ii_EDA_consentimiento_trasplantes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RrZf7UYo364q"
   },
   "source": [
    "# Análisis exploratorio de datos\n",
    "El análisis exploratorio de datos se conoce como EDA (exploratory data analysis).\n",
    "\n",
    "\n",
    "## Objetivos\n",
    "1. Presentar las principales librerías de Python para análisis exploratorio de datos y recursos visuales.\n",
    "2. Introducir y familiarizarse con la lectura y la exploración de datasets, permitiendo identificar límites y potencial de los datos así como su adecuación para el análisis.\n",
    "\n",
    "## Datasets\n",
    "Datos abiertos del GCBA. Ministerio de Salud. Instituto de Trasplantes de la Ciudad de Buenos Aires. Los descargamos de [aquí]('https://data.buenosaires.gob.ar/dataset/manifestaciones-a-la-donacion-para-trasplantes').\n",
    "\n",
    "**Manifestaciones a la donación para trasplantes**\n",
    "Información correspondiente a la Campaña de Promoción “Hoy también elegís ser donante de órganos” organizada por el Instituto de Trasplante de la Ciudad de Buenos Aires en el marco de la jornada electoral **PASO 2017** dando cumplimiento a la Ley 2508/07. Se realizó en 62 establecimientos autorizados por la Justicia Electoral de la Ciudad de Buenos Aires.\n",
    "\n",
    "1. *Manifestaciones a la donación para trasplantes*: personas que decidieron sobre la donación de órganos y tejidos en la CABA durante las Elecciones PASO 2017.\n",
    "\n",
    "2. *Establecimentos - Elecciones 2017*: ubicación de los establecimientos autorizados para las Elecciones Legislativas de Octubre 2017.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KOPfjXWA4CVS"
   },
   "outputs": [],
   "source": [
    "#importamos las librerias con las que trabajaremos\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R345q6laoBKS"
   },
   "source": [
    "### Personas que se expresaron frente a la donación de órganos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LxSlGatI4Hgp"
   },
   "outputs": [],
   "source": [
    "# este bloque es exclusivo para trabajar con colab\n",
    "# se habilita un botón que tenés que pulsar para seleccionar tu archivo de un directorio local\n",
    "from google.colab import files\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qaIKCAFG4PIW"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NACIONALIDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>CONSENTIMIENTO</th>\n",
       "      <th>CUALQUIER_ORGANO</th>\n",
       "      <th>CUALES_NO</th>\n",
       "      <th>TRASPLANTE</th>\n",
       "      <th>INVESTIGACION</th>\n",
       "      <th>COMUNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>31</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>41</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>29</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>26</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NACIONALIDAD SEXO  EDAD CONSENTIMIENTO CUALQUIER_ORGANO CUALES_NO  \\\n",
       "0    ARGENTINA    F    40             SI               SI       NaN   \n",
       "1    ARGENTINA    M    31             SI               SI       NaN   \n",
       "2    ARGENTINA    F    41             SI               SI       NaN   \n",
       "3    ARGENTINA    M    29             SI               SI       NaN   \n",
       "4    ARGENTINA    F    26             SI               SI       NaN   \n",
       "\n",
       "  TRASPLANTE INVESTIGACION  COMUNA  \n",
       "0         SI            SI       2  \n",
       "1         SI            NO       2  \n",
       "2         SI            SI       2  \n",
       "3         SI            NO       2  \n",
       "4         SI            NO       2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cargamos el dataset, instanciándolo para poder referenciarlo luego\n",
    "data = pd.read_csv('manifestaciones-a-la-donacion-para-trasplantes.csv', sep=';')\n",
    "\n",
    "# visualizamos que se haya cargado bien\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qZHQJx_R4963"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1502 entries, 0 to 1501\n",
      "Data columns (total 9 columns):\n",
      "NACIONALIDAD        1502 non-null object\n",
      "SEXO                1502 non-null object\n",
      "EDAD                1502 non-null int64\n",
      "CONSENTIMIENTO      1502 non-null object\n",
      "CUALQUIER_ORGANO    1502 non-null object\n",
      "CUALES_NO           14 non-null object\n",
      "TRASPLANTE          1502 non-null object\n",
      "INVESTIGACION       1502 non-null object\n",
      "COMUNA              1502 non-null int64\n",
      "dtypes: int64(2), object(7)\n",
      "memory usage: 105.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# chequamos dimensiones, estructura, contenido\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u5MlNgZWAXIH"
   },
   "source": [
    "Según `.info()`:\n",
