TP1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import calendar\n",
    "import warnings\n",
    "from datetime import datetime\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#Plots\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cargamos los dataframes con los CSVs de Navent\n",
    "\n",
    "fiuba1 = pd.read_csv('../datos_navent_fiuba/fiuba_1_postulantes_educacion.csv')\n",
    "postulantes_genero = pd.read_csv('../datos_navent_fiuba/fiuba_2_postulantes_genero_y_edad.csv')\n",
    "vistas = pd.read_csv('../datos_navent_fiuba/fiuba_3_vistas.csv')\n",
    "postulaciones = pd.read_csv('../datos_navent_fiuba/fiuba_4_postulaciones.csv')\n",
    "avisos_online = pd.read_csv('../datos_navent_fiuba/fiuba_5_avisos_online.csv')\n",
    "avisos_detalle = pd.read_csv('../datos_navent_fiuba/fiuba_6_avisos_detalle.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idpostulante</th>\n",
       "      <th>nombre</th>\n",
       "      <th>estado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NdJl</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>En Curso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8BkL</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>En Curso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1d2B</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>En Curso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NPBx</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>En Curso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NPBx</td>\n",
       "      <td>Master</td>\n",
       "      <td>En Curso</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  idpostulante         nombre    estado\n",
       "0         NdJl       Posgrado  En Curso\n",
       "1         8BkL  Universitario  En Curso\n",
       "2         1d2B  Universitario  En Curso\n",
       "3         NPBx  Universitario  En Curso\n",
       "4         NPBx         Master  En Curso"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Realizamos un head para analizar las columnas existentes\n",
    "fiuba1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idpostulante</th>\n",
       "      <th>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NM5M</td>\n",
       "      <td>1970-12-03</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5awk</td>\n",
       "      <td>1962-12-04</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZaO5</td>\n",
       "      <td>1978-08-10</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NdJl</td>\n",
       "      <td>1969-05-09</td>\n",
       "      <td>MASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eo2p</td>\n",
       "      <td>1981-02-16</td>\n",
       "      <td>MASC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  idpostulante fechanacimiento  sexo\n",
       "0         NM5M      1970-12-03   FEM\n",
       "1         5awk      1962-12-04   FEM\n",
       "2         ZaO5      1978-08-10   FEM\n",
       "3         NdJl      1969-05-09  MASC\n",
       "4         eo2p      1981-02-16  MASC"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postulantes_genero.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idAviso</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>idpostulante</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1111780242</td>\n",
       "      <td>2018-02-23T13:38:13.187-0500</td>\n",
       "      <td>YjVJQ6Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1112263876</td>\n",
       "      <td>2018-02-23T13:38:14.296-0500</td>\n",
       "      <td>BmVpYoR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1112327963</td>\n",
       "      <td>2018-02-23T13:38:14.329-0500</td>\n",
       "      <td>wVkBzZd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1112318643</td>\n",
       "      <td>2018-02-23T13:38:17.921-0500</td>\n",
       "      <td>OqmP9pv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1111903673</td>\n",
       "      <td>2018-02-23T13:38:18.973-0500</td>\n",
       "      <td>DrpbXDP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idAviso                     timestamp idpostulante\n",
       "0  1111780242  2018-02-23T13:38:13.187-0500      YjVJQ6Z\n",
       "1  1112263876  2018-02-23T13:38:14.296-0500      BmVpYoR\n",
       "2  1112327963  2018-02-23T13:38:14.329-0500      wVkBzZd\n",
       "3  1112318643  2018-02-23T13:38:17.921-0500      OqmP9pv\n",
       "4  1111903673  2018-02-23T13:38:18.973-0500      DrpbXDP"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vistas.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idaviso</th>\n",
       "      <th>idpostulante</th>\n",
       "      <th>fechapostulacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112257047</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-01-15 16:22:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111920714</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-02-06 09:04:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1112346945</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-02-22 09:04:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1112345547</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-02-22 09:04:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1112237522</td>\n",
