3CGiordano_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2503ed3c",
   "metadata": {},
   "source": [
    "# 3 - C - Giordano | Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2309223d",
   "metadata": {},
   "source": [
    "- La notebook debe analizar las siguientes universidades (grupo C):\n",
    ">    - Universidad Nacional de Jujuy\n",
    ">    - Universidad de Palermo\n",
    "- Trabajar creando gráficos que expliquen las siguientes dimensiones:\n",
    "> * Carrera\n",
    "> * Edad\n",
    "> * Género\n",
    "> * Localidad\n",
    "- Realizar un comparativo de inscripciones entre ambas universidades.\n",
    "- Analizar correlaciones de variables, entre:\n",
    "> * Carrera\n",
    "> * Edad\n",
    "> * Género\n",
    "> * Localidad\n",
    "- Presentar una conclusión final de lo analizado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f814dff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from datetime import date, datetime, timedelta\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aaf1616",
   "metadata": {},
   "source": [
    "# 1. Universidad Nacional de Jujuy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1775a505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>university</th>\n",
       "      <th>career</th>\n",
       "      <th>inscription_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>location</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>stephanie</td>\n",
       "      <td>washington</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1706</td>\n",
       "      <td>haedo</td>\n",
       "      <td>josephescobar@yahoo.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>contador público</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>alyssa</td>\n",
       "      <td>young</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1983</td>\n",
       "      <td>gomez</td>\n",
       "      <td>matthew86@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>andrea</td>\n",
       "      <td>rocha</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>3346</td>\n",
       "      <td>bacacay</td>\n",
       "      <td>connorford@hotmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en producción de bioimagenes</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>mary</td>\n",
       "      <td>flores</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>7009</td>\n",
       "      <td>iraola</td>\n",
       "      <td>maryjohnson@hotmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>shaun</td>\n",
       "      <td>conrad</td>\n",
       "      <td>male</td>\n",
       "      <td>18</td>\n",
       "      <td>6401</td>\n",
       "      <td>sundblad</td>\n",
       "      <td>annturner@yahoo.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         university  \\\n",
       "1069  universidad nacional de jujuy   \n",
       "862   universidad nacional de jujuy   \n",
       "734   universidad nacional de jujuy   \n",
       "567   universidad nacional de jujuy   \n",
       "916   universidad nacional de jujuy   \n",
       "\n",
       "                                                 career inscription_date  \\\n",
       "1069            licenciatura en ciencias antropológicas       2020-10-15   \n",
       "862                                    contador público       2020-09-21   \n",
       "734                 licenciatura en ciencias geológicas       2020-12-17   \n",
       "567           licenciatura en producción de bioimagenes       2020-11-30   \n",
       "916   licenciatura en planificación y diseño del pai...       2020-11-30   \n",
       "\n",
       "     first_name   last_name  gender  age postal_code  location  \\\n",
       "1069  stephanie  washington  female   18        1706     haedo   \n",
       "862      alyssa       young  female   18        1983     gomez   \n",
       "734      andrea       rocha  female   18        3346   bacacay   \n",
       "567        mary      flores  female   18        7009    iraola   \n",
       "916       shaun      conrad    male   18        6401  sundblad   \n",
       "\n",
       "                        email  \n",
       "1069  josephescobar@yahoo.com  \n",
       "862       matthew86@gmail.com  \n",
       "734    connorford@hotmail.com  \n",
       "567   maryjohnson@hotmail.com  \n",
       "916       annturner@yahoo.com  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"datasets/3CUJujuy_select.csv\", index_col=[0])\n",
    "df1 = df1.sort_values(by='age', ascending=True)\n",
    "df1[\"inscription_date\"] = pd.to_datetime(df1[\"inscription_date\"], format=\"%Y-%m-%d\")\n",
    "df1[['university', 'career', 'first_name', 'last_name', 'gender', 'postal_code', 'location', 'email']] = df1[['university', 'career', 'first_name', 'last_name', 'gender', 'postal_code', 'location', 'email']].astype('string')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4a09c16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La cantidad total de inscriptos es: 1189\n"
     ]
    }
   ],
   "source": [
    "total_unj = df1[\"university\"].count()\n",
    "print(\"La cantidad total de inscriptos es: \" + str(total_unj))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077d858c",
   "metadata": {},
   "source": [
    "### Se calcula la cantidad de inscriptos por carrera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3301e72e",
   "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>career</th>\n",
       "      <th>count_by_career</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>medicina</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>agronomía</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ingeniería electrónica</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ingeniería mecánica</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bioquímica</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>licenciatura en gestión de agroalimentos</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>contador público</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>licenciatura en relaciones del trabajo</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>licenciatura en musicoterapia</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              career  count_by_career\n",
       "0                                           medicina               47\n",
       "1                                          agronomía               46\n",
       "2                             ingeniería electrónica               44\n",
       "3                                ingeniería mecánica               44\n",
       "4                                         bioquímica               43\n",
       "5           licenciatura en gestión de agroalimentos               43\n",
       "6                                   contador público               42\n",
       "7  licenciatura en planificación y diseño del pai...               42\n",
       "8             licenciatura en relaciones del trabajo               41\n",
       "9                      licenciatura en musicoterapia               41"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_career = df1.pivot_table(index='career', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df1_career.rename(columns={\"university\": \"count_by_career\"}, inplace=True)\n",
    "df1_career.sort_values(by='count_by_career', ascending=False, inplace=True)\n",
    "df1_career.reset_index(inplace=True)\n",
    "df1_career.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d54617",
   "metadata": {},
   "source": [
    "> * La carrera con mayor número de inscriptos es: Medicina (47 inscriptos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0cf64c6",
   "metadata": {},
   "source": [
    "#### Promedio de inscriptos por carrera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01570277",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de inscriptos por carrera es: 36\n"
     ]
    }
   ],
   "source": [
    "kpi1 = df1_career[\"count_by_career\"].mean()\n",
    "print(\"El promedio de inscriptos por carrera es: \" + str(int(kpi1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b78c37",
   "metadata": {},
   "source": [
    "### Se calcula la edad promedio por carrera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e48a639b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>career</th>\n",
       "      <th>mean_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diseño industrial</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>actuario</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>licenciatura en musicoterapia</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ingeniería electrónica</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>licenciatura en ciencias de la educación</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bioquímica</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>licenciatura en artes</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>licenciatura en relaciones del trabajo</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arquitectura</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>licenciatura en gestión de agroalimentos</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>licenciatura en ciencias biológicas</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>técnico para bioterios</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>agronomía</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>licenciatura en ciencias ambientales</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>licenciatura en kinesiología y fisiatría</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>licenciatura en producción de bioimagenes</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>medicina</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>licenciatura en ciencias de la computación</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ingeniería mecánica</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ingeniería en informática</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>farmacia</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>diseño de imagen y sonido</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>licenciatura en ciencia y tecnología de alimentos</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>licenciatura en administración</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>licenciatura en terapia ocupacional</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>contador público</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>licenciatura en psicología</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>licenciatura en sociología</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ciencias veterinarias</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               career  mean_age\n",
       "7                                   diseño industrial        48\n",
       "0                                            actuario        49\n",
       "14                   licenciatura en ciencia política        50\n",
       "24                      licenciatura en musicoterapia        51\n",
       "9                              ingeniería electrónica        51\n",
       "20           licenciatura en ciencias de la educación        51\n",
       "3                                          bioquímica        52\n",
       "13                              licenciatura en artes        52\n",
       "28             licenciatura en relaciones del trabajo        53\n",
       "2                                        arquitectura        53\n",
       "25  licenciatura en planificación y diseño del pai...        53\n",
       "22           licenciatura en gestión de agroalimentos        53\n",
       "18                licenciatura en ciencias biológicas        53\n",
       "21                licenciatura en ciencias geológicas        54\n",
       "17            licenciatura en ciencias antropológicas        54\n",
       "32                             técnico para bioterios        54\n",
       "1                                           agronomía        54\n",
       "16               licenciatura en ciencias ambientales        55\n",
       "23           licenciatura en kinesiología y fisiatría        55\n",
       "26          licenciatura en producción de bioimagenes        56\n",
       "31                                           medicina        56\n",
       "19         licenciatura en ciencias de la computación        56\n",
       "11                                ingeniería mecánica        56\n",
       "10                          ingeniería en informática        56\n",
       "8                                            farmacia        58\n",
       "6                           diseño de imagen y sonido        58\n",
       "15  licenciatura en ciencia y tecnología de alimentos        59\n",
       "12                     licenciatura en administración        59\n",
       "30                licenciatura en terapia ocupacional        59\n",
       "5                                    contador público        59\n",
       "27                         licenciatura en psicología        60\n",
       "29                         licenciatura en sociología        60\n",
       "4                               ciencias veterinarias        62"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_cage = df1.pivot_table(index=\"career\", \n",
    "                          aggfunc=\"mean\", \n",
    "                          values=\"age\")\n",
    "df1_cage.rename(columns={\"age\": \"mean_age\"}, inplace=True)\n",
    "df1_cage.reset_index(inplace=True)\n",
    "df1_cage.mean_age = df1_cage.mean_age.round()\n",
    "df1_cage[\"mean_age\"] = df1_cage[\"mean_age\"].astype(\"int\")\n",
    "df1_cage.sort_values(by=\"mean_age\", ascending=True, inplace=True)\n",
    "df1_cage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31bc489a",
   "metadata": {},
   "source": [
    "#### Promedio de edad (general)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9938f5dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de edad de los inscriptos en la Universidad Nacional de Jujuy es de: 55 años.\n"
     ]
    }
   ],
   "source": [
    "kpi2 = df1_cage[\"mean_age\"].mean()\n",
    "print(\"El promedio de edad de los inscriptos en la Universidad Nacional de Jujuy es de: \" + str(round(kpi2))+ \" años.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12dbc6e7",
   "metadata": {},
   "source": [
    "### Se calcula el promedio de años desde que se han inscripto a cada carrera:\n",
    "> #### Con este fin primero se crea una columna que muestra el valor por inscripto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb3f453c",
   "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>university</th>\n",
       "      <th>career</th>\n",
       "      <th>inscription_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>location</th>\n",
       "      <th>email</th>\n",
       "      <th>years_since_inscription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>stephanie</td>\n",
       "      <td>washington</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1706</td>\n",
       "      <td>haedo</td>\n",
       "      <td>josephescobar@yahoo.com</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>contador público</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>alyssa</td>\n",
       "      <td>young</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1983</td>\n",
       "      <td>gomez</td>\n",
       "      <td>matthew86@gmail.com</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>andrea</td>\n",
       "      <td>rocha</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>3346</td>\n",
       "      <td>bacacay</td>\n",
       "      <td>connorford@hotmail.com</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en producción de bioimagenes</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>mary</td>\n",
       "      <td>flores</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>7009</td>\n",
       "      <td>iraola</td>\n",
       "      <td>maryjohnson@hotmail.com</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>shaun</td>\n",