    "* ¿Cómo identificarías si hay datos vacíos?\n",
    "* ¿Podés decir que el dataset está bien organizado? ¿Por qué?\n",
    "* ¿Eliminarías alguna fila o alguna columna?\n",
    "* ¿Transformarías algún dato? \n",
    "* ¿Ya podrías analizarlo?\n",
    "* Observando el dataset (su estructura, el nombre de las columnas, características de las respuestas, etc.), ¿cómo lo caracterizarías? ¿qué tipo de preguntas podrías responder?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NZknFk345GrK"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EDAD</th>\n",
       "      <th>COMUNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1502.000000</td>\n",
       "      <td>1502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.914780</td>\n",
       "      <td>8.032623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.259494</td>\n",
       "      <td>4.407416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              EDAD       COMUNA\n",
       "count  1502.000000  1502.000000\n",
       "mean     42.914780     8.032623\n",
       "std      15.259494     4.407416\n",
       "min      18.000000     1.000000\n",
       "25%      30.000000     4.000000\n",
       "50%      41.000000     8.000000\n",
       "75%      54.000000    12.000000\n",
       "max      99.000000    15.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# qué hace .describe()? \n",
    "# qué tipos de datos lee?\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1T2rvLRkr64t"
   },
   "source": [
    "#### Exploremos la información troncal del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D61wjeyDsLrf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "5       True\n",
       "6       True\n",
       "7       True\n",
       "8       True\n",
       "9       True\n",
       "10      True\n",
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       "24      True\n",
       "25      True\n",
       "26      True\n",
       "27      True\n",
       "28      True\n",
       "29      True\n",
       "        ... \n",
       "1472    True\n",
       "1473    True\n",
       "1474    True\n",
       "1475    True\n",
       "1476    True\n",
       "1477    True\n",
       "1478    True\n",
       "1479    True\n",
       "1480    True\n",
       "1481    True\n",
       "1482    True\n",
       "1483    True\n",
       "1484    True\n",
       "1485    True\n",
       "1486    True\n",
       "1487    True\n",
       "1488    True\n",
       "1489    True\n",
       "1490    True\n",
       "1491    True\n",
       "1492    True\n",
       "1493    True\n",
       "1494    True\n",
       "1495    True\n",
       "1496    True\n",
       "1497    True\n",
       "1498    True\n",
       "1499    True\n",
       "1500    True\n",
       "1501    True\n",
       "Name: CONSENTIMIENTO, Length: 1502, dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# esta expresión genera un booleano sobre la serie/variable/columna que le pasamos\n",
    "data['CONSENTIMIENTO']=='SI'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bj-WQktTsX0k"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       SI\n",
       "1       SI\n",
       "2       SI\n",
       "3       SI\n",
       "4       SI\n",
       "5       SI\n",
       "6       SI\n",
       "7       SI\n",
       "8       SI\n",
       "9       SI\n",
       "10      SI\n",
       "11      SI\n",
       "12      SI\n",
       "13      SI\n",
       "14      SI\n",
       "15      SI\n",
       "16      SI\n",
       "17      SI\n",
       "18      SI\n",
       "19      SI\n",
       "20      SI\n",
       "21      SI\n",
       "22      SI\n",
       "23      SI\n",
       "24      SI\n",
       "25      SI\n",
       "26      SI\n",
       "27      SI\n",
       "28      SI\n",
       "29      SI\n",
       "        ..\n",
       "1472    SI\n",
       "1473    SI\n",
       "1474    SI\n",
       "1475    SI\n",
       "1476    SI\n",
       "1477    SI\n",
       "1478    SI\n",
       "1479    SI\n",
       "1480    SI\n",
       "1481    SI\n",
       "1482    SI\n",
       "1483    SI\n",
       "1484    SI\n",
       "1485    SI\n",
       "1486    SI\n",
       "1487    SI\n",
       "1488    SI\n",
       "1489    SI\n",
       "1490    SI\n",
       "1491    SI\n",
       "1492    SI\n",
       "1493    SI\n",
       "1494    SI\n",
       "1495    SI\n",
       "1496    SI\n",
       "1497    SI\n",
       "1498    SI\n",
       "1499    SI\n",
       "1500    SI\n",
       "1501    SI\n",
       "Name: CONSENTIMIENTO, Length: 1486, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# empleamos la condición de arriba para seleccionar aquellos casos/registros/filas que cumplan con esa condición\n",
    "# aquí hacemos la selección sobre la misma columna\n",
    "# alternativamente se podría utilizar otra columna o el dataset completo, depende de lo que busquemos identificar!\n",