       "      <td>5awk</td>\n",
       "      <td>2018-01-25 18:55:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idaviso idpostulante     fechapostulacion\n",
       "0  1112257047         NM5M  2018-01-15 16:22:34\n",
       "1  1111920714         NM5M  2018-02-06 09:04:50\n",
       "2  1112346945         NM5M  2018-02-22 09:04:47\n",
       "3  1112345547         NM5M  2018-02-22 09:04:59\n",
       "4  1112237522         5awk  2018-01-25 18:55:03"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postulaciones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idaviso</th>\n",
       "      <th>idpais</th>\n",
       "      <th>titulo</th>\n",
       "      <th>descripcion</th>\n",
       "      <th>nombre_zona</th>\n",
       "      <th>ciudad</th>\n",
       "      <th>mapacalle</th>\n",
       "      <th>tipo_de_trabajo</th>\n",
       "      <th>nivel_laboral</th>\n",
       "      <th>nombre_area</th>\n",
       "      <th>denominacion_empresa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8725750</td>\n",
       "      <td>1</td>\n",
       "      <td>VENDEDOR/A PROVINCIA DE SANTA FE</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;&lt;strong&gt;Empresa:&lt;/strong&gt;&lt;/strong&gt; ...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Comercial</td>\n",
       "      <td>VENTOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17903700</td>\n",
       "      <td>1</td>\n",
       "      <td>Enfermeras</td>\n",
       "      <td>&lt;p&gt;Solicitamos para importante cadena de farma...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Salud</td>\n",
       "      <td>Farmacias Central Oeste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000150677</td>\n",
       "      <td>1</td>\n",
       "      <td>Chofer de taxi</td>\n",
       "      <td>&lt;p&gt;TE GUSTA MANEJAR? QUERES GANAR PLATA HACIEN...</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Empedrado 2336</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Transporte</td>\n",
       "      <td>FAMITAX SRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000610287</td>\n",
       "      <td>1</td>\n",
       "      <td>CHOFER DE CAMIONETA BAHIA BLANCA - PUNTA ALTA</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;Somos una empresa multinacional que...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Transporte</td>\n",
       "      <td>Wurth Argentina S.A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000872556</td>\n",
       "      <td>1</td>\n",
       "      <td>Operarios de Planta - Rubro Electrodomésticos</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;OPERARIOS DE PLANTA&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>Senior / Semi-Senior</td>\n",
       "      <td>Producción</td>\n",
       "      <td>ELECTRO OUTLET SRL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idaviso  idpais                                         titulo  \\\n",
       "0     8725750       1               VENDEDOR/A PROVINCIA DE SANTA FE   \n",
       "1    17903700       1                                     Enfermeras   \n",
       "2  1000150677       1                                 Chofer de taxi   \n",
       "3  1000610287       1  CHOFER DE CAMIONETA BAHIA BLANCA - PUNTA ALTA   \n",
       "4  1000872556       1  Operarios de Planta - Rubro Electrodomésticos   \n",
       "\n",
       "                                         descripcion        nombre_zona  \\\n",
       "0  <p><strong><strong>Empresa:</strong></strong> ...  Gran Buenos Aires   \n",
       "1  <p>Solicitamos para importante cadena de farma...  Gran Buenos Aires   \n",
       "2  <p>TE GUSTA MANEJAR? QUERES GANAR PLATA HACIEN...    Capital Federal   \n",
       "3  <p><strong>Somos una empresa multinacional que...  Gran Buenos Aires   \n",
       "4  <p><strong>OPERARIOS DE PLANTA</strong></p><p>...  Gran Buenos Aires   \n",
       "\n",
       "  ciudad       mapacalle tipo_de_trabajo         nivel_laboral nombre_area  \\\n",
       "0    NaN             NaN       Full-time  Senior / Semi-Senior   Comercial   \n",
       "1    NaN             NaN       Full-time  Senior / Semi-Senior       Salud   \n",
       "2    NaN  Empedrado 2336       Full-time  Senior / Semi-Senior  Transporte   \n",
       "3    NaN             NaN       Full-time  Senior / Semi-Senior  Transporte   \n",
       "4    NaN             NaN       Full-time  Senior / Semi-Senior  Producción   \n",
       "\n",
       "      denominacion_empresa  \n",
       "0                   VENTOR  \n",
       "1  Farmacias Central Oeste  \n",
       "2              FAMITAX SRL  \n",
       "3      Wurth Argentina S.A  \n",
       "4       ELECTRO OUTLET SRL  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avisos_detalle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idaviso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112355872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1112335374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1112374842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1111984070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1111822480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idaviso\n",