       "      <td>conrad</td>\n",
       "      <td>male</td>\n",
       "      <td>18</td>\n",
       "      <td>6401</td>\n",
       "      <td>sundblad</td>\n",
       "      <td>annturner@yahoo.com</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         university  \\\n",
       "1069  universidad nacional de jujuy   \n",
       "862   universidad nacional de jujuy   \n",
       "734   universidad nacional de jujuy   \n",
       "567   universidad nacional de jujuy   \n",
       "916   universidad nacional de jujuy   \n",
       "\n",
       "                                                 career inscription_date  \\\n",
       "1069            licenciatura en ciencias antropológicas       2020-10-15   \n",
       "862                                    contador público       2020-09-21   \n",
       "734                 licenciatura en ciencias geológicas       2020-12-17   \n",
       "567           licenciatura en producción de bioimagenes       2020-11-30   \n",
       "916   licenciatura en planificación y diseño del pai...       2020-11-30   \n",
       "\n",
       "     first_name   last_name  gender  age postal_code  location  \\\n",
       "1069  stephanie  washington  female   18        1706     haedo   \n",
       "862      alyssa       young  female   18        1983     gomez   \n",
       "734      andrea       rocha  female   18        3346   bacacay   \n",
       "567        mary      flores  female   18        7009    iraola   \n",
       "916       shaun      conrad    male   18        6401  sundblad   \n",
       "\n",
       "                        email  years_since_inscription  \n",
       "1069  josephescobar@yahoo.com                        2  \n",
       "862       matthew86@gmail.com                        2  \n",
       "734    connorford@hotmail.com                        1  \n",
       "567   maryjohnson@hotmail.com                        1  \n",
       "916       annturner@yahoo.com                        1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def datediff(x):\n",
    "    today = date.today()\n",
    "    age = today.year - x.year - ((today.month, today.day) < (x.month, x.day))\n",
    "    return age\n",
    "\n",
    "df1['years_since_inscription'] = df1['inscription_date'].apply(datediff)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84e32921",
   "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>career</th>\n",
       "      <th>years_since_inscription_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>licenciatura en administración</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>licenciatura en artes</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>licenciatura en sociología</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                career  years_since_inscription_mean\n",
       "0       licenciatura en administración                          1.26\n",
       "1                licenciatura en artes                          1.30\n",
       "2           licenciatura en sociología                          1.33\n",
       "3     licenciatura en ciencia política                          1.33\n",
       "4  licenciatura en ciencias geológicas                          1.36"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_cinsc = df1.pivot_table(index='career', \n",
    "                          aggfunc='mean', \n",
    "                          values='years_since_inscription')\n",
    "df1_cinsc.rename(columns={\"years_since_inscription\": \"years_since_inscription_mean\"}, inplace=True)\n",
    "df1_cinsc.sort_values(by=\"years_since_inscription_mean\", ascending=True, inplace=True)\n",
    "df1_cinsc.reset_index(inplace=True)\n",
    "df1_cinsc[\"years_since_inscription_mean\"] = df1_cinsc[\"years_since_inscription_mean\"].round(2)\n",
    "df1_cinsc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1d98c0",
   "metadata": {},
   "source": [
    "#### Promedio de años desde la inscripción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31da34bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de años desde la inscripción es: 1.45 años.\n"
     ]
    }
   ],
   "source": [
    "kpi3 = df1_cinsc[\"years_since_inscription_mean\"].mean()\n",
    "print(\"El promedio de años desde la inscripción es: \" + str(round(kpi3, 2)) + \" años.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a19c99",
   "metadata": {},
   "source": [
    "### Se calcula la cantidad de hombres y mujeres por carrera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4bdf1cb2",
   "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>gender</th>\n",
       "      <th>career</th>\n",
       "      <th>count_by_career</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>male</td>\n",
       "      <td>medicina</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>female</td>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>male</td>\n",
       "      <td>agronomía</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>male</td>\n",
       "      <td>ingeniería electrónica</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>male</td>\n",
       "      <td>licenciatura en kinesiología y fisiatría</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>male</td>\n",
       "      <td>licenciatura en gestión de agroalimentos</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>male</td>\n",
       "      <td>ingeniería mecánica</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>male</td>\n",
       "      <td>bioquímica</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>male</td>\n",
       "      <td>licenciatura en ciencias biológicas</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>female</td>\n",
       "      <td>licenciatura en relaciones del trabajo</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender                                             career  count_by_career\n",
       "64    male                                           medicina               27\n",
       "25  female  licenciatura en planificación y diseño del pai...               25\n",
       "34    male                                          agronomía               25\n",
       "42    male                             ingeniería electrónica               25\n",
       "56    male           licenciatura en kinesiología y fisiatría               24\n",
       "55    male           licenciatura en gestión de agroalimentos               24\n",
       "44    male                                ingeniería mecánica               23\n",
       "36    male                                         bioquímica               23\n",
       "51    male                licenciatura en ciencias biológicas               23\n",
       "28  female             licenciatura en relaciones del trabajo               22"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_cgen = df1.groupby([\"gender\", 'career'])['university'].count()\n",
    "df1_cgen = df1_cgen.to_frame()\n",
    "df1_cgen.reset_index(inplace=True)\n",
    "df1_cgen.rename(columns={\"university\": \"count_by_career\"}, inplace=True)\n",
    "df1_cgen.sort_values(by=[\"count_by_career\"], ascending=False, inplace=True)\n",
    "df1_cgen.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad980fff",
   "metadata": {},
   "source": [
    "> * La carrera más elegida por las mujeres es: Licenciatura en planificación y diseño del paisaje.\n",
    "> * La carrera más elegida por los hombres es: Medicina."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3902a1",
   "metadata": {},
   "source": [
    "### Se calcula cantidad de inscriptos por género:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8af19f70",
   "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>gender</th>\n",
       "      <th>count_by_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  count_by_gender\n",
       "0  female              549\n",
       "1    male              640"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_cgen2 = df1.pivot_table(index='gender', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df1_cgen2.rename(columns={\"university\": \"count_by_gender\"}, inplace=True)\n",
    "df1_cgen2.reset_index(inplace=True)\n",
    "df1_cgen2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf22ba2d",
   "metadata": {},
   "source": [
    "### Se calcula un nuevo campo para dividir los inscriptos por grupo etario:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5cbc1e19",
   "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>university</th>\n",
       "      <th>career</th>\n",
       "      <th>inscription_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>location</th>\n",
       "      <th>email</th>\n",
       "      <th>years_since_inscription</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>stephanie</td>\n",
       "      <td>washington</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1706</td>\n",
       "      <td>haedo</td>\n",
       "      <td>josephescobar@yahoo.com</td>\n",
       "      <td>2</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>contador público</td>\n",
       "      <td>2020-09-21</td>\n",
       "      <td>alyssa</td>\n",
       "      <td>young</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1983</td>\n",
       "      <td>gomez</td>\n",
       "      <td>matthew86@gmail.com</td>\n",
       "      <td>2</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>andrea</td>\n",
       "      <td>rocha</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>3346</td>\n",
       "      <td>bacacay</td>\n",
       "      <td>connorford@hotmail.com</td>\n",
       "      <td>1</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en producción de bioimagenes</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>mary</td>\n",
       "      <td>flores</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>7009</td>\n",
       "      <td>iraola</td>\n",
       "      <td>maryjohnson@hotmail.com</td>\n",
       "      <td>1</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>universidad nacional de jujuy</td>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>shaun</td>\n",
       "      <td>conrad</td>\n",
       "      <td>male</td>\n",
       "      <td>18</td>\n",
       "      <td>6401</td>\n",
       "      <td>sundblad</td>\n",
       "      <td>annturner@yahoo.com</td>\n",
       "      <td>1</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         university  \\\n",
       "1069  universidad nacional de jujuy   \n",
       "862   universidad nacional de jujuy   \n",
       "734   universidad nacional de jujuy   \n",
       "567   universidad nacional de jujuy   \n",
       "916   universidad nacional de jujuy   \n",
       "\n",
       "                                                 career inscription_date  \\\n",
       "1069            licenciatura en ciencias antropológicas       2020-10-15   \n",
       "862                                    contador público       2020-09-21   \n",
       "734                 licenciatura en ciencias geológicas       2020-12-17   \n",
       "567           licenciatura en producción de bioimagenes       2020-11-30   \n",
       "916   licenciatura en planificación y diseño del pai...       2020-11-30   \n",
       "\n",
       "     first_name   last_name  gender  age postal_code  location  \\\n",
       "1069  stephanie  washington  female   18        1706     haedo   \n",
       "862      alyssa       young  female   18        1983     gomez   \n",
       "734      andrea       rocha  female   18        3346   bacacay   \n",
       "567        mary      flores  female   18        7009    iraola   \n",
       "916       shaun      conrad    male   18        6401  sundblad   \n",
       "\n",
       "                        email  years_since_inscription age_group  \n",
       "1069  josephescobar@yahoo.com                        2   18 a 27  \n",
       "862       matthew86@gmail.com                        2   18 a 27  \n",
       "734    connorford@hotmail.com                        1   18 a 27  \n",
       "567   maryjohnson@hotmail.com                        1   18 a 27  \n",
       "916       annturner@yahoo.com                        1   18 a 27  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def age_group(x):\n",
    "    if x <= 27:\n",
    "        return '18 a 27'\n",
    "    elif x <= 37:\n",
    "        return '28 a 37'\n",
    "    elif x <= 47:\n",
    "        return '38 a 47'\n",
    "    elif x <= 57:\n",
    "        return '48 a 57'\n",
    "    elif x <= 67:\n",
    "        return '58 a 67'\n",
    "    elif x <= 77:\n",
    "        return '68 a 77'\n",
    "    elif x <= 87:\n",
    "        return '78 a 87'\n",
    "    else:\n",
    "        return '88 a 90'\n",
    "    \n",
    "df1['age_group'] = df1['age'].apply(age_group)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "57036d86",
   "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>age_group</th>\n",
       "      <th>count_by_age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18 a 27</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68 a 77</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58 a 67</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>78 a 87</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48 a 57</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38 a 47</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28 a 37</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>88 a 90</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_group  count_by_age_group\n",
       "0   18 a 27                 183\n",
       "5   68 a 77                 174\n",
       "4   58 a 67                 164\n",
       "6   78 a 87                 159\n",
       "3   48 a 57                 154\n",
       "2   38 a 47                 146\n",
       "1   28 a 37                 142\n",
       "7   88 a 90                  67"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_ager = df1.pivot_table(index='age_group', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df1_ager.rename(columns={\"university\": \"count_by_age_group\"}, inplace=True)\n",
    "df1_ager.reset_index(inplace=True)\n",
    "df1_ager.sort_values(by=[\"count_by_age_group\"], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a81505",
   "metadata": {},
   "source": [
    "> * El grupo etario con mayor cantidad de inscriptos es: 18 a 27 (182 inscriptos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa85b339",
   "metadata": {},
   "source": [
    "### Se calcula la localidad con más inscriptos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "978e30ff",
   "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>location</th>\n",
       "      <th>count_by_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25 de mayo</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bella vista</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anguil</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baños de la laja</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amilgancho</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           location  count_by_location\n",
       "0        25 de mayo                  6\n",
       "1       bella vista                  5\n",
       "2            anguil                  5\n",
       "3  baños de la laja                  4\n",
       "4        amilgancho                  4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_loc = df1.pivot_table(index='location', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df1_loc.rename(columns={\"university\": \"count_by_location\"}, inplace=True)\n",
    "df1_loc.sort_values(by='count_by_location', ascending=False, inplace=True)\n",
    "df1_loc.reset_index(inplace=True)\n",
    "df1_loc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f4f736",
   "metadata": {},
   "source": [
    "> * La localidad con mayor número de inscriptos es: 25 de mayo (6 inscriptos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd69803",
   "metadata": {},
   "source": [
    "# 2. Universidad de Palermo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ecf38de",
