{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Open Data Córdoba](http://opendatacordoba.org/) es un colectivo abierto y multidisciplinario sin fines de lucro cuyo objetivo principal es difundir y transparentar el uso de la tecnología y los datos masivos para beneficio del conjunto de la sociedad, especialmente en la provincia de Córdoba.\n",
    "\n",
    "El grupo [nació](http://opendatacordoba.org/blog/democracia-con-codigos-asi-nacio-opendatacordoba/) en época de elecciones, intentando echar un manto de luz a las sospechas fundadas de desprolijidades en el escrutinio provisorio de las legislativas de 2013. Luego de muchas otras iniciativas y bastante perseverancia docente de algunos de nuestros compañeros, nuestra contribución ha empezado a ser reconocida desde los medios locales, algunas agrupaciones políticas y otras organizaciones de la sociedad civil que abogan por la transparencia y el fortalecimiento de las instituciones. \n",
    "\n",
    "En este contexto, el portal de noticias [Cba24N](http://www.cba24n.com.ar), perteneciente al grupo de Servicios de Radio y Televisión (SRT) de la Universidad Nacional de Córdoba, nos ha invitado a realizar análisis de datos \"en vivo\" el próximo 25 de octubre, día de las elecciones nacionales. \n",
    "\n",
    "\n",
    "Hay muchas [ideas para ese día](https://hackdash.org/dashboards/odc2015) que ojalá podamos llevar adelante, aunque vale resaltar (<strike>atajarse</strike>) que se trata de una tarea totalmente voluntaria . Mientras tanto, el domingo pasado me dediqué a jugar con algunas agregaciones sobre el padrón de Córdoba para encontrar \"notas de color\" analizando nombres y apellidos de los votantes\n",
    "\n",
    "<!-- TEASER_END -->\n",
    "\n",
    "### Dime tu nombre y te diré cómo te llamas \n",
    "\n",
    "*Cinco de cada diez hombres... son la mitad*, [analizaba Les Luthiers](https://www.youtube.com/watch?v=Sy_sVtJlG40) con mucho humor y precisión científica. Este análisis del padrón no pretende conclusiones mucho más elaboradas que esa (ni alcanzar semejantes cotas de gracia), pero con las herramientas de software libre actuales es fácil analizar (y graficar) algunas curiosidades. \n",
    "\n",
    "Por ejemplo ¿hay más cordobeses de apellido Pérez o González? ¿Cuántas personas tienen más de dos nombres? ¿Cuáles son los nombres más comunes de cada década? \n",
    "\n",
    "Primero importo y configuro las herramientas principales, [Pandas](http://pandas.pydata.org/) y [Matplotlib](http://matplotlib.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams['lines.linewidth'] = 2\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cba = pd.read_csv('padron_cba_2015.csv', usecols=['clase', 'apellido', 'nombres', 'sexo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clase</th>\n",
       "      <th>apellido</th>\n",
       "      <th>nombres</th>\n",
       "      <th>sexo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1938</td>\n",
       "      <td>ABACA</td>\n",
       "      <td>CELINA DEL ROSARIO</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963</td>\n",
       "      <td>ABADJIAN</td>\n",
       "      <td>MARIA ALICIA SOL</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979</td>\n",
       "      <td>ABAL</td>\n",
       "      <td>CLARA</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1922</td>\n",
       "      <td>ABALO</td>\n",
       "      <td>MARIA AZUCENA</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1925</td>\n",
       "      <td>ABALO</td>\n",
       "      <td>YOLANDA JOSEFA</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clase  apellido             nombres sexo\n",
       "0   1938     ABACA  CELINA DEL ROSARIO    F\n",
       "1   1963  ABADJIAN    MARIA ALICIA SOL    F\n",
       "2   1979      ABAL               CLARA    F\n",
       "3   1922     ABALO       MARIA AZUCENA    F\n",
       "4   1925     ABALO      YOLANDA JOSEFA    F"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2783122"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cba.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5139293211005482, 0.48607067889945177)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cba[cba['sexo'] == 'F'].index)/_, len(cba[cba['sexo'] == 'M'].index)/_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clase         1910\n",
       "apellido    BASTUS\n",
       "nombres     MIGUEL\n",
       "sexo             M\n",
       "Name: 328532, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cba.ix[cba['clase'].argmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El [padrón que conseguimos](https://github.com/OpenDataCordoba/elecciones2015/tree/master/nombres)  es el que la Justicia Electoral provee a los partidos políticos para sus tareas de fiscalización. En particular el de la provincia de Córdoba tiene **2.78 millones de votantes habilitados** de los cuales el **51.4% son mujeres**. El más viejo es Don Miguel Bastus, de 105 años. \n",
    "\n",
    "\n",
    "Si bien el *dataset* del padrón incluye mucha información como circuito de cada votante y por supuesto el DNI y otros datos sensibles que por cuestiones de protección de la privacidad nunca publicaríamos, en particular me interesaba desagregar los nombres, que vienen en una sola columna, en dos o más y normalizarlos a un formato común (cada nombre o apellido comienza con mayúscula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cba.apellido = cba.apellido.apply(lambda v: v.title())\n",
