id': 41845, 'resourceURI': 'http://gateway....               30  He claims he is the legendary Norse thunder de...                 0  http://gateway.marvel.com/v1/public/characters...                                                 []                0  1011025 2014-03-05 18:58:19       Thor (Ultimate)  http://gateway.marvel.com/v1/public/characters...                19  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...               19                 24  http://gateway.marvel.com/v1/public/characters...  [{'resourceURI': 'http://gateway.marvel.com/v1...                20                 jpg  http://i.annihil.us/u/prod/marvel/i/mg/3/80/53...  [{'url': 'http://marvel.com/comics/characters/...                NaN                 NaN                                                NaN                           None             NaN                     NaN  He claims he is the legendary Norse thunder de...  He claims he is the legendary Norse thunder de...        NaN          NaN                                  [Ultimate Marvel]               NaN  (Thor) Asgard; (Golmen) Norway          NaN  this has not been updated yet                  NaN                            The Ultimates #4 (2002)                          NaN                         NaN                                         Unrevealed             NaN                         Blue           NaN                 NaN            NaN             NaN                                 Formerly Ultimates                        Blond        6'5\"             NaN  (Thor) Publicly known; (Golmen) known to autho...                 NaN             NaN         NaN           NaN           Thorult head.jpg          NaN              NaN         Guardian deity; formerly psychiatric nurse                                                NaN                NaN        NaN                                                NaN            (Thor) Asgard; (Golmen) Norway                    NaN             NaN                     NaN        NaN  Thor possesses immense superhuman strength, en...   Thor or Thorlief Golmen  (Thor) Odin (father), Loki (half-brother); (Go...                       NaN                                                NaN       NaN                      NaN                 NaN            NaN           NaN             NaN          NaN                NaN         [[Ultimate]]                    Enchanted hammer named Mjolnir.           NaN                             285 lbs."
       ]
      }
     ],
     "prompt_number": 141
    }
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   "metadata": {}
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 ]
}