    "data['CONSENTIMIENTO'][data['CONSENTIMIENTO']=='SI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XOkZgdb3tGLG"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1486"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sobre la línea de arriba, .count() permite contrar la cantidad de filas que cumple esa condición\n",
    "data['CONSENTIMIENTO'][data['CONSENTIMIENTO']=='SI'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "phfpxly7qN9m"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "porcentaje de donantes:  98.93\n"
     ]
    }
   ],
   "source": [
    "# aquí utilizamos esa construcción para contar respuestas afirmativas y negativas, guardando el resultado en memoria\n",
    "donan = data['CONSENTIMIENTO'][data['CONSENTIMIENTO']=='SI'].count()\n",
    "no_donan = data['CONSENTIMIENTO'][data['CONSENTIMIENTO']!='SI'].count()\n",
    "\n",
    "# con ello, realizamos una operación que resuma la información\n",
    "print('porcentaje de donantes: ', round(donan/len(data)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sujk96yNqU49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CUALQUIER_ORGANO'][data['CUALQUIER_ORGANO']!='SI'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G0RXgqyjqdLw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59             HIGADO-CORNEAS\n",
       "233      RIÑON-HIGADO-CORNEAS\n",
       "234              HIGADO-RIÑON\n",
       "326                      OJOS\n",
       "432                   CORNEAS\n",
       "433       CORNEAS-PIEL-HUESOS\n",
       "528                   CORNEAS\n",
       "529       CORNEAS-PIEL-HUESOS\n",
       "648       HIGADO-RIÑON-PULMON\n",
       "883     RIÑON-CORNEAS-CORAZON\n",
       "914               PIEL-HUESOS\n",
       "1002                  TEJIDOS\n",
       "1013                     PIEL\n",
       "1434                  CORNEAS\n",
       "Name: CUALES_NO, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .notnull() también opera como un booleano\n",
    "# con la misma lógica que antes, podemos seleccionar aquellas filas cuya respuesta presenta alguna especificación\n",
    "data['CUALES_NO'][data['CUALES_NO'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oJebim2tq6Xn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82      True\n",
       "83      True\n",
       "96      True\n",
       "119     True\n",
       "120     True\n",
       "121     True\n",
       "122     True\n",
       "132     True\n",
       "133     True\n",
       "167     True\n",
       "556     True\n",
       "1371    True\n",
       "1381    True\n",
       "1382    True\n",
       "1383    True\n",
       "1388    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vemos que hay 16 personas que no dieron consentimiento para donación\n",
    "# también hay 16 que no aceptaron donar para trasplante\n",
    "# serán los mismos? qué podés decir a partir de este output?\n",
    "data['TRASPLANTE'][data['TRASPLANTE']!='SI'] == data['CONSENTIMIENTO'][data['CONSENTIMIENTO']!='SI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2EXlMiSXrKA-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['INVESTIGACION'][data['INVESTIGACION']!='SI'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M5iZf8RBr4cs"
   },
   "source": [
    "#### Retomemos la exploración de las variables numéricas\n",
    "Con funciones propias de la estadística descriptiva, obtendremos información acerca de las características de los encuestados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XprNM4Ve8go8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05    21.0\n",
       "0.95    69.0\n",
       "Name: EDAD, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['EDAD'].quantile([.05, .95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4vjDvtEVlTe8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([29], dtype=int64), count=array([47]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# la moda proviene del módulo stats de SciPy\n",
    "stats.mode(data['EDAD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jPgn0BUI7xny"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "casos:  1502\n",
      "promedio:  42.91\n",
      "mediana:  41.0\n",
      "moda:  29\n",
      "desvío estándar:  15.26\n"
     ]
    }
   ],
   "source": [
    "print('casos: ', len(data))\n",
    "print('promedio: ', round(data['EDAD'].mean(),2))\n",
    "print('mediana: ', round(data['EDAD'].median(),2))\n",
    "print('moda: ', stats.mode(data['EDAD'])[0][0]) # SciPy & índices/subíndices\n",
    "print('desvío estándar: ', round(data['EDAD'].std(),2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gjK_1n288p9g"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# aquí graficamos la forma que cobra la frecuencia de la variable EDAD\n",
    "# utilizamos un método propio de PANDAS para plotear histogramas\n",
    "data['EDAD'].hist(bins=10, grid=False, figsize=(12,8), color='c', rwidth=0.9);\n",