    "cba.nombres = cba.nombres.apply(lambda v: v.title())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para separar los nombres voy a usar un [paquete](http://nameparser.readthedocs.org/en/latest/) que tiene un mínimo cuidado con casos particulares con los *van*, *del*, etc. que pueden aparecer en algunos apellidos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nameparser import HumanName\n",
    "def s(n):\n",
    "    hn = HumanName(n)\n",
    "    if hn.middle:\n",
    "        return [hn.first, hn.middle, hn.last]\n",
    "    return [hn.first, hn.last, hn.middle]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['María', 'Francisca', '']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s('María Francisca')  # devuelve hasta tres nombres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nombres_df = pd.DataFrame(cba['nombres'].apply(s).values.tolist(), \n",
    "                              columns=['primer_nombre', 'segundo_nombre', 'tercer_nombre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clase</th>\n",
       "      <th>apellido</th>\n",
       "      <th>sexo</th>\n",
       "      <th>primer_nombre</th>\n",
       "      <th>segundo_nombre</th>\n",
       "      <th>tercer_nombre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1938</td>\n",
       "      <td>Abaca</td>\n",
       "      <td>F</td>\n",
       "      <td>Celina</td>\n",
       "      <td>Del Rosario</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963</td>\n",
       "      <td>Abadjian</td>\n",
       "      <td>F</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alicia</td>\n",
       "      <td>Sol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979</td>\n",
       "      <td>Abal</td>\n",
       "      <td>F</td>\n",
       "      <td>Clara</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1922</td>\n",
       "      <td>Abalo</td>\n",
       "      <td>F</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Azucena</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1925</td>\n",
       "      <td>Abalo</td>\n",
       "      <td>F</td>\n",
       "      <td>Yolanda</td>\n",
       "      <td>Josefa</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clase  apellido sexo primer_nombre segundo_nombre tercer_nombre\n",
       "0   1938     Abaca    F        Celina    Del Rosario              \n",
       "1   1963  Abadjian    F         Maria         Alicia           Sol\n",
       "2   1979      Abal    F         Clara                             \n",
       "3   1922     Abalo    F         Maria        Azucena              \n",
       "4   1925     Abalo    F       Yolanda         Josefa              "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cba = pd.concat((cba.drop('nombres', 1), nombres_df), axis=1)\n",
    "cba.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya tenemos el dataset como queríamos, podemos sacar algunas cuentas y, mejor, hacer algunos graficos!\n",
    "\n",
    "\n",
    "### Los 20 apellidos más comunes en Córdoba\n",
    "\n",
    "Vemos que contra todo pronóstico, los Pérez apenas entran en el top 10 de apellidos más comunes y el 1.3% del padrón son los González! Quizás signifique el hallazgo de un nuevo *focus group* para campañas políticas. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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22WcZNWoUH330EUFBQQXHHDduHIMGDcJut1O9enVmzpzJmDFjyM3NZfLkydjt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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3700074f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cba.apellido.value_counts(normalize=True)[:20].sort(inplace=False).plot('barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un nombre muy cordobés\n",
    "\n",
    "La generación más jóven de votantes tiene entre 16 y 26 años. Entre las mujeres de esa edad, casi el 10% (más de 25 mil cordobesas) tiene como primer nombre María\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OHz9Oeno6mZmZLFu2DID9+/czadIkVqxYwZ///Gc2bdpU3dcsIiIiV3FdRwpO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\n/wdYGH6X5VDg+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36fd5622e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cba[(cba['clase'] >= 1990) & (cba['sexo'] == 'F')].primer_nombre.value_counts(normalize=True)[:20].sort(inplace=False).plot('barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para las votantes más longevas, de 60 para arriba, la proporción de Marías llega al 15%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YunVrPvvsM3r37k1OTg4FBQUAtupmPz8/kpOTMXN3lCN1y2a/fbKZ85k5Gyifp1M+z+Wx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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36fff3f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cba[(cba['clase'] <= 1955) & (cba['sexo'] == 'F')].primer_nombre.value_counts(normalize=True)[:20].sort(inplace=False).plot('barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el caso de los hombres jóvenes, Juan es el primer nombre más común, pero seguido de cerca por Franco y Matías."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0UlNTSUpKKpbjUH25lDWqKxeR4uTQgeLs2bMMHjyYGTNmUK9ePSwWCy+//DKp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ffec6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cba[(cba['clase'] >= 1990) & (cba['sexo'] == 'M')].primer_nombre.value_counts(normalize=True)[:20].sort(inplace=False).plot('barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y entre los hombres mayores la cosa está entre Juan y José"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eu91OUlIS1apVIyUlhfXr1wPQqVMnrFaro6Y5MzOTp556iszMTCZMmEBCQgKj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\nTjQciIiIiBMNByIiIuLk/wMWxto6u4hcwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ffe6e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cba[(cba['clase'] <= 1955) & (cba['sexo'] == 'M')].primer_nombre.value_counts(normalize=True)[:20].sort(inplace=False).plot('barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La generación \"Y(enifer)\"  \n",