   "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>university</th>\n",
       "      <th>career</th>\n",
       "      <th>inscription_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>location</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>linda</td>\n",
       "      <td>hawkins</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>5565</td>\n",
       "      <td>campo de los andes</td>\n",
       "      <td>hayeshailey@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>brianna</td>\n",
       "      <td>bartlett</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>6746</td>\n",
       "      <td>cucha cucha</td>\n",
       "      <td>saramartin@yahoo.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>warren</td>\n",
       "      <td>sexton</td>\n",
       "      <td>male</td>\n",
       "      <td>18</td>\n",
       "      <td>7165</td>\n",
       "      <td>faro querandi</td>\n",
       "      <td>jared28@hotmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias biológicas</td>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>leah</td>\n",
       "      <td>roberts</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>8360</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>moorejoseph@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>lauren</td>\n",
       "      <td>walker</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1804</td>\n",
       "      <td>canning</td>\n",
       "      <td>madisonberry@hotmail.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 university  \\\n",
       "121  universidad de palermo   \n",
       "196  universidad de palermo   \n",
       "360  universidad de palermo   \n",
       "151  universidad de palermo   \n",
       "349  universidad de palermo   \n",
       "\n",
       "                                                career inscription_date  \\\n",
       "121            licenciatura en ciencias antropológicas       2020-09-05   \n",
       "196  licenciatura en planificación y diseño del pai...       2020-10-27   \n",
       "360                   licenciatura en ciencia política       2020-09-11   \n",
       "151                licenciatura en ciencias biológicas       2020-09-30   \n",
       "349                licenciatura en ciencias geológicas       2020-09-05   \n",
       "\n",
       "    first_name last_name  gender  age postal_code            location  \\\n",
       "121      linda   hawkins  female   18        5565  campo de los andes   \n",
       "196    brianna  bartlett  female   18        6746         cucha cucha   \n",
       "360     warren    sexton    male   18        7165       faro querandi   \n",
       "151       leah   roberts  female   18        8360                <NA>   \n",
       "349     lauren    walker  female   18        1804             canning   \n",
       "\n",
       "                        email  \n",
       "121     hayeshailey@gmail.com  \n",
       "196      saramartin@yahoo.com  \n",
       "360       jared28@hotmail.com  \n",
       "151     moorejoseph@gmail.com  \n",
       "349  madisonberry@hotmail.com  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"datasets/3CUPalermo_select.csv\", index_col=[0])\n",
    "df2 = df2.sort_values(by='age', ascending=True)\n",
    "df2[\"inscription_date\"] = pd.to_datetime(df2[\"inscription_date\"], format=\"%Y-%m-%d\")\n",
    "df2[['university', 'career', 'first_name', 'last_name', 'gender', 'postal_code', 'location', 'email']] = df2[['university', 'career', 'first_name', 'last_name', 'gender', 'postal_code', 'location', 'email']].astype('string')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cdab24a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La cantidad total de inscriptos es: 607\n"
     ]
    }
   ],
   "source": [
    "total_up = df2[\"university\"].count()\n",
    "print(\"La cantidad total de inscriptos es: \" + str(total_up))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614b2e53",
   "metadata": {},
   "source": [
    "### Se calcula la cantidad de inscriptos por carrera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "63869ff5",
   "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>career</th>\n",
       "      <th>count_by_career</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>licenciatura en psicología</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>medicina</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciencias veterinarias</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ingeniería mecánica</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>licenciatura en gestión de agroalimentos</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>técnico para bioterios</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diseño de imagen y sonido</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>licenciatura en ciencias de la educación</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     career  count_by_career\n",
       "0                licenciatura en psicología               29\n",
       "1                                  medicina               28\n",
       "2                     ciencias veterinarias               23\n",
       "3       licenciatura en ciencias geológicas               23\n",
       "4                       ingeniería mecánica               22\n",
       "5  licenciatura en gestión de agroalimentos               22\n",
       "6                    técnico para bioterios               21\n",
       "7                 diseño de imagen y sonido               21\n",
       "8  licenciatura en ciencias de la educación               21\n",
       "9          licenciatura en ciencia política               20"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_career = df2.pivot_table(index='career', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df2_career.rename(columns={\"university\": \"count_by_career\"}, inplace=True)\n",
    "df2_career.sort_values(by='count_by_career', ascending=False, inplace=True)\n",
    "df2_career.reset_index(inplace=True)\n",
    "df2_career.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a15ff08",
   "metadata": {},
   "source": [
    "> * La carrera con mayor número de inscriptos es: Licenciatura en Psicología (29 inscriptos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c9c8b5",
   "metadata": {},
   "source": [
    "#### Promedio de inscriptos por carrera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "139ac321",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de inscriptos por carrera es: 18\n"
     ]
    }
   ],
   "source": [
    "kpi4 = df2_career[\"count_by_career\"].mean()\n",
    "print(\"El promedio de inscriptos por carrera es: \" + str(int(kpi4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f0b3b8",
   "metadata": {},
   "source": [
    "### Se calcula la edad promedio por carrera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a29b888",
   "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>career</th>\n",
       "      <th>mean_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>licenciatura en terapia ocupacional</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>licenciatura en kinesiología y fisiatría</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>licenciatura en musicoterapia</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>licenciatura en artes</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>farmacia</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>diseño de imagen y sonido</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ingeniería en informática</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>licenciatura en ciencias de la computación</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>contador público</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>licenciatura en ciencias biológicas</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>técnico para bioterios</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ciencias veterinarias</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>licenciatura en sociología</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>medicina</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>licenciatura en ciencias de la educación</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>actuario</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ingeniería electrónica</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bioquímica</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arquitectura</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>licenciatura en ciencia y tecnología de alimentos</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>licenciatura en ciencias ambientales</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>licenciatura en administración</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>licenciatura en gestión de agroalimentos</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ingeniería mecánica</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>licenciatura en psicología</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>licenciatura en relaciones del trabajo</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diseño industrial</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>agronomía</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>licenciatura en producción de bioimagenes</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               career  mean_age\n",
       "30                licenciatura en terapia ocupacional        28\n",
       "23           licenciatura en kinesiología y fisiatría        30\n",
       "24                      licenciatura en musicoterapia        32\n",
       "13                              licenciatura en artes        32\n",
       "8                                            farmacia        33\n",
       "6                           diseño de imagen y sonido        33\n",
       "10                          ingeniería en informática        33\n",
       "25  licenciatura en planificación y diseño del pai...        34\n",
       "19         licenciatura en ciencias de la computación        34\n",
       "5                                    contador público        34\n",
       "18                licenciatura en ciencias biológicas        35\n",
       "32                             técnico para bioterios        35\n",
       "4                               ciencias veterinarias        35\n",
       "29                         licenciatura en sociología        35\n",
       "21                licenciatura en ciencias geológicas        35\n",
       "17            licenciatura en ciencias antropológicas        35\n",
       "31                                           medicina        36\n",
       "20           licenciatura en ciencias de la educación        37\n",
       "0                                            actuario        37\n",
       "9                              ingeniería electrónica        37\n",
       "3                                          bioquímica        37\n",
       "2                                        arquitectura        37\n",
       "15  licenciatura en ciencia y tecnología de alimentos        37\n",
       "16               licenciatura en ciencias ambientales        37\n",
       "14                   licenciatura en ciencia política        38\n",
       "12                     licenciatura en administración        38\n",
       "22           licenciatura en gestión de agroalimentos        38\n",
       "11                                ingeniería mecánica        39\n",
       "27                         licenciatura en psicología        39\n",
       "28             licenciatura en relaciones del trabajo        39\n",
       "7                                   diseño industrial        40\n",
       "1                                           agronomía        40\n",
       "26          licenciatura en producción de bioimagenes        41"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_cage = df2.pivot_table(index=\"career\", \n",
    "                          aggfunc=\"mean\", \n",
    "                          values=\"age\")\n",
    "df2_cage.rename(columns={\"age\": \"mean_age\"}, inplace=True)\n",
    "df2_cage.reset_index(inplace=True)\n",
    "df2_cage.mean_age = df2_cage.mean_age.round()\n",
    "df2_cage[\"mean_age\"] = df2_cage[\"mean_age\"].astype(\"int\")\n",
    "df2_cage.sort_values(by=\"mean_age\", ascending=True, inplace=True)\n",
    "df2_cage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b8263a",
   "metadata": {},
   "source": [
    "#### Promedio de edad (general)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "179c76b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de edad de los inscriptos en la Universidad de Palermo es de: 36 años.\n"
     ]
    }
   ],
   "source": [
    "kpi5 = df2_cage[\"mean_age\"].mean()\n",
    "print(\"El promedio de edad de los inscriptos en la Universidad de Palermo es de: \" + str(round(kpi5))+ \" años.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5a7f0f",
   "metadata": {},
   "source": [
    "### Se calcula el promedio de años desde que se han inscripto a cada carrera:\n",
    "> #### Con este fin primero se crea una columna que muestra el valor por inscripto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7ca6bd6",
   "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>university</th>\n",
       "      <th>career</th>\n",
       "      <th>inscription_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>location</th>\n",
       "      <th>email</th>\n",
       "      <th>years_since_inscription</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>linda</td>\n",
       "      <td>hawkins</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>5565</td>\n",
       "      <td>campo de los andes</td>\n",
       "      <td>hayeshailey@gmail.com</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>brianna</td>\n",
       "      <td>bartlett</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>6746</td>\n",
       "      <td>cucha cucha</td>\n",
       "      <td>saramartin@yahoo.com</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>warren</td>\n",
       "      <td>sexton</td>\n",
       "      <td>male</td>\n",
       "      <td>18</td>\n",
       "      <td>7165</td>\n",
       "      <td>faro querandi</td>\n",
       "      <td>jared28@hotmail.com</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias biológicas</td>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>leah</td>\n",
       "      <td>roberts</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>8360</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>moorejoseph@gmail.com</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>lauren</td>\n",
       "      <td>walker</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1804</td>\n",
       "      <td>canning</td>\n",
       "      <td>madisonberry@hotmail.com</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 university  \\\n",
       "121  universidad de palermo   \n",
       "196  universidad de palermo   \n",
       "360  universidad de palermo   \n",
       "151  universidad de palermo   \n",
       "349  universidad de palermo   \n",
       "\n",
       "                                                career inscription_date  \\\n",
       "121            licenciatura en ciencias antropológicas       2020-09-05   \n",
       "196  licenciatura en planificación y diseño del pai...       2020-10-27   \n",
       "360                   licenciatura en ciencia política       2020-09-11   \n",
       "151                licenciatura en ciencias biológicas       2020-09-30   \n",
       "349                licenciatura en ciencias geológicas       2020-09-05   \n",
       "\n",
       "    first_name last_name  gender  age postal_code            location  \\\n",
       "121      linda   hawkins  female   18        5565  campo de los andes   \n",
       "196    brianna  bartlett  female   18        6746         cucha cucha   \n",
       "360     warren    sexton    male   18        7165       faro querandi   \n",