       "0  1112355872\n",
       "1  1112335374\n",
       "2  1112374842\n",
       "3  1111984070\n",
       "4  1111822480"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avisos_online.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idpostulante</th>\n",
       "      <th>nombre</th>\n",
       "      <th>estado</th>\n",
       "      <th>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NdJl</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>En Curso</td>\n",
       "      <td>1969-05-09</td>\n",
       "      <td>MASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NdJl</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1969-05-09</td>\n",
       "      <td>MASC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8BkL</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>En Curso</td>\n",
       "      <td>1976-07-21</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8BkL</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1976-07-21</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8BkL</td>\n",
       "      <td>Posgrado</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1976-07-21</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  idpostulante         nombre    estado fechanacimiento  sexo\n",
       "0         NdJl       Posgrado  En Curso      1969-05-09  MASC\n",
       "1         NdJl  Universitario  Graduado      1969-05-09  MASC\n",
       "2         8BkL  Universitario  En Curso      1976-07-21   FEM\n",
       "3         8BkL  Universitario  Graduado      1976-07-21   FEM\n",
       "4         8BkL       Posgrado  Graduado      1976-07-21   FEM"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mergeamos los 2 primeros dataframes para obtener una descripcion mas detallada de cada postulante al aviso\n",
    "\n",
    "datos_postulantes = pd.merge(fiuba1, postulantes_genero, on='idpostulante')\n",
    "\n",
    "datos_postulantes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idaviso</th>\n",
       "      <th>idpostulante</th>\n",
       "      <th>fechapostulacion</th>\n",
       "      <th>nombre</th>\n",
       "      <th>estado</th>\n",
       "      <th>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112257047</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-01-15 16:22:34</td>\n",
       "      <td>Secundario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1970-12-03</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1111920714</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-02-06 09:04:50</td>\n",
       "      <td>Secundario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1970-12-03</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1112346945</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-02-22 09:04:47</td>\n",
       "      <td>Secundario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1970-12-03</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1112345547</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-02-22 09:04:59</td>\n",
       "      <td>Secundario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1970-12-03</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1112237522</td>\n",
       "      <td>5awk</td>\n",
       "      <td>2018-01-25 18:55:03</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1962-12-04</td>\n",
       "      <td>FEM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idaviso idpostulante     fechapostulacion         nombre    estado  \\\n",
       "0  1112257047         NM5M  2018-01-15 16:22:34     Secundario  Graduado   \n",
       "1  1111920714         NM5M  2018-02-06 09:04:50     Secundario  Graduado   \n",
       "2  1112346945         NM5M  2018-02-22 09:04:47     Secundario  Graduado   \n",
       "3  1112345547         NM5M  2018-02-22 09:04:59     Secundario  Graduado   \n",
       "4  1112237522         5awk  2018-01-25 18:55:03  Universitario  Graduado   \n",
       "\n",
       "  fechanacimiento sexo  \n",
       "0      1970-12-03  FEM  \n",
       "1      1970-12-03  FEM  \n",
       "2      1970-12-03  FEM  \n",
       "3      1970-12-03  FEM  \n",
       "4      1962-12-04  FEM  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mergeamos ahora con el aviso en el cual se postulo\n",
    "\n",
    "postulaciones_con_datos_postulantes = pd.merge(postulaciones, datos_postulantes, on='idpostulante')\n",
    "\n",
    "postulaciones_con_datos_postulantes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idaviso</th>\n",
       "      <th>idpostulante</th>\n",
       "      <th>fechapostulacion</th>\n",
       "      <th>nombre</th>\n",
       "      <th>estado</th>\n",
       "      <th>fechanacimiento</th>\n",
       "      <th>sexo</th>\n",
       "      <th>idpais</th>\n",
       "      <th>titulo</th>\n",
       "      <th>descripcion</th>\n",
       "      <th>nombre_zona</th>\n",
       "      <th>ciudad</th>\n",
       "      <th>mapacalle</th>\n",