    "\n",
    "# una vez que ejecutes la celda, si querés, quitá el # de las dos líneas siguientes\n",
    "# seguramente cambie de formato el fondo del gráfico, debido a que antepuse un estilo preseteado de otra librería, SEABORN\n",
    "#sns.set(style='whitegrid', font_scale=1.5)\n",
    "#data['EDAD'].hist(bins=10, grid=False, figsize=(12,8), color='c', rwidth=0.9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "50RfV_Q9j1Ng"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB2MFd5V9RNV9Ymq+mRV/eSw7ISqur6qbh9+Hr8wQ4Wlt379+hx11FE555xzctRRR2X9+vVLPSQAYEKsmO+GVfWMJBcmeU6Sh5JcV1XXDstuaK1dWlWXJLkkycULMVhYSuvXr8873vGObNq0KWeccUZuu+22XHzx6KX9tre9bYlHBwAc6sa54r0yyR+11r7eWtud5PeSfF+S85NsHdbZmuSC8YYIh4bLLrssmzZtykUXXZSjjjoqF110UTZt2pTLLrtsqYcGAEyAeV/xTvKJJBur6glJHkjy4iQ3J5lqre1Iktbajqo6ca6Nq2pdknVJMjU1lZmZmTGGAovvwQcfzBlnnJGZmZns2rUrMzMzOeOMM/Lggw96/QIsoD3nWFhu5h3erbXtVbUpyfVJdiX5aJLdB7H95iSbk2TVqlVtenp6vkOBLo488sjcdtttueiiizIzM5Pp6em8+c1vzpFHHhmvX4CFs+ccC8vNOFe801rbkmRLklTVLyW5K8kXquqk4Wr3SUl2jj9MWHoXXnhhfvqnfzpvetObsnPnzpx44onZuXNnXvOa1yz10ACACTDup5qcOPz8tiTfn+TyJNckWTOssibJ1eMcAw4VZ511Vo499tjce++9+cY3vpF77703xx57bM4666ylHhoAMAHG/Rzv36yq25L8VpLXttbuS3JpkvOq6vYk5w33YeJt3LgxV111VR566KFs27YtDz30UK666qps3LhxqYcGAEyAcaeavGCOZfcmOXec/cKhaPv27Tn77LO/adnZZ5+d7du3L9GIAIBJ4psr4QCtXLkyN9544zctu/HGG7Ny5colGhEAMEmENxygDRs2ZO3atdm2bVt2796dbdu2Ze3atdmwYcNSDw0AmABjTTWBw8nq1auTjL7Bcvv27Vm5cmU2btz4N8sBAPZFeMNBWL16dVavXu0zZgGAgya8OexVVbdjtda6HQsAOLSY481hr7V20LcnX/zb89oOADh8CW8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoANfoAMALKpeX1Tm+xI41LniDQAsKl9SBiPCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAdjhXdV/buq+mRVfaKqLq+qo6rqKVV1U1XdXlXvr6pHL9RgAQBgUs07vKvqlCQ/nmRVa+0ZSY5I8ookm5K8pbV2epL7kqxdiIECAMAkG3eqyYokR1fViiSPSbIjyQuTXDk8vjXJBWMeAwAAJt68w7u19rkk/yXJZzIK7i8nuSXJ/a213cNqdyU5ZdxBAgDApFsx3w2r6vgk5yd5SpL7k/yvJC+aY9X2CNuvS7IuSaampjIzMzPfocCS8JoFWDzOsSxH8w7vJN+T5C9ba19Mkqr6QJKzkhxXVSuGq96nJrl7ro1ba5uTbE6SVatWtenp6TGGAp1dd228ZgEWiXMsy9Q4c7w/k+R5VfWYqqok5ya5Lcm2JC8b1lmT5OrxhggAAJNvnDneN2X0JsqPJPn4sK/NSS5OclFVfSrJE5JsWYBxAgDARBtnqklaa29M8sa9Ft+R5Dnj7BcAAJYb31wJAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKCDeYd3VT2tqm6ddftKVf1kVZ1QVddX1e3Dz+MXcsAAADCJ5h3erbU/b62d2Vo7M8k/SvL1JB9MckmSG1prpye5YbgPAACHtYWaanJukr9orX06yflJtg7Ltya5YIGOAQAAE2vFAu3nFUkuH36faq3tSJLW2o6qOnGuDapqXZJ1STI1NZWZmZkFGgr04TULsHicY1mOxg7vqnp0kn+R5PUHs11rbXOSzUmyatWqNj09Pe5QoJ/rro3XLMAicY5lmVqIqSYvSvKR1toXhvtfqKqTkmT4uXMBjgEAABNtIcJ7df52mkmSXJNkzfD7miRXL8AxAABgoo0V3lX1mCTnJfnArMWXJjmvqm4fHrt0nGMAAMByMNYc79ba15M8Ya9l92b0KScAAMDAN1cCAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdjBXeVXVcVV1ZVX9WVdur6vlVdUJVXV9Vtw8/j1+owQIAwKQa94r3W5Nc11r7B0memWR7kkuS3NBaOz3JDcN9AAA4rM07vKvqcUm+O8mWJGmtPdRauz/J+Um2DqttTXLBuIMEAIBJN84V76cm+WKSX6uqP62qX62qY5JMtdZ2JMnw88QFGCcAAEy0FWNu++wk61trN1XVW3MQ00qqal2SdUkyNTWVmZmZMYYC/XnNAiwe51iWo3HC+64kd7XWbhruX5lReH+hqk5qre2oqpOS7Jxr49ba5iSbk2TVqlVtenp6jKFAZ9ddG69ZgEXiHMsyNe+pJq21zyf5bFU9bVh0bpLbklyTZM2wbE2Sq8caIQAALAPjXPFOkvVJ3ltVj05yR5IfySjmr6iqtUk+k+TlYx4DAAAm3ljh3Vq7NcmqOR46d5z9AgDAcuObKwEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHSwYqkHAAvpmb/woXz5gb/ucqzTLrl20Y/x+KMflY++8XsX/TgAwOIT3iwrX37gr3PnpS9Z9OPMzMxkenp60Y/TI+4BgD5MNQEAgA6ENwAAdCC8AQCgA+ENAAAdeHMlAHDAen16lE+OYjkS3gDAAevx6VE+OYrlylQTAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKCDsT7Hu6ruTPLVJA8n2d1aW1VVJyR5f5LTktyZ5Adba/eNN0wAAJhsC3HF+5zW2pmttVXD/UuS3NBaOz3JDcN9AAA4rC3GVJPzk2wdft+a5IJFOAYAAEyUccO7JflQVd1SVeuGZVOttR1JMvw8ccxjAADAxBtrjneS72qt3V1VJya5vqr+7EA3HEJ9XZJMTU1lZmZmzKHASI/X0q5du7q9Zv1vAzjULPZ5yTmW5Wqs8G6t3T383FlVH0zynCRfqKqTWms7quqkJDsfYdvNSTYnyapVq9r09PQ4Q4GR665Nj9fSzMxMl+P0ej4AB6zDeck5luVq3lNNquqYqnrsnt+TfG+STyS5JsmaYbU1Sa4ed5AAADDpxrniPZXkg1W1Zz+/0Vq7rqr+JMkVVbU2yWeSvHz8YQIAwGSbd3i31u5I8sw5lt+b5NxxBgUAAMuNb64EAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6WLHUA4CF9NiVl+Qfbr2kz8G2Lv4hHrsySV6y+AcCABbd2OFdVUckuTnJ51prL62qpyR5X5ITknwkyataaw+Nexw4EF/dfmnuvHTxQ3VmZibT09OLfpzTLrl20Y8BAPSxEFNNfiLJ9ln3NyV5S2vt9CT3JVm7AMcAAICJNlZ4V9WpGf07+K8O9yvJC5NcOayyNckF4xwDAACWg3Gnmvxykp9J8tjh/hOS3N9a2z3cvyvJKXNtWFXrkqxLkqmpqczMzIw5FBjp8VratWtXt9es/20Ah5rFPi85x7JczTu8q+qlSXa21m6pquk9i+dYtc21fWttc5LNSbJq1arWY74sh4Hrru0y97rXHO9ezwfggHU4LznHslyNc8X7u5L8i6p6cZKjkjwuoyvgx1XViuGq96lJ7h5/mAAAMNnmPce7tfb61tqprbXTkrwiyf9prb0yybYkLxtWW5Pk6rFHCQAAE24xvkDn4iQXVdWnMprzvWURjgEAABNlQb5Ap7U2k2Rm+P2OJM9ZiP0CAIeWbl9U5kvKWIZ8cyUAcMB6fFGZLyljuVqMqSYAAMBehDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6GDFUg8AFtppl1zb50DXLf5xHn/0oxb9GABAH/MO76o6KsnvJzly2M+VrbU3VtVTkrwvyQlJPpLkVa21hxZisLA/d176ki7HOe2Sa7sdCwBYHsaZavJgkhe21p6Z5Mwk/6yqnpdkU5K3tNZOT3JfkrXjDxMAACbbvMO7jewa7j5quLUkL0xy5bB8a5ILxhohAAAsA2O9ubKqjqiqW5PsTHJ9kr9Icn9rbfewyl1JThlviAAAMPnGenNla+3hJGdW1XFJPphk5VyrzbVtVa1Lsi5JpqamMjMzM85QoDuvWeBwtdjnv127dnU7xzqX09OCfKpJa+3+qppJ8rwkx1XViuGq96lJ7n6EbTYn2Zwkq1atatPT0wsxFOjjumvjNQscljqc/2ZmZvqcY53L6WzeU02q6luGK92pqqOTfE+S7Um2JXnZsNqaJFePO0gAAJh041zxPinJ1qo6IqOAv6K19ttVdVuS91XVLyb