    "\n",
    "Se conoce como \"[Generación Y](https://es.wikipedia.org/wiki/Generaci%C3%B3n_Y)\" a los nacidos alrededor de los '90. Es justamente la generación que está en la universidad o teniendo sus primeros trabajos, y tomando con pinzas semejante generalización, parece que [tienen comportamientos particulares](http://www.lanacion.com.ar/1661962-el-desafio-de-entender-a-la-generacion-y). \n",
    "\n",
    "Si bien la denominación viene a cuento de ser la que sigue a la \"generación X\" con la que se denominaba a los que nacieron entre los '60 y '80, hay una curiosidad sobresaliente: al menos en Córdoba, esta generación es muchísimo más propensa a tener nombres que comienzan con \"Y\" (Yanina, Yenifer, Yesica, etc.). El pico se da justamente en la clase 1990, con casi 2500 personas. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v1ssX6wSwXp7IYLRCKpWgd0IYZBd1U3ekXiMvk2HdF8dwukTv8e9HqyFdVlaG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\nKiZnfxk1jc0smX2/9/9bXK3UW5x8cKCMBmsLM8b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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ffd85b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cba[(cba['primer_nombre'].str.startswith('Y')) | (cba['segundo_nombre'].str.startswith('Y'))].groupby('clase').apellido.count().plot()\n",
    "plt.title('Córdobeses/as cuyo primer o segundo nombre comienza con \"Y\"');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cuestión de cantidad\n",
    "\n",
    "Parece que lo más común es que los cordobeses tengan 2 nombres, pero esa tendencia se empezó a modificar en los '80 donde la cantidad de gente con 1 sólo nombre creció. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cOt1UUGDmuedmMXXqY5e1yczMYOnSt2nSJIBp06aQmJjIjh2/4Ovrx5w5C/5b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\nz/w/dBcViDso9uYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ffd68710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "un_nombre = cba[(cba['segundo_nombre'] == '')].groupby('clase').apellido.count().plot(label='1 nombre')\n",
    "dos_nombres = cba[(cba['tercer_nombre'] == '')].groupby('clase').apellido.count().plot(label='2 nombres')\n",
    "tres_nombres = cba[(cba['tercer_nombre'] != '')].groupby('clase').apellido.count().plot(label='3 o más nombres')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Cantidad de nombres a lo largo del siglo 20 en Córdoba');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De cualquier manera, no importa mucho cuantos nombres tengas: a todos se les dice \"Chichí\" y \"Guaso\". \n",
    "\n",
    "\n",
    "### El nombre más típico de la clase\n",
    "\n",
    "Si agrupásemos a todos los hombres y mujeres del padrón según su edad, ¿cuál sería el nombre y apellido más probable para cada grupo?\n",
    "\n",
    "Considerando que sólo se incluye a las personas vivas, sería una exageración decir que es el nombre más típico de ese año, pero es un indicio. \n",
    "\n",
    "El siguiente algoritmo busca el primer nombre, segundo nombre y apellido más probable para hombres y mujeres nacidos en cada año. Si conocen a alguien que se llame exactamente así nacido en ese año, pueden contarle que tiene el nombre más protípico de su clase: ¡qué originalidad! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 200)\n",
    "def nombre_del_año(sexo):\n",
    "    \n",
    "    nombre_por_clase = []\n",
    "    for y in cba.clase.unique():\n",
    "        electores = cba[(cba['clase'] == y) & (cba['sexo'] == sexo)]    \n",
    "        try:\n",
    "            nombre_por_clase.append([y] + [electores[attr].value_counts().index[0] for attr in \\\n",
    "                                               ['primer_nombre', 'segundo_nombre', 'apellido']])\n",
    "        except (KeyError, IndexError):\n",
    "            continue\n",
    "    return pd.DataFrame(nombre_por_clase, columns=['clase', 'primer_nombre', 'segundo_nombre', 'apellido']).sort('clase')\n",
    "\n",
    "nombre_tipico_hombres = nombre_del_año('M')\n",
    "nombre_tipico_mujeres = nombre_del_año('F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clase</th>\n",
       "      <th>primer_nombre</th>\n",
       "      <th>segundo_nombre</th>\n",
       "      <th>apellido</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1910</td>\n",
       "      <td>Miguel</td>\n",
       "      <td></td>\n",
       "      <td>Bastus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1915</td>\n",
       "      <td>Hugo</td>\n",
       "      <td></td>\n",