       "      <th>tipo_de_trabajo</th>\n",
       "      <th>nivel_laboral</th>\n",
       "      <th>nombre_area</th>\n",
       "      <th>denominacion_empresa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112257047</td>\n",
       "      <td>NM5M</td>\n",
       "      <td>2018-01-15 16:22:34</td>\n",
       "      <td>Secundario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1970-12-03</td>\n",
       "      <td>FEM</td>\n",
       "      <td>1</td>\n",
       "      <td>REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;span style=\"\"&gt;En &lt;/span&gt;&lt;/em&gt;&lt;...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Atención al Cliente</td>\n",
       "      <td>Eficasia Argentina SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1112257047</td>\n",
       "      <td>1kJqGb</td>\n",
       "      <td>2018-01-15 10:23:11</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>En Curso</td>\n",
       "      <td>1984-04-21</td>\n",
       "      <td>FEM</td>\n",
       "      <td>1</td>\n",
       "      <td>REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;span style=\"\"&gt;En &lt;/span&gt;&lt;/em&gt;&lt;...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Atención al Cliente</td>\n",
       "      <td>Eficasia Argentina SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1112257047</td>\n",
       "      <td>1kJqGb</td>\n",
       "      <td>2018-01-15 10:23:11</td>\n",
       "      <td>Terciario/Técnico</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1984-04-21</td>\n",
       "      <td>FEM</td>\n",
       "      <td>1</td>\n",
       "      <td>REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;span style=\"\"&gt;En &lt;/span&gt;&lt;/em&gt;&lt;...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Atención al Cliente</td>\n",
       "      <td>Eficasia Argentina SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1112257047</td>\n",
       "      <td>eOE9Rr</td>\n",
       "      <td>2018-01-15 10:42:07</td>\n",
       "      <td>Secundario</td>\n",
       "      <td>Graduado</td>\n",
       "      <td>1979-03-05</td>\n",
       "      <td>FEM</td>\n",
       "      <td>1</td>\n",
       "      <td>REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;span style=\"\"&gt;En &lt;/span&gt;&lt;/em&gt;&lt;...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Atención al Cliente</td>\n",
       "      <td>Eficasia Argentina SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1112257047</td>\n",
       "      <td>eOE9Rr</td>\n",
       "      <td>2018-01-15 10:42:07</td>\n",
       "      <td>Universitario</td>\n",
       "      <td>Abandonado</td>\n",
       "      <td>1979-03-05</td>\n",
       "      <td>FEM</td>\n",
       "      <td>1</td>\n",
       "      <td>REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN</td>\n",
       "      <td>&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;span style=\"\"&gt;En &lt;/span&gt;&lt;/em&gt;&lt;...</td>\n",
       "      <td>Gran Buenos Aires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part-time</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Atención al Cliente</td>\n",
       "      <td>Eficasia Argentina SA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idaviso idpostulante     fechapostulacion             nombre  \\\n",
       "0  1112257047         NM5M  2018-01-15 16:22:34         Secundario   \n",
       "1  1112257047       1kJqGb  2018-01-15 10:23:11      Universitario   \n",
       "2  1112257047       1kJqGb  2018-01-15 10:23:11  Terciario/Técnico   \n",
       "3  1112257047       eOE9Rr  2018-01-15 10:42:07         Secundario   \n",
       "4  1112257047       eOE9Rr  2018-01-15 10:42:07      Universitario   \n",
       "\n",
       "       estado fechanacimiento sexo  idpais  \\\n",
       "0    Graduado      1970-12-03  FEM       1   \n",
       "1    En Curso      1984-04-21  FEM       1   \n",
       "2    Graduado      1984-04-21  FEM       1   \n",
       "3    Graduado      1979-03-05  FEM       1   \n",
       "4  Abandonado      1979-03-05  FEM       1   \n",
       "\n",
       "                                             titulo  \\\n",
       "0  REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN   \n",
       "1  REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN   \n",
       "2  REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN   \n",
       "3  REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN   \n",
       "4  REPRESENTANTES DE ATENCIÓN AL CLIENTE/ RETENCIÓN   \n",
       "\n",
       "                                         descripcion        nombre_zona  \\\n",
       "0  <p><strong><em><span style=\"\">En </span></em><...  Gran Buenos Aires   \n",
       "1  <p><strong><em><span style=\"\">En </span></em><...  Gran Buenos Aires   \n",
       "2  <p><strong><em><span style=\"\">En </span></em><...  Gran Buenos Aires   \n",
       "3  <p><strong><em><span style=\"\">En </span></em><...  Gran Buenos Aires   \n",
       "4  <p><strong><em><span style=\"\">En </span></em><...  Gran Buenos Aires   \n",
       "\n",