50yRbFmCcAAAw0eYd3q21jyV51hzL70jynHEGBQAcurp8UZkvKWMZ8s2VAMAB6/HlYb6kjOVqrI8TBAAADozwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKAD4Q0AAB0IbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfzDu+qelJVbauq7VX1yar6iWH5CVV1fVXdPvw8fuGGCwAAk2mcK967k/xUa21lkucleW1VnZERAxXkAAAFHUlEQVTkkiQ3tNZOT3LDcB8AAA5r8w7v1tqO1tpHht+/mmR7klOSnJ9k67Da1iQXjDtIAACYdCsWYidVdVqSZyW5KclUa21HMorzqjrxEbZZl2RdkkxNTWVmZmYhhgLdeM0CLB7nWJajscO7qo5N8ptJfrK19pWqOqDtWmubk2xOklWrVrXp6elxhwL9XHdtvGYBFolzLMvUWJ9qUlWPyii639ta+8Cw+AtVddLw+ElJdo43RAAAmHzjfKpJJdmSZHtr7c2zHromyZrh9zVJrp7/8AAAYHkYZ6rJdyV5VZKPV9Wtw7I3JLk0yRVVtTbJZ5K8fLwhAgDA5Jt3eLfWbkzySBO6z53vfgEAYDnyzZUAANCB8AYAgA6ENwAAdCC8AQCggwX55kqYZAf6pU9/Z7tNB79Na21exwKYZPM5zzrHshy54s1hr7V20Ldt27bNazuAw5FzLIwIbwAA6EB4AwBAB8IbAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENAAAdCG8AAOhAeAMAQAfCGwAAOhDeAADQgfAGAIAOhDcAAHQgvAEAoAPhDQAAHQhvAADoQHgDAEAH1Vpb6jGkqr6Y5NNLPQ44CE9Mcs9SDwJgmXKOZdI8ubX2Lftb6ZAIb5g0VXVza23VUo8DYDlyjmW5MtUEAAA6EN4AANCB8Ib52bzUAwBYxpxjWZbM8QYAgA5c8QYAgA6ENxyEqnq4qm6ddTttqccEsBxUVauqd8+6v6KqvlhVv72U44KFtGKpBwAT5oHW2plLPQiAZehrSZ5RVUe31h5Icl6Szy3xmGBBueINABwq/neSlwy/r05y+RKOBRac8IaDc/SsaSYfXOrBACwz70vyiqo6Ksl3JrlpiccDC8pUEzg4ppoALJLW2seG986sTvI7SzsaWHjCGwA4lFyT5L8kmU7yhKUdCiws4Q0AHEremeTLrbWPV9X0Ug8GFpLwBgAOGa21u5K8danHAYvBN1cCAEAHPtUEAAA6EN4AANCB8AYAgA6ENwAAdCC8AQCgA+ENMIGqakNVfbKqPlZVt1bVc6tqpqr+fLh/a1VdOaz7K1X1c3tt+9+G36uqfraqbq+q/1dV26rq6Uv1vACWM5/jDTBhqur5SV6a5NmttQer6olJHj08/MrW2s17bfKzSW6tqvcmaUn+dZJnDY+9NslZSZ7ZWvt6VX1vkmuq6umttb9a9CcDcBgR3gCT56Qk97TWHkyS1to9SVJVc67cWvtKVW1I8vZh0X9ord0//H5xkunW2teHdT9UVX+Y5JVJtizeUwA4/JhqAjB5PpTkScPUkP9eVf9k1mPvnTXV5E17FrbWLk9yfJLHtdbenSRV9bgkx7TW/mKv/d+cxHQTgAXmijfAhGmt7aqqf5TkBUnOSfL+qrpkeHiuqSapqlOTfGuSVlXHttZ27eMQldGUFAAWkCveABOotfZwa22mtfbGJK9L8gP72eStSX4+yRVJ3jjs4ytJvlZVT91r3WcnuW1hRwyA8AaYMFX1tKo6fdaiM5N8eh/rvyjJiUneleQ/Jfm+qjpjePhNSX6lqo4e1v2eJGcn+Y3FGDvA4cxUE4DJc2ySt1XVcUl2J/lUknVJrsxojvcDw3r3ZPTpJ7+c5GWttZbRFe6fyeiNli9M8raM5n5/vKoeTvL5JOe31h4IAAuqRudhAABgMZlqAgAAHQhvAADoQHgDAEAHwhsAADoQ3gAA0IHwBgCADoQ3AAB0ILwBAKCD/w+0fwCLklTnSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sabés cómo interpretar un boxplot o diagrama de caja?\n",
    "# data.boxplot(by='SEXO', figsize=(12,8), layout=(1,2));\n",
    "data.boxplot('EDAD', by='SEXO', figsize=(12,8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SFX9_YKh-12D"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ARGENTINA', 'CHILENA', 'PERUANA', 'URUGUAYA', 'ESPAÑOLA',\n",
       "       'PARAGUAYA', 'BOLIVIANA', 'CANADIENSE', 'BRASILERA', 'VENEZOLANA',\n",
       "       'ITALIANA', 'MEXICANA', 'COLOMBIANA', 'FINLANDESA'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['NACIONALIDAD'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CnLhxTQQ5Srl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARGENTINA     1448\n",
       "PARAGUAYA       16\n",
       "URUGUAYA        13\n",
       "BRASILERA        5\n",
       "BOLIVIANA        3\n",
       "PERUANA          3\n",
       "CHILENA          3\n",
       "MEXICANA         2\n",
       "ESPAÑOLA         2\n",
       "VENEZOLANA       2\n",
       "ITALIANA         2\n",
       "FINLANDESA       1\n",
       "COLOMBIANA       1\n",
       "CANADIENSE       1\n",
       "Name: NACIONALIDAD, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['NACIONALIDAD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ML7H9zs0spLL"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NACIONALIDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>COMUNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PERUANA</td>\n",
       "      <td>F</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>F</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NACIONALIDAD SEXO  EDAD  COMUNA\n",
       "82      ARGENTINA    F    24       8\n",
       "83      ARGENTINA    F    37       4\n",
       "96      ARGENTINA    F    36       1\n",
       "119     ARGENTINA    M    18       4\n",
       "120       PERUANA    F    38       4\n",
       "121     ARGENTINA    M    37       4\n",
       "122     ARGENTINA    F    34       4\n",
       "132     ARGENTINA    M    51       6\n",
       "133     ARGENTINA    F    82       6\n",
       "167     ARGENTINA    M    46       4\n",
       "556     ARGENTINA    M    25      15\n",
       "1371    ARGENTINA    F    38      13\n",
       "1381    ARGENTINA    F    29       8\n",
       "1382    ARGENTINA    F    57       7\n",
       "1383    ARGENTINA    F    70       3\n",
       "1388    ARGENTINA    M    61       4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# veamos qué particularidades tienen quienes no serán donantes\n",
    "#data.loc[data['CONSENTIMIENTO']!='SI',:]\n",
    "data.loc[data['CONSENTIMIENTO']!='SI',['NACIONALIDAD', 'SEXO', 'EDAD', 'COMUNA']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MEYMPgqa5dYh"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>EDAD</th>\n",