       "151       leah   roberts  female   18        8360                <NA>   \n",
       "349     lauren    walker  female   18        1804             canning   \n",
       "\n",
       "                        email  years_since_inscription  \n",
       "121     hayeshailey@gmail.com                        2  \n",
       "196      saramartin@yahoo.com                        2  \n",
       "360       jared28@hotmail.com                        2  \n",
       "151     moorejoseph@gmail.com                        2  \n",
       "349  madisonberry@hotmail.com                        2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def datediff(x):\n",
    "    today = date.today()\n",
    "    age = today.year - x.year - ((today.month, today.day) < (x.month, x.day))\n",
    "    return age\n",
    "\n",
    "df2['years_since_inscription'] = df2['inscription_date'].apply(datediff)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18bf0720",
   "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>career</th>\n",
       "      <th>years_since_inscription_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>actuario</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>licenciatura en gestión de agroalimentos</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ingeniería mecánica</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>técnico para bioterios</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>licenciatura en ciencia y tecnología de alimentos</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              career  \\\n",
       "0                                           actuario   \n",
       "1           licenciatura en gestión de agroalimentos   \n",
       "2                                ingeniería mecánica   \n",
       "3                             técnico para bioterios   \n",
       "4  licenciatura en ciencia y tecnología de alimentos   \n",
       "\n",
       "   years_since_inscription_mean  \n",
       "0                          1.33  \n",
       "1                          1.36  \n",
       "2                          1.36  \n",
       "3                          1.38  \n",
       "4                          1.38  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_cinsc = df2.pivot_table(index='career', \n",
    "                          aggfunc='mean', \n",
    "                          values='years_since_inscription')\n",
    "df2_cinsc.rename(columns={\"years_since_inscription\": \"years_since_inscription_mean\"}, inplace=True)\n",
    "df2_cinsc.sort_values(by=\"years_since_inscription_mean\", ascending=True, inplace=True)\n",
    "df2_cinsc.reset_index(inplace=True)\n",
    "df2_cinsc[\"years_since_inscription_mean\"] = df2_cinsc[\"years_since_inscription_mean\"].round(2)\n",
    "df2_cinsc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a116cfca",
   "metadata": {},
   "source": [
    "#### Promedio de años desde la inscripción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "405e9b6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de años desde la inscripción es: 1.52 años.\n"
     ]
    }
   ],
   "source": [
    "kpi6 = df2_cinsc[\"years_since_inscription_mean\"].mean()\n",
    "print(\"El promedio de años desde la inscripción es: \" + str(round(kpi6, 2)) + \" años.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc610009",
   "metadata": {},
   "source": [
    "### Se calcula la cantidad de hombres y mujeres por carrera:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "795655e7",
   "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>gender</th>\n",
       "      <th>career</th>\n",
       "      <th>count_by_career</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>female</td>\n",
       "      <td>licenciatura en psicología</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>female</td>\n",
       "      <td>técnico para bioterios</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>male</td>\n",
       "      <td>medicina</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>female</td>\n",
       "      <td>medicina</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>bioquímica</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>male</td>\n",
       "      <td>ciencias veterinarias</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>female</td>\n",
       "      <td>diseño de imagen y sonido</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>male</td>\n",
       "      <td>licenciatura en psicología</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>male</td>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>female</td>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender                               career  count_by_career\n",
       "27  female           licenciatura en psicología               16\n",
       "32  female               técnico para bioterios               14\n",
       "64    male                             medicina               14\n",
       "31  female                             medicina               14\n",
       "3   female                           bioquímica               13\n",
       "37    male                ciencias veterinarias               13\n",
       "6   female            diseño de imagen y sonido               13\n",
       "60    male           licenciatura en psicología               13\n",
       "54    male  licenciatura en ciencias geológicas               13\n",
       "14  female     licenciatura en ciencia política               12"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_cgen = df2.groupby([\"gender\", 'career'])['university'].count()\n",
    "df2_cgen = df2_cgen.to_frame()\n",
    "df2_cgen.reset_index(inplace=True)\n",
    "df2_cgen.rename(columns={\"university\": \"count_by_career\"}, inplace=True)\n",
    "df2_cgen.sort_values(by=[\"count_by_career\"], ascending=False, inplace=True)\n",
    "df2_cgen.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5341e5b",
   "metadata": {},
   "source": [
    "> * La carrera más elegida por las mujeres es: Licenciatura en Psicología.\n",
    "> * La carrera más elegida por los hombres es: Medicina."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2514284",
   "metadata": {},
   "source": [
    "### Se calcula cantidad de inscriptos por género:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "23393819",
   "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>gender</th>\n",
       "      <th>count_by_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  count_by_gender\n",
       "0  female              317\n",
       "1    male              290"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_cgen2 = df2.pivot_table(index='gender', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df2_cgen2.rename(columns={\"university\": \"count_by_gender\"}, inplace=True)\n",
    "df2_cgen2.reset_index(inplace=True)\n",
    "df2_cgen2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205456dd",
   "metadata": {},
   "source": [
    "### Se calcula un nuevo campo para dividir los inscriptos por grupo etario:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "993cb03d",
   "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>university</th>\n",
       "      <th>career</th>\n",
       "      <th>inscription_date</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>location</th>\n",
       "      <th>email</th>\n",
       "      <th>years_since_inscription</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias antropológicas</td>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>linda</td>\n",
       "      <td>hawkins</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>5565</td>\n",
       "      <td>campo de los andes</td>\n",
       "      <td>hayeshailey@gmail.com</td>\n",
       "      <td>2</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en planificación y diseño del pai...</td>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>brianna</td>\n",
       "      <td>bartlett</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>6746</td>\n",
       "      <td>cucha cucha</td>\n",
       "      <td>saramartin@yahoo.com</td>\n",
       "      <td>2</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencia política</td>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>warren</td>\n",
       "      <td>sexton</td>\n",
       "      <td>male</td>\n",
       "      <td>18</td>\n",
       "      <td>7165</td>\n",
       "      <td>faro querandi</td>\n",
       "      <td>jared28@hotmail.com</td>\n",
       "      <td>2</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias biológicas</td>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>leah</td>\n",
       "      <td>roberts</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>8360</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>moorejoseph@gmail.com</td>\n",
       "      <td>2</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>universidad de palermo</td>\n",
       "      <td>licenciatura en ciencias geológicas</td>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>lauren</td>\n",
       "      <td>walker</td>\n",
       "      <td>female</td>\n",
       "      <td>18</td>\n",
       "      <td>1804</td>\n",
       "      <td>canning</td>\n",
       "      <td>madisonberry@hotmail.com</td>\n",
       "      <td>2</td>\n",
       "      <td>18 a 27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 university  \\\n",
       "121  universidad de palermo   \n",
       "196  universidad de palermo   \n",
       "360  universidad de palermo   \n",
       "151  universidad de palermo   \n",
       "349  universidad de palermo   \n",
       "\n",
       "                                                career inscription_date  \\\n",
       "121            licenciatura en ciencias antropológicas       2020-09-05   \n",
       "196  licenciatura en planificación y diseño del pai...       2020-10-27   \n",
       "360                   licenciatura en ciencia política       2020-09-11   \n",
       "151                licenciatura en ciencias biológicas       2020-09-30   \n",
       "349                licenciatura en ciencias geológicas       2020-09-05   \n",
       "\n",
       "    first_name last_name  gender  age postal_code            location  \\\n",
       "121      linda   hawkins  female   18        5565  campo de los andes   \n",
       "196    brianna  bartlett  female   18        6746         cucha cucha   \n",
       "360     warren    sexton    male   18        7165       faro querandi   \n",
       "151       leah   roberts  female   18        8360                <NA>   \n",
       "349     lauren    walker  female   18        1804             canning   \n",
       "\n",
       "                        email  years_since_inscription age_group  \n",
       "121     hayeshailey@gmail.com                        2   18 a 27  \n",
       "196      saramartin@yahoo.com                        2   18 a 27  \n",
       "360       jared28@hotmail.com                        2   18 a 27  \n",
       "151     moorejoseph@gmail.com                        2   18 a 27  \n",
       "349  madisonberry@hotmail.com                        2   18 a 27  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def age_group(x):\n",
    "    if x <= 27:\n",
    "        return '18 a 27'\n",
    "    elif x <= 37:\n",
    "        return '28 a 37'\n",
    "    elif x <= 47:\n",
    "        return '38 a 47'\n",
    "    elif x <= 57:\n",
    "        return '48 a 57'\n",
    "    elif x <= 67:\n",
    "        return '58 a 67'\n",
    "    elif x <= 77:\n",
    "        return '68 a 77'\n",
    "    elif x <= 87:\n",
    "        return '78 a 87'\n",
    "    else:\n",
    "        return '88 a 90'\n",
    "    \n",
    "df2['age_group'] = df2['age'].apply(age_group)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ee339b09",
   "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>age_group</th>\n",
       "      <th>count_by_age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18 a 27</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38 a 47</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28 a 37</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48 a 57</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_group  count_by_age_group\n",
       "0   18 a 27                 175\n",
       "2   38 a 47                 175\n",
       "1   28 a 37                 146\n",
       "3   48 a 57                 111"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_ager = df2.pivot_table(index='age_group', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df2_ager.rename(columns={\"university\": \"count_by_age_group\"}, inplace=True)\n",
    "df2_ager.reset_index(inplace=True)\n",
    "df2_ager.sort_values(by=[\"count_by_age_group\"], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd98891b",
   "metadata": {},
   "source": [
    "> * El grupo etario con mayor cantidad de inscriptos es: 18 a 27 y 38 a 47 (ambos con 176 inscriptos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e82dcd",
   "metadata": {},
   "source": [
    "### Se calcula la localidad con más inscriptos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4758ad49",
   "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>location</th>\n",
       "      <th>count_by_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>general rojo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>capdepont</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tristan suarez</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bonpland norte</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cerro aconcagua</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          location  count_by_location\n",
       "0     general rojo                  4\n",
       "1        capdepont                  3\n",
       "2   tristan suarez                  3\n",
       "3   bonpland norte                  3\n",
       "4  cerro aconcagua                  3"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_loc = df2.pivot_table(index='location', \n",
    "                          aggfunc='count', \n",
    "                          values='university')\n",
    "df2_loc.rename(columns={\"university\": \"count_by_location\"}, inplace=True)\n",
    "df2_loc.sort_values(by='count_by_location', ascending=False, inplace=True)\n",
    "df2_loc.reset_index(inplace=True)\n",
    "df2_loc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e54f10b",
   "metadata": {},
   "source": [
    "> * La localidad con mayor número de inscriptos es: General Rojo (4 inscriptos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010c0824",
   "metadata": {},
   "source": [
    "# 3. U.N.J./U.P. - Análisis comparativo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e7e46c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La cantidad total de inscriptos en la UNJ es: 1189\n"
     ]
    }
   ],
   "source": [
    "total_unj = df1[\"university\"].count()\n",
    "print(\"La cantidad total de inscriptos en la UNJ es: \" + str(total_unj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1230bcdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La cantidad total de inscriptos en la UP es: 607\n"
     ]
    }
   ],
   "source": [
    "total_up = df2[\"university\"].count()\n",
    "print(\"La cantidad total de inscriptos en la UP es: \" + str(total_up))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b7d171",
   "metadata": {},
   "source": [
    "> * En la UNJ hay aproximadamente el doble de inscriptos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c1005d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the figure and axes\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=2,ncols=1, figsize=(12,10), constrained_layout = True)\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "# Labels ans titles\n",
    "ax1.set_title(str.upper('Cantidad de inscriptos por carrera: U.N.J. (top 5)'), fontsize=20)\n",
    "ax1.set_xlabel(' ', fontsize=15)\n",
    "ax1.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "ax2.set_title(str.upper(f'Cantidad de inscriptos por carrera: U.P. (top 5)'), fontsize=20)\n",