       "  ciudad mapacalle tipo_de_trabajo nivel_laboral          nombre_area  \\\n",
       "0    NaN       NaN       Part-time          Otro  Atención al Cliente   \n",
       "1    NaN       NaN       Part-time          Otro  Atención al Cliente   \n",
       "2    NaN       NaN       Part-time          Otro  Atención al Cliente   \n",
       "3    NaN       NaN       Part-time          Otro  Atención al Cliente   \n",
       "4    NaN       NaN       Part-time          Otro  Atención al Cliente   \n",
       "\n",
       "     denominacion_empresa  \n",
       "0  Eficasia Argentina SA   \n",
       "1  Eficasia Argentina SA   \n",
       "2  Eficasia Argentina SA   \n",
       "3  Eficasia Argentina SA   \n",
       "4  Eficasia Argentina SA   "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mergeamos por el idaviso para obtener una descripcion de cada postulante a que se postulo\n",
    "\n",
    "datos_avisos_con_postulantes = pd.merge(postulaciones_con_datos_postulantes, avisos_detalle, on = 'idaviso')\n",
    "\n",
    "datos_avisos_con_postulantes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos genero de postulantes\n",
    "\n",
    "\n",
    "cantidad_masculinos=datos_postulantes.loc[datos_postulantes.sexo.str.contains('MASC'),:]\n",
    "cantidad_femeninos=datos_postulantes.loc[datos_postulantes.sexo.str.contains('FEM'),:]\n",
    "sizes = [len(cantidad_masculinos), len(cantidad_femeninos)]\n",
    "nombres = ['Masculino', 'Femenino']\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.title('Proporcion Sexo de postulantes', fontsize=20)\n",
    "plt.pie(sizes, labels=nombres, autopct='%1.1f%%', startangle=20, colors=['orange', 'green'], explode=(0.1, 0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficamos genero de postulantes en barras\n",
    "\n",
    "cantidad_masculinos=datos_postulantes.loc[datos_postulantes.sexo.str.contains('MASC'),:]\n",
    "cantidad_femeninos=datos_postulantes.loc[datos_postulantes.sexo.str.contains('FEM'),:]\n",
    "cantidad_otro=datos_postulantes.loc[datos_postulantes.sexo.str.contains('NO_DECLARA'),:]\n",
    "sizes = {'MASC': [len(cantidad_masculinos)], 'FEM': [len(cantidad_femeninos)], 'OTRO': [len(cantidad_otro)]}\n",
    "df = pd.DataFrame(sizes)\n",
    "\n",
    "ax= df.plot.bar(colors=['Magenta','Blue','Green'],ec='White', title='Proporcion Sexo de postulantes')\n",
    "ax.xaxis.set_visible(False)\n",
    "ax.title.set_fontsize(20)\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficos la proporcion de nivel academico\n",
    "\n",
    "cantidad_posgrado=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Posgrado'),:]\n",
    "cantidad_universitario=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Universitario'),:]\n",
    "cantidad_master=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Master'),:]\n",
    "cantidad_otro=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Otro'),:]\n",
    "cantidad_terciario=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Terciario/T\\xc3\\xa9cnico'),:]\n",
    "cantidad_secundario=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Secundario'),:]\n",
    "cantidad_doctorado=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Doctorado'),:]\n",
    "\n",
    "sizes = [len(cantidad_posgrado), len(cantidad_universitario) ,len(cantidad_master),len(cantidad_otro), len(cantidad_terciario), len(cantidad_secundario), len(cantidad_doctorado)]\n",
    "nombres = ['Posgrado', 'Universitario', 'Master', 'Otros', 'Terciario', 'Secundario', 'Doctorado']\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.title('Proporcion Nivel academico de postulantes', fontsize=20)\n",
    "plt.pie(sizes, labels=nombres, autopct='%1.1f%%', startangle=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficos la proporcion de nivel academico en barras\n",
    "\n",
    "cantidad_posgrado=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Posgrado'),:]\n",
    "cantidad_universitario=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Universitario'),:]\n",
    "cantidad_master=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Master'),:]\n",
    "cantidad_otro=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Otro'),:]\n",
    "cantidad_terciario=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Terciario/T\\xc3\\xa9cnico'),:]\n",
    "cantidad_secundario=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Secundario'),:]\n",
    "cantidad_doctorado=datos_postulantes.loc[datos_postulantes.nombre.str.contains('Doctorado'),:]\n",
    "sizes = {'Posgrado':[len(cantidad_posgrado)], 'Universitario':[len(cantidad_universitario)], 'Master': [len(cantidad_master)], 'Otro':[len(cantidad_otro)], 'Terciario':[len(cantidad_terciario)], 'Secundario': [len(cantidad_secundario)], 'Doctorado': [len(cantidad_doctorado)]}\n",
    "\n",
    "df = pd.DataFrame(sizes)\n",
    "\n",
    "ax= df.plot.bar(ec='White', title='Proporcion Nivel academico de postulantes',logy=True)\n",
    "ax.xaxis.set_visible(False)\n",
    "ax.title.set_fontsize(20)\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() + 175))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}