       "      <th>COMUNA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NACIONALIDAD</th>\n",
       "      <th>SEXO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ARGENTINA</th>\n",
       "      <th>F</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERUANA</th>\n",
       "      <th>F</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   EDAD  COMUNA\n",
       "NACIONALIDAD SEXO              \n",
       "ARGENTINA    F        9       9\n",
       "             M        6       6\n",
       "PERUANA      F        1       1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# podemos sintetizar la información a partir de un groupby\n",
    "data.loc[data['CONSENTIMIENTO']!='SI',['NACIONALIDAD', 'SEXO', 'EDAD', 'COMUNA']].groupby(['NACIONALIDAD','SEXO']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nYdKEj2ntkeF"
   },
   "outputs": [],
   "source": [
    "# cómo trabajarías con quienes sí dieron consentimiento?\n",
    "# escribí el código acá abajo!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ddApqgBEqIl_"
   },
   "source": [
    "Una opción para abordar el dataset, es reemplazar los números de las comunas por la referencia a alguno de sus barrios. De hecho, es más sencillo para reconocer si hay alguna asociación con los grupos etarios, el género declarado, la nacionalidad y el consentimiento para la donación. Tenemos un dataset complementario que, al menos en principio, podría facilitarnos ese matching entre el número de la comuna y el barrio. Veamos cómo pueden dialogar en este caso."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GxLFx1uIn00O"
   },
   "source": [
    "### Establecimientos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MX_RHlP-GY_y"
   },
   "outputs": [],
   "source": [
    "# subimos el archivo\n",
    "from google.colab import files\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iB__zDWlVijy"
   },
   "outputs": [],
   "source": [
    "comunas = pd.read_csv('establecimentos-elecciones-2017.csv', sep=';', encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bCqrD42knNcx"
   },
   "outputs": [],
   "source": [
    "# cómo haces para ver si el df fue bien cargado?\n",
    "# escribí el código acá abajo!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AUkcB0u0df1Q"
   },
   "outputs": [],
   "source": [
    "# chequeá la estructura del dataframe (usualmente referido como df): dimensiones, tipos, nulos, etc.\n",
    "# escribí el código acá abajo!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gqpkSOsnnhA9"
   },
   "source": [
    "Una vez que pudiste acceder a las columnas:\n",
    "\n",
    "* ¿Qué información nos puede servir para complementar el primer dataset? \n",
    "* ¿Se pueden integrar facilmente los dos datasets? ¿Tienen las mismas dimensiones? ¿Cómo se podrían vincular?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jJUNHreZZRh0"
   },
   "outputs": [],
   "source": [
    "# vamos a parsear los datos de la comuna\n",
    "# para poder generar una equivalencia entre las columnas 'COMUNA' de un df y otro, tenemos que adaptarlas\n",
    "# por lo tanto, quitamos la palabra 'COMUNA' de cada celda, preservando el número\n",
    "comunas['COMUNA'] = comunas['COMUNA'].apply(lambda x: x.replace('COMUNA','')).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RZimjVwVVxK_"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BARRIO</th>\n",
       "      <th>COMUNA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BOEDO</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALMAGRO</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VILLA LURO</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VILLA DEVOTO</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>COGHLAN</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>COLEGIALES</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PALERMO</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         BARRIO  COMUNA\n",
       "0      RECOLETA       2\n",
       "1      RECOLETA       2\n",
       "2         BOEDO       5\n",
       "3       ALMAGRO       5\n",
       "4    VILLA LURO      10\n",
       "5  VILLA DEVOTO      11\n",
       "6       COGHLAN      12\n",
       "7    COLEGIALES      13\n",
       "8       PALERMO      14\n",
       "9      RECOLETA       2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# acá tenemos a las columnas candidatas para el parseo\n",
    "# está todo bien en los datos?\n",
    "comunas[['BARRIO','COMUNA']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JZ-e3aMbeRzQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 'RECOLETA',\n",
       " 5: 'ALMAGRO',\n",
       " 10: 'VILLA LURO',\n",
       " 11: 'VILLA DEVOTO',\n",
       " 12: 'COGHLAN',\n",
       " 13: 'COLEGIALES',\n",
       " 14: 'PALERMO'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vamos a llevar ese subset a un diccionario para que las KEYS sean para el matching con el df DATA y los VALUE operen como etiquetas\n",