       "      <td>Candussi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1916</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Diaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1917</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gomez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1918</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gomez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1919</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Diaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1920</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1921</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Lopez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1922</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1923</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Sanchez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1924</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1925</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1926</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1927</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1928</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1929</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1930</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1931</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1932</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1933</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1934</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1935</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1936</td>\n",
       "      <td>Jose</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1937</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1938</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1939</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1940</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1941</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1942</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1943</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1944</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1945</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1946</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1947</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1948</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Lopez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1949</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1950</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1951</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1952</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1953</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1954</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1955</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1956</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1957</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1958</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1959</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1960</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1961</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1962</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1964</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1965</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1966</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1967</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1968</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1969</td>\n",
       "      <td>Jose</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1970</td>\n",
       "      <td>Carlos</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1971</td>\n",
       "      <td>Carlos</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1972</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1973</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alejandro</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1974</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1975</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alejandro</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1976</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alejandro</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1977</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alejandro</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Alberto</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979</td>\n",
       "      <td>Diego</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1980</td>\n",
       "      <td>Diego</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1981</td>\n",
       "      <td>Diego</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1982</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1983</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1984</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1985</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1986</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1987</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1988</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1989</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1990</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1991</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1992</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1993</td>\n",
       "      <td>Franco</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1994</td>\n",
       "      <td>Franco</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1995</td>\n",
       "      <td>Franco</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1996</td>\n",
       "      <td>Matias</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1997</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1998</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1999</td>\n",