    "ax2.set_xlabel(' ', fontsize=15)\n",
    "ax2.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "# Plot 1\n",
    "ax1.barh(df1_career['career'].head(5), \n",
    "        df1_career['count_by_career'].head(5).sort_values(ascending=False), \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "\n",
    "# Plot 2\n",
    "ax2.barh(df2_career['career'].head(5), \n",
    "        df2_career['count_by_career'].head(5).sort_values(ascending=False), \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff8de35",
   "metadata": {},
   "source": [
    "> * En la UNJ la carrera con mayor número de inscriptos es: Medicina (47 inscriptos)\n",
    "> * En la UP la carrera con mayor número de inscriptos es: Licenciatura en Psicología (29 inscriptos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "85f89932",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En la UNJ el promedio de inscriptos por carrera es: 36\n"
     ]
    }
   ],
   "source": [
    "kpi1 = df1_career[\"count_by_career\"].mean()\n",
    "print(\"En la UNJ el promedio de inscriptos por carrera es: \" + str(int(kpi1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "04d27c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En la UP el promedio de inscriptos por carrera es: 18\n"
     ]
    }
   ],
   "source": [
    "kpi4 = df2_career[\"count_by_career\"].mean()\n",
    "print(\"En la UP el promedio de inscriptos por carrera es: \" + str(int(kpi4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "192f31b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de edad de los inscriptos en la UNJ es de: 55 años.\n"
     ]
    }
   ],
   "source": [
    "kpi2 = df1_cage[\"mean_age\"].mean()\n",
    "print(\"El promedio de edad de los inscriptos en la UNJ es de: \" + str(round(kpi2))+ \" años.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1cb0c8a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de edad de los inscriptos en la UP es de: 36 años.\n"
     ]
    }
   ],
   "source": [
    "kpi5 = df2_cage[\"mean_age\"].mean()\n",
    "print(\"El promedio de edad de los inscriptos en la UP es de: \" + str(round(kpi5))+ \" años.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01b720b",
   "metadata": {},
   "source": [
    "> * En la UP tiende a haber menos inscriptos por carrera, y su población tiende a ser más joven."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6d9ddd61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C0NAQrVu3fu26Tp48iVu3bqFOnTr4/PPPtZZzcHBA3759kZubW6q/8dL+fgGAiIgILFu2DIsXL8aePXvw6NGjYsu3a9cOAIp99rIgPuNGRG9MaGgobt68CZlMBnd39xLLK589MzY2RsuWLQvNi4iIAAB06dKl2DqqVq0KV1dXnD17FmfOnEGjRo3UygwaNAiTJ0/Gxo0bVc9/JSYm4siRIxgxYoRObx/UdnI2NTXFzJkzS1y+KE9PT+zYsQMXLlwosayubWFkZARPT0/s2rULZ86cgZeXV6nj0qRx48aoV68eEhISEBUVpVZveHi41vbp0aOHzifsrKws7NixA1WqVEG/fv0AvErgfvrpJwQEBKBDhw7FLq98UcjUqVMRGRlZ7MlX1zYFXr3Q4uLFizhz5gxGjBgB4NU+tWPHDsyZMwcKhQIffvghXFxcir3I8Lrat28PIyMjPHz4EAqFQm1fP3DggNYXGnzyySewtbUFAPTv3x+LFy/Ghg0b8Pz5c/Tr1w+urq5o2LDha8fo6emJ//znP1r365UrV2p9xm3mzJkwNTV97Rg0Uf5gV35/gwcPxtSpU7F582bMnj1br+N9NWvWDGPGjMF3332H9evXqz0fWdSgQYMQEREBX19fREZGolu3bnB2dtb6XGdkZCSys7NRt25djc/tlkXBJFbTy1NKKz09Hdu3bwcA1bOxyr85V1dXrftAaRSsr2rVqsWW7dq1K5YuXYozZ85oLVOa44dUSW0bMjIycOXKFbRo0QLm5uavXZ/yO/f09Czx+fCuXbti3759xX7nBZX294tS0bdEmpiYYNq0aZg/f77G9m/SpAksLS1x6tQpnepn4kZEeqP8sV7w4V4hBL766iuNPwILlle+nEQIAX9/f7UfvPfv3wcA1K9fv8Q4lGWSkpI0zq9YsaLqpQkKhQI2NjbYsmUL8vLyMGrUKJ22VfnQdFFVqlQpU+JWt25dAEBKSkqJZfXZFmVVt25dJCQkaIz35MmTWnv4LC0tdU7c9u7diydPnmD06NEwMzMDAPTs2RO1atVCUFAQVq9eXewPNHd3dwwaNAg//vgjdu7ciaFDh2ot+7pt2qtXL6xatQrffPMN1q9fj/Xr1wMAateujU6dOmHMmDFaX5hTViYmJqhevTqSk5ORkpKilrgdPHhQrVdZycnJSZW4OTs7Y8eOHfjyyy+xY8cO7NixA8CrIRQ6duyIkSNH4qOPPipTjCXt16tWrdK67OTJk99I4nb37l0cO3YMzZs3V70wRNmb+9NPPyEkJAQ9evTQ6zr9/PwQGBiI+fPnY/jw4ahSpYrWsuPHj0dSUhJWrFiBpUuXYunSpQBevYyhS5cumDBhAhwdHVXllftuvXr1yhxfwYstqampOHbsGG7cuIH333+/1K8rBwpfNEhOTsahQ4eQlJSE9957DxMmTNBb3AXp+7hYmuMHAEyYMAGffPIJrKysShH1m1XabXjTEhMTkZeXp7cLWvr8zkv7+6UoR0dHbNmyBZ6enrC2tsbDhw8REhKCf//73/jPf/6DvLw8/Pe//9W4bO3atXHjxg1kZWWVeMzjrZJEpDfz5s3DvHnz8O233+LEiRPo0KEDAgMDtb6RSVn+v//9L/bu3Yvc3Fxs3rwZEydO1LoOXa4Y6nJ7w6hRoyCEwObNm5Gfn4/NmzdDLper9fQVtw5Nn6dPn+q0fFliftNlS6O4ev38/LS2z+TJk3Veh/L2WGWvCPCqF3HIkCHIyspS3R5WnG+//RYmJiaYPXs2srKyyrQ9upadNGkSkpKScODAAUyfPh1du3bF8+fPsWvXLnh4eOCbb74pse7SKi7urVu3av0elG84VRo4cCDu3buHo0ePYs6cOejVqxfy8/NVbyAcPnx4mW7HLKld4+Pjtcaoj14YTTZt2oT8/Hy1IRGU/9b57W6lUKNGDcycOROPHz/GwoULiy0rk8nw3//+F/fv38ePP/6IyZMno2PHjrh//z42bdoEV1dX1d8GoJ+/8ZMnT6qOx2vWrMGNGzfg5OSEs2fPombNmqWu7+DBg6r6tm/fjipVqmDatGm4cOGC6mKLvo9Nb+K4qOvxAwCsrKxga2srqcQNKN02vGmPHz8GgBJ7RHWlz++8tL9fiurXrx9GjBiBRo0awdTUFA0aNICvry9+/fVXVKhQAf7+/lpvm1S+ZbWk2yoBJm5EpEfKH1z5+flITU1FWFhYsVf4lOXT09Nx7Ngx1K9fH2PHjsWJEyfUytauXRsAcO/evRLjSEhIAIBir+q5uLjAxcUFW7duxZEjR3D37l2de9veBOVVwBo1apRYVrld+mqLsihNvGURGxuLiIgI2NraqvXQKRO5gj9etbGxscHEiRNx7969Ynt39NWmFStWRJ8+fbB48WKEhIQgNTUVa9euhaGhIRYsWFDs2GallZWVhdTUVAD6+R4qVKiAbt26Yf78+fj555/x6NEj7NmzB+bm5vjhhx+09t4V503vJ6WVl5eHrVu3wsDAAJ999lmheT179kTt2rXx888/48GDB3pf91dffYX69etj9erVuHv3bonlLS0tMWjQIKxYsQInT57E48eP8e9//xt5eXmYOHGiaoyuOnXqAPjfflkWyosteXl5uHfvHiZNmoQrV65g4MCByM/PL3V9BS8aZGZm4vr161iyZEmhYQD0EXdBb+K4qOvxo6yUSURxbaycV9bbd9/0NpSG8s4JfSWQ+vzOS/v7RVcuLi5o2bIlcnJycO7cOY1lXrx4AeB/7VMcJm5EVO7Mzc3RpUsX/Pzzz8jLy1ONx1KQ8jmL0NDQYut68uQJoqKiAPzvoV9tRo8ejcTERIwdOxZmZmblehuJ8uFxbWOjFaRrW+Tl5akGKS2pLUrjzz//REJCAoyMjODq6qq3egtS9nrcuHFDbSBT5QtZoqOjcfbs2RLrmj17NqpVq4Zvv/1W6xVNXdu0YBld2tTY2Bjjx4/Hp59+CgB6fUlAREQEcnNzUatWrTKNj1cSQ0NDDBw4EF999RUAaLygUpLS7Ndvwy+//ILExETk5+ejXr16aoPNP3jwoNQvMNCVqakp/vOf/yA7OxuzZs0q9fLm5uZYsGAB2rdvj+zsbNWzOm5ubjAxMUFCQkKxA8TrwsDAAPXr18eqVavg7e2NkJAQjWNq6oPyby4yMhJpaWl6qy8qKqrEOx9K8zesy/GjrJS3zCp7oooSQqguzrxOD/Sb3IbSUPbeatve0lJ+5+Hh4cjLyyu2bGm+c31TXrjKyMjQOP/x48cwMjIqdGFDGyZuRCQZcrkco0aNQkJCAlasWFFonvItYJs2bVJdadbE398f2dnZ6NKli8YXkxQ0ePBgmJubIyEhAQMGDHhjt2aV5MaNG9i3bx9kMhkGDx5cYnkfHx8YGhoiODgYMTExWstt2bIFSUlJaN68eakHfi7O/PnzAQAfffSRTi9yKa3s7GwEBgbCwMAAI0eOxOeff672Ub75UZdeN0tLS8yZMwdpaWlan0309vaGpaUlLly4UOzbvY4dO6a63as0A5sr2+l13v5YUH5+vuqWO132mddR1tgfPnyI77//HgAwZMgQvcdVFsr9pVevXhr3K+Xtkps2bdLbd1XQZ599BmdnZ+zevRuRkZFlqqPo92FmZqbqPVywYEGJy2dnZ+u0nmXLlsHExATz589/I2+PbdSoEbp06YKsrCzVc3zFKSnuxo0bw8vLC9nZ2cXWl5ycjE2bNgF4dfGuJLocP8pK+ayitp6Ya9euISMjAzY2NqhcuXKZ1/Mmt6E0rK2tUaNGjde+wKDk4eGBJk2aICkpqdiLLTExMQgODoaRkVGhW+/fhpycHFy6dAnAq320qIyMDCQmJkIul+t227BOgwYQERUDpRzbrLjyCQkJwtTUVFhaWorU1NRC8z777DMBQLi5uYm//vpLbdn169cLQ0NDYWFhIWJiYgrNKziOW0GnT58WwcHBagN6v61x3MLDw1Xj7owbN07n9c2ZM0cAEI0bN1bbViGECA4OFmZmZsLQ0FAcP35c53iLGzMoLS1NNQC3paWluHnzZqH5+hrHbefOnQKA6Nmzp9Yyz549E+bm5moDlyrjLzqQb3Z2tnjvvfdEhQoVVGWKjmG0efNmAUBYWVmpje8khBBnzpwRVlZWAlAfgHv9+vXi3LlzGmONjY1VLXf+/PkSt1+I4vez5ORk1QDcDRo0ECkpKYXml3bMpV27domQkBCNY/3dv39fNYh0wbGMShrH7cqVK0Iul6uN26VUHuO4/fXXX8LQ0FBUrVpVvHjxQmu59u3bCwAiJCSkxHXrMo5bUcePHxfA/wbnLjqO25IlS0R0dLTG9Z0+fVqYmpoKIyMjkZiYqJpecADuqVOnahyAOyUlRUycOFGnAbiVJkyYIACIb775ptD0sozjpknBAbj9/f01DsB99+5dMWjQIJ0G4L527ZqoWLGiMDQ0FOvWrVOrKyEhQbi5uQkAYsSIEWrzX+f4kZKSImJjY9X+HoujPD84OjqKJ0+eFJqXlZUlunXrJoDCg5YrxcbGitjYWL1uw927d0VsbKzIyMgoNF1be5dlmz/++GOdx5DT5XwbEhIiDAwMhJmZmfjpp5/U5l+/fl00btxYABDz5s0r0zpK8uzZM43jLmZnZ4tx48YJAMLW1lbjMfbEiRMCgPjXv/6l07r4VkkikpS6detizJgxWLVqFZYsWYJvv/1WNS8gIAC5ubnYvXs3mjdvjp49e6Jp06bIyMhAWFgYoqOjUb16dezfvx/vv/++Tusr66uuixurp2/fvnBycio07cqVK6plsrOzkZycjPPnz+P69eswMDDAlClTsGTJklKtPyMjA8uXL4ejoyO6d+8OOzs75OTk4OzZszh//jzMzMywe/dudOrUqdTbp3xNuxACz549w82bN3Hq1ClkZGSgWbNm2LFjB5o1a6Zx2eKGA7C0tCzxBSXK2ySVvayaVKpUCQMGDMC2bduwY8cOjB8/vtg6jY2N8e2332LgwIFany8aOXIknj59iunTp6NDhw7w9PSEq6srZDIZoqKiEBYWBgMDA6xcuRLDhg0rtOxvv/2GL774AjY2NmjXrh3q16+P7OxsxMXF4ejRo8jJycGkSZN0fvmNkrId8/Pz8fTpU8TExCAiIgIvX75Ey5YtsXPnTq0vQyhuOAAbGxtV79L58+exatUq1K5dG+3bt1f1VMfHx+Pw4cN48eIF+vTpo7GHUaFQFHob26NHjxAVFaW6XXnIkCGqXjdNihsOwNPTE56enlqXLWrq1KmwsLDQOG/+/PmqN8cOHTq02De3+fr6IiIiAgEBAejatavO69dVp06d8MEHH+DXX3/VOH/nzp2YPn266vlOa2trZGRkICYmBidOnIAQAsuWLVM9IwYAtWrVwvHjx9G3b1/4+/tj+/bt6Nq1Kxo0aICXL18iNjYW4eHhyM7OxoEDB3SOddasWdi8eTNWrFiBiRMn6v3FG7a2tjh69Cg+/vhjTJ06FatWrULnzp1Rp04dZGRk4OrVqzhz5gxkMhlmzJhRYn0ODg745Zdf4O3tjXHjxuG7776Dl5cXKlWqhD///BOHDx9GZmYmhgwZonrzqy50OX6sXbsW8+bNg5+fn85juQ0fPhxHjx7Fjz/+iGbNmqF3796oXbs2Hj9+jF9//RX37t2Dh4eHxjcVt2jRAoDuPeG6bMOwYcNw8uRJhIWF6fS3V5Zt/vjjj7F//34cPXoUTZo00WmZ4nTt2hU7d+7EyJEj0b9/f7Rs2RLt2rWDsbExYmJiVMffadOmlekNqbp4/PgxnJ2d4eTkBLlcDmtra6SkpCAsLAzx8fGwsrLC7t27NT6nGBISAuBVu+jktVJMIiKh3x43IV5dPa5YsaKoWLGiePDggdr8o0ePCm9vb1G3bl1hbGwsKleuLFxcXISfn594/Pixxjq19bhpU1KPW3Gfglcllest+KlYsaKoW7eu6Ny5s/jmm2+KvfJYUludP39eDBs2TNjY2AhTU1Nhbm4u7OzsxL/+9S+NvZIlUV6JVX6MjIxE1apVhb29vRg6dKjYt2+fyM7O1ris8up9cZ+GDRsWu/5bt24JAKJmzZri5cuXxZY9c+aM6mp10fg1XbkXQog2bdqoYtHW7rGxsWLs2LGiWbNmwszMTJiZmYmmTZuKsWPHarzCLYQQN2/eFP7+/qJHjx7ivffeExUrVhTGxsaifv36ol+/fuLQoUPFbktRRdvN2NhYVK9eXbi4uAhfX19x5MgRjVdvhfjf1fHiPgV7iO7duyfWrl0r+vbtK5o1ayYqVaokKlSoIGrXri169uwpAgMD1dal7FEq+DE1NRW1a9cWHTp0EFOnTtV4BVqp6H6m6aNrz60udUVFRYn69esLAOLq1avF1peRkSGqVKkiKlSoIJKTk4stW5YeNyGEiImJEYaGhhp73C5duiQWLFggvLy8VH/XJiYmonHjxmLw4MHi9OnTWuPJzs4WmzZtEj179hS1a9cWFSpUEBYWFsLe3l5MnDhRXLt2rVB5XXrJp0yZIgCIKVOmqKbpq8dN6fnz52L58uXC09NT1KhRQxgZGamO6zNnzhR37twp1XoePXok/Pz8hKurq6hcubIwNjYWdevWFd7e3uLo0aNa43id40dZ7zjIz88XgYGBonPnzqJ69erCyMhIWFpainbt2om1a9dqPQ5qOze8zjZ4eHgIAIV6N4UQYujQoQKA2LlzZ6HpZdnm7OxsUatWLdGyZcsSy5bmt8Vff/0lpk6dKuzt7YWFhYUwMTERDRs2FMOGDSv2TofS/n7RRHk3SqtWrUStWrVEhQoVhLm5uZDL5WLGjBlajyN5eXmiXr16hc5hJZH9f9BERERERESFdO/eHSEhITh27Bi6dOny2vV9++23mDVrFi5dugRnZ2c9RPhu+vnnn9G7d28EBgbq/HI0Jm5ERERERKQmOTkZTZo0Ud3ir48x2LKystC8eXPI5XL8/PPPeojy3SOEgKurKwwNDXHhwgWdxzPkM25ERERERKRy4MABhIaG4sCBA0hPT8eECRP0NnC2qakpAgMDERYWhoyMDJibm+ul3nfJgwcP0Lt3b/Tt27dUg9Czx42IiIiIiFR8fHywe/duNGrUCJ999hlmzpwJQ0PD8g7rH4+JGxERERERkcTxVkki0jsrKyvY2NiUdxhEREREkqRQKPDo0aNSLcPEjYj0zsbGBpGRkeUdBhEREZEkubm5lXoZ9ZHgiIiIiIiISFKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxRuUdABH9/TyPeo5wWXh5h0FERESkE0/hWd4hlIg9bkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SN6B2Xl5eH7777DllZWeUdChERERG9IUzcJMDCwgIAkJSUBG9v77eyTl9fX1y/fr3YMitXrkRmZuZbiUdf2rZtq9f63uZ3oovw8HD06tWr0LSpU6eiRYsWMDU1LXF5fbcPEREREb0dMiGEKO8g/uksLCyQnp5e3mGosbGxQWRkJKysrHReJjc3F0ZGRm8wqn+28PBw+Pv745dffinvUIrVXNYc3+P78g6DiIiISCeewvOtrs/NzQ2RkZGlWoY9bhKiUChgb28P4NXtb1OnToWDgwPkcjnWrFkDAIiKioKHhwdcXV3RvXt33L9/HwDg6emJGTNmoGXLlmjWrBlOnz5dbD2enp6qneWLL76Am5sb7Ozs4OfnBwBYvXo1kpKS4OXlBS8vLwD/6xkEgKCgIPj4+AAAfHx8MGXKFHh5eWHGjBm4cOEC2rZtC2dnZ7Rt2xY3b97UuL1Lly6Fu7s75HK5ar0KhQItWrTAqFGjYGdnh27duuHFixdqyyYnJ6Nfv35wdHSEo6Mjzp49qxZjaev/888/0aVLFzg6OsLFxQW3b98u9J0oFAp06NABLi4ucHFxUa3z/v376NixI5ycnGBvb69q+4J+/fVX2Nraon379pg0aZKq1ywjIwMjR46Eu7s7nJ2dcfDgQQBAVlYWRowYAQcHBzg7OyMsLEytztTUVPTt2xdyuRytW7fGtWvXAAApKSno2rUrXFxcMGbMGDRs2BCPHj1Sa58lS5bAwcEBjo6OmDlzJgBg48aNcHd3h6OjIz7++GNVj+u+fftgb28PR0dHdOzYUeP3SURERERvDhM3iQoICEB8fDwuX76Ma9euYciQIcjJycHEiRMRFBSEqKgojBw5ErNnz1Ytk5ubiwsXLmDlypWYN2+e1nqKWrhwISIjI3Ht2jWcPHkS165dw6RJk1CnTh2EhYVpTBqKunXrFkJDQ7Fs2TLY2tri1KlTuHz5MubPn49Zs2aplQ8JCUFcXBwuXLiAK1euICoqCqdOnQIAxMXFYfz48YiJiYGlpSX279+vtvykSZPg4eGBq1ev4tKlS7Czs3vt+ocMGYLx48fj6tWrOHv2LKytrQvVWbNmTRw7dgyXLl3Cnj17MGnSJADArl270L17d1y5cgVXr16Fk5NToeWysrIwZswYHDlyBBEREUhJSSnU9p06dcLFixcRFhaGadOmISMjA9999x0A4I8//sDu3bsxfPhwtWfY/Pz84OzsjGvXruG///0vhg0bBgCYN28eOnXqhEuXLqFfv364d++eWvsdOXIEBw4cwPnz53H16lVMnz4dANC/f39cvHgRV69eRYsWLbB582YAwPz583H06FFcvXoVhw4dUqsPeLWvubm5wc3NDWlI01iGiIiIiMqG97RJVGhoKMaOHau67bBatWqIjo5GdHQ0unbtCuBVb1rB5KJ///4AAFdXVygUCq31FLV3714EBAQgNzcX9+/fx/Xr1yGXy0sV74ABA2BoaAgASEtLw/DhwxEXFweZTIacnBy18iEhIQgJCYGzszMAID09HXFxcWjQoAEaNWqkSn4KbktBJ06cwA8//AAAMDQ0RJUqVV6r/ufPnyMxMRH9+vUDAI3Pi+Xk5GDChAm4cuUKDA0NcevWLQCAu7s7Ro4ciZycHPTt21ctcbtx4wYaN26MRo0aAQA+/fRTBAQEqOI8dOgQ/P39AbxK8u7du4eIiAhMnDgRAGBra4uGDRuq1qcUERGhSjo7deqEx48fIy0tDREREQgODgYA9OjRA1WrVlXbltDQUIwYMQIVK1YE8L/9Ijo6Gv/+97/x9OlTpKeno3v37gCAdu3awcfHBwMHDlTtZ0WNHj0ao0ePBvDqVkkiIiIi0h8mbhIlhIBMJlObZmdnh3PnzmlcxsTEBMCrRCY3N1drPQXFx8fD398fFy9eRNWqVeHj46P17YQF6ylaxtzcXPX/c+bMgZeXF4KDg6FQKODp6alx+77++muMGTOm0HSFQqHaDuW2aLpVsiSlrV+XRz1XrFiBWrVq4erVq8jPz1cldx07dsSpU6dw+PBhfPbZZ5g2bZqq90sZS3Fx7t+/H82bN1ebrss2FiWTyXReVtN+4ePjgwMHDsDR0RHbtm1DeHg4AGDDhg04f/48Dh8+DCcnJ1y5cgXVq1cvcT1EREREpB+8VVKiunXrhg0bNqgSsNTUVDRv3hwpKSmqxC0nJwcxMTGlrqegZ8+ewdzcHFWqVEFycjKOHDmimlepUiU8f/5c9e9atWohNjYW+fn5qh4dTdLS0lC3bl0AwLZt2zSW6d69O7Zs2aJ6KUtiYiIePnxY7LYU1LlzZ6xfvx7Aq57HZ8+evVb9lStXRr169XDgwAEAQHZ2ttobNdPS0mBtbQ0DAwMEBgYiLy8PAHD37l3UrFkTo0aNwueff45Lly4VWs7W1hZ37txR9Rzu2bOnUJxr1qxRJVuXL18G8CoZ3LlzJ4BXt6Heu3dPLbkrWCY8PBxWVlaoXLky2rdvj7179wJ41aP35MkTte3t1q0btmzZotpG5X7x/PlzWFtbIycnR1U3ANy+fRutWrXC/PnzYWVlhb/++ktrWxIRERGR/jFxkyhfX180aNAAcrkcjo6O2LVrF4yNjREUFIQZM2bA0dERTk5OqhdklKaeghwdHeHs7Aw7OzuMHDkS7dq1U80bPXo0evbsqXo5yaJFi9CrVy906tRJ7fmvgqZPn46vv/4a7dq1UyU3RXXr1g2DBw9GmzZt4ODgAG9v70JJYklWrVqFsLAwODg4wNXVVS2BLUv9gYGBWL16NeRyOdq2bYsHDx4Umj9u3Dhs374drVu3xq1bt1S9jOHh4XBycoKzszP279+PL7/8stByZmZmWLduHXr06IH27dujVq1aqls758yZg5ycHMjlctjb22POnDmqdeXl5cHBwQGDBg3Ctm3bCvUUAsDcuXMRGRkJuVyOmTNnYvv27QBePfsWEhICFxcXHDlyBNbW1qhUqVKhZXv06IHevXvDzc0NTk5Oqls1FyxYgFatWqFr166wtbVVlZ82bRocHBxgb2+Pjh07wtHRsdi2JCIiIiL94nAARG9Beno6LCwsIITA+PHj0bRpU3z11VdvZF3Z2dkwNDSEkZERzp07hy+++AJXrlx5I+vShsMBEBER0bvkXRgOgM+4Eb0FGzduxPbt2/Hy5Us4OzurPXunT/fu3cPAgQORn58PY2NjbNy48Y2ti4iIiIjeDva4EZHesceNiIiI3iXvQo8bn3EjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxRuUdABH9/VRyrQTPSM/yDoOIiIjob4M9bkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKMyjsAIvr7iXr+HLLw8PIOg4iIiP6hhKdneYegd+xxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5E/yBr1qxBenp6eYdBRERERKXExO1vzsLCAgCQlJQEb2/vt7JOX19fXL9+vdgyK1euRGZm5luJR1/atm2r1/rexnfi6emJyMhIAMD69euRmZmJ3NxcrFu37q3GQURERESvh4nbP0SdOnUQFBT0Vta1adMmvP/++8WWKUvilpub+zphvbazZ8/qtb63+Z3k5+fDxMQEM2bMwNOnTwslbm8zDiIiIiIqGyZu/xAKhQL29vYAgLy8PEydOhUODg6Qy+VYs2YNACAqKgoeHh5wdXVF9+7dcf/+fQCvem1mzJiBli1bolmzZjh9+nSx9RTs5fniiy/g5uYGOzs7+Pn5AQBWr16NpKQkeHl5wcvLC8D/egYBICgoCD4+PgAAHx8fTJkyBV5eXpgxYwYuXLiAtm3bwtnZGW3btsXNmzc1bu/SpUvh7u4OuVyuWq9CoUCLFi0watQo2NnZoVu3bnjx4oXassnJyejXrx8cHR3h6OioStgKxlja+v/880906dIFjo6OcHFxwe3btwt9JwqFAh06dICLiwtcXFw0JokKhQK2trYYPnw45HI5vL29Vcnv8ePH4ezsDAcHB4wcORLZ2dmFljUwMMD8+fPx6NEjzJw5E7dv34aTkxOmTZum074xf/58uLu7w97eHqNHj4YQQmO7ExEREdGbwcTtHyggIADx8fG4fPkyrl27hiFDhiAnJwcTJ05EUFAQoqKiMHLkSMyePVu1TG5uLi5cuICVK1di3rx5WuspauHChYiMjMS1a9dw8uRJXLt2DZMmTUKdOnUQFhaGsLCwEuO9desWQkNDsWzZMtja2uLUqVO4fPky5s+fj1mzZqmVDwkJQVxcHC5cuIArV64gKioKp06dAgDExcVh/PjxiImJgaWlJfbv36+2/KRJk+Dh4YGrV6/i0qVLsLOze+36hwwZgvHjx+Pq1as4e/YsrK2tC9VZs2ZNHDt2DJcuXcKePXswadIkjW1x8+ZNjB49GteuXUPlypWxbt06ZGVlwcfHB3v27MEff/yB3NxcrF+/Xmt7Llq0CO+99x6uXLmCpUuXFpqn7TudMGECLl68iOjoaLx48QK//PKL1vqJiIiISP+MyjsAevtCQ0MxduxYGBm9+vqrVauG6OhoREdHo2vXrgBe9bwUTC769+8PAHB1dYVCodBaT1F79+5FQEAAcnNzcf/+fVy/fh1yubxU8Q4YMACGhoYAgLS0NAwfPhxxcXGQyWTIyclRKx8SEoKQkBA4OzsDANLT0xEXF4cGDRqgUaNGcHJyUtuWgk6cOIEffvgBAGBoaIgqVaq8Vv3Pnz9HYmIi+vXrBwAwNTVVW2dOTg4mTJiAK1euwNDQELdu3dLYFvXr10e7du0AAEOHDsXq1avRtWtXNGrUCM2aNQMADB8+HN999x0mT56srUm10vadhoWFYcmSJcjMzERqairs7Ozw0UcfFVo2ICAAAQEBr/6RllbqdRMRERGRdkzc/oGEEJDJZGrT7OzscO7cOY3LmJiYAHiVyCifNdNUT0Hx8fHw9/fHxYsXUbVqVfj4+CArK0tj2YL1FC1jbm6u+v85c+bAy8sLwcHBUCgU8PT01Lh9X3/9NcaMGVNoukKhUG2Hcls03SpZktLWr8tthStWrECtWrVw9epV5Ofna0zuAKi1t0wm0+tti5q+06ysLIwbNw6RkZGoX78+5s6dq/F7HD16NEaPHv0qrubN9RYTEREREfFWyX+kbt26YcOGDaoELDU1Fc2bN0dKSooqccvJyUFMTEyp6yno2bNnMDc3R5UqVZCcnIwjR46o5lWqVAnPnz9X/btWrVqIjY1Ffn4+goODta4zLS0NdevWBQBs27ZNY5nu3btjy5YtqtfeJyYm4uHDh8VuS0GdO3dW3WqYl5eHZ8+evVb9lStXRr169XDgwAEAQHZ2ttqLWdLS0mBtbQ0DAwMEBgYiLy9PY1337t1TfUe7d+9G+/btYWtrC4VCgT///BMAEBgYCA8PD63xFG37gjR9p8okzcrKCunp6XyRCREREVE5YOL2D+Tr64sGDRpALpfD0dERu3btgrGxMYKCgjBjxgw4OjrCycmpxLcoaqqnIEdHRzg7O8POzg4jR45U3eIHvOqd6dmzp+rlJIsWLUKvXr3QqVMntee/Cpo+fTq+/vprtGvXTmty061bNwwePBht2rSBg4MDvL29tSYqmqxatQphYWFwcHCAq6urWgJblvoDAwOxevVqyOVytG3bFg8ePCg0f9y4cdi+fTtat26NW7duFeplLKhFixbYvn075HI5UlNT8cUXX8DU1BRbt27FgAED4ODgAAMDA4wdO1ZrLNWrV0e7du1gb2+PadOmFZqn6Tu1tLTEqFGj4ODggL59+8Ld3b3YbSUiIiIi/ZMJvh6O6J2gUCjQq1cvREdHl3coJZI1bw58/315h0FERET/UELD4zRS4ubmpnoLu67Y40ZERERERCRxTNyI3hE2NjbvRG8bEREREekfEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkjokbERERERGRxDFxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjijMo7ACL6+3GtVAmRnp7lHQYRERHR3wZ73IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQZlXcARPQ3FBUFyGTlHQURERH9UwlR3hHoHXvciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkjokbERERERGRxDFxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsRaXXgwAHExsaWdxhERERE/3hM3Oi1WVhYAACSkpLg7e39Vtbp6+uL69evF1tm5cqVyMzMfCvxlAdd2sDGxgaPHj0qU/1Xr17FDz/8gObNm5dpeSIiIiLSH5kQQpR3EPRus7CwQHp6enmHocbGxgaRkZGwsrLSeZnc3FwYGRm9wajerrK0gdLevXvRvXt3VKlSpdTLuslkiCz1UkRERER6IvEUx83NDZGRpfu1xB430huFQgF7e3sAQF5eHqZOnQoHBwfI5XKsWbMGABAVFQUPDw+4urqie/fuuH//PgDA09MTM2bMQMuWLdGsWTOcPn262Ho8PT1VO/sXX3wBNzc32NnZwc/PDwCwevVqJCUlwcvLC15eXgD+1zMIAEFBQfDx8QEA+Pj4YMqUKfDy8sKMGTNw4cIFtG3bFs7Ozmjbti1u3rypcXuXLl0Kd3d3yOVy1XoVCgVatGiBUaNGwc7ODt26dcOLFy/Ult23bx/s7e3h6OiIjh07AgCysrIwYsQIODg4wNnZGWFhYTq3we7du+Hg4AB7e3vMmDFDY7zLly+Hvb097O3tsXLlStX0BQsWwNbWFl27dsWnn34Kf39/AMCvv/6KY8eOAQDmz58Pd3d32NvbY/To0eD1HiIiIqK3TBC9JnNzcyGEEPHx8cLOzk4IIcS6detE//79RU5OjhBCiMePH4uXL1+KNm3aiIcPHwohhPjxxx/FiBEjhBBCeHh4iClTpgghhDh8+LDo3Lmz1nqU5S9evFhoWm5urvDw8BBXr14VQgjRsGFDkZKSohanEELs27dPDB8+XAghxPDhw8WHH34ocnNzhRBCpKWlqdZ37Ngx0b9/f7VtPnr0qBg1apTIz88XeXl54sMPPxQnT54U8fHxwtDQUFy+fFkIIcSAAQNEYGCg2vL29vYiISFBCCHEkydPhBBC+Pv7Cx8fHyGEELGxsaJ+/frixYsXJbZBYmKiqF+/vnj48KHIyckRXl5eIjg4uFAbREZGCnt7e5Geni6eP38u3n//fXHp0iVx8eJF4ejoKDIzM8WzZ89EkyZNxNKlS1Xtsm/fvkLrFEKIoUOHikOHDqlt0/fffy9cXV2Fq6uraPDqOhc//PDDDz/88MNP+XwkztXVtdTL/H3uCSNJCQ0NxdixY1W3HVarVg3R0dGIjo5G165dAbzqSbK2tlYt079/fwCAq6srFAqF1nqK2rt3LwICApCbm4v79+/j+vXrkMvlpYp3wIABMDQ0BACkpaVh+PDhiIuLg0wmQ05Ojlr5kJAQhISEwNnZGQCQnp6OuLg4NGjQAI0aNYKTk5PathTUrl07+Pj4YODAgartjoiIwMSJEwEAtra2aNiwIW7dulViG1y8eBGenp6oUaMGAGDIkCE4deoU+vbtqyoTERGBfv36wdzcHMCrtj59+jTy8/PRp08fmJmZAQA++ugjje0TFhaGJUuWIDMzE6mpqbCzs1MrO3r0aIwePRrAq1sliYiIiEh/mLjRGyGEgKzIj3chBOzs7HDu3DmNy5iYmAAADA0NkZubq7WeguLj4+Hv74+LFy+iatWq8PHxQVZWlsayBespWkaZ0ADAnDlz4OXlheDgYCgUCnh6emrcvq+//hpjxowpNF2hUKi2Q7ktmm6V3LBhA86fP4/Dhw/DyckJV65cgRBCY9wltYG25XQpo8uyWVlZGDduHCIjI1G/fn3MnTtXaxsTERER0ZvBZ9zojejWrRs2bNigSsBSU1PRvHlzpKSkqBK3nJwcxMTElLqegp49ewZzc3NUqVIFycnJOHLkiGpepUqV8Pz5c9W/a9WqhdjYWOTn5yM4OFjrOtPS0lC3bl0AwLZt2zSW6d69O7Zs2aJ6KUtiYiIePnxY7LYUdPv2bbRq1Qrz58+HlZUV/vrrL3Ts2BE7d+4EANy6dQv37t1D8+bNS2yDVq1a4eTJk3j06BHy8vKwe/dueHh4FCrTsWNHHDhwAJmZmcjIyEBwcDA6dOiA9u3b4+eff0ZWVhbS09Nx+PBhtViVSZqVlRXS09MRFBSk83YSERERkX6wx43eCF9fX9y6dQtyuRwVKlTAqFGjMGHCBAQFBWHSpElIS0tDbm4uJk+eDDs7u1LXo+To6AhnZ2fY2dmhcePGaNeunWre6NGj0bNnT1hbWyMsLAyLFi1Cr169UL9+fdjb22t9E+b06dMxfPhwLF++HJ06ddJYplu3boiNjUWbNm0AvHrxyY4dO1S3W5Zk2rRpiIuLgxACnTt3hqOjI2xtbTF27Fg4ODjAyMgI27Ztg4mJSYltYG1tjW+//RZeXl4QQuCDDz5Anz59Cq3PxcUFPj4+aNmypapdlbd59u7dG46OjmjYsCHc3NzU3iJpaWmJUaNGwcHBATY2NnB3d9dpG4mIiIhIfzgcANE/XHp6OiwsLJCZmYmOHTsiICAALi4ur1UnhwMgIiKiciXxFKcswwGwx43oH2706NG4fv06srKyMHz48NdO2oiIiIhI/5i4Ef3D7dq1q7xDICIiIqIS8OUkREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkzqi8AyCivyFXVyAysryjICIiIvrbYI8bERERERGRxDFxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOKPyDoCI/n6ikqIgmycr7zCIiIjob0L4ifIOodyxx42IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTN5I8CwsLAEBSUhK8vb3fyjp9fX1x/fr1YsusXLkSmZmZbyWeN+XKlSv49ddfyzsMIiIiIioBEzd6Z9SpUwdBQUFvZV2bNm3C+++/X2yZsiRuubm5rxOWXuXm5jJxIyIiInpHMHGjd4ZCoYC9vT0AIC8vD1OnToWDgwPkcjnWrFkDAIiKioKHhwdcXV3RvXt33L9/HwDg6emJGTNmoGXLlmjWrBlOnz5dbD2enp6IjIwEAHzxxRdwc3ODnZ0d/Pz8AACrV69GUlISvLy84OXlBeB/PYMAEBQUBB8fHwCAj48PpkyZAi8vL8yYMQMXLlxA27Zt4ezsjLZt2+LmzZsat3fp0qVwd3eHXC5XrVehUKBFixYYNWoU7Ozs0K1bN7x48UJt2Z9//hmtWrWCs7MzunTpguTkZADA3LlzMXr0aHTr1g3Dhg3DN998gz179sDJyQl79uxBRkYGRo4cCXd3dzg7O+PgwYMAgJiYGLRs2RJOTk6Qy+WIi4sry1dIRERERGVkVN4BEJVFQEAA4uPjcfnyZRgZGSE1NRU5OTmYOHEiDh48iBo1amDPnj2YPXs2tmzZAuBVD9OFCxfw66+/Yt68eQgNDdVYT1ELFy5EtWrVkJeXh86dO+PatWuYNGkSli9fjrCwMFhZWZUY761btxAaGgpDQ0M8e/YMp06dgpGREUJDQzFr1izs37+/UPmQkBDExcXhwoULEEKgd+/eOHXqFBo0aIC4uDjs3r0bGzduxMCBA7F//34MHTq00PLt27fH77//DplMhk2bNmHJkiVYtmwZgFfJbUREBMzMzLBt2zZERkZi7dq1AIBZs2ahU6dO2LJlC54+fYqWLVuiS5cu2LBhA7788ksMGTIEL1++RF5eXpm+NyIiIiIqGyZu9E4KDQ3F2LFjYWT0aheuVq0aoqOjER0dja5duwJ41ZtmbW2tWqZ///4AAFdXVygUCq31FLV3714EBAQgNzcX9+/fx/Xr1yGXy0sV74ABA2BoaAgASEtLw/DhwxEXFweZTIacnBy18iEhIQgJCYGzszMAID09HXFxcWjQoAEaNWoEJycntW0pKCEhAYMGDcL9+/fx8uVLNGrUSDWvd+/eMDMz0xhnSEgIDh06BH9/fwBAVlYW7t27hzZt2mDhwoVISEhA//790bRpU7VlAwICEBAQ8Oof7/ajf0RERESSw8SN3klCCMhkMrVpdnZ2OHfunMZlTExMAACGhoaqZ8001VNQfHw8/P39cfHiRVStWhU+Pj7IysrSWLZgPUXLmJubq/5/zpw58PLyQnBwMBQKBTw9PTVu39dff40xY8YUmq5QKFTbodwWTbdKTpw4EVOmTEHv3r0RHh6OuXPnaoxF03r379+P5s2bF5reokULtGrVCocPH0b37t2xadMmdOrUqVCZ0aNHY/To0QAAWR3tbUpEREREpcdn3Oid1K1bN2zYsEGVgKWmpqJ58+ZISUlRJW45OTmIiYkpdT0FPXv2DObm5qhSpQqSk5Nx5MgR1bxKlSrh+fPnqn/XqlULsbGxyM/PR3BwsNZ1pqWloW7dugCAbdu2aSzTvXt3bNmyBenp6QCAxMREPHz4sNht0baO7du3ay1XdBu6d++ONWvWQAgBALh8+TIA4M6dO2jcuDEmTZqE3r1749q1azrHQkRERESvj4kbvZN8fX3RoEEDyOVyODo6YteuXTA2NkZQUBBmzJgBR0dHODk54ezZs6WupyBHR0c4OzvDzs4OI0eORLt27VTzRo8ejZ49e6peTrJo0SL06tULnTp1KnSLZlHTp0/H119/jXbt2ml9Vqxbt24YPHgw2rRpAwcHB3h7exdKsEoyd+5cDBgwAB06dCj2GTwvLy9cv35d9XKSOXPmICcnB3K5HPb29pgzZw4AYM+ePbC3t4eTkxNu3LiBYcOG6RwLEREREb0+mVBeWici0hNZHRkwpuRyRERERLoQfn+vlMXNzU31BnNdsceNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkjokbERERERGRxDFxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKMyjsAIvr7ca3jiki/yPIOg4iIiOhvgz1uREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4ozKOwAi+vuJigJksvKOgoiIiKRAiPKO4O+BPW5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkjokbERERERGRxDFxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYgKycjIwPr165Gfn1/eoRARERHR/2Pi9jdnYWEBAEhKSoK3t/dbWaevry+uX79ebJmVK1ciMzPzrcTzptjY2ODRo0c6lz906BAWLVpUbJmSvqenT59i3bp1Oq+ztDHl5uZiwoQJaN++PQwMeHggIiIikgqZEEKUdxD05lhYWCA9Pb28w1BjY2ODyMhIWFlZ6bxMbm4ujIyM3mBUpVOWbXhdCoUCvXr1QnR0tNq8vLw8GBoavrVYiiOTuQGILO8wiIiISAKYbahzc3NDZGTpfivxkvo/hEKhgL29PYBXP/CnTp0KBwcHyOVyrFmzBgAQFRUFDw8PuLq6onv37rh//z4AwNPTEzNmzEDLli3RrFkznD59uth6PD09VTviF198ATc3N9jZ2cHPzw8AsHr1aiQlJcHLywteXl4A/tczCABBQUHw8fEBAPj4+GDKlCnw8vLCjBkzcOHCBbRt2xbOzs5o27Ytbt68qXF7ly5dCnd3d8jlctV6FQoFWrRogVGjRsHOzg7dunXDixcv1Jb9+eef0apVKzg7O6NLly5ITk4GADx+/BjdunWDs7MzxowZA+U1D4VCAVtbW/j6+sLe3h5DhgxBaGgo2rVrh6ZNm+LChQsAgG3btmHChAmq7Zo0aRLatm2Lxo0bIygoSO17iomJQcuWLeHk5AS5XI64uDjMnDkTt2/fhpOTE6ZNm4bw8HB4eXlh8ODBcHBwAAD07dsXrq6usLOzQ0BAgGq7fvvtN7i4uMDR0RGdO3dWi+nu3bvo3Lkz5HI5OnfujHv37hUbKxERERG9RYL+1szNzYUQQsTHxws7OzshhBDr1q0T/fv3Fzk5OUIIIR4/fixevnwp2rRpIx4+fCiEEOLHH38UI0aMEEII4eHhIaZMmSKEEOLw4cOic+fOWutRlr948WKhabm5ucLDw0NcvXpVCCFEw4YNRUpKilqcQgixb98+MXz4cCGEEMOHDxcffvihyM3NFUIIkZaWplrfsWPHRP/+/dW2+ejRo2LUqFEiPz9f5OXliQ8//FCcPHlSxMfHC0NDQ3H58mUhhBADBgwQgYGBasunpqaK/Px8IYQQGzduVG37xIkTxbx584QQQvzyyy8CgEhJSVHVe+3aNZGXlydcXFzEiBEjRH5+vjhw4IDo06ePEEKIrVu3ivHjx6u2y9vbW+Tl5YmYmBjx3nvvqX1PEyZMEDt27BBCCJGdnS0yMzMLzRdCiLCwMFGxYkVx584d1TRlm2dmZgo7Ozvx6NEj8fDhQ1GvXj1VOWWZgjH16tVLbNu2TQghxObNm1Vxa4u1qO+//164uroKV1dXATQQr66v8cMPP/zwww8///QPqXN1dS31MtK574zemtDQUIwdO1Z122G1atUQHR2N6OhodO3aFcCr3jRra2vVMv379wcAuLq6QqFQaK2nqL179yIgIAC5ubm4f/8+rl+/DrlcXqp4BwwYoLoFMC0tDcOHD0dcXBxkMhlycnLUyoeEhCAkJATOzs4AgPT0dMTFxaFBgwZo1KgRnJyc1LaloISEBAwaNAj379/Hy5cv0ahRIwDAqVOn8NNPPwEAPvzwQ1StWlW1TKNGjVQ9XnZ2dujcuTNkMhkcHBw0rgN41TNmYGCA999/X9WrV1CbNm2wcOFCJCQkoH///mjatKnGelq2bKmKEXjVoxkcHAwA+OuvvxAXF4eUlBR07NhRVU7Td3Xu3DnV9n322WeYPn26zrECwOjRozF69GgAylsliYiIiEhfmLj9AwkhIJPJ1KbZ2dnh3LlzGpcxMTEBABgaGiI3N1drPQXFx8fD398fFy9eRNWqVeHj44OsrCyNZQvWU7SMubm56v/nzJkDLy8vBAcHQ6FQwNPTU+P2ff311xgzZkyh6QqFQrUdym3RdKvkxIkTMWXKFPTu3Rvh4eGYO3euxjgLKlivgYGB6t8GBgaq9ipuGSGE2vzBgwejVatWOHz4MLp3745NmzahcePGauUKtk94eDhCQ0Nx7tw5VKxYEZ6ensjKyirxu9KkYPmSYiUiIiKiN4vPuP0DdevWDRs2bFAlFKmpqWjevDlSUlJUiVtOTg5iYmJKXU9Bz549g7m5OapUqYLk5GQcOXJENa9SpUp4/vy56t+1atVCbGws8vPzVb1FmqSlpaFu3boAXj2fpUn37t2xZcsW1UtZEhMT8fDhw2K3Rds6tm/frpresWNH7Ny5EwBw5MgRPHnyROc6y+LOnTto3LgxJk2ahN69e+PatWtq7aYp9qpVq6JixYq4ceMGfv/9dwCveu9OnjyJ+Ph4AOrfFQC0bdsWP/74IwBg586daN++/RvYKiIiIiIqCyZu/0C+vr5o0KAB5HI5HB0dsWvXLhgbGyMoKAgzZsyAo6MjnJyccPbs2VLXU5CjoyOcnZ1hZ2eHkSNHol27dqp5o0ePRs+ePVUvJ1m0aBF69eqFTp06FbpFs6jp06fj66+/Rrt27ZCXl6exTLdu3TB48GC0adMGDg4O8Pb2LjbZKWru3LkYMGAAOnToUOiNkX5+fjh16hRcXFwQEhKCBg0a6FxnWezZswf29vZwcnLCjRs3MGzYMFSvXh3t2rWDvb09pk2bprZMjx49kJubC7lcjjlz5qB169YAgBo1aiAgIAD9+/eHo6MjBg0apLbs6tWrsXXrVsjlcgQGBmLVqlVvdPuIiIiISHccDoCI9I7DARAREZESsw11HA6AiIiIiIjob4iJGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkjokbERERERGRxDFxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCTOqLwDIKK/H1dXIDKyvKMgIiIi+vtgjxsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4mRCCFHeQRDR34uFhQVsbW3LO4x3XkpKCmrUqFHeYfwtsC31g+2oH2xH/WA76gfbUX9K05YKhQKPHj0qVf1GZQmKiKg4tra2iIyMLO8w3nlubm5sRz1hW+oH21E/2I76wXbUD7aj/rzptuStkkRERERERBLHxI2IiIiIiEjimLgRkd6NHj26vEP4W2A76g/bUj/YjvrBdtQPtqN+sB315023JV9OQkREREREJHHscSMiIiIiIpI4Jm5EVGa//fYbmjdvjiZNmmDRokVq84UQmDRpEpo0aQK5XI5Lly6VQ5TSV1I77ty5E3K5HHK5HG3btsXVq1fLIUrpK6kdlS5evAhDQ0MEBQW9xejeHbq0Y3h4OJycnGBnZwcPD4+3HOG7o6S2TEtLw0cffQRHR0fY2dlh69at5RCltI0cORI1a9aEvb29xvk8z+iupLbkuUY3JbWj0hs51wgiojLIzc0VjRs3Frdv3xbZ2dlCLpeLmJiYQmUOHz4sevToIfLz88W5c+dEy5Ytyyla6dKlHc+cOSNSU1OFEEL8+uuvbEcNdGlHZTkvLy/Rs2dPsW/fvnKIVNp0accnT56IFi1aiLt37wohhEhOTi6PUCVPl7ZcuHChmD59uhBCiIcPH4qqVauK7Ozs8ghXsk6ePCmioqKEnZ2dxvk8z+iupLbkuUY3JbWjEG/uXMMeNyIqkwsXLqBJkyZo3LgxjI2N8cknn+DgwYOFyhw8eBDDhg2DTCZD69at8fTpU9y/f7+cIpYmXdqxbdu2qFq1KgCgdevWSEhIKI9QJU2XdgSANWvW4OOPP0bNmjXLIUrp06Udd+3ahf79+6NBgwYAwLbUQpe2lMlkeP78OYQQSE9PR7Vq1WBkxCF2C+rYsSOqVaumdT7PM7orqS15rtFNSe0IvLlzDRM3IiqTxMRE1K9fX/XvevXqITExsdRl/ulK20abN29Gz54930Zo7xRd98fg4GCMHTv2bYf3ztClHW/duoUnT57A09MTrq6u+OGHH952mO8EXdpywoQJiI2NRZ06deDg4IBVq1bBwIA/zUqD55k3g+easnuT5xpe1iGiMhEaXkgrk8lKXeafrjRtFBYWhs2bNyMiIuJNh/XO0aUdJ0+ejMWLF8PQ0PBthfXO0aUdc3NzERUVhePHj+PFixdo06YNWrdujWbNmr2tMN8JurTl0aNH4eTkhBMnTuD27dvo2rUrOnTogMqVK7+tMN95PM/oH881r+dNnmuYuBFRmdSrVw9//fWX6t8JCQmoU6dOqcv80+naRteuXYOvry+OHDmC6tWrv80Q3wm6tGNkZCQ++eQTAMCjR4/w66+/wsjICH379n2boUqarn/XVlZWMDc3h7m5OTp27IirV68ycStCl7bcunUrZs6cCZlMhiZNmqBRo0a4ceMGWrZs+bbDfWfxPKNfPNe8vjd5rmF/PBGVibu7O+Li4hAfH4+XL1/ixx9/RO/evQuV6d27N3744QcIIfD777+jSpUqsLa2LqeIpUmXdrx37x769++PwMBA/jjWQpd2jI+Ph0KhgEKhgLe3N9atW8ekrQhd2rFPnz44ffo0cnNzkZmZifPnz6NFixblFLF06dKWDRo0wPHjxwEAycnJuHnzJho3blwe4b6zeJ7RH55r9ONNnmvY40ZEZWJkZIS1a9eie/fuyMvLw8iRI2FnZ4cNGzYAAMaOHYsPPvgAv/76K5o0aYKKFSvyVdca6NKO8+fPx+PHjzFu3DjVMpGRkeUZtuTo0o5UMl3asUWLFujRowfkcjkMDAzg6+tb4mux/4l0acs5c+bAx8cHDg4OEEJg8eLFsLKyKufIpeXTTz9FeHg4Hj16hHr16mHevHnIyckBwPNMaZXUljzX6KakdnyTZELTzcFEREREREQkGbxVkoiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkjokbERERERGRxDFxIyIiIiIikjgmbkRERERERBLHxI2IiIiIiEjimLgRERERERFJHBM3IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBER0T/WyZMnsWrVKqSkpAAAIiMjsW7dunKOiojetkuXLmHu3Ln4448/yjsUIq1kQghR3kEQERG9bRcuXECbNm2Qn5+PevXq4cCBA9i8eTPu3r2Lw4cPl3d4RPSWPHr0CM7OznBzc8P+/fthYMB+DZImo/IOgIiIqDwcPHgQzs7OOHLkCPz8/NCmTRsYGhrit99+K+/QiOgtEUJg6NChqFevHnbt2sWkjSSNPW5ERPSPlJycjPz8fFhbWwMAUlNTUaFCBVSqVKmcIyMiIlLHywpERPSPVKtWLVXSBgDVqlVj0kZE9BpSU1NRrVo1jB8/vrxDKTfLli1DhQoVcOPGDb3XzcSNiN4KmUxW6GNoaAgrKyt06tQJO3fu1Kl8tWrV4OnpiW3btqGkmwVCQ0MxaNAgNGjQAKampqhatSrc3d0xb948PHnyROMy27ZtU63Pw8NDa90KhQIGBgaqsiXFrukTHh6ucb3Kj4WFBerVq4cuXbrgm2++QVxcnNZ4tMWhFBkZiREjRqBx48YwMzND5cqV4eDggGnTpiExMVHrctrY2NgUirVChQqoXr06HBwc8Nlnn2Hfvn14+fKlxmXnzp1bYtt4enqWOqbMzExYWlpCJpNh8ODBOsVfqVIlJCcnayzj6ekJmUyGP//8U+P8W7duYfz48bC1tYWFhQXMzc3RvHlzjBs3Djdv3tS67qysLPj7+6NVq1aoUqUKjI2NYW1tDVdXV0yYMAEnT57UeZuLtpuJiQlq1KgBFxcX+Pr64siRI8jLy9O4rI+PT4nfg4+PT6Fl8vLysHHjRnh4eKBatWqoUKECatasCblcDl9fXxw6dKhQ+fDwcLU6K1asCGtra3Ts2BHTpk3D5cuXtW5f0f1M02fbtm06tZWyLoVCoVP5grp27QqZTIb69etrbc/iKNuh6H5dsH0GDhyocVmFQgGZTIb27durzXv69Cm++eYbODk5wcLCAiYmJqhbty5at26Nf/3rX1rb9uXLl9i8eTM+/PBDWFtbw8TEBJUqVYKTkxMmT56Ma9euFSqv6W/W1NQUTZo0wejRo7W2qXIfK/odadr3zM3NYW9vj5kzZ2o9PmdkZGDlypXo1KkTatasCWNjY1haWqJly5aYPXs27ty5o9P6lZ48eYL58+ejZcuWqFq1KkxMTFC/fn0MHDgQx44d07gMoJ/jR2mUdHwvGJOu+3fRvy0DAwNYWlqibdu2+O6775Cbm1vmeL/55hu8ePECs2bNAqDbMb/gx8bGRq3O1z2fFzxGNmrUCD4+Prh+/brO21Tcsah27dpq5ceNG4eaNWti6tSpOq9DV3zGjYjeKj8/PwBATk4Obt68iQMHDiAsLAxRUVFYvnx5seX//PNPBAcH4+TJk4iMjMTatWvVymdnZ8PX1xc7duyAmZkZevbsiWbNmiE9PR0nTpzA3LlzsXbtWuzfvx8dO3bUGKORkRFOnTqFmzdvonnz5mrzN23aBCEEjIyMij3BKWPXRNPJydHREX379gUAvHjxAg8fPsT58+exYMECLFy4EBMnToS/vz+MjHQ7dAshMHPmTCxZsgRGRkbo2rUrBgwYgJcvX+Ls2bPw9/fHunXrsH37dnh7e+tUZ0FffvklLC0tkZ+fj2fPnuHmzZsIDg7Gjh070LRpUwQGBqJVq1Yal/Xw8NCaoGlqm5Ls2bMHaWlpkMlk+Omnn/D48WNUr1692GXS09Ph5+eHDRs2lGpdq1evxpQpU5Cfn4+OHTuiV69ekMlkiIqKwoYNGxAQEIDly5dj0qRJauvz8PDApUuXULt2bXz88ceoVasWkpOTERcXh4CAADx9+rTYiwaaKPezvLw8PH36FDExMQgMDMTmzZvh5uaGnTt3olmzZhqX7dOnD5ycnDTOKzg9Ly8PvXr1wm+//QZLS0t8+OGHqFevHlJTU3H79m3s2rULN27cQO/evdXqadiwoSoJfPnyJVJSUnDp0iX4+/vD398fgwcPxvfffw8LCwuNcSj3s5JifBPu3LmD48ePQyaTISEhAUeOHEGvXr30vp59+/bh3LlzaNOmjU7lk5KS0K5dOygUCjRu3BhDhgxBtWrVkJiYiBs3bmDlypUwMzODs7NzoeVu3bqFvn37IjY2FlZWVujatSsaNGiAly9f4vr169iwYQNWr16NAwcOqH2XBf9mHz9+jBMnTmDjxo0ICgrC+fPn0bRp01Jtc8F978GDB/j555+xePFiBAUF4cKFC6hWrZqq7O+//w5vb28kJiaiXr16+OCDD1CnTh1kZGTg8uXLWLx4MZYuXYrff/8dLi4uJa771KlT+Pjjj/Ho0SO0aNECQ4YMQaVKlRAXF4fDhw9j3759GDp0KDZt2gQTExONdZT1+CElyr+tvLw8xMfH46effsK5c+dw/Phx/PTTT6Wu7969e/j+++8xYsQI1K1bFwA0HuevXLmCgwcPFjrnKRX8W9fH+bzgOtLS0hAeHo7t27dj7969OHHiBFq3bq3TtlWpUgWTJ09Wm67puGVmZoYvv/wSM2bMwNmzZ9G2bVud1qETQUT0FgAQmg45oaGhQiaTCZlMJuLj40ssHxERIQwMDIRMJhN37txRmz9ixAgBQLi4uIh79+4Vmpefny/WrFkjDAwMhIWFhbh+/Xqh+Vu3bhUARN++fQUAMXXqVLX6c3NzRZ06dYS7u7uoW7euxhi1xa6Ncr3Dhw/XOP/EiROiQYMGAoAYM2aMzuubN2+eACBsbGxEdHS02vygoCBhamoqDA0NxYkTJ3SOt2HDhgJAoe9L6enTp2LixIkCgKhSpYqIjY0tNN/Pz08AEH5+fjqvTxetW7cWBgYGYtq0aQKAWLZsWYnxN2nSRBgaGqrtB0II4eHhIQCIuLi4QtO3b98uAIhq1aqJkydPqi136tQpUa1aNQFAbN++vdC8BQsWCACiW7duIjs7W23Z1NRUcebMGV03udj97MGDB2LAgAECgKhfv75ITk4uNH/48OECgNi6datO6woMDBQAhKOjo3j69Kna/IyMDLV9KCwsTAAQHh4eGuu8fPmycHBwEABEjx491OYXt5+VVlnrmjlzpgCg+u9HH31U6nVrawfl9CZNmggAom3btmrLxsfHCwCiXbt2haZ//vnnAoAYOXKkyM/PV1suKSlJREVFFZr24MEDUa9ePQFATJ48WWRmZqotl5ycLMaNGye2bdummqbtbzYvL0988MEHAoDw8fFRq0vbPqZt+vPnz8X7778vAIi5c+eqpsfGxorKlSsLAwMDsWjRIpGTk6O2rjt37ogBAwaIsLCwEtcTExMjzM3NhYGBgVi9erVa+927d0+4uLio2reo1zl+lIUu55PS7t/aykdHRwszMzMBQISHh5c61lmzZgkAJR7HSjrnKenjfF50Hfn5+ap9w9PTU6ftatiwoWjYsKFOZZUSExOFoaGhGDJkSKmWKwkTNyJ6K4o7+bRo0UIAEHv37tWpvPLkvm/fvkLTT58+LQCIqlWriqSkJK2xzJgxQwAQXbp0KTRdeaCfPXu2aNOmjahRo4Z4+fJloTIHDx4UAERAQMBbS9yEePXjxdjYWMhkMrUfZJrWFx8fL4yMjESFChXEtWvXtNa7fv16AUA0b95c5OXl6RSvLj8Shg0bJgCIPn36FJr+JhK3P/74QwAQXbt2FY8ePRLGxsbC1tZWa3ll/Pv27RMARK9evdTKaPrh9ezZM1G1alUBQPz2229a6z9y5IgquXv27Jlqes+ePQUAERwcXLYNLaKk/SwvL094enoKAOLLL78sNK+0idsXX3whAIgVK1boHF9JiZsQrxKFGjVqaGyX8k7ccnJyRO3atUXlypVFZmamcHFxEYaGhiIhIaFU6y4pcRsyZIjo06ePACCCgoIKldGWuCmPmZcvX9Y5Dl9fXwFAfPrppyWWzcrKUv1/cX+ze/fuFQCEnZ2d2rzSJm5CCLF48WIBQHzwwQeqaV26dBEAxNdff12quLWtp3PnzqpkXJukpCTV33pERESheWU9fpTV20zchPjfcWrJkiWlijM/P19YW1uL+vXrl1hWl3Oevs7nmtZx/vx5AUBUrFixxFiFKFviJoQQnp6ewsTERKSlpZV6WW34jBsRlTvx/8+rlXQff9HyFSpUKDR948aNAIBRo0YVeulEUTNmzICJiQlCQ0MRHx+vscyoUaOQkpKCgwcPqq3DwsICn3zyiU6x6outrS0GDhwIIQR2795dYvmtW7ciNzcX/fr1g4ODg9Zyvr6+qFOnDm7evFmq56tKorx975dffsGzZ8/0Vq8mAQEBAF4901K9enX06tULN27cwOnTp4tdrm/fvujYsSN++eUXhIWFlbieoKAgPHnyBC1btkT37t21luvRowfc3d2RmpqKoKAg1XTlrZu3bt3SZbNem4GBAf79738DAHbv3l3ic6HFeVOx16xZE2PGjAEAjc+6lqdDhw7hwYMHGDRoEMzMzODj44O8vDxs2bJF7+tS3s48c+ZM5OTklFi+tN/HixcvEBgYCKD4W7iVtN0eWJS2Y3FZFT0XxMfHIzQ0FKamppg+fXqJy5cUd3x8PI4fPw4TE5Ni67O2toavry+A/x1fiirt8QP43/Nec+fO1al8eSjt+VgpJiYG9+/fR7t27fQShz7P50WVZRuzs7OxY8cO/Pe//8WqVasQFhZW4jOv7dq1Q3Z2Nk6dOqXzekrCZ9yIqFyFhobi5s2bkMlkcHd3L7G88tkzY2NjtGzZstC8iIgIAECXLl2KraNq1apwdXXF2bNncebMGTRq1EitzKBBgzB58mRs3LhR9fxXYmIijhw5ghEjRuj09kFtJ2dTU1PMnDmzxOWL8vT0xI4dO3DhwoUSy+raFkZGRvD09MSuXbtw5swZeHl5lTouTRo3box69eohISEBUVFRavWGh4drbZ8ePXro/NxBVlYWduzYgSpVqqBfv34AXiVwP/30EwICAtChQ4dil1e+KGTq1KmIjIws9kSua5sCr15ocfHiRZw5cwYjRowA8Gqf2rFjB+bMmQOFQoEPP/wQLi4uxf4oeV3t27eHkZERHj58CIVCobavHzhwQOsLDT755BPY2toCAPr374/Fixdjw4YNeP78Ofr16wdXV1c0bNjwtWP09PTEf/7zH6379cqVK7U+4zZz5kyYmpq+dgyaKH+wK7+/wYMHY+rUqdi8eTNmz56t1/G+mjVrhjFjxuC7777D+vXr1Z6PLGrQoEGIiIiAr68vIiMj0a1bNzg7O2t9rjMyMhLZ2dmoW7euxud2y6JgEqvp5SmllZ6eju3btwOA6tlY5d+cq6ur1n2gNArWV7Vq1WLLdu3aFUuXLsWZM2e0linN8eNdEBMTo7qAp+35ZG2Ubevm5qaXWPR5Pi9ICIF169YBKN02PnjwAJ999lmhaY0aNcLWrVu1Ppes/E1z6tQpvT0by8SNiN4q5Y/1gi8nEULgq6++0vgjsGB55ctJhBDw9/dX+8F7//59AED9+vVLjENZJikpSeP8ihUrql6aoFAoYGNjgy1btiAvLw+jRo3SaVvnzZuncXqVKlXKlLgpH/ZOSUkpsaw+26Ks6tati4SEBI3xnjx5UmsPn6Wlpc6J2969e/HkyROMHj0aZmZmAICePXuiVq1aCAoKwurVq4v9gebu7o5Bgwbhxx9/xM6dOzF06FCtZV+3TXv16oVVq1bhm2++wfr167F+/XoAQO3atdGpUyeMGTNG6wP2ZWViYoLq1asjOTkZKSkpaj9qDh48qNarrOTk5KRK3JydnbFjxw58+eWX2LFjB3bs2AHg1RAKHTt2xMiRI/HRRx+VKcaS9utVq1ZpXXby5MlvJHG7e/cujh07hubNm6teGKLszf3pp58QEhKCHj166HWdfn5+CAwMxPz58zF8+HBUqVJFa9nx48cjKSkJK1aswNKlS7F06VIAr17s06VLF0yYMAGOjo6q8sp9t169emWOr+DFltTUVBw7dgw3btzA+++/jzlz5pS6voIXDZKTk3Ho0CEkJSXhvffew4QJE/QWd0H6Pi6W5vgBABMmTMAnn3wCKyurUkT95igvihR8OcmLFy/Qr1+/Ei96FXXv3j0A0NuFKH19V1euXFHtt8qXk1y5cgVmZmZYuHChTrGMGDECHTp0gJ2dHSpVqoQ7d+5g7dq1CAgIQM+ePXHu3LlCf29KyjdOKttGH5i4EdFbpUxmZDIZLC0t0aFDB3z++edaT3hFkx+ZTIbNmzerroJrostVT11ulRg1ahQ2bNiAzZs3Y968edi8eTPkcrlaT19J69CX0tze8abKlkZx9fr5+enldiHl7TQF9wcjIyMMGTIEy5cvR2BgYIm9F99++y2Cg4Mxe/ZseHt7a00E9NGmkyZNgq+vL44dO4azZ8/i8uXLOHv2LHbt2oVdu3Zhzpw5mD9/fon1l0ZxcW/dulXttf/aDBw4EP369UNYWBgiIiJw+fJlRERE4MCBAzhw4ACGDRumegW3vuIDXt3eVpY3jb6OTZs2IT8/X61tCvbm6jtxq1GjBmbOnIlZs2Zh4cKFWLJkidayMpkM//3vfzF9+nQcPXoUv//+Oy5duoTz589j06ZN2Lp1K9avX6+6yKSPv3FNF1ucnJwQHh5ebJKpTcGLBmZmZrCxscGQIUMwc+ZM1cUWfR+b3sRxUdfjBwBYWVlJJmkD/ndRRDkEjVwux9ChQzF27NhS1/X48WMAKLEns7Re97u6evUqrl69CuDVLb3W1tb47LPPMHPmTLz//vs6xVD09mJ7e3ts2LABFhYWWLZsGebOnYvg4GC15ZRvRn306JFO69EFn3EjordKvHopEvLz85GamoqwsLBir1Iqy6enp+PYsWOoX78+xo4dixMnTqiVLc3VrYSEBADFXx10cXGBi4sLtm7diiNHjuDu3bs697a9CcqriTVq1CixrHK79NUWZVGaeMsiNjYWERERsLW1VeuhUyZyysSuODY2Npg4cSLu3btXbO+Ovtq0YsWK6NOnDxYvXoyQkBCkpqZi7dq1MDQ0xIIFC4od26y0srKykJqaCkA/30OFChXQrVs3zJ8/Hz///DMePXqEPXv2wNzcHD/88IPW3rvivOn9pLTy8vKwdetWGBgYqN0a1bNnT9SuXRs///wzHjx4oPd1f/XVV6hfvz5Wr16Nu3fvllje0tISgwYNwooVK3Dy5Ek8fvwY//73v5GXl4eJEyeqxhmrU6cOgP/tl2Xh5+cHIQTy8vJw7949TJo0CVeuXMHAgQORn59f6vq2bt2qOr5nZmbi+vXrWLJkSaFhAPQRd0Fv4rio6/GjrJTJSHFtrJxX2tt34+PjVefjZ8+e4ffff8eECRN0HnKmIOUdD1lZWaVeVhN9nc+HDx+u2s9evnyJu3fv4ocfftA5aSuOMsHV9gzbixcvAPyvbfSBiRsRvRPMzc3RpUsX/Pzzz8jLy8Pw4cORmZlZqIzyOYvQ0NBi63ry5AmioqIAoMQHqUePHo3ExESMHTsWZmZmJd4K8yYpH4DX5b58XdsiLy9PNRi4vh4qB4A///wTCQkJMDIygqurq97qLUj5DNKNGzfUBkVVvpAlOjoaZ8+eLbGu2bNno1q1avj222+1Xh3VtU0LltGlTY2NjTF+/Hh8+umnAKDziw50ERERgdzcXNSqVeuN9FoZGhpi4MCB+OqrrwBA4wWVkpRmv34bfvnlFyQmJiI/Px/16tVTG2z+wYMHyM3NfSMvKTE1NcV//vMfZGdnqwYwLg1zc3MsWLAA7du3R3Z2tur5LDc3N5iYmCAhIaHYAeJ1YWBggPr162PVqlXw9vZGSEiIxjE19UH5NxcZGYm0tDS91RcVFYWnT58WW7Y0f8O6HD/KStmbqezRKkoIobo4o4/nAMuqZs2aALTHWVpv4nyub8ptzsjI0Dhf2RbKcvrAxI2I3ilyuRyjRo1CQkICVqxYUWie8i1gmzZtUl1p1sTf3x/Z2dno0qVLiQ8yDx48GObm5khISMCAAQPK7cR448YN7Nu3DzKZDIMHDy6xvI+PDwwNDREcHIyYmBit5bZs2YKkpCQ0b9681AM/F0d5u99HH32k04tcSis7OxuBgYEwMDDAyJEj8fnnn6t9lG9+1KXXzdLSEnPmzEFaWprWZxO9vb1haWmJCxcu4NixY1rrOnbsGC5cuICqVauWamBzZTvp6xbb/Px81TMcuuwzr6OssT98+BDff/89AGDIkCF6j6sslPtLr169NO5XytsnN23apPfboQHgs88+g7OzM3bv3o3IyMgy1VH0+zAzM1P1Hi5YsKDE5bOzs3Vaz7Jly2BiYoL58+e/kbfHNmrUCF26dEFWVpbqOb7ilBR348aN4eXlhezs7GLrS05OxqZNmwC8unhXEl2OH2WlfHbq3LlzGudfu3YNGRkZsLGxQeXKlfW67tKQy+UAXp2r9OFNnM/1TfmdNG7cWON8ZVsoB5rXC70NLEBEVAyUcmyz4sonJCQIU1NTYWlpKVJTUwvN++yzzwQA4ebmJv766y+1ZdevXy8MDQ2FhYWFiImJKTSv4DhuBZ0+fVoEBwerDQD6tsZxCw8PV427M27cOJ3XN2fOHAFANG7cWG1bhRAiODhYmJmZCUNDQ3H8+HGd4y1uDKC0tDTVANyWlpbi5s2bhebraxy3nTt3CgCiZ8+eWss8e/ZMmJubi4oVKxYaNFoZf9GBfLOzs8V7770nKlSooCpTdBymzZs3CwDCyspKbXwnIYQ4c+aMsLKyEoD6ANzr168X586d0xhrbGysarnz58+XuP1CFL+fJScnqwbgbtCggUhJSSk0v7TjuO3atUuEhIRoHOvv/v37qkGkC47FWNI4bleuXBFyuVxt3C6l8hjH7a+//hKGhoaiatWq4sWLF1rLtW/fXgAQISEhJa5bl3Hcijp+/LgA/jc4d9Fx3JYsWSKio6M1ru/06dPC1NRUGBkZicTERNX0ggNwT506VeMA3CkpKWLixIk6DcCtNGHCBAFAfPPNN4Wml2UcN00KDsDt7++vcQDuu3fvikGDBuk0APe1a9dExYoVhaGhoVi3bp1aXQkJCcLNzU0AECNGjFCb/zrHj5SUFBEbG6v291gc5fnB0dFRPHnypNC8rKws0a1bNwEUHrRcKTY2VsTGxmrdhuL+Hv78808RGxurNpapNk+fPhWGhoZq+6omug7ArY/zeUnrKEl0dLR4/Pix2nSFQqH6+1y4cKHGZZXjmf7xxx+vFUNBfDkJEb1z6tatizFjxmDVqlVYsmQJvv32W9W8gIAA5ObmYvfu3WjevDl69uyJpk2bIiMjA2FhYYiOjkb16tWxf/9+ne9xL+urrot7+Ubfvn3VrsIVfPtVdnY2kpOTcf78eVy/fh0GBgaYMmVKsS8s0LT+jIwMLF++HI6OjujevTvs7OyQk5ODs2fP4vz58zAzM8Pu3bvRqVOnUm+f8o1kQgg8e/YMN2/exKlTp5CRkYFmzZphx44daNasmcZlixsOwNLSEpMnTy523crbJJVXZTWpVKkSBgwYgG3btmHHjh0YP358sXUaGxvj22+/xcCBA7U+XzRy5Eg8ffoU06dPR4cOHeDp6QlXV1fIZDJERUUhLCwMBgYGWLlyJYYNG1Zo2d9++w1ffPEFbGxs0K5dO9SvXx/Z2dmIi4vD0aNHkZOTg0mTJun88hslZTvm5+fj6dOniImJQUREBF6+fImWLVti586dWl+IUNxwADY2NqrepfPnz2PVqlWoXbs22rdvr7qyHR8fj8OHD+PFixfo06ePxh5GhUJR6O2wjx49QlRUlOr2piFDhqh63TQpbjgAT09PeHp6al22qKlTp8LCwkLjvPnz56veHDt06NBiXzLh6+uLiIgIBAQEoGvXrjqvX1edOnXCBx98gF9//VXj/J07d2L69Omq5zutra2RkZGBmJgYnDhxAkIILFu2TPWMGADUqlULx48fR9++feHv74/t27eja9euaNCgAV6+fInY2FiEh4cjOzsbBw4c0DnWWbNmYfPmzVixYgUmTpyo95dv2Nra4ujRo/j4448xdepUrFq1Cp07d0adOnWQkZGBq1ev4syZM5DJZJgxY0aJ9Tk4OOCXX36Bt7c3xo0bh++++w5eXl6oVKkS/vzzTxw+fBiZmZkYMmSI6s2vutDl+LF27VrMmzevVC9nGj58OI4ePYoff/wRzZo1Q+/evVG7dm08fvwYv/76K+7duwcPDw+Nbypu0aIFgLL14nfu3Bl3797V+eVAVapUQefOnREeHo4nT57o5SUlb+J8Xlr79u3DokWL4OXlhUaNGqFSpUq4ffs2Dh8+jKysLHzwwQeYOnWq2nL5+fk4fvw4mjdvDnt7e/0FpLcUkIioGNBjj5sQr64eV6xYUVSsWFE8ePBAbf7Ro0eFt7e3qFu3rjA2NhaVK1cWLi4uws/PT+PVMyG097hpU1KPW3GfgleBlest+KlYsaKoW7eu6Ny5s/jmm2/UrtxqWp8258+fF8OGDRM2NjbC1NRUmJubCzs7O/Gvf/1L41XMkiiv1io/RkZGomrVqsLe3l4MHTpU7Nu3T2RnZ2tcVnn1vrhPw4YNi13/rVu3BABRs2bNEq8GnzlzRnW1umj8mq7cCyFEmzZtVLFoa/fY2FgxduxY0axZM2FmZibMzMxE06ZNxdixYzVe4RZCiJs3bwp/f3/Ro0cP8d5774mKFSsKY2NjUb9+fdGvXz9x6NChYrelqKLtZmxsLKpXry5cXFyEr6+vOHLkiMYeMiH+1xtR3KdgD9G9e/fE2rVrRd++fUWzZs1EpUqVRIUKFUTt2rVFz549RWBgoNq6lD1KBT+mpqaidu3aokOHDmLq1Kni8uXLWrev6H6m6aNrz60udUVFRYn69esLAOLq1avF1peRkSGqVKkiKlSoIJKTk4stW5YeNyGEiImJEYaGhhp73C5duiQWLFggvLy8VH/XJiYmonHjxmLw4MHi9OnTWuPJzs4WmzZtEj179hS1a9cWFSpUEBYWFsLe3l5MnDhRXLt2rVB5XXrJp0yZIgCIKVOmqKbpq8dN6fnz52L58uXC09NT1KhRQxgZGamO6zNnzhR37twp1XoePXok/Pz8hKurq6hcubIwNjYWdevWFd7e3uLo0aNa43id40dZ7zjIz88XgYGBonPnzqJ69erCyMhIWFpainbt2om1a9dqPQ5qOzfo0uNWlh7vAwcOCAAaezILKm1v2Oucz1+3xy08PFx88sknonnz5qJKlSrCyMhIWFlZiS5duojt27eL/Px8rTEDECtWrHit9RclE+IN3KBNRERERET/GHl5eXBwcICxsTEuX778zg9I/jo+/vhjnDx5Erdv3y7TcBna8OUkRERERET0WgwNDeHv74+rV6/ip59+Ku9wys2VK1cQHByMuXPn6jVpA5i4ERERERGRHnzwwQdYtWqV3sZzexfdv38fCxYsKNNA5iXhrZJEREREREQSxx43IiIiIiIiiWPiRkREREREJHEcx42I9M7KykqncV+IiIiI/okUCgUePXpUqmWYuBGR3tnY2CAyMrK8wyAiIiKSJDc3t1Ivw1sliYiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZERERERCRxTNyIiIiIiIgkjokbERERERGRxDFxIyIiIiIikjij8g6AiP5+nkc9R7gsvLzDICIiItKJp/As7xBKxB43IiIiIiIiiWPiRkREREREJHFM3IiIiIiIiCSOiRsREREREZHEMXEjIiIiIiKSOCZuREREREREEsfEjYiIiIiISOKYuBEREREREUkcEzciIiIiIiKJY+JGREREREQkcUzciIiIiIiIJI6JGxERERERkcQxcSMiIiIiIpI4Jm5EREREREQSx8SNiIiIiIhI4pi4ERERERERSRwTNyIiIiIiIolj4kZUwJo1a5Cenl7eYRARERERFfJWEzcLCwsAQFJSEry9vd/KOn19fXH9+vViy6xcuRKZmZlvJR59adu2rV7rexPfydOnT7Fu3Tq91lkchUIBe3v7Mi+/fv16ZGZmqvbT0ggPD0evXr3KvO6C3ubfiUKhwK5du97oOoiIiIjo9ZVLj1udOnUQFBT0Vta1adMmvP/++8WWKUvilpub+zphvbazZ8/qtb438Z287cTtdeTn58PExAQzZswo71BU3sbfCRM3IiIiondDuSRuBXtG8vLyMHXqVDg4OEAul2PNmjUAgKioKHh4eMDV1RXdu3fH/fv3AQCenp6YMWMGWrZsiWbNmuH06dPF1uPp6YnIyEgAwBdffAE3NzfY2dnBz88PALB69WokJSXBy8sLXl5eAFCoxyUoKAg+Pj4AAB8fH0yZMgVeXl6YMWMGLly4gLZt28LZ2Rlt27bFzZs3NW7v0qVL4e7uDrlcrlqvQqFAixYtMGrUKNjZ2aFbt2548eKF2rLJycno168fHB0d4ejoqErYCsZY2vr//PNPdOnSBY6OjnBxccHt27cLfScKhQIdOnSAi4sLXFxcNCaJc+bMwapVq1T/nj17NlavXl2ozMyZM3H79m04OTlh2rRpWmMFgB9++AFyuRyOjo747LPPVO09adIktG3bFo0bN1YlMUIITJs2Dfb29nBwcMCePXvU4svKysKIESPg4OAAZ2dnhIWFAQAyMzMxcOBAyOVyDBo0CK1atUJkZCQMDAwwf/58PHr0CADQt29fuLq6ws7ODgEBARq/199++w22trZo3749fvrpJ9X0jIwMjBw5Eu7u7nB2dsbBgwfVlk1PT0fnzp3h4uICBwcHjWUKfifbtm1D37598dFHH6FRo0ZYu3Ytli9fDmdnZ7Ru3RqpqakAgNu3b6NHjx5wdXVFhw4dcOPGjWLbcubMmTh9+jScnJywYsUKre0WExODli1bwsnJCXK5HHFxcRrbhIiIiIjeDKPyDiAgIADx8fG4fPkyjIyMkJqaipycHEycOBEHDx5EjRo1sGfPHsyePRtbtmwB8Kq368KFC/j1118xb948hIaGaqynqIULF6JatWrIy8tD586dce3aNUyaNAnLly9HWFgYrKysSoz31q1bCA0NhaGhIZ49e4ZTp07ByMgIoaGhmDVrFvbv31+ofEhICOLi4nDhwgUIIdC7d2+cOnUKDRo0QFxcHHbv3o2NGzdi4MCB2L9/P4YOHVpo+UmTJsHDwwPBwcHIy8tTe/6qLPUPGTIEM2fORL9+/ZCVlYX8/Hw8fPhQVWfNmjVx7NgxmJqaIi4uDp9++qkq+VX6/PPP0b9/f3z55ZfIz8/Hjz/+iAsXLhQqs2jRIkRHR+PKlSvFxlq9enUsXLgQZ86cgZWVVaHv7v79+4iIiMCNGzfQu3dveHt746effsKVK1dw9epVPHr0CO7u7ujYsWOhdX/33XcAgD/++AM3btxAt27dcOvWLaxbtw5Vq1bFtWvXEB0dDScnJ43f85YtW1CtWjW8ePEC7u7u+Pjjj1G9enXV/KysLIwaNQonTpxAkyZNMGjQINW8hQsXolOnTtiyZQuePn2Kli1bokuXLjA3N1eVMTU1RXBwMCpXroxHjx6hdevW6N27N2QymcZ4ACA6OhqXL19GVlYWmjRpgsWLF+Py5cv46quv8MMPP2Dy5MkYPXo0NmzYgKZNm+L8+fMYN24cTpw4obUtFy1aBH9/f/zyyy8AgGXLlmlstw0bNuDLL7/EkCFD8PLlS+Tl5WmNk4iIiIj0r9wTt9DQUIwdOxZGRq9CqVatGqKjoxEdHY2uXbsCeNWbZm1trVqmf//+AABXV1coFAqt9RS1d+9eBAQEIDc3F/fv38f169chl8tLFe+AAQNgaGgIAEhLS8Pw4cMRFxcHmUyGnJwctfIhISEICQmBs7MzgFc9LXFxcWjQoAEaNWqkShwKbktBJ06cwA8//AAAMDQ0RJUqVV6r/ufPnyMxMRH9+vUD8CqBKConJwcTJkzAlStXYGhoiFu3bqmVsbGxQfXq1XH58mUkJyfD2dm5UGKjibZYr169Cm9vb1XiXPC769u3LwwMDPD+++8jOTkZABAREYFPP/0UhoaGqFWrFjw8PHDx4sVC32VERAQmTpwIALC1tUXDhg1x69YtRERE4MsvvwQA2Nvba/3+V69ejeDgYADAX3/9hbi4uELbd+PGDTRq1AhNmzYFAAwdOlTVMxcSEoJDhw7B398fwKsk7969e2jRooVqeSEEZs2ahVOnTsHAwACJiYlI/r/27j2qqnJf4/gDQpmipJnlpVJ2JgosriqicYlCLaMkPVpWMhyKVuZOt1lWdDs1dqXb1NrmppOXzEqzTbZPah4TvJSmkJe8pG4DM3UTSqJ4SS7v+YPjOiK3hZjr1f39jMEYwZrznb/3N1dj8vjONcnL0/XXX19t/+Li4tSkSRM1adJEvr6+uvvuuyVJQUFB2rJli4qKivTNN99owIABzn1+++23Gnt5rur61r17d7366qv6+eeflZSU5Jz32dLS0pw9KFRhtfMAAABA3bk9uBljKq0yGGMUEBCgtWvXVrnPlVdeKak8yJz5rFlV45wtJydHkyZN0oYNG9SsWTMlJyfr1KlTVW579jjnbnP2qklqaqri4uKUnp6u3NxcxcbGVjm/CRMmaMSIERV+npub65zHmblUdatkbeo6vjGm1jHffPNNXXfdddq8ebPKysqqDHdS+YNfZs+erX/9618aOnToedc6bdq0as/d2XM4U7src6huG1f2zczM1PLly7V27Vo1atRIsbGxVb5XqqvZGKNPP/1UHTt2rPYY8+bNU35+vrKzs+Xt7a127dpV+3484+xeeHp6Or/39PRUSUmJysrKdPXVVztXOGvav679eeCBB9StWzd98cUX6tWrl/7rv/5Lt912W4VtUlJSlJKSIknq6FH93AEAAFB3bv9zAAkJCZoxY4YzgBUUFKhjx47Kz893Brfi4mJt27atzuOc7ejRo2rcuLF8fX2Vl5enJUuWOF9r0qSJjh075vz+uuuu044dO1RWVuZcdalKYWGh2rRpI6n8M0hV6dWrl2bOnOm8xXH//v0VbkusTXx8vN555x1J5SuPR48erdf4TZs2Vdu2bfXZZ59JKl+ROffBLIWFhWrVqpU8PT01d+7cam+L69evn5YuXaoNGzaoV69elV4/t6/V1RofH68FCxbo8OHDkiqfu3NFR0dr/vz5Ki0tVX5+vlatWqWuXbtW2mbevHmSym9v/emnn9SxY0f17NlTCxYskCRt375d33//faXxCwsL1axZMzVq1Eg//PCD1q1bV2kbf39/5eTkaM+ePZKkjz76qMI833rrLWcI2rhxY5XHaNmypby9vZWRkaG9e/fWOGdXNG3aVO3bt9cnn3wiqTyEbd68ucZ9zj1H1fXtxx9/lJ+fn0aPHq3ExERt2bKl3vUCAADAdW4PbsOGDdONN97ofDDFhx9+qCuuuEILFy7UU089peDgYIWEhNT6FMWqxjlbcHCwQkNDFRAQoKFDh6pHjx7O11JSUtSnTx/nw0lee+019e3bV7fddluFWzTPNX78eE2YMEE9evSoNtwkJCTogQceUPfu3RUUFKT+/ftX+EW5NlOnTlVGRoaCgoIUHh5eKcCez/hz587VtGnT5HA4FBUVpX/9618VXn/00Uc1Z84cRUZGateuXRVWGc92xRVXKC4uTv/xH//hvH30bNdcc4169OihwMBAPfnkk9XWGhAQoGeffVYxMTEKDg7W2LFja6y/X79+zvN822236Y033qh0i+Gjjz6q0tJSBQUFaeDAgZo9e7auvPJKPfroo8rPz5fD4dDrr78uh8NR6fbT3r17q6SkRA6HQ6mpqYqMjKxUQ8OGDZWWlqa77rpLPXv21E033eR8LTU1VcXFxXI4HAoMDFRqamql/QcPHqysrCxFRERo3rx58vf3r3HOrpo3b57ee+89BQcHKyAgoMqHnpzN4XDIy8tLwcHBevPNN6vt2/z58xUYGKiQkBD98MMPevjhhy9IvQAAAHCNh3Hl3jGgCmVlZQoLC9Mnn3xS5WeebFRaWqri4mI1bNhQe/bsUXx8vHbt2qUrrrjC3aVdVjp6dNTf9Dd3lwEAAOCSWBN7UY8XERFR6eF/tXH7Z9xwadq+fbv69u2rfv36XTKhTSr/cwBxcXEqLi6WMUbvvPMOoQ0AAADWI7jhvHTu3Fk//viju8uosyZNmtT5XzcAAAAAd3P7Z9wAAAAAADUjuAEAAACA5QhuAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYjuAGAAAAAJYjuAEAAACA5QhuAAAAAGA5L3cXAODy0yS8iWKzYt1dBgAAwGWDFTcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACzn5e4CAFx+so8dk0dmprvLAAAAcImJjXV3CbVixQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcAMAAAAAyxHcAAAAAMByBDcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcMMl4YsvvtD333/v7jIAAAAAtyC4wRpHjhzR9OnTK/186dKlWrlypQIDA89r3AMHDqh///71Le+i8fHxqfLnzz//vJYvX17jvlOmTNGJEyfqfExXxgYAAID7eBhjjLuLACQpNzdXffv21datW91dygVXUlIiLy8vl7b18fFRUVHReR2nXbt2ysrKUosWLVzep7S0VA0aNDiv41XHo2NH6W9/u6BjAgAA/F5MbOxFPV5ERISysrLqtA8rbrDG008/rT179igkJERPPvmkJGnixInq0qWLHA6HXnjhBee277//vhwOh4KDg/XQQw9JkpKTkzV69GhFRUXJz89PCxculFQeCM+s1pWWlmrcuHEKCgqSw+HQW2+9JUn66quvFBoaqqCgIA0dOlS//fZbpfpiY2P1xBNPKCoqSoGBgVq/fr0kaf369YqKilJoaKiioqK0c+dOSdLs2bM1YMAA3X333UpISFBRUZHi4+MVFhamoKAgLVq0qNpe/OlPf1JYWJji4+OVn5/vnN+ZOVVV77Rp03TgwAHFxcUpLi5OkrRs2TJ1795dYWFhGjBggDMQtmvXTi+//LJ69uypTz75pNaxz5yfzp07y+FwaNy4cXU7uQAAAKgXghus8dprr+kPf/iDNm3apIkTJ2rZsmXavXu31q9fr02bNik7O1urVq3Stm3b9Oqrr2rFihXavHmzpk6d6hzj4MGDWrNmjf77v/9bTz/9dKVjpKWlKScnRxs3btSWLVs0ePBgnTp1SsnJyZo/f76+//57lZSU6J133qmyxuPHj+ubb77R9OnTNXToUEmSv7+/Vq1apY0bN+rll1/WM88849x+7dq1mjNnjlasWKGGDRsqPT1d3333nTIyMvSnP/1JVS14Hz9+XGFhYfruu+8UExOjl156qcLr1dU7evRotW7dWhkZGcrIyNChQ4f0yiuvaPny5fruu+8UERGhyZMnO8dp2LCh1qxZo0GDBtU6dkFBgdLT07Vt2zZt2bJFzz33XJW9jYiIUEREhFRYWN1pBgAAwHkguMFay5Yt07JlyxQaGqqwsDD98MMP2r17t1asWKH+/fs7bwds3ry5c597771Xnp6e6ty5s/Ly8iqNuXz5co0cOdJ522Lz5s21c+dOtW/fXrfccoskaciQIVq1alWVNd1///2SpOjoaB09elRHjhxRYWGhBgwYoMDAQI0ZM0bbtm1zbn/HHXc46zPG6JlnnpHD4dDtt9+u/fv3V1mjp6enBg4cKEl68MEHtWbNmgqvu1rvunXrtH37dvXo0UMhISGaM2eO9u7d63z9zDFcGbtp06Zq2LChhg0bpr///e9q1KhRpX1TUlKUlZVVvuzv61tl/wAAAHB+XPvQDeAGxhhNmDBBI0aMqPDzadOmycPDo8p9rrzyygr7VzXmufvW5WOe5+7r4eGh1NRUxcXFKT09Xbm5uYo96x7pxo0bO/973rx5ys/PV3Z2try9vdWuXTudOnWqzsd0tV5jjO644w599NFHVb5+dm21je3l5aX169frq6++0scff6y3335bK1ascKkOAAAA1B8rbrBGkyZNdOzYMef3vXr10syZM52fy9q/f79++eUXxcfHa8GCBTp8+LAkqaCgwOVjJCQkaMaMGSopKXHu6+/vr9zcXP3zn/+UJM2dO1cxMTFV7j9//nxJ0po1a+Tr6ytfX18VFhaqTZs2kso/11adwsJCtWzZUt7e3srIyKiw+nW2srIy5+fNPvzwQ/Xs2bPC6zXVe3YPIyMj9fXXXzu3O3HihHbt2lVjf6obu6ioSIWFhbrzzjs1ZcoUbdq0qcZxAAAAcGGx4gZrXHPNNerRo4cCAwPVp08fTZw4UTt27FD37t0llT9t8YMPPlBAQICeffZZxcTEqEGDBgoNDa0xMJ1t2LBh2rVrlxwOh7y9vTV8+HCNGjVKs2bN0oABA1RSUqIuXbpo5MiRVe7frFkzRUVF6ejRo5o5c6Ykafz48RoyZIgmT56s2267rdpjDx48WHfffbciIiIUEhIif3//Krdr3Lixtm3bpvDwcPn6+jrD4hkNGzastt6UlBT16dNHrVq1UkZGhmbPnq3777/f+YCRV155xXkbZFWqG7ugoED33HOPTp06JWOM3nzzzeqbDAAAgAuOPwcAuCg2NlaTJk0qf/gGasSfAwAAAJcS/hwAAAAAAKDeuFUScFFmZqa7SwAAAMC/KVbcAAAAAMByBDcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcAMAAAAAyxHcAAAAAMByBDcAAAAAsBzBDQAAAAAs5+XuAgBcfsKbNFFWbKy7ywAAALhssOIGAAAAAJYjuAEAAACA5QhuAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlvNxdAIDLUHa25OHh7ioAAADKGePuCuqNFTcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcAMAAAAAyxHcAAAAAMByBDcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ24wGbPnq0DBw64uwyVlpZq6tSpKikpcXcpAAAAqCeCGy4ZUVFRF/V4d955p44cOVKnfZYsWaL169erdevWv09RdTB58mT5+PjIy8ur1m3PZ64AAAC4eGr/jQ6wxDfffHNRj7d48eI6bV9aWqq8vDxNnTr1d6rIdWVlZbr++uv10EMPubR9XecKAACAi4sVN1wyfHx8JEmZmZmKjY1V//795e/vr8GDB8sYI6k8gPj7+6tnz54aPXq0+vbtK0k6fvy4hg4dqi5duig0NFSLFi2SVH5bY1JSknr37q0OHTpo/PjxzuO1a9dOhw4dkiR98MEH6tq1q0JCQjRixAiVlpY6a3r++efVrVs3rV27VrNnz9bmzZslSY888ogiIiIUEBCgF154oco5xcbGasyYMYqOjlanTp20YcMGJSUlqUOHDnruueec21V3/KVLlyosLEzBwcGKj4+XJK1fv149e/bU5MmTFRUVpZ07d9Zpru+//74cDoeCg4Odwe8f//iHunXrptDQUN1+++3Ky8s7v5MIAACA82OAS0Tjxo2NMcZkZGSYpk2bmn379pnS0lITGRlpVq9ebU6ePGnatm1rfvzxR2OMMYMGDTJ33XWXMcaYCRMmmLlz5xpjjPn1119Nhw4dTFFRkZk1a5Zp3769OXLkiDl58qS58cYbzU8//WSMMeamm24y+fn5Zvv27aZv377m9OnTxhhjHnnkETNnzhxjjDGSzPz58501xsTEmA0bNhhjjDl8+LAxxpiSkhITExNjNm/eXGlOMTExZvz48cYYY6ZMmWJatWplDhw4YE6dOmXatGljDh06VO3xf/nllwrzPXO8wsJCU1xcbIwxZunSpSYpKckYY1ya69atW80tt9xi8vPzK4xZUFBgysrKjDHGvPvuu2bs2LGV5vK3v/3NhIeHm/DwcHOjZAxffPHFF1988cWXLV+WCQ8Pr/M+3CqJS1LXrl3Vtm1bSVJISIhyc3Pl4+MjPz8/tW/fXpJ0//33Ky0tTZK0bNkyff7555o0aZIk6dSpU/rpp58kSfHx8fL19ZUkde7cWXv37tUNN9zgPNZXX32l7OxsdenSRZJ08uRJtWzZUpLUoEED3XfffVXWuGDBAqWlpamkpEQHDx7U9u3b5XA4Km2XmJgoSQoKClJAQIBatWolSfLz89O+ffu0Zs2aKo+/bt06RUdHO+fbvHlzSdKxY8c0fPhw7d+/X8YYHT582Hms2ua6YsUK9e/fXy1atKgw5s8//6yBAwfq4MGDOn36tPOYZ0tJSVFKSookKcLDo8qeAAAA4PwQ3HBJuvLKK53/3aBBA5WUlMgYU+32xhh9+umn6tixY4Wff/vtt1WOde6+Q4YM0Z///OdK4zZs2FANGjSo9POcnBxNmjRJGzZsULNmzZScnKxTp07VOBdPT88KtXh6ejrnVdXxP//8c3lUEZCee+45xcXFaeTIkcrJyVFcXFylY9U016rGfPzxxzV27FglJiYqMzNTL774YpVzAQAAwO+Dz7jhsuHv768ff/xRubm5kqT58+c7X+vVq5feeustZ7jbuHGjy+PGx8dr4cKF+uWXXyRJBQUF2rt3b437HD16VI0bN5avr6/y8vK0ZMmSOs6m9uN3795dK1euVE5OjvPnkvTrr7/q2muvlVT+uba6HmvBggXOVbozYxYWFqpNmzaSpDlz5pz3XAAAAHB+CG64bFx11VWaPn26evfurZ49e+q6665z3haYmpqq4uJiORwOBQYGKjU11eVxO3furFdeeUUJCQlyOBy64447dPDgwRr3CQ4OVmhoqAICAjR06FD16NHjvOdV3fGvvfZapaWlKSkpScHBwRo4cKAk6cknn9SECRPUo0cPlZWV1elYAQEBevbZZxUTE6Pg4GCNHTtWkvTiiy9qwIABuvXWW523UQIAAODi8TA13V8GXGKKiork4+MjY4wee+wxdejQQWPGjHF3Wf92Ijw8lOXuIgAAAM6wLPJEREQoK6tuvy2x4obLyrvvvquQkBAFBASosLBQI0aMcHdJAAAAQL2x4gbggmPFDQAAWMWyyMOKGwAAAABchghuAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYjuAGAAAAAJYjuAEAAACA5QhuAAAAAGA5L3cXAOAyFB4uZWW5uwoAAIDLBituAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYzsvdBQC4/GQfyJbHSx7uLgMAAECSZF4w7i6h3lhxAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcAMAAAAAyxHcAAAAAMByBDcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcAMAAAAAyxHccEn58ssvtWnTJneXAQAAAFxUBLdz+Pj4SJIOHDig/v37X5RjDhs2TNu3b69xmylTpujEiRMXpR53OXeOd955p44cOeL8fsWKFfryyy8VHBxcr+OcOce2mj17tkaNGiVJmjFjht5///3f9XifffZZre8/AAAAuJeHMca4uwib+Pj4qKioyN1lVNKuXTtlZWWpRYsWLu9TUlIiLy+v37GqC+t85ng+LuY5Pp9zMHv2bGVlZentt9/+naqqKDk5WX379r2g/1Dh0dpDGnHBhgMAAKgX84JdkSciIkJZWVl12ocVt2rk5uYqMDBQklRaWqpx48YpKChIDodDb731liQpOztbMTExCg8PV69evXTw4EFJUmxsrJ566il17dpVt9xyi1avXl3jOLGxsc4T98gjjygiIkIBAQF64YUXJEnTpk3TgQMHFBcXp7i4OEkVV40WLlyo5ORkSeW/hI8dO1ZxcXF66qmntH79ekVFRSk0NFRRUVHauXNnlfOdOHGiunTpIofD4Txubm6uOnXqpOHDhysgIEAJCQk6efJkpX337NmjyMhIdenSRc8//3yF2qoa9/jx47rrrrsUHByswMBAzZ8/v8o5tmvXTocOHZIkTZ48WYGBgQoMDNSUKVPqVF9OTo66d++uLl26KDU1tdZ5n6uqcyJJixcvlr+/v3r27KnRo0erb9++kqQXX3xRKSkpSkhI0MMPP6y9e/cqPj5eDodD8fHx+umnnyRJ//jHP