    "ref = { k:v for v,k in zip(comunas['BARRIO'],comunas['COMUNA'])}\n",
    "ref"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IfkGt5FcHjiQ"
   },
   "source": [
    "¿La información está completa? ¿Perdimos algo en el camino?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HLW-_WjYaCSx"
   },
   "outputs": [],
   "source": [
    "# notaste que había 2 establecimientos en la comuna 5, uno en Almagro y otro en Boedo\n",
    "# como en nuestro df DATA no podemos diferenciarlos, incluimos ambos a la referencia y la actualizamos\n",
    "ref.update({5 : 'ALMAGRO/BOEDO'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Meo0-SNemRc4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 'RECOLETA',\n",
       " 5: 'ALMAGRO/BOEDO',\n",
       " 10: 'VILLA LURO',\n",
       " 11: 'VILLA DEVOTO',\n",
       " 12: 'COGHLAN',\n",
       " 13: 'COLEGIALES',\n",
       " 14: 'PALERMO'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1IxF76SNwcIs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1487    COLEGIALES\n",
       "1488    COLEGIALES\n",
       "1489    COLEGIALES\n",
       "1490       PALERMO\n",
       "1491       PALERMO\n",
       "1492       PALERMO\n",
       "1493       PALERMO\n",
       "1494       PALERMO\n",
       "1495       PALERMO\n",
       "1496       PALERMO\n",
       "1497    VILLA LURO\n",
       "1498    VILLA LURO\n",
       "1499    VILLA LURO\n",
       "1500    VILLA LURO\n",
       "1501    VILLA LURO\n",
       "Name: COMUNA, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cuando al .map() le pasamos un diccionario cuyas claves generan equivalencias con los valores de cqada registro...\n",
    "# es posible etiquetar o renombrar el contenido de cada celda \n",
    "data['COMUNA'].map(ref)[-15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wMafQOIMm0Cn"
   },
   "outputs": [],
   "source": [
    "# aquí guardamos ese reetiquetado en una nueva columna para no perder información\n",
    "data['COMUNA2'] = data['COMUNA'].map(ref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hl4T9mEQDY9R"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NACIONALIDAD', 'SEXO', 'EDAD', 'CONSENTIMIENTO', 'CUALQUIER_ORGANO',\n",
       "       'CUALES_NO', 'TRASPLANTE', 'INVESTIGACION', 'COMUNA', 'COMUNA2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3kEQcib_u2f1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>NACIONALIDAD</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>CUALQUIER_ORGANO</th>\n",
       "      <th>CUALES_NO</th>\n",
       "      <th>TRASPLANTE</th>\n",
       "      <th>INVESTIGACION</th>\n",
       "      <th>COMUNA</th>\n",
       "      <th>COMUNA2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSENTIMIENTO</th>\n",
       "      <th>SEXO</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 rowspan=\"2\" valign=\"top\">NO</th>\n",
       "      <th>F</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SI</th>\n",
       "      <th>F</th>\n",
       "      <td>1062</td>\n",
       "      <td>1062</td>\n",
       "      <td>1062</td>\n",
       "      <td>8</td>\n",
       "      <td>1062</td>\n",
       "      <td>1062</td>\n",
       "      <td>1062</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>424</td>\n",
       "      <td>424</td>\n",
       "      <td>424</td>\n",
       "      <td>6</td>\n",
       "      <td>424</td>\n",
       "      <td>424</td>\n",
       "      <td>424</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     NACIONALIDAD  EDAD  CUALQUIER_ORGANO  CUALES_NO  \\\n",
       "CONSENTIMIENTO SEXO                                                    \n",
       "NO             F               10    10                10          0   \n",
       "               M                6     6                 6          0   \n",
       "SI             F             1062  1062              1062          8   \n",
       "               M              424   424               424          6   \n",
       "\n",
       "                     TRASPLANTE  INVESTIGACION  COMUNA  COMUNA2  \n",
       "CONSENTIMIENTO SEXO                                              \n",
       "NO             F             10             10      10        1  \n",
       "               M              6              6       6        0  \n",
       "SI             F           1062           1062    1062      455  \n",
       "               M            424            424     424      180  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.groupby(['CONSENTIMIENTO','SEXO']).count()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6iyyS5l-DxmK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['F' 'M']\n",
      "['SI' 'NO']\n",
      "['SI' 'NO']\n"
     ]
    }
   ],
   "source": [
    "categoricas = ['SEXO', 'CONSENTIMIENTO', 'INVESTIGACION']\n",
    "\n",
    "# excluyo \n",