       "      <td>Juan</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    clase primer_nombre segundo_nombre   apellido\n",
       "84   1910        Miguel                    Bastus\n",
       "85   1915          Hugo                  Candussi\n",
       "73   1916          Jose                      Diaz\n",
       "69   1917          Jose                     Gomez\n",
       "32   1918          Jose                     Gomez\n",
       "80   1919          Jose                      Diaz\n",
       "63   1920          Jose                 Rodriguez\n",
       "76   1921          Jose                     Lopez\n",
       "3    1922          Jose                 Rodriguez\n",
       "83   1923          Jose                   Sanchez\n",
       "70   1924          Jose                  Gonzalez\n",
       "4    1925          Jose                  Gonzalez\n",
       "59   1926          Jose                  Gonzalez\n",
       "41   1927          Jose                  Gonzalez\n",
       "66   1928          Jose                  Gonzalez\n",
       "79   1929          Jose                  Gonzalez\n",
       "61   1930          Jose                  Gonzalez\n",
       "21   1931          Jose                  Gonzalez\n",
       "34   1932          Jose                  Gonzalez\n",
       "74   1933          Juan                 Rodriguez\n",
       "71   1934          Jose                 Rodriguez\n",
       "58   1935          Juan                  Gonzalez\n",
       "53   1936          Jose                 Rodriguez\n",
       "42   1937          Juan                 Rodriguez\n",
       "0    1938          Juan                 Rodriguez\n",
       "78   1939          Juan                 Rodriguez\n",
       "81   1940          Juan                  Gonzalez\n",
       "49   1941          Juan                  Gonzalez\n",
       "16   1942          Juan                 Rodriguez\n",
       "28   1943          Juan                  Gonzalez\n",
       "24   1944          Juan                  Gonzalez\n",
       "36   1945          Juan                 Rodriguez\n",
       "26   1946          Juan                  Gonzalez\n",
       "64   1947          Juan                  Gonzalez\n",
       "40   1948          Juan        Alberto      Lopez\n",
       "15   1949          Juan        Alberto   Gonzalez\n",
       "29   1950          Juan        Alberto   Gonzalez\n",
       "44   1951          Juan        Alberto   Gonzalez\n",
       "72   1952          Juan        Alberto   Gonzalez\n",
       "6    1953          Juan        Alberto   Gonzalez\n",
       "50   1954          Juan        Alberto  Rodriguez\n",
       "38   1955          Juan        Alberto  Rodriguez\n",
       "65   1956          Juan        Alberto   Gonzalez\n",
       "47   1957          Juan        Alberto   Gonzalez\n",
       "52   1958          Juan        Alberto   Gonzalez\n",
       "67   1959          Juan        Alberto   Gonzalez\n",
       "37   1960          Jose        Alberto   Gonzalez\n",
       "17   1961          Jose        Alberto  Rodriguez\n",
       "68   1962          Jose        Alberto   Gonzalez\n",
       "1    1963          Jose        Alberto   Gonzalez\n",
       "39   1964          Jose        Alberto   Gonzalez\n",
       "18   1965          Jose        Alberto  Rodriguez\n",
       "25   1966          Jose        Alberto   Gonzalez\n",
       "77   1967          Jose        Alberto  Rodriguez\n",
       "62   1968          Jose        Alberto   Gonzalez\n",
       "27   1969          Jose        Alberto   Gonzalez\n",
       "19   1970        Carlos        Alberto   Gonzalez\n",
       "23   1971        Carlos        Alberto   Gonzalez\n",
       "22   1972          Juan        Alberto   Gonzalez\n",
       "75   1973          Juan      Alejandro   Gonzalez\n",
       "14   1974          Juan        Alberto   Gonzalez\n",
       "5    1975          Juan      Alejandro  Rodriguez\n",
       "35   1976          Juan      Alejandro   Gonzalez\n",
       "33   1977          Juan      Alejandro  Rodriguez\n",
       "8    1978          Juan        Alberto  Rodriguez\n",
       "2    1979         Diego                  Gonzalez\n",
       "43   1980         Diego                  Gonzalez\n",
       "55   1981         Diego                  Gonzalez\n",
       "7    1982          Juan                  Gonzalez\n",
       "9    1983          Juan                  Gonzalez\n",
       "31   1984          Juan                  Gonzalez\n",
       "12   1985          Juan                  Gonzalez\n",
       "51   1986          Juan                  Gonzalez\n",
       "46   1987          Juan                 Rodriguez\n",
       "10   1988          Juan                  Gonzalez\n",
       "60   1989          Juan                  Gonzalez\n",
       "13   1990          Juan                  Gonzalez\n",
       "57   1991          Juan                  Gonzalez\n",
       "11   1992          Juan                  Gonzalez\n",
       "54   1993        Franco                  Gonzalez\n",
       "30   1994        Franco                  Gonzalez\n",
       "56   1995        Franco                  Gonzalez\n",
       "48   1996        Matias                  Gonzalez\n",
       "45   1997          Juan                  Gonzalez\n",
       "82   1998          Juan                 Rodriguez\n",
       "20   1999          Juan                  Gonzalez"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nombre_tipico_hombres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clase</th>\n",