9StWzeFhobq9ttvV15eXqVjv/jii5o0aZKk8vfJmDFjFB0drU6dOmnDhg1KSkpShw4d9Nxzzzn3+eCDD9S1a1eFhIRoxIgRKi0tlVT+nnn22WcVHBysyMhI5eXl6ZtvvtHnn3+uJ598UiEhIdqzZ482bdqkyMhIORwO9evXT7/++quk8vdh586d5XA4NGjQoCp7BQAAgN8Hwc0FaWlpysnJ0caNG7VlyxYNHjxYxcXFevzxx7Vw4UJlZ2dr6NChevbZZ537lJSUaP369ZoyZYpeeumlasc516uvvqqsrCxt2bJFK1eu1JYtWzR69Gi1bt1aGRkZysjIqLXeXbt2afny5frLX/4if39/rVq1Shs3btTLL7+sZ555ptL2y5Yt0+7du7V+/Xpt2rRJ2dnZWrVqlSRp9+7deuyxx7Rt2zZdffXV+vTTTyvt/8c//lF//OMftWHDBrVu3brWcZcuXarWrVtr8+bN2rp1q3r37l3jHLOzszVr1ix9++23Wrdund59911t3LixTvU98sgj2rBhg66//nqX5l3bOTl16pRGjBihJUuWaM2aNcrPz69U86JFi/Thhx9q1KhRevjhh53nfPTo0ZKknj17at26ddq4caMGDRqkN954o9pzesYVV1yhVatWaeTIkbrnnnv017/+VVu3btXs2bN1+PBh7dixQ/Pnz9fXX3+tTZs2qUGDBpo3b56k8sAcGRmpzZs3Kzo6Wu+++66ioqKUmJioiRMnatOmTfrDH/6ghx9+WK+//rq2bNmioKAg5/v3tddec753Z8yYUWutAAAAuHAunfvo3Gj58uUaOXKk85a35s2ba+vWrdq6davuuOMOSeWraa1atXLuk5SUJEkKDw9Xbm5uteOca8GCBUpLS1NJSYkOHjyo7du3y+Fw1KneAQMGqEGDBpKkwsJCDRkyRLt375aHh4eKi4srbb9s2TItW7ZMoaGhkqSioiLt3r1bN954o9q3b6+QkJBKcznb2rVr9dlnn0mSHnjgAY0bN67GcW+99VaNGzdOTz31lPr27atbb721xvmsWbNG/fr1U+PGjSWV93b16tVKTEx0qb6vv/7aGegeeughPfXUUzXWFx0dXWH/qs5JWVmZ/Pz81L59e0nS/fffr7S0NOc+iYmJuuqqq5z9+fvf/+48/vjx4yVJP//8swYOHKiDBw/q9OnTzrFqkpiYKEkKCgpSQECA8z3n5+enffv2ac2aNcrOzlaXLl0kSSdPnlTLli0llYe+M6uC4eHh+p//+Z9K4xcWFurIkSOKiYmRJA0ZMkQDBgyQJDkcDg0ePFj33nuv7r333kr7pqWl/X8PLu+PYwIAAFx0BDcXGGPk4eFR6WcBAQFau3ZtlftceeWVkqQGDRqopKSk2nHOlpOTo0mTJmnDhg1q1qyZkpOTderUqSq3PXucc7c5E3AkKTU1VXFxcUpPT1dubq5iY2OrnN+ECRM0YkTFDyXl5uY653FmLlXdilid6saVylekFi9erAkTJighIUHPP/98jeNUx9X6qup7TfWdUd05qe2joWefg+pqefzxxzV27FglJiYqMzNTL774Yo1jSv8/X09Pzwpz9/T0VElJiYwxGjJkiP785z9X2tfb29t57LPfl6764osvtGrVKn3++ef6z//8T23btq3C5/dSUlKUkpJSPsfW1b/PAQAAUHfcKumChIQEzZgxw/mLbkFBgTp27Kj8/HxncCsuLta2bdvqPM7Zjh49qsaNG8vX11d5eXlasmSJ87UmTZro2LFjzu+vu+467dixQ2VlZUpPT6/2mIWFhWrTpo2k8odeVKVXr16aOXOm84Ed+/fv1y+//FLjXM4WGRnpXNH6+OOPax33wIEDatSokR588EGNGzdO3333XZVzPCM6OlqfffaZTpw4oePHjys9Pb3WVbqz9ejRw1nXmdsGXZ13defE399fP/74o3OFb/78+dUePyoqqsLxe/bsKaniuZkzZ47L86lJfHy8Fi5c6JxHQUGB9u7dW+M+Z/fd19dXzZo1c34uc+7cuYqJiVFZWZn27dunuLg4vfHGGzpy5IiVD/EBAAC4XLHi5oJhw4Zp165dcjgc8vb21vDhwzVq1CgtXLhQo0ePVmFhoUpKSvTEE08oICCgzuOcERwcrNDQUAUEBMjPz089evRwvpaSkqI+ffqoVatWysjI0Guvvaa+ffvqhhtuUGBgYLW/RI8fP15DhgzR5MmTddttt1W5TUJCgnbs2KHu3btLKn+IxQcffOC83bI2U6ZM0YMPPqi//OUvuuuuu+Tr61vjuP/85z/15JNPytPTU97e3nrnnXeqnOMZYWFhSk5OVteuXZ19DA0NrfK2yKpMnTpVDzzwgKZOnar77ruv1nmfubVQqv6cXHXVVZo+fbp69+6tFi1aOGuryrRp0zR06FBNnDhR1157rWbNmiWp/MEjAwYMUJs2bRQZGamcnByX5lOTzp0765VXXlFCQoLKysrk7e2tv/71r7rpppuq3WfQoEEaPny4pk2bpoULF2rOnDkaOXKkTpw4IT8/P82aNUulpaV68MEHVVhYKGOMxowZo6uvvrre9QIAAMA1/DkA1NuJEyd01VVXycPDQx9//LE++ugjLVq0yN1l/e6Kiork4+MjY4wee+wxdejQQWPGjHF3WVbgzwEAAACbXA5/DoAVN9Rbdna2Ro0aJWOMrr76as2cOdPdJV0U7777rubMmaPTp08rNDS0xs/KAQAAAPXBihuAC44VNwAAYJPLYcWNh5MAAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYjuAGAAAAAJYjuAEAAACA5QhuAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFjOy90FALj8hLcOV9YLWe4uAwAA4LLBihsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYjuAGAAAAAJbzcncBAC4/2dmSh4e7qwAAAJcrY9xdwcXHihsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYjuAGAAAAAJYjuAEAAACA5QhuAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AZeBzMxMffPNNxd0zDvvvFNHjhy5oGMCAADg/Hi5uwAA9ZeZmSkfHx9FRUXVeyxjjIwxWrx48QWoDAAAABcCK26Axe69916Fh4crICBAaWlpkqSlS5cqLCxMwcHBio+PV25urmbMmKE333xTISEhWr16tZKTk7Vw4ULnOD4+PpKkoqIixcfHKywsTEFBQVq0aJEkKTc3V506ddKjjz6qsLAw7du3T+3atdOhQ4ckSZMnT1ZgYKACAwM1ZcqUi9sEAAAAsOIG2GzmzJlq3ry5Tp48qS5duuiee+7R8OHDtWrVKrVv314FBQVq3ry5Ro4cKR8fH40bN06S9N5771U5XsOGDZWenq6mTZvq0KFDioyMVGJioiRp586dmjVrlqZPn15hn+zsbM2aNUvffvutjDHq1q2bYmJiFBoaWmG7tLQ0Z7iU8i9sIwAAAP7NEdwAi02bNk3p6emSpH379iktLU3R0dFq3769JKl58+Z1Gs8Yo2eeeUarVq2Sp6en9u/fr7y8PEnSTTfdpMjIyEr7rFmzRv369VPjxo0lSUlJSVq9enWl4JaSkqKUlBRJkodHRN0mCgAAgBoR3ABLZWZmavny5Vq7dq0aNWqk2NhYBQcHa+fOnbXu6+XlpbKyMknlYe306dOSpHnz5ik/P1/Z2dny9vZWu3btdOrUKUlyBrNzGWMu0IwAAABwvviMG2CpwsJCNWvWTI0aNdIPP/ygdevW6bffftPKlSuVk5MjSSooKJAkNWnSRMeOHXPu265dO2VnZ0uSFi1apOLiYueYLVu2lLe3tzIyMrR3795a64iOjtZnn32mEydO6Pjx40pPT9ett956oacLAACAGhDcAEv17t1bJSUlcjgcSk1NVWRkpK699lqlpaUpKSlJwcHBGjhwoCTp7rvvVnp6uvPhJMOHD9fKlSvVtWtXffvtt87VtMGDBysrK0sRERGaN2+e/P39a60jLCxMycnJ6tq1q7p166Zhw4ZVuk0SAAAAvy8Pw31QAC6w8s+4Zbm7DAAAcJm61BNMRESEsrLq9rsSK24AAAAAYDmCGwAAAABYjuAGAAAAAJYjuAEAAACA5QhuAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYjuAGAAAAAJYjuAEAAACA5bzcXQCAy094uJSV5e4qAAAALh+suAEAAACA5QhuAAAAAGA5ghsAAAAAWI7gBgAAAACWI7gBAAAAgOUIbgAAAABgOYIbAAAAAFiO4AYAAAAAliO4AQAAAIDlCG4AAAAAYDmCGwAAAABYjuAGAAAAAJYjuAEAAACA5TyMMcbdRQC4vPj4+Mjf39/dZVzS8vPzde2117q7jEsaPaw/elh/9LD+6GH90cP6u9A9zM3N1aFDh+q0j9cFOzoA/B9/f39lZWW5u4xLWkREBD2sJ3pYf/Sw/uhh/dHD+qOH9WdDD7lVEgAAAAAsR3ADAAAAAMsR3ABccCkpKe4u4ZJHD+uPHtYfPaw/elh/9LD+6GH92dBDHk4CAAAAAJZjxQ0AAAAALEdwA3Deli5dqo4dO+rmm2/Wa6+9Vul1Y4xGjx6tm2++WQ6HQ999950bqrRbbT2cN2+eHA6HHA6HoqKitHnzZjdUabfaenjGhg0b1KBBAy1cuPAiVndpcKWHmZmZCgkJUUBAgGJiYi5yhfarrYeFhYW6++67FRwcrICAAM2aNcsNVdpr6NChatmypQIDA6t8netJ7WrrIdeT2tXWwzPcdj0xAHAeSkpKjJ+fn9mzZ4/57bffjMPhMNu2bauwzRdffGF69+5tysrKzNq1a03Xrl3dVK2dXOnh119/bQoKCowxxixevJgensOVHp7ZLi4uzvTp08d88sknbqjUXq708NdffzWdOnUye/fuNcYYk5eX545SreVKD1999VUzfvx4Y4wxv/zyi2nWrJn57bff3FGulVauXGmys7NNQEBAla9zPaldbT3kelK72npojHuvJ6y4ATgv69ev18033yw/Pz9dccUVGjRokBYtWlRhm0WLFunhhx+Wh4eHIiMjdeTIER08eNBNFdvHlR5GRUWpWbNmkqTIyEj9/PPP7ijVWq70UJLeeust3XfffWrZsqUbqrSbKz388MMPlZSUpBtvvFGS6OM5XOmhh4eHjh07JmOMioqK1Lx5c3l58ed0z4iOjlbz5s2rfZ3rSe1q6yHXk9rV1kPJvdcTghuA87J//37dcMMNzu/btm2r/fv313mbf2d17c97772nPn36XIzSLhmuvg/T09M1cuTIi13eJcGVHu7atUu//vqrYmNjFR4ervfff/9il2k1V3o4atQo7dixQ61bt1ZQUJCmTp0qT09+DXMV15MLi+vJ+XH39YR/6gFwXkwVD6T18PCo8zb/zurSn4yMDL333ntas2bN713WJcWVHj7xxBN6/fXX1aBBg4tV1iXFlR6WlJQoOztbX331lU6ePKnu3bsrMjJSt9xyy8Uq02qu9PDLL79USEiIVqxYoT179uiOO+7QrbfeqqZNm16sMi9pXE8uHK4n58/d1xOCG4Dz0rZtW+3bt8/5/c8//6zWrVvXeZt/Z672Z8uWLRo2bJiWLFmia6655mKWaD1XepiVlaVBgwZJkg4dOqTFixfLy8tL995778Us1Vqu/r/cokULNW7cWI0bN1Z0dLQ2b95McPs/rvRw1qxZevrpp+Xh4aGbb75Z7du31w8//KCuXbte7HIvSVxPLgyuJ/Xj7usJa/QAzkuXLl20e/du5eTk6PTp0/r444+VmJhYYZvExES9//77MsZo3bp18vX1VatWrdxUsX1c6eFPP/2kpKQkzZ07l1+Sq+BKD3NycpSbm6vc3Fz1799f06dPJ7SdxZUe3nPPPVq9erVKSkp04sQJffvtt+rUqZObKraPKz288cYb9dVXX0mS8vLytHPnTvn5+bmj3EsS15P643pSf+6+nrDiBuC8eHl56e2331avXr1UWlqqoUOHKiAgQDNmzJAkjRw5UnfeeacWL16sm2++WY0aNeLx1+dwpYcvv/yyDh8+rEcffdS5T1ZWljvLtoorPUTNXOlhp06d1Lt3bzkcDnl6emrYsGG1Pi7734krPUxNTVVycrKCgoJkjNHrr7+uFi1auLlye9x///3KzMzUoUOH1LZtW7300ksqLi6WxPXEVbX1kOtJ7Wrrobt5mKpuGgYAAAAAWINbJQEAAADAcgQ3AAAAALAcwQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcAMAAAAAyxHcAAAAAMByBDcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ0AAAAALEdwAwAAAADLEdwAAAAAwHIENwAAAACwHMENAAAAACxHcAMAAAAAyxHcAAAAAMByBDcAAAAAsBzBDQAAAAAsR3ADAAAAAMsR3AAAAADAcgQ3AAAAALAcwQ0AAAAALPe/mN6SFBg72EgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the figure and axes\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=2,ncols=1, figsize=(12,10), constrained_layout = True)\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "# Labels ans titles\n",
    "ax1.set_title(str.upper('Promedio de años desde la inscripción: U.N.J. (top 5)'), fontsize=20)\n",
    "ax1.set_xlabel(' ', fontsize=15)\n",
    "ax1.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "ax2.set_title(str.upper(f'Promedio de años desde la inscripción: U.P. (top 5)'), fontsize=20)\n",
    "ax2.set_xlabel(' ', fontsize=15)\n",
    "ax2.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "# Plot 1\n",
    "ax1.barh(df1_cinsc['career'].head(5), \n",
    "        df1_cinsc['years_since_inscription_mean'].head(5).sort_values(ascending=True), \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "\n",
    "# Plot 2\n",
    "ax2.barh(df2_cinsc['career'].head(5), \n",
    "        df2_cinsc['years_since_inscription_mean'].head(5).sort_values(ascending=True), \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ade8f19d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de años desde la inscripción es: 1.45 años.\n"
     ]
    }
   ],
   "source": [
    "kpi3 = df1_cinsc[\"years_since_inscription_mean\"].mean()\n",
    "print(\"El promedio de años desde la inscripción es: \" + str(round(kpi3, 2)) + \" años.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9bed2586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de años desde la inscripción es: 1.52 años.\n"
     ]
    }
   ],
   "source": [
    "kpi6 = df2_cinsc[\"years_since_inscription_mean\"].mean()\n",
    "print(\"El promedio de años desde la inscripción es: \" + str(round(kpi6, 2)) + \" años.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f0b690",
   "metadata": {},
   "source": [
    "> * En el promedio de años desde la inscripción no se observan diferencias significativas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "61d38afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the figure and axes\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2, figsize=(12,10), constrained_layout = True)\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "# Labels ans titles\n",
    "ax1.set_title(str.upper('Cant. por género: U.N.J.'), fontsize=20)\n",
    "ax1.set_xlabel(' ', fontsize=15)\n",
    "ax1.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "explode_pie = (0.05, 0)\n",
    "\n",
    "ax2.set_title(str.upper(f'Cant. por género: U.P.'), fontsize=20)\n",
    "ax2.set_xlabel(' ', fontsize=15)\n",
    "ax2.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "# Plot 1\n",
    "ax1.pie(df1_cgen2['count_by_gender'],\n",
    "        labels=df1_cgen2['gender'],\n",
    "        explode=explode_pie,\n",
    "        autopct='%1.1f%%',\n",
    "        shadow=True, \n",
    "        startangle=90,\n",
    "        counterclock=False,\n",
    "        textprops=dict(color=\"k\")\n",
    "        #color=['b','g','r','c','m']\n",
    "       )\n",
    "\n",
    "# Plot 2\n",
    "ax2.pie(df2_cgen2['count_by_gender'],\n",
    "        labels=df2_cgen2['gender'],\n",
    "        explode=explode_pie,\n",
    "        autopct='%1.1f%%',\n",
    "        shadow=True, \n",
    "        startangle=90,\n",
    "        counterclock=False,\n",
    "        textprops=dict(color=\"k\")\n",
    "        #color=['b','g','r','c','m']\n",
    "       )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da687c3c",
   "metadata": {},
   "source": [
    "> * En la UNJ el 53,8% de los inscriptos son hombres.\n",
    "> * En la UP el 52,2% de los inscriptos son mujeres.\n",
    "\n",
    "> * En la UNJ la carrera más elegida por las mujeres es: Licenciatura en planificación y diseño del paisaje.\n",
    "> * En la UNJ la carrera más elegida por los hombres es: Medicina.\n",
    "\n",
    "> * En la UP la carrera más elegida por las mujeres es: Licenciatura en Psicología.\n",
    "> * En la UP la carrera más elegida por los hombres es: Medicina.\n",
    "\n",
    "> * Medicina es la carrera más elegida por los hombres en ambas universidades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3debe79c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the figure and axes\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2, figsize=(15,8), constrained_layout = True)\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "# Labels ans titles\n",
    "ax1.set_title(str.upper('Rango etario: U.N.J.'), fontsize=20)\n",
    "ax1.set_xlabel(' ', fontsize=15)\n",
    "ax1.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "ax2.set_title(str.upper('Rango etario: U.P.'), fontsize=20)\n",
    "ax2.set_xlabel(' ', fontsize=15)\n",
    "ax2.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "# Plot 1\n",
    "ax1.bar(df1_ager['age_group'], \n",
    "        df1_ager['count_by_age_group'], \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "\n",
    "# Plot 2\n",
    "ax2.bar(df2_ager['age_group'], \n",
    "        df2_ager['count_by_age_group'], \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d697766",
   "metadata": {},
   "source": [
    "> * En la UNJ el grupo etario con mayor cantidad de inscriptos es: 18 a 27 (182 inscriptos)\n",
    "> * En la UP el grupo etario con mayor cantidad de inscriptos es: 18 a 27 y 38 a 47 (ambos con 176 inscriptos)\n",
    "\n",
    "> * En ambas universidades parece haber un aumento significativo de las inscripciones entre los 18 y los 27 años. Sin embargo, en la UP también se observa un número significativo entre los 38 y los 47 años.\n",
    " \n",
    "> * Por otro lado, en la UNJ las inscripciones se registran en todos los rangos (de 18 a 90 años). En la UP no se registran inscripciones después de los 57 años."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "293afa21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the figure and axes\n",
    "fig, (ax1,ax2) = plt.subplots(nrows=2,ncols=1, figsize=(12,10), constrained_layout = True)\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "# Labels ans titles\n",
    "ax1.set_title(str.upper('Inscriptos por localidad: U.N.J. (top 5)'), fontsize=20)\n",
    "ax1.set_xlabel(' ', fontsize=15)\n",
    "ax1.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "ax2.set_title(str.upper('Inscriptos por localidad: U.P. (top 5)'), fontsize=20)\n",
    "ax2.set_xlabel(' ', fontsize=15)\n",
    "ax2.set_ylabel(' ', fontsize=15, rotation='vertical')\n",
    "\n",
    "# Plot 1\n",
    "ax1.barh(df1_loc['location'].head(5), \n",
    "        df1_loc['count_by_location'].head(5).sort_values(ascending=False), \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "\n",
    "# Plot 2\n",
    "ax2.barh(df2_loc['location'].head(5), \n",
    "        df2_loc['count_by_location'].head(5).sort_values(ascending=False), \n",
    "        color=['b','g','r','c','m']\n",
    "       )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00954d2c",
   "metadata": {},
   "source": [
    "> * En la UNJ la localidad con mayor número de inscriptos es: 25 de Mayo (6 inscriptos)\n",
    "> * En la UP la localidad con mayor número de inscriptos es: General Rojo (4 inscriptos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267d7baa",
   "metadata": {},
   "source": [
    "# 4. Conclusiones:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ce42bc",
   "metadata": {},
   "source": [
    "Tratándose de dos datasets que fueron creados de forma aleatoria resulta forzado el análisis. Sin embargo, se señalaron algunos indicadores, similitudes y diferencias que pudieron observarse en ambas universidades. En resumen:\n",
    "* En la UNJ hay aproximadamente el doble de inscriptos\n",
    "* En la UNJ la carrera con mayor número de inscriptos es: Medicina (47 inscriptos)\n",
    "* En la UP la carrera con mayor número de inscriptos es: Licenciatura en Psicología (29 inscriptos)\n",
    "* En la UP tiende a haber menos inscriptos por carrera, y su población tiende a ser más joven.\n",
    "* En el promedio de años desde la inscripción no se observan diferencias significativas.\n",
    "* En la UNJ el 53,8% de los inscriptos son hombres.\n",
    "* En la UP el 52,2% de los inscriptos son mujeres.\n",
    "* En la UNJ la carrera más elegida por las mujeres es: Licenciatura en planificación y diseño del paisaje.\n",
    "* En la UNJ la carrera más elegida por los hombres es: Medicina.\n",
    "* En la UP la carrera más elegida por las mujeres es: Licenciatura en Psicología.\n",
    "* En la UP la carrera más elegida por los hombres es: Medicina.\n",
    "* Medicina es la carrera más elegida por los hombres en ambas universidades.\n",
    "* En la UNJ el grupo etario con mayor cantidad de inscriptos es: 18 a 27 (182 inscriptos)\n",
    "* En la UP el grupo etario con mayor cantidad de inscriptos es: 18 a 27 y 38 a 47 (ambos con 176 inscriptos)\n",
    "* En ambas universidades parece haber un aumento significativo de las inscripciones entre los 18 y los 27 años. Sin embargo, en la UP también se observa un número significativo entre los 38 y los 47 años.\n",
    "* Por otro lado, en la UNJ las inscripciones se registran en todos los rangos (de 18 a 90 años). En la UP no se registran inscripciones después de los 57 años.\n",
    "* En la UNJ la localidad con mayor número de inscriptos es: 25 de Mayo (6 inscriptos)\n",
    "* En la UP la localidad con mayor número de inscriptos es: General Rojo (4 inscriptos)"
   ]
  }
 ],
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