    "# NACIONALIDAD y COMUNA (quiero conservar sus etiquetas)\n",
    "# EDAD (ya numérica)\n",
    "# CUALES_NO (14 casos, detalle arriba)\n",
    "# CUALQUIER_ORGANO_SI (30 con NO)\n",
    "# TRASPLANTE (es redundante)\n",
    "\n",
    "for i in categoricas:\n",
    "    print(data[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xypogMAgEGCd"
   },
   "outputs": [],
   "source": [
    "# generamos un df de variables dummies (datos numéricos binarios [0, 1]) para facilitar algunos procedimientos \n",
    "dummies = pd.get_dummies(data[categoricas])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LHfXk5GyEocR"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NACIONALIDAD</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>COMUNA</th>\n",
       "      <th>COMUNA2</th>\n",
       "      <th>SEXO_F</th>\n",
       "      <th>CONSENTIMIENTO_SI</th>\n",
       "      <th>INVESTIGACION_SI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>RECOLETA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NACIONALIDAD  EDAD  COMUNA   COMUNA2  SEXO_F  CONSENTIMIENTO_SI  \\\n",
       "0    ARGENTINA    40       2  RECOLETA       1                  1   \n",
       "1    ARGENTINA    31       2  RECOLETA       0                  1   \n",
       "2    ARGENTINA    41       2  RECOLETA       1                  1   \n",
       "3    ARGENTINA    29       2  RECOLETA       0                  1   \n",
       "4    ARGENTINA    26       2  RECOLETA       1                  1   \n",
       "\n",
       "   INVESTIGACION_SI  \n",
       "0                 1  \n",
       "1                 0  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# armamos selecciones de las columnas que nos interesa conservar en un nuevo df, ya integrado\n",
    "data_seleccion = ['NACIONALIDAD', 'EDAD', 'COMUNA', 'COMUNA2']\n",
    "dummies_seleccion = ['SEXO_F', 'CONSENTIMIENTO_SI', 'INVESTIGACION_SI']\n",
    "\n",
    "#construimos el nuevo df, con partes de cada uno de los df existentes\n",
    "newdata = data[data_seleccion].join(dummies[dummies_seleccion])\n",
    "newdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1b_ZB0rKy4H8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN              57.656458\n",
       "ALMAGRO/BOEDO     8.455393\n",
       "COLEGIALES        7.856192\n",
       "RECOLETA          6.058589\n",
       "COGHLAN           5.858855\n",
       "PALERMO           5.659121\n",
       "VILLA DEVOTO      4.860186\n",
       "VILLA LURO        3.595206\n",
       "Name: COMUNA2, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chequeamos estructura de los datos por comuna\n",
    "newdata['COMUNA2'].value_counts(normalize=True, sort=True, ascending=False, dropna=False)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j1wonjB3u5hR"
   },
   "source": [
    "Habían notado que prácticamente la mitad de las comunas no estaba categorizada o carecía de establecimiento de referencia? \n",
    "\n",
    "De hecho, más de la mitad de la información al respecto no ha sido disponibilizada! \n",
    "\n",
    "\n",
    "### Desafío\n",
    "Una alternativa para resulver este tema es armar un diccionario propio para el parseo, completo. Podés recurrir al GCBA o a Wikipedia para armar una lista completa de las referencias por comuna { número : barrios afectados } y reescribir la columna 'COMUNA 2'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z0bslp-Lv8MS"
   },
   "outputs": [],
   "source": [
    "# cómo comenzarías?\n",
    "# escribí el código acá abajo!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CVHMESqkVd1A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1012.12x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hay una serie de gráficos que puede ser útil para identificar algunos agrupamientos y/o tendencias\n",
    "sns.pairplot(data=newdata, hue='CONSENTIMIENTO_SI', palette='husl');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tDnwp2WJwRYU"
   },
   "source": [
    "En este caso, es difícil por la conglomeración de dummies. De hechom muchos puntos se apilan en el mismo par ordenado. De ahí que nos conviene graficar las series con histograms o swarmplots. \n",
    "\n",
    "[Aquí](https://seaborn.pydata.org/generated/seaborn.pairplot.html) también podés ver algunos ejemplos de datos más funcionales para el pairplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R95PGjmRoc7S"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid', font_scale=1)\n",
    "sns.swarmplot(y='EDAD', x='CONSENTIMIENTO_SI', data=newdata, color='b');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Chcu1eIspC1P"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(y='EDAD', x='SEXO_F', data=newdata, color='c');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cuD6onAPxQpy"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "EDA_consentimiento_trasplantes.ipynb",
   "private_outputs": true,
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}