       "      <th>primer_nombre</th>\n",
       "      <th>segundo_nombre</th>\n",
       "      <th>apellido</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1911</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Elvira</td>\n",
       "      <td>Frencia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1912</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Magdalena</td>\n",
       "      <td>Rocha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1913</td>\n",
       "      <td>Petrona</td>\n",
       "      <td>Josefina</td>\n",
       "      <td>Coronda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1914</td>\n",
       "      <td>Adela</td>\n",
       "      <td></td>\n",
       "      <td>Cena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1915</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Camagni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1916</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1917</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Diaz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1918</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1919</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1920</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Lopez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1921</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1922</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1923</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Garcia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1924</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1925</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Fernandez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1926</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1927</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1928</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Lopez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1929</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1930</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1931</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1932</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1933</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1934</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1935</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1936</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1937</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Lopez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1938</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1939</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1940</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1941</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1942</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1943</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1944</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1945</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1946</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1947</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1948</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1949</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1950</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1951</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1952</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1953</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1954</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1955</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1956</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1957</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1958</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1959</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1960</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1961</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1962</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1964</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1965</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1966</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1967</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1968</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Beatriz</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1969</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1970</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1971</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1972</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1973</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1974</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1975</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1976</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Alejandra</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1977</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1978</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Soledad</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Soledad</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1980</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Soledad</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1981</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Soledad</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1982</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Soledad</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1983</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Soledad</td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1984</td>\n",
       "      <td>Maria</td>\n",
       "      <td>Soledad</td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1985</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1986</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1987</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1988</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1989</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Rodriguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1990</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1991</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1992</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1993</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1994</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1995</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1996</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1997</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1998</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1999</td>\n",
       "      <td>Maria</td>\n",
       "      <td></td>\n",
       "      <td>Gonzalez</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    clase primer_nombre segundo_nombre   apellido\n",
       "84   1911         Maria         Elvira    Frencia\n",
       "87   1912         Maria      Magdalena      Rocha\n",
       "88   1913       Petrona       Josefina    Coronda\n",
       "86   1914         Adela                      Cena\n",
       "85   1915         Maria                   Camagni\n",
       "73   1916         Maria                  Gonzalez\n",
       "69   1917         Maria                      Diaz\n",
       "32   1918         Maria                  Gonzalez\n",
       "80   1919         Maria                  Gonzalez\n",
       "63   1920         Maria                     Lopez\n",
       "76   1921         Maria                  Gonzalez\n",
       "3    1922         Maria                 Rodriguez\n",
       "83   1923         Maria                    Garcia\n",
       "70   1924         Maria                 Rodriguez\n",
       "4    1925         Maria                 Fernandez\n",
       "59   1926         Maria                 Rodriguez\n",
       "41   1927         Maria                 Rodriguez\n",
       "66   1928         Maria                     Lopez\n",
       "79   1929         Maria                 Rodriguez\n",
       "61   1930         Maria                  Gonzalez\n",
       "21   1931         Maria                 Rodriguez\n",
       "34   1932         Maria                 Rodriguez\n",
       "74   1933         Maria                 Rodriguez\n",
       "71   1934         Maria                 Rodriguez\n",
       "58   1935         Maria                 Rodriguez\n",
       "53   1936         Maria                  Gonzalez\n",
       "42   1937         Maria                     Lopez\n",
       "0    1938         Maria                  Gonzalez\n",
       "78   1939         Maria                  Gonzalez\n",
       "81   1940         Maria                  Gonzalez\n",
       "49   1941         Maria                  Gonzalez\n",
       "16   1942         Maria                 Rodriguez\n",
       "28   1943         Maria                 Rodriguez\n",
       "24   1944         Maria                  Gonzalez\n",
       "36   1945         Maria                 Rodriguez\n",
       "26   1946         Maria          Maria   Gonzalez\n",
       "64   1947         Maria          Maria  Rodriguez\n",
       "40   1948         Maria          Maria   Gonzalez\n",
       "15   1949         Maria          Maria   Gonzalez\n",
       "29   1950         Maria          Maria   Gonzalez\n",
       "44   1951         Maria          Maria  Rodriguez\n",
       "72   1952         Maria          Maria   Gonzalez\n",
       "6    1953         Maria          Maria  Rodriguez\n",
       "50   1954         Maria          Maria   Gonzalez\n",
       "38   1955         Maria          Maria   Gonzalez\n",
       "65   1956         Maria        Beatriz   Gonzalez\n",
       "47   1957         Maria        Beatriz   Gonzalez\n",
       "52   1958         Maria        Beatriz   Gonzalez\n",
       "67   1959         Maria        Beatriz   Gonzalez\n",
       "37   1960         Maria        Beatriz   Gonzalez\n",
       "17   1961         Maria        Beatriz   Gonzalez\n",
       "68   1962         Maria        Beatriz   Gonzalez\n",
       "1    1963         Maria        Beatriz   Gonzalez\n",
       "39   1964         Maria        Beatriz   Gonzalez\n",
       "18   1965         Maria        Beatriz   Gonzalez\n",
       "25   1966         Maria        Beatriz   Gonzalez\n",
       "77   1967         Maria        Beatriz   Gonzalez\n",
       "62   1968         Maria        Beatriz   Gonzalez\n",
       "27   1969         Maria      Alejandra   Gonzalez\n",
       "19   1970         Maria      Alejandra   Gonzalez\n",
       "23   1971         Maria      Alejandra   Gonzalez\n",
       "22   1972         Maria      Alejandra   Gonzalez\n",
       "75   1973         Maria      Alejandra   Gonzalez\n",
       "14   1974         Maria      Alejandra   Gonzalez\n",
       "5    1975         Maria      Alejandra   Gonzalez\n",
       "35   1976         Maria      Alejandra   Gonzalez\n",
       "33   1977         Maria                  Gonzalez\n",
       "8    1978         Maria        Soledad   Gonzalez\n",
       "2    1979         Maria        Soledad   Gonzalez\n",
       "43   1980         Maria        Soledad   Gonzalez\n",
       "55   1981         Maria        Soledad   Gonzalez\n",
       "7    1982         Maria        Soledad   Gonzalez\n",
       "9    1983         Maria        Soledad  Rodriguez\n",
       "31   1984         Maria        Soledad   Gonzalez\n",
       "12   1985         Maria                  Gonzalez\n",
       "51   1986         Maria                  Gonzalez\n",
       "46   1987         Maria                  Gonzalez\n",
       "10   1988         Maria                  Gonzalez\n",
       "60   1989         Maria                 Rodriguez\n",
       "13   1990         Maria                  Gonzalez\n",
       "57   1991         Maria                  Gonzalez\n",
       "11   1992         Maria                  Gonzalez\n",
       "54   1993         Maria                  Gonzalez\n",
       "30   1994         Maria                  Gonzalez\n",
       "56   1995         Maria                  Gonzalez\n",
       "48   1996         Maria                  Gonzalez\n",
       "45   1997         Maria                  Gonzalez\n",
       "82   1998         Maria                  Gonzalez\n",
       "20   1999         Maria                  Gonzalez"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nombre_tipico_mujeres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparando apellidos: Gaitán vs Lobo\n",
    "\n",
    "De chico tenía la idea (un poco errada) de que mi apellido no era muy común. Salvo algunos [casos bochornosos](http://tn.com.ar/politica/escandalo-sexual-en-neuquen-un-funcionario-intercambiaba-sexo-por-favores-en-ofici_050364), no conocí otros Gaitán en Neuquén que no sean de mi familia cercana, aunque sabía que había mucha parentela lejana en Córdoba y en otros países como Colombia era muy frecuente. \n",
    "\n",
    "Los Lobo, el apellido de mi mujer, en cambio, parece que son muchos menos (y crecen demográficamente a menor ritmo) de lo que la \"intuición\" indica. \n",
    "\n",
    "Comparemos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f36ffca3e80>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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qymZbjPYk14DZ+MtythRR4CWkCVHGW9jFxQAArFjfZWxmAf6wqDqbkxUF/++v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ffd0f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaitanes = cba[(cba['apellido'] == 'Gaitan')].groupby('clase').apellido.count()\n",
    "lobos = cba[(cba['apellido'] == 'Lobo')].groupby('clase').apellido.count()\n",
    "gaitanes.plot(label='Gaitán'), lobos.plot(label='Lobo')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Haciendo la curva acumulada, puede verse el crecimiento absoluto de cada apellido a lo largo del siglo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2t9uN2+3G4XAcdayISDz5x/ZaXl1bSW5GEl+9aiwWs7ZEkDPnpAeOmT/2A+h2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\n7nfR8rHLzC2+Vtp87V3B7HcTNEIn/gQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36ffd6b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaitanes.cumsum().plot(label='Gaitán'), lobos.cumsum().plot(label='Lobo')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En Córdoba somos 4 veces más los Gaitán, aunque si la muestra fuese mi familia y la de mi esposa, sería casi al reves: ¡son 9 hermanos!\n",
    "\n",
    "### ¡Hacé tu propia comparación!\n",
    "\n",
    "¿Te interesa saber cuanta gente se llama como vos? Hicimos una pequeñisima aplicación web (de [código libre](https://github.com/mgaitan/nombres_cba) que permite consultar la misma base de datos del padrón de Córdoba y generar gráficos similares a los últimos **para los nombres y apellidos que vos elijas**. Se puede probar acá http://lab.opendatacordoba.org\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### ¿Qué más se podría analizar?\n",
    "\n",
    "Dado que además de la clase contamos con la sección y circuito electoral de cada votante, se podrían analizar agregaciones geolocalizadas, por ejemplo para ver el nombre más tipico de cada departamento o provincia a lo largo del tiempo como en [este estudio](http://jezebel.com/map-sixty-years-of-the-most-popular-names-for-girls-s-1443501909). \n",
    "\n",
    "Teniendo algunas reglas de clasificación (o mejor, una base de datos) que ponderen con algún coeficiente de precisión la procedencia de un apellido, sería posible agrupar (y geolocalizar) ¿hay algun barrio de córdoba con más personas de raíces arábigas?\n",
    "\n",
    "Comparar gráficas entre distintas ciudades del país abre otro sinnúmero de posibilidades. Por poner un simple ejemplo *¿Cuál es la región que más proporción de niñas llamadas Eva tuvo alredodor de 1950?* *¿Qué partido ha ganado en las últimas elecciones allí?*\n",
    "\n",
    "Por supuesto, hay cosas más interesantes y significativas de las que ocuparse, pero quisimos encontrar algunas perlitas, que puedan distendernos ante tanto spot de campaña. Y de paso mostrar que, teniendo datos, con muy pocas lineas de código se pueden responder preguntas que de otra manera sólo pueden ser contestadas con especulaciones, generandose mitos amparados en la \"intuición\" popular o periodística, que puede ser mucho menos precisa que una consulta a las fuentes. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}